Search

Introduction: Diseases of the Central Nervous System (CNS) affect millions of people worldwide, with the number of people affected quickly growing. Unfortunately, the successful development of CNS-acting drugs is less than 10%, and this is attributed to the complexity of the CNS, unexpected side effects, difficulties in penetrating the blood-brain barrier and lack of biomarkers. Areas covered: Herein, the authors first review how pharmacokinetic/pharmacodynamic (PK/PD) models are designed to predict the dose-dependent time course of effect, and how they are used to translate drug effects from animal to man. Then, the authors discuss how pharmacometabolomics gives insight into system-wide pharmacological effects and why it is a promising method to study interspecies differences. Finally, the authors advocate the application of PK/PD-metabolomics modeling to advance translational CNS drug development by discussing its opportunities and challenges. Expert opinion: It is envisioned that PK/PD-metabolomics will increase our understanding of CNS drug effects and improve translational CNS drug development, thereby increasing success rates. The successful future development of this concept will require multi-level and longitudinal biomarker evaluation over a large dose range, multi-tissue biomarker evaluation, and the generation of a proof of principle by application to multiple CNS drugs in multiple species.

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass $1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In today's development paradigm, late-stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model-based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples. [ABSTRACT FROM AUTHOR]