1. Decentralized Clinical Trials (DCTs)

Decentralized clinical trials (DCTs) are transforming the traditional model by leveraging technology to conduct trials outside conventional clinical settings. This approach brings the study to the patient, utilizing digital health technologies, remote monitoring, and telemedicine to collect data.

Benefits:

• Increased Participation: DCTs reduce geographical barriers, enabling more diverse patient participation.
• Improved Patient Convenience: Participants can undergo assessments and provide data from the comfort of their homes, enhancing retention rates.
• Faster Recruitment: With broader reach, DCTs can accelerate participant recruitment, reducing trial timelines.

2. Advanced Data Analytics and Artificial Intelligence (AI)

The integration of advanced data analytics and artificial intelligence (AI) is revolutionizing how clinical trial data is collected, analyzed, and interpreted. These technologies enable researchers to uncover patterns and insights that were previously unattainable.

Applications:

• Predictive Analytics: AI can predict patient outcomes, optimize trial design, and identify potential risks early in the trial process.
• Real-Time Monitoring: Advanced analytics allow for continuous monitoring of patient data, ensuring timely intervention if adverse events occur.
• Data Integration: Combining data from various sources (e.g., electronic health records, wearable devices) provides a comprehensive view of patient health and trial progress.

3. Personalized Medicine and Precision Trials

The shift towards personalized medicine is driving the development of precision trials, where treatments are tailored to individual patient profiles based on genetic, biomarker, phenotypic, or psychosocial characteristics.

Key Aspects:

• Targeted Therapies: Trials are increasingly focusing on specific patient subgroups most likely to benefit from the intervention.
• Biomarker-Driven Studies: Identifying and utilizing biomarkers for patient selection and treatment efficacy assessment.
• Adaptive Trial Designs: These designs allow modifications based on interim results, improving trial efficiency and success rates.

4. Patient-Centric Approaches

A growing emphasis on patient-centric approaches ensures that trials are designed and conducted with the participant’s needs and experiences in mind. This trend recognizes patients as partners rather than subjects.

Strategies:

• Enhanced Communication: Transparent and ongoing communication with participants about trial progress and findings.
• Patient Advocacy Involvement: Involving patient advocacy groups in trial design to ensure relevance and improve patient engagement.
• Flexible Protocols: Designing protocols that accommodate patients’ lifestyles and reduce the burden of participation.

Blockchain for Data Security and Transparency

The use of blockchain technology in clinical trials offers enhanced security, transparency, and traceability of data. This technology addresses many challenges associated with data integrity and regulatory compliance.

Advantages:

• Immutable Records: Blockchain provides an unalterable ledger of all trial activities, ensuring data integrity and trustworthiness.
• Enhanced Privacy: Secure data sharing while maintaining patient confidentiality.
• Regulatory Compliance: Facilitates adherence to regulatory requirements by providing transparent and verifiable data trails.

6. Increased Focus on Rare Diseases and Orphan Drugs

There is a heightened focus on developing treatments for rare diseases and orphan drugs. Advances in genetics and molecular biology enable the identification of disease mechanisms and potential therapeutic targets for these conditions.

Developments:

• Collaborative Research: Partnerships between academia, industry, and patient organizations to pool resources and expertise.
• Regulatory Support: Incentives and support from regulatory bodies to expedite the development and approval of treatments for rare diseases.
• Innovative Trial Designs: Use of innovative trial methodologies, such as platform trials and basket trials, to evaluate multiple therapies simultaneously.

7. Virtual and Augmented Reality (VR/AR)

Virtual reality (VR) and augmented reality (AR) are emerging tools in clinical trials, offering novel ways to enhance patient engagement and training for clinical staff.

Applications:

• Patient Education: VR/AR can provide immersive educational experiences, helping patients understand trial procedures and expectations.
• Simulation Training: Training for clinical staff using VR simulations to improve protocol adherence and patient interactions.
• Pain and Stress Management: VR applications to manage patient pain and anxiety during procedures.

8. Regulatory Evolution

Regulatory agencies worldwide are adapting to the changing landscape of clinical trials by introducing flexible and adaptive regulatory frameworks. These changes aim to facilitate innovation while ensuring patient safety and data integrity.

Regulatory Trends:

• Guidance on DCTs: Development of guidelines to support the implementation of decentralized trials.
• Adaptive Design Acceptance: Increased acceptance of adaptive trial designs to accelerate development timelines.
• Real-World Evidence (RWE): Encouraging the use of real-world evidence to supplement traditional clinical trial data for regulatory decisions.

