AI in Biomedical Research: Regulatory & Technology Perspectives

Life Sciences, Drug Discovery & Development, Preclinical,
  • Monday, October 30, 2023

Discover a groundbreaking webinar delving into the use of artificial intelligence (AI) and machine learning (ML) in preclinical drug research and related regulatory frameworks. The challenges and opportunities of integrating AI into preclinical drug research and development workflows are of increasing importance, especially in the context of regulatory science expectations. On the one hand, legislative priorities seek to reduce the cost of healthcare and drugs, while also reducing the burden of testing on animals through the use of technology like AI.

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Alternatively, political and community constituencies are concerned about the use of technology like AI. Nevertheless, agencies such as The United States Food and Drug Administration (FDA) have recognized the growing impact of this new technology on the pharmaceutical and biologics industry, confirming that in 2021 more than 100 drug and biologic application submissions included AI or ML components.

The European Medicines Agency (EMA) also recognizes AI’s potential to generate new insights and improve processes. As the technology develops, academic and end-user communities move ahead to advance more and more applications of AI in the field of biomedical research, close collaboration with regulators is critical to ensure positive qualification and implementation of this emerging technology.

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Join this interactive webinar to watch leaders from the FDA and VeriSIM Life share their perspectives on where and how AI is being applied within the preclinical phase of the drug development lifecycle, how scientific regulators are working with stakeholders today to responsibly leverage AI, and what needs to be done to continue evolving the state of AI for the benefit of all patients. Additional topics to discuss will include:

  • The use of ML to augment and complement the use of animal models in preclinical research
  • Techniques supporting improved preclinical toxicological knowledge and toxicogenomic research
  • Applying chemical pattern analysis and bioanalytics to predict drug interactions and support drug repositioning
  • Concepts of bias and explainability in preclinical AI model development
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Speakers

Dr. Szczepan Baran, VeriSIM Life

Dr. Szczepan Baran, MS, VMD, CSO, VeriSIM Life

Dr. Szczepan Baran, a Visionary Veterinarian Scientist with two decades of dedicated experience, stands at the forefront of integrating technological innovations into drug development and clinical trials. With a relentless commitment to revolutionizing patient access to cutting-edge medicines, he harnesses the power of digital technologies and data analytics to propel scientific boundaries and redefine patient engagement. As the Chief Scientific Officer at VeriSIM Life, Dr. Baran architects the R&D strategy, driving both foundational and applied research initiatives while pioneering transformative processes, technologies and products. Dr. Baran previously served as a Head of Emerging Technologies at Novartis, where he led an integrated digital enterprise strategy focusing on developing and incorporating patient-relevant AI technologies and conducted decentralized clinical trials (DCT).

In parallel, Dr. Baran focused on regulatory engagement to identify adaptation hurdles and develop regulatory validation and qualification pathways for emerging technologies. Before joining VeriSIM Life, Dr. Baran held multiple start-up and faculty positions and served on numerous boards, including chairing the IQ MPS Affiliate, leading the IQ MPS Affiliate Regulatory Outreach Group and serving as an ad hoc member of National Academies on the NHP Model Systems Committee.

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Currently, he serves as an advisory board member at BRIDGE, a member of the NIH Advisory Committee to the Director (ACD) Working Group on Catalyzing the Development and Use of Alternative Methods to Advance Biomedical Research, a founder and co-chair of the AI and Translational Digital Biomarker Initiatives of the 3Rs Collaborative and serves on the scientific advisory committee on Alternative Toxicological Methods (SACATM) as an ad hoc member.

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Dr. Weida Tong, National Center for Toxicological Research

Dr. Weida Tong, PhD, Director, Division of Bioinformatics and Biostatistics, FDA National Center for Toxicological Research –

Dr. Weida Tong is the Director of the Division of Bioinformatics and Biostatistics at The FDA’s National Center for Toxicological Research (NCTR). He has been an FDA Senior Biomedical Research and Biomedical Product Assessment Service expert since 2011, an Arkansas Research Alliance fellow since 2016 and a member of the Arkansas Academy of Computing since 2021. He has served on science advisory boards for several multi-institutional projects in Europe and the US and holds adjunct appointments at several universities.

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His primary research interests are in the fields of bioinformatics, artificial intelligence (AI), molecular modeling and data analytics for biomarker discovery, drug safety and repurposing, pharmacogenomics/toxicogenomics and precision medicine. Currently, he directs several FDA mission-critical projects in his division:

  • Supervising The FDA-led community-wide microarray and sequencing quality control (MAQC/SEQC) consortium to analyze the technical performance and practical utility of emerging genomics technologies with an emphasis on regulatory application and precision medicine
  • Developing the liver toxicity knowledge base to address drug safety concerns related to drug-induced liver injury (DILI)
  • Designing and developing computer-based technology to support The FDA’s effort on bioinformatics and scientific computing (e.g., development of The FDA genomic tool, ArrayTrack, to support pharmacogenomics data review in FDA)
  • Developing machine learning and AI for digital health and drug repositioning
  • Conducting molecular modeling and quantitative structure-activity relationship models on various toxicological endpoints such as endocrine disruption and carcinogenicity

Dr. Tong has published over 300 peer-reviewed papers and book chapters.

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Who Should Attend?

This webinar will appeal to leaders and professionals from the preclinical drug discovery and development divisions of pharma and biotech organizations, including but not limited to:

  • CEO, CSO, Chief Innovation/Development Officer
  • Vice President/Executive Director/Sr. Director/Director
    • Research & development
    • External innovation
    • Emerging technologies
    • Strategic partnerships
  • Director/Head/Principal Scientist
    • Translational sciences
    • Toxicology
    • PK/PD, DMPK
  • Technical Lead, preclinical development

What You Will Learn

Attendees will benefit from these key takeaways:

  • The use of artificial intelligence (AI) in preclinical drug development
  • The US regulatory framework for evaluating drug applications featuring AI/ML (machine learning)-derived research artifacts, or AI/ML-informed research complementing in vitro/in vivo artifacts
  • Best practice considerations for researchers considering, or already implementing AI techniques in their workflows
  • Resources and communities researchers can avail themselves of to educate and prepare for alignment and collaboration with regulatory agencies

Xtalks Partner

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VeriSIM Life

VeriSIM Life has developed a sophisticated computational platform that leverages advanced AI and ML techniques to improve drug discovery and development by significantly reducing the time and money it takes to bring a drug to market. BIOiSIM® is a first-in-class ‘virtual drug development engine’ that offers unprecedented value for the drug development industry by narrowing down the number of drug compounds that offer anticipated value for the treatment or cure of specific illnesses or diseases. The platform predicts the likelihood of a candidate’s success in clinical trials early in the preclinical stage, while reducing unnecessary experimentation and better informing key program decisions. For more information, visit www.verisimlife.com.

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