Extending ligand efficacy indices with compound pharmacokinetic characteristics towards holistic Compound Quality Scores

The suitability of a small molecule to become an oral drug is often assessed by simple physicochemical rules, the application of ligand efficacy scores (combining physicochemical properties with potency) or by multi-parameter composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce a new type of Compound Quality Scores (specifically called dose-scores and cmax-scores), which explicitly include predicted or when available experimentally determined PK parameters, such as volume of distribution, clearance and plasma protein binding. Combined with on-target potency, these scores are surrogates for an estimated dose or the corresponding cmax. These Compound Quality Scores allow for prioritization of compounds in test cascades, and by integrating machine learning based potency and PK predictions, these scores allow prioritization for synthesis. We demonstrate the complementary and in most cases the superiority to existing efficiency metrics (such as ligand efficiency scores) by project examples.