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 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.
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 in the microbiota. Genetic variation (polymorphism) accounts for some of the variability in the effect of drugs.
In general, drugs are metabolized more slowly in fetal, neonatal and elderly humans and animals than in adults. With N-acetyltransferases (involved in Phase II reactions), individual variation creates a group of people who acetylate slowly (slow acetylators) and those who acetylate quickly, split roughly 50:50 in the population of Canada.
This variation may have dramatic consequences, as the slow acetylators are more prone to dose-dependent toxicity.
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 diseases.
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.