The Exponential Growth of AI in Pharma for 2020.

The growth of Artificial Intelligence (‘AI’) is becoming prevalent for many industries expanding beyond just research, sales and marketing. Today, AI automation has been used by the Pharma industry since the '70s, and pharma companies are seeking to expand the Use Cases beyond just operational efficiency. But there are limitations and obstacles such as adoption, budget, and trust that limits companies to utilize the technology. As of 2020, more pharma companies, hospitals, clinics, labs, and manufacturers are embracing the technology for Efficiency, Predictive Analytics and Machine Learning (‘ML’) are the key drivers. There is a strong belief and vision that AI and ML will provide the innovation and efficiency to improve Drug Discovery in the coming years. In fact, there have been observations already that AI is helping to discover new lead compounds through becoming the computer chemist. There are also developments in the UK eyeing on how AI can affect the efficiency of Drug Design. Since adoption is becoming widespread within the pharmaceutical industry, here are some of the benefits and reasons Pharma is utilizing AI and ML more in 2020.

1) AI can help in developing new drugs:

One reason why Drug Developers are incorporating AI into their processes is that it can help in the development life cycle. There is a study from the Massachusetts Institute of Technology (MIT) that discovered a 13.8% success rate in terms of drugs passing clinical trials. Pharma companies are paying $161 million to $2 billion just to get the entire clinical trial process completed and successfully gain the approval of the FDA. However, this is a low success rate for the billions that are spent in research, development, and trials. With AI to help with the development of new drugs, Pharma companies are looking forward to increasing the success rates of new drugs while minimizing the cost of production.

2) AI can help in the efficiency of clinical studies:

Another benefit and reason why Pharma companies are using AI is to increase the efficiency of clinical studies, especially in the earlier phases. Most clinical studies are still in papers and journals rather than in electronic systems. To test the effect of drugs in patients, they still need to take note of the contributing and non-contributing factors, to determine the effects or adverse reaction to the drug. This traditional way is undeniably slow given the number of protocols that have been developed for most disease states and conditions. There are even some companies and clinics that still use fax machines to deliver and request patient records (don’t even get me started on this one!). 

If AI is incorporated into clinical studies, there is a possibility to minimize the 80% clinical trials that fail in the industry, as per the study by Cognizant. Data extraction and analysis will be easier and medical records will be more secure and accessible. The technology may not necessarily bring us to a higher approval rate immediately; however, our ability to fail faster would also provide financial savings that could be reallocated to the portfolio for investment into another drug, device or product.

3) AI can help companies match patients with appropriate clinical studies:

By leveraging AI as part of the patient/investigator recruitment process, the process to complete this part of the trial could be expedited with a greater level of accuracy. This could result in “the first patient in, the last patient in and locking the database” in a much faster manner thus lowing the cost for the trial initiation. In addition, investigator verification could be conducted electronically as part of an ongoing activity during the trial. By developing algorithms that match patient/condition/disease state/treatment/location/ with specific clinical studies, Sponsors and CRO’s will have greater visibility and transparency into the analysis of the structure and unstructured data from the medical records of the patients thus deploying automation into various phases of the trial. This can happen in real-time, allowing investigators and researchers the opportunity to review data and results faster resulting in the overall speed and completion of the trial.

The outlook for AI in Pharma for 2020 looks extremely promising and creates opportunities for companies, researchers, patients, products and the industry as a whole. The key factors to adoption are to deploy AI into different parts of the process to determine the level of automation and accuracy. The goal of AI is to enhance and innovate processes, not to disrupt and replace the human factor. AI is used to support our vision and efforts and the Pharma industry is the perfect place for AI and ML to grow exponentially.   

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