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.Electronic supplementary materialThe online version of this article (10.1007/s10928-017-9562-9) contains supplementary material, which is available to authorized users.

Biodistribution and efficacy of an anti-TENB2 antibody-drug conjugate in a patient-derived model of prostate cancer

“…gPKPDSim [23] was used to fit a two-compartment model with non-linear clearance (Vm, Km) to previously published PK data for anti-TENB2 ADC [21] for parameter estimation (Figure 2). The parameter values (± estimation error) estimated from PK data of ADC at doses ranging from 0.342 to 10.5 mg/kg were 55.5 ± 0.990 mL/kg for V1, 58.6 ± 3.3 mL/kg for V2, 8.97 ± 0.477 mL/kg/day for CL, 105 ± 24.3 mL/kg/day for Cld, 38.1 ± 3.44 μg/day/kg for Vm, and 0.142 ± 0.0960 μg/mL for Km.…”

“…An integrated, multi-purpose user-friendly GUI application (gPKPDSim) [23] was used to estimate PK parameters from our previously published PK data of ADC in normal mice [21] using a two-compartment model with non-linear clearance (Vm, Km) as previously described [23]. The ODE15s algorithm was used to solve the differential equations, with the data weighted by a proportional error model as previously described (Hosseini et al, 2018).…”

In silico authentication of amygdalin as a potent anticancer compound in the bitter kernels of family Rosaceae

“…Moreover, RAS signaling pathways are the key regulators of several aspects of normal cell growth and malignant transformation ( Downward, 2003 ). In Silico modeling and simulation techniques which are widely adopted for drug developmental process ( Deisboeck et al, 2009 , Hosseini et al, 2018 ) were used to determine the activity of amygdalin in up-regulation or down-regulation of cancer pathways.…”

gQSPSim: A SimBiology‐Based GUI for Standardized QSP Model Development and Application

“…This model was selected because of its broad use and familiarity to the modeling community. A schematic of this model is shown in figure 1 of Hosseini et al …”

“…14 This model was selected because of its broad use and familiarity to the modeling community. A schematic of this model is shown in figure 1 of Hosseini et al 15 In the following, we demonstrate how to use gQSPSim to (i) perform optimization to obtain reference parameter values to fit the mean of clinical data, (ii) develop a virtual cohort to capture the variability observed in the clinical patients, (iii) generate a virtual population that closely matches the clinical population statistics, and (iv) run model-based predictions for novel scenarios using the virtual population. Detailed instructions for reproducing this case study are provided in Supplementary Instructions S1.…”