For small and mid-sized pharmaceutical and biotech companies, “affordable clinical research” should not mean simply finding the lowest rate card.
The largest savings often come from somewhere else: avoiding unnecessary complexity, reducing rework, applying controls proportionately, and using technology only where it solves a real problem.
Several developments over the past two years point in the same direction.
The industry is moving from transformation-by-hype toward evidence-based pragmatism.
DCTs are settling into a hybrid reality
Decentralized clinical trials were once presented as a possible replacement for the traditional site-based model.
That has not happened.
A 2025 analysis of 1,097 drug trials with decentralized attributes initiated between 2014 and 2023 found that 97.6% used a hybrid model, while 78.3% incorporated only one decentralized attribute. Trial completion was not significantly different from trials without decentralized elements: 82.4% versus 83.3%. DCT activity rose sharply during COVID-19 and then declined again afterwards [1].
That does not mean decentralization failed.
It suggests something more useful: decentralized elements work best as tools, not as an ideology.
eCOA/ePRO, remote visits, BYOD, local assessments, direct-to-patient services, and remote monitoring can reduce participant burden or solve specific operational problems. But there is little reason to decentralize an activity simply because the technology exists.
For cost-conscious sponsors, the question is increasingly:
Which elements make this particular study better?
Not:
How digital can we make it?
AI is becoming useful, but adoption is still uneven
The same shift is happening with AI.
A global Tufts CSDD survey of 302 respondents assessed AI/ML implementation across 36 clinical-development activities. Averaged across those activities, 36.9% had not yet implemented AI/ML, 30.3% were beginning or piloting, 22.1% were partially implementing, and only 10.7% were at full implementation [2].
That is a much more interesting picture than either “AI changes nothing” or “AI replaces clinical development.”
AI is already useful for reducing manual effort in areas such as document processing, coding assistance, data review, monitoring, feasibility, and analytical workflows.
But regulatory expectations are also becoming clearer.
FDA’s 2025 draft guidance proposes a risk-based credibility framework tied to the AI model’s context of use, while EMA’s reflection paper takes a similarly lifecycle- and risk-oriented approach [3,4].
The practical lesson is straightforward:
Automate tasks, not accountability.
The best AI use cases today are bounded, reviewable, and supported by appropriate human expertise.
Risk proportionality is becoming an operating model
ICH E6(R3) is perhaps the clearest expression of the same principle.
Rather than prescribing identical controls for every activity, the guideline emphasizes quality by design, critical-to-quality factors, proportionality, fit-for-purpose processes, and risk-based oversight.
Importantly, implementation is regional rather than a single global “go-live” date. Principles and Annex 1 became effective in the EU in July 2025; FDA issued final E6(R3) guidance in September 2025; and Health Canada implemented it in April 2026. Annex 2 reached ICH Step 4 in June 2026, with regional implementation continuing [5,6].
The UK provides another useful example.
Its amended Clinical Trial Regulations took effect on 28 April 2026. Valid applications now receive an initial combined regulatory and ethics decision within 30 calendar days. Eligible lower-risk “notifiable” trials can receive automatic MHRA authorisation within 14 days, although ethics review and the combined decision still apply [7].
For smaller sponsors, this is good news.
A proportionate system rewards organization that understand where risk actually sits, rather than organizations that simply add more review layers.
Capital discipline still matters
The biotech funding environment remains selective.
Nature Reviews Drug Discovery described a difficult funding market as one of the major forces shaping 2025 biopharma dealmaking, alongside pricing pressure and looming loss of exclusivity for major products [8].
For smaller sponsors, that translates directly into operational pressure.
Every unnecessary handoff costs money.
Every late specification change creates rework.
Every unclear responsibility creates management overhead.
Every technology platform that does not solve a defined problem consumes runway.
Cost efficiency, therefore, is not simply about paying less per hour.
It is about building a study operating model in which fewer hours are wasted.
Therapeutic complexity keeps increasing
At the same time, the science is not becoming simpler.
As of June 2025, more than 6,000 interventional cell-therapy trials had been registered globally, with the field diversifying beyond conventional CAR-T approaches [9].
For pluripotent stem-cell-derived therapies alone, a 2025 review identified 116 regulatory-approved clinical trials testing 83 products, with more than 1,200 participants dosed by the end of 2024 [10].
Complex modalities bring complex endpoints, data flows, safety considerations, external vendors, analysis requirements, and regulatory expectations.
This creates an important tension for smaller companies:
they need specialized expertise, but they often do not need the organizational overhead of a very large delivery structure.
That is where flexible specialist teams, FSP models, and focused project-based support can be particularly useful.
RWE is becoming more practical, but it is not a shortcut
Real-world data and real-world evidence are also moving closer to mainstream regulatory use.
