Individual variation in drug response

Individual variation in drug response

Prescribers have numerous sources of guidance about how to use drugs appropriately (e.g. dose, route, frequency, duration) for many conditions. However, this advice is based on average dose–response data derived from observations in many individuals. They can never be certain about the actual dose–response relationships for the particular patients that they treat. They can never be certain that their choice will be effective or safe for their individual patient and must recognize the need to monitor the outcome of their prescription. The inter-individual variation from the norm is often predictable and good prescribers are able to anticipate it and adjust their practice accordingly.

Therapy optimization

Patients with a given disease may differ dramatically in the desired and undesired effects of one and the same standard drug therapy. Extreme examples are:

  • total non-responsiveness and
  • severe adverse effects

reported for virtually all drugs in smaller or larger patient subsets. These phenomena are driven by hidden or unconsidered heterogeneity among patients and within the disease entity itself. Easiest to avoid are undesired effects caused by inconsideration of therapy-relevant, clinically manifest patient conditions such as comorbidities or pregnancy. The more hidden heterogeneity is gradually comprehended and dealt with using a fast-growing array of disease and patient markers. Marker discovery is largely driven by the molecular analyses of patient subgroups with distinct treatment responses identified in clinical studies. This deserves emphasis as clinical studies are frequently but incorrectly thought to reduce treatment individuality.

Validated patient and disease heterogeneity markers are applied to maximize outcomes and to reduce toxicities and costs of drug therapies. Specifically, they help to:

  • select and later adjust drug and dosage
  • verify treatment adherence
  • avoid or early detect undesired drug effects.

Along the way, disease markers gradually transform the traditional organ- and histology-based disease classification into one more relevant for treatment decisions. Therapies are also optimized by the consideration of the patient’s treatment objectives and drug preferences.

Drugs and patients in need of

All therapies require optimization. The individual intensity and urgency is determined by the pharmacological and toxicological drug properties, by the course of the disease, and by patient-specific factors. Drugs requiring optimization and monitoring are usually rich in serious undesired effects and/or they are safe and effective only in a narrow concentration range. Patient-specific factors include his/her therapeutic objectives and drug preferences, contraindications such as age, pregnancy, or comorbidities, and the individual, usually hardly predictable profile of undesired drug effects. Responsiveness prediction based on (tumor) genotyping is used mainly in oncology and it is partly driven by high costs of newer drugs.  

Tools

Therapy optimization utilizes both surrogate (laboratory) markers and clinical symptoms constituting a contraindication or suggestive of an undesired drug effect. Therapy-optimizing markers are technologically indistinguishable from those applicable to diagnosis or prediction of progression and prognosis. A specific exception are concentration measurements of active or toxic drug forms. From the viewpoint of pharmacology, therapy optimizing markers interrogate either the pharmacokinetics or pharmacodynamics of a drug. Predictive markers are deployed prior to, assessing markers during or following treatments. Predictive markers are based on averages of many patients and they are therefore less accurate than assessment markers - in an individual patient, the interrogated pharmacokinetic or pharmacodynamics effects may be modified by additional factors specific to this patient.  

Pharmacodynamics markers are more informative for the desired or undesired drug effects than pharmacokinetic ones. This is due to the more immediate relatedness between clinical effects and pharmacodynamics than with pharmacokinetics (drug application -> pharmacokinetics -> pharmacodynamics -> clinical effects). For this reason, once established, pharmacodynamics markers tend to be more widely used than pharmacokinetics markers. However, pharmacodynamics markers are much more difficult to develop since they frequently require invasive sampling of diseased tissue. In consequence, most pharmacodynamics markers interrogate processes taking place (e.g. coagulation tests during anticoagulant therapy) or measurable (e.g. transaminases during drug hepatotoxicity) in the easily accessible blood compartment. If affected by functionally relevant genetic variability, a drug’s pharmacodynamics can be also interrogated by genotyping of blood-derived DNA, as can pharmacokinetics.

