Rebuilding the Case for Diagnostics Investment (2/2): A Framework for Investors

Published on LinkedIn on Nov 20th

In Part 1/2 (https://www.linkedin.com/pulse/rebuilding-case-diagnostics-investment-12-reflections-pereira-leal-swvbf), I discussed why diagnostics face a systematic funding gap despite being essential infrastructure for precision medicine, and outlined five strategic directions for entrepreneurs building diagnostic companies. Those reflections emerged from building Ophiomics and follow from the reflection I’ve been making for my book, Precision Diagnostics: A Founder’s Journey (https://amzn.to/4iEwUjp).

This second article on Diagnostics Investment flips the perspective. Over the years, I’ve been asked by different investors to analyze their diagnostics portfolio companies and advise on potential investments. I’ve observed that even sophisticated investors consistently misprice diagnostics opportunities because they’re using the wrong mental models. It’s not a lack of intelligence or diligence: it’s category confusion.

A critical caveat before we proceed: This discussion and the framework I suggest applies specifically to diagnostics that represent medical innovation – tests that change clinical pathways, alter treatment decisions, and require physician behavior change. These are fundamentally different from commodity diagnostics like routine blood counts or basic metabolic panels, which have straightforward adoption dynamics and face minimal market barriers once regulatory approval is achieved, approval that in itself tends to be trivial.

When someone asks “how can you have regulatory approval but no sales?” – they’re thinking about commodities. Medical innovation diagnostics can take 5-7 years after approval to achieve meaningful market penetration because they require clinical guideline updates, physician education, payer coverage decisions, and workflow integration. This isn’t failure; it’s the adoption curve for anything that changes how medicine is practiced.

In this article I suggest a different investment framework – one grounded in business model de-risking rather than validation checkpoints alone. I’ll outline the core concepts here, but this is just a starting point. I’ll to return to these ideas in more detail at a later point, through a dedicated series on diagnostics investment strategy.

The Mispricing Thesis: Why Diagnostics Are Systematically Undervalued

Diagnostics aren’t weak assets. They’re misunderstood assets. And that misunderstanding creates a genuine arbitrage opportunity.

The mispricing happens in three dimensions:

Mispricing of Time: Investors expect either biotech timelines (8-12 years from concept to market) or SaaS velocity (18 months to scale). Diagnostics sit awkwardly between these poles. You can achieve CE mark in 2-3 years, but meaningful adoption takes 5-7 years. This isn’t failure – it’s the physics of the category. Clinical guidelines take time to update. Reimbursement decisions follow evidence, and evidence accumulates gradually. Physicians change behavior slowly, even with compelling data.

When investors evaluate diagnostics against biotech timelines, they see “too fast to be rigorous.” When they evaluate against SaaS timelines, they see “too slow to be venture-backable.” Both perspectives miss the point.

Mispricing of Value: Most VCs model diagnostics revenue as test volume multiplied by test price. They’re looking for 10x returns from product sales. But diagnostics value actually accrues in three places: (1) test revenue, (2) pharma partnerships and companion diagnostic arrangements, and (3) proprietary datasets that enable downstream products or licensing.

A diagnostics company modeling only test revenue is like a software company modeling only license fees while ignoring services, platform extensions, and data monetization. Investors who see only the first revenue stream systematically undervalue the asset.

Mispricing of Defensibility: In software, defensibility comes from network effects. In biotech, it comes from patent exclusivity. Diagnostics have something potentially more durable: validated evidence moats.

Once your test is embedded in clinical guidelines, supported by prospective trials, and backed by five years of real-world data, you’re nearly impossible to displace. A competitor would need to replicate your evidence base, which takes years and capital they probably don’t have. But building this moat takes time that investors often mistake for weakness. Frequently founders do too!

The opportunity however isn’t in the upside of diagnostics – it’s in the mispricing itself. Investors who correctly value diagnostics assets can identify opportunities the market is systematically ignoring.

