In this QCast episode, co-hosts Jullia and Tom unpack Phase 1 clinical trial designs, the crucial first-in-human studies that establish safety, pharmacokinetics and tolerability before larger trials begin. We’ll guide you through single ascending- and multiple ascending-dose stages, demystify modern adaptive escalation methods like BOIN, and explain how food-effect, drug–drug interaction and bioavailability assessments shape dosing and label guidance.
You’ll discover how to streamline your first-in-human programme by combining objectives into a single adaptive protocol, using incomplete-block crossover or factorial layouts, and building in interim-analysis gates. Whether you’re a clinical-operations lead, biostatistician or regulatory specialist, this episode will equip you with the practical insights to design faster, leaner and more informative Phase 1 trials.
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Key Takeaways
What are Phase 1 Clinical Trial Designs?
Phase 1 trials are first-in-human studies that establish safety, tolerability and how the body handles a new drug. They set dose ranges, uncover early pharmacokinetic and pharmacodynamic signals, and provide the benchmarks for all later phases.
Core Components of Phase 1 Design
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Single Ascending Dose (SAD): Small cohorts each receive one dose; safety and blood-level data determine the next dose.
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Multiple Ascending Dose (MAD): Volunteers take repeated doses over days to reveal accumulation and steady-state tolerability.
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Adaptive Escalation (e.g. BOIN): Real-time probability updates guide whether to raise, hold or lower the dose, targeting a predefined toxicity rate.
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Food-Effect Studies: Cross-over design where each person takes the drug once with a high-fat meal and once fasted to show how food alters absorption.
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Drug–Drug Interaction Trials: Co-administration with enzyme inhibitors or inducers to detect exposure changes that inform label warnings.
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Bioavailability & Bioequivalence: Comparison of oral versus intravenous dosing or test versus reference formulations; regulators require the 90%confidence interval of exposure ratios to lie between 80 and 125%.
Why Integrated Phase 1 Designs Outperform Traditional Methods
By combining multiple objectives into one adaptive protocol and using efficient cohort structures, teams can:
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Speed Up Start-Up: One protocol covering SAD, MAD, food-effect and interaction parts avoids repeated ethics and site initiations.
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Reduce Sample Size: Incomplete-block cross-overs and factorial layouts let each volunteer contribute to multiple comparisons.
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Enable Early Decisions: Predefined interim-analysis gates allow stopping for success, futility or safety, cutting unnecessary follow-on work.
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Improve Dose Selection: Model-based escalation methods use all accumulated data to hone in on the true therapeutic window.
Operational Essentials for Implementation
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Define Your Objectives: Clarify whether you need safety only, or also pharmacokinetic and pharmacodynamic insights.
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Engage Statisticians Early: Run simulations to project patient numbers and dose paths before drafting the protocol.
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Build In Flexibility: Include optional parts, interim-review windows and amendment-free add/drop clauses.
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Standardise Procedures: Lock down meal composition, sampling schedules and data-management workflows to ensure consistency.
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Leverage Modern Tools: Use software for real-time dose recommendations and centralised data review to keep the study on track.
Common Pitfalls to Avoid
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Relying solely on three-plus-three without considering more efficient designs.
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Treating food-effect or interaction studies as afterthoughts rather than integral parts.
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Underestimating the logistical complexity of cross-over schedules and interim analyses.
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Omitting clear label-driven endpoints—ensure every study part ties back to prescribing instructions or regulatory requirements.
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Failing to document flexibility clauses upfront, leading to costly protocol amendments.
Full Transcript
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma, or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Jullia
Today we’re focusing on Phase 1 clinical trial designs. This is the stage where a new compound meets its first human volunteers, and every choice we make, from dose levels to scheduling, can affect safety, cost, and timeline. We’ll guide you through single ascending dose and multiple ascending dose stages, the latest in dose escalation methods, food effect and interaction studies, and even how to build efficiency into your protocol. By the end of this episode, you’ll know exactly what questions to ask and decisions to lock down before you start the first-in-human work.
Tom
Thanks, Jullia. It’s easy to think of Phase 1 as just a safety check, but today we’ll show you how it also shapes pharmacokinetics, tolerability, and label language down the line. Whether you’re a clinician, statistician, or project manager, we hope you come away from this discussion with practical pointers to make your Phase 1 studies faster, leaner, and more informative.
Tom
So let’s open up by covering the basics. Phase 1a versus Phase 1b. Many of our listeners will know the terms, but let’s take a moment to clarify what each stage actually tests. Why do sponsors typically run both, and what distinct data does each deliver?
