Biochemical reaction network topology defines dose-dependent Drug-Drug interactions.


Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
03 2023
Historique:
received: 20 09 2022
revised: 28 12 2022
accepted: 22 01 2023
pubmed: 23 2 2023
medline: 15 3 2023
entrez: 22 2 2023
Statut: ppublish

Résumé

Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.

Identifiants

pubmed: 36805215
pii: S0010-4825(23)00049-5
doi: 10.1016/j.compbiomed.2023.106584
pii:
doi:

Substances chimiques

Drug Combinations 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

106584

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None Declared.

Auteurs

Mehrad Babaei (M)

Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands; Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy. Electronic address: m.babaei@lacdr.leidenuniv.nl.

Tom M J Evers (TMJ)

Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands. Electronic address: t.m.j.evers@lacdr.leidenuniv.nl.

Fereshteh Shokri (F)

Leiden University Medical Center, Leiden, 2333ZA, the Netherlands. Electronic address: fereshteh.m.shokri@gmail.com.

Lucia Altucci (L)

Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy; BIOGEM, Molecular Biology and Genetics Research Institute, Ariano Irpino, Italy. Electronic address: lucia.altucci@unicampania.it.

Elizabeth C M de Lange (ECM)

Predictive Pharmacology, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands. Electronic address: ecmdelange@lacdr.leidenuniv.nl.

Alireza Mashaghi (A)

Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands. Electronic address: a.mashaghi.tabari@lacdr.leidenuniv.nl.

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