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Everyone who has worked in a lab has encountered a lab protocol or SOP at some point, but why are these documents important? Adherence to protocol ensures efficiency, Standardisation, and safety in daily lab processes. 

In this blog, we will highlight: 

1. Key differences between lab protocols and SOPs 
2. The importance of standardization in lab procedures 
3. How to write an effective lab procedure
4. Streamlining lab protocol creation and sharing with digital lab solutions

Laboratory protocols and Standard Operating Procedures (SOPs) are fundamental frameworks that ensure the consistency, safety, and accuracy of scientific experiments.

Lab protocols are detailed step-by-step instructions designed to carry out a specific experimental task or lab process. On the other hand, SOPs provide a comprehensive set of guidelines for almost any lab procedure, from ensuring lab cleanliness to proper equipment use, safe handling of hazardous substances, and lab maintenance procedures.

Together, lab protocols and SOPs form the backbone of daily laboratory operations, enabling scientists to conduct their work efficiently, securely and in compliance with regulatory standards. They serve as a blueprint for conducting research in a consistent and optimal manner.

Read on for our quick guide to lab procedures and protocols. We'll examine different procedure types, highlight the importance of standardization, and give our top tips on how to write effective SOPs for your lab—including how to streamline protocol writing with advanced digital lab solutions.

What are the main differences between Lab Protocols and SOPs?

The terms "lab protocol" and "SOP" are sometimes used interchangeably in the context of laboratory environments, although they often have distinct meanings depending on the context and specific use:

Lab Protocols:

• Purpose: A lab protocol typically refers to a set of instructions designed to carry out a specific experiment or a testing procedure. It details the steps necessary to perform a scientific experiment, a clinical test, or any sequence of actions in a lab setting.
• Specificity: Protocols are usually very specific and can be more detailed about experimental conditions, such as concentrations, temperatures, and equipment settings.
• Flexibility: While they should be followed as written to ensure consistency and reproducibility of results, protocols might be slightly adjusted or optimized according to specific research needs or objectives.

Standard Operating Procedures (SOPs):

• Purpose: An SOP is a broader document that provides step-by-step instructions to perform any task within a lab (or other environments) consistently and correctly. SOPs are used not only for experiments but for any procedure that should follow a specific standard to ensure lab safety and compliance.
• Scope: They cover a wider range of activities beyond scientific experiments, including general lab maintenance, daily procedures, data documentation, and other operational processes.
• Regulatory Compliance: SOPs are often required to comply with industry standards and regulatory guidelines. They are designed to ensure that different individuals can perform the same task in the same manner over time, enhancing efficiency and quality control.

While there may be some overlap between lab protocols and SOPs, both are crucial for maintaining high standards of practice, though their applications and the rigidity with which they must be followed can differ.

Importance of standardization in lab procedures

Lab protocols and procedures are central to establishing standardization within daily lab operations, ensuring that every process is performed consistently and effectively. 

Standardization in lab procedures is crucial for several reasons, particularly in scientific research, diagnostic testing, and any setting where precise and reproducible results are necessary. Here are some key aspects of why standardization is so important:

Consistency and Reproducibility

Standardization ensures that experiments or tests conducted in a laboratory yield the same results, irrespective of who performs them or when they are performed. This consistency is crucial for validating experiments and confirming results across different studies or even different laboratories.

Quality Control

Standard procedures help maintain high quality and reliability in experimental results. By following a standardized protocol, labs can minimize variability that might arise from different methodologies or operator errors, ensuring the data's accuracy and reliability.

Lab Safety

SOPs include detailed safety guidelines that are crucial for handling hazardous materials, operating complex machinery and ensuring general laboratory safety. Standardisation in these procedures helps prevent accidents and ensures the safety of lab personnel.

Efficiency and Time Management

Having a set of well-defined, standardized procedures reduces the time needed for training and briefing. New personnel can quickly learn and adapt to established methods, which increases the overall efficiency of the laboratory. It also helps to manage resources better by eliminating unnecessary variations in the use of materials and equipment.

Regulatory Compliance

Many laboratories operate under strict regulatory conditions imposed by governmental and international bodies, especially in the pharmaceutical, medical, and food industries. Standardized procedures ensure that labs comply with these regulations, helping to maintain certifications and avoid legal or ethical violations.

Cost Efficiency

By reducing errors and reiterations of experiments, standardised procedures can lead to significant cost savings. Consistent protocols minimise the waste of expensive reagents and samples, which can have a substantial impact on lab budgets.

In summary, the standardization of lab procedures is integral to the scientific process, ensuring that results are accurate, reliable, and verifiable. This not only boosts the credibility of the scientific findings but also enhances operational efficiency within laboratories.

How to write an effective lab procedure

When writing a lab protocol or SOP, it can be useful to follow a stepwise process. Follow this guide to start writing effective lab protocols or SOPs for your lab:

1. Define the Purpose

Start by clearly stating the purpose of the protocol or SOP. Specify what the procedure will accomplish and why it is important. This sets the context and ensures all users understand the intent behind the procedure.

2. Scope and Application

Outline the scope of the protocol or SOP. Explain when and where it should be used and by whom. This section should also list any prerequisites, such as necessary qualifications, training, or conditions required before performing the task.

3. List of Materials and Equipment

Provide a detailed list of all materials, equipment and software needed to complete the procedure. Include specific models, settings and any safety gear required.

4. Detailed Steps

Break down the procedure into clear, numbered steps. Each step should be concise and describe precisely what needs to be done. Use bullet points for clarity and ensure that the steps are in the correct order.

5. Safety and Compliance Notes

Include any safety instructions related to the procedure. Highlight critical steps, potential hazards, and emergency response actions. Ensure all compliance guidelines are met and documented within the SOP.

6. Quality Control Checks

Integrate checkpoints or quality control measures to ensure the procedure's output meets the required standards. Specify how results should be evaluated and recorded.

7. Documentation and Reporting

Detail how the results should be documented, who should receive the reports, and the format for these reports. Ensure the process aligns with data integrity and record-keeping requirements.

8. Review and Approval

Before finalizing, the protocol or SOP should be reviewed by senior staff or peers. This review ensures accuracy and practicality. Once reviewed, it should be approved by the required authority.

9. Revision History

Keep a revision log at the end of the document to track changes. Include dates, the nature of revisions, and who approved the changes.

Adopting a structured approach such as this helps create comprehensive and reliable lab protocols and SOPs, enhancing the efficacy of daily laboratory operations.

Streamlining lab protocol creation and sharing with digital lab solutions

Whether your lab relies on paper-based lab protocols or stores key protocol documents in an online file-sharing system, your lab could benefit from a comprehensive digital lab solution tailored to creating, sharing, and accessing key lab protocols and SOPs.

SciSure (formerly eLabNext) for Protocol and SOP Management is an advanced digital system for creating and sharing dynamic and interactive protocols and SOPs. The platform allows lab personnel to easily access necessary protocols anywhere, anytime, reinforcing compliance and facilitating seamless collaboration through a shared protocol library and integrated communication tools.

This powerful digital lab solution offers a user-friendly interface, making it easy for labs to generate step-by-step interactive protocols that guide laboratory processes. Every change made to a protocol is tracked, ensuring transparency and accuracy throughout your work processes. In addition, our platform comes equipped with a wide range of templates for common lab protocols, saving you time when creating protocols. 

SciSure can enhance adherence, lab safety, and collaboration within your team via a shared library for protocols and SOPs.

AI protocol generation and beyond

In addition to the robust protocol creation and sharing that can be achieved with our platform, SciSure has recently developed an AI-powered Protocol Generation Add-On – an innovative tool designed to transform and streamline your lab's protocol and SOP creation experience.

Powered by advanced generative AI software, the add-on overcomes the tedious task of manually creating protocols, automating the generation of comprehensive protocols from a brief description of your experiment, significantly cutting down your time and effort.

Available as an add-on, the generative AI tool eases the burden of documentation. Beyond protocols and SOPs, the tool can also assist in swiftly producing detailed lab reports and experiment summaries, allowing you to dedicate more time to research and less to paperwork.

A better way to manage lab procedures?

Understanding and utilizing lab protocols and SOPs is not just about maintaining standards; it's about enhancing the efficiency, safety, and efficacy of scientific research.

Adopting digital tools can greatly enhance the creation, management and adherence to these crucial documents. Whether you're drafting your first SOP or looking to refine existing procedures, adopting digital solutions from SciSure can not only streamline the protocol creation process, but also ensures that all procedures are up-to-date, consistent and accessible, reducing human error and increasing productivity.

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Biomedical research is critical in advancing healthcare and understanding biological processes. This work often involves handling potentially hazardous biological agents, making biosafety a critical aspect of laboratory operations. As research continues to innovate and explore new frontiers, the need for robust biosafety programs remains a priority.

Understanding Biosafety Levels (BSLs)

The Centers for Disease Control and Prevention (CDC) define biosafety levels through the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines. The BSL framework is intended to categorize laboratories based on the risk posed by the biological agents they handle. This framework outlines four biosafety levels (BSL-1 to BSL-4) with increasing stringency in safety practices and containment measures.

• BSL-1: This is the basic level for handling minimal risk agents, suitable for work involving well-characterized agents not known to cause disease consistently in immunocompetent adult humans. Basic practices such as hand washing, the use of personal protective equipment like lab coats and gloves, and good laboratory hygiene are typically sufficient.
• BSL-2: This level applies to laboratories working with moderate-risk agents that can cause human disease but are typically spread through direct contact. Additional precautions beyond BSL-1 include controlled access to the laboratory, specific training for personnel, and the use of appropriate personal protective equipment.
• BSL-3: This level is for handling high-risk agents that can cause serious or fatal diseases via inhalation or exposure to bodily fluids. In addition to BSL-2 controls, BSL-3 facilities have additional engineering controls such as specialized ventilation systems to prevent the release of infectious aerosols.
• BSL-4: This is the highest biosafety level for laboratories working with dangerous and exotic agents that pose a high individual risk of aerosol-transmitted infections and life-threatening disease that are frequently fatal and for which there are no vaccines or treatments. This level is required for related agents with unknown risk or route of transmission. Most stringent safety and containment measures including complete isolation from the outside environment through multiple airlocks and highly specialized ventilation systems.

Key Components of Biosafety

Effective biosafety controls include several key components:

• Facility Design: The laboratory design itself plays a critical role in biosafety. Well-designed facilities incorporate physical barriers like self-closing doors, airlocks, sealed walls, and HEPA-filtered ventilation systems to control airflow and prevent the escape of pathogens. The layout of the lab should also minimize the risk of contamination by separating areas with different biosafety levels.
• Engineered Equipment: Safety equipment plays a critical role in biosafety. Primary barriers in biosafety are physical containment measures (e.g., biological safety cabinets (BSCs), centrifuge safety cups) that protect laboratory personnel and the environment from exposure to infectious agents. They are the first line of defense against splashes, spills, and aerosols that can contain harmful biological agents.
• Safe Work Practices, Training, and Procedures: Safe lab practices minimize the risk of exposure to infectious agents by ensuring aseptic techniques, having established protocols for handling biological materials, and ensuring proper disposal of biohazardous waste.
• Risk Assessment: A biosafety risk assessment is a systematic process that identifies, evaluates, and mitigates risks associated with the use of biological agents. It aims to:
  • Identify procedural hazards and hazardous characteristics of biological agents that are handled.
  • Classifies biological agents into risk groups according to their degree of risk of infectivity, virulence, pathogenicity, availability of preventive measures and effective treatments, and potential damage to the environment.
  • Determine the appropriate biosafety level for controls and restriction understanding.
  • Consider biosecurity which focuses on the prevention of theft, loss, and misuse of hazardous biological agents and toxins, equipment, and/or valuable information.
  • Identify and implement controls to minimize the risk of exposure to biological agents for workers, the environment, and the community.
  • Ensure regulatory compliance with local and national biosafety regulations.

Learn how to create your own biosafety risk assessments in our guide.

• Personal Protective Equipment: Personal protective equipment (PPE) like gloves, gowns, and respirators are the last line of defense and provide additional protection. This combination minimizes the risk of exposure to infectious agents during laboratory procedures.

