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Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens

IR@IMTECH: CSIR-Institute of Microbial Technology, Chandigarh


Field	Value

Title	Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens

Creator	Passi, Anurag Jolly, Bani Sharma, Tina Pandya, Ashma Bhardwaj, Anshu

Subject	QR Microbiology

Description	Antimicrobial resistance (AMR) is the outcome of genotypic and phenotypic diversification of lineages that is rapidly increasing among various pathogenic strains globally. In 2017 the World Health Organization (WHO) released a list of priority pathogens that can only be tackled with the discovery of novel antibiotics. New drug discovery and development entails high costs and attrition rates and therefore new strategies to address AMR are urgently needed. With the accumulation of large volumes of pharmacological and “-omics” data and strong analytical tools, in silico approaches play an increasingly important role in the discovery of novel antibiotics as well as repurposing of known drugs for new indications. This chapter discusses the available high-throughput chemical-biology integrative data platforms including network-based approaches for drug repurposing. The chapter also discusses contemporary and futuristic methods/resources for exploring and extending the existing targets and chemical space with semantic linked data technologies and deep-learning methods that are being adapted for drug repurposing.

Publisher	Elsevier Science

Contributor	Roy, Kunal

Date	2019

Type	Book Section PeerReviewed

Identifier	Passi, Anurag and Jolly, Bani and Sharma, Tina and Pandya, Ashma and Bhardwaj, Anshu (2019) Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens. In: In Silico Drug Design: Repurposing Techniques and Methodologies. Elsevier Science, USA, pp. 229-253. ISBN 978-0-12-816125-8

Relation	http://crdd.osdd.net/open/2419/