Conclusion

The next five years promise significant advancements in clinical trials, driven by technology, personalized medicine, patient-centric approaches, and regulatory evolution. These trends are poised to enhance the efficiency, accuracy, and inclusivity of clinical research, ultimately leading to the development of better treatments and improved patient outcomes. By embracing these innovations, the clinical trial industry is set to transform and meet the challenges of modern healthcare.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

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Typically, you can find the SoA located on a few pages in a clinical trial protocol created on a word document. It appears in a table that creates a chronological visit outline and specific checklist of each assessment expected at that visit. Further detailed information about each visit and assessment not included in the table is often found in embedded in the protocol in numerous different sections, in lengthy footnotes or appendices. In a digital SoA, every assessment or measurement is defined alongside industry standards with inclusion of what would be written in these footnotes or other sections of the protocol. Creating a digital SoA, combines this information into a searchable format and eliminates manual review across different sections of the protocol, reducing the risk for missed required datapoints. Every assessment or measurement used in protocol design that was created in the digital SoA platform, would be defined universally enhancing the understanding of what is to be expected out of that requirement and laying the foundation for a Digital Protocol.

Furthermore, sponsors and study teams, even within an organization, often  refer to the same specific assessment by different names. Within a digital SoA, a standardized language is employed allowing for easier communication and shared understanding across key stakeholders, improved interoperability with downstream consumers of the SoA content, and streamlined updates for protocol amendments. For example, the digital SoA can quickly be updated from standardized information as amendments are approved, minimizing the room for error, protocol deviations, and missed system updates. Research staff and Clinical operations teams can refer to a single source of truth that holds all relevant information and rely on its accuracy to guide their practices.

This standardization also drives enhanced data management, one of the most prominent areas in clinical trials that can benefit from a digital SoA. Traditionally, electronic data capture (EDC) systems are created by a Data Manager that reviews the SoA document and translates it into defined system requirements and specific data standards³. This digitized information removes the need for translation and sets the stage for automation to associate the assessments listed in the SoA with the comparable data standards, system requirements and data collection forms³. EDC can be developed in direct accordance with the protocol requirements within every patient visit. Navigation within those EDC platforms would directly correlate with the protocol, in a user-friendly platform. Digital SoA takes a traditionally time consuming, tedious process and streamlines it to benefit data collection across the trial. Further, a digital SoA promotes a data driven design by its ability to provide real-time insights on decisions made by Data Managers or research staff. For example, a digital SoA can provide a snapshot of a what data would be impacted if a lab assessment is missed. Data snapshot abilities can reduce the burden on a Data Manager, allowing users togenerate queries showing the impact of different decisions and scenarios prior to them occurring. A study team can guide their decisions for each patient based upon real-time, real-world data and see the risks, safety information, patient overviews, data impacts, etc. that each decision may cause.

Additionally, data management teams need consistency. The information found within a digital SoA is reusable for future protocols and creates standardized repeatable workflows such as downstream integrations and document generation⁴. Interoperability is improved through standardization using a consistent data model such as USDM (Unified Study Definition Model) supported by industry standards (e.g., ICH M11: Clinical electronic Structured Harmonized Protocol (CeSHarP)⁵ , CDISC controlled terminology⁶, etc.)As each assessment and measurement is defined, this consistency in data model and terminology allows information to more easily flow downstream across platforms and documents like EDC, CTMS, IRT, lab manuals, etc. bridging the gap between systems and reducing manual effort for the study. All in all, a digital SoA supports the data management process, while maintaining improved efficiency and accuracy in trials.

The concept of turning a clinical trial “digital” is no stranger to researchers. Technology has continually changed the face of clinical trials and overcome challenges through its developments. The benefits that are seen can be endless – processes have been streamlined, costs are reduced, patient data is better obtained and protected, accuracy and ability to adhere to protocols are improved, etc. Implementing a digital SoA is just one of those impressive developments. The SoA carries the weight of success for researchers, therefore, its development into something more is important to consider as the field continues to further digitize and embrace technology.

References

1. Tufts Center for the Study of Drug Development. Impact Reports – Rising Protocol Design Complexity is Driving Rapid Growth in Clinical Trial Data. csddtuftsedu. 2021;23(1). https://csdd.tufts.edu/impact-reports.
2. Ken Getz, M.B.A. K. Shining a Light on the Inefficiencies in Amendment Implementation. wwwappliedclinicaltrialsonlinecom. 2023;32(12). https://www.appliedclinicaltrialsonline.com/view/shining-a-light-on-the-inefficiencies-in-amendment-implementation
3. Georgieff T. Navigating toward a Digital Clinical Trial Protocol. wwwappliedclinicaltrialsonlinecom. 2023;32(12). Accessed May 3, 2024. https://www.appliedclinicaltrialsonline.com/view/navigating-toward-a-digital-clinical-trial-protocol
4. XTalks – Faro Health Inc. Clinical Data Management Insights: Using Digital SoA to Solve Modern Clinical Trial Challenges. Xtalks. Published August 24, 2023. Accessed May 7, 2024. https://xtalks.com/webinars/clinical-data-management-insights-using-digital-soa-to-solve-modern-clinical-trial-challenges/.
5. International Council for Harmonisation of Technical Requirements. ICH M11: Clinical Electronic Structured Harmonised Protocol (CeSHarP) FDA and Health Canada Regional ICH Consultation.; 2023. Accessed May 7, 2024. https://www.fda.gov/media/167334/download.
6. CDISC Digital Data Flow for Clinical Trial Protocols. Accessed June 21, 2024 https://www.cdisc.org/ddf

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

The post The Benefits of Implementing Digitized SoA in Clinical Research Protocols appeared first on Astrix.