Jullia
So in the single ascending dose stage, small cohorts often comprised of three people each get just one dose of the drug. We collect blood samples to map how the body absorbs, distributes, metabolises and eliminates the compound. Safety signals and blood-level results guide whether we increase the dose for the next group. Once we establish that single-dose safety margin, we move into the multiple ascending dose stage. Here, volunteers receive repeated doses over several days. That tells us if the drug builds up in the system or if new tolerability issues emerge. Running both stages gives us a clear picture of dose-related risk and dynamics before any larger trials begin.
Tom
Next up is dose escalation. The standard three-plus-three design is still widely used, but it has limitations. Could you tell us a little about the newer methods sponsors are adopting to make dose finding more efficient and precise?
Jullia
That’s a good point, Tom. So, the classic three-plus-three rule treats three participants at one dose and only increases if no more than one shows a dose-limiting toxicity. It’s simple but doesn’t use all available data. A newer approach is the Bayesian Optimal Interval design, or BOIN. In fact, this is a topic we’ve already covered on the show a few episodes back. This method sets a target toxicity rate, often around 25 per cent, and updates probability estimates after each cohort. The tool then recommends whether to escalate, stay or de-escalate. That real-time feedback reduces the number of participants exposed to doses too low to be effective or too high to be safe. Many teams now also combine single and multiple dose stages into one adaptive protocol, saving setup time and letting data flow continuously from one part to the next.
Tom
Beyond escalation, Phase 1 also often explores how meals, for example, affect exposure. Why is it important to meal-test volunteers early on, and how do interaction studies fit into Phase 1?
Jullia
In a food-effect study, each volunteer takes the drug once after a high-fat meal and once on an empty stomach, in a cross-over schedule. Because every person experiences both conditions, we isolate how a meal changes absorption without large sample sizes. That informs prescribing instructions, whether to take the drug with food or on an empty stomach. For drug-to-drug interaction work, we co-administer the compound with known enzyme inhibitors or inducers. Blood samples then reveal any significant increase or decrease in exposure. These studies protect against unforeseen safety issues when patients take multiple medications, and they guide labelling for concomitant use.
Tom
On top of food-effect studies, other medicines and new formulations can also affect exposure. So, before we shift to efficiency tactics, let’s quickly cover bioavailability and bioequivalence. What should our listeners know?
Jullia
So bioavailability compares how much drug enters the systemic circulation after oral dosing versus an intravenous reference. We look at measures like area under the curve, which represents total exposure. Bioequivalence tests whether a test formulation matches a reference product. In practice, this is most often a branded tablet versus a generic one. Regulators require that the 90 per cent confidence interval of the exposure ratio falls between 80 and 125 per cent. If it does, the formulations are considered statistically “not different”. Meeting these criteria ensures therapeutic equivalence without direct efficacy trials, saving time and cost for generic approvals.
Tom
Now let’s talk cost and timeline. Phase 1 can be expensive and slow. What practical design tactics can move the needle without compromising data quality?
Jullia
Great question, Tom. First, integrate multiple objectives under one protocol. Instead of separate single-dose, multiple-dose and food-effect studies, plan them as sequential parts with interim review gates. Second, use incomplete-block cross-over designs when you have several formulations or dose levels. This means each volunteer only tests a subset, but you still compare all conditions overall. Third, apply different layouts if you need to test two factors at once, such as dose and meal type. Lastly, include predefined interim analyses so the study can stop early for success, failure or safety concerns. These measures cut participant numbers, clinic days and overall expense, while preserving strong and reliable data.
Tom
So, what should listeners do next when planning their first-in-human trial?
Jullia
Start by defining your questions. For example, do you only need safety or also pharmacokinetic and pharmacodynamic insights? Bring statisticians in at protocol drafting to align escalation rules with your risk tolerance. Then, run simulations in software before you commit to a design. This will reveal likely patient counts and dose paths. Next, build flexibility into your protocol. Allow parts to be added or removed without formal amendments, and make sure to schedule interim reviews. Finally, nail down operations. This can include everything from standardising meals, clear sample-handling workflows, to a solid data management plan. This is because even the best design can fail without smooth execution.
Tom
Before we wrap up, let’s go over our top three takeaways. First, Phase 1 is more than a safety check. It sets pharmacokinetic and tolerability benchmarks for later phases. Second, adaptive escalation methods like BOIN make better use of participant data and improve safety. Lastly, integrated designs and interim-analysis rules deliver multiple datasets in one protocol, saving time and cost without cutting corners.
Jullia
Thanks, Tom. And with that, we’ve come to the end of this episode on Phase 1 clinical trial designs. We hope this episode gave you a clear, step-by-step approach to Phase 1 design. If you enjoyed today’s discussion, don’t forget to subscribe to QCast so you never miss an episode, and share our show with others. And, if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for joining us, and we’ll see you in the next episode.
About QCast
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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