Patrice Nony

Depressive symptoms are associated with unhealthy lifestyles in hypertensive patients with the metabolic syndrome

Objective Metabolic syndrome results from a complex interaction between lifestyle and genetic factors. Among this population, adhesion to healthy recommendations is a cornerstone of cardiovascular disease prevention. We examined the association between depression and multiple unhealthy behaviours in hypertensive patients with the metabolic syndrome. Research design and methods Eight hundred and forty consecutive hypertensive subjects with the metabolic syndrome were studied in our secondary-care centre. Separated scores reflecting unhealthy behaviours (physical inactivity, smoking and unhealthy diet) were combined to produce a global unhealthy lifestyle score. The Hospital Anxiety and Depression scale was used to assess and quantify depression. We performed a separate analysis for each sex. Results The prevalence of depression (13.0 versus 7.3%, P < 0.001) was greater in women than in men. Presence of depression was significantly associated in both men and women with unhealthy diet (in particular, excessive cholesterol and total caloric intake) but also with decreased physical activity in men and with smoking habits in women. In both sexes, the global unhealthy lifestyle score, reflecting a cluster of unhealthy behaviours, was positively correlated with the depression score. In multivariate analysis, the depression score appeared in both sexes as an independent determinant of unhealthy lifestyle. Conclusions Among hypertensive subjects with the metabolic syndrome, depressive symptoms along a continuum of severity are independently associated with multiple unhealthy lifestyles. This suggests that even minor forms of depression may impact on adhesion to health behaviours beyond major depressive symptoms and/or psychiatric condition.

Experimental designs for small randomised clinical trials: An algorithm for choice

BackgroundSmall clinical trials are necessary when there are difficulties in recruiting enough patients for conventional frequentist statistical analyses to provide an appropriate answer. These trials are often necessary for the study of rare diseases as well as specific study populations e.g. children. It has been estimated that there are between 6,000 and 8,000 rare diseases that cover a broad range of diseases and patients. In the European Union these diseases affect up to 30 million people, with about 50% of those affected being children. Therapies for treating these rare diseases need their efficacy and safety evaluated but due to the small number of potential trial participants, a standard randomised controlled trial is often not feasible. There are a number of alternative trial designs to the usual parallel group design, each of which offers specific advantages, but they also have specific limitations. Thus the choice of the most appropriate design is not simple.MethodsPubMed was searched to identify publications about the characteristics of different trial designs that can be used in randomised, comparative small clinical trials. In addition, the contents tables from 11 journals were hand-searched. An algorithm was developed using decision nodes based on the characteristics of the identified trial designs.ResultsWe identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the evaluation of therapies in orphan diseases. The main characteristics and the advantages and limitations of these designs were summarised and used to develop an algorithm that may be used to help select an appropriate design for a given clinical situation. We used examples from publications of given disease-treatment-outcome situations, in which the investigators had used a particular trial design, to illustrate the use of the algorithm for the identification of possible alternative designs.ConclusionsThe algorithm that we propose could be a useful tool for the choice of an appropriate trial design in the development of orphan drugs for a given disease-treatment-outcome situation.

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.

Using Pharmacokinetic-Pharmacodynamic Relationships to Predict the Effect of Poor Compliance

Since it is difficult to improve patient compliance to drug prescriptions, an alternative is to select a drug with less consequences for poor compliance, that is, a drug that has the capacity of ‘forgiveness’. Forgiveness is the property of a drug which, when compared with another medicine with different pharmacokinetics and/or concentration-effect relationships, blunts the consequences of missing one or two doses in a row, or has a greater variability in the timing of intake. Simulations show that drugs with a concentration-effect relationship modelled with an effect compartment, for example a delayed response, have more forgiveness. A marker of forgiveness would be of some help for doctors deciding which drug to prescribe to patients who are poor compilers.

A pharmacokinetic simulation model for ivabradine in healthy volunteers.

