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Pharmaceuticals
Special Issues
Drug Safety and Real-World Evidence: From Databases to Artificial Intelligence
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Drug Safety and Real-World Evidence: From Databases to Artificial Intelligence

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "AI in Drug Development".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 726

Special Issue Editor

Dr. Maria Antonietta Barbieri

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
Interests: pharmacovigilance; pharmacoepidemiology; real-world data; adverse drug reactions; post-marketing surveillance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drug safety and proper communication remain essential pillars of public health. While randomized clinical trials are fundamental to establish efficacy and safety before marketing authorization, their intrinsic limitations may not fully capture a medicine’s benefit–risk profile in routine clinical practice.

The increasing availability of real-world data from electronic health records, administrative claims, registries, and spontaneous reporting system databases has transformed pharmacovigilance and pharmacoepidemiology. At the same time, the adoption of automated and digital tools is accelerating the generation of evidence. In this evolving landscape, artificial intelligence (AI), machine learning, and natural language processing are emerging as key enablers for signal detection, enhanced risk prediction, and timely decision support.

Real-world pharmacovigilance and pharmacoepidemiology studies are therefore crucial to optimize prescribing, evaluate effectiveness, monitor drug–drug interactions, and characterize short- and long-term safety, especially in underrepresented and vulnerable populations and across innovative and complex therapies. Furthermore, integrating AI-driven approaches may strengthen proactive safety monitoring and improve patient-centered outcomes.

Authors are invited to submit original articles as well as systematic reviews, meta-analyses, and review articles to this Special Issue to contribute to the implementation of real-world data for public health.

We look forward to hearing from you.

Dr. Maria Antonietta Barbieri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drug safety
  • real-world evidence
  • patient’s clinical outcomes
  • pharmacovigilance
  • pharmacoepidemiology
  • artificial intelligence
  • adverse drug reactions
  • inappropriate prescribing

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Published Papers (1 paper)

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Research

13 pages, 947 KB  
Article
Signal Detection and Machine Learning-Based Prediction of Cytokine Release Syndrome in B-Cell Maturation Antigen-Targeting Immunotherapies Using FAERS Data
by Suhyeon Moon, Dong-Won Kang, Yeo Jin Choi and Sooyoung Shin
Pharmaceuticals 2026, 19(5), 669; https://doi.org/10.3390/ph19050669 - 25 Apr 2026
Viewed by 457
Abstract
Background/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains [...] Read more.
Background/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods: A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16–3.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53–8.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions: CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies. Full article
(This article belongs to the Special Issue Drug Safety and Real-World Evidence: From Databases to Artificial Intelligence)
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