Maintaining Biosafety in the Workplace

Improving biosafety hazard identification, evaluation and control can be attained by doing the following:

• Form a Biosafety Committee: A dedicated committee with expertise in biosafety and laboratory operations should oversee the implementation and monitoring of biosafety research and operations. This committee, composed of laboratory personnel, EHS professionals, and operational stakeholders should oversee all aspects of biosafety within the facility.  Their responsibilities include reviewing proposed research activities for EHS concerns, approving the use of biological agents, and ensuring compliance with biosafety requirements.
• Conduct Regular Risk Assessments: Employers must conduct biosafety risk assessments to determine which employees have exposures from work activities or conditions that are reasonably anticipated to elevate risk of contracting a disease caused by an infectious agent. Laboratories that perform procedures with biohazardous materials that contain transmissible pathogens are likely to have occupational exposure to these agents. A biosafety risk assessment evaluates risks, so the appropriate control measures are implemented to prevent illness. Regularly assess the risks associated with the biological agents being handled and adjust protocols as needed.
• Develop Emergency Preparedness and Response Plans: Emergency preparedness and response plans must cover procedures to handle spills and exposures, an exposure control plan, accessing emergency contact information, and how to report an incident involving biohazards. Facilities where human blood or other potentially infectious materials (OPIM) exist as an occupational hazard should have a specific exposure control plan. The purpose of the exposure control plan is to describe practices to prevent or minimize exposure to human blood, bloodborne pathogens (BBP), or other potentially infectious materials (OPIM).
• Training and Competency: The priority of biosafety training is to reduce the risks of biological agents through awareness and work practices. Do the following in biosafety training:
  • Communicate containment measures associated with biological risk groups and biosafety levels.
  • Educate laboratory personnel on safe practices for handling biological materials and the prevention of laboratory acquired infections.
  • Ensure compliance with regulations.

Challenges and Solutions in Biosafety Management

Challenges to managing effective biosafety programs include:

• Keeping Up with Emerging Threats: New and emerging infectious diseases can pose unforeseen risks.
  • Regular biosafety program reviews and updates are critical to address evolving threats. Design biosafety programs with the ability to adapt to new threats.  
  • Encourage collaboration and information sharing between researchers, institutions, and public health agencies to stay informed about emerging pathogens.
  • Regularly review risk assessments to categorize biological agents and adjust biosafety levels as needed.

• Resource Limitations: Implementing and maintaining stringent biosafety measures can be resource-intensive, especially for smaller labs. Biosafety programs require investment in proper facilities, equipment, training, and qualified personnel.
  • Explore grant opportunities and public-private partnerships to secure funding for biosafety infrastructure and training.
  • Focus resources on areas with the highest risk profiles.
  • Technology like hazardous substance inventory software can help address some of these challenges by streamlining processes, improving data management, and facilitating regulatory compliance.
• Training Needs: Ensuring continuous training for all laboratory personnel requires ongoing commitment and resources.
  • Implement training programs to keep staff up-to-date on the latest biosafety protocols and techniques.
  • Conduct regular assessments to evaluate staff proficiency in biosafety procedures.
  • Best practices like sharing resources and collaborating with institutions or industry groups can optimize training programs and cost-effectiveness.

Biosafety Regulatory Bodies and International Standards

Organizations like the CDC, NIH, and WHO play a crucial role in establishing and promoting global biosafety standards. Their guidelines offer a framework for national regulations and promote international collaboration in research.

• Centers for Disease Control and Prevention (CDC) - In collaboration with the National Institutes of Health (NIH), the CDC publishes the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines.  This document serves as the primary reference for safe laboratory practices in the US, outlining recommendations for containment measures, biosafety levels, and safe work practices for various biological agents.

• National Institutes of Health (NIH) – In addition to the BMBL guidelines mentioned above, the NIH sets biosafety requirements for institutions that receive NIH funding for research involving biological agents.

• World Health Organization (WHO) - The WHO issues biosafety guidelines that provide recommendations for laboratory practices in resource-limited settings and other international contexts.

Conclusion

In conclusion, biosafety controls are crucial during the design of laboratories and operational stages. Engineering controls, safe work practices, risk assessments, biosafety stakeholder engagement, and PPE will assist in mitigating risks to as low as reasonably achievable. By proactively identifying potential and emerging hazards associated with biological agents – including storage, use, and modification – and including the appropriate cross-functional interested parties in management of biosafety, organizations can establish a strong foundation for continual improvement of biosafety programs.

Materials that are hazardous due to their biological or infectious properties are called biohazardous materials or simply biohazards. Research laboratories work with a variety of biological agents including recombinant or synthetically derived nucleic acid, blood, tissues, body fluids, cell lines, bacteria, viruses, viral vectors, plasmids, fungi, prions, or parasites that cause disease in humans, animals, or plants. Recognizing these hazards and their associated risks is essential for implementing effective health & safety controls.

Understanding Biological Hazards

To ensure the safety of people, the environment, and the public, it’s crucial to have proper biosafety measures in place. Certain workplace safety laws require employers that are exposed to transmissible infectious pathogens to have effective written safety plans and controls in place. Laboratories that perform procedures with biohazardous materials that contain transmissible pathogens are likely to have occupational exposure to these agents.

Here are some common biohazard examples found in laboratories:

• Blood and Other Potentially Infectious Materials (OPIM): Human and animal blood, along with other bodily fluids, can transmit various pathogens.
• Laboratory Cultures and Samples:Working with cultured microorganisms and biological research materials carries the risk of exposure to infectious agents.
• Contaminated Sharps: Used syringes, needles, and other sharp instruments contaminated with biohazardous materials pose a significant risk of puncture wounds and potential infection.
• Fungal Growth: Mold and yeast can thrive in laboratory environments, causing respiratory problems and allergic reactions.
• Waste from Animals: Animal waste or contaminated bedding can harbor harmful pathogens.

Risk Groups and Biohazard Safety Levels

Biological agents are classified according to their risk level when considering infectivity, pathogenicity and availability of preventive measures and treatments for the corresponding disease. The National Institute of Health has established classification of biological agents into four risk groups (1-4).

The Centers for Disease Control and Prevention (CDC) define biosafety levels through the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines. The BSL framework is intended to categorize laboratories based on the risk posed by the biological agents they handle. This framework outlines four biosafety levels (BSL-1 to BSL-4) with increasing stringency in safety practices and containment measures. See our Biosafety Guidelines guide for more information on these risk groups and BSLs.

5 Opportunities to Prevent Biohazards Through Effective Biosafety and Biosecurity Governance

1. A single, centralized system for biological registration approvals

There's a growing demand for stricter regulations regarding oversight. Organizations in the US receiving federal funding have to comply with NIH Guidelines, and the White House OSTP recently introduced a comprehensive Policy for Oversight of Dual Use Research of Concern and Pathogens with Enhanced Pandemic Potential (DURC), effective from May 6, 2025. Similarly, the EU has its own directives concerning exposure to biological agents.

The emphasis is shifting from considering robust registration and approval systems for work involving biological agents as optional to regarding them as essential. Having a single, centralized system for biological registration approval ensures an organization adheres to high standards for biosafety, regulatory compliance, and internal processes with efficient oversight of projects, biological materials, and potential exposures.

2. Biosecurity - Who is working on what? Where?

Understanding who is working on what and where within a biomedical lab presents a pivotal opportunity for enhancing overall biohazard prevention strategies. By implementing comprehensive biological registration and authorization systems, organizations can gain real-time visibility into project activities, including tracking material, personnel, and space exposure levels.

Streamlining registration management processes reduces approval times and facilitates efficient communication, ensuring robust oversight and risk management throughout the project lifecycle.

Furthermore, driving consistency in materials management through standardized biomaterials libraries enables organizations to establish controls over materials usage, personnel, and locations, thereby enhancing biosecurity and internal compliance processes. This holistic approach to understanding lab activities fosters a culture of accountability and transparency, ultimately mitigating risks associated with biohazard incidents and safeguarding the well-being of personnel and the broader community.

3. Institutional Biosafety Committee Oversight for Biologicals Projects

Expanding the requirement for institutional biosafety committees to encompass all institutions engaged in high-containment laboratory activities presents a significant opportunity to enhance effective biosafety and biosecurity governance. Currently, only federally funded institutions conducting recombinant or synthetic nucleic acid research are mandated to have such committees, leaving privately funded institutions exempt from compliance. This discrepancy in regulatory oversight underscores the need for a level playing field, ensuring that all institutions conducting similar high-risk activities adhere to standardized review processes and oversight mechanisms.

By mandating a universal requirement for local oversight bodies, potential risks associated with laboratory-acquired infections and biological incidents can be mitigated. This approach not only ensures a consistent level of review and approval across all institutions but also addresses public concerns regarding the oversight and input of risky research conducted within communities. While there are challenges such as additional bureaucracy for non-compliant organizations and the need to establish and operate biosafety committees, the benefits of standardized oversight outweigh the disadvantages, promoting transparency, safety, and community trust in biosafety practices.

4. De-risking the Organization and Personnel with Training and Assessment of Biohazards

In high-containment laboratories like BSL-3 and BSL-4 facilities, personnel handle indigenous or exotic agents capable of causing severe or fatal diseases. Training in the safe handling of such pathogens is crucial for personnel, who must be supervised by knowledgeable scientists. However, the consistency and rigor of this training vary across institutions, highlighting the need for standardized assessment and guidance to ensure personnel proficiency and risk awareness.

Establishing minimum education and training requirements for personnel in high-containment laboratories offers several benefits for effective biosafety governance. It fosters confidence in the competence of individuals working with hazardous pathogens, promoting trust within the community. Moreover, by ensuring that personnel understand and can manage risks effectively, such standards enhance overall laboratory safety. However, implementing new requirements may pose challenges, particularly in adapting regulations across diverse research operations and ensuring compliance among existing workforce members. Despite these obstacles, standardized training protocols are essential for safeguarding personnel, institutions, and the wider community from biohazard risks.

5. Inspections and Medical Surveillance

By implementing robust biosafety management protocols with software such as SciSure for Health & Safety (formerly SciShield), organizations can identify personnel handling biological agents and mitigate exposure risks effectively. Leveraging Learning Management Systems (LMS) presents an opportunity to automate training assignments tailored to individual staff members' risk exposures, ensuring they receive necessary medical clearances, vaccinations, and skill enhancements. Simultaneously, optimizing equipment management processes ensures the availability and functionality of safety gear, bolstering biosecurity measures.

Strategic biosafety inspections of laboratory spaces provide organizations with a chance to ensure compliance with safety protocols, promptly addressing vulnerabilities. Embracing medical surveillance systems enables proactive tracking and management of personnel health, facilitating timely interventions such as medical clearances and vaccinations. By capitalizing on these opportunities, organizations can fortify their biosafety and biosecurity frameworks, creating safer environments for personnel and the wider community.

Managing Biological Hazards with Software

Implementing a robust biosafety program is essential to protect yourself, your colleagues, and the environment from biohazards. Investing in biological software solutions can significantly enhance your biohazard management practices, promoting safety, regulatory compliance, and overall lab hazard awareness.

Biotech R&D has birthed some of the most impressive innovations, from recombinant DNA to genome editing. While the road to commercialization has always been challenging, many fundamental barriers to innovation have grown bigger. Currently, there’s an overload of unstructured data and ideas. Translating these into world-changing ideas presents a huge organizational and logistical challenge.

With the rise of generative AI in the past year, a solution to some of these issues is on the horizon. Generative AI can promote divergent thinking, challenge the bias of experts, evaluate and refine ideas, and facilitate collaboration across niche research areas. It’s also streamlining the data lifecycle and changing the creative aspects of biotech lab operations, such as automating and improving the quality of content, from lab notebooks to published scientific literature. 

In the following blog, we look at what generative AI is, how it works, and its applications in biotech and the broader life sciences.

What is Generative AI? 

Generative AI, or Gen AI, is a class of artificial intelligence techniques and algorithms that generate new data samples or content, including audio, code, images, text, simulations, and videos. 

Unlike discriminative models that focus on classification or prediction tasks based on existing data, generative models learn the underlying patterns and structures of the data to generate new instances that are statistically similar to the training data.

Gen AI Encompasses a Wide Range of Algorithms and Approaches

Gen AI has applications across various domains, including image generation, text synthesis, music composition, drug discovery, and content creation. It enables the generation of realistic and diverse data samples, facilitates data augmentation for training machine learning models, and fosters creativity and innovation in AI-driven applications.

There are several algorithms and approaches, including:

• Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a competitive manner. The generator learns to generate realistic data samples, such as images, text, or audio, while the discriminator learns to distinguish between real and generated samples. Through adversarial training, GANs produce high-quality synthetic data that closely resembles the distribution of real data.
• Variational Autoencoders (VAEs): VAEs are probabilistic generative models that learn to encode and decode data samples into a lower-dimensional latent space. By sampling from the latent space and decoding the samples back into the original data space, VAEs can generate new data samples that capture the variability and structure of the training data. VAEs are commonly used for generating images, text, and other complex data types.
• Autoregressive Models: AutoRegressive models, such as autoregressive neural networks (ARNs) and autoregressive moving average (ARMA) models, generate data sequences by modeling the conditional probability distribution of each data point given the previous observations. By iterative sampling from the conditional distribution, autoregressive models generate sequences of data samples, such as time series data, speech, or text.
• Transformers: Transformers are a class of deep learning architectures that have achieved state-of-the-art performance in natural language processing (NLP) tasks. Transformers, particularly variants like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), can generate coherent and contextually relevant text by modeling the relationships between words or tokens in a sequence.