Ivabradine is a novel bradycardic agent that has been developed for the prevention of angina. Ivabradine has an active metabolite S-18982. The aim of this study is to develop a pharmacokinetic simulation model. Pharmacokinetic data from two studies were pooled and included data from a total of 66 healthy male volunteers. The data were collected following single dose intravenous and multiple dose oral administration of ivabradine. The multiple dose regimens were administered every 12 h and there were seven active dosing levels. The modelling was performed using the NONMEM software. The model was assessed in terms of its ability to describe the original data set used in its construction and also data arising from a different clinical pharmacology study involving 12 additional subjects. The pharmacokinetics of ivabradine and S-18982 were best described by two linked two compartment intravenous bolus and first-order input, with first-pass loss, and first-order output model. When the model was used for simulation it produced an adequate description of both the original data and data arising from a different clinical pharmacology study.

Bridging the gap between therapeutic research results and physician prescribing decisions: knowledge transfer, a prerequisite to knowledge translation

Background: A wide gap continues to exist between available therapeutic research results and physician’s prescribing. Numerous explanations account for this gap, but one central reason is the difficulty in transferring comprehensive research information to practicing clinicians. This problem arises from information overload and the growing complexity of research findings. We propose a multistep process that can be used to develop systems to bridge this information/prescription gap. The steps include: comprehensively collecting and summarizing clinical trial reports, scoring and ranking these according to their level of evidence, exploring and synthesizing the data using meta-analyses, summarizing these results, representing them in an easily understandable form, and transmitting the overview findings to prescribers at the time they need them. Discussion: This ambitious endeavor is needed to ensure that prescribers have access to pertinent research results for use in their prescription decisions. We demonstrate in this article that there are no theoretical or technical obstacles to make the proposed system workable.

Quantitative Prediction of the Impact of Drug Interactions and Genetic Polymorphisms on Cytochrome P450 2C9 Substrate Exposure

Background and ObjectiveCytochrome P450 (CYP) 2C9 is the most common CYP2C enzyme and makes up approximately onethird of total CYP protein content in the liver. It metabolises more than 100 drugs. The exposure of drugs mainly eliminated by CYP2C9 may be dramatically modified by drug–drug interactions (DDIs) and genetic variations. The objective of this study was to develop a modelling approach to predict the impact of genetic polymorphisms and DDIs on drug exposure in drugs metabolised by CYP2C9. We then developed dosing recommendations based on genotypes and compared them to current Epar/Vidal dosing guidelines.MethodsWe created two models. The genetic model was designed to predict the impact of CYP2C9 polymorphisms on drug exposure. It links the area under the concentration–time curve (AUC) ratio (mutant to wild-type patients) to two parameters: the fractional contribution of CYP2C9 to oral clearance in vivo (i.e. CR or contribution ratio), and the fractional activity of the allele combination with respect to the homozygous wild type (i.e. FA or fraction of activity). Data were available for 77 couples (substrate, genotype). We used a three-step approach: (1) initial estimates of CRs and FAs were calculated using a first bibliographic dataset; (2) external validation of these estimates was then performed through the comparison between the AUC ratios predicted by the model and the observed values, using a second published dataset; and (3) refined estimates of CRs and FAs were obtained using Bayesian orthogonal regression involving the whole dataset and initial estimates of CRs and FAs. Posterior distributions of AUC ratios, CRs and FAs were estimated using Monte-Carlo Markov chain simulation. The drug interaction model was designed to predict the impact of DDIs on drug exposure. It links the AUC ratio (ratio of drug given in combination to drug given alone) to several parameters: the CR, the inhibition ratio (IR) of an inhibitor, and the increase in clearance (IC) due to an inducer. Data were available for 80 DDIs. IRs and ICs were calculated using the interaction model and an external validation was performed. Doses adjustments were calculated in order to obtain equal values for drug exposure in extensive and poor metabolisers and then compared to Epar/Vidal dosing guidelines.ResultsCRs were assessed for 26 substrates, FAs for five genotype classes including CYP2C9*2 and *3 allelic variants, IRs for 27 inhibitors and ICs for two inducers. For the genetic model, the mean prediction error of AUC ratios was −0.01, while the mean prediction absolute error was 0.36. For the drug interaction model, the mean prediction error of AUC ratios was 0.01, while the mean prediction absolute error was 0.22. Of the 26 substrates and CYP2C9*2 and *3 variants investigated, 30 couples (substrate, genotype) lead to a dose adjustment, as opposed to only ten couples identified in the Epar/Vidal recommendations.ConclusionThese models were already used for CYP2D6. They are accurate at predicting the impact of drug interactions and genetic polymorphisms on CYP2C9 substrate exposure. This approach will contribute to the development of personalized medicine, i.e. individualized drug therapy with specific dosing recommendations based on CYP genotype or drug associations.

