The Fundamental Flaws of the Biotech Anatomy
In a 2006 Harvard Business Review article, Gary Pisano contended that the biotech industry struggled to establish itself as a genuine, value-generating business sector. Despite attracting more than $300 billion in capital, and the extraordinary commercial success of companies such as Amgen and Genentech, most biotechnology firms had earned no profit.
The reason?
The underlying ecosystem and structure of the Biotech Anatomy, which details how drugs transition from university bench to patient bedside, are fundamentally flawed.
After nearly two decades since that article was written, is the business case for commercializing science stronger? If so, how has the Biotech Anatomy changed?
Let’s examine how these dynamics have evolved.
Where We Started
In 1976, Genentech demonstrated the potential of biotechnology in drug development. Along with semiconductor and computer hardware companies, they also pioneered a business model that has significantly influenced how biotech companies are built. This model integrated three key components:
Technology Transfer - The transfer of technology from academic institutions to industry by establishing new biotech startups instead of licensing directly to existing Big Pharma companies;
Financing - Access to venture capital and public equity markets for critical funding, rewarding the early investors, scientists, and academic institutions for their willingness to take on risk;
Partnering - A knowledge market where emerging biotech companies trade their intellectual property with established Big Pharma for financial backing.
The initial triumphs of biotechs like Genentech and Amgen, and later by the complete sequencing of the human genome in 2003, fueled optimism among scientists and financial analysts that biotechnology would lead to a surge in profitable therapeutic innovations.
The prevailing view was that smaller biotech startups, with their focused research capabilities, were inherently more efficient than the large, vertically-integrated pharmaceutical companies mired in red tape; thus, it was suggested that Big Pharma should concentrate on late-stage clinical development, distribution, and sales while leaving the pioneering research and development to the agile small biotechs.
The Flaws of the Biotech Anatomy
The "Biotech Anatomy" refers to the ecosystem comprising the sector's key stakeholders, including start-ups, established Big Pharma companies, academic institutions, private and public investors, and patients.
It also encompasses the frameworks facilitating interactions among these stakeholders, such as scientific collaborations, markets for capital, intellectual property, and therapeutic products, as well as the regulatory and governance framework shaping these interactions.
Pisano argued that biotech was fundamentally flawed as scientific ventures carry too much uncertainty, drug development lacks the ability to be modularized by independent players, and most R&D knowledge is tacit.
To overcome these issues, biotech as an industry needed a significant reformulation of how it did business, including open-sourcing of intellectual property by universities, greater vertical integration, and fewer biotech startups.
To a large extent, none of these recommendations were followed and biotech has only grown and become a more mature, stable industry. But let’s review each of these flaws one by one.
Flaw #1: Science Carries Much Uncertainty
“Deep Tech” ventures that involve engineering, such as Elon Musk’s SpaceX, are clearly very challenging. However, in engineering, there is always a right pathway of getting from A to B. More importantly, this pathway can be precisely mapped and repeated by others.
But biological science isn’t engineering.
For better and worse, human biological systems of health and disease are extremely difficult to interrogate and manipulate in predictable, repeatable ways. Much more is unknown than known about how the human body works, which increases the risk of drug development.
Whether a drug candidate is safe and effective can be determined only through a lengthy process of trial and error. Historically, only one out of about 6,000 preclinical compounds and only about 10% of drug candidates beginning clinical trials have ultimately been approved for commercial sale.
Repeatability of any particular scientific result is challenging, no matter how spectacular. Even within fairly standardized clinical development practices, interpretations of the data can vary dramatically. Companies can and do interpret these results in different ways. Even with similar interpretations, they can come to different conclusions about whether to continue to the next phase of clinical development, based on their risk tolerance.
How has biotech been able to reduce scientific uncertainty?
Despite its inherent uncertainties, the biotech industry has significantly advanced in mitigating risks through improved methodologies and technologies over the past two decades.
Precision medicine and genetic profiling, for instance, have vastly improved our ability to predict treatment outcomes, thereby reducing the unpredictable nature of drug development. Furthermore, advancements in AI and machine learning have enhanced our ability to analyze biological data and predict viable pathways for drug development, addressing the unpredictable nature of biological systems (For example, Recursion Pharmaceuticals).
The scientific community, recognizing the critical importance of reproducibility, has taken concerted steps to address the challenges associated with scientific repeatability.
A key player in this initiative is the National Institutes of Health (NIH), which has implemented several measures to enhance the transparency and reproducibility of research findings. The NIH now requires more rigorous documentation of methodologies in grant applications and has increased support for studies that validate important research findings. Moreover, the NIH promotes open science practices, such as the sharing of data and research protocols, which allow for easier replication of studies by independent researchers.
These efforts are complemented by broader community actions, including the establishment of journals dedicated to publishing replication studies and the development of new statistical standards to improve the reliability of published research.
