Drug metabolism

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. 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. 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.

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. 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.

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. 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. 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. 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.

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).

The hepatic extraction ratio can be defined experimentally as:

E_{H} = \frac{C_{in} - C_{out{C_{in

</math>

where <math>E_{H}</math> is the hepatic extraction ratio, <math>C_{in}</math> is the drug concentration entering the liver, and <math>C_{out}</math> 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:

E_{H} = \frac{f_{u} \cdot CL_{int{Q_{H} + f_{u} \cdot CL_{int

</math>

where <math>f_{u}</math> is the unbound fraction of drug in blood, <math>CL_{int}</math> is intrinsic hepatic metabolic clearance, and <math>Q_{H}</math> 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.

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. 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. 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.

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

V_{T}\frac{dC_{T{dt}=Q_{T}C_{A}-Q_{T}C_{V,T}

</math>

where <math>V_{T}</math> is tissue volume, <math>C_{T}</math> is tissue drug concentration, <math>Q_{T}</math> is tissue blood flow, <math>C_{A}</math> is arterial drug concentration, and <math>C_{V,T}</math> 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.

Intrinsic clearance measured in vitro can be scaled to whole-liver intrinsic clearance as: 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.

History

Systematic studies of drug metabolism emerged from nineteenth-century investigations into how administered medicinal and aromatic compounds were chemically transformed in the body.

Drug metabolism

Other factors

Prodrug activation: codeine

Food–drug interaction: grapefruit juice

Hepatic extraction and clearance

Physiologically-based pharmacokinetic modeling

In vitro to in vivo extrapolation

History

Further reading

External links