Using multiple drug exposure levels to optimize power in pharmacogenetic trials

Gender differences in drug toxicity

Clinical data suggest that gender dimorphic profiles are emerging in terms of both drug efficacy and adverse drug reactions (ADRs). With an increasing emphasis on individualised therapies and the need to prevent drug attrition there is a compelling need to understand the molecular basis for gender dimorphic profiles in ADRs and the consequences. Classes of agents exhibiting gender-based variation in pharmaceutical efficacy and toxicity include anaesthetics, HIV-1 therapies and antiarrhythmic drugs. Body weight differences are often cited as a reason for differences in drug pharmacokinetics and subsequent toxicity. However, some studies accounted for these factors and still found significance suggesting that dosage versus body weight does not explain the outcome. Here, we present an overview of current understanding of gender-specific drug toxicity and present rational molecular explanations for these adverse events. There is mounting evidence in support of hormonal effects underpinning the majority of the ADR differences observed between the sexes.

Gene-environment interactions for complex traits: definitions, methodological requirements and challenges

Genetic and environmental risk factors and their interactions contribute to the development of complex diseases. In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application. Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means. This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice. We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results. We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa. Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified. These issues as well as a critical appraisal of common methodological concerns are finally discussed.

Relevance of assessing drug concentration exposure in pharmacogenetic and imaging studies

Pharmacodynamic differences are difficult to interpret without drug concentration data. In particular, variability in drug exposure may confound the interpretation of pharmacogenetic, therapeutic outcome, and neuroimaging studies. Inter-individual variability in concentrations can be quite high due to variable adherence and pharmacokinetics. For example, clearance may be influenced by genetics, drug interactions, age and illness. We review findings that acute responses to selective serotonin reuptake inhibitors can have a concentration-response relationship using positron emission tomography and neuroendocrine measures. We also present preliminary evidence that the concentration-response relationship for paroxetine is influenced by genotypic differences at the serotonin transporter promoter. In large clinical studies, the accurate assessment of drug exposure can be challenging, with several techniques used to assess exposure. Population pharmacokinetics (Pop PK) is a method that is ideally suited for analysing concentration data from large trials because both patient-specific and population parameters can be determined with only a small number of plasma samples per patient. As opposed to relying on prescribed doses or a single trough level, the ability to determine more accurately exposure with Pop PK reduces the heterogeneity introduced by exposure variability. Pop PK hierarchic Bayesian approaches have been effective for characterizing anticonvulsants, antibiotics, antineoplastics and antiarrhythmics. We have recently successfully incorporated these pop PK analyses into routine assessments of elderly patients in clinical trials of selective serotonin reuptake inhibitors (SSRIs) and second generation antipsychotics. For the design and interpretation of neuroimaging, pharmacogenetic, and behavioural studies, the assessment of drug concentration exposure is therefore feasible and has potentially important ramifications.

Candidate genes for antidepressant response to selective serotonin reuptake inhibitors

Selective serotonin reuptake inhibitors (SSRIs) can safely and successfully treat major depression, although a substantial number of patients benefit only partially or not at all from treatment. Genetic polymorphisms may play a major role in determining the response to SSRI treatment. Nonetheless, it is likely that efficacy is determined by multiple genes, with individual genetic polymorphisms having a limited effect size. Initial studies have identified the promoter polymorphism in the gene coding for the serotonin reuptake transporter as moderating efficacy for several SSRIs. The goal of this review is to suggest additional plausible polymorphisms that may be involved in antidepressant efficacy. These include genes affecting intracellular transductional cascades; neuronal growth factors; stress-related hormones, such as corticotropin-releasing hormone and glucocorticoid receptors; ion channels and synaptic efficacy; and adaptations of monoaminergic pathways. Association analyses to examine these candidate genes may facilitate identification of patients for targeted alternative therapies. Determining which genes are involved may also assist in identifying future, novel treatments.

Neuroendocrine response to intravenous citalopram in healthy control subjects: pharmacokinetic influences

RATIONALE

The neuroendocrine response to intravenous citalopram may provide an acute, functional, in vivo measure of the neural serotonin (5-HT) system.

OBJECTIVE

To refine the quantification of acute neuroendocrine responses following intravenous citalopram in studies of 5-HT function.

METHODS

In 75 adult healthy subjects taking part in four similar protocols, we measured plasma prolactin and cortisol, as well as serial citalopram concentrations following intravenous citalopram (10 mg, 20 mg, 40 mg, 0.33 mg/kg) and placebo. The relationship between the AUC for intravenous citalopram during the first 150 min (AUC(150)) and the magnitude of the neuroendocrine response was determined. The role of pharmacokinetic (PK) parameters, as well as sensitivity to placebo injections, in influencing the neuroendocrine response to citalopram was then evaluated.

RESULTS

Citalopram produced a dose-dependent increase in cortisol and prolactin. The maximal increase from baseline correlated significantly but modestly with citalopram's AUC(150) (prolactin r(2)=0.23, P<0.0001; cortisol r(2)=0.3, P<0.0001). Additionally, citalopram's AUC(150) was affected by between-subject differences in both the peripheral and central volume of distribution. However, the neuroendocrine responses to citalopram did not correlate with the responses to placebo.

CONCLUSIONS

The parenteral citalopram challenge test is characterized by a modest concentration-response relationship, with concentration influenced by variable PK factors. Accounting for individual differences in drug distribution may improve the power of the citalopram challenge test, when used as an in vivo measure of central 5-HT function.

Pharmacogenomics and ???Individualized Drug Therapy???

Since 1965 there have been more than 800 pharmacogenetics/genomics reviews - most suggesting that we are on the verge of offering individualized drug therapy to everyone. However, there are numerous reasons why this approach will be extremely difficult to achieve in the foreseeable future. Drug treatment outcome represents a complex phenotype, encoded by dozens, if not hundreds, of genes, and affected by many environmental factors; therefore, we will almost always see a gradient of response. Phenotyping assays of blood enzyme activities (if feasible) are generally more successful than DNA genotyping for predicting unequivocal outcomes of drug therapy in each and every patient. Phenotyping with probe drugs has generally not succeeded, because of the overlapping substrate specificities not only of drug-metabolizing enzymes but also transporters, receptors, ion channels, transcription factors, and other drug targets; drug-drug interactions, enzyme induction and inhibition, and multiple (enzyme, transporter, second-messenger, signal transduction) pathways also present enormous problems. Genotyping to predict drug disposition, efficacy, toxicity, and clinical outcome has been proposed, but the success of genotyping in individualized drug therapy currently appears unlikely because of the many shortcomings (frequency of DNA variant sites, ethnic differences, admixture) and complexities (plasticity of the genome, multiple mechanisms for determining sizes and locations of haplotype blocks) of this approach. Genomics is an important tool in basic research; yet, it is unrealistic to include genotyping within the realm of tests available to the practicing clinician in the foreseeable future. The same can be said for transcriptomics and proteomics, which also rely on available sources (tumors, biopsies, excreta). The newly emerging fields of metabonomics and phenomics might offer solutions to anticipating and decreasing individual risk for adverse drug reactions in each individual patient; however, tests based on these approaches are not expected to become available to the practicing clinician for at least the next 5-10 years.