Regulatory network analysis defines unique drug mechanisms of action and facilitates patient-drug matching in alopecia areata clinical trials
Open accessAbstract
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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Matching pretreatment biopsy profiles to inferred networks was proposed to guide patient-drug matching in clinical trials.
“By matching pretreatment biopsy profiles to these inferred networks, the authors proposed that these molecular profiles can guide patient—drug matching in clinical trials (Chen et al, 2021).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Chen et al, 2021
Regulatory network analysis of transcriptomics from clinical trial biopsies defined distinct drug mechanisms of response for treatments such as ruxolitinib and tofacitinib.
“Chen et al (2021) analyzed transcriptomics from clinical trial biopsies using regulatory network analysis to define distinct drug mechanisms of response for treatments such as ruxolitinib and tofacitinib.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Chen et al, 2021
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Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
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- Authors
- Chen, J. C., Dai, Z., & Christiano, A. M.
- Affiliations
- Columbia University Irving Medical Center (US)
- Corresponding author
- Chen, James C.
- Christiano, Angela M.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021-01
- Volume
- 19
- Pages
- 4751-4758
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC8403543
- OpenAlex
- W3195096183
- Cited by
- 9
- References
- 18
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 1
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR069632, P50AR070588)
- Topics
- Health Sciences › Medicine › Urology › Hair Growth and Disorders
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- alopecia areata, JAK/STAT inhibitors, drug mechanisms of action, gene regulatory networks, treatment response prediction, computational model, autoimmune disease, personalized medicine
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