Pascal Girard

Characterizing patterns of drug-taking behavior with a multiple drug regimen in an AIDS clinical trial.

Objective:To characterize drug-taking behavior using continuous electronic monitoring in an AIDS clinical trial. Setting:This was a substudy of AIDS Clinical Trials Group (ACTG) protocol 175, a phase II/III study of dideoxynucleoside monotherapy versus combination therapy in asymptomatic HIV-positive subjects. Participants were required to comply with regimens containing zidovudine, zalcitabine and didanosine, or matching placebos; the total daily pill count was 16. Design:For participants at two ACTG 175 sites, electronic devices were used to monitor drug-taking behavior of all study medications over a period of approximately 90 days. Measurements:Four indices of drug-taking behavior were calculated and their distributions and relationship to the prescribed regimen were examined. Results:Data from 41 subjects were analyzed. Of the prescribed doses of zidovudine, zalcitabine and didanosine, 88, 84 and 82%, respectively, were taken. Of these, 55, 66 and 79%, respectively, were taken at the prescribed dosing frequency. The median percentage of days on which participants failed to take any of the doses was 2–5%. There was a trend towards lower adherence in the combination therapy arms compared with those assigned to receive monotherapy. In this analysis, older patients demonstrated better adherence, although patient characteristics, in general, were poorly predictive of adherence. Conclusion:Drug-taking behavior for all three active study medications differed from that prescribed. One result of this erratic adherence was that study participants sustained little antiretroviral effect during more than 25% of the monitoring period.

Do we need full compliance data for population pharmacokinetic analysis

For population pharmacokinetic analysis of multiple oral doses one of the key issues is knowing as precisely as possible the dose inputs in order to fit a model to the input-output (dose-concentration) relationship. Recently developed electronic monitoring devices, placed on pill containers, permit precise records to be obtained over months, of the time/date opening of the container. Such records are reported to be the most reliable measurement of drug taking behavior for ambulatory patients. To investigate strategies for using and summarizing this new abundant information, a Markov chain process model was developed, that simulates compliance data from real data from electronically monitored patients, and data simulations and analyses were conducted. Results indicate that traditional population pharmacokinetic analysis methods that ignore actual dosing information tend to estimate biased clearance and volume and markedly overestimate random interindividual variability. The best dosing information summarization strategies consist of initially estimating population pharmacokinetic parameters, using no covariates and only a limited number of dose records, the latter chosen based on an a priori estimate of the half-life of the drug in the compartment of interest; then resummarizing the dose records using either population or individual posterior Bayes parameter estimates from the first population fit; and finally reestimating the population parameters using the newly summarized dose records. Such summarization strategies yield the same parameter estimates as using full dosing information records while reducing by at least 75% the CPU time needed for a population pharmacokinetic analysis.

An effect model for the assessment of drug benefit: example of antiarrhythmic drugs in postmyocardial infarction patients.

Summary An effect model is a function that defines the relationship between the clinical efficacy of a treatment and specific covariates. The simplest effect model defines the probability of failure in treated patients as a linear function of the probability for these patients if they received no treatment. We used this approach to explore the effects of Class I antiarrhythmic agents in patients after myocardial infarction. Evidence from one large trial, the Cardiac Arrhythmic Suppression Trial (CAST), and the pooling of data from several smaller trials suggests that these agents have harmful effects in postmyocardial infarction patients. The relevance of results from pooled data is dependent on the homogeneity of the trials and is assessed by a heterogeneity test that is dependent on the analytical method used, i.e., odds ratio or rate difference methods, which correspond to two different effect models. We have developed an effect model that considers both iatrogenic effects of these drugs, i.e., depression of ventricular function and arrhythmogenic effects. When applied to the data from 13 published trials (including CAST), we found that these drugs may be beneficial in high-risk patients (with a 1-year mortality rate of ≥15%) and that the background lethal iatrogenic effect is likely to affect low- and very low-risk patients (1-year mortality rate of ≤5%). The accuracy of the proposed model was confirmed with use of the results from the recent CAST II study.

Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris.

Ivabradine is a new bradycardic agent with a potential indication for stable angina pectoris. To investigate the best compromise between efficacy, safety, drug regimen, and number of patients to include in a phase III study, we conducted Monte Carlo simulations using a full therapeutic model. The binary clinical outcome, chest pain, was simulated using a physiologic model in which the coronary reserve was derived from the heart rate. Safety was defined as being heart rate dependent. Using real data to build a pharmacokinetic–pharmacodynamic model controlling drug effect (i.e., heart rate decrease), and resampling heart rate profiles from the database, 100 clinical trials (N=200) were simulated for five oral doses (2.5, 5, 10, 20, and 40 mg QD or BID) of ivabradine. Only 25% of the simulated trials showed a significant effect of ivabradine with doses up to 10 mg QD, and 48 and 55% of the trials with doses of 10 mg BID and 20 mg QD, respectively, and more than 80% of the trials with a 40 mg daily dose. For safety, 4% of patients had at least one adverse event in the untreated group, and from 5 to 13% in the treated groups for the lowest to the highest dose, respectively. The number of subjects to include in a future trial to obtain a 15% decrease in chest pain under the assumption of a 68% base risk, is 239 subjects per group with 10 mg BID or 196 with 20 mg QD. These results illustrate how clinical trial simulations including a PK/PD model as well as a physiopathologic mechanistic model, describing the relationship between the intermediate and clinical endpoint, and the resampling of real patients from a large database can help in designing future phase III trials.

Inter‐Study Variability in Population Pharmacokinetic Meta‐Analysis: When and how to Estimate It?

