Academic Journal
From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review.
| Τίτλος: | From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review. |
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| Συγγραφείς: | Gałuszewski, Michał, Olszewski, Jan, Jankowska, Karolina, Wójcik, Krzysztof, Bielecka-Wajdman, Anna |
| Πηγή: | Journal of Clinical Medicine; Apr2026, Vol. 15 Issue 7, p2801, 22p |
| Θεματικοί όροι: | Drug repositioning, Artificial intelligence, Electronic health records, Natural language processing, Pharmaceutical technology, Translational research, Graph algorithms |
| Περίληψη: | Background/Objectives: Drug repositioning has emerged as a promising strategy to address the innovation crisis in pharmaceutical development. While artificial intelligence enables efficient in silico hypothesis generation, clinical translation remains challenging. This study aims to evaluate the role of Real-World Evidence (RWE) in validating AI-generated drug-repositioning candidates. Methods: A comprehensive literature review was conducted in PubMed using a predefined search strategy integrating drug repositioning, artificial intelligence, and real-world data. After multi-stage screening, 22 original research articles were included for analysis. Results: Network-based algorithms and natural language processing dominated AI-driven hypothesis generation. Validation using Electronic Health Records and insurance databases enabled retrospective assessment of drug efficacy across large populations. Successful applications were identified in neurodegenerative, metabolic, infectious, autoimmune, and psychiatric diseases. Conclusions: The integration of AI-based analytics with RWE provides a promising framework for the preliminary verification of computational predictions, potentially informing the translational pathway toward clinical practice. However, the effectiveness of this approach remains dependent on data quality and the specific therapeutic context, requiring further standardization of clinical data. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Clinical Medicine is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=20770383&ISBN=&volume=15&issue=7&date=20260401&spage=2801&pages=2801-2822&title=Journal of Clinical Medicine&atitle=From%20In%20Silico%20Hypothesis%20to%20Validation%3A%20The%20Role%20of%20Real-World%20Evidence%20in%20the%20Preliminary%20Verification%20of%20AI-Generated%20Drug-Repositioning%20Candidates%3A%20A%20Comprehensive%20Review.&aulast=Ga%C5%82uszewski%2C%20Micha%C5%82&id=DOI:10.3390/jcm15072801 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gałuszewski%2C+Michał%22">Gałuszewski, Michał</searchLink><br /><searchLink fieldCode="AR" term="%22Olszewski%2C+Jan%22">Olszewski, Jan</searchLink><br /><searchLink fieldCode="AR" term="%22Jankowska%2C+Karolina%22">Jankowska, Karolina</searchLink><br /><searchLink fieldCode="AR" term="%22Wójcik%2C+Krzysztof%22">Wójcik, Krzysztof</searchLink><br /><searchLink fieldCode="AR" term="%22Bielecka-Wajdman%2C+Anna%22">Bielecka-Wajdman, Anna</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Clinical Medicine; Apr2026, Vol. 15 Issue 7, p2801, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Drug+repositioning%22">Drug repositioning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+health+records%22">Electronic health records</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+technology%22">Pharmaceutical technology</searchLink><br /><searchLink fieldCode="DE" term="%22Translational+research%22">Translational research</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background/Objectives: Drug repositioning has emerged as a promising strategy to address the innovation crisis in pharmaceutical development. While artificial intelligence enables efficient in silico hypothesis generation, clinical translation remains challenging. This study aims to evaluate the role of Real-World Evidence (RWE) in validating AI-generated drug-repositioning candidates. Methods: A comprehensive literature review was conducted in PubMed using a predefined search strategy integrating drug repositioning, artificial intelligence, and real-world data. After multi-stage screening, 22 original research articles were included for analysis. Results: Network-based algorithms and natural language processing dominated AI-driven hypothesis generation. Validation using Electronic Health Records and insurance databases enabled retrospective assessment of drug efficacy across large populations. Successful applications were identified in neurodegenerative, metabolic, infectious, autoimmune, and psychiatric diseases. Conclusions: The integration of AI-based analytics with RWE provides a promising framework for the preliminary verification of computational predictions, potentially informing the translational pathway toward clinical practice. However, the effectiveness of this approach remains dependent on data quality and the specific therapeutic context, requiring further standardization of clinical data. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Clinical Medicine is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/jcm15072801 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 2801 Subjects: – SubjectFull: Drug repositioning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Electronic health records Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Pharmaceutical technology Type: general – SubjectFull: Translational research Type: general – SubjectFull: Graph algorithms Type: general Titles: – TitleFull: From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gałuszewski, Michał – PersonEntity: Name: NameFull: Olszewski, Jan – PersonEntity: Name: NameFull: Jankowska, Karolina – PersonEntity: Name: NameFull: Wójcik, Krzysztof – PersonEntity: Name: NameFull: Bielecka-Wajdman, Anna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20770383 Numbering: – Type: volume Value: 15 – Type: issue Value: 7 Titles: – TitleFull: Journal of Clinical Medicine Type: main |
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