<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>Darzin International Company</PublisherName>
      <JournalTitle>Multidisciplinary Engineering Science Open</JournalTitle>
      <Issn></Issn>
      <Volume>1</Volume>
      <Issue>Serial Number 1</Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>02</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Organs-on-Chips and Microphysiological Systems: Disease Modeling, Readouts, and Regulatory Adoption</ArticleTitle>
    <VernacularTitle>Organs-on-Chips and Microphysiological Systems: Disease Modeling, Readouts, and Regulatory Adoption</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>11</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2023</Year>
        <Month>11</Month>
        <Day>17</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p class="Abstract" style="line-height: 150%;"&gt;&lt;span style="font-size: 8.0pt; line-height: 150%;"&gt;This review aims to synthesize current advancements in organs-on-chips and microphysiological systems, focusing on their applications in disease modeling, analytical readouts, and regulatory and translational adoption. A qualitative literature review was conducted on 19 peer-reviewed articles selected through purposive sampling from Scopus, PubMed, and Web of Science. The review included studies addressing organ-on-chip design, disease modeling, biosensor integration, multi-organ systems, and regulatory considerations. Data were analyzed using thematic synthesis in NVivo 14, employing open, axial, and selective coding to identify key concepts and themes, with theoretical saturation achieved after analysis of the 17th article. The analysis identified four main themes. First, technological foundations highlighted microfabrication, biomaterials, dynamic perfusion, co-culture systems, and integrated biosensors as essential for replicating organ-level physiology. Second, disease modeling and therapeutic testing demonstrated that OoCs accurately recapitulate organ-specific pathophysiology, support multi-organ crosstalk, enable predictive drug screening, and facilitate personalized medicine through patient-derived cells. Third, analytical readouts and computational integration emphasized the role of multi-parametric sensors, omics profiling, and AI-driven computational modeling in enhancing mechanistic understanding, reproducibility, and predictive capability. Fourth, regulatory and translational dimensions showed growing acceptance by agencies such as the FDA and EMA, the necessity for standardization and validation, ethical considerations in cell sourcing, and increasing industrial adoption, although challenges remain in scalability, cost, and harmonization of protocols. Organs-on-chips and microphysiological systems represent a transformative approach in biomedical research, offering human-relevant models that enhance disease understanding, therapeutic evaluation, and regulatory assessment. While technological and translational challenges persist, these platforms provide a predictive, ethical, and scalable alternative to conventional preclinical models, supporting the advancement of personalized medicine and drug development.&lt;/span&gt;&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Organs-on-chips</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Microphysiological systems</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Disease modeling</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Analytical readouts</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Regulatory adoption</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Personalized medicine</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Translational research</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://jmesopen.com/index.php/jmesopen/article/download/37/32</ArchiveCopySource>
  </Article>
</ArticleSet>
