<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/NCT03018925</url>
  </required_header>
  <id_info>
    <org_study_id>IISP 53713</org_study_id>
    <nct_id>NCT03018925</nct_id>
  </id_info>
  <brief_title>Golimumab Effect in the Modulation of Gut Microbiota in Ulcerative Colitis</brief_title>
  <acronym>GOLI2015</acronym>
  <official_title>Golimumab Effect in the Modulation of Gut Microbiota in Ulcerative Colitis: Pilot Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      This study aims to characterize the changes on intestinal microbiota pattern associated with&#xD;
      the use of golimumab in order to determine if intestinal microbiota markers may correlate&#xD;
      with golimumab therapeutic effect in patients naïve &amp; non-naïve to anti-TNF treatment&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Golimumab effect in the modulation of gut microbiota in Ulcerative Colitis. Pilot Study.&#xD;
&#xD;
        1. BACKGROUND Recently, a consistent body of evidence indicates that the gut microbiota&#xD;
           ability to modulate and direct the host immune response is considered one of the pivotal&#xD;
           factors that modulate the delicate balance between health and disease in digestive&#xD;
           diseases. In the case of inflammatory bowel disease (IBD), the gut microbiota has a&#xD;
           central role in maintaining the microbiological homeostasis of the patient. Recent&#xD;
           studies describe the existence of different patterns of intestinal microbiota from&#xD;
           patients with IBD and healthy individuals . The alteration of the microbiological&#xD;
           pattern not associated with disease is known as dysbiosis and is directly related to the&#xD;
           degree of inflammation of the intestinal mucosa, and, thus, the clinic status: a greater&#xD;
           dysbiosis, greater clinical correlation with disease state, so that the gut microbiota&#xD;
           is a direct marker of the state of colonic inflammation .&#xD;
&#xD;
      The state of dysbiosis is measured through different microbiological indices of the total&#xD;
      population of bacteria in the colon. Changes in rates of these indices are the parameters&#xD;
      that are measured to characterize the gut microbiota of the patients. In the case of&#xD;
      ulcerative colitis (UC) studies have shown that intestinal microbiota is a central factor in&#xD;
      maintaining the balance between deep remission and the presence of a flare-up. Despite&#xD;
      advances, in-depth knowledge of how the gut microbiota interacts with the intestinal mucosa&#xD;
      and alters the intestinal barrier and the molecular/genetic mechanism is still unknown. Some&#xD;
      genes and associated cofactors have been identified but the exact mechanism of interaction&#xD;
      has not yet been described. The data indicate that, presumably, the mechanism of regulation&#xD;
      is a multifactorial process.&#xD;
&#xD;
      In this way, a deep understanding of gut microbiota and its interactions with the host and&#xD;
      the colon will be of great interest, in order to shape the future of IBD treatment,&#xD;
      especially of UC. Thus, IBD patients' intestinal microbiota could be either a therapeutic&#xD;
      target itself (fecal transplantation) or be consider as an adjustable adjuvant with&#xD;
      immunomodulators The group of anti-TNF drugs are the most recently incorporated in the&#xD;
      therapeutic arsenal of UC, in some cases changing the natural curse of the disease. However,&#xD;
      it is still unknown how these drugs modulate the intestinal microbiota and how they interfere&#xD;
      with it, although, probably, they develop a role. Recent results from studies by our group,&#xD;
      indicate that on entering clinical remission, gut microbiota is modified to patterns less&#xD;
      related with dysbiosis. Given the increasing importance of the use of anti-TNF drugs, it is&#xD;
      of great interest to discriminate between the patterns associated with dysbiosis and those&#xD;
      related with healthy mucosa, and how they are modified as a result of the use of anti-TNF&#xD;
      drugs. In this way, previous results of our group analyzing the changes of intestinal&#xD;
      microbiota associated with Adalimumab anti-TNF drug treatment have shown that during the&#xD;
      progression of the patient into remission, the mucosal dysbiosis pattern changes. On the&#xD;
      other hand, our group has also observed that after drug treatment failure, the gut microbiota&#xD;
      returns to a pattern closer to dysbiosis. For that reason, gut microbiota could be considered&#xD;
      as an excellent indicator of the real drug effectiveness in the patient.&#xD;
&#xD;
      Regarding Golimumab, a recently introduced anti-TNF drug therapy in UC, it is still unknown&#xD;
      how it is able to modulate the intestinal microbiota to remission-related patterns, since to&#xD;
      date there are not available studies about the relationship between Golimumab and this&#xD;
      phenomenon.&#xD;
&#xD;
      The use of prebiotics and probiotics has shown some effectiveness as adjuvants in the&#xD;
      treatment of UC . For that reason, further characterization of the gut microbiota patterns is&#xD;
      very important to develop new strategies for adjuvant ability to modulate it, especially in&#xD;
      patients receiving anti-TNF drugs and do not achieve complete remission. Similarly to recent&#xD;
      studies, we suggest that the modulation of gut microbiota could optimize the response&#xD;
      outcomes in patients treated with Golimumab.&#xD;
&#xD;
      In conclusion, based on current trends in the literature, we suggest that modulation of the&#xD;
      intestinal microbiota and the characterization of remission-related patterns, will have a&#xD;
      huge impact on the management of patients with UC. Moreover, the modulation of gut microbiota&#xD;
      together with the anti-TNF drug effectiveness could be the most promising field in the&#xD;
      management of inflammatory bowel disease.&#xD;
&#xD;
      2. HYPOTHESIS AND OBJECTIVES 2.1 Hypothesis&#xD;
&#xD;
        1. Intestinal microbiota profile change according to UC activity.&#xD;
&#xD;
        2. The Intestinal microbiota profile correlated to clinical remission is represented by&#xD;
           stable intestinal microbiota biodiversity.&#xD;
&#xD;
        3. Assess if Intestinal Microbiota is a useful tool to measure Golimumab effectiveness in&#xD;
           patients naïve to anti-TNF treatment and patients recurrent to anti-TNF treatment.&#xD;
&#xD;
      2.2 Objectives&#xD;
&#xD;
        1. To correlate clinical remission under Golimumab treatment with normal intestinal&#xD;
           microbiota profile biodiversity&#xD;
&#xD;
