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

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.

SUBMITTER: Chen JC

PROVIDER: S-EPMC8403543 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Chen James C JC Dai Zhenpeng Z Christiano Angela M AM

Computational and structural biotechnology journal 20210819

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 pa ...[more]

PMID: 34504667

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Project description:IntroductionIndividuals with alopecia areata (AA) may experience significant impacts on their health-related quality of life. The novel Alopecia Areata Patient Priority Outcomes (AAPPO) questionnaire has been developed to assess hair loss signs, emotional symptoms, and activity limitations associated with AA. The objective of this study was to evaluate psychometric properties and establish scoring of the AAPPO in adults and adolescents with AA.MethodsScoring and measurement properties of the AAPPO were examined using baseline and 2-week follow-up data from a prospective, noninterventional, web-based study of 121 patients with AA (85 adults aged ≥ 18 years, 36 adolescents aged 12-17 years) with Severity of Alopecia Tool (SALT) ≥ 25% scalp hair loss.ResultsExploratory and confirmatory factor analysis supported four single Hair Loss (HL) items, an Emotional Symptoms domain (ES; 4 items), and an Activity Limitations domain (AL; 3 items). Among all patients, the multi-item ES and AL domains had strong internal consistency (α ≥ 0.87); all HL items and domain scores had strong test-retest reliability (weighted kappa or intraclass correlation coefficients ≥ 0.78). All HL item scores demonstrated strong construct validity (r ≥ 0.52) compared with the patient-reported Alopecia Areata Symptom and Impact Scale (AASIS) hair loss subscale score; ES and AL domain scores exhibited strong construct validity (r ≥ 0.66) compared with the SF-36 Mental Component Summary (MCS) score. Using SALT scores, HL mean item scores were better (lower) in the 25-49% SALT subgroup versus those with highest SALT scores (76-100%); however, ES mean domain scores were better in the SALT 76-100% subgroup in the same comparison (p < 0.0001). Using AASIS and MCS score-created subgroups, ES and AL mean domain scores demonstrated hypothesized differences across subgroups (all p values < 0.0001).ConclusionThe AAPPO questionnaire is a reliable, valid disease-specific measure of hair loss severity and impact in individuals with AA.

Dataset Information

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

Publications

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

Similar Datasets

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