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Cited by 25Pharmaceutical Practices and Patient OutcomesPharmacovigilance and Adverse Drug ReactionsPharmacogenetics and Drug Metabolism

Advancing drug-drug interactions research: integrating AI-powered prediction, vulnerable populations, and regulatory insights

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Huang, Wenzhun, Wang, Xiao, Chen, Yunhao, Yu, Changqing, Zhang, Shanwen·2025·Frontiers in Pharmacology
DOI 10.3389/fphar.2025.1618701PMC PMC12380558
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Abstract

Drug-drug interactions (DDIs) pose a significant and intricate challenge in clinical pharmacotherapy, especially among older adults who often have chronic conditions that necessitate multiple medications. These interactions can undermine the effectiveness of treatments or lead to adverse drug reactions (ADRs), which in turn can increase illness rates and strain healthcare resources. Traditional methods for detecting DDIs, such as clinical trials and spontaneous reporting systems, tend to be retrospective and frequently fall short in identifying rare, population-specific, or complex DDIs. However, recent advancements in artificial intelligence (AI), systems pharmacology, and real-world data analytics have paved the way for more proactive and integrated strategies for predicting DDIs. Innovative techniques like graph neural networks (GNNs), natural language processing, and knowledge graph modeling are being increasingly utilized in clinical decision support systems (CDSS) to improve the detection, interpretation, and prevention of DDIs across various patient demographics. This review aims to provide a thorough overview of the latest trends and future directions in DDIs research, structured around five main areas: (1) epidemiological trends and high-risk drug combinations, (2) mechanistic classification of DDIs, (3) methodologies for detection and prediction, particularly those driven by AI, (4) considerations for vulnerable populations, and (5) regulatory frameworks and pathways for innovation. Special emphasis is placed on the role of pharmacogenomic insights and real-world evidence in developing personalized strategies for assessing DDIs risks. By connecting fundamental pharmacological principles with advanced computational technologies, this review seeks to guide clinicians, researchers, and regulatory bodies. The integration of AI, multi-omics data, and digital health systems has the potential to significantly enhance the safety, accuracy, and scalability of DDIs management in contemporary healthcare.

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  • Huang et al. (2025) noted that AI-powered DDI prediction must balance precision with clinical explainability through domain-specific calibration.

    “This polarity mirrors the challenge outlined by Huang et al. (2025) [35], who noted that AI-powered DDI prediction must balance precision with clinical explainability through domain-specific calibration.”

    From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as Huang et al. (2025) [35]

    Section 7.1 says DDI models can make precise predictions while remaining difficult to interpret, limiting clinical use; section 4.7 also calls explainable AI essential to clinical relevance. The paper supports the need to address both precision and explainability, but the cited passages do not identify domain-specific calibration as the way to do so. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Huang et al. (2025) emphasised that AI-driven DDI research is making progress but still lacks domain-specific tuning and explainability.

    “Moreover, Huang et al. (2025) [35] emphasised that AI-driven DDI research is making progress but still lacks domain-specific tuning and explainability, which our work begins to address.”

    From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as Huang et al. (2025) [35]

    Sections 4.7 and 7.1 describe substantial progress in AI-based DDI prediction alongside persistent interpretability problems. Section 4.3 calls for more personalized, context-aware models, but the passages do not specifically identify a lack of domain-specific tuning. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • No study has directly compared AI with clinicians in dentistry or oral surgery, fields in which robust DDI data remain scarce.

    “While AI-driven DDI checks have been investigated in general medicine (intensive care and pharmacovigilance), no study has directly compared AI with clinicians in dentistry or oral surgery, fields in which robust DDI data remain scarce [35].”

    From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as [35]

    Section 2.4 discusses AI analysis of pharmacovigilance data, and Section 8.1 discusses AI-enabled DDI prediction generally. Neither addresses dentistry or oral surgery, the availability of DDI data in those fields, or whether a study has directly compared AI with clinicians there. Searches for those terms and comparisons found no passage that verifies the claim. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Huang, W., Wang, X., Chen, Y., Yu, C., & Zhang, S.
Affiliations
  • Xijing University (CN)
  • Qilu University of Technology (CN)
Corresponding author
Huang, Wenzhun
ORCID
Venue
Frontiers in Pharmacology
Publisher
Frontiers Media SA
Published
2025-08-13
Volume
16
Pages
1618701-1618701
ISSN
1663-9812 (ISSN-L)
DOI
10.3389/fphar.2025.1618701
PMC
PMC12380558
OpenAlex
W4413181219
Cited by
25
References
3
Citations by year
2026: 25
FWCI
3.10
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Pharmacology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (62273284, 62172338, 62072378)
Topics
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Keywords
drug-drug interactions, adverse drug reactions, older adults, polypharmacy, artificial intelligence, graph neural networks, clinical decision support systems, vulnerable populations, pharmacogenomics, real-world evidence, regulatory framework, DDI prediction, multi-omics data, digital health

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