With the frequent usage of antibiotics to treat a wide range of common bacterial infections, from urinary tract infections, to sepsis and more, bacteria are becoming more resistant to treatment.
This is a global phenomenon on the rise which requires urgent attention. Failing to tackle antimicrobial resistance (AMR) will result in at least 10 million extra deaths globally by 2050 – more than the number of people who currently die from cancer.
Pharmaceutical companies worldwide urgently need to strengthen their investment in antibiotics development, but they all face the problem of high costs and long development cycles. A quick look at the numbers confirms this. According to industry groups PhRMA and BIO, on average it takes more than US$1 billion and over 10 years to put a new drug on the market, from development to approval. But even with all that investment, 90% of clinical drug development fails. A faster, more effective approach to drug development is needed. Artificial Intelligence (AI) can play an important role in the research part of drug R&D, readying more candidate drugs for clinical trials, cheaper and faster, contributing to the digital transformation of pharmaceutical companies and shorter waits by patients.
AI can potentially reduce costs by up to 70%
In 2021, a survey of biopharma professionals by GlobalData revealed that AI was expected to have the greatest impact on the pharma. It is perhaps not surprising that since 2015 there have been over 100 partnerships between AI vendors and major pharma companies. AI has significant application potential in the drug discovery field as it can speed up the time needed to research and develop new drug compounds in drug discovery, helping researchers to discover novel chronic disease treatments in months as opposed to years. It can also vastly reduce the research and development costs, by up to as much as 70%in some cases. As AI can help identify specific compounds likely to be more successful in drug trials it also helps increase the success rate of drugs in trial. This is part of the reason why all of the world’s top ten drug makers such as GSK, Novartis, Pfizer and Sanofi, are now investing in AI either via collaborations or technology aquisition (The Guardian). The market size for the ‘Artificial Intelligence for drug discovery and development’ was valued at $520 million in 2019 and according to Grand View Research is expected to reach $4,815 million by 2027, registering a CAGR of 31.6% from 2020 to 2027.
Drug X – the first new class of antibiotics in 40 years
A major challenge in new drug discovery lies in the screening of hundreds of millions of existing drug molecules. Traditionally, drug screening performed by experts in labs is costly, slow, and has a high failure rate. This is why the application of AI is attracting interest from the industry as a viable alternative. In 2019, more than 1.2 million people died as a direct result of antimicrobial-resistant bacterial (AMR) infections. This figure is higher than the number of deaths caused by HIV in the same year. The WHO estimates that an expected USD $1.2 trillion in expenditure per year expected by 2050 due to the rise of AMR.
To tackle the problem of AMR, Dr. Liu Bing and his team at the First Affiliated Hospital of the Medical School at Xi’an Jiaotong University have successfully developed the super antimicrobial drug Drug X, which if approved, will be the world’s first new class of antibiotics with new target proteins in nearly 40 years. The discovery time for lead compounds was shortened to just one month, and R&D costs were slashed by 70%. This new drug achieves antimicrobial effects by targeting histone-like proteins (which bind to DNA) from HU (RNA-binding proteins involved in diverse biological processes) to inhibit bacterial DNA replication.
