IQVIA on the adoption of AI and machine learning – OutSourcing-Pharma.com

Artificial intelligence (AI) and machine learning (ML) have become central topics in the pharma industry in 2019. Greater levels of investment are being funneled in this direction and a greater number of partnerships have sprung up around the areas.

The potential in relation to the pharma industry have often centered around drug discovery. The potential is there for the technology to reduce the cost of developing a new drug, which has been estimated to be approximately $2-3bn (1.8-2.7bn).

As a result, a number of large pharma companies have signed partnership deals to unlock the promise of faster drug discovery, such as Pfizers deal with CytoReason and Novo Nordisks with e-therapeutics.

Wider than this, there is the potential to improve patient recruitment to clinical trials, another notorious stumbling block in drug development.

Outsourcing-Pharma (OSP) asked Yilian Yuan (YY), SVP of data science and advanced analytics at Iqvia, for analysis on how the pharma industry is approaching the opportunity provided by AI and ML so far, and how this is likely to develop over the coming years.

OSP: How would you characterize the pharma industrys adoption of AI, so far?

YY: In general, the pharma industry recognizes the value of AI/ML, and some pharma companies have made significant investments to build the infrastructure and talent pool necessary to bring AI/ML capabilities into the R&D and commercial sectors. However, implementation can be challenging. To overcome these challenges pharma companies should undertake the following steps:

Because of the many challenges, pharma has been slow to take up AI/ML. Extra effort will be needed for the industry to fully leverage AI/ML and to realize the positive impact on business and improved patient care.

OSP: How do you see further adoption of AI/ML for pharma in 2020 and beyond?

YY: I see more and more pharma companies taking various approaches to realize the value of AI/ML, and they fall into two categories:

We also see some companies taking a combination approach to get the benefits of both.

OSP: The industry generally has a reputation of being cautious when it comes to the adoption of new technologies what are the dangers of this when applied to AI/ML?

YY: The development and commercialization of innovative treatment options for the market is costly and competitive. AI/ML can leverage real-world data to innovate clinical trial design and execution, e.g., smart patient recruitment, and select sites that can quickly enroll patients. On the commercialization side, AI/ML enables proactive and precise engagements with health care providers and patients and the ability to identify patients with high risk of exacerbation or noncompliance with the trial regimen, which can trigger interventions by nurse educators.

Pharma companies that are slow to adopt AI/ML will be left behind in the race to bring new products to market and the right products to the right patients at the right time.

OSP: Are there are any particular areas of drug discovery where the technology can have the most impact?

YY: There are many areas where AI/ML will have a positive impact on drug discovery:

OSP: Are there any noteworthy industries that are leading the way in using this technology? What can the pharma industry learn from them?

YY: The automotive industry faces fierce competition and has leveraged AI/ML to do precision marketing on its websites, with tailored messages and select products for potential buyers. Perhaps pharma can learn from them and use AI/ML to develop personalized medicine to improve patient care.

Many industries use robotic process automation to automate processes like finance systems, which pharma could do as well. Pharma is a heavily regulated industry with many reporting documents generated for clinical trials and for product usage and adverse events. These documents must be translated into many languages. Tech companies have developed auto translation services with the help of AI/ML. Existing auto translation with AI/ML can be enhanced with pharma vocabulary to cut down on the cost of translating documents into multiple languages and increase the speed of this type of work.

Yilian Yuan leads a team of data scientists, statisticians and research experts to help clients address a broad range of business and industry issues. Dr. Yuan has an extensive background in applying econometric and statistical modeling, predictive modeling and machine learning, discrete choice modeling and quantitative market research, combined with patient-level longitudinal data to provide actionable insights for pharma clients to improve business performance.

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IQVIA on the adoption of AI and machine learning - OutSourcing-Pharma.com