AI and Pharma Industry: A New Love Story, By D. Conterno (2019)
The medical industry has become a Research and Development (R & R&D) sector where Artificial Intelligence (AI) has made its entry compelling. They are many signs that we are moving more and more toward speciality AI applications. The quality of any AI application is directly related to the quality and consistency of the input data.
One of the most important areas of progress in AI will be the quality and consistency of available data. Therefore, the Internet of Things (IoT), and its ability to capture orderly and standardised data, will be the next revolution.
To this day, the primary medical areas affected by AI have generally been:
- Robotic Surgery: For example, laser eye surgery and hair transplants are typical simple yet straightforward procedures for effective treatments.
- Image Analysis: AI automated systems assist experts in examining X-rays, retina scans and other images.
- Genetic Analysis: As genome scans have become a routine part of medicine, AI tools that quickly draw insights from the data are essential.
- Pathology: Experimental systems have so far proved adept at analysing biopsy samples. The next step will be to get them approved for clinical use.
- Clinical Decision Support: A new ground that has, somehow, not yet proven its value. A typical example of this technology will be for predicting septic shock.
- Virtual Nursing: Systems can check on patients between office visits and provide automatic alerts to doctors.
- Medical Administration: New AI-enabled tools can increase efficiency in tasks like billing and insurance claims.
- Mental Health: Mining mobile phone and social media data can be used by researchers for monitoring depression.
IBM AI multi-purpose solution, called Watson (an IBM supercomputer that combines AI and sophisticated analytical software for optimal performance as a "question answering" machine), failed to deploy significant applications that would have made an outstanding contribution to the medical industry. For example, Eliot Siegel, a professor of radiology and vice-chair of information systems at the University of Maryland, collaborated with IBM on the diagnostic research. While he thinks AI-enabled tools will be indispensable to doctors within a decade, he eventually stated that he was not confident IBM would build them. "I don't think they're on the cutting edge of AI," says Siegel. "The most exciting things are going on at Google, Apple, and Amazon."
Martin Kohn, who originally came to IBM with a medical degree from Harvard University and an engineering degree from MIT, was excited to help Watson tackle the language of medicine, thus creating some superdoctors; he left IBM in 2014, saying that the company fell into a common trap: "Merely proving that you have powerful technology is not sufficient," he says. "Prove to me that it will do something useful to make my life better and my patients' lives better."
In the medical industry, the search for new drugs is a lengthy and costly process. Even with the use of AI, innovation can be a challenging path to follow. However, a French biotech company, Pharnext, established in 2007, has just achieved a significant milestone in using AI for creating new treatments and has become a model of a successful AI biotech company. The success of this company is partly because Pharnext uses quality, not raw, data that is reliable and proven. Their database is complete enough as its need for new data is low. Pharnext ingenious technique is also to work on molecules already commercialised to build molecular combinations. The benefits of this approach are many:
- The amount of data is controlled
- The quality of the molecules being screened is already established
- As their databases are based on old molecules, the information is often of good quality and verified, allowing for high quality and good data formatting. Every day, molecules fall into the public domain. Pharnext approach can therefore restart tests with new molecules regularly. This process will be significantly mastered and perfected on an ongoing basis as time goes by. Potentially, Pharnext can perform in-silico research and discover new treatment options.
- It allows a medical project to reduce the time from inception to market delivery by 5 years compared to traditional research methods. This means that a 15 years project is reduced to 10 years, thus achieving a 33% reduction.
Pharnext success allowed them to create a partnership with other biotech companies. For example, Galapagos, a Belgian pharmaceutical company, worked with Pharnext to explore improvements and create a new pipeline combination of synergistic drugs covering various indications in inflammatory and neurodegenerative diseases. "These new combinations are centred around Galapagos' candidate drugs in the inflammatory field, which will be associated, thanks to our patented technology, with other molecules already marketed without a patent," Daniel Cohen told Les Echos in 2017.
Furthermore, Pharnext also partnered with Tasly in China. Tasly is a giant in the field of traditional medicines. Tasly employs more than 10,000 people and is part of the 10 largest companies in the sector. Here, the partnership is twofold:
Tasly will open the Chinese market to Pharnext drugs
Pharnext puts at Tasly's service technology as part of a joint research platform
Yan Kaijing, CEO of Tasly Pharmaceuticals, stated: "Building on both the benefits of Tasly's research and development platform and its dense implementation in the Chinese hospital network; and on the remarkable technology Pharnext's drug discovery, we will develop combinations of Pharnext high-potential drugs to meet unmet medical needs. Supported pharmacology of biological disease networks. This partnership will allow us to harness the immense potential of medicines from traditional Chinese medicine to modernised medicine, thanks to a precise and new characterisation of the mechanism of action of each combination".
The future of AI in the biotech and medical industry is thrilling as it is now achieving a major technological transition from what is fundamentally a problematic, administrative process ridden and unpredictable research industry to a more matured one that will benefit doctors, medical and paramedical support agencies, hospitals, drug research companies and ultimately patients.