        2. To characterize the pattern of intestinal microbiota associated with the use of&#xD;
           Golimumab and temporal dynamics of microbial change.&#xD;
&#xD;
        3. Assess the effect of golimumab on the degree of colic dysbiosis in the treatment of&#xD;
           Ulcerative Colitis naive to Golimumab.&#xD;
&#xD;
      3. MATERIAL AND METHODS 3.1 Type of study/design Multicenter transversal pilot Study 3.2&#xD;
      Study population The proposed study will include 15 UC anti-TNF naïve patients from Hospital&#xD;
      Universitari Dr. Josep Trueta. Investigators will consider remission when patients have an&#xD;
      endoscopic Mayo score ≤1, and activity index score, Mayo= 0 points.&#xD;
&#xD;
      3.3 Interventions NOTE: &quot;The assignment of a patient to a particular therapeutic strategy is&#xD;
      not predetermined in advance by the study protocol, instead clinical practice. Plus, the&#xD;
      decision to prescribe a particular drug is clearly unlinked from the decision to include the&#xD;
      patient in the study. Patients will not undergo any intervention, whether diagnostic or&#xD;
      monitoring, other than the usual clinical practice, and epidemiological methods shall be used&#xD;
      for the analysis of the data collected&quot;&#xD;
&#xD;
      3.3.1 Treatments GOLIMUMAB induction with 200mg at week 0, and 100mg at week 2. Under 80kg,&#xD;
      the follow up treatment will be 50mg/month, and 100mg/month in patients over 80kg, as&#xD;
      clinical practice.&#xD;
&#xD;
      3.4 Variables 3.4.1 Demographic variables&#xD;
&#xD;
        -  Age expressed in years (y)&#xD;
&#xD;
        -  Sex Male (M)/ Female (F)&#xD;
&#xD;
        -  Body Mass Index&#xD;
&#xD;
        -  Ethnic: Caucasian/ African/ Asiatic/ American&#xD;
&#xD;
        -  Tobacco: YES / NO / EX-Smoker&#xD;
&#xD;
        -  Age of UC diagnosis expressed in years (y)&#xD;
&#xD;
        -  Familiar Antecedents (YES/NO) which kind of disease?&#xD;
&#xD;
        -  Localization Left (L)/ Righ (R) / extended (E) 3.4.2 Clinical variables&#xD;
&#xD;
        -  Mayo clinic score (Clinical colitis activity index) (Schroeder KW et al. NEngl J Med&#xD;
           1987; 317: 1625-9). Score range: 0-9 points&#xD;
&#xD;
        -  CRP (C-reactive protein): blood C-reactive protein concentration (mg/L)&#xD;
&#xD;
        -  Faecal calprotectin: stool sample expressed in concentration of calprotectin in ug for g&#xD;
           of feces (ug/g)&#xD;
&#xD;
        -  Standard Analysis:&#xD;
&#xD;
      Hemoglobin: blood hemoglobin concentration (g / dl) Platelets: blood platelets concentration&#xD;
      (x103/ ul) Leucocytes: blood leucocytes concentration (x103/ ul) Albumin: blood albumin&#xD;
      concentration (g / dl) Creatinine levels: blood creatinine concentration (mg/ dl) 3.4.3&#xD;
      Endoscopic variables • Mayo (Endoscopic score of Ulcerative Colitis) (Schroeder KW et al.&#xD;
      1987). Mayo Endoscopic Score is based only on Endoscopic Findings. Mayo Score range from 0 to&#xD;
      3.&#xD;
&#xD;
      o Note: Investigators will send the image to a centralized digital platform, where Mayo score&#xD;
      will be assessed by 2 independent professionals in order to minimize inter-observer bias.&#xD;
&#xD;
      3.4.4 Microbiological variables&#xD;
&#xD;
        -  Operational Taxonomic Units (OTUs)&#xD;
&#xD;
        -  Abundance and bacterial load. 3.5 Methods The proposed study will include 15 UC patients&#xD;
           over 18 years with informed consent signed, under treatment with anti-TNF according to&#xD;
           clinical practice. All of them will have been screened for opportunistic infections.&#xD;
           Patients will be anti-TNF naïve patients.&#xD;
&#xD;
      Stool samples will be collected before starting Anti-TNF treatment (A), and at weeks 4 (B), 9&#xD;
      (C), 13 (D), 26 (E), 39 (F), and 52 (G) to complete the study. The monitoring period will be&#xD;
      one year. Investigators will collect demographic variables (age, sex, tobacco, age of&#xD;
      diagnosis, localization,…), clinical data (partial Mayo clinical score, CRP, albumin,&#xD;
      hemoglobin, creatinine, leucocytes, platelets…), and microbiological variables at A, B, C, D,&#xD;
      E, F and G. Also, 7 calprotectin sampling (at weeks 4 (B), 9 (C), 13 (D), 26 (E), 39 (F), and&#xD;
      52 (G) as a clinical practice) and Mayo endoscopic index (at baseline and at the end of the&#xD;
      monitoring period as a clinical practice). Follow-up visits will also take place within&#xD;
      routine clinical practice. For better follow-up the evolution of the patient, investigators&#xD;
      will perform additional tests included in routine clinical practice as a rectosigmoidoscopy&#xD;
      at week 12 after starting Anti-TNF treatment.&#xD;
&#xD;
      Investigators will consider remission when patients have an endoscopic Mayo score ≤1, and&#xD;
      activity index score, Mayo clinical score =0 points.&#xD;
&#xD;
      Moreover, depending on the evolution of the patient, additional tests will also be performed&#xD;
      as routine clinical protocol during the monitoring period.&#xD;
&#xD;
      NOTE: Any test performed during the study and / or additional testing is routine clinical&#xD;
      practice according to clinical judgment and criteria of the physician.&#xD;
&#xD;
      3.5.1 Sample processing&#xD;
&#xD;
      DNA Extraction:&#xD;
&#xD;
      Before microbiological analyses, genomic DNA of 16s RNA gene will be extracted using&#xD;
      NucleoSpin® Soil Kit (Machery-Nagel GmbH &amp; Co., Germany). DNA concentration will be&#xD;
      determined with Qubit® BR (Invitrogen) Kit.&#xD;
&#xD;
      Bacterial 16S rRNA Gene Amplification by Pyrosequencing For pyrosequencing purposes, the 16S&#xD;
      rRNA gene was partially amplified from extracted genomic DNA using the universal bacterial&#xD;
      primers GC-357F 5'- CGCCCG CCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCC- CCT ACG GGA GGC AGC AG-3'&#xD;
      (341Y357) and 907R 5'-CCG TCA ATT CCT TTG AGT TT-3' (907Y926). PCR was performed with a&#xD;
      GeneAmp PCR System ® 2700 (AppliedBiosystems)). Then we purify the PCR product with kit&#xD;
      Ampure (Agencourt AMPure, Beckman Coulter Inc), and quantify the PCR products with Qubit® BR&#xD;
      (Invitrogen) Kit. The pyrosequencing will be performed with a 454 Life Genome Sciences&#xD;
      Sequencer FLX.&#xD;
&#xD;
      Sequence Editing and Analysis High-quality consensus sequences will be obtained and manually&#xD;
      refined with the Bioedit software package. Alignments were carried out with ClustalW24&#xD;
      software. Consensus sequences were compared with those in GenBank and the Ribosomal Database&#xD;
      Project by using BLASTN 2.2.10. Sequences will be grouped by number of operational taxonomic&#xD;
