TABLE 1.
Factors That Influence Assessment of Prospect of Direct Benefit | Tools to Support and Maximize Benefit and Minimize Burden |
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Evidence to support the proof of concept | |
• Biological plausibility ο Scientific justification for the proposed mechanism of action and the expected effect in the condition of interest |
In vitro data can provide valuable information about the product’s activity, particularly for studies conducted using cells or tissues derived from patients with the condition of interest |
• Nonclinical data ο In vitro mechanistic studies ο In vivo studies in animal disease models |
In vivo data obtained by using an animal disease model can provide insight into the product’s impact on disease pathophysiology and can help guide dosing decisions for a clinical trial |
• Clinical data ο Studies in adults with the condition ο Previous studies in adults or pediatric patients with relevant conditions |
Pediatric extrapolation of adult efficacy data to children can reduce the amount of data needed in children and allow for less burdensome pediatric trials |
Structure of the study intervention | |
Modeling and simulation of adult and/or pediatric pharmacokinetic/pharmacokinetic data to identify a pediatric dose based on predicted exposure-response relationship | |
• Dosing justification ο Evidence to support that the proposed dosages for study are likely to have the intended treatment effect |
PBPK modeling incorporating underlying physiologic parameters and product-specific information to predict the dose-exposure relationship Adaptive trial design to allow for dose exploration and optimization within the context of a trial designed to offer prospect of direct benefit |
• Trial duration ο Proposed trial duration is long enough for participants to experience a potential treatment effect |
Consider treatment duration decisions made in clinical practice Measure clinically meaningful or validated surrogate end points when possible |
PBPK, physiologically based pharmacokinetic.