The Portfolio Advantage: Why Diagnostics Concentration Makes Strategic Sense

Before examining how individual diagnostics companies should be evaluated, consider the portfolio-level mathematics that make diagnostics attractive as a concentrated investment strategy.

As I outlined in Part 1, drug development costs roughly 10x more than diagnostics development (around $2.6 billion vs. $20-100 million from concept to market), with only 7.9% of drugs entering Phase 1 trials ultimately reaching approval.[1] Deploy $100 million in pharma venture and you’re buying minority stakes in 5-10 drug candidates, betting that one 50-100x winner will compensate for nine failures.

Deploy that same $100 million in diagnostics and you can build controlling positions in 3-5 companies or meaningful stakes in 10-15, with 60-70% reaching sustainable revenue because pivots, indication adjustments, and platform redeployment are possible in ways that drug development doesn’t allow. You don’t need one miracle outcome – you need three solid performers to achieve comparable portfolio returns with dramatically better risk-adjusted profiles. I insist on this important point: you don’t need a miracle, and if you are banking on a new Ozempic, you might as well play the lottery – the chances are probably equivalent.

But the advantages compound beyond simple probability math when you concentrate capital in diagnostics:

Technology complementarities: A portfolio of diagnostics companies can share platform technologies, assay development expertise, and manufacturing relationships in ways that pharma portfolios cannot. Your liquid biopsy company’s circulating tumor DNA expertise might accelerate your liver disease company’s biomarker validation. Or your metabolomic company may collaborate with the ctDNA company to develop the hiper-sensitive and -precise multi-omic test that alone none could.

Data synergies: Multiple diagnostics companies generate datasets that become more valuable in aggregate. Cross-disease insights, multi-modal data integration, and shared machine learning infrastructure create portfolio-wide moats that individual companies couldn’t build alone.

Shared commercial infrastructure: Diagnostics companies selling into similar customer segments (oncologists, pathologists, hospital systems) can share commercial resources, KOL relationships, and market access strategies. Your first portfolio company builds the relationships; subsequent companies leverage them.

Coordinated market development: Multiple diagnostics companies can collectively advocate for guideline changes, engage payers on value-based contracting frameworks, and build the evidence infrastructure that benefits the entire portfolio.

This portfolio-level value creation is unique to diagnostics and invisible to investors evaluating companies one at a time against generic venture criteria. A specialized diagnostics fund with 10-12 companies isn’t just diversifying risk – it’s creating synergies that amplify returns across the entire portfolio.

Current Practice: The Standard Framework and Why It Fails

The venture industry has developed a reasonably coherent framework for mapping diagnostics development to investment stages. This isn’t speculation – it’s documented in multiple industry sources:

MDCA (Medical Device Consultants & Associates) published comprehensive guidance on IVD development pathways from concept to market approval[2]
BCG’s 2023 report “Bringing Advanced Diagnostics to Market” outlines capital requirements and commercialization challenges[3]
Active diagnostics investors like The Scenarionist have analyzed what VCs look for at each stage[4]
MedTech-focused VCs have published staging frameworks for Series A, B, and C funding[5]

These sources converge on a similar model – what I’ll call the standard validation framework:

8 stages over 7-10 years:[6]

Concept/Feasibility (Pre-Seed: $50K-$500K) → De-risks scientific validity
Proof of Concept (Seed: $500K-$3M) → De-risks technical feasibility
Analytical Validation (Series A: $3M-$10M) → De-risks technical performance
Clinical Validation (Series A: $8M-$20M) → De-risks clinical validity
Regulatory Submission (A-B Bridge: $10M-$25M) → De-risks regulatory pathway
Market Approval (Series B: $15M-$50M) → De-risks regulatory clearance
Commercialization (B-C: $30M-$100M) → De-risks commercial viability
Market Expansion (Series C+: $50M-$200M) → De-risks market position

Note: Capital values above reflect US market analysis; European diagnostics companies typically require 30-50% lower capital at equivalent stages due to different regulatory pathways, market structures, and investor expectations.

