Characterization of CYP3A5 Selective Inhibitors for Reaction Phenotyping of Drug Candidates.

CYP3A is one of the most important classes of enzymes and is involved in the metabolism of over 70% drugs. While several selective CYP3A4 inhibitors have been identified, the search for a selective CYP3A5 inhibitor has turned out to be rather challenging. Recently, several selective CYP3A5 inhibitors have been identified through high-throughput screening of ~ 11,000 compounds and hit expansion using human recombinant enzymes. We set forth to characterize the three most selective CYP3A5 inhibitors in a more physiologically relevant system of human liver microsomes to understand if these inhibitors can be used for reaction phenotyping studies in drug discovery settings. Gomisin A and T-5 were used as selective substrate reactions for CYP3A4 and CYP3A5 to determine IC50 values of the two enzymes. The results showed that clobetasol propionate and loteprednol etabonate were potent and selective CYP3A5 reversible inhibitors with selectivity of 24-fold against CYP3A4 and 39-fold or more against the other major CYPs. The selectivity of difluprednate in HLM is much weaker than that in the recombinant enzymes due to hydrolysis of the acetate group in HLM. Based on the selectivity data, loteprednol etabonate can be utilized as an orthogonal approach, when experimental fraction metabolized of CYP3A5 is greater than 0.5, to understand CYP3A5 contribution to drug metabolism and its clinical significance. Future endeavors to identify even more selective CYP3A5 inhibitors are warranted to enable accurate determination of CYP3A5 contribution to metabolism versus CYP3A4.

An improved method for cytochrome p450 reaction phenotyping using a sequential qualitative-then-quantitative approach.

Cytochrome P450 reaction phenotyping to determine the fraction of metabolism values (fm) for individual enzymes is a standard study in the evaluation of a new drug. However, there are technical challenges in these studies caused by shortcomings in the selectivity of P450 inhibitors and unreliable scaling procedures for recombinant P450 (rCYP) data. In this investigation, a two-step "qualitative-then-quantitative" approach to P450 reaction phenotyping is described. In the first step, each rCYP is tested qualitatively for potential to generate metabolites. In the second step, selective inhibitors for the P450s identified in step1 are tested for their effects on metabolism using full inhibition curves. Forty-eight drugs were evaluated in step 1 and there were no examples of missing an enzyme important to in vivo clearance. Five drugs (escitalopram, fluvastatin, pioglitazone, propranolol, and risperidone) were selected for full phenotyping in step2 to determine fm values, with findings compared to fm values estimated from single inhibitor concentration data and rCYP with intersystem-extrapolation-factor corrections. The two-step approach yielded fm values for major drug clearing enzymes that are close to those estimated from clinical data: escitalopram and CYP2C19 (0.42 vs 0.36-0.82), fluvastatin and CYP2C9 (0.76 vs 0.76), pioglitazone and CYP2C8 (0.72 vs 0.73), propranolol and CYP2D6 (0.68 vs 0.37-0.56) and risperidone and CYP2D6 (0.60 vs 0.66-0.88). Reaction phenotyping data generated in this fashion should offer better input to physiologically-based pharmacokinetic models for prediction of DDI and impact of genetic polymorphisms on drug clearance. The qualitative-then-quantitative approach is proposed as a replacement to standard reaction phenotyping strategies. Significance Statement P450 reaction phenotyping is important for projecting drug-drug interactions and interpatient variability in drug exposure. However, currently recommended practices can frequently fail to provide reliable estimates of the fractional contributions of specific P450 enzymes (fm) to drug clearance. In this report, we describe a two-step qualitative-then-quantitative reaction phenotyping approach that yields more accurate estimates of fm.

Defining the Selectivity of Chemical Inhibitors Used for Cytochrome P450 Reaction Phenotyping: Overcoming Selectivity Limitations with a Six-Parameter Inhibition Curve-Fitting Approach.

The utility of chemical inhibitors in cytochrome P450 (CYP) reaction phenotyping is highly dependent on their selectivity and potency for their target CYP isoforms. In the present study, seventeen inhibitors of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4/5 commonly used in reaction phenotyping were evaluated for their cross-enzyme selectivity in pooled human liver microsomes. The data were evaluated using a statistical desirability analysis to identify (1) inhibitors of superior selectivity for reaction phenotyping and (2) optimal concentrations for each. Among the inhibitors evaluated, α-naphthoflavone, furafylline, sulfaphenazole, tienilic acid, N-benzylnirvanol, and quinidine were most selective, such that their respective target enzymes were inhibited by ~95% without inhibiting any other CYP enzyme by more than 10%. Other commonly employed inhibitors, such as ketoconazole and montelukast, among others, were of insufficient selectivity to yield a concentration that could adequately inhibit their target enzymes without affecting other CYP enzymes. To overcome these shortcomings, an experimental design was developed wherein dose response data from a densely sampled multi-concentration inhibition curve are analyzed by a six-parameter inhibition curve function, allowing accounting of the inhibition of off-target CYP isoforms inhibition and more reliable determination of maximum targeted enzyme inhibition. The approach was exemplified using rosiglitazone N-demethylation, catalyzed by both CYP2C8 and 3A4, and was able to discern the off-target inhibition by ketoconazole and montelukast from the inhibition of the targeted enzyme. This methodology yields more accurate estimates of CYP contributions in reaction phenotyping. Significance Statement Isoform-selective chemical inhibitors are important tools for identifying and quantifying enzyme contributions as part of a CYP reaction phenotyping assessment for projecting drug-drug interactions. However, currently employed practices fail to adequately compensate for shortcomings in inhibitor selectivity and the resulting confounding impact on estimates of the CYP enzyme contribution to drug clearance. In this report, we describe a detailed IC50 study design with 6-parameter modeling approach that yields more accurate estimates of enzyme contribution.