In this QCast episode, co-hosts Jullia and Tom explore SAD and MAD studies and why they remain such an important part of early clinical development. They clarify what these studies mean in practical terms: Single Ascending Dose studies are used to understand how a drug behaves after a single administration, while Multiple Ascending Dose studies extend that picture by looking at repeated dosing over time. The conversation focuses on where these designs matter most, including first-in-human development, early dose escalation decisions, repeat-dose planning, and the early assessment of safety, tolerability, and pharmacokinetics.
They also discuss why SAD and MAD studies are often closely linked, how the findings from SAD can shape the way MAD is run, and what tends to determine whether these studies generate reliable, decision-ready data. Along the way, Jullia and Tom highlight common pressure points such as unclear escalation rules, slow data turnaround, inconsistent sampling, and operational drift at site level. They also look at the practical value these studies provide, including better-informed dose selection, a clearer understanding of accumulation and steady state, and stronger foundations for later-stage study design.
🎧 Listen to the Episode:
Key Takeaways
What SAD and MAD Studies Mean in Early Development
SAD and MAD studies are core Phase 1 designs used to build an early understanding of how a new drug behaves in humans. SAD focuses on the effects of a single dose, helping teams assess safety, tolerability, and pharmacokinetics at escalating dose levels. MAD builds on that by evaluating repeated dosing, which helps teams understand accumulation, repeat-dose tolerability, and how exposure changes over time.
Why These Studies Matter for Dose Planning
These studies help sponsors make better early decisions about dose levels, dosing schedules, and the overall direction of development. SAD provides the first controlled view of human exposure, while MAD gives a more realistic picture of how a regimen may perform over several days. Together, they help narrow uncertainty before later clinical studies begin.
What Makes SAD and MAD Studies Work in Practice
Strong execution depends on more than protocol design alone. Teams need clear escalation criteria, defined stopping rules, rapid access to safety and pharmacokinetic data, and close operational control over dosing and sample timing. When governance, data flow, and site delivery are aligned, SAD and MAD studies are much more likely to produce results that are both safe to generate and useful for what comes next.
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.
Tom
Today we’re talking about SAD and MAD studies. They come up all the time in early development, but people don’t always stop to unpack what they’re actually doing. So let’s start there. What are they for?
Jullia
So SAD is Single Ascending Dose, and MAD is Multiple Ascending Dose. They’re both early Phase 1 study designs, and they’re usually there to help teams understand how a new drug behaves in humans for the first time. With SAD, you’re looking at a single dose and building an initial picture of safety, tolerability, and pharmacokinetics, or PK. MAD takes that further by looking at repeated dosing, so you can see whether the drug accumulates, how exposure changes over time, and whether anything starts to show up that matters for later dose planning.
Tom
So SAD gives you the first read, and MAD starts to tell you what repeated use might look like. Is that why they’re often linked so closely in development plans?
Jullia
Yes, and usually because the learning carries straight across. What you see in SAD helps shape the way MAD is run, especially around dose levels, sampling, and review points. Quite a few teams now use combined protocols for that reason. It can make the early programme move more smoothly, but only if the study has been set up properly and the decision-making is tight.
Tom
I assume that’s probably where people get a bit overconfident. A combined design can sound efficient on paper, but there’s a lot going on underneath it. What needs the most care up front?
Jullia
Well the logic for dose escalation, first of all. Teams need a sensible starting dose, based on the nonclinical package, and they need a clear plan for how dose increases will be reviewed. You also need defined stopping rules, clear criteria for moving between cohorts, and the right people reviewing the emerging data. If those pieces are vague, the study gets harder to manage very quickly.
Tom
And escalation isn’t automatic, even though people sometimes talk about it that way.
Jullia
Yes, you’re not just moving up a ladder because the protocol says so. Each step depends on what the data are showing. That includes safety findings, PK results, and whether the observed exposure matches what the team expected going in. In the early cohorts, that often starts with sentinel dosing, where a small number of participants are dosed first and watched closely before the rest of the cohort proceeds.
