<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/NCT04496037</url>
  </required_header>
  <id_info>
    <org_study_id>19HH5204</org_study_id>
    <nct_id>NCT04496037</nct_id>
  </id_info>
  <brief_title>Mechanistic Evaluation of Treatments for Acute Antibody-Mediated Rejection of the Kidney Transplant</brief_title>
  <acronym>MOT-AMR</acronym>
  <official_title>Mechanistic Evaluation of Treatments for Acute Antibody-Mediated Rejection of the Kidney Transplant</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Imperial College London</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Cambridge</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Viapath LLP</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Imperial College London</source>
  <oversight_info>
    <has_dmc>Yes</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 best treatment for kidney failure is a kidney transplant, but a transplanted kidney only&#xD;
      works for about 10 to 15 years on average. One of the reasons that a transplanted kidney can&#xD;
      stop working is that the body develops antibodies against it. Antibodies are proteins&#xD;
      produced by the body to fight infections. They play a vital role in dealing with infection&#xD;
      but in some transplant patients they can 'fight' the organ, meaning it could stop working.&#xD;
      This is called acute antibody-mediated rejection (AAMR). A patient experiencing AAMR can be&#xD;
      treated to extend the life of the transplanted kidney, but the chances of the kidney still&#xD;
      working 4 years later are reduced. There is currently a clinical trial for UK kidney&#xD;
      transplant patients who develop AAMR, called TAR:GET-1. Patients participating in TAR:GET-1&#xD;
      will either receive the standard treatment that is currently given in this situation, or the&#xD;
      standard treatment with the addition of rituximab. TAR:GET-1 will answer the question: does&#xD;
      adding rituximab to standard treatment lengthen the life of a kidney transplant? A second,&#xD;
      sub-study is being proposed of the patients enrolled in TAR:GET-1, that will use the existing&#xD;
      blood and biopsy samples already taken during TAR:GET-1 plus an optional extra biopsy of the&#xD;
      kidney transplant, to improve our understanding of how the treatments of AAMR work. Patients&#xD;
      enrolled in TAR:GET-1 will have had a blood test and biopsy of the transplanted kidney to&#xD;
      establish the diagnosis of AAMR. They will also have had further blood tests after treatment,&#xD;
      and may also have further biopsies taken if their clinician needs these as part of normal&#xD;
      care. Material left over in these samples can be used to analyse how treatment works. In&#xD;
      addition, patients will be asked if they agree to an extra biopsy of the transplanted kidney&#xD;
      6 months after the treatment begins. These samples will be analysed at a deeper level than&#xD;
      would normally be done, looking at the antibodies and biopsies in detail to answer 2 key&#xD;
      questions: 1) Can the unique characteristics noted in an individual patient's antibodies and&#xD;
      biopsy predict whether a kidney transplant will be lost as a result of AAMR?; 2) Can we tell&#xD;
      treatment is working by looking at the changes in a patient's antibodies and biopsies before&#xD;
      and after treatment? The answers to these questions will help us understand AAMR and how its&#xD;
      treatments work, and potentially improve our ability to select the right treatment for the&#xD;
      right patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Design: Prospective observational study of patients enrolled in TAR:GET-1&#xD;
&#xD;
      Objectives: TAR-GET-1 (starting in 2019) is a multi-centre UK clinical trial that will&#xD;
      compare standard of care (SOC) treatment of Acute Antibody-Mediated Rejection of the Kidney&#xD;
      Transplant (AAMR) to standard of care plus rituximab (SOCR). This trial presents a rare&#xD;
      opportunity to investigate the mechanisms of action of treatments (including rituximab) in&#xD;
      AAMR. The rationale for using rituximab is the depletion of CD20-positive precursors to&#xD;
      antibody-producing cells. However, rituximab has effects on other B cell subtypes and not all&#xD;
      studies have shown antibody level reductions. Furthermore, AAMR is a heterogeneous disease,&#xD;
      as defined by variability in mechanistic markers of AAMR in blood (B cell and antibody&#xD;
      characteristics) and in transplant biopsies (histological features and gene expression).&#xD;
&#xD;
      The objectives of this study are:&#xD;
&#xD;
        1. To investigate how treatment with rituximab affects these mechanistic markers, by&#xD;
           comparing SOC to SOCR&#xD;
&#xD;
        2. To predict treatment benefit early, by analysing the effect of treatment on the&#xD;
           mechanistic markers&#xD;
&#xD;
      Methodology: In patients enrolled in TAR:GET-1 (n=170), mechanistic markers will be analysed&#xD;
      with established proof of concept (B cell and antibody characteristics, histological and&#xD;
      transcriptional markers in the biopsy) at baseline and on follow-up, making use of clinical&#xD;