      units or phylotypes with the DOTUR program26 using the farthest neighbor method at a&#xD;
      precision level of 0.01, i.e., 99% minimum similarity for any pair of sequences to belong to&#xD;
      the same phylotype, on a distance matrix with the Jukes-Cantor correction calculated with the&#xD;
      DNADIST program of the Phylip software package.&#xD;
&#xD;
      3.6 Statistical analysis Statistical analysis will be performed with the SPSSx version 11.0.&#xD;
      Significance of distances between groups was checked using an analysis of variance. Pearson_s&#xD;
      x2 test was used to compare the prevalence of genus and species.&#xD;
&#xD;
      Clinical and laboratory data will be correlated with the values of quantitative microbial&#xD;
      indices using Receiver Operating Characteristic (ROC) curves.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>October 2016</start_date>
  <completion_date type="Actual">October 2020</completion_date>
  <primary_completion_date type="Actual">March 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Golimumab Induced Shifts in the Abundance of Bacterial Markers</measure>
    <time_frame>Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52</time_frame>
    <description>Shifts of abundance of Eubacteria (EUB), A. municiphila (AKK), M. smithii (MSM), Bacteroidetes (BAC), Ruminococcus sp. (RUM), F. prausnitzii (FPRA) and E. coli (ECO) levels will be measured prior (week 0) and progressively all through golimumab therapy until week 54. Results will be expressed as 16S gene copies of microbes per gram of faeces.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Golimumab Induced Shifts in Calprotectin Faecal Sample Levels</measure>
    <time_frame>Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52</time_frame>
    <description>Shifts on calprotectin levels will be measured prior (week 0) and progressively all through golimumab therapy until week 52. Results will be expressed as microgram of calprotectin per gram of faeces</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Golimumab Induced Shifts in Clinical Response Based on Partial Mayo Score</measure>
    <time_frame>Baseline (week 0) and week 52</time_frame>
    <description>Change from baseline (week 0) in the Partial Mayo score at week 52 of Golimumab treatment.&#xD;
The clinical Mayo Score or partial Mayo Score (PMS) uses the three non-invasive components of the full Mayo Score (stool frequency, rectal bleeding and physician's global assessement), and thus excluding the endoscopic findings scoring. Maximum score values range from 0 to 9 [Schroeder et al 2005 NEJM; Rutgeerts et al 2005 NEJM]. Calculations are based on the sum of scores for the three parameters. Values ranging: &lt;2, disease in clinical remission; 2-4, mild activity; 5-7, moderate activity; and &gt;7 severe activity.</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">15</enrollment>
  <condition>Ulcerative Colitis</condition>
  <arm_group>
    <arm_group_label>Ulcerative Colitis</arm_group_label>
    <description>The proposed study will include 15 UC anti-TNF naïve patients . We will consider remission when patients have an endoscopic Mayo score ≤1, and activity index score, Mayo= 0 points.&#xD;
Stool samples will be collected before starting Anti-TNF treatment (M0), and then every 3 months (M1, M2, M3 and M4) to complete the study.&#xD;
GOLIMUMAB induction with 200mg at week 0, and 100mg at week 2. Under 70kg, the follow up treatment will be 50mg/month, and 100mg/month in patients over 70kg, as clinical practice.</description>
  </arm_group>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Golimumab</intervention_name>
    <description>The treatment followed was an induction of 200 mg of Golimumab at week 0, and 100 mg at week 2. Under 70 Kg, the follow-up treatment dose would be 50 mg/month, and 100 mg/month in patients above 70 Kg asindicated in standard clinical practice.</description>
    <arm_group_label>Ulcerative Colitis</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      STOOL SAMPLE&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        The proposed study will include 15 UC patients over 18 years, that have signed the informed&#xD;
        consent, subjected to treatment with anti-TNF by clinical practice and who have made&#xD;
        screening for opportunistic infections. Patients will be anti-TNF naïve patients.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  &gt; 18 years&#xD;
&#xD;
          -  Signed the informed consent&#xD;
&#xD;
          -  Anti-TNF naïve&#xD;
&#xD;
          -  Screening for opportunistic infections&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Active tuberculosis or another chronic infections&#xD;
&#xD;
          -  Antibiotic treatment prior 1 month&#xD;
&#xD;
          -  Probiotics &amp; Prebiotics&#xD;
&#xD;
          -  Gestation and lactation&#xD;
&#xD;
          -  Heart disfunction&#xD;
&#xD;
          -  Colectomy&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Xavier XA Aldeguer, Dr.</last_name>
    <role>Principal Investigator</role>
    <affiliation>Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Hospital Doctor Josep Trueta</name>
      <address>
        <city>Girona</city>
        <state>Catalonia</state>
        <zip>17007</zip>
        <country>Spain</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Spain</country>
  </location_countries>
  <verification_date>June 2021</verification_date>
  <study_first_submitted>December 13, 2016</study_first_submitted>
  <study_first_submitted_qc>January 10, 2017</study_first_submitted_qc>
  <study_first_posted type="Estimate">January 12, 2017</study_first_posted>
  <results_first_submitted>October 21, 2020</results_first_submitted>
  <results_first_submitted_qc>June 28, 2021</results_first_submitted_qc>
  <results_first_posted type="Actual">July 19, 2021</results_first_posted>
  <last_update_submitted>June 28, 2021</last_update_submitted>
  <last_update_submitted_qc>June 28, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">July 19, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta</investigator_affiliation>
    <investigator_full_name>Xavier Aldeguer</investigator_full_name>
    <investigator_title>Head of Gastroenterology Department</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Colitis</mesh_term>
    <mesh_term>Colitis, Ulcerative</mesh_term>
    <mesh_term>Ulcer</mesh_term>
  </condition_browse>
  <intervention_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Golimumab</mesh_term>
  </intervention_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol and Statistical Analysis Plan</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>November 10, 2015</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/25/NCT03018925/Prot_SAP_000.pdf</document_url>
    </provided_document>
  </provided_document_section>

