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Unexploited Antineoplastic Effects of Commercially Available Anti-Diabetic Drugs.

ABSTRACT: The development of efficacious antitumor compounds with minimal toxicity is a hot research topic. Numerous cancer cell targeted agents are evaluated daily in laboratories for their antitumorigenicity at the pre-clinical level, but the process of their introduction into the market is costly and time-consuming. More importantly, even if these new antitumor agents manage to gain approval, clinicians have no former experience with them. Accruing evidence supports the idea that several medications already used to treat pathologies other than cancer display pleiotropic effects, exhibiting multi-level anti-cancer activity and chemosensitizing properties. This review aims to present the anticancer properties of marketed drugs (i.e., metformin and pioglitazone) used for the management of diabetes mellitus (DM) type II. Mode of action, pre-clinical in vitro and in vivo or clinical data as well as clinical applicability are discussed here. Given the precious multi-year clinical experience with these non-antineoplastic drugs their repurposing in oncology is a challenging alternative that would aid towards the development of therapeutic schemes with less toxicity than those of conventional chemotherapeutic agents. More importantly, harnessing the antitumor function of these agents would save precious time from bench to bedside to aid the fight in the arena of cancer.

SUBMITTER: Papanagnou P

PROVIDER: S-EPMC4932542 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Unexploited Antineoplastic Effects of Commercially Available Anti-Diabetic Drugs.

Papanagnou Panagiota P Stivarou Theodora T Tsironi Maria M

Pharmaceuticals (Basel, Switzerland) 20160506 2

The development of efficacious antitumor compounds with minimal toxicity is a hot research topic. Numerous cancer cell targeted agents are evaluated daily in laboratories for their antitumorigenicity at the pre-clinical level, but the process of their introduction into the market is costly and time-consuming. More importantly, even if these new antitumor agents manage to gain approval, clinicians have no former experience with them. Accruing evidence supports the idea that several medications al ...[more]

PMID: 27164115

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Project description:BackgroundThe outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.Methods and findingsWe perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions.ConclusionsOur study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Dataset Information

Unexploited Antineoplastic Effects of Commercially Available Anti-Diabetic Drugs.

Publications

Unexploited Antineoplastic Effects of Commercially Available Anti-Diabetic Drugs.

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