Using artificial intelligence (AI) machine learning, a new class of antibiotics to treat drug-resistant staph infections has been found for the first time in over 60 years; this is a historic development to solve the antimicrobial resistance (AMR) challenge.
Researchers from Harvard University, the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, and the Massachusetts Institute of Technology (MIT) used artificial intelligence and machine learning to make this significant discovery that will improve global health care.
Globally, antibiotic resistance is a major cause of death and a hazard to public health. The UK Government commissioned a paper called The Review on Antimicrobial Resistance, which predicted that 10 million people will die each year from AMR by the year 2050.
A 2019 study published in The Lancet estimated that 1.27 million fatalities worldwide were directly caused by antimicrobial resistance and 4.95 million deaths were linked to bacterial resistance to antibiotics. According to the U.S. Centers for Disease Control’s 2019 report, Antibiotic Resistance Threats in the United States, there are over 2.8 million antimicrobial-resistant illnesses and 35,000 related fatalities in the country each year (CDC).
Antimicrobials include drugs including antibiotics, antifungals, antivirals, and antiparasitics that either eradicate or stop the growth of microorganisms. When bacteria mutate or adapt, antimicrobial resistance (AMR) occurs and renders antibiotics useless. When hazardous viruses, bacteria, parasites, and fungus become resistant to antibiotics and antimicrobial medications, this natural process can be sped up by misuse or overuse.
Drug-resistant bacterial strains are a result of various factors, such as the overprescription of antibiotics for human use and their excessive usage in animal feed. A Review of Antibiotic Use in Food Animals: Perspective, Policy, and Potential by Landers et al. states that antibiotics like tetracyclines or tylosin are fed to 88% of swine, tylosin is fed to 42% of beef calves, and almost all dairy cows receive prophylactic doses of antibiotics such as beta-lactams, cephalosporins, or penicillins after lactation.
The World Health Organization (WHO) reported in its 2022 Global Antimicrobial Resistance and Use Surveillance System (GLASS) report that the median rate for methicillin-resistant Staphylococcus aureus and third-generation cephalosporin-resistant Escherichia coli (E. coli) in 76 countries is 35% and over 40%, respectively.
Staphylococcus aureus, or Staph for short, is a Gram-positive bacterium that causes a wide range of illnesses in humans, including fatal pneumonia, sepsis, and skin infections. Over 120,000 deaths globally were attributed to methicillin-resistant S. aureus (MRSA) in 2019, according to a January 2022 research by the Institute for Health Metrics and Evaluation.
The co-authors of the paper, James Collins, Ph.D., an MIT professor, noted, “Our study demonstrates that graph neural networks can be better understood and explained using graph-based searches for chemical substructure rationales that recapitulate model predictions.”
The researchers employed Chemprop, an AI platform for graph neural networks (GNNs). To create its predictions, AI graph neural networks use data from each molecule’s atoms and chemical bonds. Artificial neural networks known as “graph neural networks” are capable of processing graph data structures to carry out tasks including analysis, classification, and prediction.
The researchers stated, “An enticing implication of the current study is that deep learning models in drug discovery can be made explicable.”
After screening more than 39,300 compounds for the methicillin-susceptible strain S. aureus RN4220’s growth inhibitory potential, the scientists identified 512 active candidate compounds. Using the atoms and bonds of a new chemical’s molecular chemistry, ensembles of AI graph neural networks were trained using the screening data to predict whether or not the compound suppresses bacterial growth.
Eliminating substances that are hazardous to or may harm human cells is a crucial part of the drug-discovery process. In order to predict cytotoxicity as well, the researchers counter-screened the training database containing more than 39,300 chemicals. They counter-screened for cytotoxicity in human liver cancer cells (HepG2) in order to comprehend both liver toxicity and general cell toxicity. The researchers counter-screened human primary skeletal muscle cells (HSkMCs) and human lung fibroblast cells (IMR-90) to get insights regarding in vivo cell toxicity.
Out of the 512 active antibacterial candidate compounds, 40 percent showed cytotoxicity based on the orthogonal models used to predict cytotoxicity. This means that 306 compounds did not exhibit any cytotoxicity for the three cell types that were screened.
The researchers then used all of the training databases to retrain ensembles of 20 AI models, creating four AI ensembles that could forecast the cytotoxicity and antibiotic capabilities of the three different cell types (HepG2, HSkMCs, and IMR-90). Over 12 million compounds were fed into these four AI ensembles as input data, with over 11.2 million coming from the Mcule commercialized database and over 799,000 from a Broad Institute database.
Following additional screening, 283 compounds were selected by the researchers and tested for experimental growth suppression against MRSA in a laboratory setting. Two antibiotic candidate compounds were found as a result, and mice were used for testing.
