Roles of Individual Human Cytochrome P450 Enzymes in Drug Metabolism

Abstract

Our knowledge of the roles of individual cytochrome P450 (P450) enzymes in drug metabolism has developed considerably in the past 30 years, and this base has been of considerable use in avoiding serious issues with drug interactions and issues due to variations. Some newer approaches are being considered for "phenotyping" metabolism reactions with new drug candidates. Endogenous biomarkers are being used for noninvasive estimation of levels of individual P450 enzymes. There is also the matter of some remaining "orphan" P450s, which have yet to be assigned reactions. Practical problems that continue in drug development include predicting drug-drug interactions, predicting the effects of polymorphic and other P450 variations, and evaluating interspecies differences in drug metabolism, particularly in the context of "metabolism in safety testing" regulatory issues ["disproportionate (human) metabolites"]. SIGNIFICANCE STATEMENT: Cytochrome P450 enzymes are the major catalysts involved in drug metabolism. The characterization of their individual roles has major implications in drug development and clinical practice.

Fig. 1

Plot of oral bioavailability in animal species versus oral bioavailability in humans (in percentages). Diamonds are for mouse, circles for rat, triangles for dog, and squares for nonhuman primates (NHP) (Musther et al., 2014). Reprinted from Eur. J. Pharmaceut. Sci., Volume 57, Musther, H., Olivares-Morales, A., Hatley, O. J., Liu, B., and Rostami Hodjegan, A. Animal vs. human oral drug bioavailability: do they correlate? Pages 280-291, Copyright (2014), with permission from the European Journal of Pharmaceutical Sciences/Elsevier.

Fig. 2

Percentage of FDA-approved new small-molecule drugs (n = 245 from years 2015–2020) metabolized by various enzymes (Bhutani et al., 2021). Redrawn from J. Med. Chem., Volume 64, Bhutani, P., Joshi G., Raja N.l, Bachhav N, Rajanna PK, Bhutani H, Paul AT, and Kumar R (2021) US FDA approved drugs from 2015-June 2020: a perspective. Pages 2339-2381. Copyright (2021), with permission from the American Chemical Society.

Fig. 3

Distribution of in vivo metabolism of debrisoquine in 258 British individuals. The metabolic ratio is the ratio of urinary debrisoquine to urinary 4’-hydroxydebrisoquine. A clear “antipode” break between EMs and PMs is seen at log₁₀ = 1 (ratio of 10). EM, extensive metabolizers; PM, poor metabolizers; UM, ultra-rapid metabolizers. Redrawn from Evans et al., (1980).

Fig. 4

Biotransformation reactions of the antihistamine terfenadine (Yun et al., 1993; Guengerich, 2014).

Fig. 5

O-Demethylation of codeine to form morphine and subsequent products (Gasche et al., 2004; Ofoegbu and B Ettienne, 2021).

Fig. 6

Major routes of metabolism of acetaminophen.

Fig. 7

N-Demethylation of clozapine.

Fig. 8

Inhibition curves for metabolism of risperidone (Doran et al., 2022b). (A and B) 9-Hydroxylation (of risperidone). (C) 7-Hydroxylation. (D) N-Dealkylation. The red points and lines indicate the appropriate risperidone reaction. Black points and lines indicate inhibition of marker activities: (A and C), dextromethorphan O-demethylation (2D6); (B and D), midazolam 1’-hydroxylation (P450 3A4). Reprinted from Drug Metab. Dispos., Volume 50, Doran, A. C., Dantonio, A. L., Gualtieri, G. M., Balesano, A., Landers, C., Burchett, W., Goosen, T. C., and Obach, R. S. An improved method for cytochrome P450 reaction phenotyping using a sequential qualitative-then-quantitative approach. Pages 1272-1286, Copyright (2022), with permission from the American Society for Pharmacology and Experimental Therapeutics.

Fig. 9

An overall approach for P450 reaction phenotyping (from Pfizer) (Doran et al., 2022b). Reprinted from Drug Metab. Dispos., Volume 50, Doran, A. C., Dantonio, A. L., Gualtieri, G. M., Balesano, A., Landers, C., Burchett, W., Goosen, T. C., and Obach, R. S. An improved method for cytochrome P450 reaction phenotyping using a sequential qualitative-then-quantitative approach. Pages 1272-1286, Copyright (2022), with permission from the American Society for Pharmacology and Experimental Therapeutics.

Fig. 10

Percentages of total P450 in human liver samples accounted for by each P450. The data points were compiled (Guengerich, 2022a,b) from four sets with multiple liver samples (Shimada et al., 1994; Kawakami et al., 2011; Achour et al., 2014) and one with a single liver sample high in P450 1A1 (Lang et al., 2019). Estimates were made immunochemically in one case (Shimada et al., 1994) and by LC-MS proteomic methods in the others (Kawakami et al., 2011; Achour et al., 2014; Lang et al., 2019). The value for P450 1A1 represents the mean of 30 samples (Lang et al., 2019). Individual colors have no specific meaning but are added to facilitate visualization. See also Paine et al., (2006) for estimates of the intestinal P450s.

Fig. 11

Two potential endogenous biomarkers for monitoring changes in P450 3A4 in humans. See text for discussion.

Fig. 12

Biotransformation of a lead drug candidate (BMS-A) by P450 11A1 and possible relevance to adrenal toxicity (Zhang et al., 2012). The epoxides (in brackets) are hypothetical.

Fig. 13

A hypothetical example of HPLC patterns of drug biotransformation products generated from a drug in vitro by different species. In this case, the rat is a better model for humans than the mouse.

Fig. 14

Biotransformation of ozanimod (Bai et al., 2021; Surapaneni et al., 2021). “+16” indicates the putative addition of an oxygen atom but at an undefined site. Gluc, glucuronic acid; NAT, N-acetyl transferase.

Fig. 15

Species differences in the metabolism of a drug (drug 1) to inert products and toxic products, with the amounts of the products represented by the size of the oval or box. In the idealized model, the same amounts of inert and toxic products are generated in all three species. In the MIST examples, the amount of potentially toxic product is much greater than in the rat or mouse.

Fig. 16

Boundary line for k_obs for time-dependent inhibition and relation to in vivo DDI (Eng et al., 2021). (A) Fifty drugs were evaluated for P450 3A4 time-dependent inhibition in human liver microsomes (at 30 μM unless noted otherwise) and ranked by k_obs, the first-order rate of inactivation, as judged using midazolam 1’-hydroxylation (O), presented on a log₁₀ scale (right y-axis). The filled bars show the in vivo drug-drug interactions as judged by the AUC with the drug divided by the AUC without the drug, clinical DDI magnitude (AUC_R). (B) The study in (A) was repeated in human hepatocytes. The stippled line indicates a twofold in vivo difference. Also indicated are P < 0.05 statistical limits and a k_obs “boundary” of the lowest in vitro value with twofold in vivo difference. AUC, area under the curve. Reprinted from Drug Metab. Dispos., Vol. 49, Eng, H., Tseng, E., Cerny, M. A., Goosen, T. C. and Obach, R. S., Cytochrome P450 3 A time-dependent inhibition assays are too sensitive for identification of drugs causing clinically significant drug-drug interactions: a comparison of human liver microsomes and hepatocytes and definition of boundaries for inactivation rate constants, pages 442-450, Copyright (2021), with permission from the American Society for Pharmacology and Experimental Therapeutics.