How Does Gen AI Work?

Gen AI uses various techniques – including neural networks and deep learning algorithms – to identify patterns and generate new outcomes based on them. The training process for a generative model involves feeding it a large dataset of examples, such as images, text, audio, and videos. While traditional AI aims to perform specific tasks based on predefined rules and patterns, gen AI goes beyond this limitation and strives to create entirely new data that resembles human-created content. A language model is an example of gen AI, using a probabilistic model of a natural language that can generate probabilities of a series of words based on text corpora in one or multiple languages it was trained on. Large language models, in their most advanced form, are a combination of feedforward neural networks and transformers.

In the area of natural language processing, there is also a neural network, a method in artificial intelligence that teaches computers to process data in a way inspired by the human brain. Deep learning is a type of machine learning process that uses interconnected nodes or neurons in a layered structure that resembles the human brain. These algorithms can take different data inputs and be used for speech and voice recognition.

How is Gen AI Currently Used in Biotech?

Gen AI is increasingly utilized in biotech and life sciences across several applications, leveraging its ability to generate realistic and diverse data samples. Some key areas where Gen AI is currently being used in biotech and life sciences include:

• Drug Discovery and Development: Gen AI generates novel molecular structures with desired properties for drug candidates. Generative models like GANs and VAEs can generate new chemical compounds with specific pharmacological properties, helping identify potential drug candidates and accelerating the drug discovery process.
• Protein Design and Engineering: Gen AI techniques are employed to design and engineer proteins with enhanced functionalities or specific biological activities. Researchers can design novel enzymes, antibodies, or therapeutics for various applications, including enzyme engineering, drug delivery, and immunotherapy, by generating protein sequences or structures with desired properties.
• Biological Image Synthesis: Generative models synthesize realistic biological images, such as microscopy images of cells, tissues, or organisms. These synthesized images can be used to augment training datasets for image analysis algorithms, improve the generalization of machine learning models, and generate data for virtual screening and testing of algorithms in silico.
• Omics Data Generation: Gen AI techniques are applied to generate synthetic omics data, including genomics, transcriptomics, proteomics, and metabolomics data. Synthetic omics data can be used to supplement real experimental data, simulate biological processes, and validate computational models, enabling researchers to explore complex biological systems and discover biomarkers or therapeutic targets.
• Text and Literature Generation: Generative models generate text-based content, such as scientific articles, literature reviews, or drug interaction reports. These generated texts can assist researchers in literature mining, knowledge discovery, and data summarisation, facilitating literature-based research and biotech and life sciences decision-making.
• Biomolecule Design and Synthesis: Gen AI techniques are used to design and synthesize novel biomolecules, such as peptides, aptamers, or nucleic acids, with specific functions or properties. By generating sequences or structures with desired characteristics, researchers can develop biomolecules for diagnostics, therapeutics, and biosensing applications.

Overall, Gen AI is revolutionizing biotech and life sciences by enabling the generation of novel data samples, molecules, and biological entities, fostering innovation, and accelerating research and development efforts in various domains. As the field continues to advance, Gen AI is expected to play an increasingly pivotal role in shaping the future of biotechnology and life sciences, including clinical research. Gen AI can help identify which patient cohorts will respond best to specific drugs, developing more personalized medicine. On the operational and marketing sides, generative AI can optimize the supply chain, manufacturing processes, and marketing and advertising strategy. 

What Are the Concerns About Using Generative AI in Biotech?

• Data Quality and Bias: Gen AI models rely heavily on the quality and representativeness of training data. In biotech and life sciences, datasets may suffer from biases, inaccuracies, or limited diversity, impacting performance and generalization ability. Biases in training data can lead to the generation of biased or unrealistic samples, hindering the reliability and validity of generated results.
• Interpretability and Trustworthiness: Many Gen AI techniques, such as deep neural networks, are complex and opaque, making it challenging to interpret and trust the generated outputs. In critical applications like drug discovery and biomolecule design, it is crucial to understand how and why generative models generate specific outputs. Lack of interpretability can impede the adoption of Gen AI in decision-making processes and regulatory approval, limiting its utility in real-world applications.
• Mapping Data: Mapping data in biotech and life sciences presents challenges due to the complexity and heterogeneity of biological systems and the diversity of data types and sources. Integrating and mapping heterogeneous data types requires harmonizing data structures, ontologies, and metadata to ensure data consistency across datasets. Challenges in mapping data can affect the accuracy and reliability of Gen AI models trained on such data, impacting their performance and applicability in real-world scenarios. Addressing issues related to mapping data is essential to ensure the quality and reliability of data inputs for Gen AI models in biotech and life sciences. 

Day-to-Day Usage of AI for Lab Operations

Above, we’ve listed the broader research applications in which Gen AI can be used. However, in day-to-day lab operations, the average life sciences and biotech scientist can utilize Gen AI in several ways. Gen AI can replace manual and repetitive tasks, freeing up resources for more complex and creative tasks. 

• Data Analysis and Interpretation: Gen AI can assist scientists in analyzing and interpreting experimental data more efficiently. By employing machine learning algorithms, scientists can train models to recognize patterns, identify correlations, and extract meaningful insights from complex datasets, such as omics data, biological images, or high-throughput screening results. Gen AI can automate data analysis tasks, streamline data interpretation processes, and provide actionable insights to guide experimental design and decision-making in the lab.
• Experimental Design and Planning: Gen AI can aid scientists in designing and planning experiments by generating hypotheses, optimizing experimental conditions, and predicting outcomes. By leveraging predictive modeling techniques, scientists can simulate experimental scenarios, predict experimental outcomes, and identify optimal experimental parameters to achieve desired objectives. Gen AI can assist in experimental design optimization, resource allocation, and risk assessment, helping scientists make informed decisions and maximize experimental efficiency in the lab.
• Literature Mining and Knowledge Discovery: Gen AI can assist scientists in literature mining and knowledge discovery by analyzing scientific literature, extracting relevant information, and synthesizing knowledge from diverse sources. Natural language processing (NLP) techniques can extract key concepts, identify relationships between scientific entities, and summarise findings from research articles, patents, and databases. Gen AI can automate literature review processes, facilitate literature-based research, and accelerate knowledge discovery in specific research areas, enabling scientists to stay up-to-date with the latest advancements and make informed decisions in the lab.

Summary

Integrating generative AI into the biotech industry brings significant benefits and notable concerns. By leveraging generative AI, researchers can tackle the challenge of navigating through vast amounts of unstructured data and ideas, fostering divergent thinking, and facilitating collaboration across niche research areas. Moreover, generative AI streamlines various aspects of biotech lab operations, from automating content generation to improving the quality of scientific literature. However, concerns regarding data quality and bias, interpretability, trustworthiness, and data mapping remain pertinent. Despite these challenges, the day-to-day usage of generative AI in lab operations holds promise, enabling scientists to enhance data analysis and interpretation, optimize experimental design and planning, and accelerate literature mining and knowledge discovery. As generative AI continues to evolve, its role in revolutionizing biotech research and development is poised to expand, shaping the future of precision medicine, drug discovery, and other critical areas within the life sciences.

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A Digital Lab Platform (DLP) helps streamline complex and time-consuming tasks by automating, integrating, or optimizing lab operations and workflows. This allows scientists to focus on research rather than administrative tasks, resulting in more efficient use of resources and fewer manual errors. Through an open ecosystem design that seamlessly integrates with existing tools and services, the Developer Hub extends the digital lab platform capabilities to provide a scalable and future-proof solution for life science research and development laboratories. 

In contrast to the one-dimensional nature of traditional ELNs (Electronic Laboratory Notebooks) and LIMS (Laboratory Information Management Systems) systems, the foundation of eLabNext serves as a unifying force, connecting various laboratory operations and making the platform multi-dimensional. This truly transformative platform is designed to improve efficiency, speed of innovation, and seamless integration of digital tools within the laboratory in the world of life science research and development. As a result, the Developer Hub is changing the way scientists and developers work together to improve the way laboratories operate and is leading the way in a new era of digitization for scientists around the world. 

Innovative Digital Features to Accelerate Lab Operations

An open ecosystem is a powerful tool for facilitating sharing and collaboration within and across organizations. By enabling easy integration with a wide range of tools and services, a digital lab platform contributes to a more connected and collaborative research environment. eLabNext Developer is built to provide a broad range of tools to users to customize features for a digital laboratory environment. The laboratory information management platform is designed as an open ecosystem that seamlessly integrates with various tools and services, which increases the system’s flexibility and capabilities. This includes a powerful Software Development Kit (SDK) and an Application Programming Interface (API), which allow developers to innovate, automate, and improve their workflows and system operation. Together, these features empower developers with everything they need to access their next-generation all-in-one Digital Lab Platform capabilities. 

Key SciSure Developer Features for Lab Digitalization

• The first-to-market developer hub for the life science R&D lab platform with a comprehensive, all-in-one platform for access to developer tools for building, deploying, testing, and releasing add-ons to the Marketplace. 
• SciSure extension or customization of workflows and functionality in eLabNext, or extend and integrate existing IT and information systems using our Software Development Kit (SDK) and API. Further increase efficiency, data consistency, and process automation in the lab. 
• Device, product and service providers can use SciSure to connect their products and services to a digital lab used by over 60,000 scientists worldwide to enable the complete digitisation of the laboratory. 
• As a truly scalable digital lab platform, the Developer Hubwill empower R&D laboratories and organizations to adapt to changing market needs and new products by easily integrating software tools, products, and services as they become available. 

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In-House Buildouts vs. Using Readily-Available Software and the Path towards Digital Sustainability

When considering whether to develop in-house software or purchase an off-the-shelf solution for your biotech applications (including lab operations, data analysis, or protein platform analysis), it is important to look at the history of the trends. 

A Brief History of Software as a Service (SaaS) in the Life Sciences

Early Development (Pre-Internet Era - Before the 1990s): Before the widespread adoption of the Internet in the 1990s, life science and pharma labs predominantly developed their own in-house software solutions to meet specific research needs. Custom-built software was standard, tailored to the unique requirements of each lab. Development timelines were lengthy, and costs were high due to the need for specialized expertise and infrastructure.

Proliferation of Commercial Software (1990s - 2000s): With advancements in computing technology and the availability of commercial software, many life science and pharma labs began adopting commercial off-the-shelf (COTS) software solutions in the 1990s and 2000s. These solutions offered standardized features, functionalities, and workflows, reducing the need for extensive in-house development efforts and providing cost-effective alternatives to custom-built software.

Emergence of SaaS Models (Early 21st Century—2000s): The early 21st century saw the rise of Software as a Service (SaaS) models, coinciding with the growth of the Internet and cloud computing. Labs started transitioning from on-premises software to cloud-based SaaS solutions in the 2000s to streamline research workflows, reduce IT overhead, and access specialized features and expertise. SaaS offerings provide advantages such as rapid deployment, scalability, and subscription-based pricing models.

Adoption of Biotech SaaS Solutions (Recent Years - 2010s onwards): In recent years, there has been a growing trend towards adopting specialized biotech SaaS solutions tailored to the unique needs of the life science and pharma industries. Labs increasingly recognize the benefits of leveraging ready-to-use biotech SaaS solutions, such as cost savings, rapid deployment, continuous updates, and compatibility with existing systems. This trend has accelerated since the 2010s, with widespread adoption across the industry.

5 Benefits of Subscribing to SaaS Rather Than Building In-House Software

1. Cost and Time Savings: Off-the-shelf biotech SaaS solutions eliminate the need for extensive development efforts and associated costs, such as hiring specialized developers, infrastructure setup, and ongoing maintenance. By leveraging pre-built solutions, organizations can significantly reduce upfront investment and time-to-market, allowing them to allocate resources more efficiently and focus on core research activities.

2. Access to Specialised Expertise and Features: Off-the-shelf biotech SaaS platforms are often developed by specialized vendors with domain expertise in life sciences and biotechnology. These solutions typically offer advanced features, functionalities, and workflows tailored to specific research needs, providing access to state-of-the-art technologies and methodologies that may be challenging to replicate in-house. By utilizing specialized expertise, organizations can benefit from best practices, industry standards, and cutting-edge innovations without requiring extensive internal development efforts.

