The results of this meta-analysis indicate that increased exposure to sunitinib is associated with improved clinical outcomes (longer TTP, longer OS, greater chance of antitumor response), as well as some increased risk of adverse effects. A sunitinib 50-mg starting dose seems reasonable, providing clinical benefit with acceptably low risk of adverse events.

Axitinib is a potent and selective inhibitor of vascular endothelial growth factor receptors 1, 2, and 3, approved for second-line therapy for advanced renal cell carcinoma (RCC). Axitinib population pharmacokinetic and pharmacokinetic/pharmacodynamic relationships were evaluated. Using nonlinear mixed effects modeling with pooled data from 383 healthy volunteers, 181 patients with metastatic RCC, and 26 patients with other solid tumors in 17 trials, the disposition of axitinib was best described by a 2-compartment model with first-order absorption and a lag time, with estimated mean systemic clearance (CL) of 14.6 L/h and central volume of distribution (Vc) of 47.3 L. Of 12 covariates tested, age over 60 years and Japanese ethnicity were associated with decreased CL, whereas Vc increased with body weight. However, the magnitude of predicted changes in exposure based on these covariates does not warrant dose adjustments. Multivariate Cox proportional hazard regression and logistic regression analyses showed that higher exposure and diastolic blood pressure were independently associated with longer progression-free and overall survivals and higher probability of partial response in metastatic RCC patients. These findings support axitinib dose titration to increase plasma exposure in patients who tolerate axitinib, and also demonstrate diastolic blood pressure as a potential marker of efficacy.

The model has aided the analysis and interpretation of the clinical data. The use of a model-based approach for selecting doses can accelerate drug development by replacing some arms or trials with simulations.

We review approaches used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP). Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the “null scenario” or to update joint smoking and MRTP habits in the “alternative scenario”. The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between the scenarios. Estimating the effects on population health depends on frequency of use of the MRTP and smoking, and on the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized. Despite methodological differences, most modellers have assumed that the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, is very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction.