While in vitro in vivo extrapolation (IVIVE) approaches are suitable for drug candidates that are eliminated by metabolizing enzymes, more sophisticated prediction approaches, e.g., PBPK models are required for drug candidates where transporter-mediated clearance mechanisms are involved. This talk will provide an update on tools for assessing in vitro transporter clearance parameters, PBPK model building, curve fitting and verification using preclinical PK data. The presentation will also provide case examples and highlight the gaps in human PK prediction.     

Many in silico and in vitro safety models are used during lead optimization to identify safety-related liabilities. A chemistry-based assessment of each model is essential to understand a model’s applicability and the relevance of any prediction from them. We will present several case studies of such analyses, including the in silico prediction of in vitro 3T3 phototoxicity and the use of in vitro cytotoxicity to predict in vivo toxicity findings.

Artificial intelligence is making its mark in drug development in many ways. We have been building artificial intelligence models of metabolism and reactivity. Metabolism can both render toxic molecules safe and safe molecules toxic. The artificial intelligence models we use quantitatively summarize the knowledge from thousands of published studies. The hope is that we could more accurately modeling the properties of medicines, to determining whether metabolism renders drugs toxic or safe. This is just one of many places where artificial intelligence could give traction on the difficult questions facing the industry.

Chemically-reactive drug metabolites, formed by enzyme-mediated metabolic activation usually in the liver, are perceived as an unwanted feature of drug candidates. These reactive metabolites may covalently bind to protein nucleophiles in vivo leading to subsequent immune-based idiosyncratic toxicities such as hepatotoxicity. The goal is to eliminate or minimize metabolic activation liabilities of drug candidates leading to the increased probability of safer drugs being developed. This presentation will review up-to-date risk assessment of reactive drug metabolites in drug discovery and development.

Accurate prediction of human pharmacokinetic properties is critically important to progress compounds with favorable PK properties. Of particular importance is the prediction of hepatic clearance, which largely determines drug exposure and contributes to projections of dose, drug half-life and bioavailability. This lecture will cover common in vitro techniques used to predict hepatic clearance of new chemical entities, as well as recent advancements and current challenges in IVIVE.

Drug-drug interactions (DDIs) mediated by hepatic transporters can have important implications in drug efficacy and safety. However, the accuracy and efficiency for the assessment of transporter mediated DDIs need improvement. This presentation will review some new tools to improve the DDI assessment, including cellular models combined with in silico tools to aid data interpretation, in vitro assays to characterize transporter probe cocktail and endogenous biomarkers which can be used for in vivo DDI assessment.