A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Abstract

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision-making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug-specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Figure 1

A portion of a graphical representation (PhysioMap) of a type 2 Diabetes PhysioPD Research Platform developed by Rosa & Co using JDesigner software.86 Major biological processes are represented graphically as subsystems or modules (e.g., glucose metabolism, insulin and glucagon, incretins, etc.). They are linked mathematically by the use of an aliasing function that allows display of the same node in multiple places on the diagram. The detailed section represents key regulated processes in glucose metabolism. For example, glycogenolysis (the reaction arrow going from liver glycogen [“Glycogen_Liver”] to glucose 6 phosphate [“G6P_Periportal”]) is regulated by glucose (“Conc_Glucose”) and glucagon concentrations (“Conc_Glucagon”). The graphical layout facilitates communication and review of the model by research team members with biological content knowledge.

Figure 2

Graphical illustration of the eight criteria of the model qualification method.

Figure 3

Illustration of the virtual patient (VP) concept. Several VPs were created to explore hypotheses of patient differences underlying response or nonresponse to three cycles of blinatumomab, a bi‐specific T‐cell engaging antibody in B‐lineage acute lymphoblastic leukemia (B‐ALL).62, 87 All VPs share the same model structure, and all have similar levels of malignant cells at the start of the trial. All VPs have parameter values that are within reported ranges, but the VPs differ in the values chosen within those ranges for some sensitive parameters, such as the malignant cell doubling rate. Some combinations of parameters were found to lead to treatment nonresponse (e.g., VP1), some to response (e.g., VP2), and some to relapse after initial response (e.g., VP3).