Biomarker Endpoints Applied in Pharmacokinetic Pharmacodynamic Modeling

Perhaps the broadest impact made by biomarker assays has been in the assessment of pharmacodynamic effects of drug candidates both preclinically and in the clinic; the data generated by such pharmacodynamic profiling can allow for extensive modeling of drug concentration-effect relationships to be performed. In colloquial terms, pharmacokinetics (PK) describes what the body does to a drug, while pharmacodynamics (PD) describes what a drug does to the body. The mathematical modeling of quantitative relationships between observed plasma or tissue drug concentrations (pharmacokinetics) and the measured pharmacological effect(s) (pharmacodynamics) is referred to as PK/PD modeling.93 Application of PK/PD modeling and simulations is useful in preclinical as well as clinical phases of drug testing, and can provide a detailed kinetic view of drug action over time, which ultimately can lead to dose optimization provided that the pharmacodynamic endpoint is well measured and tightly linked mechanistically to drug activity (indeed, the quality of information generated by modeling can only be as good as the input data). A well-characterized biomarker that is linked to target activity or is directly downstream of target signaling is a valid choice for pharmacodynamic input, so long as the bioanalytical assay for quantification of the biomarker is robust and, particularly in the clinical setting, can be performed under conditions that meet Good Laboratory Practice (GLP) conditions or are at least GLP-like83,90; of course, the analytical method that measures pharmacokinetic endpoints must also be reliable and quantitative, though this is routinely the case for synthetic drugs.

When surrogate endpoints or biomarkers with at least a preliminary linkage to clinical efficacy are used in the PD component, it may be possible to predict and subsequently test for a drug dose that provides sufficient biological activity but with a lowered risk for adverse side effects; this concept has been termed the optimal biological dose95 (in contrast with the more traditional and empirically determined maximum tolerated dose concept) and holds great promise for the refinement of therapeutic dosing regimens. The key prerequisite for broad practical application of this concept in the clinic is that the correlation between the pharmacodynamic biomarker endpoint, drug exposure, and

Drug

Biomarker

Time after dose

Figure 2 Graphical illustrations of two general concepts in PK/PD modeling. (a) An idealized scenario in which a biomarker signal is shown to decrease as levels of drug are increased over time. (b) The interplay between three endpoints, one of which is a measure of clinical efficacy and the other two are biomarkers; one of the biomarkers (biomarker 2) shows a pharmacodynamic pattern that closely follows that of the efficacy signal, and thus biomarker 2 may be of use in determining an optimal biological dose (indicated by the blue line).

Biomarker 1 Biomarker 2 Efficacy

Drug dose

Figure 2 Graphical illustrations of two general concepts in PK/PD modeling. (a) An idealized scenario in which a biomarker signal is shown to decrease as levels of drug are increased over time. (b) The interplay between three endpoints, one of which is a measure of clinical efficacy and the other two are biomarkers; one of the biomarkers (biomarker 2) shows a pharmacodynamic pattern that closely follows that of the efficacy signal, and thus biomarker 2 may be of use in determining an optimal biological dose (indicated by the blue line).

clinical efficacy must be firmly established, ideally in more than one clinical study of reasonable statistical power. Another facet where biomarkers are useful is in defining the PK/PD relationship for any metabolic products of a given drug, i.e., in order to test the pharmacological and biological activity of major metabolites in comparison to the parent compound (this is best done in preclinical models where each metabolite can be first tested in isolation). Figure 2 illustrates, in a generalized, hypothetical format, two concepts that are integral to PK/PD relationships; one is the temporal relationship between drug levels and changes in the level of a biomarker endpoint (in this illustration, the biomarker decreases over time as the drug level rises), while the other is the dose-dependent relationship between an efficacy endpoint and two pharmacodynamic biomarkers, one of which (biomarker 2) shows a dose-response pattern that is closer to that of the efficacy endpoint (and hence is more relevant than the other biomarker for determination of the optimal biological dose).

As the molecular underpinnings behind disease processes and drug mechanisms begin to become better characterized, regulatory agencies are increasingly focusing attention toward biomarkers and their potential role in drug approvals or usage guidelines. As previously mentioned, a subset of biomarkers known as surrogate endpoints have already been established, in select indications, to be predictors of clinical benefit suitable for regulatory approval decision making prior to definitive assessment of clinical outcome per se (e.g., survival). Further, there have been in recent years a few examples of therapeutics which are prescribed on the basis of presence or absence of specific molecular determinants in the disease tissue; a prime case in point is the aforementioned trastuzumab, which is prescribed only for breast cancer patients whose tumor cells express the HER2 protein. Additional cases where molecular or pharmacogenomic information (e.g., in polymorphisms in metabolic enzymes) is described in drug labels are likely to emerge in coming years. However, regulatory agencies are rightly taking a cautious approach toward reliance on either pharmacodynamic or predictive biomarkers in their deliberations. The limiting factors are the difficulties in establishing the true clinical validity of potential surrogate endpoints, and, on a broader scale, the substantial gaps that exist in the scientific

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