iopharmaceutical development and the broader healthcare industry are faced with growing challenges. The rising cost of healthcare is not sustainable, and the development of biopharmaceutical therapies is too expensive and too slow. Can new technologies such as artificial intelligence (AI), machine learning (ML), “bots,” and other digital tools, help accelerate the pace of development and approval of new therapies? At DIA Europe 2019, a diverse panel looked at the potential impact of new technologies and necessary steps to leverage them.

Impact of Digitization on Acceleration of New Therapies

Meanwhile, existing technology is changing at a dramatic pace. It is estimated that the advances made in the next five years will outstrip all those made in the previous twenty! For example, smart phones now have chips with around three billion transistors. A chip with 120 billion transistors–enough to store the structure of all currently known molecules (about 10⁶⁵)–will soon be available. With our knowledge of genomics, protein structures, transduction of signals in cells, and new AI methods, identifying molecules that can influence the activities of cells can be as rapid as 20 microseconds per molecule. If we can catch up with other industries in applying these powerful technologies, our ability to improve patient lives will be boundless.

Technology as Fundamental Transformation

Access to data is critical, both from an ethical standpoint and in terms of our ability to leverage technology. Without the proper governance, concerns about appropriate data sharing and use will continue to hamper rather than facilitate access. For example, many patients express willingness to share their health data but don’t want it used in a commercial way. We need governance about who should have access to the data and how to ensure fairness and equity for patients and researchers alike.

Further, we need to agree on business models to support further development and use of technology and data. Two types of models are currently operating: a model of central technology development with separate sale of technology and data access, and a model of free technology with monetization and payment for data. Lack of agreement on a universally accepted model contributes to uneasiness around the practice of monetizing data as well as expensive systems that may be less accessible.

True Power of AI: Predictive Applications

What is the practical meaning of these ideas, and what are our next steps?

The ability to clearly see the outcomes of therapeutic interventions and to positively impact their improvement through this continuous learning system would stimulate creative thinking and allow a more natural healthcare market to evolve.

On a practical level, we must challenge the notion that nothing can be better than the gold standard of the Randomized Controlled Trial (RCT). For example, can we use data from a placebo and standard of care database instead of control arms in every single trial? A TransCelerate project is developing such a database, which now includes data from over 100,000 patients.

We also need methodologies to combine both structured and unstructured data. Instead of trying to structure all data, we can save time and effort by tapping into the value of both. Data sharing will drive process advancement. The technology for building a whole data ecosystem is already here. A data ecosystem that includes open sourcing and data sharing among sponsor, CRO, patient, healthcare provider, and regulators will ensure benefit for all.