Role of Statistical Random-Effects Linear Models in Personalized Medicine

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

Plasma carnitine concentrations in Medium-chain acyl-CoA dehydrogenase deficiency: lessons from an observational cohort study

Our aim was to study the effect of secondary carnitine deficiency (SCD) and carnitine supplementation on important outcome measures for persons with medium-chain Acyl-CoA dehydrogenase deficiency (MCADD). We performed a large retrospective observational study using all recorded visits of persons with MCADD in the University Medical Center Groningen, the Netherlands, between October 1994 and October 2019. Frequency and duration of acute unscheduled preventive hospital visits, exercise tolerance, fatigue, and muscle pain were considered important clinical outcomes and were studied in relation to (acyl)carnitine profile and carnitine supplementation status. The study encompassed 1228 visits of 93 persons with MCADD. >60% had SCD during follow-up. This included only persons with severe MCADD. Carnitine supplementation and SCD were unrelated to the frequency and duration of the acute unscheduled preventive hospital visits (P > 0.05). The relative risk for fatigue, muscle ache, or exercise intolerance was equal between persons with and without SCD (RR 1.6, 95% CI 0.48-5.10, P = 0.4662). No episodes of metabolic crisis were recorded in non-carnitine-supplemented persons with MCADD and SCD. In some persons with MCADD, SCD resolved without carnitine supplementation. There is absence of real-world evidence in favor of routine carnitine analysis and carnitine supplementation in the follow-up of persons with MCADD.

Dose-Related Reference Range as a Tool in Therapeutic Drug Monitoring

BACKGROUND

Therapeutic drug monitoring (TDM) aims to individualize drug therapy. This systematic review provides a state-of-the-art overview of the benefits of adding the dose-related reference range (DRR) as a second reference range to the set of tools used by TDM for measurement and evaluation. It discusses alternative pharmacokinetic approaches for individualization of drug therapy.

METHODS

Literature was searched in PubMed. Textbooks provided Bateman transformations for calculating expected drug concentrations at various times after drug application in "normal patients," that is, the population of phase II clinical trials. The review compiles conditions and prerequisites for these transformations to be valid.

RESULTS

Relating a measured drug concentration to the orienting therapeutic reference range provides pharmacodynamic information for improving the benefit-to-risk ratio of desired drug effects versus adverse drug effects. The discriminating DRR considers a patient's individual pharmacokinetic situation. DRR is statistically based on the pharmacokinetic parameters total clearance, time to reach maximal concentrations, and elimination half-life. Relating the measured drug concentration to a range rather than a particular value, DRR determines if individual patients do or do not belong to the population of "normal patients." Once a patient is identified to be outside the population of "normal patients," the clinical-pharmacological TDM report elaborates the cause. It consists of the measured value, the TDM 9-field-board, the elimination pathways table, and a medication recommendation taking into account clinical information. The internet-based platform KONBEST supports editing of the clinical-pharmacological TDM report. It is personally signed and send to the therapist.

CONCLUSIONS

The DRR embedded into a clinical-pharmacological TDM report allows adjusting a patient's medication to the patient's individual needs (individualization of drug therapy).

Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

Mirabegron as effective as oxybutynin for ureteral stent symptoms

OBJECTIVE

Ureteral stents usually cause pain and lower urinary tract discomfort. This study aimed to compare the effect of mirabegron with oxybutynin in relieving ureteral stent-related symptoms over time.

METHODS

A prospective, longitudinal, randomized, single-blinded study was conducted. Patients who had a ureteral stent inserted after urolithiasis treatment were classified into two groups and received either oxybutynin 5 mg/day (Group O) or mirabegron 50 mg/day (Group M). The Ureteral Stent Symptoms Questionnaire (USSQ) was applied on the 3rd, 6th, and 15th postoperative days. Group domain scores were compared, and a mixed linear model was used to better assess score differences.

