Pharma companies slow to adopt AI will be ‘left behind’

By Ben Hargreaves contact

- Last updated on GMT

(Image: Getty/Rawpixel)
(Image: Getty/Rawpixel)

Related tags: machine learning, Artificial intelligence, IQVIA

Though pharma companies now accept the value of AI and machine learning, extra effort is required by the industry to realize the potential business and patient benefits, says Iqvia exec.

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 Pfizer’s deal with CytoReason​ and Novo Nordisk’s 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.

Yilian Yuan_Headshot
Yilian Yuan, SVP of data science and advanced analytics at Iqvia

OSP: How would you characterize the pharma industry’s 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:

  • Assemble the foundational data. Vast healthcare data is available, but it is located in many different places internally (e.g., R&D, therapeutic franchise, marketing and sales, national accounts, finance, etc.) and externally (media agencies, promotion partners, patient outreach partners, etc.), and on many disparate systems
  • Integrate and curate data from pharma, healthcare systems, distributions systems, payers, patients, etc. For example, lab data is not reported the same way across labs and across countries. De-identify so you can link data across the continuum of care without violating privacy regulations
  • Be sure to build machine learning algorithms specifically for pharma. Pharma is much more complicated than other industries because many stakeholders are involved in decisions related to patient care. In a number of cases, customized AI/ML analytic approaches must be developed for pharma applications
  • Implement change management. Sometimes the value of AI/ML is realized by doing things differently based on the deep insights uncovered by AI/ML. This often requires change of   management and leadership support

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:

  1. Partnership. By partnering with tech companies, pharma companies can build the data and analytics platforms necessary to host all data assets and analytics across the entire business, from R&D to commercialization. These collaborations will foster the development of fit-for-purpose machine learning and deep-learning tools for pharma to help increase the speed of developing new medicines, drive down cost and improve operational efficiencies. This approach usually leads to ownership of and/or access to the intellectual property of AI/ML and takes a significant amount of time to realize impact
  2. Outsourcing. Pharma companies outsource the work to companies with the advanced technology stack, data integration and curation expertise, pharma and disease domain expertise and AI/ML analytic capabilities. This approach requires selecting a partner who can deliver on the promise. It usually takes a shorter time to realize the AI/ML value than to build from scratch, because the right partners bring all the necessary ingredients and experience to the table and help accelerate the application of AI/ML to deliver value

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:

  • Enable in silico​ discovery of potential compounds for further investigations
  • Predict side effects or drug interactions based on chemical characteristics of potential drug candidates from analyzing past clinical trial data of chemical entities with similar characteristics
  • Analyze clinical trial data to identify patient subgroups that may benefit from a product that did not make the end points of the clinical trial
  • Analyze real-world data to identify potential new indications for further investigation

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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