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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03269474
Other study ID # 41142
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date November 28, 2017
Est. completion date December 31, 2024

Study information

Verified date February 2024
Source Stanford University
Contact Monica Martin
Phone 650-723-0636
Email momartin@stanford.edu
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 60
Est. completion date December 31, 2024
Est. primary completion date December 30, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 0 Years and older
Eligibility 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

Study Design


Related Conditions & MeSH terms


Intervention

Procedure:
Experimental Group
Subjects with EB diagnosis

Locations

Country Name City State
United States Pediatric Dermatology Clinic at Stanford Children's Hospital Palo Alto California

Sponsors (1)

Lead Sponsor Collaborator
Joyce Teng

Country where clinical trial is conducted

United States, 

References & Publications (17)

Bchetnia M, Tremblay ML, Leclerc G, Duperee A, Powell J, McCuaig C, Morin C, Legendre-Guillemin V, Laprise C. Expression signature of epidermolysis bullosa simplex. Hum Genet. 2012 Mar;131(3):393-406. doi: 10.1007/s00439-011-1077-7. Epub 2011 Aug 30. — View Citation

Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016 May;34(5):525-7. doi: 10.1038/nbt.3519. Epub 2016 Apr 4. Erratum In: Nat Biotechnol. 2016 Aug 9;34(8):888. — View Citation

Cohn HI, Teng JM. Advancement in management of epidermolysis bullosa. Curr Opin Pediatr. 2016 Aug;28(4):507-16. doi: 10.1097/MOP.0000000000000380. — View Citation

Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014 Jan;42(Database issue):D1091-7. doi: 10.1093/nar/gkt1068. Epub 2013 Nov 6. — View Citation

Lee B, Geyfman M, Andersen B, Dai X. Analysis of gene expression in skin using laser capture microdissection. Methods Mol Biol. 2013;989:109-17. doi: 10.1007/978-1-62703-330-5_10. — View Citation

Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016 Jan;17(1):2-12. doi: 10.1093/bib/bbv020. Epub 2015 Mar 31. — View Citation

Lovendorf MB, Mitsui H, Zibert JR, Ropke MA, Hafner M, Dyring-Andersen B, Bonefeld CM, Krueger JG, Skov L. Laser capture microdissection followed by next-generation sequencing identifies disease-related microRNAs in psoriatic skin that reflect systemic mi — View Citation

Low YS, Daugherty AC, Schroeder EA, Chen W, Seto T, Weber S, Lim M, Hastie T, Mathur M, Desai M, Farrington C, Radin AA, Sirota M, Kenkare P, Thompson CA, Yu PP, Gomez SL, Sledge GW Jr, Kurian AW, Shah NH. Synergistic drug combinations from electronic hea — View Citation

McLaren PJ, Mayne M, Rosser S, Moffatt T, Becker KG, Plummer FA, Fowke KR. Antigen-specific gene expression profiles of peripheral blood mononuclear cells do not reflect those of T-lymphocyte subsets. Clin Diagn Lab Immunol. 2004 Sep;11(5):977-82. doi: 10.1128/CDLI.11.5.977-982.2004. — View Citation

Nystrom A, Thriene K, Mittapalli V, Kern JS, Kiritsi D, Dengjel J, Bruckner-Tuderman L. Losartan ameliorates dystrophic epidermolysis bullosa and uncovers new disease mechanisms. EMBO Mol Med. 2015 Sep;7(9):1211-28. doi: 10.15252/emmm.201505061. — View Citation

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139-40. doi: 10.1093/bioinformatics/btp616. Epub 2009 Nov 11. — View Citation

Roth W, Reuter U, Wohlenberg C, Bruckner-Tuderman L, Magin TM. Cytokines as genetic modifiers in K5-/- mice and in human epidermolysis bullosa simplex. Hum Mutat. 2009 May;30(5):832-41. doi: 10.1002/humu.20981. — View Citation

Sleasman JW, Leon BH, Aleixo LF, Rojas M, Goodenow MM. Immunomagnetic selection of purified monocyte and lymphocyte populations from peripheral blood mononuclear cells following cryopreservation. Clin Diagn Lab Immunol. 1997 Nov;4(6):653-8. doi: 10.1128/cdli.4.6.653-658.1997. — View Citation

Subramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP. GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics. 2007 Dec 1;23(23):3251-3. doi: 10.1093/bioinformatics/btm369. Epub 2007 Jul 20. — View Citation

Sugaya N, Kanai S, Furuya T. Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions. Database (Oxford). 2012 Oct 10;2012:bas034. doi: 10.1093/database/bas034. Print 2012. — View Citation

Uitto J, Bruckner-Tuderman L, Christiano AM, McGrath JA, Has C, South AP, Kopelan B, Robinson EC. Progress toward Treatment and Cure of Epidermolysis Bullosa: Summary of the DEBRA International Research Symposium EB2015. J Invest Dermatol. 2016 Feb;136(2) — View Citation

Wally V, Kitzmueller S, Lagler F, Moder A, Hitzl W, Wolkersdorfer M, Hofbauer P, Felder TK, Dornauer M, Diem A, Eiler N, Bauer JW. Topical diacerein for epidermolysis bullosa: a randomized controlled pilot study. Orphanet J Rare Dis. 2013 May 7;8:69. doi: — View Citation

* Note: There are 17 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Characterize gene expression changes in EB using RNA sequencing (RNA-seq) and Computational Profiling Potential Drug Targets 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. Through the completion of study in 1 year.
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