3rd October 2018

AI in Pharma

There a couple of ways the industry is implementing AI strategies

Some big pharmaceutical companies are looking to AI driven drug discovery start-ups to get access to expertise and tools to have an effective AI strategy. For example, in June 2017, Genetech announced a partnership with GNS healthcare to identify and validate noval cancer drug targets using the company’s propriety casual machine leaning and simulation AI platform. This strategy works quite well as start-ups get access to vast amounts of data big pharma can provide, and the more data available the better results AI and machine learning can achieve.
Another way to go is to adopt internal AI capabilities, some pharma companies are adopting a cloud strategy as these cloud providers are increasingly improving AI and machine learning (ML) capabilities available as a service.

What areas are pharma companies investing in AI?

Drug Discovery – One of the main areas of focus for AI among pharma companies is Drug Discovery. The current drug discovery process is lengthy and expensive. It can take up to 15 years to translate a drug discovery idea from initial inception to a market ready product. The Pharma industry is said to spend well over $2 billion per drug, that’s partly because all the drugs that didn’t make it to market still have to be paid for.

In an article written by Professor Jackie Hunter who has held senior positions at GSK, Proximagen and is now CEO of BenevolentBio, she explains how Artificial Intelligence and machine learning present the pharmaceutical industry with a real opportunity to do R&D differently, so that it can operate more efficiently and substantially improve success at the early stages of drug development. The long-term benefits of this will mean that the vast resources and money used to develop drugs in the current process will be deployed more effectively to give not only a better return on the investment but also a substantial increase in the delivery of new medicines for serious diseases.
Therefore, Scientists using AI can test more compounds and do so with improved accuracy and reproducibility. This will reduce the time and cost it takes to bring new drugs to market.

Other applications for AI include:

Diagnosing and treating diseases – In regards to Oncology Stanford University researchers have trained an algorithm to diagnose skin cancer using deep learning, algorithm was trained to detect skin cancer or melanoma using “130,000 images of skin lesions representing over 2,000 different diseases.”

Radiology and Radiotherapy – Google’s DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments.
Clinical Trial Research – Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits, for example, as well as genetic information when looking to target specific populations; this would result in smaller, quicker, and less expensive trials overall.

Dr Muhammed Ali, who is MSD International’s executive director for healthcare solutions strategy across Europe and Canada has said;
“AI in its current state is only as good as the data it receives … it isn’t a like-for-like person replacement. he added, scientists will effectively ‘evolve’ to become more integrated with technology than ever before. “If you think of science-fiction cyborgs who are part-human, part-machine, that’s where we are moving in terms of thinking, rather than anything physical.”“We are about to see augmented learning faster for researchers and scientists who are to be more ‘educated’ at getting to discover, trial and design personalised medicine. The future of AI will force everyone to think AI digital-first and make decisions that are digitally data-driven.”

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