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Computational Drug Repurposing for All EBS Cases

Computational Drug Repurposing for All Epidermolysis Bullosa Simplex (EBS) Cases

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03269474
Enrollment
60
Registered
2017-08-31
Start date
2017-11-28
Completion date
2024-12-31
Last updated
2024-02-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Epidermolysis Bullosa, Epidermolysis Bullosa Dystrophica, Epidermolysis Bullosa, Junctional, Epidermolysis Bullosa Simplex, Genetic Skin Disease, Healthy

Keywords

epidermolysis bullosa, genetic expression, drug repurposing, computational approaches, drug discovery

Brief summary

The study will compare gene expression differences between blistered and non-blistered skin from individuals with all subtypes of EB, as well as normal skin from non-EB subjects. State of the art computational analysis will be performed to help identify new drugs that might help all EB wound healing and reduce pain. Researchers will focus on drugs that have already been approved for treatment of other dermatologic or non-dermatologic diseases, and therefore be repurposed for treatment of EB. Drug development is a very expensive process taking decades for execution. Drug repurposing on the other hand, significantly reduces the cost and shortens the amount of time that is needed to bring effective treatments to clinical use. To date, there is no specific treatment targeting the physiology and immunologic response in EB patients during wound healing. Market availability of repurposed medications will provide all EB patients rapid access to treatments, thus improving their quality of life.

Detailed description

Although gene, cell, and protein-based therapies are in development for patients suffering from all subtypes of epidermolysis bullosa (EB), new pharmacological treatments are in dire need. Characterizing molecular changes in EB, including gene expression, can identify new therapeutic targets and drugs that modulate those targets. However, sifting through gene expression information to identify the most promising drug targets is a complex data challenge. The goal of the study will identify a computational approach to evaluate and identify existing drugs approved for other diseases that can be repurposed for EB patients. The study will perform an unprecedented characterization of gene expression changes in EB patients compared to healthy, non-EB individuals across multiple tissues. Using a validated computational drug discovery platform, researchers will analyze gene expression and drug data using unique algorithms. In the first year, a list of ten, safety drugs more probable to treat the EB disease state will be identified. The most promising drugs discovered will then be tested in the clinic setting.

Interventions

PROCEDUREExperimental Group

Subjects with EB diagnosis

Sponsors

Joyce Teng
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
0 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Subjects of all ages * Diagnosis of all subtypes of EB subjects * Healthy, non-EB subjects * Ability to complete study visit to collect tissue and blood specimen

Exclusion criteria

* Pregnancy, breast feeding * Prior history of liver disease * Serious known concurrent medical illness or infection, which could potentially present a safety risk and/or prevent tissue collection from subjects

Design outcomes

Primary

MeasureTime frameDescription
Characterize gene expression changes in EB using RNA sequencing (RNA-seq) and Computational Profiling Potential Drug TargetsThrough the completion of study in 1 year.Using bioinformatic algorithms to identify changes in gene expression and review of over 2000 FDA-approved drugs based on predicted modulation of gene expression changes using a computational evolutionary algorithm system.

Countries

United States

Contacts

Primary ContactMonica Martin
momartin@stanford.edu650-723-0636

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026