This framework works beautifully for what it was designed to do: map validation milestones to capital deployment for a single diagnostic test following a linear development pathway toward FDA or CE mark approval.

The problem: This framework treats all diagnostics companies as if they’re building the same thing. It can’t distinguish between:

A platform company versus a single-test company
A data business versus a testing business
A companion diagnostic versus a standalone test
A lab service model versus a distributed device

More fundamentally, the standard framework maps stages to validation checkpoints (analytical → clinical → regulatory → commercial) rather than business model proof points. And critically, it focuses almost entirely on technical, clinical, and regulatory validation while largely ignoring market de-risking – arguably the most important dimension for diagnostics that represent medical innovation.

This creates four critical failures:

First, it ignores market adoption risk: The framework assumes that once you achieve regulatory approval, commercialization naturally follows. But diagnostics face unique market adoption challenges: changing physician behavior, achieving guideline inclusion, demonstrating health economic value to payers, integrating into existing clinical workflows. These market barriers often prove more formidable than regulatory ones. A technically validated, regulatory-approved test with no market adoption pathway is worthless – yet the standard framework treats market de-risking as an afterthought that happens in “Stage 7: Commercialization.”

Second, it creates false “Valleys of Death”: Industry research identifies Stages 4-5 as the danger zone – $15M-$40M in cumulative capital requirement with zero revenue over 18-48 months.[7] But this “valley” only exists if you evaluate diagnostics as if they should generate revenue like SaaS companies. A companion diagnostic partnered with pharma at Stage 3 doesn’t face this problem – pharma funds the clinical validation. The valley exists because we’re applying the wrong expectations about when revenue should appear.

Third, it treats regulatory clearance as a universal stage gate: The framework suggests FDA/CE approval creates a 3-5x valuation step-up.[8] True for some diagnostics, irrelevant for others. A lab-developed test (LDT) in the United States never gets FDA approval but can scale to $100M+ revenue. A software-as-medical-device diagnostic might achieve CE mark in 18 months with €200K. Treating “regulatory clearance” as a universal inflection point is intellectually lazy.

Fourth, it assumes all diagnostics follow the same trajectory to exit: Early exits post-approval pre-revenue ($50M-$300M range), growth exits at revenue scale ($500M-$2B), mature exits at market leadership ($2B-$10B).[9] But platform companies might exit much earlier (Grail to Illumina pre-revenue at $8B valuation) while single-test companies might never exit at all, instead becoming profitable lifestyle businesses.

The standard framework isn’t wrong – it’s incomplete. It describes one pathway well but assumes that pathway is universal.

A New Framework: Business Model De-Risking Stages

I believe that investment stages should map to proving critical business model assumptions, not just completing validation milestones.

This requires investors to articulate, before they invest: “What kind of diagnostics company are we building?” The answer determines what needs to be de-risked and in what sequence.

But the reality is that we don’t always know the answer at the outset. Part of what makes diagnostics uniquely suited to venture investment – despite the conventional wisdom – is that the technology allows for experimentation, learning, and pivoting in ways that drug development simply doesn’t permit.

Unlike therapeutics where you commit to a molecular target and indication early and can’t easily change course, diagnostics companies can test multiple clinical applications, pivot between market segments, adjust their business model from standalone to companion, or shift from test sales to data licensing. This flexibility should be welcomed, not penalized, when it represents genuine learning and improvement of the business case.

The framework I’m proposing acknowledges this reality. It asks investors to map the assumptions they’re testing at each stage, recognizing that those assumptions might evolve as the company learns what actually creates value.

Let me illustrate with three examples:

Example 1: Platform Company versus Single-Test Company

Single-Test Company Stages:

Seed: Prove analytical and clinical validity of the test
Series A: Prove regulatory pathway is clear and achievable; begin market de-risking
Series B: Prove market approval and early commercial traction with real physician adoption
Series C: Prove commercial scale and market penetration

Platform Company Stages:

Seed: Prove the first test works (analytical + clinical validation)
Series A: Prove you can build a second test on the same platform infrastructure without starting from scratch; begin market testing
Series B: Prove customers value the platform economics (multi-test adoption, not just buying Test #1)
Series C: Prove platform economics compound (marginal cost of new tests approaches zero)

See the difference? The platform company front-loads validation risk but back-loads business model proof. By Series A, you need evidence that platformization actually works – not just that you’ve validated one test. Current frameworks can’t distinguish this.

