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

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.

Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models.

BACKGROUND: QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out. RESULTS: The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each class of descriptors was carried out and the effect of number of descriptors (1-10) on statistical parameters was tested. Charge-based descriptors were found in 20 out of 39 models (approx. 50%), valency-based descriptor in 14 (approx. 36%) and bond order-based descriptor in 11 (approx. 28%) in comparison to other descriptors. The use of conceptual DFT descriptors does not improve the statistical quality of the models in most cases. CONCLUSION: Analysis is done with various models where the number of descriptors is increased from 1 to 10; it is interesting to note that in most cases 3 descriptor-based models are adequate. The study reveals that quantum chemical descriptors are the most important class of descriptors in modelling these series of compounds followed by electrostatic, constitutional, geometrical, topological and conceptual DFT descriptors. Cell lines in nasopharyngeal (2) cancer average R2 = 0.90 followed by cell lines in melanoma cancer (4) with average R2 = 0.81 gave the best statistical values.