Drug metabolism

Short description: Biochemical modification of drugs or foreign compounds by living organisms

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Drug metabolism is the biochemical modification and biotransformation of drugs by humans and other animals, usually through specialized enzymatic systems. It is a major aspect of pharmacokinetics and clinical pharmacology, influencing pharmacokinetics, therapeutic efficacy, toxicity, and elimination of pharmaceutical compounds, as well as drug interactions, prodrug activation, and interindividual variation in drug response.[1]

In the broader context of foreign‑compound handling, drug metabolism is a clinically oriented subset of xenobiotic metabolism that focuses on therapeutic agents and their impact on dose–exposure relationships, clinical response, and toxicity in patients, whereas the metabolism of environmental pollutants, dietary constituents, and other non‑pharmaceutical xenobiotics is covered in xenobiotic metabolism.[2] In humans, most drug metabolism occurs in the liver through Phase I and Phase II enzymatic reactions, particularly those involving cytochrome P450 enzymes, which generally convert lipophilic drug molecules into more readily excreted hydrophilic metabolites with direct consequences for absorption, distribution, metabolism, and excretion.[3]

## Contents

* 1 Lipophilicity, permeability, and metabolic clearance
* 2 Phases
* 3 Sites in the body
* 4 Modifying factors
* 4.1 Drug interactions
* 4.2 Physiological factors
* 4.2.1 Age
* 4.2.2 Gut microbiota
* 4.2.3 Pharmacogenetics
* 4.3 Regulation by xenobiotic receptors
* 4.4 Other factors
* 4.4.1 Disease
* 5 Clinical significance
* 5.1 First-pass metabolism
* 5.2 Prodrugs
* 5.3 Active metabolites
* 5.4 Drug toxicity
* 5.5 Therapeutic index
* 5.6 Personalized medicine
* 5.7 Multidrug resistance
* 6 Clinical examples
* 6.1 Prodrug activation: clopidogrel
* 6.2 Prodrug activation: codeine
* 6.3 Food–drug interaction: grapefruit juice
* 6.4 Enzyme induction: rifampicin
* 6.5 Reactive metabolite toxicity: acetaminophen
* 7 Pharmacokinetic integration
* 7.1 Hepatic extraction and clearance
* 7.2 Physiologically-based pharmacokinetic modeling
* 7.3 In vitro to in vivo extrapolation
* 7.4 Regulatory applications
* 8 History
* 9 See also
* 10 References
* 11 Further reading
* 12 External links

## Lipophilicity, permeability, and metabolic clearance

Many pharmaceutical drugs are lipophilic compounds that diffuse across cell membranes and enter cells without requiring specific transport proteins. This property is important for drug absorption and distribution, but it also means that organisms cannot completely prevent exposure to membrane-permeable drugs and other foreign compounds. As a result, drug metabolism systems evolved to chemically modify and eliminate a wide range of structurally diverse compounds.[4]

Cell membranes act as hydrophobic permeability barriers that restrict the passive diffusion of most hydrophilic molecules. The uptake of endogenous metabolites and many therapeutic agents is therefore mediated by selective transport systems.[5] In contrast, many hydrophobic drugs can readily cross biological membranes and therefore require enzymatic conversion into more polar metabolites that can be excreted through urine or bile.

Drug-metabolizing enzymes consequently display broad substrate specificities and are capable of acting on many chemically unrelated compounds.[4] In humans, these systems are concentrated primarily in the liver and include enzymes such as the cytochrome P450 oxidases, which catalyse reactions that increase the polarity of drugs and facilitate their elimination.

In addition to metabolizing xenobiotic drugs, cells also contain specialized systems that detoxify reactive by-products generated during normal metabolism. Examples include the glyoxalase system, which removes the reactive aldehyde methylglyoxal,[6] and antioxidant systems that eliminate reactive oxygen species.[7]

##  Phases

Phase I and Phase II metabolism of a lipophilic drug.

Drug metabolism in humans and other animals is commonly described using a three-phase framework derived from the broader principles of xenobiotic metabolism. Phase I reactions introduce or expose functional groups, phase II reactions conjugate drugs or their metabolites to endogenous polar molecules, and phase III processes export these species from cells into bile or urine. Together, these steps convert lipophilic drugs into more polar metabolites and define overall clearance, bioavailability, and the potential for drug–drug interactions.[8][9][10]

## Sites in the body

Quantitatively, the smooth endoplasmic reticulum of the liver cell is the principal organ of drug metabolism, although every biological tissue has some ability to metabolize drugs. Factors responsible for the liver's contribution to drug metabolism include that it is a large organ, that it is the first organ perfused by chemicals absorbed in the gut, and that there are very high concentrations of most drug-metabolizing enzyme systems relative to other organs. If a drug is taken into the gastrointestinal tract (GI tract), where it enters the hepatic portal system through the portal vein, it becomes well-metabolized and is said to show the first pass effect.

