Dataset Information

Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer.

ABSTRACT:

Background

In the field of drug discovery, assessing the potential of multidrug therapies is a difficult task because of the combinatorial complexity (both theoretical and experimental) and because of the requirements on the selectivity of the therapy. To cope with this problem, we have developed a novel method for the systematic in silico investigation of synergistic effects of currently available drugs on genome-scale metabolic networks.

Results

The algorithm finds the optimal combination of drugs which guarantees the inhibition of an objective function, while minimizing the side effect on the other cellular processes. Two different applications are considered: finding drug synergisms for human metabolic diseases (like diabetes, obesity and hypertension) and finding antitumoral drug combinations with minimal side effect on the normal human cell. The results we obtain are consistent with some of the available therapeutic indications and predict new multiple drug treatments. A cluster analysis on all possible interactions among the currently available drugs indicates a limited variety on the metabolic targets for the approved drugs.

Conclusion

The in silico prediction of drug synergisms can represent an important tool for the repurposing of drugs in a realistic perspective which considers also the selectivity of the therapy. Moreover, for a more profitable exploitation of drug-drug interactions, we have shown that also experimental drugs which have a different mechanism of action can be reconsider as potential ingredients of new multicompound therapeutic indications. Needless to say the clues provided by a computational study like ours need in any case to be thoroughly evaluated experimentally.

SUBMITTER: Facchetti G

PROVIDER: S-EPMC3744170 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Publications

Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer.

Facchetti Giuseppe G Zampieri Mattia M Altafini Claudio C

BMC systems biology 20120829

<h4>Background</h4>In the field of drug discovery, assessing the potential of multidrug therapies is a difficult task because of the combinatorial complexity (both theoretical and experimental) and because of the requirements on the selectivity of the therapy. To cope with this problem, we have developed a novel method for the systematic in silico investigation of synergistic effects of currently available drugs on genome-scale metabolic networks.<h4>Results</h4>The algorithm finds the optimal c ...[more]

PMID: 22932283

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Project description:PURPOSE OF REVIEW:Reduction of serum low-density lipoprotein cholesterol (LDL-C) levels by statins, ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors has been shown to significantly reduce cardiovascular events risk. However, fasting and postprandial hypertriglyceridemia as well as reduced high-density lipoprotein cholesterol (HDL-C) remain as residual risk factors of atherosclerotic cardiovascular diseases (ASCVD). To treat patients with hypertriglyceridemia and/or low HDL-C, drugs such as fibrates, nicotinic acids, and n-3 polyunsaturated fatty acids have been used. However, fibrates were demonstrated to cause side effects such as liver dysfunction and increase in creatinine levels, and thus large-scale clinical trials of fibrates have shown negative results for prevention of ASCVD. The failure could be attributed to their low selectivity and potency for binding to peroxisome proliferator-activated receptor (PPAR) α. To resolve these issues, the concept of selective PPARα modulator (SPPARMα) with a superior balance of efficacy and safety has been proposed and pemafibrate (K-877) has been developed. RECENT FINDINGS:Pemafibrate, one of SPPARMsα, was synthesized by Kowa Company, Ltd. for better efficiency and safety. Clinical trials in Japan have established the superiority of pemafibrate on effects on serum triglycerides (TG) reduction and HDL-C elevation as well safety. Although available fibrates showed worsening of liver and kidney function test values, pemafibrate indicated improved liver function test values and was less likely to increase serum creatinine or decrease estimated glomerular filtration rate (eGFR). Very few drug-drug interactions were observed even when used concomitantly with statins. Furthermore, pemafibrate is metabolized in the liver and excreted into the bile, while many of available fibrates are mainly excreted from the kidney. Therefore, pemafibrate can be used safely even in patients with impaired renal function since there is no significant increase in its blood concentration. A large-scale trial of pemafibrate, PROMINENT, for dyslipidemic patients with type 2 diabetes is ongoing. Pemafibrate is one of novel SPPARMsα and has superior benefit-risk balance compared to conventional fibrates and can be applicable for patients for whom the usage of existing fibrates is difficult such as those who are taking statins or patients with renal dysfunction. In the current review, all the recent data on pemafibrate will be summarized.

Dataset Information

Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer.

Background

Results

Conclusion

Publications

Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer.

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