Pharmacodynamic models validation

Miyazaki, M., Mukai, H., Iwanaga, K., Morimoto, K., and Kakemi, M., Pharmacokinetic-pharmacodynamic modeling of human insulin validity of pharmacological availability as a substitute for extent of bioavailability, /. Pharni. Pharmacol., 53, 1235-1246, 2001. 

An important outcome of these studies is the opportunity that it offers to discuss the implications of the presence of nonlinear dynamics in processes such as the secretion of cortisol. Based on the aforementioned discussion it is evident that the concepts of deterministic nonlinear dynamics should be adopted in pharmacodynamic modeling when supported by experimental and physiologic data. This is valid not only for the sake of more detailed study, but mainly because nonlinear dynamics suggest a whole new rationale fundamentally different from the classical approach. Moreover, the clinical pharmacologist should be aware of the limitations of chaotic models for long-term prediction, which is contrary to the routine use of classical models. 

Holford NHG, Peace KE. Results and validation of a population pharmacodynamic model for cognitive effects in Alzheimer patient treated with tacrine. Proc Natl Acad Sci USA 1992 89 11471-5. 

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Poulin P. 2015b. Albumin and uptake of drugs in cells additional validation exercises of a recently published equation that quantifies the albumin-facUitated uptake mechanism(s) in physiologically based pharmacokinetic and pharmacodynamic modeling research. J Pharm Sci 104. doi 10.1002/jps.24676. 

Phase I studies evaluate the pharmacokinetics and safety of the drug in a small number (tens) of healthy volunteers. Phase I studies are sometimes conducted in a small patient population (Proof of Concept studies) with a specific objective such as the validation of the relevance of preclinical models in man. The purpose of these studies may be the rapid elimination of potential failures from the pipeline, definition of biological markers for efficacy or toxicity, or demonstration of early evidence of efficacy. These studies have a potential go/no-go decision criteria such as safety, tolerability, bioavailability/PK, pharmacodynamics, and efficacy. Dosage forms used in Phase I or Proof of Concept studies must be developed with the objectives of the clinical study in mind. 

A number of limitations related to PK/PD modeling are also a reality in situations where predictability of the animal model to man is questionable, where the time course of the pharmacodynamic effect cannot be assessed for drug candidates and when, for example, no accessible/ valid pharmacodynamic endpoint for PK/PD is available. The relevance of the animal model for human could be addressed to some extent at least by measuring relative potency in animal versus man in vitro. In situations where no relevant PD endpoint is available (e.g., for CNS efficacy models), effects at target level (i.e., enzyme inhibition, receptor occupancy) might represent a valuable alternative. In this context however the level and duration of target effect required for clinical efficacy requires careful considerations. 

The primary objective of the early efLcacy studies is to validate the pharmacology model with a compound that is known to interact with the desired receptor and develop the Pharmacokinetics-Pharmacodynamics (PK-PD) relationship for further screening during lead optimization (Neervan-nan, 2006). It is essential that the excipients selected forthe vehicle do not interfere with the measured end points especially, for a disease-relevant animal model that has no clinically effective drugs to validate an animal model. In this situation, vehicles should be used as negative controls in the studies. 

After optimization, scientists test the lead compounds in more sophisticated models including pharmacokinetics, pharmacodynamics, and toxicity. The optimal molecule selected from these assessments is then declared a new dmg candidate and moves on to the next phase (development). If a program is successful, it may take a total of 3-6 years from target selection and validation through lead generation, lead optimization, and preclinical evaluation in animals to candidate selection for a potential new medicine. 

In the direct-link model, concentration-effect relationships are established without accounting for the intrinsic pharmacodynamic temporal behavior, and the relationships are valid only under the assumption of effect site, prereceptor equilibrium H3. In contrast, indirect-link models are required if there is a temporal dissociation between the time courses of concentration and effect, and the observed delay in the concentration-effect relationship is most likely caused by a functional delay between the concentrations in the plasma and at the effect site. 

Beyond pharmacokinetics and pharmacodynamics, population modeling and parameter estimation are applications of a statistical model that has general validity, the nonlinear mixed effects model. The model has wide applicability in all areas, in the biomedical science and elsewhere, where a parametric functional relationship between some input and some response is studied and where random variability across individuals is of concern [458]. 

The content of this chapter is not inclusive, in the sense of taking one through the complete development and validation of a complex PBPK/PD model, which would describe the kinetics of each component, together with the pharmacodynamic interactions between selected NAs and countermeasures. Rather, this chapter briefly describes some examples of progress made in quantitative modeling and explores how specific countermeasures interfere in this NA-induced cascade of events, and how such quantitative approaches could be used to develop improved treatment regimens. 

Consideration should be given to the selection of relevant animal models or other test systems so that scientifically valid information can be derived. Selection factors can include the pharmacodynamic responsiveness of the model, pharmacokinetic profile, species, strain, gender, and age of the experimental animals, the susceptibility, sensitivity, and reproducibility of the test system and available background data on the substance. Data from humans (e.g., in vitro metabolism), when available, should also be considered in the test system selection. 

Biopharmaceutical research often involves the collection of repeated measures on experimental units (such as patients or healthy volunteers) in the form of longitudinal data and/or multilevel hierarchical data. Responses collected on the same experimental unit are typically correlated and, as a result, classical modeling methods that assume independent observations do not lead to valid inferences. Mixed effects models, which allow some or all of the parameters to vary with experimental unit through the inclusion of random effects, can flexibly account for the within-unit correlation often observed with repeated measures and provide proper inference. This chapter discusses the use of mixed effects models to analyze biopharmaceutical data, more specihcally pharmacokinetic (PK) and pharmacodynamic (PD) data. Different types of PK and PD data are considered to illustrate the use of the three most important classes of mixed effects models linear, nonlinear, and generalized linear. 

There are several approaches to population model development that have been discussed in the literature (7, 9, 15-17). The traditional approach has been to make scatterplots of weighted residuals versus covariates and look at trends in the plot to infer some sort of relationship. The covariates identified with the scatterplots are then tested against each of the parameters in a population model, one covariate at a time. Covariates identified are used to create a full model and the final irreducible, given the data, is obtained by backward elimination. The drawback of this approach is that it is only valid for covariates that act independently on the pharmacokinetic (PK) or pharmacokinetic/pharmacodynamic (PK/PD) parameters, and the understanding of the dimensionality of the covariate diata is not taken into account. 

Pharmacokinetic/pharmacodynamic (PK/PD) knowledge creation is the process of building on current understanding of data that is already acquired by generating more data (information) that can be translated into knowledge. It entails the use of (valid) models to synthesize data, estimate inestimable uncertainty, or supplement data for further knowledge acquisition (1, 2). 

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