Other sites of extrahepatic drug metabolism include epithelial cells of the GI tract, lungs, kidneys, and skin. These sites are usually responsible for localized toxicity reactions.

## Modifying factors

The duration and intensity of the pharmacological action of many lipophilic drugs are determined by the rate at which they are metabolized to inactive products. The Cytochrome P450 monooxygenase system (CYP) is particularly important in this process. In general, factors that increase metabolic activity (enzyme induction) decrease drug exposure, whereas inhibition increases drug concentrations and prolongs drug action. However, for prodrugs that require metabolic activation, enzyme induction may increase toxicity rather than reduce it.[11]

### Drug interactions

Many clinically important drug interactions result from induction or inhibition of drug-metabolizing enzymes. For example, the prodrugs cyclophosphamide and ifosfamide are activated primarily by CYP2B6 and CYP3A4 into cytotoxic metabolites such as phosphoramide mustard and chloroacetaldehyde. Co-administration of strong CYP inducers such as phenytoin or rifampicin can accelerate bioactivation and increase the risk of toxicity, including severe myelosuppression and hemorrhagic cystitis.[12][13][14]

Drug interactions are commonly evaluated relative to theoretical models of noninteraction, including Loewe additivity and Bliss independence.[15][16][17] Interactions may be synergistic, producing greater-than-expected effects or toxicity, or antagonistic, reducing therapeutic efficacy.

### Physiological factors

Physiological factors affecting drug metabolism include age, sex, diet, enterohepatic circulation, gut microbiota, and inherited genetic variation (pharmacogenetics).[18][19]

#### Age

Drug metabolism is generally slower in fetal, neonatal, and elderly individuals than in healthy adults.

#### Gut microbiota

Gut microorganisms can alter drug metabolism through chemical modification of drugs, thereby affecting efficacy and toxicity. For example, Eggerthella lenta can inactivate digoxin.[19]

#### Pharmacogenetics

Genetic polymorphisms in drug-metabolizing enzymes contribute substantially to interindividual variability in drug response.[19] One well-known example is the alcohol flush reaction, caused by variants in ALDH2, which reduce aldehyde dehydrogenase activity and impair acetaldehyde metabolism.[20][21][22]

Polymorphisms in enzymes such as N-acetyltransferase, CYP2D6, CYP3A4, DPYD, and UGT1A1 can alter drug clearance and toxicity risk. Slow acetylators with reduced NAT2 activity are more susceptible to adverse effects from drugs such as isoniazid, hydralazine, procainamide, and phenelzine.[23][24]

Genotyping for some drug-metabolizing enzymes is now recommended prior to therapy. For example, DPYD testing is used before treatment with 5-fluorouracil or capecitabine, and UGT1A1 testing before irinotecan administration, in order to reduce the risk of severe toxicity.[25]

### Regulation by xenobiotic receptors

Expression of drug‑metabolizing enzymes and transporters is dynamically regulated by ligand‑activated xenobiotic‑sensing nuclear receptors, including the pregnane X receptor (PXR), constitutive androstane receptor (CAR), and aryl hydrocarbon receptor (AhR). When activated by therapeutic drugs and other xenobiotics, these receptors upregulate overlapping sets of Phase I and Phase II enzymes and efflux transporters, leading to enzyme induction, altered drug interaction profiles, and changes in clearance and exposure that can necessitate dose adjustment or alternative therapy.[26][27] The broader roles of these receptors in coordinating xenobiotic defense across different organisms and chemical classes are described in xenobiotic metabolism#Xenobiotic sensing and regulation.

### Other factors

Dose, frequency, route of administration, tissue distribution, and protein binding can also influence drug metabolism.[28]

#### Disease

Pathological conditions including liver, kidney, and heart disease may substantially alter drug metabolism and clearance.[29][30][31]

## Clinical significance

Drug metabolism has major clinical implications because it determines drug exposure, therapeutic response, and the risk of adverse effects in individual patients.[32][33][34] Variability in metabolic capacity due to genetic polymorphisms, comorbid disease, age, and drug–drug interactions can lead to underexposure with loss of efficacy or overexposure with toxicity at standard doses.[32][35] For these reasons, characterization of metabolic pathways is an essential component of dose selection, clinical trial design, and rational management of combination drug therapy.[34][35]

### First-pass metabolism

First-pass metabolism (first-pass effect) refers to presystemic biotransformation, primarily in the gut wall and liver, that occurs after oral administration and reduces the fraction of active drug reaching the systemic circulation.[32][33][36] The extent of first-pass metabolism influences oral bioavailability and may necessitate higher oral doses, alternative routes of administration, or specific formulation strategies.[36][37]

