The duration and intensity of pharmacological action of most lipophilic drugs are determined by the rate they are metabolized to inactive products. The
Cytochrome P450 monooxygenase system (CYP) is a crucial pathway in this regard. In general, anything that
increases the rate of metabolism (e.g.,
enzyme induction) of a pharmacologically active metabolite will
decrease the duration and intensity of the drug action. The opposite is also true, as in
enzyme inhibition. However, in cases where an enzyme is responsible for metabolizing a pro-drug into a drug, enzyme induction can accelerate this conversion and increase drug levels, potentially causing toxicity. For example, chemotherapy prodrugs like
cyclophosphamide (CPA) and
ifosfamide (Ifex), which are initially inactive, become toxic as they are metabolized into
cytotoxic compounds (such as
phosphoramide mustard and
chloroacetaldehyde) primarily from liver enzymes
CYP2B6 and
CYP3A4.
Co-administration of a strong CYP inducer, such as
phenytoin or
rifampicin, accelerates metabolism and increases the rate of
bioactivation which causes a higher concentration of cytotoxic metabolites that may lead to higher toxicity. This drug–drug interaction may enhance the risk of adverse effects, most notably severe
myelosuppression and
hemorrhagic cystitis. Typically,
drug-drug interactions are formally quantified by comparing the observed combined effect of two co-administered drugs against a theoretical baseline of no interaction. This concept, commonly referred to as the
additive effect, explains the synergistic interaction, or lack thereof, between drugs. In order to validly quantify the effect, two primary null models are used: loewe additivity and bliss independence.
Loewe additivity (dosage additivity) postulates that if two drugs share the same mechanism of action, their combined effects should be identical to the effect achieved from taking a higher dose of either drug alone. Bliss independence (response additivity) postulates that if two drugs act independently of each other, their combined effect should be the product of their individual effects. Both models identify two combined effects that signal a true drug interaction, as they deviate from the additive baseline: a synergistic effect, where the observed combined effect is greater than predicted which results in higher efficacy or toxicity levels; and an antagonistic effect, where the observed combined effect is less than predicted which often results in
drug therapy problems. The therapeutic index (TI) of a drug is the measurement of its efficacy, calculated as the ratio of the median toxic dose (TD50) to the median effective dose (ED50). Various Cytochrome P450 metabolic enzymes are inhibited or induced by many drugs. For example, chronic alcohol consumption will induce Cytochrome P450 enzymes, like
CYP2E1, which enhances the metabolism of ethanol. As a consequence, the induction of CYP2E1 will increase a person's tolerance levels and reduce the toxicity of ethanol. Additionally, CYP2E1 is involved with the metabolism of acetaldehyde (CH₃CHO), a metabolite of alcohol that is highly reactive and toxic, which can contribute to an alcohol-induced liver injury along with overoxidation. Various
physiological and
pathological factors can also affect drug metabolism. Physiological factors that can influence drug metabolism include age, individual variation (e.g.,
pharmacogenetics),
enterohepatic circulation,
nutrition,
sex differences or
gut microbiota. This last factor has significance because gut microorganisms are able to chemically modify the structure of drugs through degradation and biotransformation processes, thus altering the activity and toxicity of drugs. These processes can decrease the efficacy of drugs, as is the case of
digoxin in the presence of
Eggerthella lenta (E. lenta) in the
microbiota. Genetic variation (
polymorphism) accounts for some of the variability in the effect of drugs. As of 2019, approximately 560 million people (8% of the world's population in 2019) had this genetic mutation, which posed various health risks like metabolic disorders or an increased cancer risk. In general, drugs are metabolized more slowly in
fetal,
neonatal and
elderly humans and
animals than in
adults. Inherited genetic variations in drug-metabolizing enzymes result in different catalytic activity levels. For example,
N-acetyltransferases (involved in
Phase II reactions), individual variation creates a group of people who acetylate slowly (
slow acetylators) and those who acetylate quickly (
rapid acetylators), split roughly 50:50 in the population of Canada. However, variability in
NAT2 alleles distribution across different populations is high, and some ethnicities have a higher proportion of slow acetylators. This variation in metabolizing capacity may have dramatic consequences, as the
slow acetylators are more prone to dose-dependent toxicity.
NAT2 enzyme is a primary metabolizer of antituberculosis (
isoniazid), some antihypertensive (
hydralazine), anti-arrhythmic drugs (
procainamide), antidepressants (
phenelzine) and many more and increased toxicity as well as drug adverse reactions in slow acetylators have been widely reported. Similar phenomena of altered metabolism due to inherited variations have been described for other drug-metabolizing enzymes, like
CYP2D6,
CYP3A4,
DPYD,
UGT1A1.
DPYD and
UGT1A1 genotyping is now required before administration of the corresponding substrate compounds (
5-FU and
capecitabine for DPYD and
irinotecan for UGT1A1) to determine the activity of DPYD and UGT1A1 enzyme and reduce the dose of the drug in order to avoid severe adverse reactions. Dose, frequency, route of administration, tissue distribution, and protein binding of the drug affect its metabolism.
Pathological factors can also influence drug metabolism, including
liver,
kidney, or
heart disease.
In silico modelling and simulation methods allow drug metabolism to be predicted in virtual patient populations prior to performing clinical studies in human subjects. This can be used to identify individuals most at risk from adverse reaction. == History ==