New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear

BACKGROUND AND OBJECTIVES A relation between the size of treatment efficacy and severity of the disease has been postulated and observed as linear for a few therapies. We have called this relation the effect model. Our objectives were to demonstrate that the relation is general and not necessarily linear. STUDY DESIGN AND SETTING We extend the number of observed effect model. Then we established three numerical models of treatment activity corresponding to the three modes of action we have identified. Using these models, we simulated the relation. RESULTS Empirical evidence confirms the effect model and suggests that it may be linear over a short range of event frequency. However, it provides an incomplete understanding of the phenomenon because of the inescapable limitations of data from randomized clinical trials. Numerical modeling and simulation show that the real effect model is likely to be more complicated. It is probably linear only in rare instances. The effect model is general. It depends on factors related to the individual, disease and outcome. CONCLUSION Contrarily to common, assumption, since the effect model is often curvilinear, the relative risk cannot be granted as constant. The effect model should be taken into account when discovering and developing new therapies, when making, health care policy decisions or adjusting clinical decisions to the patient risk profile.

SCORE should be preferred to Framingham to predict cardiovascular death in French population

Background Numerous studies have examined the validity of available scores to predict the absolute cardiovascular risk. Design We developed a virtual population based on data representative of the French population and compared the performances of the two most popular risk equations to predict cardiovascular death: Framingham and SCORE. Methods A population was built based on official French demographic statistics and summarized data from representative observational studies. The 10-year coronary and cardiovascular death risk and their ratio were computed for each individual by SCORE and Framingham equations. The resulting rates were compared with those derived from national vital statistics. Results Framingham overestimated French coronary deaths by 2.8 in men and 1.9 in women, and cardiovascular deaths by 1.5 in men and 1.3 in women. SCORE overestimated coronary death by 1.6 in men and 1.7 in women, and underestimated cardiovascular death by 0.94 in men and 0.85 in women. Our results revealed an exaggerated representation of coronary among cardiovascular death predicted by Framingham, with coronary death exceeding cardiovascular death in some individual profiles. Sensitivity analyses gave some insights to explain the internal inconsistency of the Framingham equations. Conclusion Evidence is that SCORE should be preferred to Framingham to predict cardiovascular death risk in French population. This discrepancy between prediction scores is likely to be observed in other populations. To improve the validation of risk equations, specific guidelines should be issued to harmonize the outcomes definition across epidemiologic studies. Prediction models should be calibrated for risk differences in the space and time dimensions.

Methodological choices for the clinical development of medical devices.

Clinical evidence available for the assessment of medical devices (MDs) is frequently insufficient. New MDs should be subjected to high quality clinical studies to demonstrate their benefit to patients. The randomized controlled trial (RCT) is the study design reaching the highest level of evidence in order to demonstrate the efficacy of a new MD. However, the clinical context of some MDs makes it difficult to carry out a conventional RCT. The objectives of this review are to present problems related to conducting conventional RCTs and to identify other experimental designs, their limitations, and their applications. A systematic literature search was conducted for the period January 2000 to July 2012 by searching medical bibliographic databases. Problems related to conducting conventional RCTs of MDs were identified: timing the assessment, eligible population and recruitment, acceptability, blinding, choice of comparator group, and learning curve. Other types of experimental designs have been described. Zelen’s design trials and randomized consent design trials facilitate the recruitment of patients, but can cause ethical problems to arise. Expertise-based RCTs involve randomization to a team that specializes in a given intervention. Sometimes, the feasibility of an expertise-based randomized trial may be greater than that of a conventional trial. Cross-over trials reduce the number of patients, but are not applicable when a learning curve is required. Sequential trials have the advantage of allowing a trial to be stopped early depending on the results of first inclusions, but they require an independent committee. Bayesian methods combine existing information with information from the ongoing trial. These methods are particularly useful in situations where the number of subjects is small. The disadvantage is the risk of including erroneous prior information. Other types of experimental designs exist when conventional trials cannot always be applied to the clinical development of MDs.