3. Rapid Deployment and Scalability: Off-the-shelf biotech SaaS solutions are designed for rapid deployment and scalability, allowing organizations to rapidly implement and scale their research workflows as needed. These platforms typically offer cloud-based infrastructure, automated provisioning, and flexible pricing models, enabling seamless scalability to accommodate growing data volumes, research projects, and user requirements. By leveraging SaaS solutions, organizations can respond more effectively to changing research needs and market demands, ensuring agility and competitiveness in the dynamic biotech landscape.

4. Continuous Updates and Maintenance: Off-the-shelf biotech SaaS solutions are continuously updated and maintained by the vendor, ensuring access to the latest features, security patches, and performance optimizations. By outsourcing software maintenance and updates to the vendor, organizations can minimize the burden on internal IT teams and avoid disruptions to research workflows caused by outdated or unsupported software versions. Continuous updates also enable organizations to stay ahead of regulatory requirements, industry trends, and emerging technologies, ensuring the long-term relevance and sustainability of their research infrastructure.

5. Compatibility and Integration: Off-the-shelf biotech SaaS solutions are designed to be compatible with existing research tools, laboratory equipment, and data management systems, facilitating seamless integration and interoperability. These platforms often offer standardized data formats, APIs, and integration capabilities, enabling organizations to consolidate and streamline their research workflows across multiple applications and platforms. By leveraging compatible SaaS solutions, organizations can maximize the value of their existing investments, improve data accessibility and collaboration, and enhance overall research productivity and efficiency.

New Trend: White Label Software in Big Pharma

The trend of big pharma purchasing white-label software and using it internally reflects a strategic shift towards maintaining control over proprietary data and processes while leveraging external technology solutions. Rather than relying on third-party vendors for software development and data management, big pharma companies customize and deploy white-label software solutions internally, enabling them to safeguard sensitive data, streamline operations, and maintain a competitive edge. 

White Label Software Examples

Customized Laboratory Information Management Systems (LIMS): Big pharma companies may purchase white-label LIMS software and tailor it to their specific laboratory workflows and data management needs. By deploying customized LIMS internally, they can securely manage and track experimental data, samples, and workflows without relying on external vendors.

In-House Clinical Trial Management Platforms: Rather than outsourcing clinical trial management to third-party vendors, big pharma companies are investing in white label clinical trial management platforms that can be customised to meet their unique requirements. This allows them to control trial data, patient records, and regulatory compliance while streamlining the clinical trial process.

Proprietary Data Analytics Platforms: Big pharma companies often require advanced data analytics capabilities to analyze large-scale biological datasets, identify potential drug targets, and optimize research strategies. By purchasing white-label data analytics platforms and customizing them internally, they can leverage powerful analytics tools while protecting proprietary data and intellectual property.

Internal Collaboration and Communication Tools: To facilitate collaboration and communication among research teams, big pharma companies may adopt white-label collaboration platforms, project management tools, and communication software. These internal solutions enable secure collaboration, document sharing, and real-time communication while ensuring data privacy and confidentiality.

Regulatory Compliance and Quality Management Systems: Big pharma companies must adhere to stringent regulatory requirements and quality standards throughout drug development. By internally deploying white-label regulatory compliance and quality management systems, they can ensure compliance with regulatory guidelines, track quality metrics, and manage audit trails without exposing sensitive data to third-party vendors.

The trend of big pharma purchasing white-label software and using it internally underscores the importance of data privacy, security, and control in the highly regulated pharmaceutical industry. By customizing and deploying internal software solutions, big pharma companies can harness the benefits of external technology while safeguarding proprietary data and maintaining compliance with regulatory standards.

Digital Sustainability's 3 Main Pillars: Expand, Integrate, and Support!

To ensure sustainability (that is, maintaining optimal performance in the long term) when implementing digital tools, particularly when purchasing SaaS solutions, biotech companies should focus on the following:

Scalability: Choose SaaS solutions that offer scalability to accommodate the company's growth and evolving needs. Prioritise platforms that can seamlessly scale resources, such as storage, computing power, and user licenses, as the company expands its operations, increases data volumes, or introduces new products and services. Regularly assess scalability requirements and adjust subscription plans or configurations to support ongoing growth and innovation.

Integration and Customisation: Select SaaS solutions that offer robust integration capabilities and customization options to align with the company's existing systems, workflows, and unique requirements. Ensure that the chosen platforms support open APIs, data interoperability standards, and flexible configuration settings, allowing seamless integration with internal databases, laboratory equipment, and third-party applications. Collaborate closely with the SaaS vendor to tailor the solution to the company's specific needs, workflows, and business objectives, leveraging customization features, workflow automation tools, and professional services as needed.

Maintenance and Support: Prioritize SaaS solutions that provide reliable maintenance, support, and updates to ensure ongoing performance, security, and compliance. Choose vendors with a proven track record of delivering timely software updates, patches, and enhancements, as well as responsive customer support services and technical assistance. Establish clear service-level agreements (SLAs) and communication channels with the vendor to address any issues, resolve technical challenges, and provide training and support to end-users. Regularly review and optimize software configurations, monitor performance metrics, and conduct user feedback sessions to identify improvement areas and ensure the digital tools' long-term sustainability.

By implementing these strategies, a biotech company can scale up its operations, integrate and customize digital tools to meet its specific needs and ensure ongoing maintenance and support for sustainable digital transformation. This approach enables the company to leverage the benefits of SaaS solutions while maximizing efficiency, innovation, and competitiveness in the rapidly evolving biotech industry.

The Big But! 

If your company has a unique workflow or requires a platform or solution that cannot be fulfilled by off-the-shelf SaaS options and decides to build software in-house, integrating it with existing SaaS workflows presents a unique challenge. However, there are several steps the company can take to ensure successful integration:

1. Identify Integration Points: Analyse the in-house software's functionality and determine where it intersects with existing SaaS workflows. Identify integration points where data exchange or interaction between the in-house software and SaaS platforms is necessary.

2. Standardize Data Formats and Protocols: Establish standardized data formats, protocols, and APIs for data exchange between the in-house software and SaaS platforms. Ensure compatibility with common data standards and industry-specific formats to facilitate seamless integration and interoperability.

3. Implement Middleware or Integration Tools: Use middleware or integration tools to facilitate communication and data exchange between in-house software and SaaS platforms. Implement APIs, web services, or middleware solutions that can translate data between different systems, synchronize data in real time, and orchestrate workflows across multiple platforms.

4. Customize SaaS Platforms: Work with SaaS vendors to customize their platforms or APIs to accommodate the unique requirements of the in-house software. Collaborate closely with vendors to develop custom integrations, plugins, or extensions that enable seamless interoperability with the in-house solution while leveraging the scalability, reliability, and features of the SaaS platforms.

5. Develop Custom Connectors or Plugins: Build custom connectors, plugins, or adapters to facilitate integration between the in-house software and SaaS platforms. Develop custom scripts, APIs, or middleware components that bridge the gap between different systems, allowing data to flow bi-directionally and workflows to be synchronized effectively.

6. Implement Data Governance and Security Measures: Establish data governance policies, access controls, and security measures to protect sensitive information and ensure compliance with regulatory requirements. Implement encryption, authentication, and authorization mechanisms to secure data transmission and access between the in-house software and SaaS platforms.

7). Test and Validate Integration: Conduct thorough testing and validation of the integration between the in-house software and SaaS platforms to ensure reliability, accuracy, and performance. Test data exchange workflows, error handling mechanisms, and system interactions under various scenarios to identify and address any issues or discrepancies.

8. Provide Training and Support: Offer training and support to end-users to familiarise them with the integrated workflows and software interfaces. Provide documentation, tutorials, and user guides to help users navigate the integrated environment effectively and maximize productivity.

Conclusion

By following these steps, life science companies can effectively integrate their in-house software with existing SaaS workflows, enabling seamless data exchange, collaboration, and workflow orchestration across the organization. This approach allows the company to leverage the benefits of in-house and SaaS solutions while optimizing efficiency, innovation, and competitiveness in its operations.

However, if you're beginning your digital journey, there are many benefits to choosing a SaaS over building an in-house platform. 

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What are Safety Data Sheets (SDS)? And what is the Globally Harmonized System of Classification and Labeling of Chemicals (GHS)?

How do chemical manufacturers, suppliers and users communicate chemical hazards and handing information to each other across the globe?  The Globally Harmonized System of Classification and Labeling of Chemicals (GHS) is a United Nations led international system provides a consistent approach to classifying and communicating the hazards of chemicals ensuring that chemicals are evaluated using the same criteria regardless of country, resulting in standardized labels and Safety Data Sheets (SDS).  

The GHS is the culmination of more than a decade of work. There were many individuals involved, from a multitude of countries, international organizations and other stakeholders. Their expertise spanned a wide gamut of subject matter expert areas including toxicology and fire protection. The GHS improves safety for workers around the world by ensuring they understand the risks associated with the chemicals they handle. GHS also simplifies international trade by eliminating the need for companies to re-label chemicals for different countries. There is currently a GHS Sub-Committee that is responsible for maintaining the GHS, promoting its implementation, and providing additional guidance as needs arise. The GHS guidance document is regularly revised and updated to reflect best practices and regulations. The latest version of the GHS guidance can be found here.

Safety Data Sheets (SDS) are critical for anyone who handles chemicals, from scientists in chemical laboratories to house cleaners using disinfectants. An SDS provides important information about a chemical's properties, hazards, and safe handling practices. In short, it's your guide to using chemicals safely and effectively.

What is the composition of a Safety Data Sheet?

An SDS is a comprehensive guide for storing and handling a chemical. Here's a breakdown of the key sections:

• Identification: This section gives you the chemical or product name, its intended uses, and the supplier contact details including emergency phone number. It also clarifies any restrictions on how the chemical should be used.
• Hazard(s) Identification: This section details the hazards of the chemical, including its classification (flammable, toxic, etc.), pictograms that illustrate these hazards, and specific GHS hazard statements and standardized codes that communicate the risks of exposure to the user.
• Composition/Information on Ingredients: This section reveals the ingredients that make up the chemical, including synonyms and chemical formulas. It highlights any substances that pose risks.
• First-Aid Measures: Here's where to find critical information on what to do in case of accidental exposure to the chemical, including inhalation, skin contact, ingestion, and eye contact.
• Fire-Fighting Measures: If a fire breaks out, this section provides guidance on the best way to extinguish it, what specific hazards to be aware of from the burning chemical, and the recommended protective actions and gear for firefighters.
• Accidental Release Measures:  This section outlines the steps to take in case of an accidental release, including how to protect yourself, the environment, and how to clean up the spill safely.
• Handling and Storage: Learn how to handle the chemical safely during everyday use and how to store it properly to prevent incidents or degradation.
• Exposure Controls/Personal Protection: This section details the occupational exposure limits (OELs) for the chemical and recommends appropriate engineering controls or personal protective equipment (PPE) to minimize exposure risks.
• Physical and Chemical Properties: Familiarize yourself with the chemical's basic properties like appearance, odor, boiling point, and flash point in this section.
• Stability and Reactivity: This section explains how stable the chemical is and what conditions could trigger hazardous reactions. It also identifies incompatible materials to avoid storing near the chemical.
• Toxicological Information: This section includes details on the potential health effects of exposure to the chemical, including both acute (such as irritation) and chronic (such as carcinogenicity) effects.
• Ecological Information: This section focuses on the chemical's impact on the environment, including its toxicity to aquatic and terrestrial organisms, its persistence, and how it moves through the soil.
• Disposal Considerations: This section provides guidance on proper waste treatment methods and how to handle contaminated waste containers.
• Transport Information: If you need to transport the chemical, this section provides information on UN identification numbers, shipping classifications, and any special precautions for transport.
• Regulatory Information: This section details relevant safety, health, and environmental regulations governing the specific chemical.
• Other Information: This section might include the date of the latest SDS revision, additional safety advice, and references for further information.

The GHS uses a set of pictograms to visually communicate hazards.

Taken from: https://www.osha.gov/sites/default/files/publications/OSHA3491QuickCardPictogram.pdf

Conclusion

The SDS should provide comprehensive information about a substance or mixture for use in workplace chemical control regulatory frameworks. Both employers and workers use it as a source of information about hazards, including environmental hazards, and to obtain advice on safety precautions. This information is a reference for the management of hazardous chemicals in the workplace. An SDS should be produced for all substances and mixtures which meet the harmonized criteria for physical, health, or environmental hazards under the GHS and for all mixtures which contain ingredients that meet the criteria for carcinogenic, toxic to reproduction or specific target organ toxicity in concentrations exceeding the cut-off limits for SDS specified in the GHS guidance.  SDSs are often mandated by regulations, and having them on-hand ensures compliance.  The information in an SDS is valuable for risk assessments and training programs, promoting an overall safer work environment. Other factors to manage with SDSs at your organization are:  

• Accessibility and Understanding: Many employers provide SDSs electronically in a centralized system that is accessible to all employees and/or they keep paper copies. Familiarize yourself with how to access and read an SDS. SciSure can help you manage and access SDS documents electronically, thereby ensuring you always have the most up to date SDSs onhand. Learn more about our solutions on the Chemical Safety Software page.

