From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development
The pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtwo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1636809/full |
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| author | Carlos Henrique Vieira-Vieira Sarang Sanjay Kulkarni Adam Zalewski Jobst Löffler Jonas Münch Annika Kreuchwig |
| author_facet | Carlos Henrique Vieira-Vieira Sarang Sanjay Kulkarni Adam Zalewski Jobst Löffler Jonas Münch Annika Kreuchwig |
| author_sort | Carlos Henrique Vieira-Vieira |
| collection | DOAJ |
| description | The pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtworks. PRINCE integrates decades of structured and unstructured safety study reports, leveraging a multi-agent architecture based on Large Language Models (LLMs) and advanced data retrieval methodologies, such as Retrieval-Augmented Generation and Text-to-SQL. In this paper, we describe the three-step evolution of PRINCE from a data search tool based on keyword matching to a resourceful research assistant capable of answering complex questions and drafting regulatory-critical documents. We highlight the iterative development process, guided by user feedback, that ensures alignment with evolving research needs and maximizes utility. Finally, we discuss the importance of building trust-based solutions and how transparency and explainability have been integrated into PRINCE. In particular, the integration of a human-in-the-loop approach enhances the accuracy and retains human accountability. We believe that the development and deployment of the PRINCE chatbot demonstrate the transformative potential of AI in the pharmaceutical industry, significantly improving data accessibility and research efficiency, while prioritizing data governance and compliance. |
| format | Article |
| id | doaj-art-013ed62ceaff46dbbdfdba0b5ec2792f |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-013ed62ceaff46dbbdfdba0b5ec2792f2025-08-20T03:44:14ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.16368091636809From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug developmentCarlos Henrique Vieira-Vieira0Sarang Sanjay Kulkarni1Adam Zalewski2Jobst Löffler3Jonas Münch4Annika Kreuchwig5Bayer Research and Development, Pharmaceuticals, Preclinical Development, Berlin, GermanyThoughtworks Technologies (India) Private Ltd., Pune, IndiaBayer Research and Development, Pharmaceuticals, Preclinical Development, Berlin, GermanyBayer Pharma Drug Innovation, Technology and Engineering, Leverkusen, GermanyBayer Digital Transformation and IT Pharma, Berlin, GermanyBayer Research and Development, Pharmaceuticals, Preclinical Development, Berlin, GermanyThe pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtworks. PRINCE integrates decades of structured and unstructured safety study reports, leveraging a multi-agent architecture based on Large Language Models (LLMs) and advanced data retrieval methodologies, such as Retrieval-Augmented Generation and Text-to-SQL. In this paper, we describe the three-step evolution of PRINCE from a data search tool based on keyword matching to a resourceful research assistant capable of answering complex questions and drafting regulatory-critical documents. We highlight the iterative development process, guided by user feedback, that ensures alignment with evolving research needs and maximizes utility. Finally, we discuss the importance of building trust-based solutions and how transparency and explainability have been integrated into PRINCE. In particular, the integration of a human-in-the-loop approach enhances the accuracy and retains human accountability. We believe that the development and deployment of the PRINCE chatbot demonstrate the transformative potential of AI in the pharmaceutical industry, significantly improving data accessibility and research efficiency, while prioritizing data governance and compliance.https://www.frontiersin.org/articles/10.3389/frai.2025.1636809/fullpharmaceutical industrypreclinicallarge-language modelchatbotretrieval-augmented generationgenerative artificial intelligence |
| spellingShingle | Carlos Henrique Vieira-Vieira Sarang Sanjay Kulkarni Adam Zalewski Jobst Löffler Jonas Münch Annika Kreuchwig From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development Frontiers in Artificial Intelligence pharmaceutical industry preclinical large-language model chatbot retrieval-augmented generation generative artificial intelligence |
| title | From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development |
| title_full | From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development |
| title_fullStr | From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development |
| title_full_unstemmed | From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development |
| title_short | From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development |
| title_sort | from data silos to insights the prince multi agent knowledge engine for preclinical drug development |
| topic | pharmaceutical industry preclinical large-language model chatbot retrieval-augmented generation generative artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1636809/full |
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