PP012—Not-in-trial simulations: Predicting cardiovascular risk from clinical trials to real life conditions
Safety signals regarding drug effects on cardiac conductivity have been found after the approval of medicines, despite evidence suggesting that they could be deemed safe during development. Such a discrepancy may be caused by the known differences between real-life conditions and the so-called clinical trial population, which represents a subset of the target patient population, as defined by the many inclusion and exclusion criteria in clinical protocols. No formal quantitative method is
... ble to assess the implications of differences betwen experimental conditions and therapeutic use of the drug. This study demonstrates the relevance of pharmacokineticpharmacodynamic (PKPD) relationships to characterize drug-induced QTc-interval prolongation and to assess the implications discrepancies between clinical trials and real life conditions. Patients (or Materials) and Methods: d,l-sotalol data from healthy subjects and from the Rotterdam Study cohort were used as paradigm compound to assess treatment response in a Phase I setting and in real-life situation, respectively. Using not-trial-simulation principles and nonlinear mixed effects modeling, drug-induced effects were estimated across populations to discriminate the potential implications of other relevant factors. Results: Inclusion criteria were shown to restrict the representativeness of the trial population compared with real-life conditions. A significant part of the typical patient population was excluded from trials based on weight and baseline QT-interval measurements. Relative risk was statistically different between sotalol users with and without heart failure, hypertension, diabetes, and myocardial infarction. Although drug-induced effects do cause an increase in relative risk of QT interval prolongation, the presence of diabetes represented an increase in relative risk from 4.0 to 6.5, whereas for myocardial infarction it increased to 15.5 (P < 0.01). Conclusion: Our results show that drug-induced effects on QTcinterval do not fully explain the distribution of QTc values observed in the population. The increased prevalence of high QTc values in a real-life population can be assigned to comorbidities and concomitant medications. This discrepancy substantiates the need to account for these factors when evaluating cardiovascular risk of novel medicinal products. Moreover, the concept of not-in-trial simulations can be used as a tool for risk management, integrating pharmacokineticpharmacodynamic relationships as the basis for discriminating drug-specific properties from other relevant factors in noncontrolled settings.