Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study

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http://hdl.handle.net/10138/343565

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Federico , A , Fratello , M , Scala , G , Möbus , L , Pavel , A , del Giudice , G , Ceccarelli , M , Costa , V , Ciccodicola , A , Fortino , V , Serra , A & Greco , D 2022 , ' Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study ' , Cancers , vol. 14 , no. 8 , 2043 . https://doi.org/10.3390/cancers14082043

Title: Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study
Author: Federico, Antonio; Fratello, Michele; Scala, Giovanni; Möbus, Lena; Pavel, Alisa; del Giudice, Giusy; Ceccarelli, Michele; Costa, Valerio; Ciccodicola, Alfredo; Fortino, Vittorio; Serra, Angela; Greco, Dario
Contributor organization: Institute of Biotechnology
Date: 2022-04
Language: eng
Number of pages: 16
Belongs to series: Cancers
ISSN: 2072-6694
DOI: https://doi.org/10.3390/cancers14082043
URI: http://hdl.handle.net/10138/343565
Abstract: Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.
Subject: druggability
network
systems pharmacology
cancer
cancer therapy
drug repositioning
drug combinations
IMMUNE CHECKPOINT INHIBITORS
HISTAMINE
CHEMOTHERAPY
ANTAGONIST
EXPRESSION
RECEPTORS
PACKAGE
TISSUES
GROWTH
GENES
1182 Biochemistry, cell and molecular biology
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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