• Changes to an SDS: Regulations and toxicological understanding of chemicals can evolve. It's crucial to ensure the Safety Data Sheets in your inventory are up to date. Suppliers should also periodically review the information on which the SDS for a substance or mixture is based, even if no new and significant information has been provided. This requires searching chemical hazard databases for new information. Typically, suppliers should review SDS information every 3 to 5 years.
• Creating and Managing SDS: If you're a chemical manufacturer or distributor, there are resources to create and manage your SDSs.

Schedule a demo with us here to learn more.

This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

eLabNext is proud to announce it is now a Business Supporter of the World Wildlife Fund Netherlands (WWF-NL). This supporting collaboration has facilitated the protection of an area of the Atlantic Rainforest equivalent to eight football fields from deforestation over the past year. This contribution underscores eLabNext's commitment to sustainability and the tangible results of its collaboration with the WWF. For every new team member who joined the company last year, eLabNext donated €2,500 to the WWF.

In the world of scientific research, the shift towards more sustainable operations is crucial. eLabNext is at the forefront of this transformation, dedicated to modernizing life science R&D labs by transitioning from traditional pen and paper to a digital research environment. Our aim is to foster an eco-friendly approach to scientific work, leveraging our DLP to enhance efficiency and reduce environmental impact. "This partnership is a big part of how we're trying to apply our green values and motivating the broader scientific community to join in on making an impact," says Bastiaan Spijk, Head of Business Operations & People at eLabNext.  

At the heart of eLabNext's sustainability ethos are its digital solutions designed to minimize environmental impact. By championing digital sample management and reducing waste, eLabNext helps labs transition toward more sustainable operations. These initiatives are part of a broader strategy to promote eco-friendly practices, including optimizing resource use and enhancing energy efficiency, thereby contributing to a greener planet. For instance, the eLabNext Digital Lab Platform enables researchers to manage their samples more efficiently, reducing the need for physical storage. This not only supports environmental goals but also improves lab productivity and data reliability, showcasing how sustainable practices can enhance scientific outcomes. 

In many places, the Atlantic Rainforest shows what happens when you deforest: animals unique to the area are on the brink of extinction, the climate is becoming drier and hotter, and water shortages are starting to develop. But it is not too late! Together with the indigenous and local peoples, we will restore and protect the forest and ensure that a bright future dawns again for people as well as animals.

Merijn van Leeuwen, Coordinator Amazon & Atlantic Forest WWF Netherlands 

Doubling down on deforestation 

eLabNext has broadened its dedication to environmental sustainability by partnering with Trees For All and One Tree Planted, in addition to our original collaboration with WWF. To involve the scientific community in efforts to combat deforestation, we have introduced a referral program. This initiative invites individuals from the life sciences community and beyond to recommend new potential customers to eLabNext. As a token of our appreciation, we offer both a monetary incentive and an equal donation to our environmental charity partners in the name of the referrer. Additionally, participants receive a certificate to acknowledge their contribution and dedication to environmental preservation. 

"Our referral program is more than just a way to grow our business; it's a testament to our dedication to sustainability and helping labs go paperless," says Hovik Torkomyan, Head of Global Marketing at eLabNext. "By offering our clients and the wider life science community the opportunity to support reforestation efforts directly, we're not just rewarding them; we're also making a tangible impact on the environment. When a referrer chooses to support Trees For All or One Tree Planted through our program, doubling the donation in their name, it's a powerful statement of shared values and collective action toward a greener future. 

About World Wildlife Fund NL  

The World Wildlife Fund (WWF) started as a protector of animals like the panda. Our challenge is now much greater. Nature loss and climate change affect all life on earth today. That is why WWF is on a mission: We will make our world' Nature Positive'. 

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In life science and biotech laboratories, “big data” has gotten more significant than ever before and shows no signs of stopping. The data pool in most labs is highly diverse (think ‘omics, imaging, etc.), large-scale, and ever-increasing.

This massive amount of diverse data requires constant wrangling. When properly orchestrated, this diverse data can be fully harmonized according to FAIR guidelines, delivering insights that drive scientific breakthroughs. Yet, there are notable challenges and unique hurdles to managing and shaping the challenging topography of the data landscape.

In the following blog, we’ll discuss the impact of these various challenges and provide a solution.

Diversity of Data Types

• The Challenge: Life science laboratories are prolific generators of diverse data types, including genomics, proteomics, metabolomics, and imaging data. The intricate challenge lies in seamlessly integrating and structuring this heterogeneous data into a cohesive framework. Furthermore, the complexity and heterogeneity of this data create issues with integration.
•  The Impact: The incompatibility between various data types acts as a stumbling block, impeding comprehensive analysis and hindering the extraction of profound insights from these multifaceted datasets.

Volume and Scale

• The Challenge: The relentless generation of data in life science and biotech experiments, fueled by advancements like high-throughput technologies, introduces an overwhelming volume that can surpass the capacities of traditional data structuring methods. Genomics alone will generate 2 to 40 exabytes in 2025.
•  The Impact: The sheer magnitude of data becomes a resource-intensive burden, slowing down the analysis process and potentially creating bottlenecks in accessing critical information. As a result, there’s a “data storage crisis” looming over the industry.

Lack of Standardization

• The Challenge: The absence of standardized data formats and structures across laboratories and research institutions presents a formidable challenge, introducing hurdles in data interoperability.
•  The Impact: The resultant lack of harmony in data standards complicates data sharing and collaborative efforts, which is now required by all labs receiving NIH funding. Researchers grapple with integrating and deciphering datasets produced under disparate standards, impeding seamless collaboration and insights extraction.

Temporal and Longitudinal Data

• The Challenge: Longitudinal studies and time-course experiments introduce a temporal dimension, necessitating the structuring of data points across different time intervals.
•  The Impact: The intricate task of structuring temporal data becomes pivotal. Misalignment or improper representation of time-dependent data compromises the accuracy of analyses and poses challenges in identifying dynamic patterns critical for scientific interpretation.

Metadata Complexity

• The Challenge: Many laboratory scientists, particularly those who manage samples using manual, paper-based record-keeping, find capturing and organizing metadata, including experimental conditions, sample details, and procedural information, challenging.
•  The Impact: The completeness and consistency of metadata emerge as linchpins for contextualizing experimental data. Incomplete or inconsistent metadata creates hurdles in reproducing experiments and comparing study results.

Data Security and Compliance

• The Challenge: Ensuring data security and compliance with stringent regulatory requirements, such as GDPR in Europe or HIPAA in the United States, adds additional complexity.
•  The Impact: Unfortunately, cyberattacks, natural disasters, and other calamities can threaten your data. The consequences of inadequate data security measures loom large, with potential breaches jeopardizing the confidentiality of sensitive information and compromising adherence to regulatory standards.

Evolution of Analytical Techniques

• The Challenge: The rapid evolution of analytical techniques and technologies outpaces existing data structures, rendering them outdated.
•  The Impact: Laboratories struggle to adapt data structuring methodologies to accommodate emerging analytical approaches. The lag in adaptation results in inefficiencies and missed opportunities to harness the full potential of cutting-edge technologies.

User Adoption and Training

• The Challenge: Researchers may resist the adoption of standardized data structuring practices due to unfamiliarity or a lack of training.
•  The Impact: The consequential inconsistencies in data structuring hinder collaborative efforts, impede effective data sharing, and disrupt the implementation of standardized analyses. Bridging this gap demands targeted training initiatives and a cultural shift towards embracing structured data methodologies.

Integrating and Mapping the Data

• The Challenge: Mapping biological knowledge from data involves representing complex biological concepts, relationships, and processes in a computationally tractable format. Developing interpretable and semantically rich knowledge representations requires domain expertise, ontological frameworks, and natural language processing techniques to effectively capture and formalize biological knowledge.
•  The Impact: Resolving challenges in mapping life sciences data leads to increased efficiency in data analysis, standardization of methodologies, and improved access to diverse datasets, fostering accelerated scientific discovery and collaboration.

The Solution to Data Management and Mapping Challenges

A strategic blend of technological solutions, standardization efforts, and targeted training initiatives becomes imperative to tackle these challenges. Only through meticulous data structuring can laboratories unlock the full potential of their research endeavors, paving the way for new horizons in life science and biotechnology. This comprehensive approach ensures that data in life science and biotech laboratories is structured optimally, fostering meaningful interpretation, collaboration, and innovation.

Digital lab platforms, such as those offered by SciSure (formerly eLabNext), enable researchers to take a comprehensive approach to data structuring, integration, and management. Contact us today to learn more!

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Optimizing your laboratory's inventory management regime is crucial not only for maintaining the flow of everyday operations but also for upholding research integrity and safety. Thanks to the emergence of smart digital lab solutions, traditional, paper-based inventory logs are swiftly becoming obsolete. 

In this blog, we will highlight:

• Why efficient inventory management is essential
• What lab inventory management involves
• Best practices for lab inventory management
• The benefits of digitalizing your lab's inventory management

Robust inventory management should be the backbone of any successful lab operation. Despite this, many laboratories continue to use outdated methods, like paper-based inventory logging, leading to inefficiencies, waste, and potential errors. Cutting-edge digital solutions, like SciSure (formerly eLabNext) are revolutionizing the way laboratories manage and track their resources. 

Are you ready to master the art of lab inventory management? Read on for our best practices and top tips to help you achieve optimal organization, efficiency, and cost-saving in your laboratory. 

Why is efficient inventory management essential?

Efficient lab inventory management underpins successful daily lab operations. It gives lab personnel streamlined access to the necessary resources, minimizes waste, and reduces costs, all while improving overall lab productivity. By standardizing inventory infrastructure and processes, labs can maintain research integrity by ensuring consistent organization, storage, and application of reagents and materials.

Maximizing the efficiency of inventory management systems is critical for a wide variety of laboratory and clinical settings. From biobanks, to academic research labs, contract research organisations, to commercial laboratories and hospital pathology departments; standardized inventory management is essential for ensuring that lab operations remain robust and reliable.

What does lab inventory management involve?

Lab inventory management encompasses all activities related to procuring, handling, storing and overseeing laboratory materials and equipment. The importance of this process cannot be overstated, as it deals with the availability and quality of the critical components required for scientific operations. 

Typical inventory management processes include tracking stock reagent levels, ensuring correct procurement and overseeing proper usage and storage of sensitive materials. Traditional inventory practices, such as keeping paper-based inventory log books, often fail to meet these challenges effectively due to their segmented and inaccessible nature, along with susceptibility to human error.

Best practices for lab inventory management

Substantial benefits can be achieved by instilling a robust lab inventory management regime. By adopting these best practices, you can not only ensure a smoother operational flow within your lab but also significantly mitigate potential errors, enhance safety compliance and achieve considerable cost efficiencies.

1. Perform regular stock/inventory checks

Regular stock checks maintain the balance of lab inventory, preventing the accumulation of unnecessary items, while identifying those that are expired or nearing expiration. This practice is crucial as expired materials can lead to research disruptions and failed experiments. As such, consistent stock checking can minimise waste, optimize budget utilization and help maintain quality control in the lab. 

2. Accurate labelling and documentation

Thorough labeling and record-keeping are fundamental for seamless lab operation. When managed poorly, labelling errors can lead to contaminated samples, incorrect usage of materials and other costly mistakes. Proper documentation and labelling can significantly improve research reproducibility, reliability and ultimately maintain the integrity of a laboratory's work.

3. Ensure proper storage conditions

Storing reagents and samples in optimal conditions is critical for preserving their integrity. This includes adopting good cold storage management practices, such as regular defrosting, shelving rotation and avoiding large empty spaces in fridges. It is vital to monitor the storage timeframes of sensitive materials as they can become unusable due to heat, cold or moisture exposure.

4. Adopt a first-in, first-out policy

Using older stock first reduces the risk of having to discard expired items and helps in managing costs more efficiently. This system ensures that materials are used in the order they were received, reducing waste and ensuring all items are utilized before their expiration date. 

5. Get on board with digital inventory management systems

Digital lab inventory systems offer an off-the-shelf solution for tracking and managing lab inventory. By digitalizing your lab's inventory management, you can effortlessly monitor material usage, inventory levels, expiration dates, instrument maintenance and more. By enabling you to standardize your inventory management regime across the lab, digital systems can substantially reduce human errors, minimise waste and optimize cost utilization. 

