Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

ABSTRACT

Introduction: The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%).

Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic–PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology.

Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike ‘black box’ approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.

KEYWORDS:

• Quantitative systems pharmacology
• PB–PK modeling
• virtual patient
• failure analysis
• clinical trial

Declaration of interest

H Geerts is an employee of In Silico Biosciences. R Peck is an employee of Roche Pharmaceuticals. R Gieschke is a consultant to Roche Pharmaceuticals. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This manuscript was not funded.