Example 2: Data-as-Product Business Model

If you’re building a company where proprietary datasets are the primary asset:

Seed: Prove you can generate datasets competitors can’t replicate (unique patient populations, longitudinal data, multimodal integration)
Series A: Prove the dataset generates insights that change clinical or research decisions
Series B: Prove third parties will pay for access to the data (pharma for drug development, payers for risk stratification, providers for clinical decision support); demonstrate market appetite
Series C: Prove the data moat compounds over time (more data → better models → more customers → more data)

None of this appears in the standard “analytical → clinical → regulatory → commercial” progression. Yet companies like Tempus and Flatiron Health built billion-dollar businesses on exactly this model.

Example 3: Companion Diagnostic

For a companion diagnostic co-developed with a pharmaceutical partner:

Seed: Prove analytical validity and establish pharma partnership
Series A: Integrate diagnostic into pharma’s Phase 2/3 trials (pharma funds this); validate market need through pharma’s clinical program
Series B: Achieve co-approval with the therapeutic
Series C: Expand to additional indications or therapeutic partners

The entire financial structure is different. Pharma partnership at Seed fundamentally changes the risk profile – you’re not funding clinical validation alone, you’re not facing the “Valley of Death” that single-test companies experience, and market de-risking happens through pharma’s commercial apparatus.

The Multi-Monetization Problem: Why Real Diagnostics Companies Are Even More Complex

The examples above illustrate different business models, but they still oversimplify. Most diagnostics companies don’t pursue a single monetization strategy – they pursue multiple revenue streams simultaneously:

Test revenue + proprietary datasets + pharma consulting services
Companion diagnostic + standalone expansion + data licensing
Lab service + distributed device + software platform

When this happens, the de-risking logic becomes interdependent, not sequential. And this is where even sophisticated frameworks break down.

Consider a company pursuing test revenue + data licensing:

You can’t de-risk the data business without test adoption – you need actual patient testing to generate valuable datasets. But test adoption might be slower if you’re restricting data access to build proprietary moats, which hurts network effects that could accelerate adoption. Meanwhile, pharma companies might pay for early access to your datasets, potentially funding the clinical validation that makes your test credible. This means data revenue could arrive BEFORE test revenue – a complete sequence violation of the standard framework.

What about market de-risking? You’re simultaneously trying to prove that physicians will order your test AND that pharma will license your data – two completely different market validation exercises requiring different capabilities and customer relationships.

What stage are you in? Series A for clinical validation? Or Series B for commercial viability? The answer is “both” or “neither” – the framework simply doesn’t fit.

Or consider a companion diagnostic with standalone ambitions:

Your regulatory pathway differs fundamentally depending on which market you prioritize. FDA approval is required for standalone use, but companion diagnostics can proceed through pharma’s drug trials without separate device approval initially. Your go-to-market strategy differs – B2B sales to pharma for the companion indication, direct-to-provider sales for standalone use. Your capital requirements differ – pharma typically funds companion clinical validation, but standalone requires self-funded trials.

Market de-risking becomes doubly complex: you need to prove pharma will partner with you AND that you can build direct clinical adoption. These require fundamentally different market development strategies.

You literally cannot de-risk both pathways simultaneously with limited venture capital. Yet investors evaluating you at Series A need to understand which bet you’re making, and most pitch decks present both opportunities as if they’re additive rather than mutually exclusive in terms of capital deployment.

