AI Can Make Drug Reviews More Transparent at FDA

FDA drug regulation runs on a fragile asset: trust. Trust that reviewers are weighing benefits and risks the same way across therapeutic areas. Trust that evidence beats ideology. Trust that a leadership change won’t whipsaw priorities.

That’s why the recent leadership turbulence and internal anxiety at the FDA’s Center for Drug Evaluation and Research (CDER)—highlighted by reporting that staff worry a new acting director could bring bias and instability—should matter to every pharma and biotech team building a pipeline. When the review environment feels unpredictable, companies change behavior: they add “just in case” studies, delay submissions, widen safety databases, and spend more time managing perception than learning from patients.

Here’s my stance: regulatory science needs a more auditable operating system. Not a replacement for human judgment, but a stronger backbone for it. In our “AI in Pharmaceuticals & Drug Discovery” series, we keep coming back to the same theme—AI is most valuable when it makes complex decisions more transparent, consistent, and testable. CDER’s current strain is exactly the kind of environment where that approach can pay off.

Leadership instability hurts timelines more than most teams admit

Answer first: Frequent shifts at the top of a drug review organization create inconsistent expectations, which leads to longer development plans, more defensive evidence packages, and slower approvals.

CDER’s job is hard even in stable times: reviewers must synthesize preclinical signals, clinical endpoints, subgroup effects, manufacturing quality, post-market risk, and benefit-risk tradeoffs for different patient populations. When staff believe leadership might re-litigate settled questions—or apply extra scrutiny selectively—teams inside and outside the agency start optimizing for politics, not clarity.

The operational consequences show up fast:

• More “insurance” studies: Sponsors expand trials to preempt shifting goalposts.
• Heavier briefing packages: Submissions balloon, but signal-to-noise drops.
• More meetings, fewer decisions: Process expands as people seek alignment.
• Reviewer morale declines: High turnover and slower throughput become self-reinforcing.

In December 2025, this is especially timely. Many development teams are locking 2026 budgets right now. If your regulatory assumption is “the bar is moving,” you’ll fund buffers instead of breakthroughs.

The real risk isn’t bias—it’s unprovable decision-making

Answer first: The danger isn’t that humans have perspectives; it’s that the rationale behind decisions isn’t consistently documented in a way others can audit, challenge, and learn from.

The STAT reporting describes staff concerns that a leader could bring bias and instability. Whether those fears are validated isn’t the only point. The point is that perceived bias can be nearly as damaging as actual bias, because it changes how stakeholders behave.

This matters because CDER decision-making is often a blend of:

• Formal guidance and precedent
• Statistical inference and uncertainty tolerance
• Clinical judgment
• Safety philosophy (risk aversion varies by context)
• Internal consistency across divisions

When those ingredients aren’t traceable, the system looks arbitrary—even when it isn’t.

What “transparent” should mean in 2026

For pharma and biotech teams, transparency doesn’t mean broadcasting confidential data. It means:

1. Clear decision criteria (what evidence thresholds were applied)
2. Consistent reasoning patterns across similar cases
3. Audit trails for changes in position (what changed and why)
4. Quantified uncertainty rather than hand-wavy language

That’s exactly where AI for regulatory review—done responsibly—can help.

Where AI fits: not deciding approvals, but making reviews auditable

Answer first: The best use of AI in FDA-style reviews is creating structured, explainable summaries and consistency checks that reduce noise and surface contradictions.

The first mistake most companies make is treating “AI in regulatory” like an autopilot. That’s unrealistic and (frankly) a bad idea. The practical opportunity is narrower and more powerful: AI as a documentation and consistency engine.

Think of three layers:

1) Evidence ingestion and structured summarization

Regulatory submissions include mountains of text: protocols, SAPs, CSR narratives, safety listings, CMC documents, pharmacology summaries, and more. Reviewers spend valuable time locating “what matters” and validating it.

Modern NLP can:

• Extract endpoints, estimands, missingness handling, and multiplicity strategy
• Map adverse events to standardized vocabularies and flag imbalance patterns
• Summarize subgroup findings with uncertainty bars (not just p-values)
• Build traceable links from claims to tables/figures in the source document

The win isn’t speed alone. It’s repeatability: the same extraction logic can be applied across products, reducing the “it depends who read it” effect.

2) Consistency checks across similar decisions

A big driver of perceived bias is inconsistency. AI can’t resolve policy disputes, but it can reliably answer: “Are we applying the same logic as last time?”

Examples of checks an internal tool could run:

• Compare benefit-risk framing language across similar indications
• Detect when surrogate endpoints were accepted in one case but questioned in another
• Flag when post-market commitments differ dramatically without stated justification
• Identify outlier demands (e.g., unusually large safety database expectations)

Done well, this becomes a quality system for regulatory reasoning.

