Use Case
Combination oncology
Modern cancer care increasingly relies on multi‑agent regimens—targeted therapies, immunotherapies, cytotoxics, and supportive drugs. While combinations can improve outcomes, they create dosing complexity and safety risk that traditional, per‑agent dose rules don’t capture.
Clinical context
- Combining agents is a core strategy across tumor types to enhance efficacy and overcome resistance. See overview reviews on combination therapy. (Mokhtari 2017)
- Drug‑development trends show high interest in combination regimens and growing trial complexity. (Chen 2025)
Why dosing is hard
- Heterogeneity: organ function, prior toxicity, co‑medications, pharmacogenomics.
- Evidence gaps: trials optimize per‑agent MTD or RP2D; combinations often extrapolate.
- Dynamic toxicity: time‑varying AEs, supportive care, dose holds and restarts.
What OptimDosing enables
Optimization surfaces
Model dose‑response and dose‑toxicity for pairs or triplets to identify safe zones.
Patient‑fit logic
Individualize doses using labs, organ function and prior AE history.
Transparent rationale
Generate explanation artifacts for protocol or eTMF use.
Example engagements
- Pre‑IND simulation: explore candidate dose pairs using prior evidence and mechanistic priors.
- Adaptive protocol support: algorithm‑guided escalation/de‑escalation with safety bounds.
- Real‑world combination audit: retrospective dose‑safety analysis across regimens.
References
- Mokhtari RB, et al. Combination therapy in combating cancer. Cancers (Basel), 2017.
- Chen EY, et al. Trends in complexity of single‑agent and combination cancer therapies. The Oncologist, 2025.