Open Science as Competitive Infrastructure: Why Pharma Should Want Open Standards

The instinct to treat biological design data as proprietary is understandable. It is also strategically wrong. Open standards do not erode competitive advantage — they create the conditions in which competitive advantage is possible.

Ashley Morgan · August 2026 · 13 min read

Keywords

1. The Proprietary Instinct and Its Costs

This series has argued, across four articles, that biology has built extraordinary discovery infrastructure and has not built design infrastructure — and that the consequences are measurable in batch failures, manufacturing delays, and therapies that reach patients later than they should. This final article makes the strategic case for why the solution must be open: not as an act of generosity, but as a matter of competitive logic.

Ask a pharmaceutical executive whether their organisation’s biological design data should be open, and the answer will almost certainly be no. The reasoning is intuitive: biological design data represents years of research investment, hard-won empirical knowledge about what works and what does not, and real competitive value in a market where being first with a better formulation can be worth billions.

The instinct is not wrong. It is, however, based on a misidentification of where competitive value actually lives in the biological design process.

The competitive value in nanomedicine does not live in the fact that a company knows that a particular ionisable lipid at a particular molar fraction produces particles of a particular size with a particular encapsulation efficiency. That knowledge is valuable. But it is valuable because of what the company does with it — the therapeutic programme it enables, the manufacturing process built around it, the clinical data generated from it, the regulatory approval obtained for it. The value is in the application, not in the design parameters that describe the application.

This distinction matters because conflating the two leads to a posture that protects design documentation as if it were a trade secret while simultaneously suffering all the costs that come from the absence of shared standards: difficulty reproducing published results, inefficient manufacturing transfer, slow regulatory review, and the inability to build on the accumulated knowledge of the field in a systematic way.

The competitive value in nanomedicine does not live in design parameters. It lives in what those parameters enable — and open standards do not give that away.

2. The Precedents Are Clear

2.1 HTML and the Web

In 1993, the web was a collection of incompatible systems. CERN’s WorldWideWeb, Gopher, WAIS, and FTP all served similar purposes and none could talk to the others. Tim Berners-Lee’s proposal for HTML — a common markup language for hypertext documents — was not a business plan. It was an infrastructure proposal. The businesses came after.

Every company that subsequently built a valuable business on the web — Amazon, Google, Facebook, every e-commerce retailer, every media company with a digital presence — built it on top of open standards they did not own and did not control. HTML, HTTP, TCP/IP — the infrastructure layer was open. The competitive layer was proprietary. Nobody who built a successful web business wishes that HTML had remained proprietary to CERN.

2.2 DICOM in Medical Imaging

Before DICOM — the Digital Imaging and Communications in Medicine standard — every medical imaging manufacturer had its own proprietary format for storing and transmitting images. A hospital that bought a GE scanner could not easily share images with a system running Siemens software. Radiologists working across institutions could not access each other’s archives. The incompatibility was not a competitive advantage for the manufacturers — it was a tax on the whole system that added cost and friction without creating value.

DICOM was developed collaboratively by the American College of Radiology and the National Electrical Manufacturers Association in the 1980s. Its adoption was initially slow and contested — the same proprietary instinct that resists open biological design standards resisted it. It is now universal. Every manufacturer supports it. The competitive battles in medical imaging are fought on image quality, AI analysis capabilities, workflow software, and service contracts — not on whether the images can be transferred between systems.

The lesson is not that standards eliminate competition. It is that they redirect competition toward the dimensions that actually matter, and away from the infrastructure layer where competition creates no value and enormous friction.

2.3 The Human Genome Project

The Bermuda Principles, agreed in 1996 at a meeting organised by the Wellcome Trust, established that human genome sequence data should be deposited in public databases within 24 hours of generation and made freely available without restriction. This was a radical commitment at the time. Gene sequence data was widely regarded as potentially enormously valuable, and several major research institutions were resistant to releasing it before publication.

The decision to make the human genome sequence open created, within a decade, a global research infrastructure — databases, analysis tools, research programmes — that no single organisation could have built or afforded alone. Every pharmaceutical company that uses human genome data in its drug discovery programmes — which is essentially all of them — benefits from that decision daily. The organisations that fought hardest for proprietary genome data in the 1990s are now among the most enthusiastic users of the open databases that resulted from the Bermuda Principles.

3. The Network Effects of Biological Design Standards

3.1 Why Standards Have Different Economics Than Products

The economics of open standards are fundamentally different from the economics of products. A product is rivalrous — if I use it, you cannot. A standard is non-rivalrous — my use of SBOL does not reduce your ability to use SBOL. In fact, my use of SBOL increases the value of SBOL for you, because every additional participant in a standard network increases the number of parties with whom you can exchange compatible data, the number of tools that support the standard, and the pressure on regulatory bodies to accommodate it.

This non-rivalry is what creates network effects. Network effects are self-reinforcing: the more organisations adopt a standard, the more valuable adoption becomes for each of them, which drives further adoption. Once a standard achieves sufficient adoption to generate strong network effects, non-adoption becomes increasingly costly. The standard effectively becomes mandatory, not because anyone requires it, but because the cost of incompatibility with the majority of your collaborators, customers, and regulators exceeds the benefit of maintaining a proprietary alternative.

3.2 Where the Field Is on the Adoption Curve

SBOL for nanomedicine is currently at the early stage of the adoption curve — past the initial academic proof-of-concept phase, with tooling in major platforms like Benchling, but before the network effects that make adoption self-reinforcing have taken hold. This is the strategically significant moment.

Early adopters of a standard before network effects take hold bear transition costs — the effort of building SBOL workflows, training staff, and integrating structured data into existing systems — without yet receiving the full network benefit, because relatively few counterparties also use the standard. This is the collective action problem that slows standard adoption and that regulatory mandate typically resolves.

