Document Type

Article

Publication Date

11-1-2023

Keywords

JMG, Animals, Mice, Alzheimer Disease, Disease Models, Animal, Pharmaceutical Preparations

JAX Source

Alzheimers Dement. 2023;19(11):5289-95

ISSN

1552-5279

PMID

37157089

DOI

https://doi.org/10.1002/alz.13108

Grant

This grant was supported by the U54 AG054345 NIH/NIA grant issued to the IU/Jax/PITT MODEL-AD center.

Abstract

We propose an unbiased methodology to rank compounds for advancement into comprehensive preclinical testing for Alzheimer's disease (AD). Translation of compounds to the clinic in AD has been hampered by poor predictive validity of models, compounds with limited pharmaceutical properties, and studies that lack rigor. To overcome this, MODEL-AD's Preclinical Testing Core developed a standardized pipeline for assessing efficacy in AD mouse models. We hypothesize that rank-ordering compounds based upon pharmacokinetic, efficacy, and toxicity properties in preclinical models will enhance successful translation to the clinic. Previously compound selection was based solely on physiochemical properties, with arbitrary cutoff limits, making ranking challenging. Since no gold standard exists for systematic prioritization, validating a selection criteria has remained elusive. The STOP-AD framework evaluates the drug-like properties to rank compounds for in vivo studies, and uses an unbiased approach that overcomes the validation limitation by performing Monte-Carlo simulations. HIGHLIGHTS: Promising preclinical studies for AD drugs have not translated to clinical success. Systematic assessment of AD drug candidates may increase clinical translatability. We describe a well-defined framework for compound selection with clear selection metrics.

Comments

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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