RESULTS

Ureteral Stent Symptoms Questionnaire scores were similar in both groups during all three postoperative days (p>0.05). A longitudinal analysis showed that global quality of life and general health improved over time, independently of the use of any of the medications (p<0.05), while urinary symptoms and body pain scores were lower over time in participants receiving oxybutynin.

CONCLUSION

Both mirabegron and oxybutynin are equivalent in relieving ureteral stent symptoms. Moreover, some stent symptoms seem to decrease over time despite the use of medication.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients

Background

Two-dimensional personalized medicine (2-PM) models are tools for measuring individual benefits of medical treatments for chronic diseases which have potential applications in personalized medicine. These models assume normality for the distribution of random effects. It is necessary to examine the appropriateness of this assumption. Here, we propose a graphical approach to assessing the goodness-of-fit of 2-PM models with continuous responses.

Methods

We propose benefit quantile-quantile (BQQ) plots which compare the empirical quantiles of individual benefits from a patient sample predicted through an empirical Bayes (EB) approach versus the quantiles of the theoretical distribution of individual benefits derived from the assumption of normality for the random effects. We examine the performance of the approach by conducting a simulation study that compared 2-PM models with non-normal distributions for the random effects versus models with comparable normal distributions. Cramer-von Mises discrepancies were used to quantify the performance of the approach. The approach was illustrated with data from a clinical trial of imipramine for patients with depression.

Results

Simulations showed that BQQ plots were able to capture deviations from the normality assumption for the random effects and did not show any asymmetric deviations from the y = x line when the random effects were normally distributed. For the depression data, the points of the BQQ plot were scattered around closely to the y = x line, without presenting any asymmetric deviations. This implied the adequacy of the normality assumption for the random effects and the goodness-of-fit of the 2-PM model for the imipramine data.

Conclusion

BQQ plots are sensitive to violations of the normality assumption for the random effects, suggesting that the approach is a useful tool for examining the goodness-of-fit of random-effects linear models when the goal is to measure individual treatment benefits.

Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental health disorders. Stimulant drugs as the most commonly used treatment and first-line therapy for ADHD have side effects. One of the newest approaches to select the best choices and optimize dosages of medications is personalized medicine.

METHODS

This historical cohort study was carried out on the data taken from the period of 2008 to 2015. Eligible subjects were included in the study randomly. We used mixed-effects logistic regression models to personalize the dosage of Methylphenidate (MPH) in ADHD. The patients' heterogeneity was considered using subject-specific random effects, which are treated as the realizations of a stochastic process. To recommend a personalized dosage for a new patient, a two-step procedure was proposed. In the first step, we obtained estimates for population parameters. In the second step, the dosage of the drug for a new patient was updated at each follow-up.

RESULTS

Of the 221 children enrolled in the study, 169 (76.5%) were male and 52 (23.5%) were females. The overall mean age at the beginning of the study is 82.5 (± 26.5) months. In multivariable mixed logit model, three variables (severity of ADHD, time duration receiving MPH, and dosage of MPH) had a significant relationship with improvement. Based on this model the personalized dosage of MPH was obtained.

CONCLUSIONS

To determine the dosage of MPH for a new patient, the more the severity of baseline is, the more of an initial dose is required. To recommend the dose in the next times, first, the estimation of random coefficient should be updated. The optimum dose increased when the severity of ADHD increased. Also, the results show that the optimum dose of MPH as one proceeds through the period of treatment will decreased.

Development of a methodology to make individual estimates of the precision of liquid chromatography-tandem mass spectrometry drug assay results for use in population pharmacokinetic modeling and the optimization of dosage regimens

Background

The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE’s for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes.

Methods

Following method validation, three independent experiments were conducted to develop AEE’s using various least squares linear or nonlinear, and median-based linear regression techniques. SD’s were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 (“small” sample sets) or 20 (“large” sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE’s were developed by combining the SD’s of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated.

Results

Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil’s regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD’s, including SD’s below the lower limit of quantification.

Conclusions

In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.

