PII-103 - SPATIALCNS-PBPK: AN R/SHINY WEB-BASED PLATFORM FOR PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING OF SPATIAL PHARMACOKINETICS IN THE HUMAN CENTRAL NERVOUS SYSTEM AND BRAIN TUMORS

C. Wickramasinghe¹, S. Kim¹, J. Li²; ¹Karmanos Cancer Institute, Detroit, United States, ²Karmanos Cancer Institute, Detroit, Michigan, United States.

Background: Quantitative knowledge of drug penetration and exposure in the human central nervous system (CNS) is critical to rational development of new drugs and optimal use of existing drugs for brain cancer treatment. We have developed and validated a novel 9-compartment CNS (9-CNS) physiologically based pharmacokinetic (PBPK) model that enables mechanistic prediction of spatial pharmacokinetics of systemically administered drugs in the human CNS and brain tumors. To allow a broad range of users to easily apply the developed model, we packaged the 9-CNS PBPK model into a user-friendly, web-based R/Shiny platform, named SpatialCNS-PBPK application (app).

Methods: The 9-CNS model consists of 9 compartments including 2 brain parenchyma compartments (i.e., brain tissue adjacent to CSF tract and deep brain parenchymal), 3 CSF compartments (i.e., ventriclur, cranial and spinal subarachnoid CSF), and 3 tumor compartments (i.e., tumor rim, bulk tumor, and tumor core). The drug distribution and fluid transfer into and out of each compartment are described by systems of linear nonhomogeneous ordinary differential equations that contain drug- and system-specific parameters. The SpatialCNS-PBPK app is programmed in R and utilizes Shiny as the web application framework, taking advantage of key R packages.

Results: As compared to the existing 4-compartment CNS model implemented in Simcyp Simulator V18 (Certara Inc.) that assumes the brain as a homogeneous compartment, the 9-CNS model accounts for the brain and tumor regional physiological and pathological differences, thus enabling prediction of spatial heterogeneity of drug penetration and exposure in the CNS and brain tumors. This tutorial presents the development of the SpatialCNS-PBPK app, describes the key app features (including model simulation, sensitivity analysis, and pharmacokinetic parameter calculation), and provides a user guide with application examples.

Conclusion: The developed SpatialCNS-PBPK app improves our ability to more readily and reliably predict the spatial pharmacokinetics in the human CNS and brain tumors. It promises to be an invaluable computational tool to assist the development of more effective drugs and optimal use of current therapies to treat brain cancer.