Blog

From identifying novel drug targets to optimizing the manufacturing process, AI is transforming the way small molecule drugs are discovered, designed, and developed. AI-driven drug development holds remarkable potential for accelerating the availability of life-changing therapies through small molecule drug development. Read on to connect the linkages between small molecule drug chemistry, the drug discovery and development process, and the application of AI to deliver safe and effective drugs for clinical use in patients.

In the ever-evolving landscape of drug development, scientists are continually seeking innovative approaches to streamline processes, reduce costs, and enhance efficiency. One such approach is Quantitative Systems Pharmacology (QSP) modeling, a multidisciplinary field that integrates mathematics, biology, and pharmacology to facilitate a deeper understanding of the complexities underlying disease and drug response. With the growing prominence of Artificial Intelligence (AI), QSP modeling is poised for transformative advancements that could revolutionize the drug development pipeline.

VeriSIM Life’s BIOiSIM® platform can address the challenges of TD formulation development because its hybrid AI models capture the complex interplay between the API, excipients, and physiology. BIOiSIM uses a unique combination of mathematical and AI-driven techniques to analyze and predict a drug’s behavior in the human body. The platform's models provide deep insight into drug distribution in various skin layers and underlying tissues, such as joint synovial membranes, and capture a drug’s systemic exposure and clearance post-transdermal application.

A recent workshop on the "Convergence of AI and Digital Measures for Seamless Preclinical to Clinical Translation" brought together, for the first time, experts from clinical and preclinical into one room to explore the transformative potential of combinatorial power of digital measures and AI technologies in drug development. Through focused sessions and case studies, we explored the current implementation and validation processes for AI/ML-derived digital measures and addressed the challenges and obstacles to their adoption. Our goal was to identify strategies for integrating digital measures and AI to enhance the translation from preclinical to clinical stages, improve data precision, and create a synergistic effect for a comprehensive understanding of these technologies.