AI-Assisted Drug Design: Benefits of the Pharma Industry Paradigm Shift
Article by Dr. Qiao Nan, Head of Huawei Cloud EI Health
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.
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 trials. 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 acquisition. The market size for ‘artificial intelligence for drug discovery and development was valued at US$520 million in 2019 and according to Grand View Research is expected to reach US$4,815 million by 2027, registering a CAGR of 31.6% from 2020 to 2027.
A major challenge in new drug discovery lies in the screening of hundreds of millions of existing drug molecules. Traditionally, drug screening performed by experts in labs is costly, slow, and has a high failure rate. Recognizing the potential of AI, Huawei Cloud is taking an ongoing, long-term approach to investment in the healthcare industry, including AI-Aided drug design research. One such effort is in drug discovery. Huawei Cloud Pangu Drug Molecule Model jointly developed with Shanghai Institute of Materia Medica can assist pharmaceutical companies to design small molecule drugs.
The model has been trained using the data of 1.7 billion compounds and can predict the physicochemical properties of drug compounds that are known to work and be safe and score them based on their ‘druglikeness’. Researchers can then do targeted experiments to verify drug compounds that have the highest scores. Moreover, the Pangu Drug Molecule Model’s molecule optimizer can be used to optimize the structure of lead compounds, minimizing the potential side effects of the new drugs on humans.
This model is the basis for the Huawei Cloud AI-Aided Drug Design Service, the first commercial AI-assisted pharmaceutical SaaS platform in China. With the service, pharmaceutical companies can reduce the costs of trial and error, accelerating the discovery of lead compounds from several years to just one month. Starting with the APAC region, then the Middle East, this service will be made available overseas.
More than 1.2 million people died in 2019 as a direct result of antimicrobial-resistant bacterial (AMR) infections. This figure is higher than the number of deaths caused by HIV in the same year. The WHO estimates that an expected US$ 1.2 trillion in expenditure per year is expected by 2050 due to the rise of AMR. Using Huawei Cloud AI-Aided Drug Design Services, Dr. Liu Bing of the First Affiliated Hospital of the Medical School at Xi’an Jiaotong University led his team to develop Drug X, a drug used to target antimicrobial resistance (AMR). AMR is a growing and expensive problem. The discovery of lead compounds was accelerated to just one month, and R&D costs were slashed by 70%.
Research on super antimicrobial drugs has never been more urgent. Drug X has been verified by animal experiments and is undergoing preclinical research for an Investigational New Drug (IND) application. Patent applications for Drug X have been filed in multiple countries. If successful, Drug X has the potential to solve the severe challenge posed by patients facing antimicrobial-resistant (AMR) bacterial infections with no available effective drugs.
Using the platform, Dr. Liu and his team were able to screen potentially viable compounds from a large-scale database of billions of small molecules that are easy to synthesize. The super-high computing power provided by Huawei Cloud has resulted in a tenfold improvement in drug screening efficiency.
The Pangu Drug Molecule Model has effectively supported commercial services or drug pipeline development cooperation between many pharmaceutical companies and research institutes. But this is just the beginning. With continuous progress and breakthroughs in our independent R&D to address these challenging issues, Huawei Cloud will continue to support and upgrade the medical industry at home and abroad, and work with practitioners in the medical industry to improve human health.
Beyond molecule design and testing, AI algorithms mean that the number of physical tests can be reduced so that they are only necessary to validate results. This is a saving on time and money. However, at this stage, it must be acknowledged that the application of AI in drug discovery is not without challenges. For example, the need for data and samples; the lack of interoperability as layers become more complex; and the translation of traditional research into machine learning.
For the moment, traditional pharma companies still maintain an advantage in terms of regulatory understanding and experience; drug development know-how and experience, and fundamental scientific expertise. The initial success of Pangu Drug Molecule Model is therefore encouraging and exciting for the industry. As the pharma industry faces a revolutionary paradigm shift, safety and efficacy remain the priority.
The views in this article is that of the author and may not reflect the views of Tech Wire Asia.