  <clinical_results>

    <participant_flow>
      <group_list>
        <group group_id="P1">
          <title>Ulcerative Colitis</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected before starting anti-TNFα treatment (A), 4 weeks (B), 2 months after (C), 3 months (D), 6 months (E), 9 months (F) and finally 1 year after starting treatment (G). The monitoring period was one year long.&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
      </group_list>
      <period_list>
        <period>
          <title>Overall Study</title>
          <milestone_list>
            <milestone>
              <title>STARTED</title>
              <participants_list>
                <participants group_id="P1" count="15"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="15"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>NOT COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="0"/>
              </participants_list>
            </milestone>
          </milestone_list>
        </period>
      </period_list>
    </participant_flow>

    <baseline>
      <population>Age baseline: &gt;=18 years</population>
      <group_list>
        <group group_id="B1">
          <title>Ulcerative Colitis</title>
          <description>The proposed study will include 15 UC anti-TNF naïve patients . We will consider remission when patients have an endoscopic Mayo score ≤1, and activity index score, Mayo= 0 points.&#xD;
Stool samples will be collected before starting Anti-TNF treatment (M0), and then every 3 months (M1, M2, M3 and M4) to complete the study.&#xD;
GOLIMUMAB induction with 200mg at week 0, and 100mg at week 2. Under 70kg, the follow up treatment will be 50mg/month, and 100mg/month in patients over 70kg, as clinical practice.&#xD;
Golimumab</description>
        </group>
      </group_list>
      <analyzed_list>
        <analyzed>
          <units>Participants</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="15"/>
          </count_list>
        </analyzed>
        <analyzed>
          <units>faeces</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="105"/>
          </count_list>
        </analyzed>
      </analyzed_list>
      <measure_list>
        <measure>
          <title>Age</title>
          <units>years</units>
          <param>Mean</param>
          <dispersion>Full Range</dispersion>
          <units_analyzed>Participants</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="15"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>faeces</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="105"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="42" lower_limit="18" upper_limit="99"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Sex: Female, Male</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <units_analyzed>Participants</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="15"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>faeces</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="105"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Female</title>
                  <measurement_list>
                    <measurement group_id="B1" value="8"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Male</title>
                  <measurement_list>
                    <measurement group_id="B1" value="7"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Race and Ethnicity Not Collected</title>
          <population>Race and Ethnicity were not collected from any participant.</population>
          <units>Participants</units>
          <param>Count of Participants</param>
          <units_analyzed>Participants</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="0"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>faeces</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="105"/>
              </count_list>
            </analyzed>
          </analyzed_list>
        </measure>
        <measure>
          <title>Abundance</title>
          <description>Total abundance (16S gene copies per gram of faeces) for each analysed gene marker (EUB, FPRA, ECO, PHG-I, PHG-II, AKK, BAC, RUM, and MSM)</description>
          <units>16S gene copies per gram of faeces</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <units_analyzed>faeces</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="15"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>faeces</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="105"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <title>Total abundance of EUB 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="44895329" spread="60712475"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of FPRA 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="632588897" spread="973329718"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of ECO 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="13920879614" spread="23430581289"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of PHG-I 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="5595001683" spread="15764400311"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of PHG-II 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="13444198425" spread="45637222044"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of AKK 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="1137084734" spread="983517092"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of BAC 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="72930687" spread="119110706"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of RUM 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="266628585" spread="670089788"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Total abundance of MSM 16S gene copies per gram of faeces</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="1694557243" spread="2573008595"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>faecal calprotectin</title>
          <description>calprotectin values (microgram of faecal calprotectin per gram of faeces)</description>
          <units>microgram calprotectin per gram faeces</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <units_analyzed>faeces</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="15"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>faeces</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="B1" value="105"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="959.52" spread="781.41"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </measure_list>
    </baseline>