But early adopters also gain something that late adopters cannot: they shape the standard. Organisations that engage with the SBOL community now, that contribute nanomedicine-specific extensions to the data model, that develop and publish implementation patterns for their workflows, will have disproportionate influence over the standard as it matures. That influence is itself a competitive advantage — the ability to ensure that the standard that eventually becomes ubiquitous reflects your workflows, your parameter vocabularies, and your data structures rather than those of your competitors.

4. What Is and Is Not Competitively Sensitive

The most persistent objection to open biological design standards is that they require organisations to disclose competitively sensitive information. It is worth being precise about what is and is not sensitive.

What is genuinely competitively sensitive: the specific ionisable lipid structures that a company has developed and patented, the proprietary formulation optimisation algorithms built on years of internal data, the clinical data that establishes the efficacy and safety of a specific therapeutic, and the manufacturing process improvements that have taken years of engineering effort to develop.

What is not competitively sensitive — and what structured design standards primarily capture: the classes of parameters that describe an LNP system (lipid composition, process conditions, characterisation properties), the ontological vocabularies used to express those parameters, the data model that connects parameters to design objects, and the provenance structure that links designs to experiments and regulatory submissions.

A SBOL-encoded design record for a proprietary LNP formulation does not disclose the formulation. It describes it in a structured format that can be reviewed by a regulator, transferred to a CDMO, or compared computationally against other designs. The proprietary content — the specific lipid structure, the optimised molar ratios, the process parameters that produce the critical quality attributes — is protected by patent, by trade secret law, and by the regulatory exclusivity periods that pharmaceutical development earns. The standard does not change any of that.

A SBOL-encoded design record does not disclose the formulation. It describes it in a format that regulators can review, CDMOs can implement, and the field can build on. Those are not the same thing.

5. The Strategic Case for Proactive Adoption

5.1 Getting Ahead of the Mandate

Regulatory mandate for structured biological design data is coming. The FDA’s structured data programme, the EMA’s data standardisation work, and the MHRA’s regulatory modernisation initiative all point in the same direction. The timeline is uncertain — regulatory transitions move slowly — but the direction is not. At some point within the next decade, structured CMC data submission will be required for biological therapeutics, including nanomedicines.

The question for pharmaceutical organisations is not whether to adopt structured design data but when. Late adoption — waiting for the mandate — means building the capability under regulatory pressure, on a timeline set by the agency rather than by the organisation, and without the period of iterative learning that early adoption provides. It also means entering the structured data world at the same time as competitors, without the accumulated design libraries, staff expertise, and process integration that early movers will have built.

5.2 The Collaborative Dividend

Open standards also enable a form of collaboration that proprietary data structures preclude. Academic-industry partnerships, CDMO relationships, regulatory interactions, and consortium-based research programmes all involve the exchange of design data. When that data is in incompatible proprietary formats, the exchange requires translation — often manual translation — that adds time, cost, and error. When it is in a common open format, the exchange is computational and the human effort goes into the substance of the collaboration rather than the logistics of data compatibility.

The pharmaceutical industry spends hundreds of billions of dollars annually on R&D, much of it in precompetitive research that multiple companies fund simultaneously without coordinating. Open standards create the infrastructure for that coordination — not by requiring companies to share proprietary results, but by ensuring that when they choose to collaborate, the data they exchange is compatible without translation.

6. A Call for Leadership

The adoption of open standards in biological design will not happen spontaneously. The collective action problem is real, and without either regulatory mandate or industry leadership, the incentive for any individual organisation to bear early adoption costs for a future collective benefit is insufficient.

What is needed is leadership from organisations with the standing to shape the trajectory — large pharmaceutical companies with the resources to invest in early adoption, regulatory agencies with the authority to signal the direction of travel, research-intensive universities with the intellectual capital to develop the standards, and funding bodies with the mandate to require open data practices as a condition of support.

Some of this is already happening. The Wellcome Trust’s open data requirements, the NIH’s data sharing mandates, the FDA’s structured data programme — these are the leading edges of a transition that will eventually reach biological design data. The question is whether the pharmaceutical industry will lead that transition or be dragged into it.

The case for leadership is not altruistic. It is strategic. The organisations that build open standards competency now — that engage with the SBOL community, that develop implementation patterns, that advocate for sensible regulatory frameworks — will shape the infrastructure that the whole field eventually uses. That is power, not sacrifice. It is the same power that the founders of the internet exercised when they made TCP/IP open, that the radiologists and manufacturers exercised when they developed DICOM, and that the Wellcome Trust exercised when it convened the Bermuda meeting.

7. Conclusion

Open science is not the opposite of competitive science. It is the foundation on which competitive science is built.

The biological design community has an opportunity, right now, to build that foundation for nanomedicine. The standard exists. The tools exist. The regulatory direction is clear. What is missing is the recognition that the proprietary posture toward design documentation is protecting something that is not, in the end, where competitive advantage lives — while imposing costs on the field as a whole that are paid by everyone including the organisations doing the protecting.

The network effects of open biological design standards will eventually make adoption self-reinforcing. The organisations that join the network early will have shaped it. The organisations that join late will implement a standard designed by others.

The choice of which of those to be is available right now. It will not remain available indefinitely.

This is the fifth and final article in a series examining biological design infrastructure for nanomedicine — from the documentation failure at the heart of the field, through the formal framework for addressing it, the case study that makes the cost visible, the platform that makes the stakes clear, and finally the strategic argument for why the solution must be open. The argument across all five is the same: biology has discovery infrastructure. It is time to build the design infrastructure to match.

References

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