The use of modeling and simulation to guide clinical development of olmesartan medoxomil in pediatric subjects.

Modeling and simulation were used extensively in the development of an indication for the use of olmesartan medoxomil in pediatric patients with hypertension. Simulations based on models developed in adult patients indicated that two dose groups were sufficient to estimate a dose-response relationship, thereby reducing by one-third the number of subjects required for the phase III pediatric study. Model-based predictions for blood pressure reduction agreed with the observed results of the subsequent phase III study, showing statistically significant dose-response relationships with respect to both systolic and diastolic blood pressure. Previously established pharmacokinetic and exposure-response relationships in adults, adjusted for the influence of body weight on clearance (wt(0.80)), were confirmed in the pediatric population. Together, these findings support an olmesartan dosing recommendation in pediatric subjects aged 6 to 16 years of 10 mg for subjects weighing <35 kg and 20 mg for those weighing ≥35 kg.

Use of an exposure-response model to aid early drug development of an oral sphingosine 1-phosphate receptor modulator.

Pharmacokinetic (PK) and exposure-response modeling of a selective sphingosine 1-phosphate receptor-1 modulator (CS-0777) was conducted in an iterative process to guide early clinical development decisions. A model based on preclinical data from monkeys was extrapolated to humans to support a single ascending dose (SAD) study. The model was updated after each cohort, providing guidance on both maximal inhibition and time to recovery for lymphocyte counts. A 2-compartment PK model with first-order absorption and elimination was found to describe the monkey and human datasets. The relationship between lymphocyte counts and active metabolite (M-1) concentrations was modeled via an indirect response model, whereby M-1 inhibited the reentry of lymphocytes to the circulation. The indirect-response model based on SAD data had an Imax of approximately 85% and an IC50 of 0.24 ng/mL. Additionally, based on SAD data, similar models were developed for lymphocyte subsets, including CD4 cells. Subsequently, simulations were utilized to design a multiple ascending dose study with adaptive dosing regimens that would meet targeted pharmacodynamic (PD) response thresholds (eg, minimum 40% reduction in lymphocytes) while maintaining CD4 counts above a reasonable safety threshold. In conclusion, model-based development and use of adaptive designs for dose optimization can reduce the time and number of subjects needed in early clinical development.

Assessing the public health benefits of reduced ozone concentrations.

In this paper we examine scientific evidence and related uncertainties in two steps of benefit-cost analyses of ozone reduction: estimating the health improvements attributable to reductions in ozone and determining the appropriate monetary values of these improvements. Although substantial evidence exists on molecular and physiologic impacts, the evidence needed to establish concentration-response functions is somewhat limited. Furthermore, because exposure to ozone depends on factors such as air conditioning use, past epidemiologic studies may not be directly applicable in unstudied settings. To evaluate the evidence likely to contribute significantly to benefits, we focus on four health outcomes: premature mortality, chronic asthma, respiratory hospital admissions, and minor restricted activity days. We determine concentration-response functions for these health outcomes for a hypothetical case study in Houston, Texas, using probabilistic weighting reflecting our judgment of the strength of the evidence and the possibility of confounding. We make a similar presentation for valuation, where uncertainty is due primarily to the lack of willingness-to-pay data for the population affected by ozone. We estimate that the annual monetary value of health benefits from reducing ozone concentrations in Houston is approximately $10 per person per microgram per cubic meter (24-hr average) reduced (95% confidence interval, $0.70-$40). The central estimate exceeds past estimates by approximately a factor of five, driven by the inclusion of mortality. We discuss the implications of our findings for future analyses and determine areas of research that might help reduce the uncertainties in benefit estimation.

Assessing the impact of differential measurement error on estimates of fine particle mortality.

In air pollution epidemiology, error in measurements of correlated pollutants has been advanced as a reason to distrust regressions that find statistically significant weak associations. Much of the related debate in the literature and elsewhere has been qualitative. To promote quantitative evaluation of such errors, this paper develops an air pollution time-series model based on correlations among unit-normal variables. Assuming there are no other sources of bias present, the model shows the expected amount of relative bias in the regression coefficients of a bivariate regression of coarse and fine particulate matter measurements on daily mortality. The model only requires information on instrumental error and spatial variability, along with the observed regression coefficients and information on the true fine-course correlation. Analytical results show that if one pollutant is truly more harmful than the other, then it must be measured more precisely than the other in order not to bias the ratio of the fine and course regression coefficients. Utilizing published data, a case study of the Harvard Six-Cities study illustrates use of the model and emphasizes the need for data on spatial variability across the study area. Current epidemiology time-series regressions can use this model to address the general concern of correlated pollutants with differing measurement errors.