Addressing the Metabolic Stability of Antituberculars through Machine Learning

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 t1/2 < 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 t1/2 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.