Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug–drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state‐of‐the‐art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this “White Paper” alongside unresolved issues that were outlined for future debates.

ABSTRACT:The ability to predict in vivo clearance from in vitro intrinsic clearance for compounds metabolized by aldehyde oxidase has not been demonstrated. To date, there is no established scaling method for predicting aldehyde oxidase-mediated clearance using in vitro or animal data. This challenge is exacerbated by the fact that rats and dogs, two of the laboratory animal species commonly used to develop in vitro-in vivo correlations of clearance, differ from humans with regard to expression of aldehyde oxidase. The objective of this investigation was to develop an in vitro-in vivo correlation of intrinsic clearance for aldehyde oxidase, using 11 drugs known to be metabolized by this enzyme. . These compounds were assayed using two in vitro systems (pooled human liver cytosol and liver S-9 fractions) to calculate scaled unbound intrinsic clearance, and they were then compared with calculated in vivo unbound intrinsic clearance. The investigation provided a relative scale that can be used for in vitro-in vivo correlation of aldehyde oxidase clearance and suggests limits as to when a potential new drug candidate that is metabolized by this enzyme will possess acceptable human clearance, or when structural modification is required to reduce aldehyde oxidase catalyzed metabolism.

ABSTRACT:Several antihistamine drugs including terfenadine, ebastine, and astemizole have been identified as substrates for CYP2J2. The overall importance of this enzyme in drug metabolism has not been fully explored. In this study, 139 marketed therapeutic agents and compounds were screened as potential CYP2J2 substrates. Eight novel substrates were identified that vary in size and overall topology from relatively rigid structures (amiodarone) to larger complex structures (cyclosporine). The substrates displayed in vitro intrinsic clearance values ranging from 0.06 to 3.98 l/min/pmol CYP2J2. Substrates identified for CYP2J2 are also metabolized by CYP3A4. Extracted ion chromatograms of metabolites observed for albendazole, amiodarone, astemizole, thioridazine, mesoridazine, and danazol showed marked differences in the regioselectivity of CYP2J2 and CYP3A4. CYP3A4 commonly metabolized compounds at multiple sites, whereas CYP2J2 metabolism was more restrictive and limited, in general, to a single site for large compounds. Although the CYP2J2 active site can accommodate large substrates, it may be more narrow than CYP3A4, limiting metabolism to moieties that can extend closer toward the active heme iron. For albendazole, CYP2J2 forms a unique metabolite compared with CYP3A4. Albendazole and amiodarone were evaluated in various in vitro systems including recombinant CYP2J2 and CYP3A4, pooled human liver microsomes (HLM), and human intestinal microsomes (HIM). The Michaelis-Menten-derived intrinsic clearance of N-desethyl amiodarone was 4.6 greater in HLM than in HIM and 17-fold greater in recombinant CYP3A4 than in recombinant CYP2J2. The resulting data suggest that CYP2J2 may be an unrecognized participant in first-pass metabolism, but its contribution is minor relative to that of CYP3A4.

During the process of drug discovery, the pharmaceutical industry is faced with numerous challenges. One challenge is the successful prediction of the major routes of human clearance of new medications. For compounds cleared by metabolism, accurate predictions help provide an early risk assessment of their potential to exhibit significant interpatient differences in pharmacokinetics via routes of metabolism catalyzed by functionally polymorphic enzymes and/or clinically significant metabolic drug-drug interactions. This review details the most recent and emerging in vitro strategies used by drug metabolism and pharmacokinetic scientists to better determine rates and routes of metabolic clearance and how to translate these parameters to estimate the amount these routes contribute to overall clearance, commonly referred to as fraction metabolized. The enzymes covered in this review include cytochrome P450s together with other enzymatic pathways whose involvement in metabolic clearance has become increasingly important as efforts to mitigate cytochrome P450 clearance are successful. Advances in the prediction of the fraction metabolized include newly developed methods to differentiate CYP3A4 from the polymorphic enzyme CYP3A5, scaling tools for UDP-glucuronosyltranferase, and estimation of fraction metabolized for substrates of aldehyde oxidase. IntroductionIn an era where combination drug therapy to treat several conditions simultaneously is common, drug companies emphasize the need for optimal absorption, distribution, metabolism, and excretion (ADME) properties, with the purpose of optimization of efficacy and minimization of the risk of adverse events. This includes a proper assignment and an extensive understanding of the routes of metabolism to aid in the prediction of human pharmacokinetics, and to help avoid a potential "object drug" scenario when coadministration is likely. The attributes of the coadministered drug and/or the patient has the potential to increase the probability of a drug-drug interaction (DDI), i.e., enzyme inhibitor, inducer, polymorphic genotype. Hence, the greater the percentage attributed to a single metabolic route, the greater the potential for a DDI and possible "black box warning" being issued as part of the drug package insert. Furthermore, the pharmacokinetic implications of a single polymorphic enzyme being responsible for a majority of the metabolism of a drug may have either an efficacy (extensive metabolizers) or toxicological (poor metabolizers) impact on exposure. To address these concerns, early drug discovery teams strive for balanced metabolism across multiple enzymes and clearance mechanisms (hepatic, renal, biliary). These discovery efforts center on in vitro reaction phenotyping to support enhanced chemical design.There is a general agreement among the major regulatory agencies that pharmaceutical companies should provide a characterization of the metabolic profile of a new chemical entity (NCE) and understand the enzymology of the major clearance mechanisms ...