The Chemistry, Manufacturing, and Controls (CMC) Module 3 component of the Common Technical Document (CTD) required for pharmaceutical regulatory submissions requires extensive documentation, including drug substance and drug product information, manufacturing processes, analytical methods, stability studies, and more. Managing the manual transcription and compilation of this documentation consisting of text annotations, images, data, tables, etc. from various sources while ensuring accuracy, consistency, clarity, and coherence across different sections of the report can be very challenging and can often times result in data integrity errors and risks.

This webinar will provide valuable insights into:

• What is the value of using a Structured Content & Data Management (SCDM) guided workflow solution in biotech and pharmaceutical product development?
• Why are the CMC Module 3 data, content, and tables report generation challenges so difficult?
• How can I improve data integrity, minimize errors, redundancies, and discrepancies, while enhancing the reliability and accuracy of information of my CMC Module 3 and other e-Reports?
• Who can benefit from the Compass BIO® solution which can enable the automatic generation of complex data tables and reports and the ability to use a single set of When in biotech and pharmaceutical product development could a SCDM guided workflow solution be used?

The post Enable Your Drug Development CMC Digital Transformation Strategy with a SCDM Guided Workflow System appeared first on Astrix.

Informed consent is not only an ethical requirement but also a legal one, with specific guidelines outlined in the Common Rule and FDA regulations. Other informed consent regulations include: The Office of Human Research Protections (OHRP) regulation 45 CFR 46.116, General Data Protection Regulation (GDPR) in Europe, and the National Statement on Ethical Conduct in Human Research in Australia. These regulations ensure that participants are fully informed about the risks and benefits of the study, as well as any alternative treatments or procedures. The regulations also require researchers to minimize the risk of harm to participants and to protect their confidentiality and privacy.

However, creating an informed consent document can be a complex and challenging task. The guidelines for Informed Consent Forms (ICFs) can use technical jargon that may be difficult for participants to understand, and researchers may struggle to balance the requirements of the regulations with the need for clear and concise information. Preparing an informed consent document is a crucial step in conducting ethical and responsible research, and understanding the basics can help researchers navigate the process with greater ease. In this blog, we shall delve into the intricate art of crafting informed consent documents that comply with the exacting standards of the FDA and Common Rule guidelines.

FDA and Common Rule: Basic Elements for Informed Consent Writing

The FDA and Common Rule require nine fundamental elements to be included in informed consent documents:

1. Introduction: The introduction should identify the research project, explain its purpose, and describe the procedures that will be performed.
2. Purpose of the research: The purpose of the research should be clearly explained, including the scientific goals, the procedures involved, and the expected duration of the study.
3. Foreseeable risks and discomforts: The potential risks and discomforts associated with the research should be described in detail, including any physical, psychological, social, or economic harm that may result.
4. Possible benefits: Any potential benefits of participating in the study should be explained, including direct benefits to the participant, benefits to others, and benefits to society.
5. Possible alternatives: The participant should be informed of any available alternative treatments or procedures and the benefits and risks associated with each.
6. The Extent of confidentiality: The extent to which confidentiality will be maintained should be described, including any exceptions to confidentiality.
7. Terms for compensation for injury: The terms for compensation for any injury that may result from participation in the study should be explained.
8. Statement of voluntary participation: The participant should be informed that participation is voluntary and that they can withdraw from the study at any time without penalty.
9. Clinical trial disclosure statement for Phase II studies: If the study is a Phase II clinical trial, a statement disclosing whether the drug or device being tested has been approved by the FDA for any use should be included. A Phase II study must also include the following statement as mentioned on the FDA website:

“A description of this clinical trial will be available on http://www.ClinicalTrials.gov, as required by U.S. Law. This website will not include information that can identify you. At most, the website will include a summary of the results. You can search this website at any time.”

FDA and Common Rule: Additional Elements for Informed Consent

In addition to the 9 basic elements of informed consent required by the FDA and Common Rule, there are 10 additional elements that are essential to ensure that research participants are fully informed before deciding to participate in a study. These additional elements address specific concerns and considerations that may arise in certain types of research studies and provide important information for participants to make an informed decision about their participation.  In this section, we will discuss each of these 10 additional elements in detail.