      data, surplus diagnostic material from TAR:GET-1, and material from optional extra blood&#xD;
      samples for research taken as part of TAR:GET-1, at 3,6 and 12 months. An optional protocol&#xD;
      biopsy at 6 months post treatment will also be offered.&#xD;
&#xD;
      TARGET-1 is assessing DSA MFI and target and B lymphocyte numbers at baseline and 3 months&#xD;
      post-treatment. This study will in addition analyse:&#xD;
&#xD;
        1. Serum DSA MFI and target at 6 months; complement-activating ability at baseline, 3&#xD;
           months and 6 months;&#xD;
&#xD;
        2. Biopsy Banff histological scores at baseline and in post-treatment indication biopsies&#xD;
           and an optional protocol biopsy;&#xD;
&#xD;
        3. Biopsy AAMR-related transcript signature, assessed using Nanostring nCounter technology,&#xD;
           at baseline and in post-treatment biopsies indication biopsies and an optional protocol&#xD;
           biopsy;&#xD;
&#xD;
        4. B lymphocyte numbers at 6 months.&#xD;
&#xD;
      Differences pre- and post-treatment will be analysed comparing SOC and SOCR. Mediation&#xD;
      analysis models will be used to decompose total effect of treatment effect on graft loss at 4&#xD;
      years (primary end point) into an indirect effect, which measures how much of the effect acts&#xD;
      through the intermediate variable(s), and a residual direct effect.&#xD;
&#xD;
      Number of Subjects and Study populations: 170 subjects enrolled in TAR:GET-1&#xD;
&#xD;
      Outcome measures: same as primary outcome of TAR:GET-1 (graft loss at 4 years after&#xD;
      initiation of treatment)&#xD;
&#xD;
      Inclusion Criteria: All patients enrolled in TAR:GET-1 ; Signed informed consent prior to any&#xD;
      study specific procedures.&#xD;
&#xD;
      Exclusion Criteria: None&#xD;
&#xD;
      Duration of study: 8 years (duration is dictated by the need for primary outcome of TAR-GET-1&#xD;
      to be reached)&#xD;
&#xD;
      Statistical Methods: Data from this research project will be merged and linked with data from&#xD;
      the TAR:GET-1 clinical trial by the statistician team.&#xD;
&#xD;
      Descriptive data: This will include description of all the biological characteristics&#xD;
      analysed in 2 populations (rituximab + SOC and SOC) at baseline and at all follow-up time&#xD;
      points. Summary statistics (mean, median, SD, range for continuous variables, and frequency&#xD;
      tables for categorical variables) will be reported broken down by treatment, with exploratory&#xD;
      figures (box-and-whisker plots for continuous, and stacked bar-charts for categorical).&#xD;
&#xD;
      Outcome analysis: Appropriate measures of differences between treatment groups will be&#xD;
      estimated with point estimates, 95% confidence intervals, and p-values. Depending on the type&#xD;
      of variable (continuous, categorical, time-to-event), regression techniques (linear,&#xD;
      logistic, cox) and transformation of the endpoints will be considered.&#xD;
&#xD;
      Differences in characteristics comparing pre- to post-treatment samples at sequential time&#xD;
      points will be analysed comparing the 2 groups of patients (SOCR versus SOC) using r mixed&#xD;
      effect models repeat measurement techniques.&#xD;
&#xD;
      Mediation analysis, a special case of regression analysis, will be used to document that the&#xD;
      treatment effects a change in the biomarker. Mediation analysis models will be used to&#xD;
      decompose total effect of treatment effect on graft loss into an indirect effect, which&#xD;
      measures how much of the effect acts through the intermediate variable(s), and a residual&#xD;
      direct effect. All the mediation analyses will be based on the Intention-to-Treat principle.&#xD;
      The same form of outcome models will be fitted as for the efficacy analyses, including the&#xD;
      mediator as a covariate. The mediation package within R will be used which was developed&#xD;
      specifically to make valid causal inference in explanatory analyses of the mechanisms of&#xD;
      treatment-induced change in clinical outcomes in randomised clinical trials. All estimates of&#xD;
      treatment differences will be summarised using effect size estimates and 95% confidence&#xD;
      intervals (Dunn 2013). Collaborator for mediation analysis: Prof Ian White, Professor of&#xD;
      Statistical Methods for Medicine, UCL.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Not yet recruiting</overall_status>
  <start_date type="Anticipated">September 1, 2020</start_date>
  <completion_date type="Anticipated">August 31, 2028</completion_date>
  <primary_completion_date type="Anticipated">July 1, 2027</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>Allograft Survival as assessed by statistical model</measure>
    <time_frame>4 years</time_frame>
    <description>Statistical model measuring allograft survival as defined as duration from the date of randomisation to the date of eGFR ≤15 mL/min/1.72 m2, or the date of renal replacement therapy (date of starting dialysis dependency, transplantation etc), whichever occurs first.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">170</enrollment>