Together, these initiatives represent a robust response to the reproducibility crisis, aiming to strengthen the foundation of scientific inquiry and ensure that research outputs are both reliable and actionable.
Flaw #2: Drug Development is Monolithic
Drug research and development cannot be conducted in a modular fashion, meaning that the various biotech disciplines must work as an integrated system or whole. The myriad functional and technical tasks in drug R&D are typically highly interconnected.
Developing a drug successfully necessitates a collaborative, interconnected approach rather than attempting to tackle problems in isolation. Every technical decision—from selecting the target, developing the molecule, formulating the drug, designing clinical trials, identifying the target patient population, to choosing a manufacturing process—is interdependent. Solutions emerge from the continuous and extensive exchange of information among various scientific disciplines, requiring teams to collaborate closely and integrate their efforts seamlessly.
There are two approaches to R&D integration:
Vertical integration - This is (or, as we’ll see, was) the Big Pharma model, where an individual company holds all of the requisite pieces of the puzzle, developing a drug from discovery to market.
Network integration - This is the biotech model, where companies integrate through alliances, partnerships, licensing arrangements, collaborations, and outsourced contracts.
Given the complexities of biotech, cross-disciplinary integration spanning scientific, technical, and functional realms is crucial. While modularity allows for the distribution of tasks among organizations with distinct specializations within the system, it demands clear interfaces and standards to ensure that the various components of the system interact and operate cohesively.
Historically, most biotech firms originated from small groups of dedicated scientists aiming to further develop specific discoveries made in academic settings. This led to the creation of numerous specialized 'islands' of expertise within the biotech landscape. The sector has traditionally depended on a market for know-how to connect these disparate islands. Pisano is concerned that this network market is insufficient to support the necessary exchange of information and collaborative problem-solving required for drug development. He would recommend that we reduce the number of independent biotechs and move more towards vertical integration of select subset.
Has biotech successfully created a modular, integrated network for drug development?
The biotech industry has become much more modular over the last two decades rather than vertically integrated. Additionally, the number of biotech startups has significantly scaled, evidenced by the increase in venture capital coming into biotech, technological advancements such as CRISPR, and increased number of academic spin-offs.
However, the way academic institutions, biotech start-ups, and large biopharma companies work together to create medicines has significantly changed and optimized around each of the player’s strengths and capabilities.
Academic medical centers have taken a much more active role in drug development, moving technologies over the valley of death by supporting critical translational science. **In 2006, the National Institutes of Health launched the Clinical and Translational Science Awards (CTSAs) to bolster translational research funding. Presently, over 60 academic medical institutions nationwide benefit from the CTSA Program, each receiving about $10 million annually. The primary objective of these grants is to enhance the efficiency and quality of clinical and translational science, expediting the development of new treatments.
Small biotech companies remain the life blood of the therapeutic development engine. Over the last two decades, small biotech companies have indisputably become the source of therapeutic innovation, as evidenced by their disproportionately high contribution to new drug discoveries. According to a report by HBM Partners, small and emerging biotechs accounted for over 63% of new prescription drug approvals in the past five years alone. These firms have demonstrated exceptional capacity to drive the bulk of early-stage innovation, significantly outpacing larger pharmaceutical companies in originating novel drug entities. This trend is supported by data showing a notable shift in the landscape of drug development; where once Big Pharma dominated, now smaller entities thrive through specialization and agility. Their focused approach allows them to rapidly adapt and pursue high-risk, high-reward projects that often lead to breakthroughs in areas like gene therapy, immunotherapies, and orphan drugs. Furthermore, a growing number of strategic partnerships and licensing agreements between these small firms and large pharmaceutical companies underscore the critical role of biotechs in filling the innovation pipeline, affirming their status as a cornerstone of contemporary therapeutic development.
Finally, Big Pharma is no longer vertically-integrated within the virtualization of the biotech ecosystem. **Just as Big Pharma has shifted its early drug discovery and development to academic medical centers and biotech startups, it has also moved away from traditional in-house Contract Research Organization (CRO) tasks such as drug manufacturing, toxicology safety testing, DNA sequencing, and, to some degree, regulatory affairs.
Flaw #3: Most R&D Knowledge is Tacit
Highly related to the above two flaws is that much of scientific knowledge is intuitive or tacit, dramatically hindering collective learning, reducing the potential for modularization, and making scientific studies very difficult to repeat.
New hypotheses and scientific results are evaluated and interpreted under the constraints of imperfect information. Decisions about which therapeutic candidates to pursue or discard are made in the fog of limited knowledge and experience, even when this knowledge and experience may exist in a separate, siloed academic or commercial entity. This, to a large extent, is why we have such a high failure rate in drug development.
