Study Sequencing: The Study That Should Have Come First

Dessi McEntee
Mar 6
5 min read

Updated: 12 hours ago

Welcome back to The Nonclinical, a newsletter where we unpack how nonclinical toxicology actually works inside drug development — beyond the protocol and into real decisions.

Most nonclinical teams spend a lot of time thinking about which studies to run.

Very few spend enough time thinking about when they should be run.

That distinction sounds minor, but it isn't. Study sequencing is one of the most consequential — and most consistently underestimated — decisions in IND-enabling development. Get the order right and your program builds on itself, each study sharpening your understanding before the next one starts.

Get it wrong and you're either filling gaps under pressure, or defending conclusions that rest on shaky foundations.

The frustrating part is that sequencing mistakes rarely look like mistakes when they're made.

Instead, they look like efficiency.

🔣 Why Study Sequencing Gets Treated as a Logistics Problem

The default framing around study sequencing is scheduling: What can we start first? What's on the critical path? How do we compress the timeline?

These are real questions. But they're the wrong starting point.

Sequencing is fundamentally an informational problem. Each study in your nonclinical program is generating data that should be informing the design, interpretation, and execution of what comes next. When you sequence studies based on what's fastest to initiate rather than what information you actually need at each decision point, you break that chain.

The result is a program that looks busy and productive, but is quietly accumulating risk.

Based on my experience, there are 3 study sequencing mistakes I see most often:

1. Running a GLP pivotal study before the dose range is genuinely established

This is the most common and the most costly.

Dose range-finding studies exist for a reason. They're meant to define tolerability, characterize exposure, identify target organs, and give you the scientific basis for selecting GLP doses that will actually generate a clean NOAEL.

But teams under timeline pressure routinely start GLP studies with doses selected from limited non-GLP data, literature precedent, or "what seems reasonable." The reasoning is usually that they can't afford to wait for DRF results before initiating the pivotal study.

The problem isn't the risk tolerance. The problem is what happens when the GLP doses are wrong.

If your high dose produces unexpected lethality or severe toxicity, the study may need to be repeated. If your doses are too low and fail to demonstrate any adverse effects, you have a NOAEL but no toxicological characterization of margins — which regulators will notice. If your TK at the high dose doesn't support adequate exposure multiples over the anticipated clinical dose, your safety margins become a conversation rather than a conclusion.

A well-designed DRF isn't overhead. It's the study that makes your GLP study defensible.

2. Skipping or shortening the non-GLP before the GLP

Related to the above, but distinct: teams sometimes skip dose range-finding entirely and go directly to a GLP study, or run a 7-day DRF when the GLP design would really benefit from 14-day tolerability data.

The logic is usually one of the following: We have enough from literature. We've seen this target before. The molecule is similar to something we already ran.

These are assumptions, not data. And the gap between assumption and data is exactly where programs get into trouble.

A 7-day DRF tells you something about acute tolerability. It does not tell you what a 28-day repeat exposure looks like in terms of target organ progression, reversibility of early signals, or dose-dependent TK. If you're running a 28-day GLP to support a 28-day clinical study, that information matters.

The time saved by shortening or skipping the non-GLP study is almost always smaller than the time lost if the GLP needs to be repeated or defended against avoidable questions.

3. Starting a longer-duration study before a shorter-duration study has answered the key questions

The pressure to run studies in parallel is real. Sponsors want to compress timelines, and CROs are often willing to start multiple studies simultaneously.

There are situations where parallel execution makes sense. But it needs to be a deliberate decision, not a default.

A 4-week study should ideally tell you something about target organ liability, dose-dependent effects, and TK that informs how you design and monitor your 13-week study. If you start both at the same time, you're designing the 13-week in partial blindness — and if the 4-week reveals something meaningful, you may find yourself either amending an already-running protocol or sitting on a 13-week dataset that raises more questions than it answers.

Parallel execution without informational overlap is not a compressed timeline. It's two separate bets running simultaneously, with limited ability to course-correct if either one goes sideways.

🧗 What Good Sequencing Actually Looks Like

Good sequencing starts with a simple question: What do I need to know before I start this study, and which study gives me that information?

Applied to a typical IND-enabling program, that question produces something like this:

Non-GLP single dose or 3-5 day MTD → Establish rough tolerability, identify starting dose range, flag any acute signals that change the picture.

Non-GLP repeat dose (14-day or 28-day) → Establish dose-dependent tolerability, confirm TK, identify target organs, select doses for pivotal GLP with confidence.

GLP pivotal (28-day or 13-week, depending on clinical design) → Generate the definitive nonclinical safety dataset with a defensible NOAEL, characterized margins, and a clean interpretation narrative.

Each study builds on the last. The DRF makes the GLP defensible. The GLP makes the IND narrative defensible.

This isn't a novel insight. It's how well-resourced programs have always done it. What changes in the biotech context is that timeline pressure and resource constraints push teams to skip steps or compress them in ways that create fragility downstream.

👩⚖️ The Regulatory Consequence

Regulators reviewing your IND nonclinical section are not just evaluating what the data shows. They're evaluating whether you understood what the data means — and whether your program was designed to answer the right questions in the right order.

A well-sequenced program tells a coherent story. The studies fit together. The doses make sense in context. The interpretation is grounded in a clear progression from early signal to definitive characterization.

A poorly sequenced program often produces technically acceptable studies that don't quite add up. Doses that seem arbitrary. TK that wasn't available at the right time. Interpretation that feels reactive rather than anticipatory.

That's not necessarily a clinical hold. But it does generate questions. And questions late in the review process are exactly what you were trying to avoid.

👉 The Bottom Line

Study sequencing is a strategic decision, not a scheduling exercise.

The order in which you run studies determines what information you have — and don't have — at every decision point in your program. It shapes the defensibility of your doses, the clarity of your interpretation, and the coherence of the story you tell regulators.

One study should inform the next. When that's true across your entire nonclinical program, you don't just have data...

You have a strategy.

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Dessi McEntee is a board-certified toxicologist and Fractional Head of Toxicology, providing embedded nonclinical leadership to IND-bound biotech teams without internal tox. Drawing on 15+ years of extensive experience guiding programs from early development through clinical entry, she focuses on reducing regulatory risk through sound nonclinical strategy and interpretation.

The Nonclinical is written to help scientists and development teams better understand how nonclinical decisions shape clinical and regulatory outcomes.

Learn more at www.toxistrategy.com