FDA’s Advancing RWE Program explicitly supports proposals intended to provide pivotal evidence for new labeling claims or to satisfy post-approval requirements. FDA’s public reporting also shows increasing numbers of protocols containing RWE being discussed with CDER [11].
But this should not be interpreted as:
“RWE can replace the clinical trial.”
The more useful interpretation is:
RWE gives sponsors another evidence option when the data source, design, estimand, analysis, and regulatory question are genuinely fit for purpose.
For rare diseases, precision oncology, external controls, feasibility work, long-term follow-up, and post-approval research, that can be extremely valuable.
It can also reduce unnecessary data collection, but only when the evidence strategy is designed correctly from the beginning.
Diversity planning is moving upstream, but the rules differ
This is another area where precision matters.
In the US, FDORA created a statutory requirement for Diversity Action Plans for certain studies, while FDA’s detailed implementing guidance remains in draft as of 2026 [12].
In the UK, HRA and MHRA are developing an Inclusion and Diversity Plan framework, but importantly, the HRA explicitly states that this is guidance rather than a new legal requirement under the 2026 regulatory reforms [13].
The practical direction, however, is clear:
participant representativeness should be considered during study design – not added as a corrective exercise once recruitment problems appear.
What affordability means at TUNECT
For us, affordable clinical research does not mean cutting quality.
It means removing unnecessary complexity.
- Design around what is critical to quality
Not every data point, output, process, or risk deserves the same level of control.
We focus resources where errors could materially affect participant safety, trial reliability, or regulatory interpretation.
- Use technology because it solves a problem
AI, DCT tools, automation, RWD, and new platforms can all create value.
But technology should earn its place in the study.
We prefer specific, validated use cases over large “digital transformation” exercises without a measurable benefit.
- Right-size the delivery model
A sponsor may need a senior biostatistician, a programming team, data management expertise, or regulatory-ready documentation.
That does not automatically mean it needs a large full-service operating structure around every activity.
Specialist teams can be added where the study actually needs them.
- Make quality proportionate – not optional
Risk-based quality management does not mean doing less QC.
It means applying the right QC to the right risk.
Independent programming, targeted validation, automated checks, specification review, and expert review can all play a role depending on the criticality of the output.
- Keep governance simple and visible
Clear ownership, escalation paths, assumptions, decisions, and timelines reduce more cost than another layer of management ever will.
For smaller sponsors in particular, transparency is an efficiency tool.
The clinical-development environment in 2026 is technologically more capable, scientifically more complex, and increasingly risk-proportionate.
That combination creates an opportunity. The most effective operating model is no longer necessarily the one with the most technology, the most vendors, or the most process.
It is the one that can explain why each element is there.
Affordable clinical research is not a stripped-down version of quality.
It is disciplined design: fewer unnecessary layers, fewer avoidable handoffs, less rework – and more attention where it actually matters.
At TUNECT, that is the model we continue to build around.
References
[1] Jiang Y, et al. Understanding the gap between expectations and reality in decentralized clinical trials. npj Digital Medicine. 2025;8:408. doi:10.1038/s41746-025-01811-y.[2] Lamberti MJ, et al. The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development. Therapeutic Innovation & Regulatory Science. 2025;59(5):1074–1086. doi:10.1007/s43441-025-00803-0.
[3] FDA. Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance. January 2025.
[4] EMA. Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. EMA/CHMP/CVMP/83833/2023. 2024.
[5] ICH. E6(R3) Guideline for Good Clinical Practice: Principles and Annex 1. Final guideline.
[6] ICH. E6(R3) Good Clinical Practice: Annex 2. Step 4, June 2026.
[7] MHRA/HRA. The Medicines for Human Use (Clinical Trials) (Amendment) Regulations 2025 and accompanying 2026 implementation guidance.
[8] Giglio P, Micklus A. Biopharma dealmaking in 2025. Nature Reviews Drug Discovery. 2026;25:92–93. doi:10.1038/d41573-026-00013-9.
[9] Benthani F, Upadhaya S, Zhou A. Cancer cell therapies: global clinical trial trends and emerging directions. Nature Reviews Drug Discovery. 2025;24:898–899. doi:10.1038/d41573-025-00180-1.
[10] Kirkeby A, Main H, Carpenter M. Pluripotent stem-cell-derived therapies in clinical trial: A 2025 update. Cell Stem Cell. 2025;32(1):10–37. doi:10.1016/j.stem.2024.12.005.
[11] FDA. Advancing Real-World Evidence Program and FDA CDER Real-World Evidence Submissions reporting.
[12] FDA. Diversity Action Plans to Improve Enrollment of Participants from Underrepresented Populations in Clinical Studies. Draft Guidance. June 2024.
[13] Health Research Authority. Clinical trials regulations reform: Diversity and inclusion in clinical trials. 2026.