Digital and Advanced Analytics: From Hype to Tipping Point in R&D

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Martin Dewhurst, co-leader for McKinsey’s global Pharmaceuticals and Medical Products Practice, discusses the enormous potential value of digital and analytical advancements for R&D.

 

 

One of the areas with the most transformational impact is the potential to double R&D productivity over the next five to ten years.

 

After years of buzz around digital and advanced analytics in pharma, are they finally reaching a tipping point or will they remain more hype than reality?

Admittedly, some of the most optimistic predictions of recent years, such as the broad availability of genetic tests and gene therapy for a wide range of diseases, have yet to come to full fruition. Nor has access to a wealth of new scientific data been followed by the expected flood of new medicines. But, we believe the signs of transformation are now visible (e.g., in much expanded early stage pipelines) and the industry should soon start to realize tremendous value from the maturing of new technologies (both science and technology) with the potential to improve outcomes for patients (including more curative therapies).

These technologies will drive a step-change across the value chain from research, development, regulatory, and safety through manufacturing and supply to market access and commercial, as well as enabling efficiencies in support functions such as HR, finance, and strategy. Over the next five years, companies in the forefront of this digital and analytics wave could expect to see EBITDA (Earnings before interest, tax, depreciation and amortization) improvements equivalent to 15 to 30 percent or more.

One of the areas with the most transformational impact is the potential to double R&D productivity over the next five to ten years. This will be propelled by the use of new data sources such as real-world evidence and data mining of genomic and other sources; the application of advanced analytics in areas such as meta-analysis of cross-institutional data and computational modeling; process optimization via digitization and automation; and the introduction of sensors and wearable technology to enable remote patient monitoring giving deeper insights into how a patient experiences their disease and what will drive improved outcomes.

 

Drug discovery will be driven by data, whether in disease pathway analytics, the identification of high unmet medical need, or the mining of real-world evidence data to yield new insights.

 

To unlock this value, companies will have to overcome a number of obstacles, including the ambiguity of the regulatory environment in novel/emerging arenas, the fragmentation of data across many “silos”;  the complexity of processes for safeguarding data privacy, cybersecurity, and IP protection; and the difficulty of acquiring digital and analytics expertise.

At the same time, many forces are combining to help pharma realise the enormous potential of digital and advanced analytics: The vast amounts of new data being generated (we estimate over 90% of the world’s data has been generated in the last two years), the exponential increase in digital processing and storage capacity, dramatic advances in artificial intelligence and machine learning, improvements in statistical methods, the convergence of innovation in drugs and molecules, software and hardware which, together, generate disruptive insights.  

Consider a few examples of what this could mean for pharma: clinical trials will be monitored and optimized in real time with the aid of machine-learning techniques, yielding immediate insight to enhance and accelerate decision making.  Drug discovery will be driven by data, whether in disease pathway analytics, the identification of high unmet medical need, or the mining of real-world evidence data to yield new insights. And, pharma products and offerings will be increasingly personalised to meet individual patients’ needs.

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Digital and analytics have indeed reached a tipping point, and the companies that fully embrace the potential have an opportunity to capture enormous value in R&D.  It is now 25 years since the first sequencing of the human genome, over 20 years since Kasparov lost to Deep Blue at chess – advances in both science and AI arenas have now established a platform for transformational impact.  Most leading companies have already made moves and have many pilots/experiments. The next step is to scale these and move digital and analytics from the periphery to the core of their R&D model – to create a full data, analytics and tech-enabled R&D.

 

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