Publications & Blog

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.

One major difficulty in FIH dosing is considering variability in human response. The complexity of possible individual responses is challenging to account for using traditional FIH dosing methods, and failure to properly consider these possible variations can result in heightened risk of adverse effects in clinical trial participants. It can also increase the already high cost/time investment of clinical trials. This underscores the need for more sophisticated approaches in FIH dosing that can account for the myriad factors that influence individual human response.

Artificial intelligence (AI) has emerged as a game-changer in unlocking the potential of real-world evidence (RWE) to revolutionize the way we design, test, and deliver new treatments. RWE, which encompasses a wealth of data from electronic health records, patient-reported outcomes, and other sources, offers invaluable insights into drug safety and effectiveness in real-world settings. AI is becoming a transformative force in harnessing RWE to improve drug development and for addressing challenges that come with RWE such as data quality, patient privacy, and bias.