The benefits of digitalizing your lab's inventory management

Digitalization is by far the most effective way to ensure that your lab's inventory management remains efficient and sustainable.

With SciSure's LIMS capabilities, you can effortlessly manage all aspects of inventory tracking, from procurement and stock checks to storage and usage monitoring. With a robust smart digital system in place, your lab will have a solid foundation for successful research outcomes and streamlined workflows. From small start-up labs to large institutions, SciSure can be tailored to fit to your specific requirements. 

Let's take a look at some of the smart digital features that can galvanize your lab inventory management processes:

Visualize your whole inventory

Lab inventory management systems help you digitally visualize your entire lab inventory, making it easy to browse through and locate samples and specimens in your lab storage setup. Offering a centralised platform for all key inventory information, including sample tracking, storage locations, stock levels and procurement information, lab inventory management systems provide all lab staff with access to accurate and up-to-date storage data in real-time.

Streamline procurement processes

Manual procurement of key lab reagents can be tedious, especially when multiple vendors are involved. With the right lab inventory management system in place, you can streamline your procurement procedures by automating purchases when stock levels reach a pre-defined value. This feature helps improve cost efficiency while reducing potential delays in lab processes. Additionally, having all procurement information stored digitally minimizes incorrect ordering due to human error.

Benefit from barcode automation

One of the most significant benefits of digitalizing your lab inventory management is the implementation of barcode technology. With digital solutions like our SciSure Digital Lab Platform, you can quickly generate barcodes for all your lab materials and equipment, streamlining tracking, usage monitoring and facilitating automated reorder processes. Barcode automation significantly reduces the time spent on manual stock checks.

Achieve augmented biobanking

Biobanks, which store biological specimens for research and diagnostic purposes, require robust inventory management. With a LIMS, biobanks can enhance processes by digitalizing sample labeling and tracking, optimal storage conditions, cell passage requirements, and more.

Customize user roles and permissions

By integrating lab inventory management systems into your lab, you can assign individual user roles and permissions, ensuring that only authorized personnel have access to sensitive information and procurement abilities. This feature helps maintain the integrity of data and minimizes the risk of unauthorized use or misplacement of valuable materials.

Reduce instrument downtime  

Lab equipment and instruments are valuable assets that require careful maintenance and management to ensure their longevity. With lab inventory management systems, you can digitally monitor instrument usage, maintenance schedules, and calibration data. This feature helps your lab reduce instrument downtime due to unexpected malfunctions while ensuring optimal performance.

Unlock further features with ELN connectivity

SciSure offers LIMS and ELN capabilities on a single interface. This integration enables the connection of samples and reagents with study data, facilitating enhanced data connectivity, robust audit trails and easier data retrieval. With ELN connectivity, your lab can achieve improved quality control processes, simplified collaboration and streamlined research workflows.

Time to go digital?

The shift towards digital inventory management is not just a trend but a necessary evolution in lab operations. Adopting a system like SciSure can lead to substantial improvements in operational efficiency, cost efficiency and research outputs, making it a worthwhile investment for any laboratory striving for scientific excellence.

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Managing lab operations effectively is no easy feat. It requires attention to detail, standardized processes, and a deep understanding of scientific workflows. Thankfully, digital solutions like SciSure (formerly eLabNext) are transforming the way in which modern labs manage their daily operations, making them more streamlined, automated and collaborative.

In this blog, you will learn: 

1. What’s involved in lab operations?
2. Operational challenges faced by non-digitalized labs
3. How to optimize lab operations with digital tools

In today’s fast-paced labs, maintaining control over data, samples, workflows, and communication is critical. Digital lab platforms like SciSure have become essential for improving operational efficiency, ensuring compliance, and maintaining data integrity. Read on as we explore the intricacies of lab operations and provide actionable insights on optimizing workflows using cutting-edge digital tools.

What’s involved in lab operations?

Lab operations encompass a variety of tasks, such as sample tracking, data management, inventory control, compliance monitoring, and result analysis. These tasks must be carried out with precision to ensure accurate results and maintain quality standards. 

Every laboratory operation—from quality control to sample management and data analysis to reporting—relies fundamentally on precision and efficiency. Traditional manual methods of managing these processes can be time-consuming, error-prone, and challenging to scale. From manual record keeping to paper-based inventory logs, to hand-written labeling, traditional lab management systems can be a barrier to streamlined lab processes and operations

In light of this, digital tools are swiftly becoming essential for efficient lab management. Let’s take a look at some of the procedures and roles that contribute to everyday lab operations.

Essential laboratory operations

Sample management is one of the core functions of a laboratory and involves tracking samples from receipt to final analysis. Without proper sample management, there is a risk of cross-contamination, mislabeling, or loss of samples, which can result in inaccurate results and compromise data integrity. 

Lab protocols make sure that scientific procedures are carried out consistently and in compliance with regulatory and industry standards. Proper protocol management involves the creation, approval and regular updating of SOPs (Standard Operating Procedures) that guide lab personnel in carrying out experiments accurately. Effective protocol management not only minimizes errors but also ensures reproducibility across experiments.

Data management is another pivotal operation within laboratories. It involves the systematic collection, storage, and analysis of data generated from experiments and testing. Effective data management ensures that data is accurate, retrievable, and secure. This is critical not only for the integrity of research findings and test results but also for maintaining compliance with regulatory standards.‍

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Inventory control plays a vital role in lab operations, as it ensures that all necessary reagents and equipment are available when needed—without interruption. Proper inventory management helps to avoid unnecessary downtime that can delay experiments, as well as to prevent the wastage of resources and ensure cost efficiency.

Lab data security is essential for protecting digital assets within the lab environment. This includes the safeguarding of sensitive data, such as research findings, intellectual property, and personally identifiable information (PII), as well as the physical protection of lab samples, equipment, and reagents. Implementing strong data encryption, role-based access controls, and automated backups are key measures to ensure lab data security. 

Together, these operations form the backbone of effective and reliable lab management, ensuring that laboratories can fulfill their critical role in scientific discovery and industry quality assurance with efficiency and integrity.

Laboratory roles

Roles within a laboratory are as diverse as lab operations themselves. Technicians, scientists, data analysts, and managers work in concert to propel the lab's mission forward. A close-knit collaboration, paired with seamless communication, formulates the essence of dynamic laboratory operations—each role proving pivotal to the collective endeavor. 

• Laboratory managers oversee the laboratory’s operations, ensuring compliance with regulatory standards, managing budgets, and coordinating the efforts of the lab team. They play a key role in strategizing and ensuring that the laboratory's objectives are met efficiently and effectively.
• Technicians are the hands-on workforce, conducting experiments, maintaining equipment, and ensuring the smooth execution of daily tasks. Their meticulous attention to detail is crucial in the accurate execution of lab procedures.
• Laboratory scientists drive the laboratory's research and development efforts, formulating hypotheses, designing experiments, and analyzing data to advance scientific knowledge. They are pivotal in steering the lab’s focus toward innovative territories.
• Data analysts specialize in interpreting complex datasets extracting meaningful insights from experiments. Their expertise in statistical analysis and data visualization supports decision-making processes and underscores the integrity of research outcomes.
  

Each of these diverse lab roles contributes to the effectiveness of lab operations. Their specialized skills and collaborative efforts are key to managing the complexities of laboratory work, ensuring that every aspect of the laboratory's function is carried out with precision and care.

Operational challenges faced by non-digitalized labs

Achieving flawless lab operations is a multifaceted challenge, involving every aspect of daily tasks. Before we share some of our top tips and best practices for managing this, let’s consider the hurdles. Traditional lab management systems—such as paper lab records and physical folder-based inventory logs—can be a hindrance to various aspects of laboratory operations, compromising lab efficiency and efficacy.

• Data management and integrity: Managing the vast amounts of data generated in laboratories with paper-based records is not only cumbersome but prone to errors. These systems make it difficult to track changes, leading to potential compromises in data integrity. When managed in disparate platforms, data silos form, making study data challenging to integrate, analyze and share.

• Inventory management: Manually managing inventory is time-consuming and often results in errors such as incorrect stock levels or expired reagents. This can lead to delays in experiments and affect the accuracy of test results.

• Compliance with regulations: Adhering to the myriad of regulations in the scientific and quality assurance fields requires meticulous record-keeping and process validation. Manual record-keeping systems, which lack real-time updating and comprehensive audit trails, make compliance more challenging and time-consuming.
  

What are the legal implications if your lab is not digitized? Read our whitepaper

• Efficient use of resources: Traditional management systems like manual inventory logs often lead to inefficiencies in the allocation and use of resources, including personnel, equipment, and time. Manual scheduling and inventory management are prone to errors, resulting in wastage and delays.

• Sample tracking and management: Tracking samples manually increases the risk of mislabeling, loss, or contamination. This can compromise the validity of experiments and necessitate the repetition of work, leading to increased costs and delays in project timelines.

• Collaboration and communication: In the absence of a centralized digital system, collaboration and communication within the lab and with external partners can be inefficient. This affects the timely sharing of data and results, slowing down progress and decision-making.
  

Reliance on non-digital methods or legacy systems gives rise to a range of operational challenges, impacting efficiency, compliance, and the overall reliability of your lab outputs.

How to optimize lab operations with digital tools

In today’s rapidly evolving scientific landscape, labs are handling increasingly complex workflows, vast amounts of data, and stricter compliance requirements. Managing lab operations manually can quickly become inefficient, prone to errors, and costly. This is where digital tools, like SciSure, can transform your lab operations, offering a more streamlined, efficient, and secure way to manage everyday tasks.

Here are some key benefits of adopting digital solutions for optimizing lab operations:‍

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1. Enhanced data management

Effective data management is at the heart of any successful laboratory. SciSure centralizes all your lab data into one secure digital hub, providing easy access and retrieval while eliminating data silos. This ensures that all lab personnel can view, update, and analyze the most current data at any time, enhancing collaboration and accuracy.

• Centralized data repository: Store experiment results, protocols, inventory logs, and compliance records in one easily accessible place.
• Advanced search and retrieval: Quickly find specific datasets, samples, or documents, reducing time wasted sifting through physical or fragmented digital records.
• Real-time data access: All authorized users can instantly access and update data, ensuring everyone works with the most up-to-date information.
  

2. Automated lab workflows

Automation is revolutionizing laboratory operations by reducing manual interventions and minimizing errors, ultimately boosting productivity and precision. SciSure incorporates powerful automation tools that streamline repetitive tasks and integrate seamlessly with lab equipment, allowing lab personnel to focus on more complex scientific activities.

• Automated sample handling: SciSure's platform automates sample tracking and handling from receipt to disposal, reducing the risk of mislabeling, loss, or contamination. This ensures that samples are always correctly labeled and stored, streamlining workflows and preventing costly mistakes.
• Workflow automation: Automate routine lab workflows, such as experiment scheduling, protocol execution, and data entry. This removes the need for manual monitoring and reduces human error while also speeding up the pace of research and testing.
• Integration with lab equipment: SciSure integrates with lab instruments and equipment to automate data collection and analysis, reducing the need for manual data entry and ensuring consistent, accurate results. This integration also enables automatic calibration checks and real-time monitoring of experiments, optimizing lab efficiency.
  

3. Improved sample and inventory management

Tracking samples and managing lab inventory manually can lead to errors, wasted resources, and disruptions in workflow. With SciSure, sample management and inventory tracking are automated, reducing human error and improving efficiency.

• Barcode tracking: Barcode and RFID technology ensures precise sample tracking from receipt to disposal.
• Inventory automation: Automated inventory systems send alerts when stock levels are low, ensuring that supplies are always available and eliminating overstock or stockout situations.
• Waste reduction: Efficient resource tracking helps labs avoid wasting reagents, materials, and time.
  

4. Streamlined protocol and SOP management

Standardizing and managing lab protocols and SOPs (Standard Operating Procedures) is crucial for ensuring consistency and compliance. SciSure's protocol management features enable seamless creation, editing, and sharing of protocols, helping labs maintain up-to-date procedures.

• Version control: Automatically track protocol changes and ensure all team members are using the latest versions.
• Approval workflows: Set up approval processes to ensure SOPs are reviewed and authorized before being used in lab operations.
• Real-time access: Share protocols digitally with all relevant staff to reduce delays and prevent inconsistencies in how procedures are followed.
  

5. Enhanced compliance and security

Maintaining compliance with industry regulations and ensuring data security is a top priority for labs. Digital solutions simplify compliance management by automating record-keeping, providing audit trails, and securing sensitive information.

