Infectious diseases, caused by transmissible pathogens including bacteria, eukarya, and viruses, continue to challenge scientists and clinicians despite advances in medicine and basic research over the past few decades. Limitations to the fast and accurate detection of infections, as well as expanding antimicrobial resistance, exacerbate these challenges. Basic research has aimed to address these challenges, including development of anti-infective therapies, preventative measures, and fast and accurate diagnostic tools. In particular, systems and synthetic biology approaches have led to biotechnological and medical innovations—including drug treatments and modalities, vaccines and diagnostics—that have improved how we deal with infectious diseases.
Problematic pathogens include carbapenem-resistant Enterobacteriaceae, methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant tuberculosis (XDR-M), vancomycin-resistant Enterococcus (VRE), extended-spectrum beta-lactamase (ESBL)-producing bacteria, drug-resistant Candida auris, Neisseria gonorrhoeae, Plasmodium falciparum and Toxoplasma gondii. Challenges include practicing antimicrobial stewardship (the appropriate and responsible use of anti-infective drugs), developing new classes of anti-infective drugs, potentiating existing drugs against resistant infections, and understanding drug mechanisms of action. Anti-infective drugs, comprising antibacterials, antivirals, antifungals, and anti-parasitics, have become less effective treatments as a result of the spread of drug resistance.
There is therefore an urgent need for new anti-infective treatments, particularly ones that represent unprecedented chemical spaces or therapeutic modalities. AI, and in particular machine learning (MAL), a subfield of AI which uses data to train machines to make predictions, has foremost been helpful in facilitating searches of small molecule databases. MAL approaches to anti-infective drug discovery have centered on training models to identify new drugs or new uses of existing drugs. a major benefit of ML approaches is that they can virtually screen compound libraries at a scale (>1 billion compounds) that would be impossible to screen empirically. Anti-infective drug discovery has benefitted particularly from AI integration for several reasons.
One of these is that, in contrast to cancer or other diseases in which mechanism-driven approaches have remained dominant, infectious diseases are generally phenotype-driven; that is, these diseases proceed from the physiological characteristics of infectious agents, rather than their genetic or molecular compositions. Drug development is a lengthy and intricate process influenced by numerous factors such as safety, cost, manufacturing, and clinical trial outcomes. Drugs can be toxic in different ways and MAL models predicting toxicity have been limited by factors such as the lack of high-quality datasets. Absorption, distribution, metabolism, excretion, chemical instability and metabolic breakdown, are also needed to filter out drug candidates that are unselective or unsuitable for medicinal use.
Scientists from Massachusetts Institute of Technology (MIT), the Broad Institute of MIT and Harvard, the Wyss Institute for Biologically Inspired Engineering and the Leibniz Institute of Polymer Research in Dresden, Germany, have discovered one of the first new classes of antibiotics identified in the past 60 years, and the first discovered leveraging an AI-powered platform built around explainable deep learning. In their study, the researchers virtually screened more than 12 million candidate compounds to identify this new class of antibiotics, which show potential to address antibiotic resistance. In this groundbreaking approach, the team of researchers trained deep learning models on experimentally generated data to predict the antibiotic activity and toxicity of any compound.
Drawing inspiration from AI used in other contexts, including DeepMind’s AlphaGo gaming technology, the authors designed new models to explain which parts of a molecule were important for antibiotic activity. The result was the identification of a new class of antibiotics with potent activity against multidrug-resistant pathogens. In one series of experiments, the researchers tested a candidate antibiotic in mouse models of MRSA infection and found that it was efficacious both topically and systemically, indicating that the compound could be suitable for further development as a treatment for severe and sepsis-related bacterial infections. This is an important validation of how important the integration of AI and explainable deep learning will be to overcoming some of the most vexing challenges in medicine, in this case antibiotic resistance.
Building on these validating studies and similar approaches, the Integrated Biosciences team is poised to further accelerate their integration of synthetic biology and a deep understanding of cellular stress to address a significant unmet need for new treatments targeting age-related diseases.
- Edited by Dr. Gianfrancesco Cormaci, PhD, specialist in Clinical Biochemistry.
Scientific references
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