Clinically Significant Drug-Drug Interactions with Agents for Attention-Deficit/Hyperactivity Disorder

This article provides an overview of the pharmacokinetic drug-drug interactions (DDIs) for agents prescribed for attention-deficit/hyperactivity disorder (ADHD). Polypharmacy in the treatment of patients with ADHD leads to high exposures to DDIs and possibly adverse safety outcomes. We performed a systematic search of DDI reports for ADHD agents in Embase and Medline. We also searched for agents in the pharmacological pipeline, which include (1) mazindol, molindone and viloxazine, which were previously prescribed for other indications; (2) centanafadine and AR-08, never before approved; and (3) two extracts (Polygala tenuifolia extract and the French maritime pine bark extracts). The identified literature included case reports, cross-sectional, cross-over and placebo-controlled studies of patient cohorts and healthy volunteers. The DDIs were classified as follows: ADHD agents acting as perpetrators, i.e., affecting the clearance of co-prescribed agents (victim drugs), or ADHD agents being the victim drugs, being affected by other agents. Ratios for changes in pharmacokinetic parameters before and after the DDI were used as a rough estimate of the extent of the DDI. Alcohol may increase plasma dextroamphetamine concentrations by presystemic effects. Until studies are done to orient clinicians regarding dosing changes, clinicians need to be aware of the potential for cytochrome P450 (CYP) 2D6 inhibitors to increase amphetamine levels, which is equivalent to increasing dosages. Atomoxetine is a wide therapeutic window drug. The CYP2D6 poor metabolizers who do not have CYP2D6 activity had better atomoxetine response, but also an increased risk of adverse effects. CYP2D6 inhibitors have been used to increase atomoxetine response in CYP2D6 extensive metabolizers. Guanfacine is mainly metabolized by CYP3A4, which can be induced and inhibited. The package insert recommends that in guanfacine-treated patients, after adding potent CYP3A4 inducers, the guanfacine dose should be doubled; after adding potent CYP3A4 inhibitors the guanfacine dose should be halved. Based on a phenobarbital case report and our experience with CYP3A4-metabolized antipsychotics, these correction factors may be too low. According to two case reports, carbamazepine is a clinically relevant inducer of methylphenidate (MPH). A case series study suggested that MPH may be associated with important elevations in imipramine concentrations. Due to the absence of or limitations in the data, no comments for clinicians can be provided on the pharmacokinetic DDIs for clonidine, centanafadine, mazindol, molindone, AR-08, P. tenuifolia extract and the French maritime pine bark extracts. According to currently available data, clinicians should not expect that ADHD drugs modify each other's serum concentrations. A summary table for clinicians provides our current recommendations on pharmacokinetic DDIs of ADHD agents based on our literature review and the package inserts; whenever it was possible, we provide information on serum concentrations and dose correction factors. There will be a need to periodically update these recommendations and these correction factors as new knowledge becomes available.

The Effect of Body Weight Changes on Total Plasma Clozapine Concentrations Determined by Applying a Statistical Model to the Data From a Double-Blind Trial

PURPOSE/BACKGROUND

Some therapeutic drug monitoring studies suggest that increased weight is associated with small increases in clozapine concentrations. The goal of this study was to reanalyze a US double-blind study using a sophisticated statistical model to test whether weight gains from baseline or increases in percentage of body fat from baseline, computed from a published equation, are associated with increased total plasma clozapine concentrations after controlling for the effects of smoking and sex.

METHODS/PROCEDURES

Using data from a multidosage randomized double-blind US clozapine trial previously published, a random intercept linear model of steady-state total plasma clozapine concentrations was fitted to 424 concentrations from 47 patients.

FINDINGS/RESULTS

After adjusting for sex and smoking, (1) a 1-kg gain in body weight during clozapine treatment was significantly associated with a 1.4% increase in total plasma clozapine concentrations (95% confidence interval = 0.55 to 2.3) and (2) a 1-point increase in percentage of body fat during clozapine treatment was significantly associated with a 5.4% increase in total clozapine concentration (2.5 to 8.3) in females and 1.4% (-1.1 to 4.0) in males.

