A Goldilocks Regulatory Framework to Support鈥�

Despite an explosion in novel biotech modalities, technologies, and life science product development, approvals and scale-up have remained resource intensive and slow; but a global pandemic has offered a glimpse of how rapidly life science innovations could scale.

It鈥檚 2010, and you want to get somewhere by car. You basically have four options: a private automobile, a rental car, a taxi or a hired driver. Let鈥檚 say you also need a place to stay: your options are usually a hotel, vacation rental or friend鈥檚 house.

By 2020, all that changed: there were more than five million Ubers on the road, serving 100 million users with a whopping five billion rides. Airbnb had seven million active listings, with 50 million-plus bookings totaling more than 240 million nights.

This degree of change and rapid consumer adoption were enabled by a virtually nonexistent national regulatory environment, the downsides of which have become increasingly clear: side-stepped employment laws, misuse by a small subset of participants, and the aggregation and repurposing of consumer and provider data without explicit permissions.

Life science innovations at speed

The contrast with the life sciences environment is large. We have witnessed an explosion in new biotech modalities and technologies, transforming the potential to more precisely and predictably target unmet medical needs. Despite these advances, the development, approval and scale-up of innovative life science products have remained resource intensive, slow, and often unsuccessful. Many observers associate this inertia with a highly prescribed, heavily stage-gated regulatory process that overindexes for risk. Yet, a global pandemic has offered a glimpse of how life science innovations could scale and deploy as rapidly as many tech innovations have.

In January 2020, only those participating in small clinical trials had taken an mRNA-based medicine. By January 2022, more than 1.2 billion people had taken multiple lifesaving doses of mRNA vaccines for COVID-19. A total of 2.7 billion doses have been delivered around the world and at a cost per dose less than the coronavirus tests being used to track infections. These products were developed by two insurgent biotech companies most people had never heard of: Moderna and BioNTech (partnered with Pfizer).

This innovation at scale was made possible despite a regulatory environment which, when functioning at its usual pace and manner, typically requires a multiyear, stage-gated developmental and regulatory approval and labeling gauntlet, followed by a distinct government evaluation for reimbursement, coverage and/or site of delivery decisions, where required. All of this greatly challenges innovator supply scale-up planning and execution. It was, in fact, a willingness by regulators to think outside the box and partner closely with innovators that facilitated the rapid innovation and impact of COVID-19 products at such scale, deploying a technology with superior efficacy, safety and ability to respond to variants compared to traditional vaccine modalities.

If we are to scale life science innovations in time for them to make a difference in patients鈥� lives, especially when it comes to addressing the 鈥渟low鈥� pandemics of chronic disease, cognitive health, climate change and malnutrition, we need a regulatory reset.

What can we learn from these contrasts?

What鈥檚 the regulatory approach that is, as Goldilocks might put it, 鈥渏ust the right bowl of porridge鈥�: neither too stringent nor too lax? If we are to scale life science innovations in time for them to make a difference in patients鈥� lives, especially when it comes to addressing the 鈥渟low鈥� pandemics of chronic disease, cognitive health, climate change and malnutrition, we need a regulatory reset and a Goldilocks solution that speeds adoption and scale-up while safeguarding safety, efficacy and societal economics.

The current U.S. life sciences regulatory framework, which serves as either the foundation or point of departure for most other countries, has its origins in the early 1900s, when the pathology of disease and treatment was poorly understood. A range of products made unsubstantiated safety and benefit claims directly to consumers. In reality, these 鈥渕edicines鈥� often had few if any benefits and were in some cases harmful. Initially, the regulatory focus was on preventing misbranding and adulteration of food and drugs in commerce; however, over the subsequent decades 鈥� and amid continuing scandals involving consumer harm 鈥� the regulatory scheme expanded to include premarket review of safety and, for drugs, ultimately premarket review of effectiveness for the intended purpose.

Not surprisingly given this historical context, the regulatory frameworks created by government agencies broadly cover human and animal health, regardless of the types of new technologies being utilized. Further complicating the issue are regulatory silos for each type of technology modality (e.g., chemical compounds, biologicals, devices, diagnostics, food supplements). These regulatory frameworks focus first on safety. Then, they link efficacy data to intended use claims and approved labeling (providing legal protection to the company), and establish consequences for off-label promotion. For drugs, regulations assume consumers need the support of 鈥渓earned intermediaries鈥� such as physicians to make the ultimate prescription decision. How the societal economics of a product鈥檚 impact factors into regulation varies by country and is either explicitly not considered or plays a secondary role.

Compare that to the regulatory frameworks governing communications, transportation and other public services: those tend to be much more limited and technology-specific, requiring changes in regulatory authority as new technologies and innovative approaches are adopted for widespread use 鈥� thus leaving potential gaps in regulation that can be exploited. But those regulatory approaches also implicitly support the power of networked ecosystems and economics to strongly incent the corporate service providers to take actions in the best interest of their customers and society, or pay the consequence as their poor decisions become visible to the public and get amplified.

Exceptions to the life science regulators鈥� typical approach do exist and may be instructive in looking for that optimal 鈥淕oldilocks鈥� model. Even before COVID-19, the typically slow and cumbersome Food and Drug Administration (FDA) drug regulatory scheme had developed mechanisms to address small patient populations and urgent patient needs in a more-timely manner. The FDA Orphan Drug Act of 1983 led to a dramatic shift in the number of approved and marketed rare disease drugs to the great benefit of many individuals suffering from rare diseases. Accelerated approval mechanisms for life-threatening diseases such as AIDS and cancer also have been created based upon using surrogate endpoints.

