Simulation of clinical trials

Figure 21.2 The exposure-response road map passes through pharmacokinetics and pharmacodynamics. This sequence of events is essentially the same as that which informs compnter simulation of clinical trials, with the addition of complicating, bnt important, factors snch as protocol adherence and dropouts.

Holford NH, Kimko HC, Monteleone JP, Peck CC. Simulation of clinical trials. Anna Rev Pharmacol Toxicol 2000 40 209-34. 

The covariate distribution model defines the distribution and correlation of covariates in the population to be studied. The aim of a covariate distribution model is to create a virtual patient population that reflects the target population for simulations including patients covariates. This model is of great importance for the realistic simulation of clinical trials. 

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. 

Stochastic simulation was another step forward in the arena of pharmacometrics. Simulation had been widely used in the aerospace industry, engineering, and econometrics prior to its application in pharmacometrics. Simulation of clinical trials first appeared in the clinical pharmacology hterature in 1971 (40) but has only recently gained momentum as a useful tool for examining the power, efficiency, robustness, and informativeness of complex clinical trial structure (41). 

Complex pharmacokinetic/pharmacodynamic (PK/PD) simulations are usually developed in a modular manner. Each component or subsystem of the overall simulation is developed one-by-one and then each component is linked to run in a continuous manner (see Figure 33.2). Simulation of clinical trials consists of a covariate model and input-output model coupled to a trial execution model (10). The covariate model defines patient-specific characteristics (e.g., age, weight, clearance, volume of distribution). The input-output model consists of all those elements that link the known inputs into the system (e.g., dose, dosing regimen, PK model, PK/PD model, covariate-PK/PD relationships, disease progression) to the outputs of the system (e.g., exposure, PD response, outcome, or survival). In a stochastic simulation, random error is introduced into the appropriate subsystems. For example, between-subject variability may be introduced among the PK parameters, like clearance. The outputs of the system are driven by the inputs... 

It is important to incorporate this I/O model parameter uncertainty in the simulation of clinical trials. In order to implement parameter or model uncertainty in the simulation model, the typical values (mean values) of model parameters are usually defined as random variables (usually normally distributed), where the variance of the distribution is defined as standard error squared. The limits of the distribution can be defined at the discretion of the pharmacometrician. For a normal distribution, for example, this would be 0 + 2 SE, where 6 is the parameter. This would include 95% of the simulated distribution. When the simulation is performed, each replicate will have different typical starting values for the system parameters. The... 

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. 

De Ridder, F. Predicting the outcome of phase III trials using phase II data a case study of clinical trial simulation in late stage drug development. Basic Clin Pharmacol Toxicol 2005, 96 235-241. 

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 use of clinical trial simulation to support dose selection ... 

Kenna and Sheiner (41) used a simulation study to show that the MPML method— which uses an aU compliance data-dosage history questionnaire, Cq, available from all subjects and combines that with dosing history obtained with MEMS, C, from a random fraction of subjects, effectively calibrating Cq to C—is superior to other methods that use only one compliance measure, or both, or neither where neither was intention-to-treat. The authors showed that the MPML approach yielded efficient dose-response estimates over a wide range of clinical trial designs, effect sizes, and varying quality and quantity of compliance information. The method was shown to maintain good performance even when its key assumptions were violated and compliance data were sparse. 

P. L. S. Chan, N. H. G. Holford, and J. Nntt, Apphcation of clinical trial simulation to evaluate the ELLDOPA trial design. Movement Disorders Society Conference, Miami, FL, November 2002. 

A well planned simulation project increases the likelihood of providing meaningful and timely simulation results that will enhance the design and improve the efficiency, robustness, power, and informativeness of preclinical and clinical studies. An increase in the efficiency and power of clinical trials should reduce the number of studies and time needed to complete the drug development process with the resultant reduction in cost of pharmacotherapy to the consumer. 

One of the most potent applications of pharmacometrics is the informative construction of clinical trials by using clinical trial simulation (CTS). Population PM models are of great value when used in CTS because estimates of typical parameters along with parameter variability can be incorporated. There are three basic types of models needed to execute a CTS an input-output model, a covariate model, and an execution model. These are described in detail in Chapter 34 of this book. Clinical trial simulation can improve pediatric study structure by examining the impact of many important factors such as dropouts, choosing varying endpoints, and deviations from protocol. Pediatric PM models find great utility when applied to CTS. 

Lockwood, P. A. et al., The use of clinical trial simulation to support dose selection application to development of a new treatment for chronic neuropathic pain, Pharm. Res., 20(11), 1752, 2003. 

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