1. Potential risks to pregnant subjects or developing fetuses: If the study involves pregnant women or women of childbearing age, this element informs them of the potential risks to their own health or that of their developing fetus.
2. Possibility of termination of participation: This element outlines the process for terminating participation in the study, including the participant’s right to withdraw at any time without penalty.
3. Costs to the participant: This element explains any costs that the participant may incur as a result of participating in the study, such as travel expenses or medical procedures that are not covered by insurance.
4. Procedure for terminating participation: This element explains the steps that the participant must take to terminate their participation in the study.
5. Commitment to update the subject on study findings: This element outlines the researcher’s commitment to keeping the participant informed about any significant findings that emerge during the course of the study.
6. Estimate of the total number of participants: This element provides an estimate of the total number of participants in the study, which can help the participant understand the scope of the research.
7. Description of the “Certificate of Confidentiality” for NIH-funded studies: This element explains the purpose and significance of the Certificate of Confidentiality, which is intended to protect the participant’s privacy and confidentiality.
8. Information on the Genetic Information Nondiscrimination Act (GINA) for genetic testing studies: This element explains the implications of GINA for genetic testing studies, including the participants’ right to privacy and protection against discrimination based on their genetic information. For infectious diseases or epidemics, this element requires including a statement that results are required by law to be reported to local health authorities.
9. Statement on reporting results for infectious diseases or pandemics: This element explains the researcher’s obligation to report any positive test results for infectious diseases or pandemics, such as HIV or COVID-19.
10. Additional statements for Common Rule studies: This element may include any additional statements or disclosures that are required under the Common Rule, which is a set of federal regulations governing research with human subjects.

In order to safeguard the rights of subjects in research that is governed by the Common Rule, it is crucial to incorporate certain essential statements. Firstly, a statement should be included regarding the possibility of removing identifiers from collected data for future research purposes without the need for further informed consent to maintain subject privacy and prevent unauthorized use of their personal information. Alternatively, a statement should be added that obtained biological samples cannot be utilized for future research without the explicit consent of the subject, particularly for sensitive or personal data such as genetic information, to protect their privacy. Thirdly, it is necessary to incorporate a statement that specifies whether the subject’s biological samples may be commercially reused. This is important to allow the subject to make an informed decision regarding the potential commercial usage of their samples. Fourthly, a statement must be included to explain the circumstances under which clinically relevant research findings will be disclosed to the subject. This is important to ensure that the subject understands the possible benefits and risks associated with participation and can make an informed decision. Finally, a statement should be added indicating if the investigator intends to perform whole genome sequencing to inform the subject about any associated risks and allow them to make an informed decision.

By including these statements in research governed by the Common Rule, researchers can ensure that they adhere to ethical and industry standards, protect the privacy of subjects, and respect their rights during the research process.

 3 Ways Laboratory Software for Clinical Research Supports Informed Consent Management

 Utilizing laboratory software for clinical research, also known as Laboratory Information Management System (LIMS), can aid clinical researchers in automating and simplifying the handling of informed consent procedures. This not only decreases the chances of human error but also eliminates the possibility of tampering with the Informed Consent Forms (ICFs).

Let’s look at 3 ways a LIMS helps support clinical researchers in consent management.

1. Manage Documents  A LIMS manages all internal and external documents including consent forms, standard operating procedures (SOPs), and quality management manuals. The system keeps track of each document’s revision history to ensure that employees only access the most recent version. Additionally, a LIMS assigns role-based access rights to staff, allowing controlled access to confidential documents. With the document management feature, users can manage the latest version of the consent form. A LIMS can also associate filled-out consent forms with individual participant records for proper tracking and consent management.
2. Protect the Privacy of Participants  A LIMS ensures the anonymity of sensitive participant data and grants role-based access rights to protected health information (PHI) of participants. Only authorized personnel can view the masked data, and any changes or views of PHI are logged in the audit trail. This feature is beneficial for audits as external auditors can request a PHI audit report at any time
3. Associate Participants with a Study  A LIMS allows users to create a clinical research study and associate participants with it. In this way, all participants are associated with the study. Thus, all the consent forms, participant data, and all SOPs that apply to a study can be organized in one place.

A clinical LIMS helps automate and streamline the management of informed consent. It minimizes human error associated with manual paper-based processes and also eliminates ICF tampering risks. What’s more, a clinical LIMS can be integrated with digital tools used for the collection of informed consent.

Conclusion

Creating an informed consent document is a complex and challenging task that requires balancing regulatory requirements with the need for clear and concise information. Laboratory software for clinical research streamlines the process of managing informed consent documents to help researchers comply with the standards of the FDA and Common Rule guidelines. Using a LIMS can be a differentiator for clinical research labs, as it can make the consent management process a breeze, leading to better participant satisfaction and improved research outcomes.

About CloudLIMS

CloudLIMS.com is an ISO 9001:2015 and SOC 2-certified informatics company. Their SaaS, in-the-cloud Laboratory Information Management System (LIMS), CloudLIMS, offers strong data security, complimentary technical support, instrument integration, hosting and data backups to help biorepositories, analytical, diagnostic testing and research laboratories, manage data, automate workflows, and follow regulatory compliance such as ISO/IEC 17025:2017, GLP, 21 CFR Part 11, HIPAA, ISO 20387:2018, CLIA, ISO 15189:2012, and ISBER Best Practices at zero upfront cost. Their mission is to digitally transform and empower laboratories across the globe to improve the quality of living.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

The post Navigating the Complexities of Informed Consent Writing: Tips and Strategies for Clinical Researchers appeared first on Astrix.

