For years, commentators have criticized pharma for clinging to legacy sales forces, rebate-driven access, and one-size-fits-all launches. But the truth is: pharma already knows the ROI case for the combined effect of Diagnostics, Data, and Digital.
When harnessed systematically, these three elements create a competitive leap. Drugs are no longer just sold - they are activated by tests, amplified by data, and accelerated to the right patients by digital engagement.
Leading companies have been piloting these synergies under the umbrella of a precision medicine niche. Now it is time to scale.
The next winners in pharma will not be those who treat diagnostics as an accessory, but those who make Diagnostics, Data, and Digital - the D-Cubed Model - the core of commercialization across all product launches.
The Evidence Base
(As presented in the appendix to the full report)
Over 100 peer-reviewed papers and industry articles have shown that biomarker-driven launches succeed faster, at lower cost, and deliver higher returns. Real-world adoption studies confirm that embedding diagnostics expands therapy uptake and strengthens payer value. Every pharma board has seen these numbers. The debate is no longer “does precision pay off” - it does - but rather “how to operationalize it portfolio-wide.”
Historically, pharma spent $500M–$1B to launch a drug, with more than 70 percent of that allocated to legacy tactics such as sales reps, DTC advertising, and congress spend. Less than 5 percent went to diagnostics or patient services, despite diagnostics determining ~70 percent of treatment decisions. The D-Cubed playbook is flipping these economics.
The Knock-Out Economics of D-Cubed
R&D advantage:
• Cost per approval: $4.6B (legacy) vs $3.5B (biomarker), ~$1.1B lower with D-Cubed, +27 percent ROI
• Time to approval: 10 years (legacy) vs 8 years (biomarker)
• Probability of success: ~26 percent with biomarker selection vs ~8 percent without
Launch advantage:
• Conversion to treatment: 55 percent (legacy) vs 75 percent (biomarker)
• Time to peak sales: 7 years vs 5 years
• Salesforce and promotion: $160M/year vs $130M/year
• Patient acquisition cost/start: $22K vs $12K
• Diagnostic enablement: $0 vs $35M/year
Portfolio advantage - per launch D-Cubed delivers:
• +$810M NPV uplift (faster uptake, longer tails)
• +$750M saved (shorter pre-launch buildup)
• +$300M preserved (slower erosion)
Scaled across portfolios, D-Cubed creates a ~$20B advantage per decade for large pharma.
The Platformization of D-Cubed Pharma
The strongest D-Cubed pharmas are starting to operate like healthtech companies - platformized, agile, and scalable:
• Test-triggered activation replaces mass detailing
• Data flywheel: each test result enriches targeting, pricing, and evidence
• Pilot to scale in quarters, not years
• Frictionless pathways from test to treatment
Executed well, D-Cubed is:
- More valuable - faster uptake, longer revenue tails, higher NPV
- More agile - reusable infrastructure across portfolios
- Less vulnerable - defends against patent cliffs and tech disintermediation
Cynic’s Corner (Part 1)
If pharma already knows diagnostics ROI, why hasn’t it shifted faster? The answer is inertia, not ignorance. Pharma is a $1T industry built on a legacy playbook. Turning that supertanker takes more than evidence - it requires rewiring budgets, KPIs, and culture.
Yet the shift is happening:
• Pilots in oncology, rare disease, and immunology
• Internal ROI models, even if not in investor decks
• Early D-Cubed experience reallocating budgets toward diagnostics, data, and digital
The real divide is between first movers and late adopters. By the time consensus arrives, D-Cubed leaders will already own the platform advantage.
D-Cubed Proof in Practice: Diagnostics Triggered, Data Led, Digitally Activated Uptake
A leading global pharma company demonstrated the economic power of the D-Cubed model by combining diagnostics triggered targeting, real-time lab intelligence, and precision digital engagement to change physician behavior at speed and surface eligible patients who would otherwise be missed. Using DXRX Signal data, machine learning and NLP identified 822 physicians whose testing behavior was suboptimal for a novel biomarker. Rather than defaulting to traditional sales promotion, the company activated a targeted, time sensitive Physician Engage program to intervene at the point of diagnostic opportunity.
A total of 271 physicians (33%) engaged with the tailored content over the 26-week period, and 75 went on to order the biomarker test, with over half of them doing so for the first time during the initial 4 weeks of engagement. Across the 26-week period, this D-Cubed activation drove 274 tests ordered and 81 new therapy eligible patients identified, accelerating access for patients who may never have been tested under legacy models.
This is the economics of D-Cubed in motion: when diagnostics, data, and digital are orchestrated, uptake accelerates, leakage reduces, and value creation begins earlier. The question is no longer whether this works, it does, but how quickly pharma chooses to scale it. Replicated across a portfolio, this model shifts from isolated success to a repeatable platform advantage that compounds over time.