The linear staging framework cannot handle this complexity. It assumes:

One primary monetization stream
Sequential validation milestones
Clear stage gates where one type of risk is retired before the next begins
Market adoption happens automatically after regulatory approval

But real diagnostics businesses have:

Multiple interdependent revenue streams
Parallel validation requirements across different business models
Ambiguous “success” criteria (what if data licensing works but test sales plateau? Is that success or failure?)
Market adoption challenges that often exceed regulatory challenges

This isn’t just an academic distinction. It’s why so many diagnostics companies raise money for “Plan A” and end up pivoting to “Plan B” midstream. Or… “Plan F”! The business model complexity and interdependencies were never properly mapped to the staging strategy. Investors thought they were funding a testing company and wake up to discover they’ve funded a data company – or vice versa. In an AI dominated world, where precision medicine demands are unclear and pressing, this experimentation is not a problem, but an integral part of finding where the value is for the technology pathway the founders initiated.

A proper framework for diagnostics investment needs to explicitly address these interdependencies. It’s not enough to ask “platform or single-test?” You need to ask: “What are ALL the monetization strategies you’re pursuing, how do they interact, what’s the critical path through them given realistic capital constraints, and how do we de-risk market adoption for each?”

Learned From Both Sides

I’ve been in diagnostics for a while as an entrepreneur, living the highs and lows, the joy of sales and the downs of insufficient traction. Over the years, I’ve also had plenty of opportunities to analyze diagnostics investments from the other side of the table, in advisory/consultant roles for investors. What strikes me is how often sophisticated investors make the same exact mistakes I made as a founder, and I see/saw other founders do. Its like none of us is learning from each other.

No wonder investors are so reluctant to purse diagnostics projects. They’re not wrong to be cautious about diagnostics – the category is genuinely difficult. But they’re often cautious about the wrong things.

The framework I’m proposing here emerged from seeing the same patterns repeat across multiple companies and investors. I’ll develop these ideas more comprehensively in a future series, but the core insight is this: diagnostics investing requires diagnostics-specific frameworks. Borrowing from biotech, SaaS, or medical devices will lead you astray every time.

What Diagnostics-Focused Investors Should Actually Provide

If stages map to business model de-risking rather than validation checkpoints, then investor value-add must shift accordingly. Capital alone isn’t enough – and in fact, capital without the right support often accelerates failure.

The question isn’t whether investors need an “army of on-call experts in diagnostics.” The question is: what capabilities should live inside the investment firm, what should be available through external advisors, and what should board composition provide?

I think that diagnostics investment funds actually need:

Regulatory Pathway Expertise: Not just funding for FDA submissions, but strategic guidance on pathway selection. Should you pursue FDA clearance, CE mark, lab-developed test status, or breakthrough designation? This decision is business-model-determinative, and most founders get it wrong. This expertise can come from partners with diagnostics operating experience, external regulatory consultants on retainer, or board advisors who’ve navigated these pathways successfully. A VC that prevents a 2-year, $5M regulatory misstep through the right advisor introduction is worth more than one that simply writes the check.

Clinical Evidence Architecture: Access to biobanks, pre-negotiated relationships with clinical research organizations, expertise in study design that will satisfy regulators and payers. Clinical validation is the longest, most expensive stage. Investors who can compress this timeline by 6-12 months through better study design and sample access – whether through internal capabilities or curated external networks – create immediate value.

Market De-Risking Capabilities: This is where most investors fail entirely. Understanding physician adoption patterns, clinical workflow integration, health system decision-making, and the pathway to guideline inclusion. These skills rarely exist in traditional VC firms and can’t be easily outsourced. Board advisors with actual clinical practice experience or health system leadership become critical here. They can ask the right questions: Have you validated market need with actual physicians? What’s your pathway to guideline inclusion? How does this integrate into existing workflows? These questions sound obvious, but they’re systematically ignored in favor of generic venture questions about TAM and competitive moats.

Payer Strategy and Reimbursement Intelligence: Understanding who makes coverage decisions within national health systems and major commercial payers, how to structure health economics dossiers, when to engage (hint: not after launch).