1 Recruitment

Subject recruitment within clinical trials is considered one of the most crucial determinants for a successful trial. There are many challenges faced in this area that can lead to failure in reaching recruitment goals and inaccurately recruiting the proper subject for the study protocol. Minimizing recruitment barriers is pertinent, therefore, this is where AI comes into play. Considerable efforts are put forth towards recruitment. For example, sites typically assess eligibility by conducting interviews, thorough EMR reviews, physical exams, calling potential patients, numerous outreach events, etc. which directly affects the amount of paperwork, employees needed, and clinic time to carry out this process. AI can be implemented to analyze large databases leading to more efficient and reliable processes and eliminate these common recruitment limitations¹. Defined inclusion and exclusion criteria, demographics, imaging parameters, and comorbidities can be identified and included in database searches performed by AI. AI is a trained system that can extract those ideal patients within an EMR system or other recruitment databases and match them with complex clinical trial criteria while minimizing the common risks faced within recruitment. Eligibility is validated, as well as the ability to predict patient retention through AI proving promising results for clinical research.

2 Data Collection

To produce results of drug efficacy and safety for eventual usage, the collection, cleaning, and management of high-quality data is necessary in the field of clinical research.  One way that AI is streamlining data collection in clinical trials is through the use of digital health technologies (DHTs). By relying on AI algorithms, automated data collection produces usable, real-time information through wearable devices, sensors, investigational product trackers, video capture, etc.² These features allow a site or sponsor to prioritize the safety of subjects, while obtaining actionable insights through data. Additionally, defining of biomarkers while continuously collecting data through AI, can validate patient drug responses, identify sudden changes, or predict patient health outcomes for the study.

3 Predictive Insights

Another key indicator for a successful clinical trial is proper study design. AI is being utilized to enhance the overall study design process through the prediction of trends in patient data, success rates, and outcomes, which leads to a reduction of the length and cost of a trial. The success rate of a trial can be predicted by AI through previous patterns, patient data, site specific data and related trials. Within patient outcome prediction, it is noted that AI is being used to simulate data that allows for a more efficient statistical outcome measure and identify patients who are progressing to reach endpoints quicker, which results in shorter trial durations². Predictive insights allow for sponsors, clinical research organizations, and research sites to make informed decisions on what trials are best suited for their needs. The risk of failure, time, and resources are reduced with this information and allow for transparency on the expected future of the trial. Additionally, this allows for design teams to make improvements upon the trial with the predictive insights provided.

AI in clinical trials is expected to continually be incorporated into the field of pharmaceutical and biotech research. The streamlining of the processes within clinical trials will be evolving over time with the help of AI. Innovation will continue to challenge the field and help grow in areas that were once unheard of. While there will be challenges that come alongside AI integration, the benefits are undeniable within clinical research and significant strides will be made towards enhancing their processes.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

References

1. Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023;5(1). doi: https://doi.org/10.1259/bjro.20220023.

2. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to Clinical trials: Opportunities and Challenges.Health Technol. Published online February 28, 2023. doi: https://doi.org/10.1007/s12553-023-00738-2.

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The pharmaceutical industry in 2024 is shaped by notable legal and regulatory developments that transpired in 2023. The Supreme Court, in a pivotal move, addressed the standard for enablement in the context of antibody genus claims and refrained from overturning Federal Circuit decisions related to written description and induced infringement concerning skinny labels. Simultaneously, the current administration unveiled new regulatory objectives impacting the pharmaceutical sector. These include announcements regarding the integration of artificial intelligence (AI) in drug development, coupled with joint efforts from the White House and the Federal Trade Commission to curtail prescription drug costs. This involves challenging the propriety of patents listed in the FDA Orange Book and leveraging “march-in” rights under the Bayh-Dole Act.

AI Integration and Regulatory Objectives

In response to these developments, the FDA introduced initiatives on artificial intelligence and machine learning in drug development. The agency issued discussion papers as part of its communication strategy with stakeholders, aiming to explore considerations for the use of AI/ML in developing drugs and biologic products. While lacking specific policy proposals, these papers underscore the FDA’s acknowledgment of the pivotal role AI/ML will play in drug development. Furthermore, the FDA expressed its commitment to adopting a flexible risk-based regulatory framework that fosters innovation with AI while ensuring patient safety.

Executive Order on AI and Intellectual Property

President Biden’s executive order on artificial intelligence in October 2023 outlined broad initiatives aimed at enhancing safety and security in the rapidly evolving AI space. This includes directives to the US Patent and Trademark Office (USPTO) director to provide guidance on inventorship in AI use and address other considerations at the intersection of AI and intellectual property within specified timeframes. Consequently, the industry anticipates requested guidance in February and July, providing stakeholders with an opportunity to review and comment, actively contributing to the shaping of USPTO regulations involving AI.

Federal Initiatives on Drug Costs

In parallel, federal agencies focused on reducing drug costs in 2023. The Federal Trade Commission, in September, announced objectives to combat improper Orange Book listings, which it views as barriers to generic drug manufacturers entering the pharmaceutical market. Subsequently, the FTC challenged over 100 patents through an FDA regulatory process in November 2023, alleging improper listing in the Orange Book. Responding to this, Senator Elizabeth Warren and Representative Pramila Jayapal urged targeted companies to address the allegations and voluntarily delist challenged Orange Book patents. As the industry awaits outcomes, the FTC is contemplating next steps for companies ignoring warning letters.

