Skip to main content
Project

Machine learning for digital diagnostics of antimicrobial resistance
 

Project ID
109282
Total Funding
CAD 420,800.00
IDRC Officer
Armando Heriazon
Project Status
Active
Duration
36 months

Programs and partnerships

Lead institution(s)

Summary

If action is not taken, antimicrobial resistance (AMR) will cost $100 trillion and claim 10 million lives annually by 2050. The factors driving AMR extend beyond human healthcare, with implications in veterinary medicine, agriculture, and the environment.Read more

If action is not taken, antimicrobial resistance (AMR) will cost $100 trillion and claim 10 million lives annually by 2050. The factors driving AMR extend beyond human healthcare, with implications in veterinary medicine, agriculture, and the environment. New and improved approaches for tackling AMR include better monitoring, rational drug use, different business models to generate antibiotics, innovation at all levels, and most importantly, a global approach.

This project assembles a transnational team (Canada, China, Finland, France) to apply new machine learning approaches for faster diagnosis and better monitoring of resistance. The initial focus will be on two major global pathogens that have developed multi-drug resistance: Pseudomonas aeruginosa and Streptococcus pneumoniae. The research team will develop machine learning that can orient treatment selection by assessing the level of resistance, providing a rationale for generating novel antibiotics, and assisting in the surveillance of human and livestock antimicrobial resistance.

This is one of five IDRC-funded projects developed through the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR), an international collaborative platform that coordinates global funding to support collaborative research and action on antimicrobial resistance. Through the JPIAMR, IDRC has partnered with 18 other donor agencies to fund innovative research projects on diagnostics and surveillance strategies, as well as tools and technologies that can be used to detect and monitor antimicrobial resistance in human, veterinary, and environmental settings, particularly in low- and middle-income countries.

Research outputs

Access full library of outputs Opens in new tab
Article
Language:

English

Summary

We describe the genomic and phenotypic characteristics of a novel member of Streptococcus with multidrug resistance (MDR) isolated from hospital samples. Strains SP218 and SP219 were identified as a novel Streptococcus, S. sputorum, using whole-genome sequencing and biochemical tests. Average nucleotide identity values of strains SP218 and SP219 with S. pseudopneumoniae IS7493 and S. pneumoniae ST556 were 94.3% and 93.3%, respectively. Genome-to-genome distance values of strains SP218 and SP219 with S. pseudopneumoniae IS7493 and S. pneumoniae ST556 were 56.70% (54–59.5%) and 56.40% (52.8–59.9%), respectively. The biochemical test results distinguished these strains from S. pseudopneumoniae and S. pneumoniae, particularly hydrolysis of equine urate and utilization of ribose to produce acid. These isolates were resistant to six major classes of antibiotics, which correlated with horizontal gene transfer and mutation. Notably, strain SP219 exhibited cytotoxicity against human lung epithelial cell line A549. Our results indicate the pathogenic potential of S. sputorum, and provide valuable insights into mitis group of streptococci.

Author(s)
Chao, Wang
Access full library of outputs Opens in new tab