A Scalable Screening Platform to Accelerate Early Drug Discovery for Diverse ALS Subtypes

Abstract

Development of ALS therapies is challenged by phenotypic and genetic heterogeneity and an incomplete understanding of disease mechanisms. Conducting scalable screens for discovering ALS-relevant druggable targets requires addressing several critical questions. First, which pre-clinical models represent ALS disease states and patient heterogeneity yet allow for tractable, unbiased drug target screens? Mouse or neuronal models may capture hallmarks of ALS pathophysiology but are not tractable for large-scale screens. On the other hand, overexpression or isogenic models in cancer cell lines are highly scalable but have diminished ALS relevance. Second, which patient cell models should be selected for screening? There is no obvious typical normal or ALS patient cell line. In fact, it is likely that there are multiple ALS subtypes. A method for intelligently selecting cell lines for studies and validation is needed. Third, which cell phenotypes will distinguish ALS patient subpopulations? ALS can manifest at the cellular level as aberrant protein expression levels and localization patterns (e.g. TDP-43) as well as in altered organellar structure (e.g. fragmented mitochondria). Identifying ALS subtypes will require profiling dysregulated pathways across many patients. Fourth, how can we identify cellular perturbations that push ALS subtypes towards healthy states? Incorporating a broad range of readouts to capture ALS pathophysiology will create high-dimensional datasets. Computational methods are required to identify perturbations that move cells from an ALS to healthy phenotypic regions. None of the challenges raised above can be solved in isolation they must be addressed simultaneously in an end-to-end screening platform.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2022
Accession Number
AD1189832

Entities

People

  • Steven J. Altschuler

Organizations

  • University of California Regents

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Research
  • California
  • Cell Line
  • Cells
  • Classification
  • Computational Biology
  • Computational Science
  • Experimental Design
  • Fibroblasts
  • Genes
  • Genetics
  • Genome
  • Machine Learning
  • Motor Neurons
  • New York
  • Perturbations
  • Phenotypes
  • Platforms
  • Spinal Cord
  • Students
  • Therapy
  • Universities
  • Validation

Fields of Study

  • Biology
  • Medicine

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Technology Areas

  • Biotechnology