Policy Objectives and the Bayh-Dole Act

December 2023 brought new policy objectives from the Biden Administration, aiming to reduce prescription drug prices, partly through the utilization of the Bayh-Dole Act. This legislation allows certain entities to retain title and commercialize patents resulting from federally funded research, subject to specific criteria. Simultaneously, the National Institute of Standards and Technology (NIST) released a draft inter-agency framework for “march-in” rights, allowing agencies to consider price and terms in determining whether to license patents from federally funded research.

Conclusion

Judicial interpretations of the conditions for patentability in 2023 influenced the composition of pharmaceutical patent claims. Moreover, many governmental and regulatory initiatives focused on AI advancements and attempted to lower prescription costs. It is recommended that interested parties keep an eye on proposed rules in both areas and communicate with relevant authorities to actively influence laws that affect the pharmaceutical sector.

The U.S. FDA revised its position on the use of electronic systems, records, and signatures in clinical studies in 2023 and released guidelines for the planning and execution of Decentralized Clinical Trials (DCTs). In addition to developing guidelines, the FDA set up a broad framework that included internal mechanisms for evaluating decentralized and virtual trials, workshops, demonstration projects, stakeholder participation, and a dedicated website.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of services for clinical research technology including strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

The post Key Regulatory issues to monitor for clinical trials in 2024 and beyond appeared first on Astrix.

1. The Organization for Professionals in Regulatory Affairs (TOPRA)

With a diverse range of membership categories, TOPRA is an excellent resource for individuals at all stages of their regulatory affairs career. Besides offering networking opportunities, TOPRA also provides professional development resources and keeps its members updated with the latest industry news and trends.

2. American Association of Healthcare Administrative Management (AAHAM)

Since its inception in 1968, AAHAM has steadfastly advocated for healthcare professionals involved in the revenue cycle. The association offers information, education, and support to help members navigate the complexities of healthcare administration.

3. Association of Clinical Research Professionals (ACRP)

ACRP is home to two affiliate organizations: The Academy of Pharmaceutical Physicians and Investigators (APPI) and The Academy of Clinical Research. ACRP provides training and certification programs, fosters best practices, and promotes excellence in clinical research.

4. Regulatory Affairs Professionals Society (RAPS)

As the largest global organization of its kind, RAPS focuses on the regulation of healthcare and related products. Its offerings include professional development courses, resources for regulatory strategy and compliance, and a platform for networking and collaboration.

5. American College of Healthcare Executives (ACHE)

ACHE is another significant player in the realm of healthcare management. The association provides career resources, educational programs, and a platform for healthcare executives to exchange ideas and share best practices.

6. American College of Clinical Engineering (ACCE)

Recognized as a key organization in healthcare technology management, ACCE promotes the professional interests of clinical engineers. It provides resources to enhance their technical skills and knowledge about clinical systems and devices.

Conclusion

These associations offer a wealth of resources for professionals in clinical and regulatory affairs. They provide industry-specific knowledge, networking platforms, and opportunities for professional development. By joining these organizations, professionals can stay abreast of industry trends, contribute to policy discussions, and enhance their career prospects.

About Astrix

At Astrix , we do more than just fill roles – we build careers. Whether you’re looking to break into the clinical and regulatory industry or seeking your next role, we are here to support your journey. We know that finding the perfect role isn’t just about ticking boxes; it’s about discovering a position where you can grow, contribute and thrive. That’s why we take the time to understand your aspirations, skills, and potential, ensuring that we connect you with roles that will fuel your professional growth.

For hiring managers looking to find the best talent for their team, we understand your challenges such as limited resources and budget constraints. That’s why we tailor our solutions to fit your unique needs. This approach enables us to provide solutions that not only meet your immediate needs but also your future needs.

Contact us today to learn more about our service offerings and how we can help.

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Global health authorities will move away from unstructured submissions and toward a structured format for data and content. Using structured data from source to submission eliminates manual transcription, aggregation, and data table creation. This reduces resource needs, reduces errors, accelerates filing readiness, and supports initiatives like the ISPE Pharma 4.0 initiative to provide structured data using digitalization and automation.

In this webinar, we will discuss:

• Enabling structured submissions with structured data
• Automating data table generation to reduce resource needs
• Integrating end-to-end workflow of data from source to submission
• Managing the large data sets of Module 3
• Supporting Pharma 4.0 unified data model

The post On Demand Webinar – Generating and managing structured, reusable data tables for data driven submissions appeared first on Astrix.

With the explosion of generative AI and the emergence of LLMs over the last year, what are the ramifications across the clinical document landscape? Increasingly, life sciences firms are leveraging content automation solutions to maximize the efficiency, accuracy, and standardization of reporting and writing processes.

