gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development

Abstract

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology^® to execute various common PKPD analyses. The MATLAB^®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB^®-based open-source application and is publicly available to download from MATLAB^® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Keywords: Graphical-user-interface; Modeling and simulation; Non-compartmental analysis; Pharmacokinetics and pharmacodynamics models.

Fig. 1

A schematic diagram of the PKPD models in the library. The models include a two-compartment PK model with specific and non-specific clearance, b target-mediated drug disposition (TMDD) model, c physiologic indirect response model, and d minimal physiologically based pharmacokinetic (PBPK) model. Symbols A, T and A:T denote the antibody, target and the antibody:target complex. Subscripts indicate the different compartments in each model (C: central, P: peripheral, SC: subcutaneous or (extravascular), T: tight tissues, L: leaky tissues). In the indirect response model, solid and open symbols represent inhibition and stimulation

Fig. 2

A schematic workflow of how modelers and end-users employ gPKPDSim. The expert modeler develops the model in SimBiology^® and encapsulates it into a “Session” file, which can be launched in gPKPDSim by end-users. The Session file, once loaded in gPKPDSim, enables the end-user to perform different functionalities, including simulation, data fitting (parameter estimation), population simulation, and NCA. For some of these functionalities, the user must provide a dataset. All the functionalities are supported by features such as interactive visualization, and export of results as presentation-ready figures and Excel datasets. When the user completed their tasks, the session file can be saved for future use. Note that the end-user works exclusively in the environment of gPKPDSim

Fig. 3

gPKPDSim: simulation functionality view. This view has multiple sections: 1) general settings (top left); 2) functionality-specific settings including simulation time and parameter values (bottom left); 3) plot settings including species from the SimBiology^® model and dataset groups and column headers (top right); 4) non-compartmental analysis (NCA, bottom right); 5) profile notes including a list of most recent simulations and their detailed summary (bottom middle); and 6) interactive plots (top middle). See “Methods” for detailed description of each section

Fig. 4

Case Study #1: two-compartment PK model for antibody. The results show a the goodness of fit for the model fitted to data for the low and high dose groups; b and c simulated PK profiles and AUC curves, for two different CL values at 10 mg/kg; and d and e population simulation for exploring the impact of variability on PK and AUC (median and 5–95% bounds)

Fig. 5

Case Study #2: target-mediated drug disposition model. The results show the impact of antibody affinity to target and dose level on a total antibody concentration, b free and total target concentration, and c fraction of target bound

Fig. 6

Case Study #3: physiologic indirect response model. Example 1 shows a the projected PK of pyridostigmine, and b patterns of percent gain of muscular response (using the inhibition on degradation model) after IV administration of 5 mg pyridostigmine. Example 2 shows c the projected PK of cimetidine, and d plasma concentration of prolactin (using the stimulation on synthesis model) after IV administration of 300 mg cimetidine

Fig. 7

Case Study #4: minimal physiologically based pharmacokinetic (PBPK) model. Plasma concentration versus time profiles for increasing doses from 2.6 to 1300 nmole based on target-mediated drug disposition in a central compartment, b leaky tissues, c tight tissues, or d no target in any compartment