    <outcome_list>
      <outcome>
        <type>Primary</type>
        <title>Golimumab Induced Shifts in the Abundance of Bacterial Markers</title>
        <description>Shifts of abundance of Eubacteria (EUB), A. municiphila (AKK), M. smithii (MSM), Bacteroidetes (BAC), Ruminococcus sp. (RUM), F. prausnitzii (FPRA) and E. coli (ECO) levels will be measured prior (week 0) and progressively all through golimumab therapy until week 54. Results will be expressed as 16S gene copies of microbes per gram of faeces.</description>
        <time_frame>Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Ulcerative Colitis Baseline</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected before starting anti-TNFα treatment (A).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O2">
            <title>Ulcerative Colitis Week 4</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 4 after treatment (B).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O3">
            <title>Ulcerative Colitis Week 9</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 9 after (C) starting treatment.&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O4">
            <title>Ulcerative Colitis Week 13</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 13 after starting treatment (D).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O5">
            <title>Ulcerative Colitis Week 26</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 24 after starting treatment (E).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O6">
            <title>Ulcerative Colitis Week 39</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 39 after starting treatment (F).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O7">
            <title>Ulcerative Colitis Week 52</title>
            <description>A total of 15 patients were included in the study. Stool samples were collected at week 52 after starting treatment.&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
        </group_list>
        <measure>
          <title>Golimumab Induced Shifts in the Abundance of Bacterial Markers</title>
          <description>Shifts of abundance of Eubacteria (EUB), A. municiphila (AKK), M. smithii (MSM), Bacteroidetes (BAC), Ruminococcus sp. (RUM), F. prausnitzii (FPRA) and E. coli (ECO) levels will be measured prior (week 0) and progressively all through golimumab therapy until week 54. Results will be expressed as 16S gene copies of microbes per gram of faeces.</description>
          <units>16S gene copies per gram of faeces</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="15"/>
                <count group_id="O2" value="15"/>
                <count group_id="O3" value="15"/>
                <count group_id="O4" value="15"/>
                <count group_id="O5" value="15"/>
                <count group_id="O6" value="15"/>
                <count group_id="O7" value="15"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <title>16S EUB gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="14965109899" spread="20237491739"/>
                    <measurement group_id="O2" value="132857054401767" spread="113260468624418"/>
                    <measurement group_id="O3" value="12779328208" spread="9068076455"/>
                    <measurement group_id="O4" value="12851286145" spread="9017866924"/>
                    <measurement group_id="O5" value="12454690659" spread="12454690659854"/>
                    <measurement group_id="O6" value="12503490379" spread="8532885467"/>
                    <measurement group_id="O7" value="12813434576" spread="10526337966"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S FPRA gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="175719138207" spread="270369366223"/>
                    <measurement group_id="O2" value="170223467854756" spread="236215626215223"/>
                    <measurement group_id="O3" value="155540451664" spread="189790530774"/>
                    <measurement group_id="O4" value="156132981396" spread="89588898844"/>
                    <measurement group_id="O5" value="147240545450" spread="178731186533"/>
                    <measurement group_id="O6" value="147465572493" spread="178661245731"/>
                    <measurement group_id="O7" value="147013558563" spread="220645931988"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S ECO gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3314495146263" spread="5578709830918"/>
                    <measurement group_id="O2" value="3181151220276" spread="3126128990251"/>
                    <measurement group_id="O3" value="2987333503143" spread="3353864671731"/>
                    <measurement group_id="O4" value="2953273068761" spread="3369672897008"/>
                    <measurement group_id="O5" value="2670971592149" spread="3245749806014"/>
                    <measurement group_id="O6" value="2676569979573" spread="3242227933303"/>
                    <measurement group_id="O7" value="2862128876536" spread="3694099594126"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S PHG-1 gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="1554167134258" spread="4379000086392"/>
                    <measurement group_id="O2" value="2287342976506" spread="5800677622879"/>
                    <measurement group_id="O3" value="1443246127037" spread="4465319951431"/>
                    <measurement group_id="O4" value="1454728431339" spread="4462926477230"/>
                    <measurement group_id="O5" value="1486551880693" spread="4369653984051"/>
                    <measurement group_id="O6" value="1484947845315" spread="4370115404158"/>
                    <measurement group_id="O7" value="2264764764378" spread="5588585629983"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S PHG-2 gene copiese/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3734499562508" spread="12677006123368"/>
                    <measurement group_id="O2" value="4224010739381" spread="15618887473109"/>
                    <measurement group_id="O3" value="2856262666851" spread="11831601059514"/>
                    <measurement group_id="O4" value="2850757893262" spread="11832638079600"/>
                    <measurement group_id="O5" value="3374769646848" spread="12473113644843"/>
                    <measurement group_id="O6" value="3377322959435" spread="12472534823110"/>
                    <measurement group_id="O7" value="4956971378048" spread="16148476082404"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S AKK gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="631713741289" spread="546398384696"/>
                    <measurement group_id="O2" value="676730972613383" spread="619061596613631"/>
                    <measurement group_id="O3" value="648849626045" spread="560073798388"/>
                    <measurement group_id="O4" value="656752409224" spread="555980667881"/>
                    <measurement group_id="O5" value="631530294495" spread="541410081002"/>
                    <measurement group_id="O6" value="624464830535" spread="541709460521"/>
                    <measurement group_id="O7" value="588975881273" spread="573284067068"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S RUM gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="177752390157" spread="446726525618"/>
                    <measurement group_id="O2" value="875769282253977" spread="107172042366984"/>
                    <measurement group_id="O3" value="94665231220" spread="103802651252"/>
                    <measurement group_id="O4" value="89460668888" spread="98352969991"/>
                    <measurement group_id="O5" value="175237205610" spread="443046731406"/>
                    <measurement group_id="O6" value="140434420342" spread="366893347216"/>
                    <measurement group_id="O7" value="183217036465" spread="470624954343"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S MSM gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="1412131035956" spread="2144173829971"/>
                    <measurement group_id="O2" value="1448338957715" spread="103802651252363"/>
                    <measurement group_id="O3" value="1154472147552" spread="2014237593970"/>
                    <measurement group_id="O4" value="1324144197647" spread="2030070772991"/>
                    <measurement group_id="O5" value="1419822238221" spread="2125371379113"/>
                    <measurement group_id="O6" value="1378914738249" spread="2079724959914"/>
                    <measurement group_id="O7" value="1354846963287" spread="2528371614749"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>16S BAC gene copies/gr</title>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="24310229086" spread="39703568898"/>
                    <measurement group_id="O2" value="1154472147552" spread="2014237593970"/>
                    <measurement group_id="O3" value="25492065263" spread="41712078985"/>
                    <measurement group_id="O4" value="25406506889" spread="41440365839"/>
                    <measurement group_id="O5" value="24041089844" spread="39392492447"/>
                    <measurement group_id="O6" value="24145355533" spread="39357796954"/>
                    <measurement group_id="O7" value="26282792708" spread="45453286920"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Golimumab Induced Shifts in Calprotectin Faecal Sample Levels</title>
        <description>Shifts on calprotectin levels will be measured prior (week 0) and progressively all through golimumab therapy until week 52. Results will be expressed as microgram of calprotectin per gram of faeces</description>
        <time_frame>Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Ulcerative Colitis Baseline</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected before starting anti-TNFα treatment (A).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O2">
            <title>Ulcerative Colitis Week 4</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 4 after starting treatment (B).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O3">
            <title>Ulcerative Colitis Week 9</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 9 after starting treatment (C).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O4">
            <title>Ulcerative Colitis Week 13</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 13 after starting treatment (D).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O5">
            <title>Ulcerative Colitis Week 26</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 26 after starting treatment (E).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O6">
            <title>Ulcerative Colitis Week 39</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 39 after starting treatment (F).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
          <group group_id="O7">
            <title>Ulcerative Colitis Week 52</title>
            <description>A total of 9 patients were included in the study. Stool samples were collected at week 52 after starting treatment (G).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
          </group>
        </group_list>
        <measure>
          <title>Golimumab Induced Shifts in Calprotectin Faecal Sample Levels</title>
          <description>Shifts on calprotectin levels will be measured prior (week 0) and progressively all through golimumab therapy until week 52. Results will be expressed as microgram of calprotectin per gram of faeces</description>
          <units>mg/g</units>
          <param>Mean</param>
          <dispersion>Standard Deviation</dispersion>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="9"/>
                <count group_id="O2" value="9"/>
                <count group_id="O3" value="9"/>
                <count group_id="O4" value="9"/>
                <count group_id="O5" value="9"/>
                <count group_id="O6" value="9"/>
                <count group_id="O7" value="9"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="1379.78" spread="761.52"/>
                    <measurement group_id="O2" value="626.79" spread="800.30"/>
                    <measurement group_id="O3" value="1267.50" spread="811.18"/>
                    <measurement group_id="O4" value="1036.68" spread="883.24"/>
                    <measurement group_id="O5" value="994.39" spread="714.60"/>
                    <measurement group_id="O6" value="545.64" spread="635.46"/>
                    <measurement group_id="O7" value="478.50" spread="881.02"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Golimumab Induced Shifts in Clinical Response Based on Partial Mayo Score</title>
        <description>Change from baseline (week 0) in the Partial Mayo score at week 52 of Golimumab treatment.&#xD;
The clinical Mayo Score or partial Mayo Score (PMS) uses the three non-invasive components of the full Mayo Score (stool frequency, rectal bleeding and physician's global assessement), and thus excluding the endoscopic findings scoring. Maximum score values range from 0 to 9 [Schroeder et al 2005 NEJM; Rutgeerts et al 2005 NEJM]. Calculations are based on the sum of scores for the three parameters. Values ranging: &lt;2, disease in clinical remission; 2-4, mild activity; 5-7, moderate activity; and &gt;7 severe activity.</description>
        <time_frame>Baseline (week 0) and week 52</time_frame>
        <population>Baseline (week 0) and week 52</population>
        <group_list>
          <group group_id="O1">
            <title>Partial Mayo Score Baseline</title>
            <description>A total of 15 patients were included in the study. Partial Mayo scores were collected from patients before starting Golimumab treatment (week 0)</description>
          </group>
          <group group_id="O2">
            <title>Partial Mayo Score at Week 52</title>
            <description>A total of 15 patients were included in the study. Partial Mayo scores were collected from patients at week 52 (G) after starting Golimumab treatment.</description>
          </group>
        </group_list>
        <measure>
          <title>Golimumab Induced Shifts in Clinical Response Based on Partial Mayo Score</title>
          <description>Change from baseline (week 0) in the Partial Mayo score at week 52 of Golimumab treatment.&#xD;
The clinical Mayo Score or partial Mayo Score (PMS) uses the three non-invasive components of the full Mayo Score (stool frequency, rectal bleeding and physician's global assessement), and thus excluding the endoscopic findings scoring. Maximum score values range from 0 to 9 [Schroeder et al 2005 NEJM; Rutgeerts et al 2005 NEJM]. Calculations are based on the sum of scores for the three parameters. Values ranging: &lt;2, disease in clinical remission; 2-4, mild activity; 5-7, moderate activity; and &gt;7 severe activity.</description>
          <population>Baseline (week 0) and week 52</population>
          <units>score on a scale</units>
          <param>Number</param>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="15"/>
                <count group_id="O2" value="15"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <title>Patient 1</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="2"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 2</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="9"/>
                    <measurement group_id="O2" value="5"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 3</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="2"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 4</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="0"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 5</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 6</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="6"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 7</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="5"/>
                    <measurement group_id="O2" value="2"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 8</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="7"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 9</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="8"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 10</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="2"/>
                    <measurement group_id="O2" value="1"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 11</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 12</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="15"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="2"/>
                    <measurement group_id="O2" value="5"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 13</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3"/>
                    <measurement group_id="O2" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 14</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="3"/>
                    <measurement group_id="O2" value="1"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
            <class>
              <title>Patient 15</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="O1" value="1"/>
                    <count group_id="O2" value="1"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="O1" value="4"/>
                    <measurement group_id="O2" value="1"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </outcome>
    </outcome_list>

