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TumorPred: A computational framework implemented via an R/Shiny web application for parameter estimation and sensitivity analysis in compartmental brain modeling

Charuka D. Wickramasinghe & Nelum S.S.M. Hapuhinna

Software Impacts2026https://doi.org/10.1016/j.simpa.2026.100831article
AJG 1
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0.50

What the paper says

It is difficult or infeasible to directly measure how much of a drug actually enters the human brain and a brain tumor, how long it remains there, and to estimate drug-specific or patient-specific parameters, as well as how changes in these parameters influence model outputs and pharmacokinetic characteristics. Compartmental modeling offers a powerful mathematical approach to describe drug distribution and elimination in the body using systems of differential equations. This study introduces TumorPred, an R/Shiny-based web application designed for model simulation, sensitivity analysis, and pharmacokinetic parameter calculation in a permeability-limited four-compartment brain model. The model closely mimics human brain functionality for drug delivery and aims to predict the pharmacokinetics of drugs in the brain blood, brain mass, and cranial and spinal cerebrospinal fluid (CSF) of the human brain. The app provides real-time output updates in response to input modifications and allows users to visualize and download simulated plots and data tables. The computational accuracy of TumorPred is validated against results from the Simcyp Simulator (Certara Inc.). TumorPred is freely accessible and serves as an invaluable computational tool and data-driven resource for advancing drug development and optimizing treatment strategies for more effective brain cancer therapy. • Drug distribution in CNS and brain tumors is not well understood. • PBPK modeling predicts drug pharmacokinetics. • Parameter estimation and sensitivity analysis aid PBPK model development. • R-Shiny app provides a user-friendly simulation platform.

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https://doi.org/https://doi.org/10.1016/j.simpa.2026.100831

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@article{charuka2026,
  title        = {{TumorPred: A computational framework implemented via an R/Shiny web application for parameter estimation and sensitivity analysis in compartmental brain modeling}},
  author       = {Charuka D. Wickramasinghe & Nelum S.S.M. Hapuhinna},
  journal      = {Software Impacts},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.simpa.2026.100831},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.