<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03269474</url>
  </required_header>
  <id_info>
    <org_study_id>41142</org_study_id>
    <nct_id>NCT03269474</nct_id>
  </id_info>
  <brief_title>Computational Drug Repurposing for All EBS Cases</brief_title>
  <official_title>Computational Drug Repurposing for All Epidermolysis Bullosa Simplex (EBS) Cases</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Joyce Teng</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Stanford University</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The study will compare gene expression differences between blistered and non-blistered skin&#xD;
      from individuals with all subtypes of EB, as well as normal skin from non-EB subjects. State&#xD;
      of the art computational analysis will be performed to help identify new drugs that might&#xD;
      help all EB wound healing and reduce pain. Researchers will focus on drugs that have already&#xD;
      been approved for treatment of other dermatologic or non-dermatologic diseases, and therefore&#xD;
      be repurposed for treatment of EB. Drug development is a very expensive process taking&#xD;
      decades for execution. Drug repurposing on the other hand, significantly reduces the cost and&#xD;
      shortens the amount of time that is needed to bring effective treatments to clinical use. To&#xD;
      date, there is no specific treatment targeting the physiology and immunologic response in EB&#xD;
      patients during wound healing. Market availability of repurposed medications will provide all&#xD;
      EB patients rapid access to treatments, thus improving their quality of life.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Although gene, cell, and protein-based therapies are in development for patients suffering&#xD;
      from all subtypes of epidermolysis bullosa (EB), new pharmacological treatments are in dire&#xD;
      need. Characterizing molecular changes in EB, including gene expression, can identify new&#xD;
      therapeutic targets and drugs that modulate those targets. However, sifting through gene&#xD;
      expression information to identify the most promising drug targets is a complex data&#xD;
      challenge. The goal of the study will identify a computational approach to evaluate and&#xD;
      identify existing drugs approved for other diseases that can be repurposed for EB patients.&#xD;
      The study will perform an unprecedented characterization of gene expression changes in EB&#xD;
      patients compared to healthy, non-EB individuals across multiple tissues. Using a validated&#xD;
      computational drug discovery platform, researchers will analyze gene expression and drug data&#xD;
      using unique algorithms. In the first year, a list of ten, safety drugs more probable to&#xD;
      treat the EB disease state will be identified. The most promising drugs discovered will then&#xD;
      be tested in the clinic setting.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">November 28, 2017</start_date>
  <completion_date type="Anticipated">December 31, 2023</completion_date>
  <primary_completion_date type="Anticipated">December 30, 2023</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Characterize gene expression changes in EB using RNA sequencing (RNA-seq) and Computational Profiling Potential Drug Targets</measure>
    <time_frame>Through the completion of study in 1 year.</time_frame>
    <description>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.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">60</enrollment>
  <condition>Epidermolysis Bullosa</condition>
  <condition>Healthy</condition>
  <condition>Genetic Skin Disease</condition>
  <condition>Epidermolysis Bullosa Simplex</condition>
  <condition>Epidermolysis Bullosa, Junctional</condition>
  <condition>Epidermolysis Bullosa Dystrophica</condition>
  <arm_group>
    <arm_group_label>Experimental Group</arm_group_label>
    <description>Blood and tissue specimen will be collected from subjects with an EB diagnosis. Tissue specimen will be collected from blistered and nonblistered skin.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control Group</arm_group_label>
    <description>Blood and tissue specimen will be collected from healthy subjects with non-EB. Tissue specimen will be collected from an inconspicuous skin area.</description>
  </arm_group>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>Experimental Group</intervention_name>
    <description>Subjects with EB diagnosis</description>
    <arm_group_label>Control Group</arm_group_label>
    <arm_group_label>Experimental Group</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Punch biopsies of non-blistering and blistering skin, and peripheral blood mononuclear cells&#xD;
      (PBMCs): CD8+ cytotoxic T cells and CD4+ helper T cells.&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Subject of all ages with either 1) a diagnosis of EB subjects or 2) healthy, non-EB&#xD;
        subjects&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Subjects of all ages&#xD;
&#xD;
          -  Diagnosis of all subtypes of EB subjects&#xD;
&#xD;
          -  Healthy, non-EB subjects&#xD;
&#xD;
          -  Ability to complete study visit to collect tissue and blood specimen&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Pregnancy, breast feeding&#xD;
&#xD;
          -  Prior history of liver disease&#xD;
&#xD;
          -  Serious known concurrent medical illness or infection, which could potentially present&#xD;
             a safety risk and/or prevent tissue collection from subjects&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>0 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Joyce M Teng, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Stanford University</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Monica Martin</last_name>
    <phone>650-723-0636</phone>
    <email>momartin@stanford.edu</email>
  </overall_contact>
  <location>
    <facility>
      <name>Pediatric Dermatology Clinic at Stanford Children's Hospital</name>
      <address>
        <city>Palo Alto</city>
        <state>California</state>
        <zip>94304</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Monica Martin</last_name>
      <phone>650-723-0636</phone>
      <email>PediatricDermStudy@stanford.edu</email>
    </contact>
    <contact_backup>
      <last_name>Elidia V Tafoya, MPH</last_name>
      <phone>650-724-1982</phone>
      <email>PediatricDermStudy@stanford.edu</email>
    </contact_backup>
    <investigator>