Therapy-optimizing: classification, tools, and examples

Informative for Prediction Assessment
To be deployed Prior to therapy During or after therapy
Applications

Selection

-drug

-dosage

Avoidance of adverse drug events

Selection

-drug

-dosage

Detection of adverse drug events

Verification of therapy compliance

Examples

Pharmacokinetics

-GFR estimation (e.g. metformin)

-TPMT genotyping (6-mercaptopurine)

Pharmacodynamics

-EGFR genotyping (erlotinib)

-HER2 immunohistochemistry (trastuzumab)

-echocardiography (anthracyclines)

Pharmacokinetics

-GFR estimation (e.g. metformin)

drug plasma concentration (e.g. cyclosporin A)

Pharmacodynamics

-Thrombocytosis (heparins)

-INR (warfarin & phenprocoumon)

liver transaminases (e.g. methotrexate)

-echocardiography (anthracyclines)

 

Interindividual variability - pharmacodynamics

Variation in the response to equivalent drug concentrations arises because of various factors, such as differences in receptor number and structure, receptor-coupling mechanisms and physiological changes in target organs resulting from differences in genetics, age and health. For example, the beneficial natriuresis produced by the loop diuretic furosemide is often significantly reduced at a given dose in patients with renal impairment while confusion caused by opioid analgesics is more likely in the elderly. 

Interindividual variability - pharmacogenetics

Some of the variation above may be influenced by genetic polymorphisms, which can result in altered responsiveness in drug targets (e.g. receptor function) or altered drug handling (e.g. enzyme activity). There is considerable potential that, as the nature of the relationship between genetics and response to specific drugs (pharmacogenetics) is better characterised, it will be possible to identify in advance the good responders and those who will suffer adverse effects. This move towards ‘personalised medicine’, based on genetic testing, has the potential to improve the benefit-risk ratio of the drugs that we already have but also to improve the productivity pipeline. Many drugs are currently lost in development because they cause an unacceptable rate of adverse effects in an average unselected patient group. 

This is a set of powerpoint slides with self-assessment questions interspersed throughout on drug metabolism and pharmacogenetics. The aim is to understand the mechanism of clinically significant drug interactions, recognize potentially clinically significant genetic influences on drug efficacy and toxicity, and genetic predispositions to disease due to altered drug metabolism or transport. This resource is appropriate for medical students or graduate healthcare professionals such as nursing students. Provided by Nathalie K. Zgheib, MD, Associate Professor, Pharmacology and Toxicology, Faculty of Medicine, American University of Beirut.

Average: 3 (7 votes)

Some of the variation above may be influenced by genetic polymorphisms, which can result in altered responsiveness in drug targets (e.g. receptor function) or altered drug handling (e.g. enzyme activity). There is considerable potential that, as the nature of the relationship between genetics and response to specific drugs (pharmacogenetics) is better characterised, it will be possible to identify in advance the good responders and those who will suffer adverse effects. This move towards ‘personalised medicine’, based on genetic testing, has the potential to improve the benefit-risk ratio of the drugs that we already have but also to improve the productivity pipeline. Many drugs are currently lost in development because they cause an unacceptable rate of adverse effects in an average unselected patient group. 

This slide set examines the impact of population admixture on global pharmacogenomic diversity, using data from the highly heterogeneous and admixed Brazilian population.  Results obtained with ancestry-informative markers are presented  and discussed in relation to the the tri-hybrid – Amerindian, European and African – biogeographical ancestry of Brazilians.  Links to  databases on the distribution of pharmacogenomic polymorphisms among Brazilians are provided.  The challenges and opportunities  for pharmacogenomics research and clinical implementation  in  admixed populations are explored using the anticoagulant, warfarin, as a model. Slides set provided and reproduced with permission from Guilherme Suarez-Kurtz (see also Genome-wide association study of warfarin maintenance dose in a Brazilian sample. See also Parra et al. (2015) Genome-wide association study of warfarin maintenance dose in a Brazilian sample. PMID: 26265036.

 

Average: 3 (2 votes)

Interindividual variation - pharmacokinetics

Variation in the drug concentrations achieved by equivalent doses is a much more important cause of the inter-individual variation in drug response encountered in clinical practice. There are many reasons why the absorption, metabolism and excretion of drugs might vary.