Clinical trial NCT03269474: Computational Drug Repurposing for All EBS Cases
Official title: Computational Drug Repurposing for All Epidermolysis Bullosa Simplex (EBS) Cases
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Epidermolysis Bullosa
Healthy
Genetic Skin Disease
Epidermolysis Bullosa Simplex
Epidermolysis Bullosa, Junctional
Epidermolysis Bullosa Dystrophica
Keywords: epidermolysis bullosa
genetic expression
drug repurposing
computational approaches
drug discovery
Lead sponsor: Stanford University
Sponsor class: OTHER
First posted: 2017-08-31
Enrollment: 60
Sex: ALL
Minimum age (years): 0
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Subject of all ages with either 1) a diagnosis of EB subjects or 2) healthy, non-EB subjects

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.

Eligibility criteria: 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

Primary outcomes:
measure: Characterize gene expression changes in EB using RNA sequencing (RNA-seq) and Computational Profiling Potential Drug Targets
timeFrame: Through the completion of study in 1 year.
Secondary outcomes:
Other outcomes:
Locations:
city: Palo Alto
country: United States
facility: Pediatric Dermatology Clinic at Stanford Children's Hospital
state: California
Study officials:
affiliation: Stanford University
name: Joyce M Teng, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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.
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.
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.
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.
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.
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.
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):352-358. doi: 10.1016/j.jid.2015.10.050.
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.
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: 10.1186/1750-1172-8-69.
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.
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 health records and gene expression. J Am Med Inform Assoc. 2017 May 1;24(3):565-576. doi: 10.1093/jamia/ocw161.
citation: 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.
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.
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.
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 microRNA changes in psoriasis. Exp Dermatol. 2015 Mar;24(3):187-93. doi: 10.1111/exd.12604.