The attrition rate in drug development is being reduced by continuous advances in science and technology introduced by various academic institutions and pharmaceutical companies. This has been certainly noticeable in reducing the frequency with which unfavorable absorption, distribution, metabolism, and elimination (ADME) characteristics of any candidate drug causes failure in clinical development. Nonetheless, it is important that the objectives in reducing attrition during later stages of development are matched by information generated in the earliest stage of discovery. In this review, we summarize the methodologies employed during the early stages of drug discovery and discuss new findings in the areas of (1) drug metabolism enzymes, (2) the contribution of cytochrome P450 enzymes (P450, CYP) to hepatic metabolism, (3) prediction of hepatic intrinsic clearance, (4) reaction phenotyping, and (5) the metabolic differences between highly homologous enzymes such as CYP3A4 and CYP3A5. The total contribution of P450 and UDPglucuronosyltransferases to drug metabolism is reported to be more than 80%; therefore, glucuronidation is increasingly recognized as an important clearance pathway in addition to that of P450 enzymes. When estimating the contribution of P450, interpreting the results of inhibition studies using a single P450 inhibitor can lead to false conclusions. For instance, 1-aminobenzotriazole and SKF-525A have a varying range of IC50 values for inhibition of drug oxidation reactions by different CYP450 enzymes. There are disparities between methodologies at early stage drug discovery and late stage development. For example, although the drug depletion approach for the prediction of hepatic intrinsic clearance may not be desirable at late stages of development, it is suitable at the early drug discovery stage since kinetic characterization and measurement of specific drug metabolites are not required. Data from protein binding assays in plasma and/or liver microsomes is an integral part to predicting hepatic clearance; therefore, the prediction methods for protein binding have been addressed in terms of automation and in silico prediction. The approach to reaction phenotyping using recombinant P450 microsome data are reviewed as this approach enables combining the drug depletion method with appropriate scaling factors to predict clearance values. CYP3A enzymes have broad substrate specificities and are responsible for the oxidative metabolism of more than 50% of clinically used drugs. Although CYP3A4 is the most abundant CYP3A isoform in adult human liver, CYP3A5 may contribute more to CYP3A-mediated drug oxidation by human liver microsomes than CYP3A4 does, especially in Japanese subjects, who typically have a relatively high frequency of genetic CYP3A5 expression. Lack of efficacy and presence of serious side effects in some sub-group of patients remain the biggest sources of drug failure at late stage of drug development. Advances in appreciation of inter-individual variabilities in ADME, by creation of virtual individuals and use of appropriate information from early discovery may lead to a better anticipation of variable clinical and toxicological outcome following administration of any new drug candidate. Thus may also help with dosing strategies which minimize the potential side effects and maximize the clinical benefits. Accordingly, front-loading of efforts for characterizing the candidate drugs at early stages of discovery is recommended.
Keywords: Metabolism, intrinsic clearance, human liver microsomes, recombinant P450 microsomes, prediction, scaling factor, ADME, glucuronosyltransferases, 1-aminobenzotriazole, SKF-525A, morphine, UDP-glucuronosyltransferases (UGTs), esterase, flavin-containing, midazolam, nifedipine, testosterone, diclofenac 4-hydroxylation, SFK-525A, physio-logically-based pharmacokinetics (PBPK), microsomal protein per gram of liver (MPPGL), Bufuralol, Dextromethorphan, Diclofenac, Imipramine, Metoprolol, baculovirus-infected insect cells, ABT, PBPK, MPPGL, BSA, AUC, RAF, CLint, ISEF
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