Individual Variation in Drug Response


Benton Bramwell, ND, and Matt Warnock

While studies can help us understand overall patterns of drug effects, many factors affect an individual’s clinical response. In this article, we explore examples of how disease, aging, gender, and genetic variation play important roles in shaping individual responses to drug therapy.

Effects of Disease on Pharmacokinetics and Pharmacodynamics

Probably the most obvious diseases that impact a person’s sensitivity to drugs are diseases of the liver. While other organs are also important in drug metabolism, such as the gut and kidneys, the liver serves as a main site of drug metabolism and changes in its functional tissue can alter drug pharmacokinetics. However, insults to liver tissue may be acute or chronic, be mediated by different causes such as viruses, the effects of poisoning, or autoimmunity, and occur in varying degrees of severity.1 Thus, the exact impact of liver disease on drug metabolism will vary from one person to another. Talal provides a helpful review of non-invasive imaging and functional tests that may be most useful in assessing the liver’s potential to impact drug pharmacokinetics.2

In naturopathic medical school, an introductory lecture frequently emphasizes the role of inflammation as a common and crucial intermediary between health and different states of disease. It turns out that the common facilitator of disease, inflammation, also has a rather complex relationship with drug pharmacokinetics and pharmacodynamics. It is not uncommon, for example, for patients with cirrhosis to show elevated markers of inflammation, including tnfalpha and IL-6, which may be elevated in response to bacterial translocation from the gut to the liver.3 Elevations of IL-6 may be particularly significant as the inflammatory response marked with its increase is known to suppress hepatocyte CYP activity.4 This suppression can affect pharmacokinetics by increasing drug concentration. In an interesting example of how the management of systemic disease can impact drug metabolism, suppression of CYP3A, CYP2C9, and CYP2C19 activity in patients with rheumatoid arthritis (RA) is known to be reversed by administration of a monoclonal antibody to soluble interleukin-6 (IL-6).5 A similar finding of reversing CYP3A4 suppression in patients with RA is shown with a monoclonal antibody to IL-6 receptor.6

However, inflammation-driven changes in pharmacokinetics do not always translate into predictable changes in pharmacodynamics. An example of this in one study is that while patients with RA showed significantly higher serum levels of the drug verapamil in association with increasing levels of IL-6, the ability of the drug to actually prolong the PR-interval decreased and the effect of the drug on heart rate and blood pressure did not increase. This pattern of changes may be due to receptor down-regulation.7 A similar pattern is seen in patients with the inflammation of active Crohn’s disease.8 That is, in patients with active disease the concentration of verapamil increases, but this does not coincide with an enhanced pharmacodynamic effect of verapamil; conversely, reducing the severity of the inflammatory disease increases drug response despite lower drug concentration. These findings may be among the first that eventually lead to a generalized principle that to ensure predictable pharmacodynamics, inflammation must be dealt with as a priority.

Effects of Aging

The pharmacokinetics and pharmacodynamics of many drugs seem to become somewhat less tightly regulated as we age. Many factors, such as changes in organ weight and the level of drug receptors may contribute to these changes. However, decreases in hepatic and renal blood flow in the older population seem to be among the most important causes of reduced drug clearance (the volume of plasma cleared of a drug in a specific time) and steady increases in Area Under the Curve (AUC) of many drugs after the age of 20.9 The sensitivity merited in prescribing to the geriatric population underpins the axiom, “Start low and go slow.”10 An example is metabolism of O-desmethyltramadol (ODM), the active metabolite of Tramadol. In comparing an aging group with mild renal insufficiency (common in a geriatric population) and a younger group, the researchers found that the older group took significantly longer to reach maximal plasma concentrations of ODM and that the subsequent decline in ODM concentration also took significantly longer. There was also a 15% higher maximum possible treatment effect in the older group, suggesting both an opportunity for increased benefit and also a greater potential for adverse effects in the elderly.11 Other examples of drugs, among many, that demonstrate a tendency for decreased clearance and increased concentration in older patients prone to decreasing hepatic and renal function are Cefoperazone12 and Clarithromycin.13 Unfortunately, while differences in pharmacokinetics can be identified, it is not always clear whether these changes equate to clinically meaningful changes in pharmacodynamics or outcomes related to safety. For example, while in an elderly population there is a relatively large decrease in Lisinopril’s clearance,14 this does not seem to substantially impact its clinical effects.15 Carefully clarifying meaningful pharmacodynamic differences between older and younger patients represents an important area of work as our population ages and as geriatric patients are often not included at the same rate as younger patients in clinical trials.16,17

