vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Vi Ngoc Nha Tran, Alireza Shams, Sinan Ascioglu, Antal Martinecz, Jingyi Liang, Fabrizio Clarelli, Rafal Mostowy, Ted Cohen, Pia Abel zur Wiesch

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results: In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions: The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Original languageEnglish (US)
Article number22
JournalBMC bioinformatics
Volume23
Issue number1
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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