A lifetime ago I spent a few years working at a healthtech business incubator and accelerator co-funded by a large insurance company, and more recently I’ve had conversations with insurtech leaders. What I’ve learned is that payers genuinely want to be part of the discussion – they understand that early diagnostic innovation could dramatically improve their economics. But they need to be brought in by investors or structured ecosystem players, not approached by cash-starved entrepreneurs who inevitably elicit the knee-jerk “I have no money to invest” response.

The timing and framing matter enormously. When a respected VC says “we’re funding this diagnostic and think it could change outcomes in [disease area], would you help us understand what evidence you’d need to see for coverage?” – that conversation goes very differently than when a desperate founder cold-calls asking for reimbursement.

Diagnostics without reimbursement don’t scale. Investors who understand this and can facilitate the right payer conversations at the right time prevent millions in wasted capital.

Pharma Partnership Structuring: For companion diagnostics especially – warm introductions to diagnostics and biomarker leads at pharma companies, market intelligence on deal structures, guidance on clinical trial integration and regulatory coordination. A good pharma partnership at Series A can fund your entire clinical validation program.

Data Strategy and Monetization: If data is part of your product, expertise in dataset architecture, privacy frameworks, licensing models, and real-world evidence generation. Most diagnostics companies treat data as exhaust rather than product. Investors who help structure data capture from day one create a second revenue stream.

Board Composition for Diagnostics-Specific Expertise: Perhaps most importantly, investors should recruit board members and advisors with actual diagnostics experience. Not just generic healthcare or life sciences experience, but people who’ve built diagnostic companies, navigated regulatory pathways, achieved payer coverage, and integrated tests into clinical workflows. These board members ask different questions than typical venture board members, and those questions often reveal the critical risks that standard frameworks miss. And beyond successful board members, those that failed specifically at diagnostics bring valuable insight too.

The right mix of internal capabilities, external advisor networks, and strategic board composition matters more than having everything in-house. What matters is recognizing that diagnostics companies need fundamentally different support than software or therapeutics companies.

Practical Implications: A Diagnostic Tool for Investors

So, I hazard some words of advice. Before you invest in a diagnostics company, force yourself to answer three questions:

1. What business model is this company building?

Platform or single-test? Data or testing service? Companion or standalone? Lab-service or distributed device? Be specific. If you can’t articulate the business model in one sentence, you don’t understand what you’re funding.

But be honest: you might not know yet. And that’s okay if you’re structuring early-stage capital to test hypotheses. The key is acknowledging that experimentation and pivoting are part of the process in diagnostics. Unlike drug development where pivots are nearly impossible, diagnostics companies can learn and adjust their business model as they gather evidence about what creates value.

The question becomes: Are we funding a company that knows its business model and needs to execute? Or are we funding a company that’s still learning which business model works, and how do we structure milestones to test those hypotheses efficiently?

More importantly: if they’re pursuing multiple monetization strategies, can you map the interdependencies? Which revenue stream funds which validation milestone? What’s the critical path? How do different strategies affect market de-risking requirements?

2. What are the 3-4 critical assumptions that business model depends on?

For a platform: Does the second test actually leverage the first test’s infrastructure? For data-as-product: Will pharma pay for dataset access before you have revenue traction? For a companion: Will the therapeutic succeed in trials?

But don’t forget market assumptions: Will physicians actually change their behavior? Can you achieve guideline inclusion? Will payers reimburse at economically viable rates? Will health systems adopt despite workflow friction?

These assumptions determine where risk actually lives. They’re often invisible in the standard validation framework, which focuses on analytical and clinical validation while ignoring market adoption barriers.

3. Can you help de-risk those assumptions, or are you just providing capital?

If the critical assumption is “payers will reimburse,” do you have relationships with coverage decision-makers? Can you facilitate the right introductions at the right time?

If it’s “platform architecture scales,” do you have operators who’ve built platforms before? Can you provide board advisors who’ve solved these problems?

If it’s “physicians will adopt,” do you have KOLs or health system leaders who can provide market insight and validation?

If the answer is no to all of these, partner with someone who does – or acknowledge you’re making a bet you can’t actively improve. Pure financial investors have a role in diagnostics, but they should price their capital accordingly given the limited value-add.

