Drug developers are increasingly turning to computational / in silico tools and methods to enhance novel drug investigation, as well as reduce costs and improve translation throughout the development lifecycle, from lead optimization through validation.
But despite the infusion of computational models and the improvements gained from their use, the drug failure rate remains the same (if not greater). Workflows leveraging key datasets are often siloed, as are traditional computational models, which also suffer from being overfit, error-prone and limited in their extensibility.
Integrating computational models and drug intelligence datasets at different R&D stages, along with adding machine learning (ML) to understand those interactions enables better predictions and identifies potential failure points early in a program lifecycle.
Predictive AI and ML techniques are well suited to extend to complexities like drug combinations, predicting toxicities, patient responsiveness across multiple modalities, translational differences across animal species in relation to later human effect and biological pathway discovery. When integrated with molecular and pharmacovigilance data, these techniques provide actionable insights that can guide novel candidate design, limit unnecessary experimentation, improve candidate safety confidence and increase the return on program investment.
Join this webinar to learn how exactly AI and ML techniques are taking computational modeling and informatics to new domains of applicability in the drug development lifecycle. Through illustrative examples of AI in drug development, attendees will gain a better understanding of how these techniques can be applied to enhance decision-making and increase the chances of clinical success.
Dr. Jo Varshney, PhD, DVM, CEO, VeriSIM Life
Dr. Jo Varshney is the Founder and CEO of VeriSIM Life, and President and CEO of PulmoSIM Therapeutics which is a subsidiary of VeriSIM Life. She is the inventor of VeriSIM Life’s BIOiSIM core technology. Dr. Varshney is a Doctor in Veterinary Medicine (DVM) and holds a PhD in comparative oncology/genomics from the University of Minnesota, as well as graduate degrees in comparative pathology from Penn State, and computational sciences from UC San Francisco.
Dr. Varshney’s commentary on the use of technology to improve the translation of novel therapies to successful clinical outcomes has been featured in trade publications, national media and scientific journals. Additionally, she serves on several advisory boards as a Scientific/Technical advisor, including the Critical Path Institute (C-Path), to further novel platforms to foster a technology ecosystem for enabling broader access to disease progression models and clinical trial simulation applications.
John Earl, PhD, Sr. Director, Solutions Consulting, Life Sciences & Healthcare, Clarivate
John Earl is the Senior Director of the Consulting Solutions team within the Life Science and Healthcare division of Clarivate. John holds a PhD in Pathology from the University of London as well as an MSc in Toxicology form the University of Surrey and a degree in Pharmacology from the University of Leeds.
John has led analysis and forecasting services in oncology and cardiovascular medicine in addition to launching a Chinese drug forecasting database. He has managed teams in Europe, the US and Asia and currently works with clients identifying their specific information needs and helping design tailored solutions using Clarivate’s extensive data sets, analytical tools and consulting services.
Who Should Attend?
Professionals from pharma, CROs and biotech producing units with the following roles and focus areas:
- 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
- PK/PD, DMPK
- Technical Lead, Preclinical Development
What You Will Learn
Attendees will benefit from these key takeaways:
- The analytic outcomes that can be yielded from the integration of molecular, drug exposure, toxicity and pharmacovigilance datasets
- How these datasets can be synthesized by hybrid AI into a “credit score” for drug translatability
- Why this integrated approach provides a mechanistic understanding of the observed correlation
- Illustrative case study examples including determining efficacy for multiple drug combinations, and profiling candidate toxicity based on structure properties alone
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