Impact of AI and Digitalization on R&D in Pharmaceutical Industry
Innoplexus has been a leading contributor to big data and Artificial Intelligence (AI) projects in pharma R&D. Our point of view on the impact of AI enabled technologies on R&D can be summarized in one brief statement: “Data is not the new gold – but the ability to put it together in relevant, analyzable and actionable ways is.”
The pharma industry is beginning to embrace this revolution that can affect all aspects of biopharmaceutical research. From what we see as relevant and practical capabilities of big data and AI, there will be at least three major impacts on pharma R&D:
1. Integrate all internal and external data into one well-linked and searchable platform
The ability to manage and integrate data generated at all levels of the value chain from molecule discovery to real-world use, from clinical trial design to regulatory dossiers, from reimbursement applications to adverse event reporting is widely recognized as one key success factor for the industry.
While companies take different approaches to the implementation (from “big bang” reinventions of their clinical IT systems to gradual changes in their data infrastructure), all of them believe that the foundation to compete in the new reality of “personalized medicine” is to understand data broadly, deeply, and most importantly, quickly.
2. Fail faster and more transparently by employing modelling techniques in drug discovery and portfolio decision support so that innovation is not stymied
In 2003, the first whole human genome sequencing had cost roughly $3 billion. Only 3 years later the cost for one individual decreased to $300,000 and it took days to generate the information. Now the costs are approx. $1000 or less and it takes an hour to decode an individual’s DNA.
Acceleration of the understanding of patho-mechanisms on a receptor and genetic level, brings an explosion of available data. This can be turned into information and finally, an actionable insight with the support of AI enabled modelling and decision support technologies. Such technologies will be key in supporting the monitoring of workflows which often lack transparency and well-defined decision points. AI will contribute to more structured and coordinated processes through continuous monitoring. These more efficient processes will very likely generate new analytical techniques and ways of working.
3. Tap into the wealth of information from real-world evidence to establish clinical and economic value propositions
Health economic value proposition of a new chemical entity is part of the drug development decision making. The dilemma of not delivering data on the “real world” setting of medical practice is well understood but rarely solved. Payers require “real world evidence data” to establish pricing and reimbursement levels. There are several models to deliver the required evidence: some pharmaceutical companies partner with health care providers and insurers to be able to provide economic information based on actual claims and patient data. Others offer to go at risk in “pay for performance” agreements.
AI-enabled tools will move static processes to more fluid and adaptable working procedures. They enable monitoring and adjusting the development course according to the most recent insights generated from systems and integrate all new findings in real time.
These steps will require robust and integrated big data/ AI solutions that allow determining individual patient outcomes measured in clinical and financial terms. AI-based analysis and decision support will allow pharma R&D to produce solutions more effectively and to deliver on the promise of “personalized medicine”.