  <condition>Transplant; Complication, Rejection</condition>
  <arm_group>
    <arm_group_label>Standard of care (SOC)</arm_group_label>
    <description>See NCT03994783</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Rituximab + SOC (SOCR)</arm_group_label>
    <description>See NCT03994783</description>
  </arm_group>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Rituximab</intervention_name>
    <description>See NCT03994783</description>
    <arm_group_label>Rituximab + SOC (SOCR)</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Enrolment and indication renal transplant biopsies: material left over after diagnosis&#xD;
      Optional renal transplant protocol biopsy Leftover serum from samples taken at time of biopsy&#xD;
      Extra blood sample taken at time of biopsy&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        All patients enrolled in TAR:GET-1&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        All patients enrolled in TAR:GET-1 Signed informed consent prior to any study specific&#xD;
        procedures.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        None&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>5 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Candice Roufosse, MD PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Imperial College London</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Candice Roufosse, MD PhD</last_name>
    <phone>02033138240</phone>
    <email>c.roufosse@imperial.ac.uk</email>
  </overall_contact>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 29, 2020</study_first_submitted>
  <study_first_submitted_qc>July 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">August 3, 2020</study_first_posted>
  <last_update_submitted>July 29, 2020</last_update_submitted>
  <last_update_submitted_qc>July 29, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">August 3, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <intervention_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Rituximab</mesh_term>
  </intervention_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>No</document_has_sap>
      <document_date>May 27, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/37/NCT04496037/Prot_000.pdf</document_url>
    </provided_document>
  </provided_document_section>
  <!-- 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/NCT04496037
org study id: 19HH5204
nct id: NCT04496037
lead sponsor: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The best treatment for kidney failure is a kidney transplant, but a transplanted kidney only
      works for about 10 to 15 years on average. One of the reasons that a transplanted kidney can
      stop working is that the body develops antibodies against it. Antibodies are proteins
      produced by the body to fight infections. They play a vital role in dealing with infection
      but in some transplant patients they can 'fight' the organ, meaning it could stop working.
      This is called acute antibody-mediated rejection (AAMR). A patient experiencing AAMR can be
      treated to extend the life of the transplanted kidney, but the chances of the kidney still
      working 4 years later are reduced. There is currently a clinical trial for UK kidney
      transplant patients who develop AAMR, called TAR:GET-1. Patients participating in TAR:GET-1
      will either receive the standard treatment that is currently given in this situation, or the
      standard treatment with the addition of rituximab. TAR:GET-1 will answer the question: does
      adding rituximab to standard treatment lengthen the life of a kidney transplant? A second,
      sub-study is being proposed of the patients enrolled in TAR:GET-1, that will use the existing
      blood and biopsy samples already taken during TAR:GET-1 plus an optional extra biopsy of the
      kidney transplant, to improve our understanding of how the treatments of AAMR work. Patients
      enrolled in TAR:GET-1 will have had a blood test and biopsy of the transplanted kidney to
      establish the diagnosis of AAMR. They will also have had further blood tests after treatment,
      and may also have further biopsies taken if their clinician needs these as part of normal
      care. Material left over in these samples can be used to analyse how treatment works. In
      addition, patients will be asked if they agree to an extra biopsy of the transplanted kidney
      6 months after the treatment begins. These samples will be analysed at a deeper level than
      would normally be done, looking at the antibodies and biopsies in detail to answer 2 key
      questions: 1) Can the unique characteristics noted in an individual patient's antibodies and
      biopsy predict whether a kidney transplant will be lost as a result of AAMR?; 2) Can we tell
      treatment is working by looking at the changes in a patient's antibodies and biopsies before
      and after treatment? The answers to these questions will help us understand AAMR and how its
      treatments work, and potentially improve our ability to select the right treatment for the
      right patients.
Design: Prospective observational study of patients enrolled in TAR:GET-1