### Prodrugs

A prodrug is a pharmacologically inactive or less active compound that is designed to undergo enzymatic or chemical conversion in the body to release an active drug.[32][38] Prodrug strategies exploit predictable metabolic pathways, including first-pass metabolism, to improve oral bioavailability, solubility, tissue targeting, or tolerability of therapeutic agents.[38]

### Active metabolites

Active metabolites are biotransformation products that retain pharmacological activity and can contribute substantially to both therapeutic and adverse effects of a parent drug.[32][39] Recognition of clinically important active metabolites is critical for interpreting dose–response relationships, understanding prolonged or delayed effects, and anticipating toxicity in patients with impaired clearance or altered metabolism.[39][40]

### Drug toxicity

Drug metabolism can decrease toxicity through detoxification of xenobiotics but can also generate reactive or toxic metabolites that cause organ injury.[32][41][42] Bioactivation to reactive intermediates, often mediated by cytochrome P450 enzymes, is an important mechanism underlying dose‑related and idiosyncratic hepatotoxicity, hypersensitivity reactions, and other forms of drug‑induced tissue damage.[42]

### Therapeutic index

The therapeutic index (TI) is commonly defined as the ratio of the median toxic dose (TD50) to the median effective dose (ED50).[43] Metabolic clearance is a key determinant of the therapeutic index because it influences systemic exposure to parent drug and metabolites at a given dose.[33][35] Alterations in drug metabolism caused by enzyme induction, enzyme inhibition, or genetic variation can therefore significantly change the therapeutic index of a drug, particularly for drugs with narrow safety margins. Reduced metabolism may increase drug concentrations and toxicity, whereas accelerated metabolism may reduce efficacy. Interindividual differences and drug–drug interactions that alter metabolic clearance can narrow or widen a drug’s effective and safe concentration range, sometimes necessitating dose adjustment or therapeutic drug monitoring for medicines with a narrow therapeutic index.[35]

### Personalized medicine

Variability in drug‑metabolizing enzymes provides a major rationale for personalized medicine approaches such as pharmacogenetic testing and individualized dosing regimes.[32][44] Genotype‑guided selection and dosing of drugs metabolized by polymorphic enzymes, for example CYP2D6, CYP2C19 or thiopurine S‑methyltransferase, can improve efficacy and reduce the risk of serious adverse drug reactions.[45]

### Multidrug resistance

Multidrug resistance is the ability of tumour cells, microorganisms, or other pathological cells to resist the effects of multiple structurally and functionally distinct drugs, often through mechanisms that reduce intracellular concentrations of active drug.[46][47] Altered expression or function of drug-metabolizing enzymes and associated transporters can contribute to multidrug resistance by lowering intracellular concentrations of active drug.[32][47] In oncology and infectious diseases, induction of metabolic pathways or efflux transporters in tumour or microbial cells, as well as host variability in drug metabolism, can reduce exposure to active drug and compromise treatment outcomes.[46][47]

## Clinical examples

The following examples illustrate how differences in drug-metabolizing enzymes and transport pathways translate into clinically important variability in efficacy, toxicity, and drug–drug interactions. They highlight common mechanisms, including prodrug activation, enzyme inhibition or induction, and formation of reactive metabolites, and show how these mechanisms guide prescribing decisions, drug monitoring, and guideline recommendations.

### Prodrug activation: clopidogrel

Clopidogrel is a thienopyridine antiplatelet prodrug that requires oxidative bioactivation by hepatic cytochrome P450 enzymes, particularly CYP2C19, to form an active thiol metabolite that irreversibly inhibits the P2Y12 receptor on platelets.[48][49] Reduced CYP2C19 function, due to loss-of-function genetic polymorphisms or concomitant use of CYP2C19 inhibitors, decreases the formation of the active metabolite and attenuates the antiplatelet effect.[48][49] Such variability in clopidogrel metabolism has been associated with increased rates of thrombotic events, particularly stent thrombosis and myocardial infarction, and has prompted consideration of alternative P2Y12 inhibitors or genotype-guided therapy in some clinical settings.[49][50]

### Prodrug activation: codeine

Codeine is a prodrug that requires O-demethylation by cytochrome P450 2D6 (CYP2D6) to form morphine, which contributes substantially to its analgesic effect.[51][52] Individuals who are CYP2D6 poor metabolizers may obtain little or no analgesia from standard codeine doses, whereas ultrarapid metabolizers can produce higher morphine concentrations and are at increased risk of opioid toxicity, including serious respiratory depression.[51][53][52] Concerns about variable metabolism and reports of fatal toxicity in children, particularly in CYP2D6 fast metabolizers, have led regulatory agencies and professional societies to recommend limiting or avoiding the use of codeine in paediatric patients and to favor alternative analgesic strategies.[54][55]