    <reported_events>
      <time_frame>1 year</time_frame>
      <desc>not different to clinicaltrials.gov definitions</desc>
      <group_list>
        <group group_id="E1">
          <title>Ulcerative Colitis Baseline</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected before starting anti-TNFα treatment (A).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E2">
          <title>Ulcerative Colitis Week 4</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 4 after starting treatment (B).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E3">
          <title>Ulcerative Colitis Week 9</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 8 after starting treatment (C).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E4">
          <title>Ulcerative Colitis Week 13</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 13 after starting treatment (D).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E5">
          <title>Ulcerative Colitis Week 26</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 26 after starting treatment (E).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E6">
          <title>Ulcerative Colitis Week 39</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 39 after starting treatment (F).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
        <group group_id="E7">
          <title>Ulcerative Colitis Week 52</title>
          <description>A total of 15 patients were included in the study. Stool samples were collected at week 52 after starting treatment (G).&#xD;
Patients were classified as responders (responders), responder after dose optimization of anti-TNF treatment (respond after dose optimization) and non-responders (non-responders). Two different statistical analyses were done: one considering the group of responders, responder after dose optimization and non-responders, and another statistical analysis considering a unique group of responder and responders after dose optimization versus non-responders.&#xD;
Due to the fact that there are only 2 non-responders and a minimum number of 3 individuals (n= 3) is required to perform statistics, these 2 samples have been doubled for analysis.</description>
        </group>
      </group_list>
      <serious_events>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, all-cause mortality</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E3" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E4" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E5" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E6" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E7" subjects_affected="0" subjects_at_risk="15"/>
              </event>
              <event>
                <sub_title>Total, serious adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E3" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E4" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E5" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E6" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E7" subjects_affected="0" subjects_at_risk="15"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </serious_events>
      <other_events>
        <frequency_threshold>0</frequency_threshold>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, other adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E3" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E4" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E5" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E6" subjects_affected="0" subjects_at_risk="15"/>
                <counts group_id="E7" subjects_affected="0" subjects_at_risk="15"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </other_events>
    </reported_events>

    <certain_agreements>
      <pi_employee>Principal Investigators are NOT employed by the organization sponsoring the study.</pi_employee>
      <restrictive_agreement>There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI's rights to discuss or publish trial results after the trial is completed. </restrictive_agreement>
    </certain_agreements>
    <point_of_contact>
      <name_or_title>MD PhD X Aldeguer</name_or_title>
      <organization>Institut d'Investigació Biomèdica de Girona - Hosp Univ Dr Josep Trueta</organization>
      <phone>34972940200 ext 2401</phone>
      <email>xaldeguer.girona.ics@gencat.cat; abahi@idibgi.org</email>
    </point_of_contact>
  </clinical_results>
</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/NCT03018925
org study id: IISP 53713
nct id: NCT03018925
lead sponsor: 
has dmc: No
This study aims to characterize the changes on intestinal microbiota pattern associated with
      the use of golimumab in order to determine if intestinal microbiota markers may correlate
      with golimumab therapeutic effect in patients naïve & non-naïve to anti-TNF treatment
Golimumab effect in the modulation of gut microbiota in Ulcerative Colitis. Pilot Study.

        1. BACKGROUND Recently, a consistent body of evidence indicates that the gut microbiota
           ability to modulate and direct the host immune response is considered one of the pivotal
           factors that modulate the delicate balance between health and disease in digestive
           diseases. In the case of inflammatory bowel disease (IBD), the gut microbiota has a
           central role in maintaining the microbiological homeostasis of the patient. Recent
           studies describe the existence of different patterns of intestinal microbiota from
           patients with IBD and healthy individuals . The alteration of the microbiological
           pattern not associated with disease is known as dysbiosis and is directly related to the
           degree of inflammation of the intestinal mucosa, and, thus, the clinic status: a greater
           dysbiosis, greater clinical correlation with disease state, so that the gut microbiota
           is a direct marker of the state of colonic inflammation .

