– Using genomics, transcriptomics, proteomics, and metabolomics to train predictive AI models
  – Roadmap for embedding AI models as a standard preclinical component to accelerate IND readiness
Agenda:
2:00 pm Introduction to NAM Guidance
Pooja Khanna, PhD, Senior Scientist, Merck
2:15 pm AI/ML Based Prediction Models for Predicting Clinical Immunogenicity During Preclinical Development
Guilhem Richard, PhD, CTO, EpiVax
2:40 pm Computational/QSP Based Models to Preclinical IND Enabling Activities
Panagiota (Pegy) Foteinou, PhD, Senior Director, Preclinical and Early Development, Bristol Myers Squibb  Â
3:05 pm Refreshment Break
3:20 pm Use of in silico and in vitro Human Relevant Assays and Related AI/ML Based Models to Support Preclinical IND Enabling Activities
Jochem Gokemeijer, PhD, Distinguished Scientist, Biologics, Johnson & Johnson Â
3:45 pm Computational/QSP Based Models
Timothy Hickling, PhD, Consultant, Quasor Ltd.
4:10 pm 2D and 3D Skin and Specialized Models to Develop ML Algorithms for Understanding Drug Disposition and Safety Risks in Preclinical Stage of Development
Sathy Balu-Iyer, PhD, Professor, Pharmaceutical Sciences, SUNY Buffalo
4:35-5:00 pm Panel Discussion and Key Takeaways
Vibha Jawa, PhD, Chief Scientific Officer, Epivax Inc​.