Addressing the Metabolic Stability of Antituberculars through Machine Learning
Year: 2017
Type: article
Abstract: We present the first prospective application of our mouse liver microsomal (MLM) stability Bayesian model. CD117, an antitubercular thienopyrimidine tool compound that suffers from metabolic instability (MLM t 1/2 < 1 min), was utilized to assess the predictive power of our new MLM stability model. The S-substituent was removed, a set of commercial reagents was utilized to construct a virtual library of 411 analogues, and our MLM stability model was applied to prioritize 13 analogues for synthesis and biological profiling. In MLM stability assays, all 13 analogues had superior metabolic stability to the parent compound, and six new analogues had acceptable MLM t 1/2 values greater than or equal to 60 min. It is noteworthy that whole-cell efficacy and lack of relative mammalian cell cytotoxicity could not be predicted simultaneously. These results support the utility of our new MLM stability model in chemical tool and drug discovery optimization efforts.
Source: ACS Medicinal Chemistry Letters
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Molecular Genetic Analysis of Mycobacterium Tuberculosis, Biocomputation across distributed private datasets to enhance drug discovery, G4078505261, Biomarkers and Mechanisms of Paucibacillary and Latent Tuberculosis, Center to develop therapeutic countermeasures to high-threat bacterial agents, Molecular Genetic Analysis of Mycobacterium Tuberculosis
OpenAlex ID: W2755230800