In this session, Tim Martin, Executive VP of Product at Yseop, will dive into where artificial intelligence fits into the clinical document landscape and how new drugs will be discovered using generative AI techniques over the upcoming years. He will map out:

• The benefits of deploying generative AI and natural language generation (NLG) to automate reporting for improved clinical writing.
• Why generative AI promises to reduce costs and accelerate time to market in drug discovery.
• Results from pharmaceutical companies that have implemented this technology.

About Yseop

Yseop is an international AI software corporation that specializing in developing natural language generation (NLG) technology. Its primary enterprise software platform, Yseop Copilot, aids premier life science organizations worldwide in accelerating the automation of data analysis and report generation, bringing drugs to market faster.

The post On Demand Webinar – Automate Clinical Documents with Generative AI appeared first on Astrix.

Pharmaceutical companies with which we’ve worked can find it a heavy lift to complete and streamline the seemingly myriad of tasks. We’ve seen efforts encounter challenges, due to a lack of consideration and coordination among multiple, related organizations.

In this podcast RIM expert, Heather Adinolfi discusses the strategies and tactics that can effectively:

• Identify and anticipate common hurdles,
• Mitigate the impact of common unavoidable challenges, and
• Smooth the road to success with Regulatory Information Management.

Click below to listen to the podcast

The post Astrix Podcast – Insights to Succeed Throughout the Regulatory Information Management (RIM) Journey appeared first on Astrix.

• Increasing complexity of clinical trials and the supporting bioanalytical studies
• Enormous amount and variety of data generated
• Clinical trial networks comprising many parties and sites
• Increasingly sophisticated devices for gathering clinical data

Every once in a while, a technology comes along that has the potential to act as a significant disruptive force. Blockchain technology may very well be the next big disruptive technology and is likely to find applications in nearly every major industry to reduce cost, increase efficiency and more significantly, improve trust. Trust is foundational all businesses, and blockchain enables companies to seamlessly establish both trust and transparency in data-related business processes in a non-centralized and therefore scalable and cost-effective way.

Blockchain technology was invented in 2008 with the creation of bitcoin by the pseudonymous Satoshi Nakamoto. It’s important to understand that bitcoin is itself merely an application of blockchain – bitcoin itself is a financial asset that utilizes blockchain technology as its ledger. Given the necessity of trust and transparency in financial transactions, finance transactions were a natural first use case for blockchain technologies. The ability of blockchain to facilitate trust in a decentralized with relatively low cost has drawn the attention of researchers in the pharmaceutical industry, and efforts to apply the technology to data integrity problems within clinical trials are increasing.

According to Statista, global blockchain technology revenues will experience massive growth in the coming years, with the market expected to climb to over 23.3 billion U.S. dollars in size by 2023. In this blog, we will discuss ways in which blockchain technology is being applied to enhance data integrity in clinical research.

What is Blockchain Technology?

Ledgers are the foundation of accounting: a secure record of transactions is essential to the role of money in our society. Blockchain technology utilizes a distributed peer to peer computer network to create a digital ledger (a form of a database) using cryptographic techniques to store transaction records in a verifiable and unalterable way. Each server or node in the network can independently verify each data entry and modification of any data in the chain requires alteration of all following entries in the chain and consensus of the network. In this manner, with a reasonable time and network distribution of transactions, the entire chain of entries is considered secure by design and fault tolerant due to the distribution of the data.

When applied effectively, blockchain networks inherently resist modification of the data stored in the transaction record. Transaction data stored in a blockchain can’t be stolen or hacked, as it is not kept in a central repository, but instead distributed across dozens or even thousands of geographically dispersed network nodes. The distributed nature of the network, the use of timestamped records, and cumulative cryptographic verification all assure that the stored data remains intact and immutable. Additionally, the application of public key cryptography to the transactional data, makes the entries attributable and non-repudiable. Interestingly, these are key characteristics of asserting data integrity as defined by the FDA: attributable, legible, contemporaneous, original, and accurate (ALCOA).

Furthermore, the ability of a blockchain to be an immutable audit trail makes blockchain technology perfectly suited for any company focused on data integrity and regulated or audited by 3^rd parties like the FDA. In addition, blockchain network participants can store, exchange and view information in the database without the need for establishing preexisting trust between the parties (for example, by a negotiated legal contract) – trust is in fact hardcoded into the blockchain protocol.

It is important to note that not all problems require or would be most effectively met with a blockchain solution. The creation and maintenance of the ledger is currently a costly exercise. There have been efforts to address this cost, through the design of specific implementations to facilitate application to more general problems (Ethereum for example, which is a blockchain application platform based on a distributed virtual machine); and through “blockchain as a service” offerings, with Microsoft and Amazon Web Services recently adding blockchain networks to their offerings.

Blockchain solutions are ideal for data records that are meant to be shared between partners in a network where transparency and collaboration are important. Specifically, blockchain could be considered appropriate when:

• multiple parties generate transactions that represent shared data;
• the parties need to be able to independently verify that the transactions and the current state of the shared data are valid;
• there are no trusted third-party services able to manage the shared data efficiently (e.g. an escrow);
• enhanced security is needed to ensure integrity of the system.

