Publications & Blog

The approach used in the study, especially the incorporation of knowledge-based features to enrich AI models, holds tremendous promise for not only assessing safety and toxicity assessments of drug candidates but also in other aspects such as target engagement and efficacy of these candidates, early in the development phase.

From the beginning, VSL has pioneered a mission-driven approach rooted in the belief that advanced simulations can replace outdated, animal-centric paradigms that have limited applicability to humans. The FDA now explicitly affirms that AI-based computational modeling can reliably simulate how monoclonal antibodies (mAbs) distribute through the human body, predict side effects based on molecular features, and accelerate therapeutic delivery without compromising safety. That’s exactly what VSL already enables and more!

VeriSIM Life's BIOiSIM platform is highly aligned with the core principles of new FDA documents, as it leverages advanced artificial intelligence (AI) and machine learning (ML) approach to simulate complex pharmacokinetics and pharmacodynamics models. This approach ensures robust predictions of drug behavior in diverse patient populations, aligning directly with the guideline's emphasis on physiologically based pharmacokinetics (PBPK) and quantitative systems pharmacology.

The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.