Biochemical pharmacology
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Pharmacokinetics (PK) is the study of the time course of the absorption, distribution, metabolism and excretion (ADME) of a drug, compound or new chemical entity (NCE) after its administration to the body. Following a brief introduction as to why knowledge of the PK properties of an NCE is critical to its selection as a lead candidate in a drug discovery program and/or its use as a functional research tool, the present article presents an overview of PK principles, including practical guidelines for conducting PK studies as well as the equations required for characterizing and understanding the PK of an NCE and its metabolite(s). A review of the determination of in vivo PK parameters by non-compartmental and compartmental methods is followed by a brief overview of allometric scaling. ⋯ The volume of distribution and plasma protein and tissue binding are covered as is the clearance (systemic, hepatic, renal, biliary) of both small and large molecules. A section on metabolite kinetics describes how to estimate the PK parameters of a metabolite following administration of an NCE. Lastly, mathematical models used to describe pharmacodynamics (PD), the relationship between the NCE/compound concentration at the site of action and the resulting effect, are reviewed and linked to PK models in a section on PK/PD.
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Biochemical pharmacology · Jan 2014
ReviewAnimal models of human disease: challenges in enabling translation.
Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology, target identification, and in the in vivo evaluation of novel therapeutic agents and treatments. In the wake of numerous clinical trial failures of new chemical entities (NCEs) with promising preclinical profiles, animal models in all therapeutic areas have been increasingly criticized for their limited ability to predict NCE efficacy, safety and toxicity in humans. The present review discusses some of the challenges associated with the evaluation and predictive validation of animal models, as well as methodological flaws in both preclinical and clinical study designs that may contribute to the current translational failure rate. ⋯ Additionally, the transparent integration of efficacy and safety data derived from animal models into the hierarchical data sets generated preclinically is essential in order to derive a level of predictive utility consistent with the degree of validation and inherent limitations of current animal models. The predictive value of an animal model is thus only as useful as the context in which it is interpreted. Finally, rather than dismissing animal models as not very useful in the drug discovery process, additional resources, like those successfully used in the preclinical PK assessment used for the selection of lead NCEs, must be focused on improving existing and developing new animal models.