### Food–drug interaction: grapefruit juice

Grapefruit juice contains furanocoumarins and related compounds that inhibit cytochrome P450 3A4 (CYP3A4) in the intestinal wall, leading to reduced first-pass metabolism of susceptible drugs.[56] For oral medications that undergo extensive intestinal CYP3A4-mediated presystemic metabolism, co-administration with grapefruit juice can markedly increase systemic exposure and thereby heighten the risk of concentration-dependent adverse effects.[56] This interaction illustrates the clinical importance of enzyme inhibition and first-pass metabolism in determining the bioavailability and safety of many drugs.[56]

### Enzyme induction: rifampicin

Rifampicin is a rifamycin antibiotic that acts as a potent inducer of several drug-metabolizing enzymes and transporters, including cytochrome P450 3A4 (CYP3A4) and P-glycoprotein, predominantly through activation of the pregnane X receptor (PXR).[57][58][59] Induction by rifampicin can increase the clearance and reduce the plasma concentrations of many co-administered drugs, including oral contraceptives, anticoagulants, and certain antiretroviral agents, and has been associated with loss of efficacy and therapeutic failure.[57] This interaction illustrates the clinical relevance of enzyme and transporter induction in altering drug exposure and may necessitate dose adjustment or the selection of alternative therapy.[57][58]

### Reactive metabolite toxicity: acetaminophen

At therapeutic doses, acetaminophen (paracetamol) is primarily cleared by hepatic conjugation pathways, including glucuronidation and sulfation, which produce non-toxic, water-soluble metabolites.[60][61] A small fraction is oxidized by cytochrome P450 enzymes, particularly CYP2E1, to form N-acetyl-p-benzoquinone imine (NAPQI), a highly reactive intermediate that is normally detoxified by conjugation with glutathione.[60][61] In overdose or in conditions that deplete glutathione, the accumulation of NAPQI leads to covalent binding to cellular proteins, mitochondrial oxidative stress, and centrilobular hepatocellular injury that can progress to acute liver failure.[60][61] This example illustrates how reactive metabolites generated during drug metabolism can be central mediators of toxicity.[61]

## Pharmacokinetic integration

The integration of drug metabolism data with pharmacokinetic principles enables quantitative prediction of systemic drug exposure and supports clinical dose optimization across diverse patient populations.[62][63]

### Hepatic extraction and clearance

Drug metabolism is a primary determinant of systemic drug exposure, quantified through pharmacokinetic parameters including area-under-the-curve (AUC), clearance, and elimination half-life. The hepatic extraction ratio (EH) describes the fraction of drug irreversibly removed during a single pass through the liver and ranges from 0 (no extraction) to 1 (complete extraction). This ratio is determined by the relationship between intrinsic clearance (CLint, the liver's enzymatic capacity to metabolize drug) and hepatic blood flow (QH, approximately 90 L/hour in humans).[64]

The hepatic extraction ratio can be defined experimentally as: EH=Cin−CoutCin where EH is the hepatic extraction ratio, Cin is the drug concentration entering the liver, and Cout is the drug concentration leaving the liver.

According to the well-stirred model of hepatic clearance, hepatic extraction ratio can be predicted from physiological and drug-specific parameters as: EH=fu⋅CLintQH+fu⋅CLint where fu is the unbound fraction of drug in blood, CLint is intrinsic hepatic metabolic clearance, and QH is hepatic blood flow.

For drugs with high extraction ratios (EH > 0.7), hepatic clearance becomes blood flow-limited and largely independent of enzyme activity or protein binding; conversely, drugs with low extraction ratios (EH < 0.3) exhibit clearance limited by intrinsic metabolic capacity rather than perfusion. This distinction has critical implications for first-pass metabolism following oral administration, where hepatic bioavailability may be approximated as 1 − EH for drugs that are completely absorbed and undergo negligible intestinal first-pass metabolism.[65]

### Physiologically-based pharmacokinetic modeling

Physiologically based pharmacokinetic modelling (PBPK) modeling integrates drug-specific properties (molecular weight, lipophilicity, protein binding) with physiological parameters (organ volumes, blood flows, metabolic enzyme abundances) to mechanistically predict drug disposition in diverse patient populations.[66] By incorporating tissue-specific expression of drug-metabolizing enzymes (cytochrome P450s, conjugating enzymes) and transporter proteins, PBPK models simulate how metabolic pathways influence systemic exposure, tissue distribution, and elimination across different patient populations.[67][68] These models enable prediction of untested clinical scenarios including metabolism-mediated drug-drug interactions, effects of hepatic impairment on metabolic clearance, and age-related changes in enzyme activity affecting pediatric dosing without conducting dedicated clinical trials.[69]

PBPK models commonly represent organs as interconnected compartments governed by mass-balance equations describing drug transport and partitioning between blood and tissues:[70]

VTdCTdt=QTCA−QTCV,T

where VT is tissue volume, CT is tissue drug concentration, QT is tissue blood flow, CA is arterial drug concentration, and CV,T is venous drug concentration exiting the tissue.