However, creating faster tracks to approval for drugs that address the needs of only the sickest patients has led to innovation focused on those populations, with some disturbing effects. High prices can mean only a subset of the individuals that might benefit from a drug actually have access, and incumbents end up focusing on defending patents and existing franchises rather than pursing breakthrough innovation. Underinvestment impedes product innovation for broader population needs, and business and legal gamesmanship occurs over on- versus off-label promotion and liability. Further, the absence of robust postmarketing data collection and assessment may result in too many patients not benefiting from product use, while potentially experiencing side effects.

To support the global scaling of breakthrough innovation to preempt and address fundamental human and planetary health challenges, the 鈥淕oldilocks鈥� regulatory framework for life sciences should aspire to be as innovative as the science it is regulating and as bold as the problems the innovation is looking to address.

Creating the Goldilocks regulatory framework

Given the fundamental differences between digital technology and life science innovation on multiple dimensions (especially life sciences鈥� direct impact on human health), fundamentally different regulatory approaches have made sense to date. As a technology innovator and investor with interest in life science innovation, Peter Thiel has commented: 鈥淎 big difference between biology and software is that software does what it is told, and biology doesn鈥檛.鈥� While this difference is significant, the combination of the exponential expansion of scientific knowledge driven by the 鈥�biological century鈥� and the likely impact of artificial intelligence/machine learning (AI/ML) with Big Data on both digital tech and life sciences will inevitably and dramatically narrow this gap.

Life sciences innovation will benefit from a range of factors that will allow it to become more programmable and 鈥渆ngineerable,鈥� compared to its historic serendipity-based development through lab and real-world trial-and-error. Analogous to the role of platforms in digital technology, innovative life sciences platforms (increasingly leveraging both life science and digital technologies) will enable innovators to tackle bigger human and planetary health challenges. A platform demonstrating success in addressing one need can be multiplied efficiently to address a wide range of needs. If mRNA works for COVID-19 vaccines, it can quickly, with high probability of success, be reprogrammed for new variants and new vaccines, as well as developed for alternative therapeutic applications.

To support the global scaling of breakthrough innovation to preempt and address fundamental human and planetary health challenges, the 鈥淕oldilocks鈥� regulatory framework for life sciences should aspire to be as innovative as the science it is regulating and as bold as the problems the innovation is looking to address, including:

REAL-WORLD EVIDENCE FRAMEWORK: Creating a regulatory prescribed pre- and post-approval, real-world efficacy, safety and social economics data framework that 鈥�
- Is transparent and available to providers and consumers;
- Is used to scale the required preapproval evidence consistent with the urgency of the unmet health need, the type of technology used and the target population鈥檚 risk-benefit profile;
- Is used to support allowed claims, reimbursement/pricing and usage decisions; and
- Leverages provider and consumer-market forces to reinforce and supplement the scaled governmental regulation to ensure innovator accountability.
EMBRACE PREDICTIVE AND PREEMPTIVE CAPABILITIES: Biomarkers, along with AI/ML with Big Data, will exponentially increase the ability to predict at-risk populations, target interventions toward the populations that will benefit, and use biomarkers as outcome endpoints, all of which should be embraced to support scaling preapproval requirements and a regulatory framework that also targets the preemption of disease.
LINK CLAIMS TO EVIDENCE VS. LABEL: Innovators should be able to leverage absolute, and relative to competitors, evidence accumulated postmarket (in transparent, regulatory-prescribed databases) to make claims and adjust target populations. This change may benefit from shifting responsibility for signing off on dynamic labeling from the regulators (with resulting liability protection for innovators), as is done today, to the innovators themselves, which will face market pressure from healthcare providers, patients, other customers and even competitors to be appropriately restrained.
FOCUS ON GLOBAL VS. NARROWER PATIENT SUBSEGMENT NEEDS: Complement the supportive regulatory processes for rare disease populations and those with extreme underserved needs by creating streamlined regulatory approaches that support preempting and addressing the human and planetary health needs among the broadest populations, such as the 鈥渟low鈥� pandemics of chronic disease, climate change, and cognitive health, to dramatically increase the societal return on investment and the impact of life science innovation.
BREAK DOWN TECHNOLOGY AND REGULATORY AGENCY SILOS: Create horizontal, 鈥渟ystem view鈥� regulatory capabilities that cut across the current regulatory vertical technology and agency silos, to support the breakthrough innovations that will increasingly combine biochemical, device, digital and other capabilities, and to support the new business models needed to scale to serve fundamental human and planet health needs. Given that regulators will be continuously challenged to keep up with private-sector technological advances, they will need to increasingly leverage outside experts, including finding means to leverage the expertise of companies they regulate.

The unprecedented payoff to the human race from the strategy regulators used to both authorize and support the scaling of mRNA vaccines on an 鈥渆mergency鈥� basis in the face of COVID-19 should be a beacon for the rapidly emerging potential of leveraging life sciences platforms with digital technology (including AI/ML) to create new products. Vaccines went from design to development to testing to authorization and deployment to lifesaving impact 鈥� all in record time.

We know that the lax regulation model that allowed the Airbnbs and Ubers of the tech world to scale so rapidly is not an option for human and planet health products, and arguably was insufficient for hoteling and ride hailing. But making no permanent adaptations or changes to how innovative life science is regulated after the extraordinary lessons of 2020-21 should not be an option either.

For more on innovation in regulation including the role of patients and real world data, to listen to a conversation with co-authors Kathy Biberstein and Stephen Hahn.

Image credit: iStock