Population pharmacokinetic analysis is being increasingly applied to individual data collected in different studies and pooled in a single database. However, individual pharmacokinetic parameters may change randomly from one study to another. In this article, we show by simulation that neglecting inter-study variability (ISV) does not introduce any bias for the fixed parameters or for the residual variability but may result in an overestimation of inter-individual (IIV) variability, depending on the magnitude of the ISV. Two random study-effect (RSE) estimation methods were investigated: (i) estimation, in a single step, of the three-nested random effects (inter-study, inter-individual and residual variability); (ii) estimation of residual variability and a mixture of ISV and IIV in the first step, then separation of ISV from IIV in the second. The one-stage RSE model performed well for population parameter assessment, whereas, the two-stage model yielded good estimates of IIV only with a rich sampling design. Finally, irrespective of the method used, ISV estimates were valid only when a large number of studies was pooled. The analysis of one real data set illustrated the use of an ISV model. It showed that the fixed parameter estimates were not modified, whether an RSE model was used or not, probably because of the homogeneity of the experimental designs of the studies, and suggest no study-effect in this example.

Clockwise hysteresis or proteresis

The authors propose the word “proteresis” to designate the clockwise hysteresis, i.e., when an effect increases more rapidly than the observed drug concentrations. Such a phenomenon has been recently described for aspirin and nicotine. Indeed hysteresis means “which comes after,” while “proteresis,” the greek symmetrical word, means “which comes earlier,” a more appropriate term for the described situation.

Protocol Deviations and Execution Models

An important goal of clinical trial simulation (CTS) is to develop well-designed protocols that will maximize the ability to address the stated aim(s) of a proposed clinical trial. The �?rst step in this process is to identify a useful input-output model (IO), including the model structure and its parameters, which will adequately reproduce salient characteristics that clinicians wish to observe in a future clinical study (see Chapter 2). Examples of such characteristics include drug (and metabolite) concentrations, biomarkers of therapeutic or toxicological response (e.g., changes in serum cholesterol, blood pressure, CD4 cell counts, coagulation time, neutrophil counts, hepatic and renal markers, QT prolongation or the incidence of an event, such as drug-induced rash) or clinical outcomes (e.g., time to AIDS conversion, survival time, recovery from stroke, improvement in cognitive scales).

Dose-ranging trials: guidelines for data collection and standardized descriptions.

Protocols for dose ranging trials in healthy volunteers or patients can be described by the combination of an experimental design and one or more decision rules. Generally, the doses are chosen on the basis of an up-and-down method, until the maximum tolerated (affecting one or more physiological parameters) and the minimum effective doses are found. Despite the large number of possible protocols there is no standard for the description of the experimental design or the decision rule(s). We propose a series of variables that can be used to facilitate data collection and that can adequately and uniquely characterize most dose ranging protocols.

Heterogeneous effect of quinidine on the ventricular depolarization process assessed by the spatial velocity electrocardiogram of the QRS complex. Preliminary report of a new investigative method.

The negative conduction effect of quinidine on each of the successive phases of the ventricular depolarization was investigated using an original noninvasive method: the spatial velocity electrocardiogram of the QRS complex (SVECG-QRS). We performed a randomized placebo-controlled trial in 10 healthy subjects with a single oral dose of quinidine (330 mg) or placebo. Electrocardiographic acquisition and processing (220 recordings for the complete trial) were performed using the Lyon vectorcardiographic program. For each SVECG-QRS curve, the position of seven specific points from A (onset of QRS) to G (end of QRS) were determined precisely. The six successive time intervals between these points (AB-FG) and five velocity values (B-F) were then calculated. The QRS complex was longer under quinidine than placebo (102.4 +/- 1.6 vs. 100.3 +/- 1.5 ms). The difference was at the periphery of statistical significance (p = 0.05), and this lack of statistical difference may be mainly due to the low serum levels of quinidine obtained at the peak of the concentration (1.46 +/- 0.4 mg/1). All six QRS time intervals were longer under quinidine, but only the BC interval was significantly different (9.3 +/- 1.1 vs. 18.8 +/- 1.1 ms; p < 0.05) suggesting a more pronounced negative conduction effect at the onset of ventricular depolarization. No significant modifications were observed for the velocity values. We conclude that (1) the negative conduction effect of quinidine is heterogeneous, but a further study with a higher dose of quinidine (concentration-dependent effect) is required to confirm this hypothesis and (2) the spatial velocity electrocardiogram of the QRS complex allows a detailed analysis of the ventricular conduction phases. The results of the measurement were found to be reproducible. This noninvasive tool could be used in clinical practice to assess effects of antiarrhythmic drugs on successive ventricular depolarization phases.

Bioequivalence evaluation of a fixed combination of chloroquine and proguanil in a capsule formulation versus a standard medication.

To assess the bioequivalence between a test capsule with a fixed combination of chloroquine (CAS 54-05-7) and proguanil (CAS 500-92-5), and chloroquine and proguanil administered as separate tablets, an open two-sequence, two-period cross-over randomized study was performed in twelve healthy volunteers who received a single oral dose of 100 mg chloroquine and 200 mg proguanil either in the form of one capsule or the reference tablets. Biological samples (plasma, whole blood and erythrocytes) were collected up to 43 days after drug administration. The parent drugs and their main metabolites were analyzed using high performance liquid chromatography assay. Bioequivalence was assessed for whole blood and plasma AUC and Cmax of chloroquine, proguanil, cycloguanil and 4-chlorophenylbiguanide. Bioequivalence in erythrocytes was also established except for Cmax of chloroquine. While the differences for Cmax of chloroquine in erythrocytes may be related to technical problems during the erythrocyte sampling procedure (contamination with leukocytes), bioequivalence can be concluded from the plasma concentration data. Therefore, the use of a single capsule instead of one chloroquine tablet and two proguanil tablets daily can be proposed in order to increase the prophylactic compliance without decreasing the prophylactic efficacy.