ReviewJournal of clinical pharmacology2026
Application of Model-Informed Approaches in Neuroscience Drug Development and Regulatory Decision-Making.
Review in Journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Model-informed drug development (MIDD) approaches are being increasingly used in neuroscience drug development programs and regulatory decision-making. In this work, the authors summarize a select set of cases in neurology, psychiatry, addiction, anesthesia, and analgesia, where MIDD approaches were utilized to address critical drug development questions or fill knowledge gaps. These examples include efficacy considerations such as biomarker-clinical endpoint relationships, surrogate endpoints for accelerated approval, extrapolation of efficacy, and safety aspects such as informing driving studies. Additionally, the examples also inform optimal dosing regimen selection, dose adjustments in drug interactions, life cycle management decisions such as indication expansion, formulation switching, and informing patient population selection. Together, these examples offer insights into the utility of MIDD approaches to streamline drug development and opportunities to save costs and resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.