The Galien Forum USA 2019


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Prioritization and acceleration of biopharma asset performance through advanced analytics

Technology advances in the breadth and scope of information is shaping every aspect of the business model in today’s life sciences enterprise, from R&D operations to commercialization and uptake in the marketplace.  The ability to make productive use of information – as an asset, not a castoff – is emerging as a strategic source of competitive advantage. Data used proactively from a wide range of business activities can yield outsize gains on important measures of financial performance, including revenue growth, return on capital invested, and customer satisfaction. But the real payoff may be in how next-generation tools like real world evidence (RWE), predictive analytics and artificial intelligence (AI) can help companies transform their business culture – from reactive, status quo thinking to a mindset of agility and openness to innovation.

Harnessing the efficiency and asset performance opportunities from advanced information analytics is compelling, for several reasons. First, R&D productivity is eroding due to the rising cost of bringing new products to market:  latest data from the Tufts University Center for the Study of Drug Development reveals that the average cost, minus opportunity cost, to bring a new drug to patients  has surpassed $2 billion; only 10% of candidates entering clinical development end up being approved for sale.  Second, payer resistance to price increases on existing medicines has increased the risk premium for innovation, putting more pressure on industry development pipelines to show evidence of value and unmet medical need. Third, patients are directly exposed to subsidizing drug costs through higher co-insurance and co-payments, resulting in demands for more engagement in manufacturer decisions on access, price and the services that surround a medicine once it enters active clinical use.

In other words, relying on the pre-technology, evidence-free standard of practice through the product life cycle no longer provides assurance of acceptable asset performance.  It exposes life science companies to criticism from not only the financial community, but the payers, providers and patients who expect much more from the industry’s products.

A panel of experts with diverse backgrounds and skills will explore ways the data life sciences companies accumulate in vast quantities, every day, can generate insights that raise the bar on operational excellence.  The group will review effective data analytics strategies to identify and serve key areas of unmet medical need; launch new drugs and devices successfully to the market; and achieve lasting differentiation of those assets against the competition.  Next,  RWE and  AI tools like machine learning will be considered for their role in prioritizing capital allocation decisions and limiting risk exposures, such as selecting among many assets those with the highest odds of obtaining a marketing approval; using meta-analyses to source potential new treatment opportunities from data on failed clinical trials; and evaluating patient histories to reduce the delays and uncertainties in trial design and recruitment, which is often the cause of considerable financial losses to sponsors.

To ground the discussion in actual patterns of practice, cases will be presented in the oncology and cardiovascular space, covering the relationship between advanced analytics and better asset performance, as evidenced by outcomes of treatment compared to current standard of care. Finally, and perhaps most important, the session will address the critical importance of culture change. To avoid internal inertia – even resistance – the introduction of advanced analytic capabilities must include consultation and engagement to combat the notion that such tools will supplant human interventions.

Some basic “how” questions to help participants identify practical measures to advance the session theme could include:
·       How should biopharma companies build a sustainable analytics strategy and platform?
·       How can data and analytics guide capital allocation decisions?
·       How can the competitive intelligence function leverage data available today?
·       How can biopharma and medtech companies boost their asset value proposition by leveraging RWE?