      <last_name>Joyce M Teng, MD, PhD</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Kavita Sarin, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <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>
    <PMID>15358662</PMID>
  </reference>
  <reference>
    <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>
    <PMID>9384284</PMID>
  </reference>
  <reference>
    <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.</citation>
    <PMID>27043002</PMID>
  </reference>
  <reference>
    <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>
    <PMID>19910308</PMID>
  </reference>
  <reference>
    <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>
    <PMID>24203711</PMID>
  </reference>
  <reference>
    <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>
    <PMID>23060433</PMID>
  </reference>
  <reference>
    <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>
    <PMID>17644558</PMID>
  </reference>
  <results_reference>
    <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>
    <PMID>27386970</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>26802230</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>26194911</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>23651789</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>25832646</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>27940607</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>21877134</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>19267394</PMID>
  </results_reference>
  <results_reference>
    <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>
    <PMID>23483391</PMID>
  </results_reference>
  <results_reference>
    <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.</citation>
    <PMID>25431026</PMID>
  </results_reference>
  <verification_date>January 2022</verification_date>
  <study_first_submitted>August 21, 2017</study_first_submitted>
  <study_first_submitted_qc>August 30, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 31, 2017</study_first_posted>
  <last_update_submitted>January 11, 2022</last_update_submitted>
  <last_update_submitted_qc>January 11, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">January 13, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>Stanford University</investigator_affiliation>
    <investigator_full_name>Joyce Teng</investigator_full_name>
    <investigator_title>Director of Pediatric Dermatology</investigator_title>
  </responsible_party>
  <keyword>epidermolysis bullosa</keyword>
  <keyword>genetic expression</keyword>
  <keyword>drug repurposing</keyword>
  <keyword>computational approaches</keyword>
  <keyword>drug discovery</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Epidermolysis Bullosa</mesh_term>
    <mesh_term>Epidermolysis Bullosa Simplex</mesh_term>
    <mesh_term>Epidermolysis Bullosa Dystrophica</mesh_term>
    <mesh_term>Epidermolysis Bullosa, Junctional</mesh_term>
    <mesh_term>Skin Diseases, Genetic</mesh_term>
    <mesh_term>Skin Diseases</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>As of now, there are no plans to share the data with other researchers. Once the outcome measures have been accomplished, the research team will publish results for the entire clinicaltrials.gov community and researchers for this vulnerable population study.</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03269474
org study id: 41142
nct id: NCT03269474
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Cohort
time perspective: Prospective
measure: Characterize gene expression changes in EB using RNA sequencing (RNA-seq) and Computational Profiling Potential Drug Targets
time frame: Through the completion of study in 1 year.
description: 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.
arm group label: Experimental Group
description: Blood and tissue specimen will be collected from subjects with an EB diagnosis. Tissue specimen will be collected from blistered and nonblistered skin.
arm group label: Control Group
description: Blood and tissue specimen will be collected from healthy subjects with non-EB. Tissue specimen will be collected from an inconspicuous skin area.
intervention type: Procedure
intervention name: Experimental Group
description: Subjects with EB diagnosis
arm group label: Control Group
arm group label: Experimental Group
Punch biopsies of non-blistering and blistering skin, and peripheral blood mononuclear cells
      (PBMCs): CD8+ cytotoxic T cells and CD4+ helper T cells.
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 0 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Joyce M Teng, MD, PhD
role: Principal Investigator
affiliation: Stanford University
last name: Monica Martin
phone: 650-723-0636
email: momartin@stanford.edu
facility: 
status: Recruiting
contact: 
contact backup: 
investigator: 
investigator: 
country: United States
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.
PMID: 15358662
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.
PMID: 9384284
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.
PMID: 27043002
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.
PMID: 19910308
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.
PMID: 24203711
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.
PMID: 23060433
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.
PMID: 17644558
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.
PMID: 27386970
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.
PMID: 26802230
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.
PMID: 26194911
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.
PMID: 23651789
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.
PMID: 25832646
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.
PMID: 27940607
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.
PMID: 21877134
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.
PMID: 19267394
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.
PMID: 23483391
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.
PMID: 25431026
responsible party type: Sponsor-Investigator
investigator affiliation: Stanford University
investigator full name: Joyce Teng
investigator title: Director of Pediatric Dermatology
mesh term: Epidermolysis Bullosa
mesh term: Epidermolysis Bullosa Simplex
mesh term: Epidermolysis Bullosa Dystrophica
mesh term: Epidermolysis Bullosa, Junctional
mesh term: Skin Diseases, Genetic
mesh term: Skin Diseases
sharing ipd: No
ipd description: As of now, there are no plans to share the data with other researchers. Once the outcome measures have been accomplished, the research team will publish results for the entire clinicaltrials.gov community and researchers for this vulnerable population study.