Academic Journal
AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives.
| Title: | AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives. |
|---|---|
| Authors: | Dinc, Rasit, Ardic, Nurittin |
| Source: | Life (2075-1729); Dec2025, Vol. 15 Issue 12, p1916, 23p |
| Subject Terms: | Artificial intelligence, Machine learning, Cardiovascular agents, Simulation methods & models, Animal experimentation, Safety standards |
| Company/Entity: | United States. Food & Drug Administration |
| Abstract: | Background: Animal studies remain fundamental to cardiovascular drug and device development, yet their ability to predict human responses is increasingly being questioned. The US Food and Drug Administration (FDA)'s April 2025 roadmap supports alternative testing approaches that strategically reduce animal use while increasing human relevance by combining laboratory methods, computer simulations, and artificial intelligence. This review examines AI-enabled alternative methodologies for cardiovascular safety assessment within established validation frameworks and regulatory acceptance programs. We describe machine learning approaches for predicting cardiac safety risks, automated analysis of human heart cells, and patient-specific computer simulations for evaluating medical devices. These tools can improve our understanding of biological mechanisms, focus limited animal studies on critical questions, and accelerate decision-making. Regulatory acceptance requires rigorous validation appropriate to each specific use and decision context. Conclusion: We outline practical steps for establishing credibility, including transparent data documentation, independent testing, and identifying where models can be reliably applied, and identify remaining challenges in data standardization and regulatory readiness. With ongoing alignment between regulators, standards bodies, and product developers, these alternative approaches could significantly reduce reliance on animal testing in cardiovascular research while maintaining or improving the quality of evidence. [ABSTRACT FROM AUTHOR] |
| Copyright of Life (2075-1729) 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.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20751729&ISBN=&volume=15&issue=12&date=20251201&spage=1916&pages=1916-1938&title=Life (2075-1729)&atitle=AI-Enabled%20Reduction%20of%20Animal%20Use%20in%20Cardiovascular%20Translational%20Medicine%3A%20Regulatory%20and%20Technological%20Perspectives.&aulast=Dinc%2C%20Rasit&id=DOI:10.3390/life15121916 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 190530134 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1040.81262207031 |
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| Items | – Name: Title Label: Title Group: Ti Data: AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dinc%2C+Rasit%22">Dinc, Rasit</searchLink><br /><searchLink fieldCode="AR" term="%22Ardic%2C+Nurittin%22">Ardic, Nurittin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Life (2075-1729); Dec2025, Vol. 15 Issue 12, p1916, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiovascular+agents%22">Cardiovascular agents</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+experimentation%22">Animal experimentation</searchLink><br /><searchLink fieldCode="DE" term="%22Safety+standards%22">Safety standards</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="CO" term="%22United+States%2E+Food+%26+Drug+Administration%22">United States. Food & Drug Administration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Animal studies remain fundamental to cardiovascular drug and device development, yet their ability to predict human responses is increasingly being questioned. The US Food and Drug Administration (FDA)'s April 2025 roadmap supports alternative testing approaches that strategically reduce animal use while increasing human relevance by combining laboratory methods, computer simulations, and artificial intelligence. This review examines AI-enabled alternative methodologies for cardiovascular safety assessment within established validation frameworks and regulatory acceptance programs. We describe machine learning approaches for predicting cardiac safety risks, automated analysis of human heart cells, and patient-specific computer simulations for evaluating medical devices. These tools can improve our understanding of biological mechanisms, focus limited animal studies on critical questions, and accelerate decision-making. Regulatory acceptance requires rigorous validation appropriate to each specific use and decision context. Conclusion: We outline practical steps for establishing credibility, including transparent data documentation, independent testing, and identifying where models can be reliably applied, and identify remaining challenges in data standardization and regulatory readiness. With ongoing alignment between regulators, standards bodies, and product developers, these alternative approaches could significantly reduce reliance on animal testing in cardiovascular research while maintaining or improving the quality of evidence. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Life (2075-1729) 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/life15121916 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1916 Subjects: – SubjectFull: United States. Food & Drug Administration Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Cardiovascular agents Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Animal experimentation Type: general – SubjectFull: Safety standards Type: general Titles: – TitleFull: AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dinc, Rasit – PersonEntity: Name: NameFull: Ardic, Nurittin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20751729 Numbering: – Type: volume Value: 15 – Type: issue Value: 12 Titles: – TitleFull: Life (2075-1729) Type: main |
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