      The state of dysbiosis is measured through different microbiological indices of the total
      population of bacteria in the colon. Changes in rates of these indices are the parameters
      that are measured to characterize the gut microbiota of the patients. In the case of
      ulcerative colitis (UC) studies have shown that intestinal microbiota is a central factor in
      maintaining the balance between deep remission and the presence of a flare-up. Despite
      advances, in-depth knowledge of how the gut microbiota interacts with the intestinal mucosa
      and alters the intestinal barrier and the molecular/genetic mechanism is still unknown. Some
      genes and associated cofactors have been identified but the exact mechanism of interaction
      has not yet been described. The data indicate that, presumably, the mechanism of regulation
      is a multifactorial process.

      In this way, a deep understanding of gut microbiota and its interactions with the host and
      the colon will be of great interest, in order to shape the future of IBD treatment,
      especially of UC. Thus, IBD patients' intestinal microbiota could be either a therapeutic
      target itself (fecal transplantation) or be consider as an adjustable adjuvant with
      immunomodulators The group of anti-TNF drugs are the most recently incorporated in the
      therapeutic arsenal of UC, in some cases changing the natural curse of the disease. However,
      it is still unknown how these drugs modulate the intestinal microbiota and how they interfere
      with it, although, probably, they develop a role. Recent results from studies by our group,
      indicate that on entering clinical remission, gut microbiota is modified to patterns less
      related with dysbiosis. Given the increasing importance of the use of anti-TNF drugs, it is
      of great interest to discriminate between the patterns associated with dysbiosis and those
      related with healthy mucosa, and how they are modified as a result of the use of anti-TNF
      drugs. In this way, previous results of our group analyzing the changes of intestinal
      microbiota associated with Adalimumab anti-TNF drug treatment have shown that during the
      progression of the patient into remission, the mucosal dysbiosis pattern changes. On the
      other hand, our group has also observed that after drug treatment failure, the gut microbiota
      returns to a pattern closer to dysbiosis. For that reason, gut microbiota could be considered
      as an excellent indicator of the real drug effectiveness in the patient.

      Regarding Golimumab, a recently introduced anti-TNF drug therapy in UC, it is still unknown
      how it is able to modulate the intestinal microbiota to remission-related patterns, since to
      date there are not available studies about the relationship between Golimumab and this
      phenomenon.

      The use of prebiotics and probiotics has shown some effectiveness as adjuvants in the
      treatment of UC . For that reason, further characterization of the gut microbiota patterns is
      very important to develop new strategies for adjuvant ability to modulate it, especially in
      patients receiving anti-TNF drugs and do not achieve complete remission. Similarly to recent
      studies, we suggest that the modulation of gut microbiota could optimize the response
      outcomes in patients treated with Golimumab.

      In conclusion, based on current trends in the literature, we suggest that modulation of the
      intestinal microbiota and the characterization of remission-related patterns, will have a
      huge impact on the management of patients with UC. Moreover, the modulation of gut microbiota
      together with the anti-TNF drug effectiveness could be the most promising field in the
      management of inflammatory bowel disease.

      2. HYPOTHESIS AND OBJECTIVES 2.1 Hypothesis

        1. Intestinal microbiota profile change according to UC activity.

        2. The Intestinal microbiota profile correlated to clinical remission is represented by
           stable intestinal microbiota biodiversity.

        3. Assess if Intestinal Microbiota is a useful tool to measure Golimumab effectiveness in
           patients naïve to anti-TNF treatment and patients recurrent to anti-TNF treatment.

      2.2 Objectives

        1. To correlate clinical remission under Golimumab treatment with normal intestinal
           microbiota profile biodiversity

        2. To characterize the pattern of intestinal microbiota associated with the use of
           Golimumab and temporal dynamics of microbial change.

        3. Assess the effect of golimumab on the degree of colic dysbiosis in the treatment of
           Ulcerative Colitis naive to Golimumab.

      3. MATERIAL AND METHODS 3.1 Type of study/design Multicenter transversal pilot Study 3.2
      Study population The proposed study will include 15 UC anti-TNF naïve patients from Hospital
      Universitari Dr. Josep Trueta. Investigators will consider remission when patients have an
      endoscopic Mayo score ≤1, and activity index score, Mayo= 0 points.

      3.3 Interventions NOTE: "The assignment of a patient to a particular therapeutic strategy is
      not predetermined in advance by the study protocol, instead clinical practice. Plus, the
      decision to prescribe a particular drug is clearly unlinked from the decision to include the
      patient in the study. Patients will not undergo any intervention, whether diagnostic or
      monitoring, other than the usual clinical practice, and epidemiological methods shall be used
      for the analysis of the data collected"

      3.3.1 Treatments GOLIMUMAB induction with 200mg at week 0, and 100mg at week 2. Under 80kg,
      the follow up treatment will be 50mg/month, and 100mg/month in patients over 80kg, as
      clinical practice.

      3.4 Variables 3.4.1 Demographic variables

        -  Age expressed in years (y)

        -  Sex Male (M)/ Female (F)

        -  Body Mass Index

        -  Ethnic: Caucasian/ African/ Asiatic/ American

        -  Tobacco: YES / NO / EX-Smoker

        -  Age of UC diagnosis expressed in years (y)

        -  Familiar Antecedents (YES/NO) which kind of disease?

        -  Localization Left (L)/ Righ (R) / extended (E) 3.4.2 Clinical variables

        -  Mayo clinic score (Clinical colitis activity index) (Schroeder KW et al. NEngl J Med
           1987; 317: 1625-9). Score range: 0-9 points

        -  CRP (C-reactive protein): blood C-reactive protein concentration (mg/L)

        -  Faecal calprotectin: stool sample expressed in concentration of calprotectin in ug for g
           of feces (ug/g)

        -  Standard Analysis:

      Hemoglobin: blood hemoglobin concentration (g / dl) Platelets: blood platelets concentration
      (x103/ ul) Leucocytes: blood leucocytes concentration (x103/ ul) Albumin: blood albumin
      concentration (g / dl) Creatinine levels: blood creatinine concentration (mg/ dl) 3.4.3
      Endoscopic variables • Mayo (Endoscopic score of Ulcerative Colitis) (Schroeder KW et al.
      1987). Mayo Endoscopic Score is based only on Endoscopic Findings. Mayo Score range from 0 to
      3.

      o Note: Investigators will send the image to a centralized digital platform, where Mayo score
      will be assessed by 2 independent professionals in order to minimize inter-observer bias.

      3.4.4 Microbiological variables

        -  Operational Taxonomic Units (OTUs)

        -  Abundance and bacterial load. 3.5 Methods The proposed study will include 15 UC patients
           over 18 years with informed consent signed, under treatment with anti-TNF according to
           clinical practice. All of them will have been screened for opportunistic infections.
           Patients will be anti-TNF naïve patients.

      Stool samples will be collected before starting Anti-TNF treatment (A), and at weeks 4 (B), 9
      (C), 13 (D), 26 (E), 39 (F), and 52 (G) to complete the study. The monitoring period will be
      one year. Investigators will collect demographic variables (age, sex, tobacco, age of
      diagnosis, localization,…), clinical data (partial Mayo clinical score, CRP, albumin,
      hemoglobin, creatinine, leucocytes, platelets…), and microbiological variables at A, B, C, D,
      E, F and G. Also, 7 calprotectin sampling (at weeks 4 (B), 9 (C), 13 (D), 26 (E), 39 (F), and
      52 (G) as a clinical practice) and Mayo endoscopic index (at baseline and at the end of the
      monitoring period as a clinical practice). Follow-up visits will also take place within
      routine clinical practice. For better follow-up the evolution of the patient, investigators
      will perform additional tests included in routine clinical practice as a rectosigmoidoscopy
      at week 12 after starting Anti-TNF treatment.

      Investigators will consider remission when patients have an endoscopic Mayo score ≤1, and
      activity index score, Mayo clinical score =0 points.

      Moreover, depending on the evolution of the patient, additional tests will also be performed
      as routine clinical protocol during the monitoring period.

      NOTE: Any test performed during the study and / or additional testing is routine clinical
      practice according to clinical judgment and criteria of the physician.

      3.5.1 Sample processing

      DNA Extraction:

      Before microbiological analyses, genomic DNA of 16s RNA gene will be extracted using
      NucleoSpin® Soil Kit (Machery-Nagel GmbH & Co., Germany). DNA concentration will be
      determined with Qubit® BR (Invitrogen) Kit.

      Bacterial 16S rRNA Gene Amplification by Pyrosequencing For pyrosequencing purposes, the 16S
      rRNA gene was partially amplified from extracted genomic DNA using the universal bacterial
      primers GC-357F 5'- CGCCCG CCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCC- CCT ACG GGA GGC AGC AG-3'
      (341Y357) and 907R 5'-CCG TCA ATT CCT TTG AGT TT-3' (907Y926). PCR was performed with a
      GeneAmp PCR System ® 2700 (AppliedBiosystems)). Then we purify the PCR product with kit
      Ampure (Agencourt AMPure, Beckman Coulter Inc), and quantify the PCR products with Qubit® BR
      (Invitrogen) Kit. The pyrosequencing will be performed with a 454 Life Genome Sciences
      Sequencer FLX.

      Sequence Editing and Analysis High-quality consensus sequences will be obtained and manually
      refined with the Bioedit software package. Alignments were carried out with ClustalW24
      software. Consensus sequences were compared with those in GenBank and the Ribosomal Database
      Project by using BLASTN 2.2.10. Sequences will be grouped by number of operational taxonomic
      units or phylotypes with the DOTUR program26 using the farthest neighbor method at a
      precision level of 0.01, i.e., 99% minimum similarity for any pair of sequences to belong to
      the same phylotype, on a distance matrix with the Jukes-Cantor correction calculated with the
      DNADIST program of the Phylip software package.

      3.6 Statistical analysis Statistical analysis will be performed with the SPSSx version 11.0.
      Significance of distances between groups was checked using an analysis of variance. Pearson_s
      x2 test was used to compare the prevalence of genus and species.

      Clinical and laboratory data will be correlated with the values of quantitative microbial
      indices using Receiver Operating Characteristic (ROC) curves.
observational model: Case-Only
time perspective: Prospective
measure: Golimumab Induced Shifts in the Abundance of Bacterial Markers
time frame: Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52
description: Shifts of abundance of Eubacteria (EUB), A. municiphila (AKK), M. smithii (MSM), Bacteroidetes (BAC), Ruminococcus sp. (RUM), F. prausnitzii (FPRA) and E. coli (ECO) levels will be measured prior (week 0) and progressively all through golimumab therapy until week 54. Results will be expressed as 16S gene copies of microbes per gram of faeces.
measure: Golimumab Induced Shifts in Calprotectin Faecal Sample Levels
time frame: Baseline (week 0); week 4; week 9; week 13; week 26; week 39; week 52
description: Shifts on calprotectin levels will be measured prior (week 0) and progressively all through golimumab therapy until week 52. Results will be expressed as microgram of calprotectin per gram of faeces
measure: Golimumab Induced Shifts in Clinical Response Based on Partial Mayo Score
time frame: Baseline (week 0) and week 52
description: Change from baseline (week 0) in the Partial Mayo score at week 52 of Golimumab treatment.
The clinical Mayo Score or partial Mayo Score (PMS) uses the three non-invasive components of the full Mayo Score (stool frequency, rectal bleeding and physician's global assessement), and thus excluding the endoscopic findings scoring. Maximum score values range from 0 to 9 [Schroeder et al 2005 NEJM; Rutgeerts et al 2005 NEJM]. Calculations are based on the sum of scores for the three parameters. Values ranging: <2, disease in clinical remission; 2-4, mild activity; 5-7, moderate activity; and >7 severe activity.
arm group label: Ulcerative Colitis
description: The proposed study will include 15 UC anti-TNF naïve patients . We will consider remission when patients have an endoscopic Mayo score ≤1, and activity index score, Mayo= 0 points.
Stool samples will be collected before starting Anti-TNF treatment (M0), and then every 3 months (M1, M2, M3 and M4) to complete the study.
GOLIMUMAB induction with 200mg at week 0, and 100mg at week 2. Under 70kg, the follow up treatment will be 50mg/month, and 100mg/month in patients over 70kg, as clinical practice.
intervention type: Drug
intervention name: Golimumab
description: The treatment followed was an induction of 200 mg of Golimumab at week 0, and 100 mg at week 2. Under 70 Kg, the follow-up treatment dose would be 50 mg/month, and 100 mg/month in patients above 70 Kg asindicated in standard clinical practice.
arm group label: Ulcerative Colitis
STOOL SAMPLE
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Xavier XA Aldeguer, Dr.
role: Principal Investigator
affiliation: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
facility: 
country: Spain
responsible party type: Principal Investigator
investigator affiliation: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
investigator full name: Xavier Aldeguer
investigator title: Head of Gastroenterology Department
mesh term: Colitis
mesh term: Colitis, Ulcerative
mesh term: Ulcer
mesh term: Golimumab
sharing ipd: No
provided document: 
participant flow: 
baseline: 
outcome list: 
reported events: 
certain agreements: 
point of contact: 