The Effects of Gender

There is still a lot to learn about the effects of gender on drug pharmacokinetics and pharmacodynamics. However, we are briefly highlighting here some important differences that have emerged in recent meta-analyses or data pooled from phase 3 trials. For example, a recent meta-analysis suggests that women have a greater incidence of the potentially traumatic experience of awareness under general anesthesia, while also emerging more quickly from anesthesia than men.18 Recent meta-analysis also shows that women receiving treatment for schizophrenia have a higher response to antipsychotic medication than men, with the number needed to treat (NNT) for a response in women being 6.9 and the NNT for men being 9.4.19 Based on data from phase 3 clinical trials, it also appears that women experience greater improvements in lipid levels after treatment with bempedoic acid, compared to men.20

Genetic Variation

One of the most common sources of differences in individual response to drug therapy is genetic variation in genes encoding proteins needed in drug metabolism and effect. Consider, for example, probably the most commonly used drug in the world: caffeine. Variation in the CYP1A2 gene (specifically, those who are homozygous or heterozygous for the -163C>A polymorphism) is an important contributing factor to the rate at which caffeine is metabolized.21 When combined with other factors that can affect caffeine availability, such as differences in the rate of gastric emptying, the result is considerable variability in how long it takes for caffeine to reach its maximum levels in the bloodstream. This can take as little as 20 minutes but can also take up to 120 minutes when someone consumes a cup of hot coffee, and the range is larger, 20 to 240 minutes, when someone consumes a cold energy drink.22 Moreover, while an average half-life (the time for an active amount of drug in the body to be reduced by half) for caffeine is 4-6 hours, the range of half-life is also of considerable magnitude, from 2-12 hours.23 In essence, by the time some people reach peak blood levels, active caffeine levels may already be reduced by half in others!

As diabetes is now estimated to affect about 1 in 10 Americans, and 90%-95% of these roughly 38 million people in the United States are diagnosed with type 2 diabetes,24 we feel it is important to review some of the pharmacogenetic factors impacting the treatment of this common condition. Several genetic variants are suggested to impact the pharmacokinetics or dynamics of drugs commonly used in treating type 2 diabetes. For example, a study of 478 patients taking metformin found that those carrying or homozygous for the ‘G’ allele of the SLC22A1 gene variant rs628031 G/A (a gene encoding a cation transporter25) showed increased likelihood to be responders to metformin monotherapy.26 Another gene variant of significant import is the G972R mutation of the Insulin Receptor Substrate-1 gene. This variant results in a glycine to arginine substitution at codon 972 and is present in about 10% of those with type 2 diabetes and around 6% of the general population.27 This variant is associated with reduced insulin sensitivity28 and an increased risk of treatment failure with oral antidiabetic drugs;29 it also has been reported to be a significant independent predictor of coronary heart disease,30 which is an important consideration given the overlap of type 2 diabetes and heart disease. Also important in the context of heart disease and diabetes is an effect of a mutation in the 5’ region of the PROX1 gene (a gene enabling transcription factor activity and shaping organ development31), the single-nucleotide polymorphism (SNP) known as rs340874, which is a potential risk factor for hyperglycemia due to use of atenolol.32

