Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.

Spolaor, S., Scheve, M., Firat, M., Cazzaniga, P., Besozzi, D., Nobile, M. (2021). Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization. FRONTIERS IN GENETICS, 12 [10.3389/fgene.2021.617935].

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

Spolaor S.;Cazzaniga P.;Besozzi D.;Nobile M. S.
2021

Abstract

Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.
Articolo in rivista - Articolo scientifico
cancer; combination chemotherapy; fuzzy modeling; global optimization; multi-objective optimization; therapeutic targets;
English
Spolaor, S., Scheve, M., Firat, M., Cazzaniga, P., Besozzi, D., Nobile, M. (2021). Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization. FRONTIERS IN GENETICS, 12 [10.3389/fgene.2021.617935].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/316219
Citazioni
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 3
Social impact