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Regulatory network analysis defines unique drug mechanisms of action and facilitates patient-drug matching in alopecia areata clinical trials

Authors
  • Chen, James C.
  • Dai, Zhenpeng
  • Christiano, Angela M.
Type
Published Article
Journal
Computational and Structural Biotechnology Journal
Publisher
Elsevier
Publication Date
Aug 19, 2021
Volume
19
Pages
4751–4758
Identifiers
DOI: 10.1016/j.csbj.2021.08.026
PMID: 34504667
PMCID: PMC8403543
Source
PubMed Central
Keywords
Disciplines
  • Research Article
License
Unknown

Abstract

Not all therapeutics are created equal in regards to individual patients. The problem of identifying which compound will work best with which patient is a significant burden across all disease contexts. In the context of autoimmune diseases such as alopecia areata, several formulations of JAK/STAT inhibitors have demonstrated efficacy in clinical trials. All of these compounds demonstrate different rates of response, and here we observed that this coincided with different molecular effects on patients undergoing treatment. Using these data, we have developed a computational model that is capable of predicting which patient-drug pairs have the highest likelihood of response. We achieved this by integrating inferred mechanism of action data and gene regulatory networks derived from an independent patient cohort with baseline patient data prior to beginning treatment.

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