Finally, it may be especially important to consider the presence of polymorphisms of the CYP2C9 gene, needed for sulfonylurea metabolism, in elderly subjects with type 2 diabetes receiving treatment with sulfonylurea drugs. In a study that included 103 subjects over the age of 60, the rate of hypoglcyemic episodes increased according to the number of polymorphisms present for the CYP2CP gene. Over 3 months, those with two wild type alleles experienced 0.36 0.98 episodes (mean SD), those with one polymorphism experienced 0.79 1.7 episodes, and those with two polymorphisms experienced 2.67 4.6 episodes.33

Given the impact of the ongoing opioid epidemic, we also discuss briefly some pharmacogenetic differences that impact the effectiveness of opioid drugs. Opioid pain treatment is an area where patients exhibit significant individual variability in response. For example, one study in cancer patients showed that while most patients responded to morphine, about 25% did not and in most cases did respond when changed to an alternative opioid.34 While no single genetic difference explains this variation, several genetic variants are beginning to emerge as important contributing factors. Among these is the single-nucleotide polymorphism (SNP) in the μ-opioid receptor gene known as A118G, in which there is an aspartate substitution for asparagine at position 40. A structural impact of this substitution is a removal of an N-glycosylation site in the receptor, which, while not affecting the binding of the receptor to opioid alkaloids, may increase its affinity for the endogenous ligand, -endorphin.35 The results of a meta-analysis of 23 studies (Ren, 2015) suggest that the presence of this allele is associated with higher pain scores in the first 24 hours of the postoperative period, as well as greater opioid consumption and a reduced risk of vomiting. Thus, the overall picture suggests a somewhat reduced sensitivity to the effects of opioids in patients with this SNP.36

This view is reinforced by a recent update of the analysis, expanded to include 39 studies and 7,455 patients.37 This expanded analysis confirms that patients carrying the A118G allele, which occurs in about 16% of northern and western Europeans and 46.5% in Asians, required greater levels of opioids in the first 24 hours after surgery. In addition to evidence showing that this allele affects sensitivity to opioid medications, there is also evidence from an observational study that its presence is associated with significantly increased risk of severe clinical outcomes (respiratory/cardiac arrest) in cases of overdose [OR:5.3, 95% CI, 1.2-23.8, p<0.05].38

In addition to highlighting the importance of the A118G polymorphism, the previously cited updated analysis (Li, 2023) was also able to detect a difference associated with the
CYP3A41G variant, which leads to a single G-to A-substitution resulting in reduced function of the CYP3A4 metabolizing enzyme. Those carrying the CYP3A41G variant required smaller doses of opioids in the first 24 hours after surgery compared to those not carrying the variant. As reviewed by the authors of the analysis, this is a high frequency allele in Asians (0.249 in Japanese, and 0.221 in Chinese).

To further inform clinicians about the known impacts of genetic variations on the most commonly prescribed drugs, we have cross-referenced information in FDA’s table of pharmacogenetic biomarkers and associated drug labeling text with a list of frequently prescribed drugs. This effort aims to provide a concise summary of some of the most vital pharmacogenetic information from FDA’s table for drugs that are among the 50 most commonly prescribed.

Cross-reference of FDA’s table of pharmacogenetic biomarkers in drug labeling39 with list of commonly prescribed drugs40

