Four decisions simulation can reshape.
Phase 2 protocol choices carry major consequences. QuantHealth helps you assess them with probability estimates rather than intuition.
four indication types
from dose selection to Phase 3 readiness
Dose selection and optimization
Many Phase 2 failures trace to dose selection without enough data. Too low can miss efficacy; too high can cause tolerability problems, increase dropout, and confound the endpoint. Traditional dose-escalation designs use small cohorts to identify an MTD but provide limited dose-response insight for the primary efficacy endpoint.
QuantHealth simulates the complete dose-response curve for your compound and indication, using your PK/PKPD parameters alongside published exposure-response literature for related mechanisms. For every tested dose, you see estimated P(success) and the responder subgroup breakdown. The result is not simply an optimal-dose selection. It shows the probability distribution across dose levels and where the risk-adjusted optimum falls for your protocol. Most engagements identify at least one dose level that was over- or under-targeted in the initial protocol draft.
Typical output
P(success) curve across 4 dose levels with 90% credible intervals; recommended dose with sensitivity to PK variability assumptions; one-dose-step-up scenario with tolerability trade-off narrative.
Patient population enrichment strategy
A compound that fails in an all-comers population may show a strong signal in a definable subgroup. The challenge is defining that subgroup before setting enrollment criteria. Broad criteria can inflate the sample needed, while narrow criteria can hinder enrollment and restrict label scope.
QuantHealth models virtual patients with realistic variation in biomarker levels, comorbidity burden, and responder phenotypes. Run different enrichment cut-offs to see how estimated response rate, variance, and sample-size requirements change at each threshold. Enrichment analyses often expose a threshold effect: below a certain cut-off, tightening the criteria further delivers little P(success) gain but substantially reduces enrollment. QuantHealth identifies that threshold.
Typical output
Enrichment threshold analysis across three biomarker cut-offs; P(success) and enrollment feasibility comparison; recommended inclusion criteria with rationale for each candidate threshold.
Adaptive design interim analysis planning
Adaptive designs require careful pre-specification of adaptation rules and the statistical properties of the resulting trial. Many teams cannot define those rules rigorously because they lack a model of trial behavior under different assumptions. The outcome is either a conservative fixed design or an adaptive design whose operating characteristics remain unclear.
QuantHealth simulation supplies the operating characteristics needed to pre-specify an adaptive design with confidence. Assess Type I error, power, and expected sample size across true effect sizes, then test how adaptation rules respond to core assumptions. The analysis supports the statistical analysis plan and can be included in a regulatory briefing package for a Type B meeting or Type C meeting.
Typical output
Operating characteristic curves across effect size scenarios; Type I error and power table for proposed adaptation rules; sensitivity analysis on key input assumptions; regulatory briefing annex describing simulation methodology.
Portfolio prioritization across multiple compounds
Organizations developing multiple compounds in the same indication or mechanism class must allocate resources without a systematic data-based answer. Given current knowledge, which compound has the higher probability of success at Phase 2? Teams often rely on qualitative assessment, where uncertainty can result in tens of millions in misallocated development spend.
QuantHealth's Portfolio tier runs standardized simulations across multiple compounds with the same disease module and patient population model. The comparison is like-for-like: identical endpoints and population assumptions, with different compound PK/PKPD parameters. You receive a ranked P(success) comparison across compounds, broken down by responder subtype and dose. Most portfolio analyses produce at least one ranking change relative to the team's prior assessment.
Typical output
Side-by-side P(success) comparison across 2-4 compounds; compound-specific recommended Phase 2 design; sensitivity analysis on differentiating input assumptions; portfolio recommendation narrative.
Therapeutic areas with validated disease modules
Tell us which decision you face.
One compound, one decision point, one engagement. We will tell you whether simulation can sharpen your probability estimate for that decision.