What the FDA’s Real-Time Trial Pilot Means for Biopharma Sponsors

by Laura Farmer, Opus Strategy

It’s tempting to read the FDA’s new real-time clinical trial initiative as just another agency modernization effort. But for biopharma sponsors, that would be a mistake.

The early pilots are limited but instructive. AstraZeneca is participating through its Phase 2 TRAVERSE trial evaluating Calquence with venetoclax and rituximab in treatment-naïve mantle cell lymphoma; Amgen through an early-stage study of Imdelltra/tarlatamab, its DLL3-targeted T-cell engager in small cell lung cancer. In both cases, the stated objective is to allow FDA reviewers to access predefined safety and efficacy signals in near real time, rather than waiting for data to be collected, analyzed, and submitted through traditional review cycles.

In other words: real-time review is not a faster version of the current process but a fundamentally different operating model for clinical development. To be ready, sponsors will need to navigate at least three operational shifts.

Data infrastructure comes first. Paradigm Health, which is supporting the AstraZeneca pilot, describes a model that captures data directly from EHRs and other structured and unstructured sources, evaluates FDA-defined data points algorithmically, and transmits only those signals needed for regulatory determinations. That level of integration is not a minor workflow adjustment. It requires interoperable systems, standardized endpoint definitions, site-level data discipline, and auditability strong enough to support regulatory reliance.

Site strategy becomes even more consequential. If real-time review depends on data flowing cleanly from clinical sites into regulator-facing systems, sponsors will need to rethink how they assess trial networks. Traditional measures of site prestige or enrollment potential will still matter, but they may no longer be sufficient. Digital maturity, EHR integration, data quality, and operational responsiveness may become criteria for regulatory readiness.

Cross-functional integration can no longer be deferred. Functions that have traditionally operated in sequence (clinical, regulatory, data science, AI governance) will need to operate in close alignment far earlier in development. The FDA’s related request for information on AI-enabled optimization of early-phase trials specifically identifies safety monitoring, dose selection, and early go/no-go decisions as areas of focus. Sponsors that approach this as an IT project will miss the point. The core challenge is whether an organization can define, validate, and explain the signals regulators want to see.

Still, there are caveats. This remains a pilot, and a narrow one at that. It will not replace formal submissions, and FDA officials have emphasized that the initial focus is on predefined endpoints rather than unrestricted access to patient-level data. The tension between speed and evidentiary rigor has not been resolved. Faster visibility does not automatically produce better decisions.

But the strategic signal is hard to ignore. The FDA is testing whether review can become more continuous and less episodic. For biopharma sponsors, that means regulatory advantage may increasingly depend on development infrastructure, not just clinical data quality.

The companies best positioned to benefit will be those that can answer a few practical questions now:

  • Can our trial data be trusted in near real time?
  • Are our sites capable of supporting regulatory-grade data flow?
  • Are our safety and efficacy signals defined precisely enough to support continuous regulatory monitoring?
  • Are our AI-enabled tools explainable and auditable enough to withstand regulatory scrutiny?
  • Do our clinical and regulatory teams have an operating model built for continuous engagement rather than periodic submission?

The last question may be the most important. Real-time review cannot be opted into at the end of a trial – it must be incorporated into the development process from the point of FPI.

At Opus Strategy, we help biopharma teams translate regulatory and technology shifts into practical strategic decisions. Here, the question is not whether every trial will become “real-time.” It is whether sponsors are prepared for a regulatory environment in which continuous evidence generation becomes a competitive advantage.

Precision Oncology Has a Testing Problem

by Laura Farmer & Christian Hayden, Opus Strategy

More than one in eight patients with advanced NSCLC is being systematically deprived of effective targeted therapy, despite having treatable, guideline-recommended biomarkers. That gap doesn’t reflect a shortage of targeted therapies. It reflects a failure of the testing that determines access to them.

A recent JCO Oncology Practice commentary by Voruganti et al. (“Illusion of Comprehensive Testing in Non–Small Cell Lung Cancer”) documents the scope of that problem in detail. The short version: assays marketed as comprehensive vary meaningfully in gene coverage, RNA fusion detection, sensitivity for low-frequency alterations, and reporting clarity. A negative result may reflect assay limitation rather than true absence of an actionable alteration.

For pharma and biotech companies developing precision oncology programs, this is a strategic issue as much as a clinical one. Targeted therapies only work at scale if the right patients are identified. Three areas warrant particular attention.

First, companion diagnostic strategy needs to account for RNA. DNA-based panels alone systematically under-detect fusions and splice variants (MET exon 14 skipping being one prominent example). Companies developing fusion-targeting agents or splice variant-dependent therapies should be building RNA-inclusive CDx requirements into development strategy, not treating them as an afterthought.

Second, investment in physician education around assay interpretation is lacking. Only 38% of oncologists report high confidence in using multipanel somatic testing to guide care decisions. Closing that gap is an area where pharma has both the resources and the incentive to act.

Third, the community oncology setting deserves more attention. Most patients are treated outside academic centers, where testing access and genomics literacy remain most uneven. Commercial and medical affairs strategies that assume academic-center testing quality will systematically miss a large share of eligible patients.

The broader framing matters too. As pipelines increasingly depend on nuanced biomarker selection (resistance markers, emerging targets like HER2 and cMET overexpression, co-mutation status), testing quality becomes a determinant of commercial outcomes. The companies that treat diagnostic infrastructure as a strategic priority, rather than an implementation detail, are better positioned as the precision oncology landscape matures.

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