Drug R&D is broken; how to put the tech in biotech

Drug R&D is broken; how to put the tech in biotech

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Despite headlines indicating recent growth, R&D productivity in the biotech and pharma industries has been in steady decline. So, why is an industry that was built on bold, visionary leaps of faith through much of the 20th century – think insulin, antibiotics, the Salk polio vaccine, the rise of biotechnology and monoclonal antibodies – struggling to reclaim the innovative and entrepreneurial spirit from which it sprung?

Rising costs of developing an asset and longer development timelines are partly to blame. However, it is also reflective of a more surreptitious culprit – increasingly conservative R&D management practices which focus on the predictability of drug development, but which have led the industry to a decade of incremental innovation (with a few notable exceptions such as antivirals / Hepatitis C, Immuno-oncology).

Embracing risk in drug development
Drug development is riddled with failure and, thus, a risky business. Only 5% of compounds that enter clinical development become commercial products and many products that make it to market then fail to return their own development costs. Most of the R&D budget is spent on these failures, a fact not lost on those in the industry. This explains why many companies invest primarily in already-approved drugs. In an attempt to smooth new drug output, minimize financial risk, and introduce more predictability into the process, there has been an industry-wide trend towards portfolio management techniques where detailed forecast and risk prediction numbers provide a false impression of confidence in progression through the pipeline.

Ironically, this attempt to reduce the perceived riskiness of the development process has driven pharma to the pursuit of more narrow development strategies, referring to the idea that a drug enters development with a pre-determined purpose, narrowing its development. While these strategies (validated targets, proven mechanisms of action, and more targeted patient groups) may seem less “risky” on one hand, innovation remains incremental from a commercial perspective due to a high clinical data standard, pricing, and adoption barriers, such as market access, pricing constraints and prescribing guidelines. These narrow development strategies increase the risks associated with premature specialization or choosing a development path too quickly.

Pharma continues to overlook the role of obliquity, the ability to pivot during development in the presence of new data, or serendipity, seeing opportunity in what actually happened, in drug development. To ignore the roles of obliquity and serendipity would be to ignore famous examples such as Avastin, which notoriously ‘failed’ many times before entering development, Herceptin, which pivoted 180 degrees upon seeing its phase II data, and the whole statin class which saw many ‘false fails’ before becoming one of the industry’s biggest blockbuster classes. In most of these cases, the ability to ‘know’ in advance was at odds with the appetite to explore.

While there is no low-risk strategy in drug development, it can be argued that there is good risk and bad risk – and the successful companies of the future will be those that, rather than seeking to lower absolute risk, aim to better qualify risk, and balance good risk and bad risk, known risk and unknown risk. Importantly, behavior that embraces obliquity and serendipity in development reduces real risk more than the current emphasis on badly understood or predicted risk.

Learning from the tech industry
One way to increase the balance of “good” risk or opportunity-seeking is to introduce optionality (“range”) earlier in the drug development process. Why only test a molecule for a single indication in the first instance – e.g. rheumatoid arthritis, knowing that biological pathways are redundant and literature may suggest a broader role for an anti-inflammatory in other auto-immune indications, which may be more compelling – both clinically (in terms of unmet need) and commercially (i.e. regulatory environment/pricing/competitive)? Similarly embracing and integrating new and emerging technologies (clinical trial modelling, AI/Machine Learning approaches to discovery/target identification, pan-omic characterization of patient groups, digital therapeutics) should allow for early prototyping of novel therapeutic-digital combinations which may have in the past been inconceivable.

The rigid operational infrastructure within the pharma industry, however, is not set up to embrace optionality in this way – and here we can learn a lesson from tech industry principles used in skunkworks.

A skunkworks project is, as defined by Wikipedia, “a project developed by a relatively small and loosely structured group of people who research and develop a project primarily for the sake of radical innovation”. First introduced by Lockheed Martin during World War II, the concept has been widely adopted within the tech industry (Google’s X innovation lab) to encourage and support the development of radically innovative and disruptive products to solve intractable technological problems. Clarence Kelly Johnson, the lead engineer of the original Lockheed Martin Skunkworks said,

“We are not defined by the technologies that we create, but by the process in which we create them.”

By empowering a small group of outsiders with access to a full range of perspectives and technologies, pharma can seek to replicate this potentially disruptive skunkworks approach. Leveraging perspectives from outside the traditional company R&D, asking questions of novel drug candidates, designing a plan to learn, and generating evidence earlier in the development process (potentially pre-clinically where the costs are considerably lower) can improve the odds of success in later clinical development and post-approval.

Covid-19 – Nurturing the seeds of optimism
A marginal uptick in return on R&D investment in 2020 (to 2.5% in 2020 from 1.6% in 2019) — the first in six years according to a Deloitte analysis,— suggests reason for optimism. However, whether covid-19 will prove to be the catalyst for an enduring reversal of this trend remains to be seen. Pre-competitive, cross-sector data-sharing and collaboration, supportive and engaged regulator(s) and a unifying unmet need were key ingredients to the covid-19 vaccine success story. One of the key lessons of 2020 was that alternative decision-making approaches were key to success. By learning from and seeking to recreate these elements, pharma can turn the lessons of this life-changing pandemic into an opportunity for enduring positive change for the industry at large and for the years to come.

Photo: metamorworks, Getty Images

 

 

 

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