### In vitro to in vivo extrapolation

In vitro to in vivo extrapolation (IVIVE) scales metabolic stability data from human liver microsomes or hepatocytes to predict human clearance. Intrinsic clearance measured in vitro is scaled using physiological factors (microsomal protein per gram liver, liver weight) and empirical correction factors to account for differences between simplified in vitro systems and intact liver function. While microsomal data with physiological scaling factors consistently underpredict in vivo clearance, empirical scaling factors (typically 5- to 10-fold corrections) substantially improve prediction accuracy.[71][72][73]

Intrinsic clearance measured in vitro can be scaled to whole-liver intrinsic clearance as:[73]

CLint,H=CLint,in vitro⋅MPPGL⋅LW

CLint,H is whole-liver intrinsic clearance, CLint,in vitro is intrinsic clearance measured in vitro, MPPGL is microsomal protein per gram liver, and LW is liver weight.

Predicted hepatic clearance can then be estimated using the well-stirred model:[71]

CLH=QH⋅fu⋅CLint,HQH+fu⋅CLint,H

CLH is hepatic clearance, QH is hepatic blood flow, fu is the unbound fraction of drug in blood, and CLint,H is whole-liver intrinsic clearance.

### Regulatory applications

PBPK models incorporating drug metabolism parameters are increasingly accepted by regulatory agencies including the FDA for supporting clinical pharmacology decisions, justifying clinical study designs, and waiving studies in special populations when adequately validated.[74][75][76] Common metabolism-related regulatory applications include predicting drug-drug interactions involving metabolic enzyme inhibition or induction, assessing the impact of genetic polymorphisms in drug-metabolizing enzymes on exposure, and evaluating dose adjustments needed in patients with hepatic impairment affecting metabolic capacity.[74][76] The FDA issued guidance in September 2018 outlining format and content expectations for PBPK analyses submitted to support drug applications.[74] PBPK model predictions can be used to support decisions on whether, when, and how to conduct certain clinical pharmacology studies, particularly those examining metabolic pathways and their variability, and to support dosing recommendations in product labeling, with acceptance determined on a case-by-case basis considering the quality, relevance, and reliability of the metabolic and pharmacokinetic analyses.[74][77]

## History

Systematic studies of drug metabolism emerged from nineteenth-century investigations into how administered medicinal and aromatic compounds were chemically transformed in the body.[78] Early work demonstrated that drugs could undergo reactions such as oxidation and amino acid conjugation, establishing the concept that biotransformation increases drug elimination and alters pharmacological activity.[78] By the early twentieth century, additional pathways including oxidation–reduction, methylation, acetylation, and sulfonation had been recognized in the metabolism of therapeutic agents.[78][79]

Drug metabolism became a distinct discipline during the mid-twentieth century alongside the development of modern pharmacology and pharmacokinetics. Richard Tecwyn Williams helped formalize the field by classifying metabolic reactions into phase I and phase II processes in his 1947 monograph Detoxication Mechanisms.[80][78] Subsequent research identified microsomal cytochrome P450 enzymes as the principal catalysts of phase I drug oxidation and glutathione S-transferases as major phase II enzymes.[81][82][83] These discoveries established the biochemical basis of drug clearance, drug interactions, metabolic bioactivation, and interindividual variability in therapeutic response.

Since the late twentieth century, drug metabolism research has advanced through improved characterization of enzyme systems and regulatory mechanisms, with particular emphasis on interindividual variability and prediction of human clearance.[84][85] Major developments included quantitative proteomic methods for measuring enzyme abundance in human tissues, enabling more accurate physiologically-based pharmacokinetic modeling and in vitro-in vivo extrapolation of clearance predictions.[86] Recognition of membrane transporter proteins—particularly organic anion-transporting polypeptides (OATPs), P-glycoprotein, and breast cancer resistance protein (BCRP)—as critical determinants of drug disposition led to regulatory guidance for assessing transporter-mediated drug-drug interactions alongside metabolic pathways.[84][86] Non-cytochrome P450 enzymes including UDP-glucuronosyltransferases, aldehyde oxidase, and carboxylesterases received increased attention as significant contributors to drug clearance, though predicting their in vivo activity from in vitro systems remains challenging.[86]

