Quantitative Understandings of Risk in Cell and Gene Therapies
What is risk? How can we accurately understand risk and apply this understanding to inform solutions for investors? There are no easy answers to these questions, and failing to properly understand risk can be a costly mistake. In the young but rapidly evolving field of cell and gene therapies, comprehensively understanding the complex and nuanced risk factors a new company face is certainly a daunting task, and understandably, many investors are put off by the seemingly impenetrable array of obstacles threatening a return on their money. The ‘bio-dollar’ lure touted by companies boasting headline-grabbing efficacy rates achieved primarily in relatively rare blood cancers represents the potential for substantial returns, clearly reflected by the scale of investments poured into the CAR-T race. However, while the CAR-T narrative has understandably generated much excitement, there are sure to be further casualties before eventual winners emerge, and investors must remain vigilant when exploring broader offerings from a young and quickly evolving sector.
Bringing an advanced therapy to market is a technically demanding and high-risk undertaking in an ecosystem prone to innovative disruption and that necessitates complex supply chains and original reimbursement models that often fail to deliver. Despite this, rapid evolution within the field drives increased understanding of what works, what doesn’t, and how to triage efforts to efficiently de-risk product development. Demonstrated by major M&As such as Pfizer’s acquisition of Bamboo Therapeutics, leading technology developers are increasingly realising the importance on manufacturing scalability and supply chain security, while the growing number of PhD-qualified investors represents a growing awareness of the value of technical understanding through the due diligence process. These are certainly welcome developments, but the limited number of evergreen or specialist funds investing in the space offer limited capital, and those who do invest in the space suffer from poor portfolio diversification. We must strive to make our industry as accessible and ‘user-friendly’ as possible to the wider capital market while ensuring investors are empowered to make valid and informed decisions.
Mature investment funds may have a range of tools and methodologies at their disposal to enhance their performance. Often specific to their respective industry or application, such methods are useful when describing the relationships between large and complex networks of interrelated factors, offering objective modes of decision-making which aim to compensate for the humanistic biases highlighted in behavioural economics. Quantitative methods of risk modelling as applied to equity investments leverage data from previous experience, incorporating expert opinion where data is scarce to estimate the risk/return profile of an investment opportunity. The benefits of such quantitative methods allow investors to understand how an investment might fit the needs of their portfolio, improve risk-adjusted views of return on investment, and provide objective grounds for negotiations. Meanwhile, technology developers could use such methods to identify optimally de-risked product development strategies, whether in clinical development, manufacturing, or supply chain management, as well as offer own modelling outputs in investor talks.
Such a model might also include scenario and sensitivity analyses. Scenario analysis models the response of company key performance indicators (KPIs) to specific scenarios (e.g. failure to achieve a key value inflexion), while sensitivity analysis aims to model the robustness or sensitivity of a company to single external or internal changes (e.g. rising cost-of-goods). Such methods are crucial at a time where supply chain markets are still consolidating, regulators are playing catch-up, an unpredictable political climate, and countless further confounding factors contribute great uncertainty to the industry.
Uncertainties and risks must be properly understood, quantified and modelled if we are to support the growth of our promising yet challenging industry through these troubled times. A comprehensive quantitative risk model for equity investments would be a great and valuable asset to investors and technology developers alike. Offering consistent and quantitative ways to enhance investment decisions in our field will not only generate greater returns for investors but accelerate the availability of a new generation of therapies to patients across the globe.