Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives

Abstract

Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS). Density functional theory (DFT) calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM), a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool for investigating metabolic stability, factors that affect it, and the proposed structures of metabolites at the same time. The performance of the DFT-SVM-based approach provides an opportunity to implement it in a standard drug development pipeline.

In Vitro approach for identification of a leading cytochrome P450 isoenzyme responsible for biotransformation of novel arylpiperazine drug candidates and their inhibition potency towards CYP3A4.

“…This makes it the safest compound among the tested and set to be co-administered with other drugs. Unfortunately, taking into consideration its metabolic stability value, compound 1 turns out to be more susceptible to biotransformation than referent drug, buspirone (13). Therefore, it might prove slightly less valuable, as it might not reach its molecular target when administered and will also be more prone to the first-pass effect.…”

“…So far, authors assessed their metabolic stability against first phase metabolism using human liver microsomes in in vitro test. Authors also studied metabolic pathways revealing parts of the molecules vulnerable to oxidation by microsomal enzymes (13). In the present paper, authors aimed to further contribute into biochemistry of those derivatives by studying metabolic stability involving particular cytochrome enzymes, identifying leading metabolizing CYP isoform as well as measuring potency to inhibit CYP3A4.…”

“…In the cited publications, in addition to new chemical entities, results of the structure-activity relationships have also been presented (6,7,(9)(10)(11). A study to model metabolic stability of arylpiperazines has also been conducted (13), not only describing an approach that can be applied to other compounds (14)(15)(16)(17) but also identifying the best chemometric technique to conduct such research (18).…”

Comparison of MLR, OPLS, and SVM as potent chemometric techniques used to estimate in vitro metabolic stability

“…Both the chemicals and data collection were the same as previously described in our previous report . Briefly, the studied compounds were incubated with pooled human liver microsomes along with co‐factors (NADPH) in a phosphate buffer environment.…”

“…In the case of arylpiperazines there have been attempts to design metabolically stable derivatives, even pinpointing the molecule parts responsible for their metabolic stability . In a recently published study, we presented our model for predicting half‐life values based on the support vector machine technique (SVM), which proved to be valid in a restricted chemical space . The aim of the presented study was to compare three methods used for value prediction – multiple linear regression (MLR), orthogonal partial least squares (OPLS) and SVM and determine whether SVM is actually the best possible option for developing such complex models as quantitative structure‐metabolic stability relationships.…”