## See also

* Biodegradation
* Microbial biodegradation

## References

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43. ↑ "Therapeutic index | Pharmacology Education Project". https://www.pharmacologyeducation.org/therapeutic-index.
44. ↑ "Pharmacogenomics Knowledge Base (PharmGKB)". Stanford University. https://www.pharmgkb.org/.
45. ↑ "CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network". Clinical Pharmacology and Therapeutics 89 (3): 464–467. March 2011. doi:10.1038/clpt.2010.279. PMID 21270786.
46. ↑ 46.0 46.1 "Multidrug resistance in cancer: role of ATP-dependent transporters". Nature Reviews. Cancer 2 (1): 48–58. January 2002. doi:10.1038/nrc706. PMID 11902585.
47. ↑ 47.0 47.1 47.2 "Drug-metabolizing enzymes and transporters: implications for drug–drug interactions and multidrug resistance". Clinical Pharmacokinetics 48 (8): 497–507. 2009. doi:10.2165/00003088-200948080-00001.
48. ↑ 48.0 48.1 "Clopidogrel Therapy and CYP2C19 Genotype". Medical Genetics Summaries. NCBI Bookshelf. 2013. https://www.ncbi.nlm.nih.gov/books/NBK84114/.
49. ↑ 49.0 49.1 49.2 "Pharmacogenomics of clopidogrel: evidence and perspectives". Thrombosis Research 128 (4): 307–316. October 2011. doi:10.1038/nrcardio.2011.39. PMID 21592545.
50. ↑ "Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update". Clinical Pharmacology and Therapeutics 112 (5): 959–967. November 2022. doi:10.1002/cpt.2526. PMID 35034351.
51. ↑ 51.0 51.1 "Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 update". Clinical Pharmacology and Therapeutics 95 (4): 376–382. April 2014. doi:10.1038/clpt.2013.254. PMID 24458010.
52. ↑ 52.0 52.1 "Codeine Therapy and CYP2D6 Genotype". Medical Genetics Summaries. NCBI Bookshelf. 2012. https://www.ncbi.nlm.nih.gov/books/NBK100662/.
53. ↑ "Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for codeine therapy in the context of cytochrome P450 2D6 (CYP2D6) genotype". Clinical Pharmacology and Therapeutics 91 (2): 321–326. February 2012. doi:10.1038/clpt.2011.287. PMID 22205192.
54. ↑ "The Use of Codeine and Tramadol in the Pediatric Population—What is the Verdict Now?". Journal of Pediatric Health Care 33 (1): 117–123. January 2019. doi:10.1016/j.pedhc.2018.04.016. PMID 30545525.
55. ↑ "The use of oral opioids to control children's pain in the post-codeine era". Paediatrics & Child Health 26 (2): 120–127. 2021. doi:10.1093/pch/pxaa133. PMID 33747307.
56. ↑ 56.0 56.1 56.2 "Grapefruit-medication interactions: forbidden fruit or avoidable consequences?". CMAJ 185 (4): 309–316. March 2013. doi:10.1503/cmaj.120951. PMID 23184849.
57. ↑ 57.0 57.1 57.2 "Roles of rifampicin in drug-drug interactions: underlying molecular mechanisms involving the nuclear pregnane X receptor". Annals of Clinical Microbiology and Antimicrobials 5. February 2006. doi:10.1186/1476-0711-5-3. PMID 16480505.
58. ↑ 58.0 58.1 "The pregnane X receptor: from bench to bedside". Expert Opinion on Drug Metabolism & Toxicology 4 (7): 895–908. July 2008. doi:10.1517/17425255.4.7.895. PMID 18624678.
59. ↑ "The role of intestinal P-glycoprotein in the interaction of digoxin and rifampin". The Journal of Clinical Investigation 104 (2): 147–153. July 1999. doi:10.1172/JCI6663. PMID 10411543.
60. ↑ 60.0 60.1 60.2 "Acetaminophen Toxicity". StatPearls [Internet]. StatPearls Publishing. 2026. https://www.ncbi.nlm.nih.gov/books/NBK441917/.
61. ↑ 61.0 61.1 61.2 61.3 "Recent updates on acetaminophen hepatotoxicity: the role of nrf2 in hepatoprotection". Toxicological Research 29 (3): 165–172. September 2013. doi:10.5487/TR.2013.29.3.165. PMID 24386516. Bibcode: 2013ToxRe..29..165G.
62. ↑ "Current trends in drug metabolism and pharmacokinetics". Acta Pharmaceutica Sinica. B 9 (6): 1113–1144. November 2019. doi:10.1016/j.apsb.2019.10.001. PMID 31867160.
63. ↑ "Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development". Acta Pharmaceutica Sinica. B 12 (6): 2751–2777. June 2022. doi:10.1016/j.apsb.2022.03.009. PMID 35755285.
64. ↑ "The role of quantitative ADME proteomics to support construction of physiologically based pharmacokinetic models for use in small molecule drug development". Proteomics. Clinical Applications 9 (7–8): 732–744. August 2015. doi:10.1002/prca.201400147. PMID 25676057.
65. ↑ "Hepatic clearance". Deranged Physiology: CICM Primary Exam: Pharmacokinetics. 18 December 2023. https://derangedphysiology.com/main/cicm-primary-exam/pharmacokinetics/Chapter-3336/hepatic-clearance.
