Bayes Pharma.ai / Optimal Design
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Clinical development / Optimal design
Illustrative preview
Recommended starting design

D-optimal sampling schedule

Balances information across clearance and volume while respecting the current five-sample burden and minimum spacing constraint.

Decision snapshot

The few numbers needed to judge whether this design is ready to operationalize.

ReadinessPreviewRun engine to score
Total samples12024 subjects x 5
Study window24 h0 to 24 hours
Condition no.-Lower is more stable

Where the design learns

Expected concentration profile with recommended collection points.

Expected concentrationRecommended sample

Design rationale

How this schedule supports the study objective.

Ready to continue?

Generate a protocol-ready report or use the schedule in clinical trial simulation.

Protocol-ready sampling schedule

Nominal times and practical collection windows for the clinical protocol.

SampleNominal timeAllowed windowStudy purposePriority

Robustness across assumptions

Test whether sampling times remain informative when CL and V differ from the base model.

Run the stress test to generate a consensus schedule across plausible parameter scenarios.

Sparse cohort plan

Distribute the information burden across cohorts.

Build a sparse cohort plan after reviewing the primary schedule.