Private vs. Public Blockchains

Blockchain databases can be categorized into two main types – public and private. Bitcoin is an example of a public blockchain, with thousands of computer nodes distributed worldwide doing the work required to verify the network. These nodes are run by participants in verifying the network (computational work also known as “mining”) who receive rewards paid in the cryptocurrency created by the network. Attributes of a public blockchain include public access, a high degree of control, a large distribution of work which can result in fewer verified transactions per unit time, and transaction verification and overall security by Proof of Stake or Proof Of Work (approaches that enhance stability and security of the network).

Private blockchains, on the other hand, are more common in industry applications of blockchain technology. These type of blockchains have a less randomly distributed network (nodes typically run by stakeholders), restricted access to the network, the potential for higher throughput and scalability, and transaction verification by authorized network participants.

Hybrid blockchains are a recent approach: the blockchain in general is a public blockchain with an ability to designate data that is public and fully open vs. data that remains private and accessible only to authenticated entities. Some recent private blockchain implementations are also leveraging public blockchains for the cryptographic payload only: this approach has an interesting advantage when considering data storage compliance with geographic regulations such as the European Union’s General Data Protection Regulation (GDPR).

Enhancing Data Integrity in Clinical Research with Blockchain Technology

Good quality data from clinical trials requires good security, proper content (metadata) and an immutable audit trail. Blockchain technology is a potential component of ensuring these capabilities. Blockchain data integrity has the strength of cryptographic validation of each interaction or transaction, and the entirety of the set of records. Any data integrity issue in a blockchain results in an immediate indication of where and when the problem happened, along with the contributor of the offending transaction.

Blockchain effectively allows proof of the integrity and provenance of the data independently of the production of the data. Blockchain-based, immutable, timestamped records of clinical trial results and protocols could potentially reduce the incidence of fraud and error in clinical trial records by eliminating the potential for outcome switching, selective reporting and data snooping.

Additionally, it is very difficult for researchers in the current environment of siloed informatics systems to share and maintain the provenance of data. Blockchain-based data systems could allow for seamless data sharing between organizations, thereby reducing data integrity issues stemming from errors and incomplete data.

As an example of this, researchers at University of California San Francisco (UCSF) recently developed a proof-of-concept method for using a private blockchain to make data collected in the clinical trial process immutable, traceable, and more trustworthy. The researchers used data from a completed Phase II clinical trial and tested the resilience to tampering with the data through their proof-of-concept web portal service. They demonstrated effectively that data entry, storage, and adverse event reporting can be performed in a more robust and secure manner, could withstand attacks from the network, and be resilient to corruption of data storage. For this approach to be implemented in the real world, it’s worthwhile to note that a regulator acting as a centralized authority would likely need to instantiate a private blockchain, operate the web portal, register all parties, and keep a ledger of the blockchain’s transactions.

Another example of blockchain technology in action was provided by scientist.com, a leading online marketplace for outsourced research in the life sciences, with the recent launch of their DataSmart platform based on a proprietary blockchain implementation. The platform is designed to ensure data integrity in research data from clinical and preclinical stages of drug development that is submitted electronically to regulators. DataSmart also allows pharmaceutical and biotechnology companies to demonstrate that critical supplier data has not been tampered with and remains unaltered.

To improve visibility of data quality and analysis, researchers from several different companies recently collaborated to develop TrialChain, a hybrid blockchain-based platform intended for application to biomedical research studies. This approach includes provisions to permit modifications of original data created as a routine part of downstream data analysis to be logged within the blockchain. In addition, the use of a hybrid blockchain structure in TrialChain allows for public validation of results alongside the additional authorization required for full access to data.

Finally, pharmaceutical giants Pfizer, Amgen and Sanofi have teamed up to find the most effective ways to utilize blockchain technology, from ensuring data integrity to speeding up clinical trials and ultimately lowering drug development costs.

Conclusion

With the ability to deliver immutable timestamped records, audit trails and data provenance, in a highly secure and attributable manner, blockchain technologies could indeed have a huge impact on clinical research worldwide. While blockchain obviously can’t prevent errors at the source (e.g., an incorrect recording of a test result), a blockchain-enabled clinical research system could provide a strong mechanism to ensure the integrity of the datasets themselves.

As in other industries, blockchain also holds promise for reducing costs in many aspects of the drug lifecycle – from discovery to manufacturing. From the ability to concisely and securely track data provenance as part of analytics to meeting regulatory serialization requirements in the supply chain, there are many interesting potential applications of blockchain technology.

While the use of blockchain technology in clinical research is still in its infancy, there is much interesting activity in applying blockchain technology to solve clinical data management challenges. Many pharmaceutical companies are forming outside partnerships and creating in-house initiatives to explore blockchain technologies in clinical research areas like patient recruitment, patient data sharing, data privacy, preventing data tampering and improving research reproducibility.

As with the application of any new technology, biopharma companies looking to implement blockchain technology in their clinical research should consider working with a quality informatics consultant well established in the domain, and consider very deliberately how the technology will fit in the overall systems architecture.

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