66. ↑ "Program of physiologically-based pharmacokinetic and pharmacodynamic modeling (PBPK Program)" (in en). Center for Drug Evaluation and Research. 4 September 2018. https://www.fda.gov/about-fda/cder-offices-and-divisions/program-physiologically-based-pharmacokinetic-and-pharmacodynamic-modeling-pbpk-program.
67. ↑ "Application of Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of an FDA Public Workshop on PBPK". CPT 4 (4): 226–230. April 2015. doi:10.1002/psp4.33. PMID 26225246.
68. ↑ "Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration". European Journal of Pharmaceutical Sciences 200. September 2024. doi:10.1016/j.ejps.2024.106838. PMID 38960205.
69. ↑ "Using physiologically-based pharmacokinetic modeling for predicting the effects of hepatic impairment on the pharmacokinetics of olanzapine and samidorphan given as a combination tablet". CPT 10 (9): 1071–1080. September 2021. doi:10.1002/psp4.12675. PMID 34185436.
70. ↑ "PBPK modeling and simulation in drug research and development". Acta Pharmaceutica Sinica. B 6 (5): 430–440. September 2016. doi:10.1016/j.apsb.2016.04.004. PMID 27909650.
71. ↑ 71.0 71.1 "Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches". Pharmaceutical Research 49 (8): 513–533. January 2010. doi:10.2165/11317030-000000000-00000. PMID 15771236.
72. ↑ "Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds". Drug Metabolism and Disposition 36 (7): 1385–1405. October 2008. doi:10.1124/dmd.108.020479. PMID 18426954.
73. ↑ 73.0 73.1 "Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver". Current Drug Metabolism 8 (1): 33–45. January 2007. doi:10.2174/138920007779315053. PMID 17266522.
74. ↑ 74.0 74.1 74.2 74.3 "Physiologically Based Pharmacokinetic Analyses — Format and Content: Guidance for Industry". U.S. Food and Drug Administration. September 2018. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/physiologically-based-pharmacokinetic-analyses-format-and-content-guidance-industry.
75. ↑ "Biopharmaceutics Applications of Physiologically Based Pharmacokinetic Absorption Modeling and Simulation in Regulatory Submission: An Update From the FDA's Office of Generic Drugs". The AAPS Journal 110 (10): 3285–3299. October 2021. doi:10.1016/j.xphs.2021.02.020. PMID 33619657.
76. ↑ 76.0 76.1 "The Evolution and Future Directions of PBPK Modeling in FDA Regulatory Review". Pharmaceutics 17 (11): 1413. October 2025. doi:10.3390/pharmaceutics17111413. PMID 41304751.
77. ↑ "The Use of Physiologically Based Pharmacokinetic Analyses — Biopharmaceutics Applications for Oral Drug Product Development, Manufacturing Changes, and Controls: Guidance for Industry". U.S. Food and Drug Administration. September 2020. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-physiologically-based-pharmacokinetic-analyses-biopharmaceutics-applications-oral-drug-product.
78. ↑ 78.0 78.1 78.2 78.3 "Richard Tecwyn Williams (1909–1979): an appreciation". Journal of Analytical Toxicology 33 (9): 623–626. 2009. doi:10.1093/jat/33.9.623. PMID 20040139.
79. ↑ "History of drug metabolism: the first half of the 20th century". Drug Metabolism Reviews 16 (3): 185–253. January 1985. doi:10.3109/03602538508991435. PMID 3914936.
80. ↑ "Profiles in drug metabolism and toxicology: Richard Tecwyn Williams (1909-1979)". Drug Metabolism Reviews 47 (4): 401–405. 2015. doi:10.3109/03602532.2015.1115516. PMID 26610047.
81. ↑ "Drug metabolism and pharmacogenetics: the British contribution to fields of international significance". British Journal of Pharmacology 147 Suppl 1 (Suppl 1): S89–99. January 2006. doi:10.1038/sj.bjp.0706466. PMID 16402125.
82. ↑ "A passion for P450s (rememberances of the early history of research on cytochrome P450)". Drug Metabolism and Disposition 31 (12): 1461–1473. December 2003. doi:10.1124/dmd.31.12.1461. PMID 14625342.
83. ↑ "Glutathione and glutathione-dependent enzymes represent a co-ordinately regulated defence against oxidative stress". Free Radical Research 31 (4): 273–300. October 1999. doi:10.1080/10715769900300851. PMID 10517533.
84. ↑ 84.0 84.1 "Current trends in drug metabolism and pharmacokinetics". Acta Pharmaceutica Sinica. B 9 (6): 1113–1144. 2019. doi:10.1016/j.apsb.2019.10.001. PMID 31867160.
85. ↑ "Drug Metabolism: A Half-Century Plus of Progress, Continued Needs, and New Opportunities". Drug Metabolism and Disposition 51 (1): 99–104. 2023. doi:10.1124/dmd.121.000739. PMID 35868640.
86. ↑ 86.0 86.1 86.2 "Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development". Acta Pharmaceutica Sinica. B 12 (6): 2751–2777. 2022. doi:10.1016/j.apsb.2022.03.009. PMID 35755285.

