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Activity Number:
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487
- Topics in Clinical Trials - II
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #309811
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Title:
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Oncology Dose Escalation Using Bayesian Logistic Regression Model and Pharmacokinetic Data
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Author(s):
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Arnab K Maity* and Satrajit Roychoudhury and Ray Li and Lada A. Markovtsova and Roberto Bugarini
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Companies:
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Pfizer and Pfizer Inc. and Pfizer and Pfizer and Pfizer Inc
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Keywords:
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BLRM;
DLT;
MTD;
Phase I;
PK;
Oncology
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Abstract:
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Oncology drug development starts with a dose escalation phase to find the maximal tolerable dose (MTD). Dose limiting toxicity (DLT) is the primary endpoint for dose escalation phase. Traditionally, model-based dose escalation trial designs recommend a dose for escalation based on an assumed dose-DLT relationship. Pharmacokinetic (PK) data are often available but are currently only used by clinical teams in a subjective manner to aid decision making. Formal incorporation of PK data in dose-escalation model can make the decision process more efficient and lead to an increase precision. In this talk we present a Bayesian joint modeling framework for incorporating PK data in Oncology dose escalation trial. This framework explores the dose-PK and PK-DLT relationships jointly for better model informed dose escalation decision. Utility of the proposed model is demonstrated through a real-life case study along with simulation.
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Authors who are presenting talks have a * after their name.