• Audit trails: Easily generate and track comprehensive logs of lab activities for regulatory audits.
• Role-based access controls: Limit access to sensitive data and samples, ensuring that only authorized personnel can view or modify critical information.
• Data encryption and backups: Ensure that all data is securely encrypted and automatically backed up, protecting it from unauthorized access and loss.
  

6. Seamless collaboration and communication

Collaboration is essential in a laboratory setting, and digital tools make it easier than ever for teams to work together, even remotely. SciSure's platform enhances communication by providing centralized access to data, protocols, and real-time project updates.

• Team collaboration: Enable multiple users to work on the same datasets and protocols in real-time, ensuring all team members have access to up-to-date information.
• Project tracking: Manage lab workflows by assigning tasks to staff, tracking progress, and setting deadlines to ensure smooth project execution.
• Shared resources: Share important documents, results, and protocols seamlessly across teams, improving communication and reducing the risk of miscommunication.
  

The formula for operational excellence 

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Effective lab operations are the foundation of successful scientific research. However, the complexity of managing data, samples, inventory, protocols, and compliance can create significant challenges, particularly for labs relying on manual processes or legacy systems. By adopting digital solutions like SciSure's digital lab platform, labs can streamline their workflows, improve data integrity, and reduce the risk of errors.

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From enhanced data management and sample tracking to protocol standardization and automation, digital platforms optimize every aspect of lab operations. These tools empower lab managers and staff to focus on innovation and research by reducing administrative tasks and improving efficiency.

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Ready to explore how automation and digitalization could revolutionize your lab’s day-to-day operations? Book a free personal demo today!

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This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

eLabNext, renowned for its pioneering Digital Lab Platform (DLP) that offers an all-in-one Electronic Lab Notebook (ELN) with Lab Inventory Management (LIMS) features such as sample, equipment, and SOP management for life science laboratories, is proud to announce the launch of eLabNext Developer. This groundbreaking platform is designed to democratise digital lab tool development and enhance the life sciences industry's research and development experience. Set to revolutionise laboratory operations, it offers unprecedented adaptability, extendibility, and a future-proof ecosystem. For instance, it allows Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance eLabNext. 

eLabNext Developer represents the first-ever comprehensive and democratic developer experience within the Life Science R&D community. This platform allows eLabNext customers to extend or adapt the eLabNext software functionality according to their specific needs. Moreover, it welcomes companies in the life sciences to leverage eLabNext by connecting their products or services to digital labs. This is achieved through the development and commercialisation of add-ons via the eLabNext Marketplace. 

Key Features and Benefits: 

• First-to-Market Developer Hub: A unique, all-in-one platform that provides access to developer tools for building, deploying, and releasing add-ons. 
• Adapt and Extend the eLabNext Digital Lab: Enhance current workflows or integrate with existing IT systems using the Software Development Kit (SDK) and Application Programming Interface (API) for increased efficiency and automation. 
• Access to the Digital Lab: Enables suppliers of laboratory devices or products, as well as service providers in laboratories, to bring their solutions to the digital lab used by over 60,000 scientists worldwide. 
• Future-Proof and Scalable: The platform's design accommodates evolving market needs, allowing for the easy integration of new tools and services. 
•  

A Call to Innovation

eLabNext invites the global life science community to embark on this transformative journey. By leveraging eLabNext Developer, users can unlock the full potential of their laboratories, ensuring they remain at the forefront of scientific research and development. 

"We are thrilled to launch eLabNext Developer, a platform that embodies our commitment to innovation and collaboration in the life science R&D sector," said Wouter de Jong, Co-founder and Managing Director of eLabNext. "Our platform is designed to empower users to customise their research environment like never before, marking a significant step towards the future of lab digitalisation." 

eLabNext Developer is not just a platform; it represents a movement towards creating a more connected, efficient, and innovative research community. By facilitating the easy development and deployment of add-ons, eLabNext ensures that the life sciences R&D community is well-equipped to meet both today's challenges and those of the future. 

This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

AOByte recently started a new partnership with eLabNext, an all-in-one lab management software. eLabNext solutions help to improve the quality of the research by providing all-around tools for any lab. Due to its rapid expansion, eLabNext has decided to release an SDK, allowing developers to create new add-ons that other users can install on the eLabNext dashboard. Add-ons allow users to integrate 3rd party software into dashboards, software such as Dropbox, Google Drive, etc. Add-ons also enable users to add functionality to their dashboard without waiting for their desired functionality to be released by eLabNext.

Our company is proud to accompany eLabNext on its journey. Seeing a growing demand, we’ve decided to share part of our journey of creating custom add-ons. This article is a good place to start if you’re interested in eLabNext add-on development.

Starting Add-on Development

To start add-on development, you must first turn on Developer mode from settings. Navigate to Account Settings > Developer. Developer mode is turned on by simply toggling the switch. In turned-on Developer mode, the SDK will attempt to inject an add-on JavaScript file from the “Add-on script URL” on the page load. A single JavaScript file will be loaded at runtime on page load each time you browse the eLabNext dashboard.

Now, let’s try to create a simple add-on. Before jumping into coding, here are two valuable resources: eLabNext SDK documentation and eLabNext REST API documentation.

Use the Download Template from the Developer settings page to create an empty add-on. This is a working sample add-on, which can be fed to the SDK via an HTTP server of your choice. Our team is using a NodeJS-based http-server for development purposes. The add-on below achieves a simple task of displaying the tasks table in the dashboard. It also allows users to create and delete tasks.

/*

@rootVar: SAMPLE_ADDON

@name: Sample

@description: Sample addon

@author: Stepan Smbatyan

@version: 1.0.0

*/

var SAMPLE_ADDON = {};



((context) => {

 context.init = (config) => {

   $(() => {

     context.SampleAddon = new context.SampleAddon(config);

   });

 };



 context.SampleAddon = new Class({

   Implements: [Options, Events],

   Extends: eLabSDK.Base,

   options: {},

   initialize: function (config) {

     // Store a reference to the function's context

     var self = this;

     // Set the options for the application using the provided configuration

     self.setOptions(config);



     $(document).ready(() => {

       const currentPage = Helper.History.get('pageID');



       const pageID = currentPage || new URLSearchParams(window.location.search).get('pageID');



       renderTaskPage();



       if (pageID === 'tasks') {

         getTasks().then(({ data }) => {

           renderTaskTable(data);



           addDeleteBtnListener();

         });

       }

     });

   },

 });



 // #TODO: remove context.init() when upload as add-on to marketplace

 context.init();

})(SAMPLE_ADDON);



// ======================================= DOM =======================================



/**

* Renders the task list UI by updating the browser history, creating a button and table,

* filling the table with task data, and updating the main content section with the table container.

* @param {Event} e - Optional event object. If provided, prevents the default action.

*/

const renderTaskTable = (data) => {

 const button = createAddTaskButton();

 $('#main-content')

 .html('<section id="tableContainer"></section>')

 .prepend(button.render());



 const table = createTaskTable();

 table.data = data;

 table._renderHTML();

};



/**

* Creates a custom page for tasks using eLabSDK.

* This function initializes a new CustomPage object with specified configurations.

* @returns {CustomPage} A CustomPage object representing the task page.

*/

const renderTaskPage = () => {

 return new eLabSDK.CustomPage({

   rootVar: '.nav-main-level',

   pageID: 'tasks',

   mainMenu: 'Tasks',

   subMenu: 'Task list',

 });

};



/**

* Creates a button element using the eLabSDK.GUI.Button constructor.

* The button is configured with a label, CSS class,

* and an action to show a dialog for updating tasks.

* @returns {eLabSDK.GUI.Button} - A button element configured to add a new task when clicked.

*/

const createAddTaskButton = () => {

 return new eLabSDK.GUI.Button({

   label: 'Add New Task',

   class: 'addNewTaskBtn',

   action: () => showDialog(DIALOG_CONFIGS.CREATE, createTaskAction),

 });

};



const addDeleteBtnListener = () => {

 $('.deleteBtn').on('click', (e) => {

   const id = e.currentTarget.getAttribute('_dataId');



   showDialog(DIALOG_CONFIGS.DELETE, () => deleteTaskAction(id));

 });

};



/**

* Creates a table element using the Helper.Table.create method.

* The table is configured with specified target container, data

* and columns for displaying task information.

* @returns {HTMLElement} - A table element configured to display task information.

*/

const createTaskTable = () => {

 return Helper.Table.create({

   target: 'tableContainer',

   caption: null,

   data: {},

   columns: [

     {

       name: 'Full Name',

       key: 'fullName',

       width: '20%',

       cellRender: ({ creator }) => `<b>${creator.fullName}</b>`,

     },

     {

       name: 'Title',

       key: 'title',

       width: '20%',

       cellRender: ({ title }) => `<span>${title || '-'}</span>`,

     },

     {

       name: 'Description',

       key: 'contents',

       width: '45%',

       cellRender: ({ contents }) => `<span>${contents || '-'}</span>`,

     },

     {

       name: 'Created',

       key: 'created',

       width: '10%',

       cellRender: ({ created }) => `<span>${created.split('T')[0]}</span>`,

     },

     {

       name: 'Action',

       key: 'actions',

       width: '5%',

       cellRender: ({ taskID }) => `

<p class='deleteTranslationIcon deleteBtn' _dataId="${taskID}">

<i class='fa fa-trash-alt _actionIcon' title='Delete translation'></i>

</p>

       `,

     },

   ],

 });

};



// ======================================= MODAL =======================================



/**

* Initiates the deletion of a task identified by its taskId asynchronously.

* Upon successful deletion, closes any open dialogs, reloads the page to reflect the changes.

* @param {string} taskId - The ID of the task to be deleted.

* @returns {Promise<void>} - A Promise that resolves after the task deletion and page reload.

*/

const deleteTaskAction = async (taskId) => {

 await deleteTask(taskId);

 Dialog.closeWait();

 window.location.reload();

};



/**

* Adding a new task with the provided title and description,

* closing the dialog window, and reloading the current page.

* @returns {Promise<void>} A promise that resolves once the actions are updated.

*/

const createTaskAction = async () => {

 const title = $('#title').val();

 const description = $('#description').val();



 await addTask({ title, description });

 Dialog.closeWait();

 window.location.reload();

};



/**

* Displays a dialog window with specified configuration options and a custom button,

* calling the provided callback function when the custom button is clicked.

*

* @param {Object} config - The configuration object for the dialog window.

* @param {string} config.title - The title of the dialog window.

* @param {number} config.width - The width of the dialog window.

* @param {string} config.btnOk - The label for the OK button.

* @param {string} config.btnCancelLabel - The label for the Cancel button.

* @param {string} config.content - The content to be displayed in the dialog window.

* @param {string} config.customButtonLabel - The label for the custom button.

* @param {string} config.customButtonStyle - The style for the custom button.

* @param {Function} callback - The callback function to be called when the custom button is clicked.

* @returns {void}

*/

const showDialog = (config, callback) => {

 const {

   title,

   width,

   btnOk,

   btnCancelLabel,

   content,

   customButtonLabel,

   customButtonStyle,

 } = config;



 Dialog.show({

   title,

   width,

   btnOk,

   btnCancelLabel,

   content,

   customButtons: [

     {

       label: customButtonLabel,

       style: customButtonStyle,

       fn: callback,

     },

   ],

 });

};



// ======================================= CONSTANTS =======================================



const DIALOG_CONFIGS = {

 DELETE: {

   title: 'Delete Task',

   width: '550',

   btnOk: false,

   btnCancelLabel: 'Close',

   content: '<p>Are you sure you want to delete this task?</p>',

   customButtonLabel: 'Delete Task',

   customButtonStyle: 'background:#fe810',

 },

 CREATE: {

   title: 'Add New Task',

   width: '550',

   btnOk: false,

   btnCancelLabel: 'Close',

   content: `

<section>

<input id="title"  type="text" placeholder="Title" />

<textarea id="description" placeholder="Description" style="padding-top: 8px;"/>

</section>

   `,

   customButtonLabel: 'Add Task',

   customButtonStyle: 'background:#fe810',

 },

};





// ======================================= API =======================================



/**

* Retrieves tasks by making a GET request to eLabSDK.

*

* @returns {Promise<Array>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const getTasks = () => new Promise((resolve, reject) => {

 new eLabSDK.API.Call({

   method: 'GET',

   path: 'tasks',

   onSuccess: (xhr, status, response) => {

     resolve(response);

   },

   onError: (xhr, status, response) => {

     reject(response);

   },

 }).execute();

});



/**

* Adds a new task with the provided title and description by making a POST request to eLabSDK.

*

* @param {Object} task - An object containing the title and description of the task.

* @param {string} task.title - The title of the task.

* @param {string} task.description - The description of the task.

