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Activity Number: 659 - Adaptive Dose Individualization
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Statistics and Pharmacometrics Interest Group
Abstract #329215 Presentation
Title: Individualized Adaptive Dose-Response Modeling in Multiple Myeloma: Potential to Improve Patient Care
Author(s): Dean Bottino*
Companies: Takeda Pharmaceuticals U.S.A.
Keywords: Precision medicine; Bayesian parameter estimation; Oncology; Myeloma; Modeling; Adaptive

Multiple myeloma (MM) treatment guidelines and response criteria offer thresholds that can be used to decide when a patient is in need of salvage therapy, but key opinion leaders differ regarding criteria for when, whether and how to modify therapy in practice. Indeed, when a disease's response criteria start to include categories like "Very Good Partial Response," it suggests that the limitations of categorical response definitions to provide relevant treatment decision information are being strained and that a more continuous patient-centric approach may be worth exploring.

We propose using a semi-mechanistic tumor dynamic model with Bayesian parameter estimation to: (a) Initially estimate the patient's range of predicted outcomes based on baseline measurements; (b) Continually refine the patient's future response trajectory as his or her response data becomes available; (c) Predict that patient's response trajectory under alternative treatment scenarios.

We believe that once validated, this methodology has the potential to impact the way patient-level decisions are made and ultimately improve outcomes for patients with multiple myeloma.

Authors who are presenting talks have a * after their name.

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