IMPLICATIONS/CONCLUSIONS

As hypothesized, weight increases during clozapine treatment, which probably reflect increases in fat tissue, were associated with increases in total plasma concentrations. Pending further replication in other samples, it seems likely that clozapine may deposit in body fat and that this may decrease clozapine clearance. This change may be small in most patients but may be clinically relevant in females with major gains in body fat.

Estimating individual benefits of medical or behavioral treatments in severely ill patients

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

The Art of Pharmacotherapy: Reflections on Pharmacophobia

PURPOSE/BACKGROUND

This commentary deals with the neglected issue of the art of psychopharmacology by recounting the authors' journeys.

METHODS/PROCEDURES

First, a model of medical science situated within the history of medicine is described including (1) a limitation of the mathematical model of science, (2) the distinction between mechanistic science and mathematical science, (3) how this distinction is applied to medicine, and (4) how this distinction is applied to explain pharmacology to psychiatrists. Second, the neglected art of psychopharmacology is addressed by explaining (1) where the art of psychopharmacotherapy was hiding in the first author's psychopharmacology research, (2) how the Health Belief Model was applied to the art of medicine, (3) how the second author became interested in the Health Belief Model, and (4) his studies introducing the Health Belief Model in psychopharmacology. The authors' collaboration led to: (1) study of the effect of pharmacophobia on poor adherence and (2) reflection on the limits of the art of psychopharmacology.

FINDINGS/RESULTS

Low adherence was found in 45% (116/258) of psychiatric patients with pharmacophobia versus 22% (149/682) in those with no pharmacophobia, providing an odds ratio of 2.9 (95% confidence interval, 2.2-4.0) and an adjusted odds ratio of 2.5 (95% confidence interval, 1.8-3.5) after adjusting for other variables contributing to poor adherence.

IMPLICATIONS/CONCLUSIONS

Different cognitive patterns in different patients may contribute to poor adherence. Specific interventions targeting these varying cognitive styles may be needed in different patients to improve drug adherence.

Reviving Research on Medication Attitudes for Improving Pharmacotherapy: Focusing on Adherence

There is little current interest in research into patients' attitudes toward medications. In the 1960s, psychiatric researchers including Uhlenhuth, Rickels and Covi focused on this area, but this research topic needs to be revived in the 21st century. The Health Belief Model may hold potential for doing this. This model was initially developed by 2 health psychologists, Rosenstock and Becker, to explain why patients did not follow medical interventions. The application of this model to study medication adherence in psychiatric outpatients has provided multiple findings including the conclusions that adherence is associated with: (1) the balance between internal and external health control beliefs, (2) psychological reactance, (3) patients' attitudes toward prescribed drug treatment in general and (4) the balance between the necessity of taking medications versus the concerns derived from adverse drug reactions (ADRs). Poor adherence is associated with several cognitive styles of patients, including: (1) high internal and external health control beliefs (patients who feel that their health is controlled both by external factors and their own beliefs), (2) higher psychological reactance, (3) pharmacophobia (present in 1/6 patients) and (4) skepticism about medications (a high concern for ADRs and a low belief in the necessity of taking medications). All of these findings suggest that shared decision-making is particularly important in fostering adherence in psychiatric patients. Two wider applications of this article can be made: (1) opening psychiatry to advances in clinical psychology and (2) expanding studies on attitudes toward medications to other medical disciplines.

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models

We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained. Copyright © 2016 John Wiley & Sons, Ltd.