* @returns {Promise<Object>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const addTask = ({ title, description }) => new Promise((resolve, reject) => {

 const data = {

   assigneeID: 0,

   title,

   contents: description,

 };



 new eLabSDK.API.Call({

   method: 'POST',

   path: 'tasks',

   pathParams: {},

   onSuccess: (xhr, status, response) => {

     resolve(response);

   },

   onError: (xhr, status, response) => {

     reject(response);

   },

 }).execute(data);

});



/**

* Deletes a task with the specified ID by making a DELETE request to eLabSDK.

*

* @param {string} id - The ID of the task to be deleted.

* @returns {Promise<Object>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const deleteTask = (id) => new Promise((resolve, reject) => {

 new eLabSDK.API.Call({

   method: 'DELETE',

   path: `tasks/${id}`,

   onSuccess: (xhr, status, response) => {

     resolve(response);

   },

   onError: (xhr, status, response) => {

     reject(response);

   },

 }).execute();

});

One of the crucial things to remember while creating an add-on is prioritizing using SDK and API methods over custom code. A good example will be rendering buttons or making HTTP requests. By using the methods provided by SDK, you can be assured, e.g. buttons will have correct styling, or all the necessary headers will be appended to your HTTP request.

More Complex Add-on Development

Obviously, most of the add-ons that will be created will be more complicated than this example. Naturally, while delivering more complex features, developers would like to use the power of breaking code into modules, minimizing code for production, writing test cases for their code, and using all the other advantages of modern web development. While working on the add-ons, we’ve created a boilerplate add-on, allowing users to achieve a project structure, packaging, testing, etc. The project can be found on GitHub.

Remember that the eLabNext SDK is gaining momentum; thus, the documentation needs to be completed. Please contact our team if you find yourself in a situation where help might be required. Our team will continue writing about the eLabNext add-on development process. We will cover topics like submissions of add-ons to eLab Marketplace, tips and tricks of eLabNext add-on development, talking about more complicated features development, and so on.

What is a Health Check?

A health check is a checkup on the health of the lab's digital operations and progress using impact-driven metrics.

• The Purpose: Constant productivity improvement – on all fronts!  
• Impact-driven metric examples:
  • % of samples digitized within a period of time (e.g., two freezers by Q2)
  • % of SOPs digitized within a period of time (e.g., 20 legacy SOPs)
  • % of Electronic Lab Notebook entries and reports digitized within a period (e.g., 30% of ELN entries and reports digitized by the end of the year)

The Importance of Healthchecks at SciSure

If we at SciSure (formerly eLabNext) do not know how our customers are using the system, what features they are underutilizing, which features they wish they had, how effective system deployment has been, and how implementation has impacted the internal lab culture, we are not doing our job! 

The reality is that customers define many of the new features we develop, and we take pride in understanding their needs through conversations and having an actual methodical approach to building our community of innovative and creative users and proactively seeking their opinions. In the world of Customer Success, this process is called a "Health Check." At the same time, we cannot ensure our customer's success if we do not establish responsibility and accountability to implement the system sustainably.

Health checks are similar to going to your Primary Care Physician for a physical or taking your car for an oil change. In the tech world, a health check is a periodic check that examines your lab's/business' technological and usability health. Depending on the nature of the company and the industry, these technical health checks can cover a wide range of assets, applications, policies, technology, people, or business operations.

When we talk about a Customer Success Health Check at SciSure, we refer to a comprehensive assessment of your Technical and Usability Health. This allows us to better facilitate successful usage, implementation, and customer satisfaction.

Impact Goals Assessments

Upon contract signature with SciSure, our Customer Success and Lab Digitization Specialists will meet with key stakeholders to understand the lab's short- and long-term goals. Specifically, they focus on:

1. Digital Lab Strategy: What is the organization's Digital Lab Strategy, and what are the short—and long-term goals?
   • How will you assess your success, and what are your internal key performance indicators (KPIs)?
   • What are your one-month, three-month, six-month, nine-month, and twelve-month goals? What percentage of your lab's operations and physical items would you like to have digitized or automated?
2. Lab Assets: How many storage units, equipment, and supplies should be tracked within SciSure?
3. Sample Migration and Strategy: How many legacy samples should be digitized immediately, and what is the lab's long-term Sample Strategy?
4. Sample Automation: How automated should sample management workflows be, and what does your data structure and standardization look like?
5. SOP Management: How many and which SOPs need to be digitized, edited, and used with version control in the short- and long-term?
6. Project and Report Management:
   • How many projects/programs do you need to track currently?
   • What is the structure and cadence of your Lab Reports?
   • How will the results be tracked and accessed?
7. Automation, Integration, and Customization: What is your digital tool development strategy, what integrations would you like to have, and are there any specific workflows for which you'd like to create customizations?

Having answered these questions, our team helps the customer define a series of impact goals to achieve their lab's digitization vision. This approach creates an objective framework for measuring the success of the customer's digitization efforts and identifying underutilized areas of the platform from which the customer can achieve greater value. With each health check call, Customer Success looks at the progression of these goals across three timeframes:

PAST

• What pain points was the customer's team previously experiencing?
• What digitization impact goals were previously set to address these pain points?
• Were any additional steps or calls scheduled with the SciSure team to support these goals:
  • Key user training sessions with Digital Lab Consultant
  • New feature releases/demos
  • Activation of add-ons from the Marketplace
  • Workflow implementation walkthroughs with Customer Success
  • Import templates to migrate Inventory data into the platform

PRESENT

• How well does the customer feel those goals were met:
  • Subjectively: How satisfied are users with their Digital Lab Experience compared to before the last health check call? Have they received new value from the system? Are workflows more streamlined than before?
  • Objectively: How frequently are users logging in to engage with the platform? Are they creating more samples, protocols, or experiments in the system than before? How many experiments have been signed off and completed?
• Are the impact goals still as relevant today as they were when they were set?
• Are there new pain points that need to be addressed?

FUTURE

• What new impact goals does the customer want to pursue going forward? What existing goals still need to be achieved?
• How will the customer prioritize their revised impact goal statement?

It helps to think of your lab in this context: If you take your lab to the doctor, would they say it is healthy or notice symptoms that need to be addressed?

Tools and technologies are great, but they're only as good as their implementation and the desired success they generate. Leveraging SciSure's health check framework, we plan to ensure our customers' success. To align, reach out to your Lab Digitization Specialist and/or Customer Success Specialist.

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Materials that are hazardous due to their biological or infectious properties are called biohazardous materials or simply biohazards. Research laboratories work with a variety of biological agents including recombinant or synthetically derived nucleic acid, blood, tissues, body fluids, cell lines, bacteria, viruses, viral vectors, plasmids, fungi, prions, or parasites that cause disease in humans, animals, or plants. The primary purpose of biosafety is to protect individuals and the environment from unintentional exposure to biological agents and biohazards.

Biosecurity is equally as important as biosafety. The primary purpose of biosecurity is to prevent unauthorized access, theft, misuse, or intentional release of biohazardous materials such as pathogens and toxins. This involves securing these materials to mitigate the potential risks associated with their malicious use.

What is Biosafety?

Within research laboratories, biosafety encompasses the control measures, regulations, containment principles, and administrative controls like safe work practices that are used to manage risks associated with working with the handling and use of biological agents. As stated above, these agents can represent a wide variety of potentially hazardous materials.

Goals of Biosafety:

• Minimize the risk of exposure: Protect individuals such as laboratory researchers from biological agent exposure.
• Containment of biohazards: Implement robust biosafety containment measures to prevent the inadvertent release of infectious agents from affecting personnel, the environment, or community.
• Standard Operating Procedures: Establish biosafety protocols to ensure safe handling and disposal of biological materials.

To evaluate biohazard risks, biosafety risk assessments should be conducted to determine the exposures from work activities or conditions that are reasonably anticipated to elevate risk of contracting a disease caused by an infectious agent. Laboratories that perform procedures with biohazardous materials that contain transmissible pathogens are likely to have occupational exposure to these agents. A biosafety risk assessment evaluates risks, so the appropriate control measures are implemented to prevent illness.

What is Biosecurity?

Biosecurity involves the comprehensive set of measures implemented to securely handle and store biological materials. It focuses on preventing unauthorized access, theft, misuse, or intentional release of these materials, which could have catastrophic consequences if used for malicious purposes such as bioterrorism or biological warfare.

Goals of Biosecurity:

• Prevent malicious use: Prevent the intentional misuse of biological agents that threaten public health, ecosystems, agriculture, and national and global security.
• Secure research activities and materials: Ensure the integrity of research activities and safeguard the biological materials utilized throughout the research process.

Measures and Practices:

Biosafety:

• Biological Risk Group (Risk Groups 1 – 4): Biological agents are classified according to their risk level when considering infectivity, pathogenicity and availability of preventive measures and treatments for the corresponding disease. The National Institute of Health has established classification of biological agents into four risk groups.
• Biosafety Levels (BSL-1 to BSL-4): Based on the biosafety risk assessment and biological agents, specific biosafety levels (BSL-1 to BSL-4) are designated, each with increasingly stringent containment requirements.
• Biological Safety Cabinets (BSCs): These cabinets are designed to reduce the escape of research materials and agents in the room environment, and they’re used to remove contaminants from the research work zone. There are different classes of BSCs and the selection of the class is dependent on the types of biological agents being handled. BSCs must be tested and certified.  
• Safe Work Practices, Training, and Procedures: Hazard awareness training and risk specific training should be required. Safe work practices and SOPs that include general safety rules and techniques, inventory control, and minimization of aerosols are especially important.  
• Personal Protective Equipment (PPE): Utilizing appropriate PPE, such as gloves, gowns, respirators, and eye protection are also used to reduce potential exposure.

Biosecurity:

• Physical Security: Securing access points to facilities that handle biological agents such as fencing, gates, locks, surveillance cameras, and access control systems that prevent unauthorized access.
• Personnel Security: This includes background checks, security clearances, data access and permissions, and training.
• Inventory Control: Records of biological agents including quantity, location, authorized users which helps with tracking usage and traceability. This is important for biosafety, as well.
• Cybersecurity and Data Management: Cybersecurity and data management infrastructure intend to protect sensitive information associated with research activities and biological materials. Breaches in these systems can warn of potential biosecurity threats.
• Dual-Use Research Assessments: These evaluate intended beneficial applications and potential for misuse of biological materials.

Regulatory Compliance:

Both biosafety and biosecurity are subject to regulations established at the local, national, and international level. Organizations should have up-to-date compliance registers to ensure they’re meeting the requirements that relate to biosafety such as handling and storing materials, managing biohazardous waste including transportation, protecting workers from bloodborne pathogens or other potentially infectious materials, and installed biosecurity controls.

Risk Assessment:

• Biosafety Risk Assessment: A risk assessment that involves hazard identification, hazard assessment and risk evaluation, risk management, documentation & communication, and review & update. Learn more about Biosafety Risk Assessments.
• Biosecurity Risk Assessment: This assessment identifies potential biological threats and assesses vulnerabilities in the measures and practices listed above. The outcomes of a biosecurity assessment can include emergency preparedness and response drills, training, communication, and coordination plans.

The purpose of both biosafety and biosecurity risk assessments is to identify risks or weaknesses and implement corrective and preventive actions (CAPAs) to mitigate the gaps. These types of assessments are interminable, meaning they should be periodically reviewed and updated to reflect new processes, controls procedures, or changes to the organization that could affect the biosafety and biosecurity risk levels.

Education and Training:

Comprehensive biosafety and biosecurity training are essential for the protection of people and the environment. This training equips individuals with the necessary knowledge and skills to ensure safe and secure research practices.

• Biosafety Training – Biosafety training is a general term that captures multiple programs that target biohazard risk reduction such as emergency preparedness and response training  or bloodborne pathogens training. Topics should cover basic requirements including but not limited to: relevant regulations, modes of transmission of pathogens, the importance of standard precaution measures, vaccine information (e.g., Hepatitis B), containment with BSCs, safe handling practices, PPE requirements and their limitations, decontamination of waste, incident reporting, and what to do during an emergency.
• Biosecurity Training – Biosecurity training should cover topics such as incident reporting, dual-use research considerations, ethical and legal aspects, security policies and procedures such as access control measures, data management practices, and specific security measures.

Biosafety and biosecurity are distinct in their objectives, but they mutually reinforce aspects of responsible laboratory management. Biosafety focuses on preventing unintentional exposure to biological agents and ensuring the safety of people and the environment. Biosecurity addresses intentional misuse by implementing measures to safeguard biological materials from unauthorized access, theft, or malicious use. Focus on robust biosafety and biosecurity programs can create a safe and secure environment for organizations, minimizing the risk of both accidental and intentional harm.