      Objectives: TAR-GET-1 (starting in 2019) is a multi-centre UK clinical trial that will
      compare standard of care (SOC) treatment of Acute Antibody-Mediated Rejection of the Kidney
      Transplant (AAMR) to standard of care plus rituximab (SOCR). This trial presents a rare
      opportunity to investigate the mechanisms of action of treatments (including rituximab) in
      AAMR. The rationale for using rituximab is the depletion of CD20-positive precursors to
      antibody-producing cells. However, rituximab has effects on other B cell subtypes and not all
      studies have shown antibody level reductions. Furthermore, AAMR is a heterogeneous disease,
      as defined by variability in mechanistic markers of AAMR in blood (B cell and antibody
      characteristics) and in transplant biopsies (histological features and gene expression).

      The objectives of this study are:

        1. To investigate how treatment with rituximab affects these mechanistic markers, by
           comparing SOC to SOCR

        2. To predict treatment benefit early, by analysing the effect of treatment on the
           mechanistic markers

      Methodology: In patients enrolled in TAR:GET-1 (n=170), mechanistic markers will be analysed
      with established proof of concept (B cell and antibody characteristics, histological and
      transcriptional markers in the biopsy) at baseline and on follow-up, making use of clinical
      data, surplus diagnostic material from TAR:GET-1, and material from optional extra blood
      samples for research taken as part of TAR:GET-1, at 3,6 and 12 months. An optional protocol
      biopsy at 6 months post treatment will also be offered.

      TARGET-1 is assessing DSA MFI and target and B lymphocyte numbers at baseline and 3 months
      post-treatment. This study will in addition analyse:

        1. Serum DSA MFI and target at 6 months; complement-activating ability at baseline, 3
           months and 6 months;

        2. Biopsy Banff histological scores at baseline and in post-treatment indication biopsies
           and an optional protocol biopsy;

        3. Biopsy AAMR-related transcript signature, assessed using Nanostring nCounter technology,
           at baseline and in post-treatment biopsies indication biopsies and an optional protocol
           biopsy;

        4. B lymphocyte numbers at 6 months.

      Differences pre- and post-treatment will be analysed comparing SOC and SOCR. Mediation
      analysis models will be used to decompose total effect of treatment effect on graft loss at 4
      years (primary end point) into an indirect effect, which measures how much of the effect acts
      through the intermediate variable(s), and a residual direct effect.

