Viquidacin

Unbound tissue-to-plasma partition coefficients (K_puu) were determined for 56 structurally diverse compounds in rats following intravenous infusion. Five tissues were included in the study: white adipose, brain, heart, liver, and skeletal muscle. The rank ordering of the median tissue K_puu values was: liver (4.5) > heart (1.8) > adipose (1.2) > skeletal muscle (0.6) > brain (0.05), with liver being most enriched and brain most impaired. The median K_puu values of acids and zwitterions were lower than those of bases and neutrals in all tissues but liver. Selective tissue distribution was observed, dependent upon chemotype, which demonstrated the feasibility of targeting or restricting drug exposure in certain tissues through rational design. Physicochemical attributes for K_puu were identified using recursive partitioning, which further classified compounds with enriched or impaired tissue distribution. The attributes identified provided valuable insight on design principles for asymmetric tissue distribution to improve efficacy or reduce toxicity.

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.