## Further reading

* Ioannides C (2001). Enzyme Systems That Metabolise Drugs and Other Xenobiotics. John Wiley and Sons. ISBN 0-471-89466-4.
* Ioannides C (1996). Cytochromes P450: Metabolic and Toxicological Aspects. CRC Press Inc. ISBN 0-8493-9224-1.
* Awasthi YC (2006). Toxicology of Glutathionine S-transferses. CRC Press Inc. ISBN 0-8493-2983-3.

## External links

* Databases
* Drug metabolism database
* Directory of P450-containing Systems
* University of Minnesota Biocatalysis/Biodegradation Database
* SPORCalc
* Drug metabolism
* Drug metabolism portal
* Small Molecule Drug Metabolism
* Template:Cite MeSH
* Template:Cite MeSH
* Template:Cite MeSH

* v
* t
* e

Pharmacology

Ligand (biochemistry)|

| Excitatory|

* Agonist
* Endogenous agonist
* Irreversible agonist
* Partial agonist
* Superagonist
* Physiological agonist

|

Inhibitory|

* Antagonist
* Competitive antagonist
* Irreversible antagonist
* Physiological antagonist
* Inverse agonist
* Enzyme inhibitor

|

* Drug
* Neurotransmitter
* Agonist-antagonist
* Pharmacophore

Pharmacodynamics|

| Activity at receptor|

* Mechanism of action
* Mode of action
* Binding
* Receptor (biochemistry)
* Desensitization (pharmacology)

|

Other effects of ligand|

* Selectivity (Binding, Functional)
* Pleiotropy (drugs)
* Non-specific effect of vaccines
* Adverse effects
* Toxicity (Neurotoxicity)

|

Analysis|

* Dose–response relationship
* Hill equation (biochemistry)
* Schild plot
* Del Castillo Katz model
* Cheng-Prussoff Equation
* Methods (Organ bath, Ligand binding assay, Patch-clamp)

|

Metrics|

* Efficacy
* Intrinsic activity
* Potency (EC50, IC50, ED50, LD50, TD50)
* Therapeutic index
* Affinity

|

Pharmacokinetics|

| Metrics|

* Loading dose
* Volume of distribution (Initial)
* Rate of infusion
* Onset of action
* Biological half-life
* Plasma protein binding
* Bioavailability

|

LADME|

* (L)ADME: (Liberation)
* Absorption
* Distribution
* Metabolism
* Excretion (Clearance)

|

* Compartment
* Bioequivalence

Related
fields|

| Neuroscience and psychology|

* Neuropsychopharmacology
* Neuropharmacology
* Psychopharmacology
* Electrophysiology

|

Medicine|

* Clinical pharmacology
* Pharmacy
* Medicinal chemistry
* Pharmacoepidemiology

|

Biochemistry and genetics|

* Pharmacoinformatics
* Pharmacogenetics
* Pharmacogenomics

|

Toxicology|

* Pharmacotoxicology
* Neurotoxicology

|

Drug discovery|

* Classical pharmacology
* Reverse pharmacology

|

* Photopharmacology
* Immunopharmacology
* Cell biology
* Physiology

Other|

* Coinduction (anaesthetics)
* Combination therapy
* Functional analog (chemistry)
* Polypharmacology
* Chemotherapy
* List of drugs
* WHO list of essential medicines

| Tolerance and resistance|

* Drug tolerance
* Tachyphylaxis
* Drug resistance
* Antibiotic resistance
* Multiple drug resistance

|

Antimicrobial pharmacology|

* Antimicrobial pharmacodynamics
* Minimum inhibitory concentration
* Bacteriostatic
* Minimum bactericidal concentration
* Bactericide

|

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