Modernizing data operations to enable today's Phase 1 trials

Small and emerging biotechs face a hyper-competitive early-phase environment: less R&D capital, greater pressure to be first, and increasing regulatory flexibility for compounds addressing unmet needs and small populations. In some cases, drugs are approved on Phase 1 data alone.

Early-phase trial design has thus shifted toward complexity. Simple, healthy-volunteer-only Phase 1 studies are increasingly rare; most combine healthy volunteers with small patient cohorts. In this environment, the differentiator is no longer what data you collect. It is how quickly you can turn complex data into a defensible go/no-go decision.

For most sponsors and contract research organizations (CROs), the bottleneck for data-rich Phase 1 trials is operational, not scientific. Legacy operating models add avoidable weeks through customized case report form (CRF) libraries, debates over form layout, 100% sponsor review of every table and figure (including routine, non-endpoint data), manual electronic data capture (EDC) transcription that triggers full source data verification (SDV), and sequential programming workflows. For example, we have seen sponsors who maintain as many as 50 custom EDC form variations for the same data points.

The cost of deviating from a sound standard is concrete. In one study, a sponsor team rejected the standards-based electronic CRF design we provided and directed us to rebuild the forms outside those standards; after go-live, the design was reverted to our original—by which point everyone had lost weeks, and the study carried real risk of orphaned data and clinic re-entries. On another project, a sponsor had no existing standard for a site-completed questionnaire, triggering repeated redesigns and a six-month delay before the document was finalized. Ultimately, we used the standards-based prototype we had submitted at the outset.

At Parexel, we have rebuilt our delivery model and our 2,000-plus-person Global Data Operations (GDO) organization, which spans five continents, to accelerate early-phase work. That scale also brings deep early-phase expertise: dedicated unit teams and an approximately 40-person clinical pharmacology, modeling and simulation (CPMS) group that turns around interim pharmacokinetic (PK) reports for dose escalation in 2-3 days — within five for complex studies. Since 2024, we have compressed standard post-Last Participant Last Visit (LPLV) biometrics timelines by roughly half, and even our worst-case committed timelines fall in the industry's upper quartile. As a result of that process, we have concluded that a modern early-phase operating model rests on four core principles:

1.    Standardized data capture

To streamline data capture, we have replaced bespoke sponsor forms with non-negotiable, standard templates that are Clinical Data Interchange Standards Consortium (CDISC)-compliant. CDISC-standardized forms are akin to the bumpers in a children's bowling alley — the right data is captured every time, regardless of who configures the study.

We use ClinBase™ as our validated eSource system. It captures source data at the bedside in real time, with interfaces to medical devices, barcode scanning, and a full 21 CFR (Code of Federal Regulations) Part 11 audit trail. Because there is no transcription, there is no 100% SDV requirement.

ClinBase builds occur within 5-6 weeks from the final clinical study protocol (CSP). For studies that extend beyond our units to external sites, the real differentiator is electronic data capture (EDC): we have compressed the standard EDC build from a long-standing 12-week baseline to 8 weeks, effective Q3 2025. EDC has historically been notoriously clunky in early phase; by compressing it, we eliminated the timeline penalty. ClinBase-to-EDC automation lets sponsors pivot from an in-unit study to a hybrid model (combining our EPCUs with external sites) without the usual backlog of data to re-transcribe. Customization is still available for sponsors who want it, but we can project the explicit cost and timeline impacts, surfacing trade-offs that were previously invisible.

2.    A transparent, three-tier complexity grading system

Without transparent, up-front complexity grading, sponsors get committed to timelines that CROs cannot meet, eroding trust on both sides.

At Parexel, we grade the complexity of every early-phase study as Low, Medium, or High at the request-for-proposal (RFP) stage. Complexity turns on objective variables: participant count, unique CRFs, datasets, tables, figures and listings (TFLs), test parameter validations (TPVs), study design (from bioavailability and bioequivalence through first-in-human, single ascending dose/maximum tolerated dose), and number of study parts.

Each tier carries a committed end-to-end post-LPLV biometrics window. Low-complexity studies carry a 26–30 working-day window; medium, 36-40 and high, 46-50. The window is defined as LPLV through content-approved clinical study report (CSR); third-party vendor data and dependent TFLs are reported separately.

Complexity grades are aligned across data management, biostatistics, statistical programming, medical writing, and CPMS, so the entire biometrics organization operates against the same timeline.

The goal is not to slow studies; it is to remove surprises. Sponsors know on day one what the timeline is and what they need to do to hit it. 

3.    A partnership review model

Each engagement is led by a dedicated, client-aligned Delivery Lead who owns the relationship and the committed timeline. Sponsors keep full regulatory oversight and full data visibility, but their review focuses on what drives the go/no-go decision — endpoint data — rather than on every routine vital-signs table. Final statistical analysis can be delivered 5–15 WD post-database lock (DBL) when the sponsor commits to a single, focused review round.

Committed timelines depend on operational conditions Parexel publishes openly: a single sponsor review round; the dry run completed before DBL with no post-dry-run protocol amendments; Last Data In within 2 days of LPLV; and full GDO resourcing on both sides. This model asks sponsors to trust standardized CRO quality control. That trust must be earned through transparency, not demanded.

4.    Technology and AI as the enabling layer

Technology earns its place in this model only where it removes a specific bottleneck. The work that used to sit on the critical path—transcribing, transforming, and reconciling data by hand—is what we automated first, so that expert judgment goes to interpretation rather than mechanics. Data transformation is the clearest case: since October 2025, a generative AI engine built on Palantir’s Artificial Intelligence Platform has delivered study data tabulation model (SDTM) datasets within 2 days of the data transfer mapping specifications, a step that previously took weeks. AI-enabled analysis datasets (ADaM) and tables follow on the same path through 2026.

The same logic applies downstream in medical writing, where AI assistance trims roughly a week from the first-draft CSR cycle and, according to internal (unpublished) Parexel data, cuts about 67% of the time spent converting post-text tables to in-text tables. More telling than any single efficiency figure is the sequencing it enables: authors draft the sections that do not depend on final results (Introduction, Methods, mock results, narratives, and non-data-driven appendices ) before the final tables, with human review throughout, so the post-table window can close in 16–25 working days. Automating predictable, sequential work is what makes a committed timeline credible rather than aspirational.

Technology also underwrites the partnership model. Focused sponsor review only works if sponsors can watch the data accrue rather than audit it at the end, so we have invested in continuous visibility. A single workbench, Elluminate, consolidates every data source, with near-real-time delivery metrics. Live visibility, not a final 100% review, is what lets trust take the place of re-checking.

Complex Phase 1 trials demand transparent data operations

More complex Phase 1 trials are the right answer for de-risking modern drug development. Running them through legacy data operations is what loses the months. In our experience, a cultural shift in trust between sponsors and CROs is essential to make streamlined, modernized data operations efficient. The first step to building that trust is transparency. 

A standardized, complexity-graded, partnership-based operating model preserves the scientific richness of a modern Phase 1 while still delivering a defensible go/no-go decision in weeks — protecting capital and protecting the asset's position for the next funding or partnering conversation.

Start a conversation

Continue exploring Phase I Strategies

Gain practical perspectives from the recent panel discussion featuring biotech leaders and early phase experts on the strategies that can accelerate proof of concept.

Watch on-demand

Explore more episodes