Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Ibrahim, Heba Ezzat; Abdo, A.; Kerdawy, Ahmed M. El; Eldin, Ahmed Sharaf

doi:10.1016/j.ailsci.2021.100005

Cited by 15 publications

(10 citation statements)

References 109 publications

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“…However, SRS has several limitations and difficulties, such as under-reporting and bias, in detecting drug side effects. [ 3 , 4 ] For instance, ADR reporting is influenced by a myriad of factors, including the severity of ADR, the duration of the drug's release on the market, the experience of medical professionals, and the qualifications of the doctors reporting it. [ 5 ] Professional medical reports of adverse events often lack clarity regarding the diagnosis of adverse events.…”

Section: Introductionmentioning

confidence: 99%

Analyzing adverse drug reaction using statistical and machine learning methods

et al. 2022

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Background: Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs. Methods: A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines. Results: We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest. Conclusions: Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.

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Section: Introductionmentioning

confidence: 99%

Analyzing adverse drug reaction using statistical and machine learning methods

et al. 2022

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“…Thus, further studies are required to clarify the causal relationship between dupilumab and AA, and a novel therapeutic paradigm for dupilumab in AA. Despite these limitations, signals from post-marketing data can help develop new safety information [ 59 ]. This study confirms the paradoxical effects of dupilumab on AA using a real-world database.…”

Section: Discussionmentioning

confidence: 99%

Short communication: Comments on hair disorders associated with dupilumab based on VigiBase

Park

Byun

et al. 2022

PLoS ONE

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Background Dupilumab is a human antibody that blocks the signaling of both interleukin-4 and interleukin-13 receptors. It has been approved for the treatment of moderate-to-severe atopic dermatitis. However, several case reports have reported conflicting effects of dupilumab on alopecia. Objectives This study aimed to examine dupilumab-related hair disorders using the large real-world database, VigiBase. Methods All individual case safety reports associated with dupilumab in the Uppsala Monitoring Center VigiBase until December 29, 2019, were analyzed. Hair disorder-related terms were defined in High Level Terms with “alopecias,” “pilar disorders NEC (not elsewhere classified),” and “hypertrichoses,” using the Medical Dictionary for Regulatory Activities Hierarchy. Hair disorder reports associated with dupilumab and other biologics that inhibit the Th2 axis (omalizumab, mepolizumab, reslizumab, and benralizumab) were analyzed to determine their association with hair disorders. Disproportionality analysis was performed based on the proportional reporting ratio, reporting odds ratio, and information components. Results Among the 20,548 total dupilumab adverse event (AE) reports, hair disorders were reported in 462 dupilumab cases (2.2%), most of which reported hair loss, and only eight cases reported an increase in hair growth. The paradoxical trend in hair loss and growth after dupilumab use was confirmed using a disproportionality analysis. Among the other investigated biologics on Th2 immunity, only omalizumab was associated with hair loss. Additionally, hair disorders after dupilumab treatment were more frequently reported in women than in men. The proportion of hair disorder cases was high in Europe, accounting for 20.8% of hair disorder reports, whereas only 9.7% of all dupilumab-related AEs were reported in Europe. In conclusion, our analysis using a large real-world database confirmed that dupilumab is associated with hair disorders.

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“…org/page/e2br3-individual-case-safety-report-icsr-specification-andrelated-files). This global pharmacovigilance database facilitates detection of possible adverse drug reactions; however, the processes of data analysis and whether there is adequate focus on patient safety has been questioned (Edwards, 2017;Streefland, 2018;Ibrahim et al, 2021). According to Edwards (2017, p. 365) "We must develop a more holistic evaluation of suboptimal therapeutic outcomes, and do this without apportioning unnecessary blame".…”

Section: A Role For Cultural and Religious Beliefs In The Pharmacovig...mentioning

confidence: 99%

“…Jamaica, as a full member of the World Health Organization global database since 2012, collects individual case safety reports of adverse drug reactions following guidelines established by the International Conference on Harmonization (E2B (R3) Individual case Safety Report Specification and Related Files; https://www.ich.org/page/e2br3-individual-case-safety-report-icsr-specification-and-related-files ). This global pharmacovigilance database facilitates detection of possible adverse drug reactions; however, the processes of data analysis and whether there is adequate focus on patient safety has been questioned ( Edwards, 2017 ; Streefland, 2018 ; Ibrahim et al, 2021 ). According to Edwards (2017, p. 365) “We must develop a more holistic evaluation of suboptimal therapeutic outcomes, and do this without apportioning unnecessary blame”.…”

Section: Introductionmentioning

confidence: 99%

Influence of Jamaican Cultural and Religious Beliefs on Adherence to Pharmacotherapy for Non-Communicable Diseases: A Pharmacovigilance Perspective

2022

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Worldwide, socio-cultural determinants have been shown to influence the beliefs of patients about their health and decision making for treatment. This is consistent with the evidence that cultural and religious beliefs affect illness conceptualization and behaviors of Jamaican patients living with non-communicable diseases, such as diabetes mellitus and hypertension. Despite these known socio-cultural influences, an acknowledgment of relevance of adherence to pharmacotherapy has been grossly understudied. Furthermore, while poor adherence to pharmacotherapy, especially in the management of patients living with non-communicable diseases is associated with adverse drug reactions; reporting of such information in the pharmacovigilance process is inadequate. We review previous studies on the cultural and religious beliefs within the Jamaican context that may contribute to poor adherence to pharmacotherapy, especially among those patients living with non-communicable diseases. We support the ongoing perspective that current pharmacovigilance processes need retooling with the inclusion of socio-cultural influences on adherence to pharmacotherapy.

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Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Cited by 15 publications

References 109 publications

Analyzing adverse drug reaction using statistical and machine learning methods

Analyzing adverse drug reaction using statistical and machine learning methods

Short communication: Comments on hair disorders associated with dupilumab based on VigiBase

Influence of Jamaican Cultural and Religious Beliefs on Adherence to Pharmacotherapy for Non-Communicable Diseases: A Pharmacovigilance Perspective

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