Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability

Abstract

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.

Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor

“…QSAR has been applied successfully and extensively to ind predictive models for activity of bioactive agents for the toxicity prediction [26][27][28][29], activity of peptides [30][31][32][33], drug metabolism [34][35][36], gastrointestinal absorption [37][38][39], prediction of pharmacokinetic and ADME properties [40][41][42][43][44], drug resistance and physicochemical properties [45][46][47].…”

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

“…As a measure of agreement corrected for chance, kappa value is considered as true accuracy for classification models and outperforms accuracy to evaluate the prediction capability. A model with a kappa value over 0.4 is regarded rational [21,22].…”

“…However, Bayesian approach, on account of the frequency of various descriptors occurrence, is a robust classification methodology which can differentiate actives from inactives [14,16]. Many examples have demonstrated its application in structure-activity analysis and drug discovery [14,[17][18][19][20][21]. Besides, several studies have shown that Bayesian categorization demonstrates higher EF and accuracy than molecular docking in identification of actives from decoys [22].…”

Computational Tools And Resources For Metabolism-Related Property Predictions. 2. Application To Prediction Of Half-Life Time In Human Liver Microsomes

“…Recently, several in silico predictive classification models of human or rodent liver microsomal stability have been reported [5–8]. Classification models of apparent intrinsic clearance (CL int , a compound’s rate of disappearance, be it in vitro or in vivo [typically measured in units of ml/min/kg protein], reflecting the actual metabolic capacity of the enzyme system in the limit of free access to substrate, a quantity inversely proportional to half-life time) were developed by Lee et al in 2007 [5], for 14,557 compounds using three descriptor sets and random forest as well as naive Bayesian methods as the mathematical approaches.…”

“…In 2008, Schwaighofer et al [7] developed human and rodent liver microsomal stability models for more than 3000 compounds from Bayer Schering Pharma in-house data using Dragon descriptors and a Gaussian process classifier. In 2010, Hu et al [8] used more than 15,000 compounds to build classification models for prediction of human and rodent half-life microsomal stability data based on SciTegic’s FCFP_6 fingerprints using a naive Bayesian classifier. A small number of somewhat older studies exist that report on work to directly predict metabolic stability, based on generally smaller number of compounds [9–12].…”