Pharmacodynamic-Pharmacokinetic Integration as a Guide to Medicinal Chemistry

Abstract

A primary objective of pharmacokinetic-pharmacodynamic (PKPD) reasoning is to identify key in vivo drug and system proper¬ties, enabling prediction of the magnitude and time course of drug responses under physiological and pathological conditions in animals and man. Since the pharmacological response generated by a drug is highly dependent on the actual system used to study its action, knowledge about its potency and efficacy at a given concentration or dose is insufficient to obtain a proper understanding of its pharmacodynamic profile. Hence, the output of PKPD activities extends beyond the provision of quantitative measures (models) of results, to the design of future protocols. Furthermore, because PKPD integrates DMPK (e.g. clearance) and pharmacology (e.g. potency),it provides an anchor point for compound selection, and, as such, should be viewed as an important weapon in medicinal chemistry. Here we outline key PK concepts relevant to PD, and then consider real-life experiments to illustrate the importance to the medicinal chemist of data obtained by PKPD. Useful assumptions and potential pitfalls are described, providing a holistic view of the plethora of determinants behind in vitro-in vivo correlations. By condensing complexity to simplicity, there are not only consequences for experimental design, and for the ranking and design of compounds, but it is also possible to make important predictions such as the impact of changes in drug potency and kinetics. In short, by using quantitative methods to tease apart pharmacodynamic complexities such as temporal differences and changes in plasma protein binding, it is possible to target the changes necessary for improving a compound's profile.

Systems Pharmacology: Bridging Systems Biology and Pharmacokinetics-Pharmacodynamics (PKPD) in Drug Discovery and Development

“…However, it is increasingly being recognized that successful implementation of PKPD reasoning in early drug discovery could have at least as much impact on the overall efficiency and success of pharmaceutical research as comparable investments in late-stage modelling and simulation (7,8), and this is now increasingly being reduced to practice to guide medicinal chemistry programs (11). This conclusion derives from the assertion that arguably the most significant challenge facing the pharmaceutical industry is compound attrition, resulting from the failure of preclinical efficacy and safety model data to translate into human proof-of-mechanism/concept studies (9,10).…”

“…To this end, it has been suggested that PKPD modelling and simulation can play a significant role in early preclinical drug discovery (11) and provide a framework for translational research which links, in a quantitative manner, the interactions between a drug or combination of drugs, pharmacological targets, physiological pathways and, ultimately, integrated disease systems (12). However, historically, the discipline focusing on integrating PKPD has been data driven, with strong emphasis on (bio-)statistical approaches such as non-linear mixed effect (populationbased) modelling.…”

“…A recent development in this area is based on the growing realisation that innovation could be dramatically catalysed by creating synergy at the interface between systems biology (15) and PKPD (7,11), two disciplines which until now have largely existed in 'parallel universes' with a limited track record of impactful collaboration. This has led to the emergence of systems pharmacology (15).…”

Modeling, Simulation, and Translation Framework for the Preclinical Development of Monoclonal Antibodies

“…When feasible, the systems or PBPK model developed in early discovery should be incorporated into the pharmacology understanding. Next, translational pharmacology becomes essential at the transitional phase between candidate selection and FIH (8). At this stage, the scaling of the PK and PD from animals to human using a model-based approach with no adverse effect level (NoAEL) and minimally anticipated biological effect level (MABEL) (if needed) considerations is used to guide decisions on the starting as well as escalating dose decisions in the FIH study.…”

The Rise of Systems Pharmacology in Drug Discovery and Development

“…However, it was also recognized that in order to tackle Phase II attrition additional steps are required. One such step may include implementation of PK/PD reasoning in early drug discovery [5,6] since by addressing attrition as early as possible, before the major clinical costs are incurred, the greatest potential efficiencies can be realized [7,8].…”