In silico approaches and tools for the prediction of drug metabolism and fate: A review
Introduction
As our understanding of drug fate–determining metabolic reactions has increased, drug metabolism has attracted greater attention as a critical factor in drug discovery [[1], [2], [3]]. The fate of substances such as drugs and xenobiotics administered into our body is largely governed by the three phases of drug metabolism: phase I, introduction of a reactive group by oxidation, reduction or hydrolysis, among others; phase II, conjugation with various moieties; and phase III, removal of xenobiotics and metabolites from cells in the liver and intestine. These transformation processes may convert compounds to inactive, active, or toxic metabolites. Not surprisingly, because it is responsible for the clearance of ∼70% of clinical drugs, metabolism has been intensively investigated as part of drug development efforts [4].
Natural compounds have recently attracted considerable research attention owing to their inherent advantages and high potential as drug candidates [5]. Moreover, the structural similarity of certain natural compounds with metabolites found in the human body makes metabolism a critical factor in determining the effectiveness of natural drugs [[6], [7], [8]]. For instance, historical opioid drug candidates are metabolized into more potent metabolites, such as (dihydro) codeine, which, in turn, is metabolized into (dihydro) morphine [9,10]. Considering the large number of endogenous enzymatic reactions that influence drug modification through (de)activation and (de)toxification, determining how a drug is metabolized is an important step in drug discovery.
Numerous experimental technologies have been used in recent decades to study the metabolism and fate of drugs [[11], [12], [13]]. The traditional drug discovery method—target-to-hit, hit-to-lead, and lead optimization—is expensive, costing more than $200 million for the average drug, and time-consuming, with a typical discovery period of 4–5 years [14,15]. In addition, because it is impossible to exactly replicate biological in vivo environments, such methods are relatively inaccurate and are still considered low-throughput, given the scale of combinatorial structural variations of chemical compounds.
Numerous advances in predicting drug metabolism using in silico approaches have been made as part of drug discovery efforts, and different aspects of these advances have been reviewed [[16], [17], [18], [19], [20], [21], [22]]. These include tools to predict drug metabolism based on the interactions of drugs with cytochrome P450 (CYP450) enzymes and their metabolic endpoints [18,22], tools to predict ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of drugs and related solubility, permeability and bioavailability issues [16], as well as approaches for predicting the inducibility of drug-metabolizing enzymes and transporters that affect the plasma concentration of drugs, which can cause undesirable or prolonged action or adverse effects [21].
Given these observations, in silico approaches have been increasingly used to predict the metabolic conversion of drugs [23] and as such are considered the best “fail early and fail cheap strategy,” allowing for reduced costs, time savings, and thus decreased attrition rates in late drug discovery phases. Herein, we present an introduction to fundamental approaches to in silico model development for predicting the metabolic fate of drugs as well as their toxicity. In addition, we summarize currently available in silico tools and some recent achievements in predicting drug metabolism. Finally, we discuss the future and challenges of in silico drug discovery.
Section snippets
Predictions based on quantitative structure-activity relationships and machine-learning approaches
The quantitative structure-activity relationship (QSAR) concept, developed in the early 1960s by Hansch/Fujita [24] and Free/Wilson [25] and widely used in drug discovery, assumes that molecules with similar structures potentially exhibit similar chemical and biological activities [[26], [27], [28]]. The original concept of structure-activity relationship dates back to 1868, when Crum-Brown and Fraser introduced the idea of correlating the chemical composition of a certain compound with its
Prediction of drug conversion to toxic metabolites
The metabolism of xenobiotics such as drugs and other foreign substances involves certain important enzymatic reactions, such as those mediated by CYP450 family enzymes expressed in liver and small intestines. According to literature reports, ∼90% of drugs can be efficiently metabolized by six CYP450 enzymes [104], the activity of which can be altered by factors such as genetic polymorphisms, cytokine regulation, disease state, sex, age, and hormones [[105], [106], [107]]. Another example is
Problems associated with predictive model construction
The inconsistency of available experimental data used to build in silico models is a major concern [119]. Predictive models strongly rely on experimental data for model construction; thus, high variability in experimental assays produced by biological variation and technical errors can lead to erroneous data and may therefore introduce inaccuracy into predictive models. The inaccuracy of in silico models may also result from different experimental conditions for the multiple resources
Conclusions
Because of its central importance, metabolism in biological systems has been intensively researched, especially in the field of drug discovery. The high impact of drug metabolism on drug efficacy and drug fate in biological systems has given rise to numerous in silico approaches and tools for metabolic reaction prediction in recent decades. However, the limitations of these approaches cannot be ignored. Specifically, the fact that these methods are highly dependent on experimental data is a
Conflicts of interest
The authors declare no conflicts of interest.
Acknowledgements and grants
This research was supported by a National Research Foundation of Korea grant funded by the Korean government (NRF-2016R1D1A1B03935264) and was also supported by the Bio-Synergy Research Project (NRF-2018M3A9C4076474) of the Ministry of Science, ICT and Future Planning through the National Research Foundation.
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These authors contributed equally.