In Silico Classification of Major Clearance Pathways of Drugs with Their Physiochemical Parameters

Abstract

ABSTRACT:Predicting major clearance pathways of drugs is important in understanding their pharmacokinetic properties in clinical use, such as drug-drug interactions and genetic polymorphisms, and their subsequent pharmacological/toxicological effects. In this study, we established an in silico classification method to predict the major clearance pathways of drugs by identifying the boundaries of physicochemical parameters in empirical decisions for each clearance pathway. It requires only four physicochemical parameters [charge, molecular weight (MW), lipophilicity (log D), and protein unbound fraction in plasma (f up )] that were predicted from their molecular structures without performing any benchwork experiments. The training dataset consisted of 141 approved drugs whose major clearance pathways were determined to be metabolism by CYP3A4, CYP2C9, and CYP2D6, hepatic uptake by OATPs, or renal excretion in an unchanged form. After grouping by charge, each drug was plotted in a three-dimensional space according to three axes of MW, log D, and f up . Then, rectangular boxes for each clearance pathway were drawn mathematically under the criterion of "maximizing F value (harmonic mean of precision and recall) with minimum volume," yielding to a precision of 88%, which was confirmed through two types of validation: leave-one-out method and validation using a new dataset. With further modification toward multiple pathways and/or other pathways, not only would this in silico classification system be useful for industrial scientists at the early stage of drug development, which can lead to the selection of candidate compounds with optimal pharmacokinetic properties, but also for regulators in evaluating new drugs and giving regulatory requirements that are pharmacokinetically reasonable.Once a drug enters the human body, it undergoes detoxification by the complementary functions of a wide variety of metabolic enzymes and transporters, resulting in numerous clearance pathways such as urinary elimination through glomerular filtration and renal tubular excretion, passive diffusion into the liver followed by hepatic metabolism by cytochrome P450 (P450) enzymes to an inactive metabolite, or hepatic uptake by transporters followed by excretion into the bile and then into the feces. The major clearance pathway of drugs is one of the most important pharmacokinetic features relevant for its clinical use. Understanding the major clearance pathways would enable prediction of the changes in the systemic exposure of a drug caused by drug-drug interactions or genetic polymorphisms of enzymes and transporters. Some drugs have been withdrawn from the market owing to fatal side effects that often occurred by drug-drug interactions of their major clearance pathways, such as cerivastatin and its interaction with gemfibrozil (Shitara et al., 2004). The withdrawals might have been avoided by clarifying their major clearance pathways in the human body and by recognizing the severity of drug interactions, leading to better decision-making b...

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

“…Eventually, as data accumulates for a large number of chemicals, it may become possible to predict clearance using QSAR approaches. Qualitative prediction of whether a drug is likely to be cleared by metabolism (including the CYP isoenzyme involved) or by urinary excretion on the basis of its physicochemical properties, has recently been demonstrated (Kusama et al, 2010). Of course, there is much more extensive data on drugs than on environmental chemicals.…”

“…Predicting primary metabolic pathways, along with the potential for producing active metabolites, could be supported by in silico approaches such as QSAR (Kusama et al, 2010). Knowledge built on drug data showing the role of chemical properties in metabolism, binding, and partition would help this categorization.…”

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“…40 compounds (Manga et al, 2003). 88% precision of predicted renal clearance for 141 drugs (Kusama et al, 2010).…”

In Silico Prediction of Major Drug Clearance Pathways by Support Vector Machines with Feature-Selected Descriptors

“…Although the rectangular method is visually intuitive and easily understood, expansion of the number of clearance pathways would not be practical due to an algorithmic issue and substantial time requirements for the calculation (Kusama et al, 2010). In this study, we improved the prediction performance for each clearance pathway via the introduction of the support vector machine (SVM) with Gaussian kernel function, which has a broad capacity for two-class classification owing to its ability to find a nonlinear boundary in multidimensional data (Vapnik, 2000).…”

“…There are several in silico methods to predict whether a compound is susceptible to a certain metabolic enzyme (Yamashita et al, 2008;Mishra et al, 2010;Cheng et al, 2012), but it is fairly difficult to identify the major clearance pathways of drugs in humans because multiple elements, such as metabolic clearance mediated by various enzymes, should be correctly predicted on the basis of the theoretical approach, such as the relative activity factor method (Venkatakrishnan et al, 2000). Therefore, we previously established an in silico empirical classifier using a rectangular method ("CPathPred") to predict the major clearance pathways of drugs, with only four physicochemical parameters easily predicted from their molecular structures [charge; molecular weight; octanol-water distribution coefficient, or lipophilicity (log D); and protein unbound fraction in plasma], by optimizing the boundaries of these parameters for each clearance pathway (Kusama et al, 2010). Then the major clearance pathways of 141 approved drugs that are known to be majorly metabolized by CYP3A4, CYP2C9, or CYP2D6; taken up into hepatocytes by organic anion transporting polypeptide (OATPs); or excreted into urine in an unchanged form could be reasonably predicted by CPathPred with an accuracy of 73%.…”