2023 - Leveraging AI-Driven Machine Learning and Advanced Preclinical Cancer Models for Improved Clinical Translation to the Clinic

In recent years, the biopharmaceutical industry has become keenly interested in adopting artificial intelligence (AI)-driven machine learning platforms. These AI platforms can be used for streamlining R&D efforts, reducing discovery timelines and, costs, and improving efficiency. At the same time, the demand for predictive and robust preclinical models to minimize translational failures in oncology is at an all-time high.
As researchers look to leverage genotypic features of models for early identification of predictive biomarkers, understand complex immune interactions, and elucidate combination therapies’ mechanisms of action, it’s becoming apparent that the fight against cancer will be won at the intersection of biology, biochemistry, and computer science.
During this webinar, weMichael Boice, PhD, will look at how in silico predictions of efficacy, when validated in more clinically relevant preclinical models, can bring greater certainty to early decision-making, and improve translation to the clinic. WeHe also will demonstrate how in vivo validation studies can accelerate AI-driven machine learning and rapidly improve the accuracy of compound-specific in silico models for biomarker optimization.