Application of mechanistic multiparameter optimization and large scale in vitro toin vivopharmacokinetics correlations to small molecule therapeutic projects

Abstract

ABSTRACTComputational chemistry and machine learning are used in drug discovery to predict target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g. minimizing dose, maximizing safety margins and/or minimized drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predictin vivoPK properties and validatesin vitrotoin vivocorrelation analysis to support mechanistic PK MPO. Examples of use and impact in small molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top 2ndpercentile, and 100% in the top 10thpercentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared to the rest of the compounds synthesized, highlighting the potential of this tool to reduce the reliance onin vivotesting for compound screening.