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Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to
Kristyn Pantoja1,2, Shankar Lanke2, Alain Munafo3
1Department of Statistics, Texas A&M University, College Station, Texas, USA.
Bayesian adaptive models using systemic exposure can better estimate the maximum tolerated dose (MTD) in early oncology trials. Incorporating pharmacokinetics improves safety and reliability compared to traditional methods.
Area of Science:
- Clinical Pharmacology
- Biostatistics
- Oncology Drug Development
Background:
- Maximum tolerated dose (MTD) determination is crucial for informing the therapeutic index in oncology.
- Bayesian adaptive model-based designs are increasingly standard in first-in-human trials.
Purpose of the Study:
- To illustrate the use of systemic exposure within Bayesian adaptive dose-toxicity models for MTD estimation.
- To extend traditional models by incorporating pharmacokinetic exposure for improved dose-escalation decisions.
Main Methods:
- Simulations of 1000 trials were conducted using Bayesian adaptive models incorporating pharmacokinetic exposure.
- Dose-toxicity and exposure-toxicity models were compared against rule-based designs.
- Metrics included safety, accuracy, and reliability of MTD estimation.
Main Results:
- Model-based designs outperformed rule-based methods in simulations.
- Exposure-toxicity models complemented dose-toxicity models, reducing underdose risk and increasing net reliability.
- MTD estimation accuracy decreased with high pharmacokinetic variability.
Conclusions:
- Bayesian adaptive exposure-toxicity models offer a valuable enhancement for phase I oncology dose-escalation studies.
- Leveraging pharmacokinetics provides continuous dose-escalation insights, improving decision-making.
- Controlling pharmacokinetic variability is essential for accurate MTD estimation.
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