Model pharmacometric

Gieschke, R. and Steimer, J.L., Pharmacometrics modelling and simulation tools to improve decision making in clinical drug development, Eur.. Drug Metab. Pharmacokinet., 25, 49-58, 2000. 

For impacting safety, the FDA has noted opportunities to better dehne the importance of the QT interval, for improved extrapolation of in vitro and animal data to humans, and for use of extant chnical data to help construct models to screen candidates early in drug development (e.g., hver toxicity). Pharmacometrics can have a role in developing better links for all of these models. 

The absence of the terms model qualification, model verification, model accreditation, and credible model as applied to PM models deserves explanation. The definition for qualified is having complied with specific requirements or precedent conditions. Model qualification would imply that some specific objective standard must be met for a model to be qualified, that the standards are the same for all models, and that there would be no alternative approaches to qualifying the model. Within the realm of pharmacometric (PM) models, these specific precedent conditions have not been stated, and it would be impossible and unreasonable to have a set of specific objective conditions that would apply to all models. 

Pharmacometric (PM) models have many and varied applications for drug development, regulation, and applied pharmacotherapy. Resampling techniques can be applied to model development, evaluation, and validation— most often resulting in an economy of effort once applied to these aspects of modeling (1-3). Models have been defined as either descriptive or predictive (see Chapter 8). While descriptive models require checks for reliability and stability, predictive models have the added requirement of validation (which resampling can do). 

M. H. Quenouille introduced the jackknife (JKK) in 1949 (12) and it was later popularized by Tukey in 1958, who first used the term (13). Quenouille s motivation was to construct an estimator of bias that would have broad applicability. The JKK has been applied to bias correction, the estimation of variance, and standard error of variables (4,12-16). Thus, for pharmacometrics it has the potential for improving models and has been applied in the assessment of PMM reliability (17). The JKK may not be employed as a method for model validation. 

The JKK can be used for any estimator that is a sample analog of a parameter. For instance, one can use the JKK for the sample mean as an estimator of the population mean, the sample variance as an estimator of the population variance, the sample minimum as an estimator of the population minimum, and so on. This definition can be extended to any population characteristic and is therefore of interest in pharmacometrics, especially when applied to population modeling. 

Bootstrapping the residual assumes that the residuals are not a function of the dependent variables and that the form of the error model is known. This is a strong assumption that is seldom met in regression analyses and pharmacometrics in particular. Bootstrapping pairs is less sensitive to assumptions than is bootstrapping residuals. 

However, in parametric problems the bootstrap adds little or nothing to the theory or application and one cannot explain why the typical approach to estimating parameters via formulas should be replaced by bootstrap estimates. Consequently, it is uncommon to see the parametric bootstrap used in real problems. When applied to population pharmacometric (PPM) modeling, a weakness of the parametric bootstrap is that it assumes that the model is known with a high degree of certainty. This is seldom true. 

The role of resampling methods in PMM development and validation are explored. If applied, these techniques may bring efficiency to pharmacometric model development and result in models for which one s confidence level is very high. Patient pharmacotherapy will also be improved. One can expect to see resampling more extensively applied to modeling in the future. 

Pharmacometrics and PK/PD modeling can provide important insights into gene expression and proteomics data because these approaches can reduce the dimensionality of the problem time courses of expression are expressed in terms of a limited number of model parameters that are readily interpreted by the users. 

Pharmacometric analysis enables synthesis of disjoint data into knowledge that can inform important clinical development program decisions. Via its modeling and simulation techniques, it is not only a major producer of new knowledge concerning... 

Current pharmacometrics practices and available techniques for model-based development are heavily constrained by a vision of model-based development as a subsidiary development process in an empirical development paradigm. Many of the symptoms of the less than optimal functioning of pharmacometrics in this setting are the result of a failure to appreciate the true needs and requirements of the pharmacometrics group. In this chapter, we use the term pharmacometrics enterprise to denote the need to transform the current pharmacometrics analysis process to an interdependent enterprise capable of managing the growing complexity of the critical upstream and downstream implications of a fully functional and accountable service. The design of a pharmacometrics enterprise that embodies the required elements is a novel and complex endeavor that can overtax the experience and... 

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