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.
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Acknowledgments
The authors thank Dr. Gary Walker for assistance with data retrieving. We thank Drs. Kristi Fan, Natasja Brooijmans, and Jason Cross for discussions on experimental design. Also we would like to mention our appreciation to Dr. David Diller for his help on preparing this manuscript. We thank Drs. Tarek Mansour and Will Somers for their support of this work.
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Hu, Y., Unwalla, R., Denny, R.A. et al. Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability. J Comput Aided Mol Des 24, 23–35 (2010). https://doi.org/10.1007/s10822-009-9309-9
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DOI: https://doi.org/10.1007/s10822-009-9309-9