Evaluation of a Published in Silico Model and Construction of a Novel Bayesian Model for Predicting Phospholipidosis Inducing Potential

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• 作者：Dennis J Pelletier ; Daniel Gehlhaar ; Anne Tilloy-Ellul ; Theodore O. Johnson ; Nigel Greene
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2007
• 出版时间：May 2007
• 年：2007
• 卷：47
• 期：3
• 页码：1196 - 1205
• 全文大小：113K
• 年卷期：v.47,no.3(May 2007)
• ISSN：1549-960X

文摘

The identification of phospholipidosis (PPL) during preclinical testing in animals is a recognized problemin the pharmaceutical industry. Depending on the intended indication and dosing regimen, PPL can delayor stop development of a compound in the drug discovery process. Therefore, for programs and projectswhere a PPL finding would have adverse impact on the success of the project, it would be desirable to beable to rapidly identify and screen out those compounds with the potential to induce PPL as early as possible.Currently, electron microscopy is the gold standard method for identifying phospholipidosis, but it is low-throughput and resource-demanding. Therefore, a low-cost, high-throughput screening strategy is requiredto overcome these limitations and be applicable in the drug discovery cycle. A recent publication by Ploemenet al. (Exp. Toxicol. Pathol. 2004, 55, 347-55) describes a method using the computed physicochemicalproperties pK_a and ClogP as part of a simple calculation to determine a compound's potential to inducePPL. We have evaluated this method using a set of 201 compounds, both public and proprietary, withknown in vivo PPL-inducing ability and have found the overall concordance to be 75%. We have proposedsimple modifications to the model rules, which improve the model's concordance to 80%. Finally, we describethe development of a Bayesian model using the same compound set and found its overall concordance tobe 83%.