Tom
Can you give an example of where that plays out in a really practical way?
Jullia
So say the first cohort shows the drug peaks later than expected, or clears more slowly. That can change the blood sampling schedule for the next cohort, because you may need different timepoints to capture the profile properly. Or imagine the safety review meeting is due, but PK data haven’t been turned around quickly enough. Suddenly the team is making a dose decision without the full picture. That’s one of the places these studies can start to wobble.
Tom
Right, so the science can be sound, but the timing of the data still matters.
Jullia
Very much so. Early phase studies depend on fast, reliable review cycles. Safety labs, ECGs, adverse event data, PK results, they all need to be available when decisions are being made. If the data flow is patchy, the whole escalation process becomes harder to trust.
Tom
Now let’s move into MAD, because that’s where the study starts to look a bit closer to real treatment use. What changes once you go from a single dose to repeated dosing?
Jullia
With repeated dosing, you want to know whether exposure builds up, when steady state is reached, and whether tolerability changes over several days. Something that looks fine after one dose can look different once participants have had the drug again and again. This is also where pharmacodynamic markers can start to become more useful, if there’s a sensible one to measure.
Tom
So MAD isn’t just more of the same. It’s answering the question of what the regimen might actually look like over time.
Jullia
Yes, and that’s why it matters so much for later planning. MAD helps teams think more realistically about dose level, dose schedule, and whether the intended regimen is going to be workable in the patient setting. It gives a more grounded view of repeat-dose behaviour, and that’s often what later study design needs.
Tom
Now one thing that comes up a lot in these early studies is optional extras, like food-effect work. When do those additions make sense?
Jullia
So they make sense when they’ve been thought through properly and written into the design in a controlled way. A food-effect arm can be useful if there’s reason to think meals could change absorption, or if the likely clinical use makes that question important early. The problem comes when flexibility turns into drift. If the study has too many open-ended options, it becomes harder to govern and harder to interpret cleanly once the data come together.
Tom
That’s a good example of a common misconception, actually. People hear ‘adaptive’ and assume it means you can keep changing things as you go.
Jullia
Yes, and that’s not a helpful way to think about it. Early phase flexibility still needs boundaries. The protocol should make it clear what can change, why it can change, and how those decisions will be made. Otherwise you end up with uncertainty in the one place you really want control.
Tom
So where do teams usually feel the pressure operationally?
Jullia
Timing and coordination, usually. Dose administration windows, sample timing, and review meetings matter. If site activity starts drifting, even slightly, it can make the dataset much harder to interpret. A common scenario is a cohort where dosing happens on time, but blood draws or safety checks slip just enough to muddy the PK or tolerability picture. These are often small studies, but the detail matters all the way through.
Tom
And I suppose that’s the bit people underestimate?
Jullia
Spot on. Because the participant numbers are smaller, people sometimes assume the execution is straightforward. But there’s a lot happening in a short period, and the decisions can have a big effect on what comes next in development. You need strong site delivery, quick data handling, and a review process that everyone understands before the first participant is dosed.
Tom
Now before we finish, let’s end on some practical points. If a team is about to run SAD and MAD work, what should they focus on?
Jullia
I’d keep the fundamentals tight. Make sure the escalation rules are clear and usable, the safety and PK data can get where they need to go quickly, and that the protocol only builds in flexibility where the decision path is already well defined. Those three things do a lot to keep the study usable, both medically and operationally.
Tom
So if you boil it down, good SAD and MAD studies depend on clear decisions, clean data flow, and disciplined execution.
Jullia
I’d say that’s a fair summary. SAD gives you the first human read on exposure and tolerability. MAD builds that into a better understanding of repeat dosing and accumulation. And across both, what really matters is whether the team can generate reliable data and make sensible decisions at the right time.
Jullia
With that, we’ve come to the end of today’s episode on SAD MAD studies. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. 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 tuning in, 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.
Subscribe to QCast on Apple Podcasts or Spotify to never miss an episode.