DrugPharmacogenetic biomarkerSummary of key pharmacogenetic relevance found in drug labeling
Omeprazole (proton pump inhibitor)CYP2C19A person is an extensive, intermediate, or “poor metabolizer”*41 depending on the number of functional alleles that are present for CYP2C19. At steady state, poor metabolizers have 1.5X that of the rest of the population, though the change is not considered clinically meaningful.
Metoprolol (beta blocker)CYP2D6Metoprolol is metabolized predominantly by CYP2D6, which, when inhibited by several drugs (such as quinidine and propafenone) can lead to a several-fold increase in metoprolol levels and thus increase risk of adverse effects outside of the heart tissue. In addition, CYP2D6 is absent in about 8% of Caucasians and about 2% of most other populations. Those with absent CYP2D6 are poor metabolizers of the drug.
Fluoxetine (Antidepressant: Selective Serotonin Reuptake Inhibitor)CYP2D6Fluoxetine is an inhibitor of CYP2D6 and may make those with normal CYP2D6 function resemble poor metabolizers. This may explain the association between fluoxetine use and risk of QT interval prolongation and ventricular arrhythmias, as CYP2D6 inhibitors predispose to this adverse effect.
Citalopram (Antidepressant: Selective Serotonin Reuptake Inhibitor)CYP2C19In CYP2C19 poor metabolizers the AUC is increased by 107%. Due to risk of QT prolongation, the maximum dose of Citalopram (Celexa) in poor CYP2C19 metabolizers is 20mg/d.
Buproprion (Antidepressant: Inhibits reuptake of dopamine and norepinephrine)CYP2D6Buproprion and its metabolites are CYP2D6 inhibitors and have been shown to increase the AUC of desipramine in extensive metabolizers of CYP2C19 by 5 fold. The effects of increasing desipramine were seen for at least 7 days after the last dose of bupropion.
Carvedilol
(Beta-blocker)		CYP2D6Poor metabolizers of CYP2D6 have 2 to 3 fold higher plasma concentrations of the (R+)enantiomer of Carvedilol and a 20-25% increase in the levels of Carvedilol (S+) enantiomer. During up-titration of dose, poor metabolizers of CYP2D6 had increased rates of dizziness.
Tramadol (Opioid analgesic)CYP2D6Those who are ultra-rapid metabolizers of CYP2D6 experience increased exposure to Tramadol’s active metabolite, Odesmethyltramadol, created by the action of CYP2D6. This can lead to life-threatening respiratory depression and signs of overdose, including sleepiness, confusion, and shallow breathing. The genotype associated with ultrarapid metabolizers is estimated to be present in 1-10% of Whites, 3-4% of Blacks, 1-2% of East Asians, and may be greater than 10% in Oceanian, Northern African, Middle Eastern, Ashkenazi Jews, and Puerto Ricans.
Pantoprazole (Proton pump inhibitor)CYP2C19About 3% of Caucasians and African Americans and 17-23% of East Asians are poor metabolizers of CYP2C19 due to genetic polymorphism. For adults, this may lead to half-lives of 3.5 to 10 hours, though with minimal accumulation no change in dosing is needed. In children, poor metabolizers may have oral clearance that is 10-fold less than rapid metabolizers and in this population dose reduction should be considered.
Rosuvastatin (Statin)SLCO1B1 (gene encoding the OATP1B1 transporter)Those with the polymorphism SLCO1B1 521 C/C have poor functioning of two alleles encoding for the OATP1B1 transporter and have increased levels of rosuvastatin. This polymorphism is reported to generally exist in less than 5% of most racial groups and its impacts of efficacy and safety have not been clearly established.
Meloxicam (NSAID)CYP2C9Meloxicam AUC is substantially higher in those with reduced CYP2C activity, especially in those who are poor metabolizers. The frequency of poor metabolizer genotypes is present in <5% of the population. In patients known or suspected to be poor CYP2C9 metabolizers based on genetic testing or previous experience with other CYP2C9 substrates (e.g., warfarin or phenytoin), healthcare professionals should consider dose reduction.
Clopidogrel (Antiplatelet drug)CYP2C19The action of Clopidogrel (Plavix) depends on the creation of an active metabolite, mainly through the action of CYP2C19. Those who are homozygous for nonfunctional alleles of CYP2C19 are poor metabolizers and show decreased exposure to the active metabolite and diminished inhibition of platelet aggregation. The genotype leading to poor metabolism is found in about 2% of White patients, 4% of black patients, and 14% of Chinese patients.
Glipizide (Sulfonylurea, antidiabetic drug)G6PDSulfonylurea agents such as Glipizide can lead to hemolytic anemia in patients with G6PD deficiency. This finding has been additionally reported in some patients who did not have G6PD deficiency.
Warfarin (Anticoagulant)CYP2C9Warfarin’s  S-enantiomer of warfarin is metabolized mainly by CYP2C9. Two variant alleles, CYP2C9*2 and CYP2C9*3, lead to decreased hydroxylation of S-warfarin, in vitro. The frequency of the CYP2C9*2 allele is approximately 11% and that of CYP2C9*3 is about 7%, in Caucasians. Other CYP2C9 alleles also associated with reduced enzymatic activity occur at lesser frequencies. Warfarin hinders vitamin K regeneration via VKOR inhibition. Specific VKORC1 gene variants, such as –1639G>A, affect warfarin dosing.
Tamsulosin (Alpha-1 blocker used to treat enlarged prostate)CYP2D6It is known that concurrent use of a strong CYP2D6 inhibitor, paroxetine, increases the AUC of Tamsulosin (Flomax) by 1.6. It is therefore expected that poor metabolizers of CYP2D6 would exhibit similarly elevated levels, comparing poor metabolizers to extensive metabolizers. About 7% of Caucasians are poor metabolizers, as are about 2% of African Americans.
Duloxetine (Antidepressant: serotonin noradrenaline reuptake inhibitor)CYP2D6When CYP2D6 poor metabolizers were given a CYP1A2 inhibitor (fluvoxamine) along with duloxetine, a six-fold increase in the AUC of duloxetine was seen.

