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Current Medicinal Chemistry


ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Recent Advances in the Development of Pharmaceutical Agents for Metabolic Disorders: A Computational Perspective

Author(s): Sridhara Janardhan, Varvara Dubovskaja, Alexey Lagunin, Batchu Venkateswara Rao, Garikapati Narahari Sastry* and Vladimir Poroikov*

Volume 25, Issue 39, 2018

Page: [5432 - 5463] Pages: 32

DOI: 10.2174/0929867324666171002120647

Price: $65


Background: Metabolic disorders comprise a set of different disorders varying from epidemic diseases such as diabetes mellitus to inborn metabolic orphan diseases such as phenylketonuria. Despite considerable evidence showing the importance of the computational methods in discovery and development of new pharmaceuticals, there are no systematic reviews outlining how they are utilized in the field of metabolic disorders. This review aims to discuss the necessity of the development of web-based tools and databases by integration of available information for solving Big Data problems in network pharmacology of metabolic disorders.

Methods: We undertook a structured search of bibliographic databases for peer-reviewed research literature using a focused review question and inclusion/exclusion criteria. The quality of retrieved papers was appraised using standard tools.

Results: The alterations in metabolic pathways cause various cardiovascular, hematological, neurological, gastrointestinal, immune disorders and cancer. In this regard, informatics, Big Data and modeling techniques aid in the design of novel therapeutic agents for metabolic diseases by addressing various Big Data problems in the network polypharmacology (drugs/pharmaceutical agents, proteins, genes, diseases, bioassays, ADMET and metabolic pathways), identification of privileged scaffolds, developing new diagnostic biomarkers, understanding the pathophysiology of disease and progress in personalized medicine.

Conclusion: The recent advances of developing pharmaceutical agents for various metabolic disorders by considering their pathogenesis, mechanisms of action, therapeutic and adverse effects have been summarized. We have highlighted the role of computational techniques, drug repurposing, and network-based polypharmacological approaches in the identification of new/existing medicines with improved drug-likeness properties for the rare metabolic disorders.

Keywords: Big data and modeling, drug repurposing, informatics, metabolic disorders, network polypharmacology, orphan diseases.

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