      Number of Subjects and Study populations: 170 subjects enrolled in TAR:GET-1

      Outcome measures: same as primary outcome of TAR:GET-1 (graft loss at 4 years after
      initiation of treatment)

      Inclusion Criteria: All patients enrolled in TAR:GET-1 ; Signed informed consent prior to any
      study specific procedures.

      Exclusion Criteria: None

      Duration of study: 8 years (duration is dictated by the need for primary outcome of TAR-GET-1
      to be reached)

      Statistical Methods: Data from this research project will be merged and linked with data from
      the TAR:GET-1 clinical trial by the statistician team.

      Descriptive data: This will include description of all the biological characteristics
      analysed in 2 populations (rituximab + SOC and SOC) at baseline and at all follow-up time
      points. Summary statistics (mean, median, SD, range for continuous variables, and frequency
      tables for categorical variables) will be reported broken down by treatment, with exploratory
      figures (box-and-whisker plots for continuous, and stacked bar-charts for categorical).

      Outcome analysis: Appropriate measures of differences between treatment groups will be
      estimated with point estimates, 95% confidence intervals, and p-values. Depending on the type
      of variable (continuous, categorical, time-to-event), regression techniques (linear,
      logistic, cox) and transformation of the endpoints will be considered.

      Differences in characteristics comparing pre- to post-treatment samples at sequential time
      points will be analysed comparing the 2 groups of patients (SOCR versus SOC) using r mixed
      effect models repeat measurement techniques.

      Mediation analysis, a special case of regression analysis, will be used to document that the
      treatment effects a change in the biomarker. Mediation analysis models will be used to
      decompose total effect of treatment effect on graft loss into an indirect effect, which
      measures how much of the effect acts through the intermediate variable(s), and a residual
      direct effect. All the mediation analyses will be based on the Intention-to-Treat principle.
      The same form of outcome models will be fitted as for the efficacy analyses, including the
      mediator as a covariate. The mediation package within R will be used which was developed
      specifically to make valid causal inference in explanatory analyses of the mechanisms of
      treatment-induced change in clinical outcomes in randomised clinical trials. All estimates of
      treatment differences will be summarised using effect size estimates and 95% confidence
      intervals (Dunn 2013). Collaborator for mediation analysis: Prof Ian White, Professor of
      Statistical Methods for Medicine, UCL.
observational model: Cohort
time perspective: Prospective
measure: Allograft Survival as assessed by statistical model
time frame: 4 years
description: Statistical model measuring allograft survival as defined as duration from the date of randomisation to the date of eGFR ≤15 mL/min/1.72 m2, or the date of renal replacement therapy (date of starting dialysis dependency, transplantation etc), whichever occurs first.
arm group label: Standard of care (SOC)
description: See NCT03994783
arm group label: Rituximab + SOC (SOCR)
description: See NCT03994783
intervention type: Drug
intervention name: Rituximab
description: See NCT03994783
arm group label: Rituximab + SOC (SOCR)
Enrolment and indication renal transplant biopsies: material left over after diagnosis
      Optional renal transplant protocol biopsy Leftover serum from samples taken at time of biopsy
      Extra blood sample taken at time of biopsy
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 5 Years
maximum age: N/A
healthy volunteers: No
last name: Candice Roufosse, MD PhD
role: Principal Investigator
affiliation: Imperial College London
last name: Candice Roufosse, MD PhD
phone: 02033138240
email: c.roufosse@imperial.ac.uk
responsible party type: Sponsor
mesh term: Rituximab
sharing ipd: No
provided document: 