Implementation of quantitative and systems pharmacology in large pharma

Abstract

Quantitative and systems pharmacology concepts and tools are the foundation of the model-informed drug development paradigm at Merck for integrating knowledge, enabling decisions, and enhancing submissions. Rigorous prioritization of modeling and simulation activities has enabled key drug development decisions and led to a high return on investment through significant cost avoidance. Critical factors for the successful implementation, examples on impact on decision making with associated return of investment, and drivers for continued success are discussed.

Figure 1

Within drug industry, quantitative and systems pharmacology focuses on the application: knowledge integration to enable decisions and enhance submissions. The aspiration and benefits of applying quantitative and systems pharmacology value are illustrated in the drug discovery and development continuum. Quantitative and systems pharmacology can be seen as the framework that focuses on the development of integrated models using the modeling toolbox that exists within quantitative sciences, supporting the model-informed drug discovery and development paradigm.

Figure 2

Quantitative and systems pharmacology model development generally follows a learning–confirming cycle. Key questions in a drug project are framed before start of model development, and the most appropriate modeling approach is identified. The model should reflect current physiological and statistical knowledge and is parameterized using data from various sources (ideas, literature, in-house or external studies, expert opinion, and assumptions). Transparency on the data, sources, and assumptions is critical. Previously unmeasured parameters can be identified, fit, or optimized on the basis of available data. The outputs are answers to the question at stake, enhanced understanding, and ability to explore untested scenarios through simulations. The model itself can be viewed as a representation of integrated form of knowledge.

Figure 3

Quantitative and experimental functions require a collaborative network structure and empowered matrix leadership, irrespective of the organizational structure.

Figure 4

Illustration of Merck's variety of systems pharmacology models for virtual organs, tissue and diseases developed in house and with external partners. Courtesy of the virtual tumor graphic is Physiomics PLC.

Figure 5

Schematic view of the development and qualification of finite element analysis as noninvasive marker for longitudinal prediction of bone strength and compound differentiation. From a high-resolution image of the ultradistal radius in a monkey, an engineering model of bone is created. Using finite element analysis, the deformations that the structure undergoes under a load are calculated. The force at which the bone goes into fracture is what is used to represent the strength of the bone. The finite element analysis was validated by comparing the predicted strength to actual failure load. Subsequently, high-resolution peripheral quantitative computed tomography and finite element analysis of bone strength at the distal radius in ovariectomized adult rhesus monkey demonstrated longitudinal efficacy of odanacatib and differentiation from alendronate. The finite element analysis method was incorporated in phase III trials to evaluate bone strength progression on the ultradistal radius site in humans.

Figure 6

Dose selection of Tildrakizumab for treatment of psoriasis levering comparator data analysis. (a) Comparative landscaping: efficacy (y-axis) for various subcutaneous (SC) doses of tildrakizumab (based on Ph1b data) as compared to recommended dosing regimens for adalimumab, etanercept and infliximab, ustekinumab and briakinumab with 80% confidence intervals. (b) Results from clinical trial simulations for the selected dose range for phase IIb development of tildrakizumab. (c) Results from clinical trial simulations for PASI-75 in cohorts of n = 300 based on exposure–response models. (d) Clinical trial simulations of PASI-75 response rate during treatment with 100 mg SC tildrakizumab administered in 12-, 16-, or 26-week dosing intervals.