It is worth underscoring that both inhibition and induction of differing hepatic enzymes can have toxic effects depending upon the drug that is being metabolized. For example, fluoxetine, which inhibits CYP2D6 function, can cause the QT interval (the time it takes for the ventricles to contract and relax again) to lengthen, increasing the potential for dangerous ventricular arrhythmias, while excessive induction of the same enzyme can lead to toxic levels of Tramadol’s active metabolite and increased risk of respiratory depression.

Conclusion

In addition to awareness of estimates of drug effect reported in human studies, practitioners benefit when their prescribing decisions for an individual are informed by consideration of the patient’s unique characteristics. In addition to considering a patient’s existing list of medications to screen for interactions, it is also important not to lose sight of other key factors that shape the patient’s unique response. These include the effects of pre-existing inflammation, the reduced hepatic and renal blood flow that accompanies aging, and the presence of potentially impactful genetic variants. Diligence in identifying these factors, and in the case of inflammation addressing this factor to the extent possible, may lead to improved efficiency and predictability of an individual’s response to pharmacotherapy, or at least make the clinician more aware of when especially close monitoring is needed.

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  41. While the commonly used term of “poor metabolizer” in this area of labeling/literature certainly expresses the relatively low capacity for metabolism of a drug, we would point out that the terminology seems to unfairly frame the level of function of detoxification organs based on their capacity to biotransform chemicals not generally innate to their natural environment.

Published December 16, 2023

About the Authors

Benton Bramwell, ND, is a 2002 graduate of National College of Naturopathic Medicine who practiced primarily in Utah while helping to expand the prescriptive rights of naturopathic physicians in that state. Currently, he owns and operates Bramwell Partners, LLC, providing scientific and regulatory consulting services to both dietary supplement and conventional food companies. He and his wife, Nanette, have six children and two grandchildren; they live in Manti, Utah.

Matt Warnock is an accidental herbalist, who received his MBA and Juris Doctor from BYU, then worked as an attorney, litigator, and business consultant until 2000. He then joined RidgeCrest Herbals, a family business started by his father, and started learning about
herbal medicine, focusing especially on complex herbal formulas. He has two U.S. patents for herbal formulations and methods. He lives near Salt Lake City with his wife, Carol; they are the parents of three children and four grandchildren.