Challenges and opportunities for in vitro-in vivo extrapolation of aldehyde oxidase-mediated clearance: Towards a roadmap for quantitative translation.

Underestimation of AO-mediated clearance by current in vitro assays leads to uncertainty in human dose projections, thereby reducing the likelihood of success in drug development. In the present study we first evaluated the current drug development practices for AO substrates. Next, the overall predictive performance of in vitro-in vivo extrapolation (IVIVE) of unbound hepatic intrinsic clearance (CL int,u ) and unbound hepatic intrinsic clearance by AO (CL int,u,AO ) was assessed using a comprehensive literature database of in vitro (human cytosol/ S9/ hepatocytes) and in vivo (iv/oral) data collated for 22 AO substrates (total of 100 datapoints from multiple studies). Correction for unbound fraction in the incubation (fu inc ) was done by experimental data or in silico predictions. The fraction metabolized by AO (fm AO ) determined via in vitro/in vivo approaches was found to be highly variable. The geometric mean fold errors (gmfe) for scaled CL int,u (mL/min/kg) were 10.4 for human hepatocytes, 5.6 for human liver cytosols, and 5.0 for human liver S9, respectively. Application of these gmfe's as empirical scaling factors improved predictions (45-57% within 2-fold of observed) compared with no correction (11-27% within 2-fold), with the scaling factors qualified by leave-one-out cross-validation. A road map for quantitative translation was then proposed following a critical evaluation on the i n vitro and clinical methodology to estimate in vivo fm AO In conclusion, the study provides the most robust system-specific empirical scaling factors to-date as a pragmatic approach for the prediction of in vivo CL int,u,AO in the early stages of drug development. Significance Statement Confidence remains low when predicting in vivo clearance of aldehyde oxidase (AO) substrates using in vitro systems, leading to de-prioritisation of AO substrates from the drug development pipeline to mitigate risk of unexpected and costly in vivo impact. The current study establishes a set of empirical scaling factors as a pragmatic tool to improve predictability of in vivo AO clearance. Developing clinical pharmacology strategies for AO substrates by utilizing mass balance/clinical DDI data will help build confidence in fm AO .