Oliver Schacht, CEO of OpGen looks at why technologies including artificial intelligence and machine learning could be key to combatting antimicrobial resistance.
Though the Covid-19 pandemic certainly is not over, that does not mean that the global healthcare and medical communities can afford to ignore the next pandemic -- antimicrobial resistance (AMR). Currently, the World Health Organization lists drug resistance as one of their top 10 global health issues to track in 2021. And the World Economic Forum recently announced that superbugs could be more deadly than Covid-19. Slowly but surely, the general public is becoming increasingly aware of the looming and growing threat of “the silent AMR pandemic”.
As the risk of superbugs continues to work its way into the public consciousness, the global healthcare community is turning its eyes towards the next frontier in the fight against AMR. Where existing solutions rely heavily on drug-based solutions, the future of AMR prevention and treatment optimisation lies in embracing artificial intelligence (AI) technologies and prioritising non-drug solutions such as rapid and comprehensive diagnostics.
The effective use of AI and machine learning will prove crucial in the fight against antibiotic resistance. As we’ve seen from Covid-19, outbreaks like these are not a local threat, but rather have lasting global implications. The use of AI in both diagnostics and surveillance will allow the healthcare community to more accurately, efficiently and quickly trace and anticipate the spread of future outbreaks. That said, Covid-19 caught the world by surprise, and AI will not be deployed effectively if healthcare workers are not familiar or trained in how to use these technologies. As such, we should utilise the time we have now to increase awareness among healthcare workers about the availability of such technologies and the impact these tools can have within individual patient care settings, and on a larger scale, how they can identify transmission events and disease patterns.
Though AI and whole genome sequencing (WGS) data models are already becoming increasingly accessible in high-income countries, the future of combatting AMR depends on including middle to lower-income nations, as well. Further, the community should promote AI tools that are sustainable and future-proof by being regularly updated to respond to current and evolving needs. Finally, the global healthcare community should work to create AI that is inclusive and accounts for the widest-possible potential use cases.
If the over-prescription of antibiotics in the Covid-19 pandemic has taught us anything, it is that the next wave in the fight against AMR will be driven by non-drug-based solutions. In a study that assessed the early months of the Covid-19 pandemic, it was found that 96% of hospital admissions were prescribed antibiotics before the 48 hours that it typically takes to confirm a bacterial infection. And the World Health Organization’s annual Antibacterial Pipeline Report suggests that antibiotics currently in development as well as recently approved medicines are not enough to combat the increasing emergence and spread of antimicrobial resistance. It is clear we need to prioritise alternatives moving forward.
As such, leaders in the medical community will be looking for non-drug-based advancements such as rapid testing. The general public has become familiar with rapid PCR testing throughout the course of the pandemic, but these types of tests will prove crucial in antimicrobial resistance as well. Speed, ease of use and accuracy of these tests offer far-reaching benefits. Rapid molecular, multiplex PCR diagnostic panels enable healthcare professionals to make faster appropriate antibiotic treatment decisions, in a matter of just a few hours, providing the opportunity for overall better and more cost-effective patient care.
Also, more clinicians are embracing whole genome sequencing (WGS) in the microbiology laboratories. The results from WGS provide clinicians with remarkably accurate pathogen identification, ultimately contributing to the fight against AMR. Further, automated interpretive tools are becoming progressively refined, with studies showcasing next generation sequencing (NGS) workflows capable of identifying antibiotic resistance markers with >95% sensitivity and >99% specificity and identifying pathogens correctly with 100% sensitivity and specificity.
The global threat of antimicrobial resistance is a serious concern. Going forward, we can expect the medical community to step back and look at patients’ diagnostic plans more broadly. Rapid molecular multiplex PCR testing and NGS combined with AI powered tools can provide targeted solutions which expedite the diagnosis and treatment of patients.