Simulation trials

Trial Simulator Pharsight Corporation http //w ww.pharsight.com... 

Bonate PL. Clinical trial simulation in drug development. Pharm Res 2000 17 252-6. 

Finally, the minimally required number of molecular orientations (steps in cos0 and in < >) is determined experimentally by inspection of trial simulations as illustrated in Figure 6.5 on the now familiar high-spin heme spectrum too few orientations cause so-called mosaic artifacts, which must be eliminated by increasing the step numbers. In this particular example of the high-spin heme from Figure 6.2, the gx and -values are relatively close, and 25 steps in (p suffice, but gz is well separated and the number of steps in cos0 must be increased beyond 1000 to fully eliminate mosaic artifacts. 

Figure 5.5 Comparison of rocking curve (small squares) with second trial simulation (dashed line). The effect of adding diffuse scatter is shown by the solid line...

Jumbe, N., B. Yao, R. Rovetti, G. Rossi, and A.C. Heatherington. 2002. Clinical trial simulation of a 200-microg fixed dose of darbepoetin-a in chemotherapy-induced anemia. Oncology (WiUiston. Park) 16 37-44. 

Clinical trial simulation or computer-assisted trial design or biosimulation is not a revolutionary technique. Computer simulation has been used in the automotive... 

Some factors or covariates may cause deviations from the population typical value generated from system models so that each individual patient may have different PK/PD/disease progression profiles. The relevant covariate effects on drug/disease model parameters are identified in the model development process. Clinical trial simulations should make use of input/output models incorporating... 

Fig. 1.8 Clinical trial simulation of the response (%) in blinded data after enrolment of 50% of the patients.

Fig. 1.9 Clinical trial simulation of the dose-response of the response (% responders) in Phase III trials compared with the actual observed outcome in the three trials. Solid line median. Dotted lines 5th, 25th, 75th, and 95th percentiles. Each arm of a study enrolled about 200 patients.

Once the Phase III clinical trials were started, a blinded data survey was done when 50% of the patients were enrolled. This is not an uncommon practice. Blinded data can be used to assess the appropriateness of assumptions that were made when designing the trials, such as the overall response or variability, among others. However, to judge the observed data it is necessary to have an idea of what reasonably can be expected. Due to the multitude of factors involved, clinical trial simulations can help here. The observed blinded response rate was lower than the antipicitated value, but was well within what could be expected from the simulation results (Fig. 1.8). Finally, when the true outcome of the Phase III trial was compared with the predictions from the clinical trial simulations, an overall good agreement was observed (Fig. 1.9). 

Lockwood, P., Ewy, W., Hermann, D., Hol-ford, N. Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect an example in Alzheimer s disease. Pharm Res 2006, 23 2050-2059. 

Nestorov, I. A. Sensitivity analysis of pharmacokinetic and pharmacodynamic models in clinical trial simulation and design. In Kimko, H. C., Duffull, S. B eds. Simulation for designing clinical trials. A pharmacokinetic-pharmacodynamic modeling perspective. (Drugs and the pharmaceutical sciences, volume 127) Marcel Dekker, New York, 2003. 

Girard, P. Clinical trial simulation a tool for understanding study failures and preventing them. Basic Clin Pharmacol Toxicol 2005, 96 228-234. 

Current Clinical Trial Simulation in Drug Development... 

Dickinson, G.L. et al. Clinical trial simulation of the effect of CYP2D6 polymorphism on the antitussive response to dextromethorphan./ Pharm Sci 2007, 47 175-186. 

The Trial Simulator (Pharsight Corp., http //www.pharsight.com) is a comprehensive and powerful tool for the simulation of clinical trials. Population PK/PD models developed with tools mentioned in Section 17.10.3 can be implemented in a Trial Simulator. In addition, treatment protocols, inclusion criteria, and observations can be specified. Also covariate distribution models, compliance models, and drop-out models can be specified. All of these models can be implemented via a graphical user interface. For the analysis of simulation results a special version of S-Plus is implemented and results can also be exported in different formats, like SAS. 

During the trial phase, the simulation model can be used as a vehicle to define an effective test protocol. As stressed in the American Food and Drug Administration Critical Path Opportunities List, clinical trial simulation can predict efficient designs for development programs that reduce the number of trials and patients, improve decisions on dosing, and increase informativeness [18]. 

Drug X when administered with food resulted in an approximately 25 % reduction in exposure. The approach used involves the development of a population PK/PD model and use of clinical trial simulations to predict an outcome of a virtual trial. 

The multicanonical algorithms are powerful, but the probability weight factors are not a priori known and have to be determined by iterations of short trial simulations. This process can be nontrivial and very tedius for complex systems with many degreees of freedom. 

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