vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.
Antibiotic
Binding kinetics
Computational model
Pharmacodynamics
Pharmacokinetics
Web-based tool
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
06 Jan 2022
06 Jan 2022
Historique:
received:
03
08
2020
accepted:
14
12
2021
entrez:
7
1
2022
pubmed:
8
1
2022
medline:
11
1
2022
Statut:
epublish
Résumé
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. 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. The vCOMBAT online tool is publicly available at https://combat-bacteria.org/ .
Sections du résumé
BACKGROUND
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
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
CONCLUSIONS
The vCOMBAT online tool is publicly available at https://combat-bacteria.org/ .
Identifiants
pubmed: 34991453
doi: 10.1186/s12859-021-04536-3
pii: 10.1186/s12859-021-04536-3
pmc: PMC8734216
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : JPI-EC-AMR
ID : 271176/H10
Organisme : Bill and Melinda Gates Foundation
ID : OPP1111658
Organisme : Research Council of Norway (NFR)
ID : 262686
Informations de copyright
© 2021. The Author(s).
Références
Lancet Infect Dis. 2005 Feb;5(2):115-9
pubmed: 15680781
Int J Tuberc Lung Dis. 2004 May;8(5):560-7
pubmed: 15137531
PLoS Comput Biol. 2016 Mar 11;12(3):e1004749
pubmed: 26967493
N Engl J Med. 2015 Nov 26;373(22):2149-60
pubmed: 26605929
Eur J Biochem. 1977 Nov 1;80(2):325-30
pubmed: 336370
Lancet Infect Dis. 2017 Jan;17(1):39-49
pubmed: 28100438
PLoS Med. 2019 Apr 2;16(4):e1002773
pubmed: 30939136
PLoS Comput Biol. 2017 Jan 6;13(1):e1005321
pubmed: 28060813
PLoS Comput Biol. 2020 Aug 14;16(8):e1008106
pubmed: 32797079
PLoS Med. 2019 Jul 5;16(7):e1002842
pubmed: 31276490
Pediatrics. 2000 Feb;105(2):E19
pubmed: 10654979
Sci Rep. 2017 Mar 29;7(1):502
pubmed: 28356552
Science. 2011 Sep 23;333(6050):1764-7
pubmed: 21940899
Clin Infect Dis. 2011 May;52(9):e194-9
pubmed: 21467012
PLoS Pathog. 2012 Jan;8(1):e1002487
pubmed: 22253599
Science. 2002 Mar 15;295(5562):2042-6
pubmed: 11896268
PLoS Med. 2019 Jul 9;16(7):e1002851
pubmed: 31287813
PLoS Med. 2019 Mar 22;16(3):e1002769
pubmed: 30901322
Antimicrob Agents Chemother. 1996 Dec;40(12):2765-8
pubmed: 9124837
Nature. 2014 May 29;509(7502):555-7
pubmed: 24877180
Infect Genet Evol. 2019 Oct;74:103937
pubmed: 31247337
Nat Chem Biol. 2015 Jun;11(6):382-3
pubmed: 25894084
Am J Respir Crit Care Med. 2015 May 1;191(9):1058-65
pubmed: 25654354
Cell Mol Life Sci. 2020 Feb;77(3):381-394
pubmed: 31768605
Sci Transl Med. 2015 May 13;7(287):287ra73
pubmed: 25972005