A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma.


Journal

Mathematical biosciences
ISSN: 1879-3134
Titre abrégé: Math Biosci
Pays: United States
ID NLM: 0103146

Informations de publication

Date de publication:
01 2020
Historique:
received: 19 05 2019
revised: 10 11 2019
accepted: 26 11 2019
pubmed: 7 12 2019
medline: 6 11 2020
entrez: 7 12 2019
Statut: ppublish

Résumé

Drug resistance (DR) is a phenomenon characterized by the tolerance of a disease to pharmaceutical treatment. In cancer patients, DR is one of the main challenges that limit the therapeutic potential of the existing treatments. Therefore, overcoming DR by restoring the sensitivity of cancer cells would be greatly beneficial. In this context, mathematical modeling can be used to provide novel therapeutic strategies that maximize the efficiency of anti-cancer agents and potentially overcome DR. In this paper, we present a new multiscale model devoted to the interaction of potential treatments with multiple myeloma (MM) development. In this model, MM cells are represented as individual objects that move, divide, and die by apoptosis. The fate of each cell depends on intracellular and extracellular regulation, as well as the administered treatment. The model is used to explore the combined effects of a tyrosine-kinase inhibitor (TKI) with a pentose phosphate pathway (PPP) inhibitor. We use numerical simulations to tailor effective and safe treatment regimens that may eradicate the MM tumors. The model suggests that an interval for the daily dose of the PPP inhibitor can maximize the responsiveness of MM cells to the treatment with TKIs. Then, it demonstrates that the combination of high-dose pulsatile TKI treatment with high-dose daily PPP inhibitor therapy can potentially eradicate the tumor.The predictions of numerical simulations using such a model can be considered as testable hypotheses in future pre-clinical experiments and clinical studies.

Identifiants

pubmed: 31809782
pii: S0025-5564(19)30534-6
doi: 10.1016/j.mbs.2019.108293
pii:
doi:

Substances chimiques

Antineoplastic Agents 0
Enzyme Inhibitors 0
Protein Kinase Inhibitors 0
Protein-Tyrosine Kinases EC 2.7.10.1

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

108293

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Anass Bouchnita (A)

Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala 75105, Sweden; Ecole Centrale Casablanca, Ville Verte, Bouskoura, 20000 Casablanca, Morocco. Electronic address: anass.bouchnita@centrale-casablanca.ma.

Vitaly Volpert (V)

Institut Camille Jordan, Université Lyon 1, Villeurbanne 69622, France; INRIA Team Dracula, INRIA Lyon La Doua, Villeurbanne 69603, France; Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow 117198, Russian Federation.

Mark J Koury (MJ)

Vanderbilt University Medical Center, Nashville, TN 37232-6307, USA.

Andreas Hellander (A)

Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala 75105, Sweden.

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Classifications MeSH