For generalist VCs: If you can’t answer these three questions clearly, you’re probably applying the wrong framework. Diagnostics look cheap compared to drug development ($50M total capital vs. $2.6B), but they’re not “easy biotech.” They’re a different category with different economics, and critically, different market adoption dynamics. Respect that difference or partner with specialists.

For specialized diagnostics funds: Build the right mix of internal capabilities, external advisor networks, and board-level expertise around the 3-4 most critical needs your companies face – regulatory consulting, payer strategy, pharma partnerships, clinical evidence design, and market de-risking. Structure fund life for longer timelines (12-15 years, not 10 years) that accommodate both validation timelines and market adoption curves. Create reserves for bridge rounds during the analytical-to-clinical transition that kills most diagnostics companies.

For strategic investors (pharma, Quest, LabCorp, hospital systems): Stop waiting for Series B or C to engage. The real value is created at Seed and Series A when business model decisions get locked in. If you’re pharma looking for companion diagnostics, engage at analytical validation when you can still shape the clinical program. If you’re a lab service looking for new tests, engage when platform architecture decisions are being made. If you’re a health system, engage early when workflow integration can still be designed rather than retrofitted.

The Arbitrage Window Won’t Stay Open Forever

Diagnostics are mispriced because investors evaluate them with frameworks borrowed from other categories. The opportunity is in the mispricing itself.

Whoever adopts diagnostics-specific frameworks first will identify value the market is currently ignoring. That window will narrow as more investors figure this out – but right now, it seems to me that it’s wide open.

Looking Toward the Gulf

I’ve been visiting and thinking extensively about the Gulf region over the past year, and I’m increasingly convinced the future of precision diagnostics will be written there as much as, or more than, in Boston or Basel. The GCC countries – particularly Saudi Arabia, UAE and Qatar – have unique structural advantages that could allow them to leapfrog traditional models entirely and play leadership roles in the diagnostics domain – I’ll explore what that means for diagnostics ecosystem building in a next article.

References:

[1] Ambiom (2021). “7.9% of drugs entering clinical development were approved by FDA over the past decade.” https://ambiom.com/7-9-of-drugs-entering-clinical-development-were-approved-by-fda-over-the-past-decade

[2] MDCA. “IVD Development: From Concept to Market Approval.” Medical Device Consultants & Associates. https://www.mdcassoc.com/ivd-development/

[3] Boston Consulting Group (2023). “Bringing Advanced Diagnostics to Market.” https://www.bcg.com/publications/2023/bringing-advanced-diagnostic-testing-to-market

[4] The Scenarionist. “What VCs Look For in Early-Stage Diagnostics.” https://www.thescenarionist.com/p/what-vcs-look-for-in-diagnostics-startups

[5] Mind Machine. “Understanding Series A, B, and C Funding for MedTech Startups.” https://mindmachineco.com/understanding-series-a-b-and-c-funding-for-medtech-startups/

[6] Framework synthesized from sources [2], [3], [4], and [5], plus additional industry sources including: FINDDX “IVD Product Lifecycle” (https://www.finddx.org/wp-content/uploads/2025/08/02-IVD-Product-Lifecycle.pdf) and MDIC “Developing Clinical Evidence for Regulatory and Reimbursement” (https://mdic.org/wp-content/uploads/2019/08/Clinical-Evidence-IVD-Framework-FINAL.pdf)

[7] Multiple sources converge on this “Valley of Death” characterization, including references [3], [4], and The Scenarionist’s analysis of diagnostics funding gaps.

[8] This valuation step-up estimate appears in BCG’s analysis [3] and is referenced in multiple VC assessments of diagnostics companies post-regulatory approval.

[9] Exit data from public sources including: Roche acquisition of LumiraDx diagnostics business for $295M (2023), Guardant Health market cap and revenue figures from public filings, Exact Sciences revenue data from 2024 annual reports, and Grail acquisition by Illumina at $8B valuation.