Pisano argues that the surge in start-ups entering biotech has led to a landscape dominated by relatively inexperienced players. They are essentially starting at square one of building their collective knowledge of a company like Genentech who has developed their capability over many decades of research and development.
Although scientific progress continues, drug discovery remains an art form that depends on judgment, intuition, and experience. Therefore, the extensive exchange of experiences over time plays a crucial role in these ventures, with the scope of this sharing in biotech being particularly significant.
Overall, the challenges of achieving integration and fostering collective learning within the industry are substantial.
Has biotech’s access to specialized, tacit knowledge improved?
While it is true that much of biotech R&D relies on tacit knowledge, the sector has evolved mechanisms to codify and share this knowledge more effectively than ever before.
Undoubtedly, specialized drug development requires specialized knowledge.
This hasn’t changed.
What has changed is the accessibility to this specialized knowledge. Just as Big Pharma companies have become less vertically-integrated and entire divisions have spun out to form independent CROs, the deep expertise of these organizations have been “unlocked” and more accessible by the broader biotech community. The biotech consulting industry has grown with an annualized rate of 5% from 2017 to 2022 and will continue to grow for the foreseeable future.
You need a fractional Chief Medical Officer to help you develop your Phase 1 trial using an oligonucleotide drug for age-related macular degeneration? Activate your LinkedIn network and you can probably find a person that has done several of these trials. Prefer to meet people in-person? Go to the annual Oligonucleotide Therapeutics Society annual meeting.
Pick your favorite hyper-specialized area of expertise. There’s a network and annual meeting for everyone.
Additionally, collaborative platforms, digital lab notebooks, and open-access journals are just a few examples of how scientific knowledge is being documented and shared across the globe. These tools not only improve reproducibility but also enhance collective learning, making the tacit knowledge more accessible and actionable.
Technological advancements have also played a significant role in improving knowledge sharing. The rise of digital lab notebooks and open-access repositories allows researchers to record and share their experimental procedures, raw data, and findings in real-time. Platforms like Figshare and the Open Science Framework serve as repositories where scientists can deposit their data, making it accessible to the global scientific community. This accessibility not only facilitates the replication of studies but also enables scientists to build on existing research, accelerating the pace of innovation.
Moreover, the biotech industry has witnessed the growth of preprint servers like bioRxiv, which allow researchers to publish their findings before undergoing peer review. This practice has been particularly beneficial during the COVID-19 pandemic, where rapid sharing of research results has been critical to global response efforts. Preprints increase the speed at which knowledge is disseminated, allowing other researchers to replicate studies and verify results swiftly, thus enhancing the overall robustness of scientific data.
Collaborative efforts have also been strengthened through consortiums and alliances, such as the Structural Genomics Consortium, which involve multiple academic and industry partners working together on pre-competitive research. These collaborations are structured around shared goals of reproducibility and open access to data, significantly contributing to a more reliable scientific foundation upon which further research and development can be built.
Concrete evidence of the impact of these initiatives can be seen in the increasing number of studies that report successful replication attempts. For instance, the Reproducibility Project: Cancer Biology has shown that initiatives focused on replication can improve reliability in biomedicine, with several studies being successfully replicated under newly established rigorous standards.
Conclusions
As the biotechnology sector evolves, it becomes evident that its earlier foundational challenges—primarily those identified by Gary Pisano regarding uncertainty, integration, and the tacit nature of knowledge—have not only been acknowledged but also continuously addressed over the past two decades. Innovations in scientific processes and the adaptation of new business models have significantly improved these issues, enhancing the sector's overall efficacy and reliability.
The increased emphasis on precision medicine and advanced genetic profiling has revolutionized the predictability and efficiency of therapeutic outcomes, countering the inherent uncertainties of biotechnological endeavors. These advancements, coupled with strides in artificial intelligence, have transformed data analysis, leading to more precise developmental pathways for drugs. Furthermore, the push towards open science, spearheaded by institutions like the NIH, has greatly improved the reproducibility of scientific research. By insisting on transparency and more stringent documentation, the scientific community now benefits from a more reliable and accessible body of research, facilitating better and faster verification of results.
Simultaneously, the biotech sector has evolved from a rigid, monolithic structure to a more integrated and collaborative network. This shift has been crucial in bridging the islands of specialization that Pisano referenced, enhancing the sector's modular nature without sacrificing the depth of collaborative knowledge necessary for innovation. Through strategic alliances and partnerships, knowledge is no longer siloed but shared widely, accelerating the pace of innovation and reducing redundant efforts across the industry.
By addressing its fundamental flaws through technological advancement and strategic restructuring, biotechnology has not only reinforced its foundation but also set a new standard for how science can be effectively commercialized. This evolution promises not only more rapid advancements in therapy development but also a more robust framework for future scientific endeavors, ensuring that biotechnology remains at the forefront of medical innovation.