be a determining factor in the progression of the clinical trial, clinical outcome when plated siege to quantify or hard-to short-term studies. Another important advantage of model-based methods is that they have access to functional components and structures of a biological system that can not be identified experimentally erm Equalized. The best example of such a concept is the quantification BX-912 of insulin sensitivity, as defined by the index of insulin sensitivity. The loss of insulin sensitivity is not due to the progression of diabetes directly from the glucose and insulin levels are measured in the plasma is derived from a model. In addition, M & S provide a shield U the Fa One whose drug Se treatment, the disease may change to VER.
Clinical trial simulation in contrast to the meta-analysis, clinical trial simulation allows the evaluation of the impact of a number of design features on the statistical power to an effect of treatment before exposure to patients recognize an experimental drug. In an area where most clinical studies, a conservative design, Eur J Clin Pharmacol, 67: S75 S86 S81 This BX-912 702674-56-4 methodology provides a unique opportunity to evaluate innovative designs. Pleased t, the power calculations that Stichprobengr E and variability perform Do take into account criteria t, k CTS can calculate the power in the light from a plurality of other factors. Generally used CTS two types of models. First, a model of drug action taken consideration Including Lich pharmacokinetic and pharmacodynamic factors. In the chronic model also takes into account the progression of the disease.
Unfortunately, this prevents the lack of knowledge about the mechanisms of response to treatment in many therapeutic indications of the development of mechanistic models PKPD. Therefore, the examples often refer to classical statistical models, such as the mixed model for repeated measures. These statistical models have the disadvantage that they often can not incorporate the effect of concentration and therefore can not make conclusions about the age-related differences in pharmacokinetics, as is the case for p lkerungsgruppen Pediatric Bev. Second, given an execution model CTS First Instance. These models simulate other important aspects of the test, such as differences abandoned, and protocol compliance.
In this way we may use all m Matched study design results in a candidate to be determined, that such studies for comparison of designs in a strictly quantitative. So far only very few examples exist in which the relevant factors were evaluated FA design We prospectively as part of planning a p Pediatric study. It is also important to note that the CTS, the study of factors that are not being investigated by the meta-analysis or empirical design can k Allowed. Rst K Can designs that have not been implementations not Fig. 3 Modeling and simulation can be used to support the prediction and extrapolation of the data on the early clinical development. The graphs show the impact of pharmacokinetic differences in systemic exposure in children of different age groups. Based on pharmacokinetic parameters of systemic exposure can be simulated for a range of doses. Note the nonlinearity of t in the range of doses for different age groups. Lines represent the proportion of patients who gewichtsm Achieve To ig to the following target exposure criteria different doses of abacavir. circles10 kg, 20 kg, by triangles30 squares40 kg kg. Cella et al picture. 4 The diagram shows the major components of a clinical trial simulation. In the model-ba