3) Explainable risk models as decision support

This is where people get nervous, and rightly so. The goal isn’t a black-box score that dictates approval. It’s an interpretable model that helps reviewers and sponsors talk concretely about risk.

For instance:

• A calibrated model for likely class-related adverse events given MOA, exposure, and patient factors
• Bayesian updating tools that show how new evidence shifts posterior beliefs
• Mechanistic + clinical signal fusion for early safety detection

Used as decision support, these tools can reduce “vibes-based” debate.

A strong regulatory culture isn’t one that avoids judgment. It’s one that makes judgment inspectable.

What this means for AI in drug discovery and clinical development

Answer first: Regulatory instability pushes teams to build more evidence than necessary; AI helps by prioritizing the evidence that actually reduces uncertainty.

AI in pharmaceuticals is often sold as faster molecule design or better hit-to-lead. That’s real, but it’s not the whole value story. When CDER is turbulent, AI’s more immediate advantage can be development efficiency under uncertainty.

Here’s what works in practice.

Use AI to build a “regulatory-ready” evidence graph

Instead of treating the NDA/BLA as a document dump, treat it as a connected set of claims:

• Claim: “Improves functional outcome” → which endpoint, which timepoint, which population?
• Claim: “Acceptable safety profile” → which AESIs, which exposure, which comparators?
• Claim: “Manufacturing is controlled” → which CQAs, which process controls, which release criteria?

An evidence graph lets you answer reviewer questions quickly and consistently—and it exposes weak links early.

Run “what if the bar shifts?” simulations

If leadership changes result in stricter expectations (real or perceived), teams often scramble late. A better approach is to precompute a few scenarios:

• Higher safety exposure requirement: What’s the added enrollment/time cost?
• Different primary endpoint preference: What’s the statistical power tradeoff?
• More conservative labeling: How does that affect commercial viability?

AI-based trial simulation and protocol optimization can quantify these contingencies instead of guessing.

Reduce bias in your own decision-making first

Companies love to complain about regulatory inconsistency while tolerating plenty of internal inconsistency. If you want credibility with regulators, tighten your own process:

• Standardize endpoint definitions and estimands across programs
• Use pre-registered analysis plans internally for key decisions
• Implement model governance (versioning, audit logs, validation sets)

When you show up with disciplined evidence, you’re harder to dismiss.

Practical playbook: 6 moves to build trust with regulators using AI

Answer first: Build systems that make your data easier to review, your reasoning easier to audit, and your uncertainties easier to quantify.

If you’re leading clinical development, regulatory affairs, or translational science, these are the moves I’d prioritize in 2026 planning.

1. Create an AI-assisted submission readiness layer

   • Automated completeness checks
   • Cross-document consistency validation (protocol ↔ CSR ↔ datasets)
   • Traceable citations from summary text to source tables

2. Implement explainable safety signal monitoring

   • Pre-specify AESIs
   • Use interpretable models with human-readable drivers
   • Document thresholds and escalation paths

3. Standardize benefit-risk narratives

   • Use structured templates and controlled vocabularies
   • Quantify uncertainty ranges
   • Keep a changelog for narrative shifts

4. Build a “regulatory Q&A copilot” (internally first)

   • Train it on your own program docs and meeting minutes
   • Require source grounding for every answer
   • Log every question to improve future packages

5. Harden governance for AI models used in development

   • Validation plans, bias tests, drift monitoring
   • Role-based access and audit trails
   • Clear statements of intended use (what it can’t do matters)

6. Prepare for more public scrutiny

   • Assume decisions and datasets may be debated outside technical circles
   • Produce plain-language explanations alongside technical ones
   • Stress test messaging for misinterpretation

These aren’t theoretical. They’re operational muscle that pays off whenever review dynamics get unpredictable.

People also ask: Can AI really reduce regulatory bias?

Answer first: AI can reduce bias in process—by enforcing consistency and documentation—but it can also introduce bias if trained on skewed data or used without governance.

Two truths can coexist:

• AI is excellent at finding inconsistencies, missing rationale, and outlier demands.
• AI can encode historical inequities (underrepresentation in trials, biased labels, incomplete safety reporting) unless explicitly corrected.

So the goal isn’t “AI = neutral.” The goal is AI = measurable. A decision process you can measure is a process you can improve.

The trust gap is a delivery problem—and AI can help close it

Drug development doesn’t just need smarter models. It needs more dependable interfaces between science, regulators, clinicians, and the public. When CDER staff fear instability or bias, that interface weakens, and the entire ecosystem pays for it in time and confidence.

My bet for 2026: the winners won’t be the companies that build the flashiest AI models. They’ll be the ones that use AI to produce reviewable evidence—clear, consistent, and hard to misread.

If you’re evaluating how AI can support regulatory strategy, clinical trial optimization, and drug discovery timelines, start with this question: What would it take to make our benefit-risk story auditable from raw data to final claim—without heroics?