Oncology QSP for Dose and Schedule Optimization
Better dose and schedule decisions require more than simple comparison
Choosing the right dose and schedule is one of the most important and difficult questions in oncology development, because early promising strategies can still prove suboptimal if timing, interval, sequencing, exposure pattern, or tolerability trade-offs are not well understood. These decisions often cannot be resolved through simple empirical comparison alone: multiple approaches may appear plausible, but the available data may not clearly show which is strongest, which trade-offs matter most, or where uncertainty remains highest. Oncology QSP helps address this by using mechanistic simulation to examine treatment dynamics over time, allowing teams to compare alternative strategies in a more structured and biologically informed way. Our work supports clearer reasoning around dose and schedule selection by helping teams understand why a strategy may work, where it may fail, which assumptions are driving its apparent advantage, and what next studies are most likely to improve confidence.
What this solution helps address
This solution is designed for oncology teams facing dose and schedule questions such as:
-
Which dose range is most likely to achieve meaningful activity without unnecessary burden or risk?
-
Does the current schedule make mechanistic sense, or should timing and interval assumptions be revisited?
-
Would a lower, more frequent regimen outperform a higher, less frequent one?
-
Are there meaningful differences between front-loaded, step-up, maintenance, or intermittent approaches?
-
How sensitive is expected performance to treatment timing, interval spacing, or sequence?
-
Could changing schedule improve consistency, durability, or interpretability of response?
-
If multiple strategies appear plausible, which one is strongest enough to justify the next study?
-
Which uncertainties matter most before committing to a dose or schedule decision?
These questions often arise at points where a programme has enough evidence to move forward, but not enough clarity to move forward confidently. The role of QSP here is to support better judgment under uncertainty.
How we support dose and schedule optimization
We use oncology QSP models to evaluate how different dosing strategies may influence treatment behaviour over time. Depending on the question, the available evidence, and the maturity of the modelling effort, this can include comparing candidate dose levels, testing alternative treatment intervals, and examining different schedule structures such as more frequent versus less frequent dosing, compressed versus extended cycles, and induction, maintenance, or follow-up approaches. Where relevant, we also assess how timing and sequencing may influence outcomes, especially when the order or spacing of interventions could materially change performance.
Our work looks beyond simple endpoint comparison to understand how response may evolve under different dosing assumptions and what trade-offs may emerge across competing strategies. This includes assessing where one approach may appear stronger in one dimension but introduce uncertainty, limitation, or cost in another. We also examine which assumptions are most influential in shaping the result, helping teams see where confidence is well supported and where further evidence would make the biggest difference.
The goal is not just to generate simulations, but to turn them into practical decision support. We translate model findings into clearer recommendations on what to compare, what to refine, what to test next, and which strategies are strongest enough to carry forward into the next study or internal discussion. In this way, QSP supports strategic thinking by helping teams move from a difficult and uncertain choice to a more structured and defensible decision.
Our approach
We approach dose and schedule optimization as a mechanistic decision problem, not just a modelling task. That means the work is shaped around the decision a team actually needs to make: which strategy to advance, what uncertainty to resolve, what risk to avoid, and what evidence is needed next.
We focus on making simulation useful for decision-making. That includes clarifying assumptions, comparing plausible strategies in a disciplined way, and translating findings into conclusions that teams can actually use in planning and internal discussion.
This is especially important in oncology, where dose and schedule choices often influence not only activity, but also tolerability, combination logic, interpretability of study outcomes, and confidence in the broader development path. A good model is helpful, but a good decision framework is what ultimately matters.
What makes this valuable
Dose and schedule are not secondary technical details. They often shape how a programme performs, how results are interpreted, and how quickly confidence can be built around the next stage of work. A suboptimal choice can distort study outcomes, delay learning, or send teams down a weaker path. A better-informed choice can improve the quality of evidence, strengthen strategy, and reduce wasted effort.
This is why mechanistic dose and schedule optimization can create real value: it helps teams make high-consequence choices earlier and with better reasoning than would be possible through intuition or fragmented evidence alone.