The effects of antiepileptic inducers in neuropsychopharmacology, a neglected issue. Part I: A summary of the current state for clinicians

The literature on inducers in epilepsy and bipolar disorder is seriously contaminated by false negative findings. This is part i of a comprehensive review on antiepileptic drug (AED) inducers using both mechanistic pharmacological and evidence-based medicine to provide practical recommendations to neurologists and psychiatrists concerning how to control for them. Carbamazepine, phenobarbital and phenytoin, are clinically relevant AED inducers; correction factors were calculated for studied induced drugs. These correction factors are rough simplifications for orienting clinicians, since there is great variability in the population regarding inductive effects. As new information is published, the correction factors may need to be modified. Some of the correction factors are so high that the drugs (e.g., bupropion, quetiapine or lurasidone) should not co-prescribed with potent inducers. Clobazam, eslicarbazepine, felbamate, lamotrigine, oxcarbazepine, rufinamide, topiramate, vigabatrin and valproic acid are grouped as mild inducers which may (i)be inducers only in high doses; (ii)frequently combine with inhibitory properties; and (iii)take months to reach maximum effects or de-induction, definitively longer than the potent inducers. Potent inducers, definitively, and mild inducers, possibly, have relevant effects in the endogenous metabolism of (i)sexual hormones, (ii) vitamin D, (iii)thyroid hormones, (iv)lipid metabolism, and (v)folic acid.

Can valproic acid be an inducer of clozapine metabolism?

INTRODUCTION

Prior clozapine studies indicated no effects, mild inhibition or induction of valproic acid (VPA) on clozapine metabolism. The hypotheses that (i) VPA is a net inducer of clozapine metabolism, and (ii) smoking modifies this inductive effect were tested in a therapeutic drug monitoring study.

METHODS

After excluding strong inhibitors and inducers, 353 steady-state total clozapine (clozapine plus norclozapine) concentrations provided by 151 patients were analyzed using a random intercept linear model.

RESULTS

VPA appeared to be an inducer of clozapine metabolism since total plasma clozapine concentrations in subjects taking VPA were significantly lower (27% lower; 95% confidence interval, 14-39%) after controlling for confounding variables including smoking (35% lower, 28-56%).

DISCUSSION

Prospective studies are needed to definitively establish that VPA may (i) be an inducer of clozapine metabolism when induction prevails over competitive inhibition, and (ii) be an inducer even in smokers who are under the influence of smoking inductive effects on clozapine metabolism.

Clobazam therapeutic drug monitoring: a comprehensive review of the literature with proposals to improve future studies

BACKGROUND

Clobazam was recently approved for Lennox-Gastaut syndrome in the United States. There is no published review article focused on clobazam therapeutic drug monitoring (TDM) in English.

METHODS

More than 200 clobazam articles identified by a PubMed search were carefully reviewed for information on clobazam pharmacokinetics. Clobazam is mainly metabolized by a cytochrome P450 (CYP) isoenzyme, CYP3A4, to its active metabolite, N-desmethylclobazam. Then, N-desmethylclobazam is mainly metabolized by CYP2C19 unless the individual has no CYP2C19 activity [poor metabolizer (PM)].

RESULTS

Using a mechanistic approach to reinterpret the published findings of steady-state TDM and single-dosing pharmacokinetic studies, 4 different serum clobazam concentration ratios were studied. The available limited steady-state TDM data suggest that the serum N-desmethylclobazam/clobazam ratio can be useful for clinicians, including identifying CYP2C19 PMs (ratio >25 in the absence of inhibitors). There are 3 possible concentration/dose (C/D) ratios. The clobazam C/D ratio has the potential to measure the contribution of CYP3A4 activity to the clearance of clobazam from the body. The N-desmethylclobazam C/D ratio does not seem to be a good measure of clobazam clearance and should be substituted with the total (clobazam + N-desmethylclobazam) C/D ratio.

CONCLUSIONS

Future clobazam TDM studies need to use trough concentrations after steady state has been reached (>3 weeks in normal individuals and several months in CYP2C19 PMs). These future studies need to explore the potential of clobazam and total C/D ratios. Better studies on the relative potency of N-desmethylclobazam compared with the parent compound are needed to provide weighted total serum concentrations that correct for the possible lower N-desmethylclobazam pharmacodynamic activity. Standardization and more studies of C/D ratios from clobazam and other drugs can be helpful to move TDM forward.