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Fusaroli M, Raschi E, Poluzzi E, Hauben M. The evolving role of disproportionality analysis in pharmacovigilance. Expert Opin Drug Saf 2024; 23:981-994. [PMID: 38913869 DOI: 10.1080/14740338.2024.2368817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/12/2024] [Indexed: 06/26/2024]
Abstract
INTRODUCTION From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated. AREAS COVERED This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data. EXPERT OPINION Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.
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Affiliation(s)
- Michele Fusaroli
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Emanuel Raschi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Elisabetta Poluzzi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Manfred Hauben
- Department of Family and Community Medicine, New York Medical College, Valhalla, NY, USA
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Cocco M, Carnovale C, Clementi E, Barbieri MA, Battini V, Sessa M. Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review. Expert Rev Clin Pharmacol 2024; 17:441-453. [PMID: 38619027 DOI: 10.1080/17512433.2024.2343875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/12/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility. AREAS COVERED The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times. EXPERT OPINION Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.
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Affiliation(s)
- Marianna Cocco
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Drug Sciences, University of Pavia, Pavia, Italy
| | - Carla Carnovale
- Pharmacovigilance & Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST Fatebenefratelli-Sacco University Hospital, Università Degli Studi di Milano, Milan, Italy
| | - Emilio Clementi
- Pharmacovigilance & Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST Fatebenefratelli-Sacco University Hospital, Università Degli Studi di Milano, Milan, Italy
- Scientific Institute, IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Maria Antonietta Barbieri
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Vera Battini
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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Noguchi Y, Yan M, Yoshimura T. Comment on: Drugs That Interact With Colchicine via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS). Ann Pharmacother 2024; 58:196-197. [PMID: 37232293 DOI: 10.1177/10600280231168858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023] Open
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Malone DC, Gómez-Lumbreras A, Boyce RD, Villa-Zapata L, Tan MS, Hansten PD, Horn J. Reply: Drugs That Interact With Colchicine Via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS). Ann Pharmacother 2024; 58:198-199. [PMID: 37243500 DOI: 10.1177/10600280231168860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023] Open
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Patera AC, Maidment J, Maroj B, Mohamed A, Twomey K. A Science-Based Methodology Framework for the Assessment of Combination Safety Risks in Clinical Trials. Pharmaceut Med 2023; 37:183-202. [PMID: 37099245 DOI: 10.1007/s40290-023-00465-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/26/2023] [Indexed: 04/27/2023]
Abstract
Multiple components factor into the assessment of combination safety risks when two or more novel individual products are used in combination in clinical trials. These include, but are not limited to, biology, biochemistry, pharmacology, class effects, and preclinical and clinical findings (such as adverse drug reactions, drug target and mechanism of action, target expression, signaling, and drug-drug interactions). This paper presents a science-based methodology framework for the assessment of combination safety risks when two or more investigational products are used in clinical trials. The aim of this methodology framework is to improve prediction of the risks, to enable the appropriate safety risk mitigation and management to be put in place for the combination, and the development of the project combination safety strategy.
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Affiliation(s)
- Andriani C Patera
- Patient Safety Oncology, Oncology R&D, AstraZeneca, 101 Orchard Ridge Way, Gaithersburg, MD, 20878, USA.
| | - Julie Maidment
- Patient Safety Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Brijesh Maroj
- Patient Safety Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ahmed Mohamed
- Patient Safety Oncology, Oncology R&D, AstraZeneca, 101 Orchard Ridge Way, Gaithersburg, MD, 20878, USA
| | - Ken Twomey
- Patient Safety Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
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Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceut Med 2022; 36:295-306. [PMID: 35904529 DOI: 10.1007/s40290-022-00441-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
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Affiliation(s)
- Maribel Salas
- Daiichi Sankyo, Inc. & Center for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 211 Mount Airy Rd, Basking Ridge, NJ, USA
| | - Jan Petracek
- Institute of Pharmacovigilance, Hvezdova 2b, 14000, Prague, Czech Republic
| | - Priyanka Yalamanchili
- Daiichi Sankyo, Inc. & Rutgers University, 211 Mount Airy Rd, Basking Ridge, NJ, USA.
| | | | | | - Sameer Dhingra
- Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, India
| | | | - Tina Bostic
- PPD, part of Thermo Fisher Scientific, Wilmington, NC, USA
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Noguchi Y, Yoshizawa S, Aoyama K, Kubo S, Tachi T, Teramachi H. Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions. Pharmaceutics 2021; 13:pharmaceutics13101531. [PMID: 34683823 PMCID: PMC8537362 DOI: 10.3390/pharmaceutics13101531] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/14/2021] [Accepted: 09/17/2021] [Indexed: 02/08/2023] Open
Abstract
The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.
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Affiliation(s)
- Yoshihiro Noguchi
- Correspondence: or (Y.N.); (H.T.); Tel.: +81-58-230-8100 (Y.N. & H.T.)
| | | | | | | | | | - Hitomi Teramachi
- Correspondence: or (Y.N.); (H.T.); Tel.: +81-58-230-8100 (Y.N. & H.T.)
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Okunaka M, Kano D, Matsui R, Kawasaki T, Uesawa Y. Evaluation of the Expression Profile of Irinotecan-Induced Diarrhea in Patients with Colorectal Cancer. Pharmaceuticals (Basel) 2021; 14:ph14040377. [PMID: 33921605 PMCID: PMC8073045 DOI: 10.3390/ph14040377] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 12/03/2022] Open
Abstract
Irinotecan (CPT-11) is widely used for the treatment of unresectable colorectal cancer in combination with fluoropyrimidines, such as 5-fluorouracil and S-1. Diarrhea is one of the adverse effects associated with CPT-11 and frequently reported by patients treated with CPT-11-containing regimens combined with oral fluoropyrimidines. However, the mechanisms involved in this process, as well as whether fluctuations in the frequency and differences in the onset time of diarrhea with each CPT-11-containing regimen are caused by drug interactions remain unclear. Therefore, we examined the incidence of diarrhea caused by each CPT-11-containing regimen in patients with colorectal cancer using data from the large voluntary reporting Japanese Adverse Drug Event Report (JADER) database. Firstly, we searched for suspected drugs related to the occurrence of diarrhea using reported odds ratio and calculated the signal score to assess drug–drug interactions. Subsequently, we conducted a time-to-onset analysis using Weibull distribution. The results showed that the combination of CPT-11 with S-1 increased the frequency of diarrhea due to a pharmacological interaction but delayed its onset. The present results may contribute to the appropriate management of drug-induced adverse effects by healthcare professionals.
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Affiliation(s)
- Mashiro Okunaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan;
- Department of Pharmacy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (D.K.); (R.M.); (T.K.)
| | - Daisuke Kano
- Department of Pharmacy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (D.K.); (R.M.); (T.K.)
| | - Reiko Matsui
- Department of Pharmacy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (D.K.); (R.M.); (T.K.)
| | - Toshikatsu Kawasaki
- Department of Pharmacy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (D.K.); (R.M.); (T.K.)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan;
- Correspondence: ; Tel.: +81-42-495-8983
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Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio. Pharmaceuticals (Basel) 2020; 14:ph14010004. [PMID: 33374503 PMCID: PMC7822185 DOI: 10.3390/ph14010004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/21/2020] [Accepted: 12/21/2020] [Indexed: 12/18/2022] Open
Abstract
There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.
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