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Application of Combination High‐Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product‐Based Combination Drugs
Author(s) -
Isgut Monica,
Rao Mukkavilli,
Yang Chunhua,
Subrahmanyam Vangala,
Rida Padmashree C. G.,
Aneja Ritu
Publication year - 2018
Publication title -
medicinal research reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.868
H-Index - 130
eISSN - 1098-1128
pISSN - 0198-6325
DOI - 10.1002/med.21444
Subject(s) - drug discovery , natural product , computational biology , identification (biology) , drug , phenotypic screening , pharmacology , computer science , biology , biochemical engineering , bioinformatics , phenotype , biochemistry , botany , gene , engineering
Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product‐based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs.