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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">87</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:A116C711-4C18-5A38-8F1E-5E97753A8A64</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Folia Medica</journal-title>
        <abbrev-journal-title xml:lang="en">FM</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0204-8043</issn>
      <issn pub-type="epub">1314-2143</issn>
      <publisher>
        <publisher-name>Plovdiv Medical University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/folmed.68.e170410</article-id>
      <article-id pub-id-type="publisher-id">170410</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Pharmacy</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Therapeutic drug monitoring of adalimumab for dose optimization during maintenance therapy in patients with ulcerative colitis: real-world data</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Mansur Kadhim</surname>
            <given-names>Ahmed</given-names>
          </name>
          <email xlink:type="simple">ahmed.abd2200p@copharm.uobaghdad.edu.iq</email>
          <uri content-type="orcid">https://orcid.org/0009-0004-2197-4952</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Jabbar Kadhim</surname>
            <given-names>Dheyaa</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0002-2654-1441</uri>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Jawad Hussein</surname>
            <given-names>Raghad</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0009-0009-4191-7257</uri>
          <xref ref-type="aff" rid="A3">3</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">Wasit Health Directorate, Ministry of Health, Baghdad, Iraq</addr-line>
        <institution>College of Pharmacy, University of Baghdad</institution>
        <addr-line content-type="city">Baghdad</addr-line>
        <country>Iraq</country>
        <uri content-type="ror">https://ror.org/007f1da21</uri>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">Department of Clinical Pharmacy, College of Pharmacy, University of Baghdad, Baghdad, Iraq</addr-line>
        <institution>Wasit Health Directorate, Ministry of Health</institution>
        <addr-line content-type="city">Baghdad</addr-line>
        <country>Iraq</country>
      </aff>
      <aff id="A3">
        <label>3</label>
        <addr-line content-type="verbatim">Gastroenterology and Hepatology Teaching Hospital, Medical City, Baghdad, Iraq</addr-line>
        <institution>Gastroenterology and Hepatology Teaching Hospital, Medical City</institution>
        <addr-line content-type="city">Baghdad</addr-line>
        <country>Iraq</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p><bold>Corresponding author</bold>: Ahmed Mansur Kadhim, Wasit Health Directorate, Ministry of Health, Baghdad, Iraq; Department of Clinical Pharmacy, College of Pharmacy, University of Baghdad, Baghdad, Iraq; Email: <email xlink:type="simple">ahmed.abd2200p@copharm.uobaghdad.edu.iq</email></p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>03</day>
        <month>04</month>
        <year>2026</year>
      </pub-date>
      <volume>68</volume>
      <issue>2</issue>
      <elocation-id>e170410</elocation-id>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/1C293CC9-85CA-52B2-B8B8-AA952DABDA3B">1C293CC9-85CA-52B2-B8B8-AA952DABDA3B</uri>
      <history>
        <date date-type="received">
          <day>29</day>
          <month>08</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>17</day>
          <month>11</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Ahmed Mansur Kadhim, Dheyaa Jabbar Kadhim, Raghad Jawad Hussein</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abstract</label>
        <p><bold>Introduction</bold>: Ulcerative colitis (<abbrev xlink:title="Ulcerative colitis">UC</abbrev>) is a chronic inflammatory disease that primarily affects the colon. Tumor necrosis factor-α inhibitors (like adalimumab) are effective agents for <abbrev xlink:title="Ulcerative colitis">UC</abbrev>. However, loss of response may occur. Proactive therapeutic drug monitoring involves measuring drug levels at regular intervals in patients in remission to maintain therapeutic concentrations and potentially prevent loss of response.</p>
        <p><bold>Aim</bold>: This study aims to evaluate adalimumab trough level (<abbrev xlink:title="trough level">TL</abbrev>), the development of anti-drug antibodies (<abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>), and their relationships with clinical and laboratory variables in Iraqi patients with ulcerative colitis receiving adalimumab therapy.</p>
        <p><bold>Patients and methods</bold>: The present study was cross-sectional and conducted from April 2024 to November 2024. It included 44 <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients allocated into 2 groups: group 1 (patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range) and group 2 (patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range).</p>
        <p><bold>Results</bold>: Out of 44 patients, 23 patients reached target <abbrev xlink:title="trough level">TL</abbrev>, while 21 patients did not. Based on the <abbrev xlink:title="trough level">TL</abbrev>, developments of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and clinical state of patients, recommendations were made to escalate the dose for 13 patients, switch therapy for 16 patients, de-escalate the dose for 10 patients, and continue therapy for 5 patients. Additionally, it was found that neutrophil count, erythrocyte sedimentation rate, and C-reactive protein were higher, and hemoglobin and packed cell volume were lower in patients who did not reach the target adalimumab <abbrev xlink:title="trough level">TL</abbrev> (<italic>p</italic>&lt;0.05).</p>
        <p><bold>Conclusions</bold>: Therapeutic drug monitoring for adalimumab can be an important tool for optimizing <abbrev xlink:title="Ulcerative colitis">UC</abbrev> treatment and explaining the potential causes of non-responsiveness to this medicine.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>adalimumab</kwd>
        <kwd>anti-drug antibodies</kwd>
        <kwd>inflammatory bowel disease</kwd>
        <kwd>therapeutic drug monitoring</kwd>
        <kwd>ulcerative colitis</kwd>
      </kwd-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="sec1">
        <title>Citation</title>
        <p>Kadhim AM, Kadhim DJ, Hussein RJ. Therapeutic drug monitoring of adalimumab for dose optimization during maintenance therapy in patients with ulcerative colitis: real-world data. Folia Med (Plovdiv) 2026;68(2):е170410. <ext-link ext-link-type="doi" xlink:href="10.3897/folmed.68.e170410">doi: 10.3897/folmed.68.e170410</ext-link>.</p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="Introduction" id="sec2">
      <title>Introduction</title>
      <p>Inflammatory bowel diseases (<abbrev xlink:title="Inflammatory bowel diseases">IBDs</abbrev>), which include ulcerative colitis (<abbrev xlink:title="Ulcerative colitis">UC</abbrev>) and Crohn’s disease (<abbrev xlink:title="Crohn’s disease">CD</abbrev>), consist of chronic recurrent inflammatory diseases of unknown etiology that affect the gastrointestinal tract (<abbrev xlink:title="gastrointestinal tract">GIT</abbrev>).<sup>[<xref ref-type="bibr" rid="B1">1</xref>]</sup> While the inflammation in <abbrev xlink:title="Ulcerative colitis">UC</abbrev> is limited to the colon and rectum, the inflammation in <abbrev xlink:title="Crohn’s disease">CD</abbrev> can affect all parts of the <abbrev xlink:title="gastrointestinal tract">GIT</abbrev>, from the mouth to the anus, and is linked to discontinuous transmural lesions of the gut wall.<sup>[<xref ref-type="bibr" rid="B2">2</xref>]</sup> Regardless of sex, IBD can start at any age; however, the most significant peak of onset occurs between the ages of 15 and 45. The incidence is steadily rising to the point that it qualifies as a global health issue.<sup>[<xref ref-type="bibr" rid="B3">3</xref>]</sup></p>
      <p>The pathogenesis of ulcerative colitis involves the activation of different immune cells, which results in the secretion of pro-inflammatory cytokines, i.e., <abbrev xlink:title="tumor necrosis factor-α">TNF-α</abbrev>, interleukin-1 (<abbrev xlink:title="interleukin-1">IL-1</abbrev>), IFN-γ, IL-6, and IL-23, which increases the permeability of the intestinal barrier and thus promotes inflammation in the intestinal mucosa.<sup>[<xref ref-type="bibr" rid="B4">4</xref>]</sup></p>
      <p>Tumor necrosis factor alpha (<abbrev xlink:title="tumor necrosis factor-α">TNF-α</abbrev>) inhibitors are frequently employed in treating autoimmune diseases such as IBD (for both moderate-to-severe <abbrev xlink:title="Crohn’s disease">CD</abbrev> and <abbrev xlink:title="Ulcerative colitis">UC</abbrev>).<sup>[<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B6">6</xref>]</sup> Infliximab, adalimumab, golimumab, and certolizumab are <abbrev xlink:title="tumor necrosis factor-α">TNF-α</abbrev> inhibitors that have been employed in the clinical setting of IBD; each has a unique pharmacological profile and varying efficacy.<sup>[<xref ref-type="bibr" rid="B5">5</xref>]</sup> They are utilized when other treatments are ineffective in improving the disease’s signs and symptoms.<sup>[<xref ref-type="bibr" rid="B7">7</xref>]</sup> Despite anti-<abbrev xlink:title="tumor necrosis factor-α">TNF-α</abbrev> biologics showing favorable therapeutic effects in achieving clinical, endoscopic, and histologic remission in IBD<sup>[<xref ref-type="bibr" rid="B8">8</xref>]</sup>, they are not without drawbacks, namely a higher risk of serious infection and loss of response in 30–50% of patients.<sup>[<xref ref-type="bibr" rid="B9">9</xref>]</sup> This encompasses a gradual decline in responsiveness over time as well as an initial absence of response.<sup>[<xref ref-type="bibr" rid="B10">10</xref>]</sup> Additionally, response may be changed by patient variables (such as smoking status and the duration of the condition). Lastly, the response seems to be influenced by genetic differences.<sup>[<xref ref-type="bibr" rid="B11">11</xref>]</sup></p>
      <p>Three major mechanisms of biologic treatment failure have been proposed. The first one is the non-immune-mediated pharmacokinetic failure related to the rapid drug clearance. The second mechanism is the immune-mediated pharmacokinetic failure caused by the production of neutralizing anti-drug antibodies (<abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>) against biologics (presenting as secondary loss of response during the maintenance phase of biologics treatment). The third mechanism is the mechanistic failure where the IBD may be driven by inflammatory mechanisms not blocked by the applied biologics.<sup>[<xref ref-type="bibr" rid="B12">12</xref>]</sup></p>
      <p>Drug trough concentrations refer to measuring drug levels just before the subsequent dose.<sup>[<xref ref-type="bibr" rid="B13">13</xref>]</sup> Higher trough levels (<abbrev xlink:title="trough level">TL</abbrev>) of the drug equate to higher exposure and result in better clinical outcomes.<sup>[<xref ref-type="bibr" rid="B14">14</xref>]</sup></p>
      <p>In order to optimize treatment for IBD and to achieve more demanding, objective end goals, therapeutic drug monitoring (<abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev>) has become a popular approach. Since low trough levels/concentrations or the development of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> are variously linked to treatment failure, the <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> of biological drugs includes measuring levels of serum drug concentrations and <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> to explain primary non-response or secondary loss of response.<sup>[<xref ref-type="bibr" rid="B15">15</xref>]</sup></p>
      <p>The <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> can be implemented in two main ways: reactive <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> and proactive <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev>. Reactive <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> involves measuring drug and <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> levels in patients experiencing a primary non-response or secondary loss of response. This approach helps in rationalizing management by identifying reasons for treatment failure.<sup>[<xref ref-type="bibr" rid="B16">16</xref>]</sup> Proactive <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev>, on the other hand, involves monitoring drug trough levels/concentrations at regular intervals in patients in remission to maintain therapeutic concentrations and potentially prevent loss of response.<sup>[<xref ref-type="bibr" rid="B17">17</xref>]</sup></p>
      <p>Adalimumab is a fully humanized IgG1 monoclonal antibody that is administered by the subcutaneous (<abbrev xlink:title="subcutaneous">SC</abbrev>) route. When used for <abbrev xlink:title="Ulcerative colitis">UC</abbrev>, adalimumab is given at a dose of 160 mg (day 1), and then 80 mg 2 weeks later (day 15), followed by a maintenance dose (beginning week 4, day 29) of 40 mg <abbrev xlink:title="subcutaneous">SC</abbrev> every 2 weeks.<sup>[<xref ref-type="bibr" rid="B18">18</xref>]</sup> The recommended target <abbrev xlink:title="trough level">TL</abbrev> of adalimumab at maintenance when used for IBD is 8–12 µg/mL.<sup>[<xref ref-type="bibr" rid="B19">19</xref>]</sup> In order to provide more individualized and efficient patient care, studies are still being done to determine the best way to use <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> in adalimumab therapy for IBD.<sup>[<xref ref-type="bibr" rid="B20">20</xref>]</sup></p>
    </sec>
    <sec sec-type="Aim" id="sec3">
      <title>Aim</title>
      <p>This study aims to evaluate <abbrev xlink:title="trough level">TL</abbrev>, the development of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and their relationships with clinical and laboratory variables in Iraqi patients with ulcerative colitis receiving adalimumab therapy.</p>
    </sec>
    <sec sec-type="Patients and methods" id="sec4">
      <title>Patients and methods</title>
      <sec sec-type="Study design" id="sec5">
        <title>Study design</title>
        <p>The research employed a cross-sectional observational design to examine the treatment outcomes of adalimumab in patients diagnosed with <abbrev xlink:title="Ulcerative colitis">UC</abbrev> and on maintenance therapy. The patients received treatment in accordance with clinical practice guidelines and the severity of their diseases while being supervised by a gastroenterologist.<sup>[<xref ref-type="bibr" rid="B21">21</xref>]</sup></p>
      </sec>
      <sec sec-type="Setting" id="sec6">
        <title>Setting</title>
        <p>The present study was conducted at the Gastroenterology and Hepatology Teaching Hospital, Medical City, Baghdad, Iraq, and lasted from April 2024 to November 2024.</p>
      </sec>
      <sec sec-type="Ethical consideration" id="sec7">
        <title>Ethical consideration</title>
        <p>The research proposal specifies the current study’s goals, and the suggested data collection methodologies were submitted to the College of Pharmacy, University of Baghdad, with clearance from the Scientific and Ethical Committee (IDRECAUBCP742024k, date: 7-4-2024). The Iraqi Ministry of Health also gave its clearance. Informed consent was obtained from all individual participants included in the study. The participation was entirely voluntary, and no incentives were offered.</p>
      </sec>
      <sec sec-type="Inclusion criteria" id="sec8">
        <title>Inclusion criteria</title>
        <p>The study included <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients who were over 18 years of age and who had been previously diagnosed with <abbrev xlink:title="Ulcerative colitis">UC</abbrev>. These patients received protocols of treatment prescribed by physicians at the Hepatology Teaching Hospital. All the patients were receiving maintenance treatment for more than three months with adalimumab + azathioprine + 5-aminosalicylic acid.</p>
      </sec>
      <sec sec-type="Exclusion criteria" id="sec9">
        <title>Exclusion criteria</title>
        <p>Patients who had the following coexisting diseases were excluded: immune system disorders (rheumatoid arthritis, psoriasis, psoriatic arthritis, ankylosing spondylitis, and systemic lupus erythematosus). Other exclusions include Paget’s disease, diabetes mellitus, asthma, chronic obstructive pulmonary disease, severe hepatic, cardiovascular, renal diseases and organ transplant recipients, and current cancer treatments or iron supplementation or the use of systemic or rectal steroids in the past 8 weeks.</p>
      </sec>
      <sec sec-type="Study groups" id="sec10">
        <title>Study groups</title>
        <p>The total sample size of eligible ulcerative colitis patients was 44. A convenient sampling method was followed wherein all eligible participants were enrolled after verbal consent. The study used this sampling method due to the complexity of identifying and recruiting eligible participants based on inclusion and exclusion criteria, especially those related to specific treatments.</p>
        <p>The eligible patients were allocated into two main groups: group 1 (patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range of 8–12 µg/mL) and group 2 (patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range). Patient response assessment (by physician) was done by using Partial Mayo Score (<abbrev xlink:title="Partial Mayo Score">PMS</abbrev>). Score &lt;2: remission. Score 2-4: mild activity. Score 5-7: moderate activity. Score &gt;7: severe activity.<sup>[<xref ref-type="bibr" rid="B22">22</xref>]</sup></p>
      </sec>
      <sec sec-type="Data collection" id="sec11">
        <title>Data collection</title>
        <p>Demographic data such as age, sex, weight, height, disease duration, smoking status, family history, and previous biological therapy were collected via direct patient interviews using a patient data chart specially designed for this study.</p>
      </sec>
      <sec sec-type="Sample preparation and laboratory parameters" id="sec12">
        <title>Sample preparation and laboratory parameters</title>
        <p>A 10-mL venous-blood sample was drawn from each patient. Blood samples were collected immediately before the next scheduled dose to confirm that trough levels were measured accurately and then split into three samples: one was taken in an erythrocyte sedimentation rate (<abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>) tube for the <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev> test, and the other was taken in an EDTA tube for the complete blood picture test. On the day of obtaining the sample, the complete blood count tests, <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>, and C-reactive protein (<abbrev xlink:title="C-reactive protein">CRP</abbrev>) were carried out, and the last portion was left to clot. Later, centrifuging for 20 minutes at 2,000–3,000 rpm was used to remove the clot. Serum samples from the resulting supernatant were separated, stored in Eppendorf tubes, and preserved in a deep freezer at −80°C until the time of analysis of biomarkers. The ADVIA 120 Hematology System was used to perform an automated assay for measuring hemoglobin level, packed cell volume (<abbrev xlink:title="packed cell volume">PCV</abbrev>), mean platelet volume (<abbrev xlink:title="mean platelet volume">MPV</abbrev>), and platelet count. ELISA testing had been carried out in step with the manufacturer’s instructions. The <abbrev xlink:title="trough level">TL</abbrev> of the adalimumab ELISA kit from Matriks Biotek [Turkey (Cat. ADA-SPEC-ADA.), detection limit (ng/mL) 18.75, spike recovery (%): between 85-115, precision: intra-assay and inter-assay CVs &lt;30%]; the <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> kit from Matriks Biotek [Turkey (Cat. ADA-QNS-HUM.), detection limit (ng/mL) 7.5, spike recovery (%): between 85-115, precision: intra-assay and inter-assay CVs &lt;30%]; and the serum calprotectin (<abbrev xlink:title="calprotectin">CALP</abbrev>) kit from Fine Test Biotech [China (Cat. No. EH4140), detection limit (sensitivity): 0.375 ng/mL, precision: Intra-assay and inter-assay CVs &lt;30%] were used for the ELISA tests.</p>
        <p>These tests are conducted using the sandwich type as the guiding premise, which are plate-based assays for detecting and quantifying a specific protein in a complex mixture. Concisely, standards and serum samples were incubated in the microtiter plate pre-coated with the human monoclonal <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> for adalimumab detection, pre-coated with the adalimumab for anti-adalimumab antibody detection, and pre-coated with the human anti-<abbrev xlink:title="calprotectin">CALP</abbrev> antibody for <abbrev xlink:title="calprotectin">CALP</abbrev> detection. Standards and serum samples were added and bound to antibodies coated on the wells and then incubated. After incubation, wash buffer removed unbound conjugates. Then, (for <abbrev xlink:title="calprotectin">CALP</abbrev> detection) biotinylated detection antibody was added to bind with <abbrev xlink:title="calprotectin">CALP</abbrev> conjugated on the coated antibody. After washing off unbound conjugates, HRP-streptavidin was added and bound to either adalimumab, <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, or <abbrev xlink:title="calprotectin">CALP</abbrev>. Following incubation, the wells were washed, and the bound enzymatic activity was detected by the addition of the tetramethylbenzidine (TMB) chromogen substrate. To end, the reaction terminated with an acidic stop solution. The color developed is proportionate to the amount of free adalimumab, <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and <abbrev xlink:title="calprotectin">CALP</abbrev> in the sample or standard. The wavelength at which the absorbance was measured was 450 nm. After that, the proportional measurement of concentration in the samples was determined using the standard curve.<sup>[<xref ref-type="bibr" rid="B23">23</xref>-<xref ref-type="bibr" rid="B25">25</xref>]</sup></p>
      </sec>
      <sec sec-type="Statistical analysis" id="sec13">
        <title>Statistical analysis</title>
        <p>The Shapiro-Wilk test was used to evaluate if a variable follows a normal distribution. Variables that follow a normal distribution were given as mean and standard deviation (<abbrev xlink:title="standard deviation">SD</abbrev>), while variables that do not follow a normal distribution were stated as median and interquartile range (<abbrev xlink:title="interquartile range">IQR</abbrev>). When the variables had a normal distribution, the difference between Group 1 and Group 2 was evaluated using an independent t-test. If the data did not, a Mann-Whitney U test was used. The chi-square test was used to determine the difference between categorical variables. The binary logistic regression analysis was used to determine the relationship between various factors and <abbrev xlink:title="trough level">TL</abbrev> attainment. All statistical analyses were performed using SPSS 27 (Chicago, USA), and <italic>p</italic>-values less than 0.05 were considered significant.</p>
      </sec>
    </sec>
    <sec sec-type="Results" id="sec14">
      <title>Results</title>
      <sec sec-type="Demographic and disease characteristics differences between groups" id="sec15">
        <title>Demographic and disease characteristics differences between groups</title>
        <p><bold>Table <xref ref-type="table" rid="T1">1</xref></bold> demonstrates the patient demographic and disease characteristics of 44 <abbrev xlink:title="Ulcerative colitis">UC</abbrev> [23 patients with trough levels within or above the therapeutic range (Group 1), 21 patients below the therapeutic range (Group 2)]. Patients included 18 females (40.9%) and 26 males (59.1%), with no statistically significant difference found between the groups in both sexes (<italic>p</italic>&gt;0.05).</p>
        <table-wrap id="T1" position="float" orientation="portrait">
          <label>Table 1.</label>
          <caption>
            <p>Demographic and clinical data of the study participants </p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="2">
                  <bold>Variable</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Group 1 (23 patients)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Group 2 (21 patients)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Statistical test [t(df), U, χ<sup>2</sup> (df)] at 95% CI</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold><italic>P</italic>-value</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="2" colspan="1">Sex</td>
                <td rowspan="1" colspan="1">Female, n (%)</td>
                <td rowspan="1" colspan="1">8 (18.2)</td>
                <td rowspan="1" colspan="1">10 (22.7)</td>
                <td rowspan="2" colspan="1">χ<sup>2</sup>(1)=0.748</td>
                <td rowspan="2" colspan="1">0.387</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Male, n (%)</td>
                <td rowspan="1" colspan="1">15 (34.1)</td>
                <td rowspan="1" colspan="1">11 (25.0)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="2">Age, year (mean±<abbrev xlink:title="standard deviation">SD</abbrev>)</td>
                <td rowspan="1" colspan="1">36.83±14.721</td>
                <td rowspan="1" colspan="1">37.43±12.396</td>
                <td rowspan="1" colspan="1">t(42)=−0.146</td>
                <td rowspan="1" colspan="1">0.885</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="2">BMI, kg/m<sup>2</sup> (mean±<abbrev xlink:title="standard deviation">SD</abbrev>)</td>
                <td rowspan="1" colspan="1">27.464±4.586</td>
                <td rowspan="1" colspan="1">24.373±4.648</td>
                <td rowspan="1" colspan="1">
                  <bold>t(40.66)=2.097</bold>
                </td>
                <td rowspan="1" colspan="1"><bold>0.042</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="2">Duration of disease, year (median ± <abbrev xlink:title="interquartile range">IQR</abbrev>)</td>
                <td rowspan="1" colspan="1">5.50 (3.00–9.00)</td>
                <td rowspan="1" colspan="1">6.00 (4.00–9.00)</td>
                <td rowspan="1" colspan="1">U=229.50</td>
                <td rowspan="1" colspan="1">0.618</td>
              </tr>
              <tr>
                <td rowspan="2" colspan="1">Biological treatment</td>
                <td rowspan="1" colspan="1">Naïve, n (%)</td>
                <td rowspan="1" colspan="1">14 (31.8)</td>
                <td rowspan="1" colspan="1">11 (25.0)</td>
                <td rowspan="2" colspan="1">χ<sup>2</sup>(1)=0.322</td>
                <td rowspan="2" colspan="1">0.570</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Previous biological treatment, n (%)</td>
                <td rowspan="1" colspan="1">9 (20.5)</td>
                <td rowspan="1" colspan="1">10 (22.7)</td>
              </tr>
              <tr>
                <td rowspan="2" colspan="1">Response</td>
                <td rowspan="1" colspan="1">Remission, n (%)</td>
                <td rowspan="1" colspan="1">15 (34.1)</td>
                <td rowspan="1" colspan="1">7 (15.9)</td>
                <td rowspan="2" colspan="1">
                  <bold>χ<sup>2</sup>(1)=4.464</bold>
                </td>
                <td rowspan="2" colspan="1"><bold>0.035</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Active, n (%)</td>
                <td rowspan="1" colspan="1">8 (18.2)</td>
                <td rowspan="1" colspan="1">14 (31.8)</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>* significant difference (<italic>p</italic>&lt;0.05). Two-sample t-test [t(df)] was used for statistical analysis of age and BMI, the Mann-Whitney U test was used for statistical analysis of the duration of the disease, and the chi-square test (χ<sup>2</sup>) was used for statistical analysis of sex, biological treatment, and response. BMI: body mass index; CI: confidence interval of the difference; df: degree of freedom; <abbrev xlink:title="interquartile range">IQR</abbrev>: interquartile range; <abbrev xlink:title="standard deviation">SD</abbrev>: standard deviation; <abbrev xlink:title="trough level">TL</abbrev>: trough level; <abbrev xlink:title="Ulcerative colitis">UC</abbrev>: ulcerative colitis</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>The mean age of the study groups was 36.83±14.721 years for patients in Group 1 and 37.43±12.396 years for patients in Group 2, with no statistically significant difference found between the groups (<italic>p</italic>&gt;0.05).</p>
        <p>The mean BMI for patients in Group 1 and Group 2 was 27.464±4.586 kg/m<sup>2</sup> and 24.373±4.648 kg/m<sup>2</sup>, respectively. The BMI was significantly higher in group 1 patients compared to group 2 (<italic>p</italic>=0.042).</p>
        <p>The median for duration of the disease for <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients in group 1 and group 2 was 5.50 (3.00-9.00) years and 6.00 (4.00-9.00), respectively. No significant difference was found between <abbrev xlink:title="Ulcerative colitis">UC</abbrev> groups with respect to the duration of the disease (<italic>p</italic>&gt;0.05).</p>
        <p>Regarding the biological treatment, the study included 25 naive patients (56.8%) and 19 patients with previous biological treatment (43.2%), with no statistically significant difference found between the groups (<italic>p</italic>&gt;0.05).</p>
        <p>A significant association between response and achievement of <abbrev xlink:title="trough level">TL</abbrev> was found between <abbrev xlink:title="Ulcerative colitis">UC</abbrev> groups [a higher number of patients achieved remission in group 1 compared to group 2 (<italic>p</italic>=0.035)].</p>
      </sec>
      <sec sec-type="Classification of the patients with recommendations based on TL, ADAs, and disease activity" id="sec16">
        <title>Classification of the patients with recommendations based on TL, ADAs, and disease activity</title>
        <p><bold>Table <xref ref-type="table" rid="T2">2</xref></bold> shows the patient classification and recommendations based on <abbrev xlink:title="trough level">TL</abbrev>, <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and disease activity, with recommendations to escalate the dose for 13 patients (29.55%), switch therapy for 16 patients (36.36%), de-escalate the dose for 10 patients (22.73%), and continue therapy for 5 patients (11.36%). Out of 44 patients, the results of the current study showed that 6 patients had low <abbrev xlink:title="trough level">TL</abbrev> with negative <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> and were in active disease (non-immune pharmacokinetic failure), and 7 patients had low <abbrev xlink:title="trough level">TL</abbrev> and were in remission. Concerning these patients, it is therefore advised to receive higher doses of their medications or shorter intervals between doses and monitor response.</p>
        <table-wrap id="T2" position="float" orientation="portrait">
          <label>Table 2.</label>
          <caption>
            <p>Classification of <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients and the recommendations made based on their <abbrev xlink:title="trough level">TL</abbrev>, <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and disease activity <bold/></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="3">
                  <bold>Patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range (21 patients)</bold>
                </td>
                <td rowspan="1" colspan="3">
                  <bold>Patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range (23 patients)</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Seven patients in remission</td>
                <td rowspan="1" colspan="2">Fourteen patients with active diseases</td>
                <td rowspan="1" colspan="2">Fifteen patients in remission</td>
                <td rowspan="1" colspan="1">Eight patients with active disease</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Seven patients with negative <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev></td>
                <td rowspan="1" colspan="1">Six patients with negative <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> (non-immune ph. k. failure)</td>
                <td rowspan="1" colspan="1">Eight patients with high positive <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> (Immune ph. k. failure)*</td>
                <td rowspan="1" colspan="1">Five patients within therapeutic range</td>
                <td rowspan="1" colspan="1">Ten patients above the therapeutic range</td>
                <td rowspan="1" colspan="1">Eight patients with mechanistic failure</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="6">Recommendations made</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Escalate the dose or shorten the interval between doses.</td>
                <td rowspan="1" colspan="1">Escalate the dose or shorten the interval between doses</td>
                <td rowspan="1" colspan="1">Switching therapy</td>
                <td rowspan="1" colspan="1">Continue therapy</td>
                <td rowspan="1" colspan="1">De-escalate the dose or lengthen the interval between doses</td>
                <td rowspan="1" colspan="1">Switching therapy</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p><abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>: anti-drug antibodies; ph. k.: pharmacokinetic; * Those who have low trough levels and high titers of anti-drug antibodies.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>Furthermore, there were 8 patients in active disease despite achieving target <abbrev xlink:title="trough level">TL</abbrev> who needed to switch therapy (mechanistic failure happens when the patient does not respond despite optimum drug <abbrev xlink:title="trough level">TL</abbrev>). In addition, 8 patients who did not achieve target <abbrev xlink:title="trough level">TL</abbrev> were in active disease with high positive <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> and also need to switch therapy (immune pharmacokinetic failure happens in patients who have low or undetectable <abbrev xlink:title="trough level">TL</abbrev> and high levels of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>). Moreover, 5 patients were in remission state with the achievement of target <abbrev xlink:title="trough level">TL</abbrev> (need to continue therapy), and 10 patients were in remission state with <abbrev xlink:title="trough level">TL</abbrev> above the target (need to de-escalate the dose or lengthen the interval between doses).</p>
      </sec>
      <sec sec-type="Differences between groups according to disease activity, TL, ADAs, and other laboratory variables" id="sec17">
        <title>Differences between groups according to disease activity, TL, ADAs, and other laboratory variables</title>
        <p><bold>Table <xref ref-type="table" rid="T3">3</xref></bold> shows the differences between group 1 and group 2 <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients in different variables. The <abbrev xlink:title="Partial Mayo Score">PMS</abbrev> shows no significant differences between patients in both groups (<italic>p</italic>&gt;0.05). The <abbrev xlink:title="trough level">TL</abbrev> was significantly higher in-group 1 compared to group 2 (<italic>p</italic>≤0.001). While the level of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> was significantly higher in-group 2 compared to group 1 (<italic>p</italic>=0.003).</p>
        <table-wrap id="T3" position="float" orientation="portrait">
          <label>Table 3.</label>
          <caption>
            <p>Difference between <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range (Group 1) and patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range (Group 2) according to different variables </p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Variable</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Group 1 (23 patients)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Group 2 (21 patients)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Statistical test [t(df), U] at 95% CI</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold><italic>P</italic>-value</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Partial Mayo Score">PMS</abbrev>
                </td>
                <td rowspan="1" colspan="1">1 (1-6)</td>
                <td rowspan="1" colspan="1">2 (1-5.5)</td>
                <td rowspan="1" colspan="1">U=174.50</td>
                <td rowspan="1" colspan="1">0.104<sup>b</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="trough level">TL</abbrev> (µg/mL)</td>
                <td rowspan="1" colspan="1">12.78 (9.53-17.43)</td>
                <td rowspan="1" colspan="1">2.760 (0.66-5.68)</td>
                <td rowspan="1" colspan="1">U=2.00</td>
                <td rowspan="1" colspan="1"><bold>&lt;0.001</bold>*<sup>b</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> (ng/mL)</td>
                <td rowspan="1" colspan="1">16.03 (7.56-35.18)</td>
                <td rowspan="1" colspan="1">268.09 (17.74-1264.74)</td>
                <td rowspan="1" colspan="1">U=116.00</td>
                <td rowspan="1" colspan="1"><bold>0.003</bold>*<bold><sup>b</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">HGB (g/dL)</td>
                <td rowspan="1" colspan="1">12.98±2.08</td>
                <td rowspan="1" colspan="1">11.476±1.82</td>
                <td rowspan="1" colspan="1">t(41.92)=2.55</td>
                <td rowspan="1" colspan="1"><bold>0.014</bold>*<bold><sup>a</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="packed cell volume">PCV</abbrev> (%)</td>
                <td rowspan="1" colspan="1">39.71±4.75</td>
                <td rowspan="1" colspan="1">35.25±5.52</td>
                <td rowspan="1" colspan="1">t(39.65)=2.855</td>
                <td rowspan="1" colspan="1"><bold>0.007</bold>*<bold><sup>a</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">MCV (fL)</td>
                <td rowspan="1" colspan="1">80.31±6.96</td>
                <td rowspan="1" colspan="1">78.73±8.29</td>
                <td rowspan="1" colspan="1">t(42)=0.684</td>
                <td rowspan="1" colspan="1">0.498<sup>a</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">WBC count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">6.92±1.97</td>
                <td rowspan="1" colspan="1">7.91±2.53</td>
                <td rowspan="1" colspan="1">t(42)=−1.457</td>
                <td rowspan="1" colspan="1">0.153<sup>a</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">LYM count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">2.62±0.818</td>
                <td rowspan="1" colspan="1">2.18±0.93</td>
                <td rowspan="1" colspan="1">t(42)=1.686</td>
                <td rowspan="1" colspan="1">0.099<sup>a</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">NEUT count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">3.570±1.280</td>
                <td rowspan="1" colspan="1">4.67±1.448</td>
                <td rowspan="1" colspan="1">t(40.14)=−2.663</td>
                <td rowspan="1" colspan="1"><bold>0.011</bold>*<bold><sup>a</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">PLT count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">251 (203-339)</td>
                <td rowspan="1" colspan="1">366 (292-366)</td>
                <td rowspan="1" colspan="1">U=188.50</td>
                <td rowspan="1" colspan="1">0.213<sup>b</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="mean platelet volume">MPV</abbrev> (fL)</td>
                <td rowspan="1" colspan="1">9.52±1.39</td>
                <td rowspan="1" colspan="1">8.39±1.02</td>
                <td rowspan="1" colspan="1">t(42)=1.789</td>
                <td rowspan="1" colspan="1">0.081<sup>a</sup></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev> (mm/hr)</td>
                <td rowspan="1" colspan="1">22.00 (9.00-31.00)</td>
                <td rowspan="1" colspan="1">32.00 (14.50-66.00)</td>
                <td rowspan="1" colspan="1">U=145.50</td>
                <td rowspan="1" colspan="1"><bold>0.024</bold>*<bold><sup>b</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="C-reactive protein">CRP</abbrev> (mg/L)</td>
                <td rowspan="1" colspan="1">9.28 (5.87-16.00)</td>
                <td rowspan="1" colspan="1">19.90 (7.86-62.50)</td>
                <td rowspan="1" colspan="1">U=134.50</td>
                <td rowspan="1" colspan="1"><bold>0.012</bold>*<bold><sup>b</sup></bold></td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="calprotectin">CALP</abbrev> (ng/mL)</td>
                <td rowspan="1" colspan="1">4284.91±1196.44</td>
                <td rowspan="1" colspan="1">4767.42±1415.80</td>
                <td rowspan="1" colspan="1">t(42)=−1.225</td>
                <td rowspan="1" colspan="1">0.228<sup>a</sup></td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>* significant difference (<italic>p</italic>&lt;0.05). <bold><sup>a</sup></bold> A two-sample t-test [t(df)] was used, and the data presented as mean ± <abbrev xlink:title="standard deviation">SD</abbrev>. <bold><sup>b</sup></bold> Mann-Whitney U test was used, and the data presented as median (<abbrev xlink:title="interquartile range">IQR</abbrev>). <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>: anti-drug antibodies; <abbrev xlink:title="calprotectin">CALP</abbrev>: calprotectin; CI: confidence interval of the difference; <abbrev xlink:title="C-reactive protein">CRP</abbrev>: C-reactive protein; df: degree of freedom; <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>: erythrocyte sedimentation rate; HGB: hemoglobin; LYM: lymphocyte; MCV: mean corpuscular volume; <abbrev xlink:title="mean platelet volume">MPV</abbrev>: mean platelet volume; NEUT: neutrophil count; <abbrev xlink:title="packed cell volume">PCV</abbrev>: packed cell volume; PLT: platelet; <abbrev xlink:title="Partial Mayo Score">PMS</abbrev>: Partial Mayo Score; <abbrev xlink:title="trough level">TL</abbrev>: trough level; WBC: white blood cells.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>In addition, HGB and <abbrev xlink:title="packed cell volume">PCV</abbrev> were significantly higher in group 1 compared to group 2 (<italic>p</italic>=0.014 and 0.007, respectively). While there was no significant difference in the MCV between the groups (<italic>p</italic>&gt;0.05).</p>
        <p>The levels of WBC, lymphocyte count, PLT, and <abbrev xlink:title="mean platelet volume">MPV</abbrev> showed no significant differences between the groups (<italic>p</italic>&gt;0.05). On the other hand, there was a significantly lower level of neutrophil count in group 1 compared to group 2 (<italic>p</italic>=0.011).</p>
        <p>The levels of <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev> and <abbrev xlink:title="C-reactive protein">CRP</abbrev> were significantly lower in group 1 compared to group 2 (<italic>p</italic>=0.024 and 0.012, respectively). The level of serum calprotectin was lower in <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients in group 1 than patients in group 2, in spite of not being statistically significant(<italic>p</italic>&gt;0.05).</p>
      </sec>
      <sec sec-type="Target TL achievement association and prediction by univariate and multivariate regression analysis" id="sec18">
        <title>Target TL achievement association and prediction by univariate and multivariate regression analysis</title>
        <p>Using univariate binary logistic regression, only <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>, BMI, <abbrev xlink:title="C-reactive protein">CRP</abbrev>, HGB, <abbrev xlink:title="packed cell volume">PCV</abbrev>, and neutrophil count had a significant effect on reaching the goal <abbrev xlink:title="trough level">TL</abbrev> (<italic>plt</italic>;0.05) <bold>(Table <xref ref-type="table" rid="T4">4</xref>)</bold>.</p>
        <table-wrap id="T4" position="float" orientation="portrait">
          <label>Table 4.</label>
          <caption>
            <p>Target <abbrev xlink:title="trough level">TL</abbrev> achievement association and prediction by univariate analysis for <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="5">
                  <bold>Univariate analysis <sup>a</sup></bold>
                </td>
              </tr>
              <tr>
                <td rowspan="2" colspan="1">
                  <bold>Variable</bold>
                </td>
                <td rowspan="2" colspan="1">
                  <bold>OR [EXP(B)]</bold>
                </td>
                <td rowspan="1" colspan="2">
                  <bold>95% CI for EXP(B)</bold>
                </td>
                <td rowspan="2" colspan="1">
                  <bold><italic>p-</italic>value</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Lower</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Upper</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="Partial Mayo Score">PMS</abbrev> score</td>
                <td rowspan="1" colspan="1">1.101</td>
                <td rowspan="1" colspan="1">0.872</td>
                <td rowspan="1" colspan="1">1.390</td>
                <td rowspan="1" colspan="1">0.418</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">BMI (kg/m<sup>2</sup>)</td>
                <td rowspan="1" colspan="1">0.871</td>
                <td rowspan="1" colspan="1">0.871</td>
                <td rowspan="1" colspan="1">0.998</td>
                <td rowspan="1" colspan="1"><bold>0.047</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> (ng/mL)</td>
                <td rowspan="1" colspan="1">1.007</td>
                <td rowspan="1" colspan="1">1.000</td>
                <td rowspan="1" colspan="1">1.014</td>
                <td rowspan="1" colspan="1">0.052</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">HGB (g/dL)</td>
                <td rowspan="1" colspan="1">0.675</td>
                <td rowspan="1" colspan="1">0.4830</td>
                <td rowspan="1" colspan="1">0.944</td>
                <td rowspan="1" colspan="1"><bold>0.022</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="packed cell volume">PCV</abbrev> (%)</td>
                <td rowspan="1" colspan="1">0.842</td>
                <td rowspan="1" colspan="1">0.736</td>
                <td rowspan="1" colspan="1">0.963</td>
                <td rowspan="1" colspan="1"><bold>0.012</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">MCV (fL)</td>
                <td rowspan="1" colspan="1">0.972</td>
                <td rowspan="1" colspan="1">0.898</td>
                <td rowspan="1" colspan="1">1.053</td>
                <td rowspan="1" colspan="1">0.488</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">WBC count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">1.226</td>
                <td rowspan="1" colspan="1">0.925</td>
                <td rowspan="1" colspan="1">1.624</td>
                <td rowspan="1" colspan="1">0.157</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">LYM count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">0.548</td>
                <td rowspan="1" colspan="1">0.265</td>
                <td rowspan="1" colspan="1">1.130</td>
                <td rowspan="1" colspan="1">0.103</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">NEUT count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">1.821</td>
                <td rowspan="1" colspan="1">1.110</td>
                <td rowspan="1" colspan="1">2.986</td>
                <td rowspan="1" colspan="1"><bold>0.018</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">PLT count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">1.004</td>
                <td rowspan="1" colspan="1">0.997</td>
                <td rowspan="1" colspan="1">1.010</td>
                <td rowspan="1" colspan="1">0.256</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="mean platelet volume">MPV</abbrev> (fL)</td>
                <td rowspan="1" colspan="1">0.624</td>
                <td rowspan="1" colspan="1">0.362</td>
                <td rowspan="1" colspan="1">1.076</td>
                <td rowspan="1" colspan="1">0.090</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev> (mm/hr)</td>
                <td rowspan="1" colspan="1">1.037</td>
                <td rowspan="1" colspan="1">1.005</td>
                <td rowspan="1" colspan="1">1.069</td>
                <td rowspan="1" colspan="1"><bold>0.021</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="C-reactive protein">CRP</abbrev> (mg/L)</td>
                <td rowspan="1" colspan="1">1.048</td>
                <td rowspan="1" colspan="1">1.004</td>
                <td rowspan="1" colspan="1">1.093</td>
                <td rowspan="1" colspan="1"><bold>0.020</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="calprotectin">CALP</abbrev> (ng/mL)</td>
                <td rowspan="1" colspan="1">1.000</td>
                <td rowspan="1" colspan="1">1.000</td>
                <td rowspan="1" colspan="1">1.001</td>
                <td rowspan="1" colspan="1">0.225</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>* significant difference; <bold><sup>a</sup></bold> Binary logistic regression is used (Enter). <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>: anti-drug antibodies; BMI: body mass index; <abbrev xlink:title="calprotectin">CALP</abbrev>: calprotectin; <abbrev xlink:title="C-reactive protein">CRP</abbrev>: C-reactive protein; <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>: erythrocyte sedimentation rate; HGB: hemoglobin; LYM: lymphocyte; MCV: mean corpuscular volume; <abbrev xlink:title="mean platelet volume">MPV</abbrev>: mean platelet volume; NEUT: neutrophil; <abbrev xlink:title="packed cell volume">PCV</abbrev>: packed cell volume; PLT: platelet; <abbrev xlink:title="Partial Mayo Score">PMS</abbrev>: Partial Mayo Score; <abbrev xlink:title="trough level">TL</abbrev>: trough level; WBC: white blood cells</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>While in multivariate binary regression (backward technique) that includes <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>, <abbrev xlink:title="C-reactive protein">CRP</abbrev>, HGB, BMI, <abbrev xlink:title="packed cell volume">PCV</abbrev>%, and neutrophil count variables, only the model containing <abbrev xlink:title="packed cell volume">PCV</abbrev>% (positive association), neutrophil count, and <abbrev xlink:title="C-reactive protein">CRP</abbrev> (negative association) had a significant effect on reaching the goal <abbrev xlink:title="trough level">TL</abbrev> [OR=0.773 (0.627-0.954), <italic>p</italic>=0.016], [OR=1.958 (1.070-3.585), <italic>p</italic>=0.029], and [OR=1.062 (1.000-1.128), <italic>p</italic>=0.049], respectively <bold>(Table <xref ref-type="table" rid="T5">5</xref>)</bold>.</p>
        <table-wrap id="T5" position="float" orientation="portrait">
          <label>Table 5.</label>
          <caption>
            <p>Target <abbrev xlink:title="trough level">TL</abbrev> achievement prediction by multivariate analysis for <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients </p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="5">
                  <bold>Multivariate analysis <sup>b</sup></bold>
                </td>
              </tr>
              <tr>
                <td rowspan="2" colspan="1">
                  <bold>Variable</bold>
                </td>
                <td rowspan="2" colspan="1">
                  <bold>OR [EXP(B)]</bold>
                </td>
                <td rowspan="1" colspan="2">
                  <bold>95% CI for EXP(B)</bold>
                </td>
                <td rowspan="2" colspan="1">
                  <bold><italic>P</italic>-value</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Lower</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Upper</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">BMI (kg/m<sup>2</sup>)</td>
                <td rowspan="1" colspan="1">0.934</td>
                <td rowspan="1" colspan="1">0.770</td>
                <td rowspan="1" colspan="1">1.133</td>
                <td rowspan="1" colspan="1">0.489 NS</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">HGB (g/dL)</td>
                <td rowspan="1" colspan="1">1.519</td>
                <td rowspan="1" colspan="1">0.643</td>
                <td rowspan="1" colspan="1">3.590</td>
                <td rowspan="1" colspan="1">0.341 NS</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="packed cell volume">PCV</abbrev> (%)</td>
                <td rowspan="1" colspan="1">0.773</td>
                <td rowspan="1" colspan="1">0.627</td>
                <td rowspan="1" colspan="1">0.954</td>
                <td rowspan="1" colspan="1"><bold>0.016</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">NEUT Count (*10<sup>3</sup>/µL)</td>
                <td rowspan="1" colspan="1">1.958</td>
                <td rowspan="1" colspan="1">1.070</td>
                <td rowspan="1" colspan="1">3.585</td>
                <td rowspan="1" colspan="1"><bold>0.029</bold>*</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev> (mm/hr)</td>
                <td rowspan="1" colspan="1">0.972</td>
                <td rowspan="1" colspan="1">0.923</td>
                <td rowspan="1" colspan="1">1.023</td>
                <td rowspan="1" colspan="1">0.271 NS</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"><abbrev xlink:title="C-reactive protein">CRP</abbrev> (mg/L)</td>
                <td rowspan="1" colspan="1">1.062</td>
                <td rowspan="1" colspan="1">1.000</td>
                <td rowspan="1" colspan="1">1.128</td>
                <td rowspan="1" colspan="1"><bold>0.049</bold>*</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>* significant difference; NS: non-significant difference; <bold><sup>b</sup></bold> Binary logistic regression was used (backward technique). BMI: body mass index; <abbrev xlink:title="C-reactive protein">CRP</abbrev>: C-reactive protein; <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>: erythrocyte sedimentation rate; HGB: hemoglobin; NEUT: neutrophil; <abbrev xlink:title="packed cell volume">PCV</abbrev>: packed cell volume.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
      </sec>
    </sec>
    <sec sec-type="Discussion" id="sec19">
      <title>Discussion</title>
      <p>In the current study, 44 <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients receiving adalimumab were included. Therapeutic drug monitoring was used to assess adalimumab <abbrev xlink:title="trough level">TL</abbrev> and the existence of <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> to adalimumab, which can help with therapy optimization. The observed differences in patients’ outcomes directly informed clinical recommendations, with a significant proportion of patients requiring a change in treatment strategy. This highlights the potential for using these markers to stratify patients early in their treatment, leading to more timely and effective therapeutic adjustments and potentially improving overall outcomes.</p>
      <p>Concerning the <abbrev xlink:title="trough level">TL</abbrev>, <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, and disease activity of the patients, recommendations were made to increase the dose or shorten the intervals between doses and monitor the response for patients who had low <abbrev xlink:title="trough level">TL</abbrev> with negative <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> and were in active disease (non-immune pharmacokinetic failure) and for patients who had low <abbrev xlink:title="trough level">TL</abbrev> and were in remission.<sup>[<xref ref-type="bibr" rid="B26">26</xref>,<xref ref-type="bibr" rid="B27">27</xref>]</sup> Furthermore, recommendations were made to switch therapy for patients who were in active disease despite achieving target <abbrev xlink:title="trough level">TL</abbrev> (mechanistic failure) and for patients who did not achieve target <abbrev xlink:title="trough level">TL</abbrev> and were in active disease with high positive <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> (immune pharmacokinetic failure).<sup>[<xref ref-type="bibr" rid="B28">28</xref>]</sup> Additionally, for patients who were in remission with the achievement of target <abbrev xlink:title="trough level">TL</abbrev>, recommendations were made to continue therapy, and for those who were in remission with <abbrev xlink:title="trough level">TL</abbrev> above the target, recommendations were made to de-escalate the dose or lengthen the interval between doses. Studies in real-world settings continue to confirm <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> effectiveness with observed clinical remission rates. The optimal adalimumab <abbrev xlink:title="trough level">TL</abbrev> for <abbrev xlink:title="Ulcerative colitis">UC</abbrev> are often considered similar to those for <abbrev xlink:title="Crohn’s disease">CD</abbrev>, aiming for levels associated with mucosal healing and sustained remission.<sup>[<xref ref-type="bibr" rid="B28">28</xref>-<xref ref-type="bibr" rid="B31">31</xref>]</sup></p>
      <p>Additionally, the current study shows that <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> were higher in patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range when compared to patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range. A Saudi cohort study (n=392 IBD patients receiving anti-TNF therapy, including adalimumab): <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> were negative in 73.1% of patients and weakly positive in 9.8%; 17.1% of patients had positive <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>, with sub-therapeutic anti-TNF drug levels significantly associated with <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> positivity (<italic>p</italic>&lt;0.001).<sup>[<xref ref-type="bibr" rid="B32">32</xref>]</sup></p>
      <p>Furthermore, routine inflammatory markers (<abbrev xlink:title="C-reactive protein">CRP</abbrev> and <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>) and routine blood tests (HGB, <abbrev xlink:title="packed cell volume">PCV</abbrev>, MCV, white blood cell count, lymphocyte count, neutrophil count, <abbrev xlink:title="mean platelet volume">MPV</abbrev>, and platelet count) were examined in the current study. It was found that neutrophil count, <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>, and <abbrev xlink:title="C-reactive protein">CRP</abbrev> were higher, and HGB and <abbrev xlink:title="packed cell volume">PCV</abbrev> were lower in patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range. An IBD cohort study published in 2020 showed a significant negative correlation between anti-TNF trough levels and some laboratory markers, including serum <abbrev xlink:title="C-reactive protein">CRP</abbrev>.<sup>[<xref ref-type="bibr" rid="B33">33</xref>]</sup> According to a study by Roblin et al., a combination of <abbrev xlink:title="C-reactive protein">CRP</abbrev>, <abbrev xlink:title="trough level">TL</abbrev>, and <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev> can be used to accurately predict a loss of response to infliximab.<sup>[<xref ref-type="bibr" rid="B34">34</xref>]</sup> Also, the current study demonstrates that <abbrev xlink:title="C-reactive protein">CRP</abbrev> could be used as a new noninvasive biomarker to predict a loss of response to adalimumab. Moreover, and for routine disease activity assessment, the recent guidelines for <abbrev xlink:title="Ulcerative colitis">UC</abbrev> management suggest measuring <abbrev xlink:title="C-reactive protein">CRP</abbrev> and fecal calprotectin in asymptomatic patients to avoid more expensive and invasive testing, such as endoscopy.<sup>[<xref ref-type="bibr" rid="B35">35</xref>]</sup> For IBD patients achieving mucosal healing, their WBC count, PLT count, <abbrev xlink:title="erythrocyte sedimentation rate">ESR</abbrev>, <abbrev xlink:title="C-reactive protein">CRP</abbrev>, and neutrophil/lymphocyte ratio levels were significantly lower than those in patients that did not achieve mucosal healing (all <italic>p</italic>&lt;0.05) and could be used as noninvasive markers for predicting mucosal healing in patients with IBD.<sup>[<xref ref-type="bibr" rid="B36">36</xref>]</sup> Anemia (low HGB and <abbrev xlink:title="packed cell volume">PCV</abbrev>) is highly prevalent in IBD patients and directly linked to chronic inflammation.</p>
      <p>The higher level of HGB and <abbrev xlink:title="packed cell volume">PCV</abbrev> in patients with <abbrev xlink:title="trough level">TL</abbrev> within or above the therapeutic range is consistent with a prior study showing that anti-TNF treatment, such as adalimumab, raises HGB levels and lowers anemia rates in IBD patients. This benefit occurred concurrently with a decrease in disease activity measured by <abbrev xlink:title="C-reactive protein">CRP</abbrev> and was unrelated to iron supplementation during treatment.<sup>[<xref ref-type="bibr" rid="B37">37</xref>]</sup></p>
      <p>Calprotectin is an acute-phase protein that regulates neutrophil migration. Its quantity corresponds with neutrophil migration and indicates the intensity of inflammation in IBD.<sup>[<xref ref-type="bibr" rid="B38">38</xref>]</sup></p>
      <p>However, this is the first study, to the best of our knowledge, to examine the association between serum <abbrev xlink:title="calprotectin">CALP</abbrev> and adalimumab <abbrev xlink:title="trough level">TL</abbrev> and find that serum <abbrev xlink:title="calprotectin">CALP</abbrev> was higher in <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients with <abbrev xlink:title="trough level">TL</abbrev> below the therapeutic range, in spite of not being statistically significant. A previous study in Japan (2019) found that serum <abbrev xlink:title="calprotectin">CALP</abbrev> is an inflammatory biomarker in IBD, but it might be more effective in evaluating <abbrev xlink:title="Crohn’s disease">CD</abbrev> patients than <abbrev xlink:title="Ulcerative colitis">UC</abbrev> patients.<sup>[<xref ref-type="bibr" rid="B39">39</xref>]</sup></p>
      <p>A recent Iraqi study by Saleh HH et al. concluded that serum <abbrev xlink:title="calprotectin">CALP</abbrev> is a new marker that could be used in evaluating and predicting how well <abbrev xlink:title="Crohn’s disease">CD</abbrev> patients will respond to infliximab and achieve target infliximab <abbrev xlink:title="trough level">TL</abbrev>.<sup>[<xref ref-type="bibr" rid="B40">40</xref>]</sup></p>
    </sec>
    <sec sec-type="Study limitations" id="sec20">
      <title>Study limitations</title>
      <p>The present study is not without its limitations, which can be enumerated as follows: firstly, the sample size is limited; secondly, <abbrev xlink:title="therapeutic drug monitoring">TDM</abbrev> results (<abbrev xlink:title="trough level">TL</abbrev> and <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>) were delayed because samples were obtained from patients and stored for approximately six months before laboratory measurements were completed, resulting in delayed therapy recommendations to physicians and patients.</p>
    </sec>
    <sec sec-type="Conclusion" id="sec21">
      <title>Conclusion</title>
      <p>Therapeutic drug monitoring for adalimumab (through the measurement of <abbrev xlink:title="trough level">TL</abbrev> and <abbrev xlink:title="anti-drug antibodies">ADAs</abbrev>) has the potential to serve as a valuable instrument in formulating suitable recommendations to optimize <abbrev xlink:title="Ulcerative colitis">UC</abbrev> treatment (escalating the dose for 13 patients, switching therapy for 16 patients, de-escalating the dose for 10 patients, and continuing therapy for 5 patients). Multivariate analysis of the studied variables indicates that <abbrev xlink:title="packed cell volume">PCV</abbrev>, neutrophil count, and <abbrev xlink:title="C-reactive protein">CRP</abbrev> could serve as indicators to predict <abbrev xlink:title="trough level">TL</abbrev> accomplishment and subsequent response to adalimumab therapy.</p>
    </sec>
    <sec sec-type="Ethical approval" id="sec22">
      <title>Ethical approval</title>
      <p>Ethical approval for the study was granted by the Scientific and Ethical Committee of the College of Pharmacy in the University of Baghdad (Protocol IDRECAUBCP742024k, date: 7-4-2024). The Iraqi Ministry of Health also gave its clearance.</p>
    </sec>
    <sec sec-type="Conflict of interest" id="sec23">
      <title>Conflict of interest</title>
      <p>The authors have declared that they have no conflict of interest, financial or otherwise.</p>
    </sec>
    <sec sec-type="Ethical statements" id="sec24">
      <title>Ethical statements</title>
      <list list-type="bullet">
        <list-item>
          <p>The authors declared that no clinical trials were used in the present study.
</p>
        </list-item>
        <list-item>
          <p>The authors declared that no experiments on humans or human tissues were performed for the present study.
</p>
        </list-item>
        <list-item>
          <p>Informed consent was obtained from all individual participants included in the study, and participation was entirely voluntary, with no incentives offered.
</p>
        </list-item>
        <list-item>
          <p>The authors declared that no experiments on animals were performed for the present study.
</p>
        </list-item>
        <list-item>
          <p>The authors declared that no commercially available immortalized human and animal cell lines were used in the present study.
</p>
        </list-item>
      </list>
    </sec>
    <sec sec-type="Use of AI" id="sec25">
      <title>Use of AI</title>
      <p>No use of AI was reported.</p>
    </sec>
    <sec sec-type="Funding" id="sec26">
      <title>Funding</title>
      <p>The authors have no funding to report.</p>
    </sec>
    <sec sec-type="Author contributions" id="sec27">
      <title>Author contributions</title>
      <p>Each named author has substantially contributed to conducting the underlying research and drafting this manuscript—conceptualization and methodology: AM and DJ; investigation: AM, RJ; formal analysis: AM and DJ; visualization and writing–original draft: AM and DJ; project administration: AM, DJ, and RJ; writing–review and editing: AM, DJ, and RJ; funding acquisition: AM; supervision: DJ, RJ. All authors have read and agreed to the final version of the manuscript and have agreed to the Folia Medica’s submission policies.</p>
    </sec>
    <sec sec-type="Data availability" id="sec28">
      <title>Data availability</title>
      <p>All data used are referenced or included in the article.</p>
    </sec>
  </body>
  <back>
    <ack>
      <title>Acknowledgements</title>
      <p>We would like to express our gratitude to all participants in this study.</p>
    </ack>
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