Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance - Imchen, Madangchanok; Moopantakath, Jamseel; Kumavath, Ranjith; Barh, Debmalya; Tiwari, Sandeep; Ghosh, Preetam; Azevedo, Vasco (Frontiers in Genetics, 2020)
A multitude of factors such as drug misuse, lack of strong regulatory measures, improper sewage disposals, low quality medicine and medications have been attributed to the emergence of drug resistant microbes. Emergence and outbreaks of multidrug resistance to the last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into recent advances in the computational approaches for the surveillance of antibiotic resistome as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature, the current status of combinatorial and adjuvant based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancement in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of outmost importance. Therefore, we discuss integrative applications of whole genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiome upon drug administration.