Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions

Hashiguchi, Masayuki; Imai, Susumu; Uehara, Kaku; Maruyama, Junya; Shimizu, Mikiko; Mochizuki, Mayumi

doi:10.1371/journal.pone.0144263

Cited by 16 publications

(18 citation statements)

References 18 publications

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“…Mass, charge, and pI value of peptide were calculated using R package “ peptides ” ( Osorio et al, 2015 ), which is specifically designed for the quick and easy calculation of different AMPs features. This would help in their better classification and design.…”

Section: Methodsmentioning

confidence: 99%

In Silico Approach for Prediction of Antifungal Peptides

et al. 2018

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This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp).

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Section: Methodsmentioning

confidence: 99%

In Silico Approach for Prediction of Antifungal Peptides

et al. 2018

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“…Hence, this study attempted to analyse if the ADR reporting forms contain essential elements to apply causality assessment. .If ADR form includes comments on whether the ADR is known or unknown, it will help faster processing and reduce the signal generation time (Hashiguchi et al 2015). As per this study, it is observed that the forms don't have this entry.…”

Section: Discussionmentioning

confidence: 81%

A qualitative study to analyse adverse drug reaction reporting forms

Thamizhvani

R²,

Bhat.C³

et al. 2016

IJPT

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Introduction: Adverse reactions to drugs cause increase in the hospital admissions. They also cause increased financial burdens to the patients. They can be reduced by increasing the awareness about adverse drug reactions. ADR reporting can create a database help in this regard. To make ADR reporting effective, good ADR reporting form is needed. This study was started to analyse the existing ADR forms of different countries and identify the possible improvements that can be made. Material and methods: ADR Reporting forms submitted to the Regional Pharmacovigilance Centre were analysed to identify the difficulties faced by the reporters while filling them. ADR reporting forms of different countries were also collected and analysed. Adequacies of these forms were analysed. Based on this qualitative analysis, areas for improvement were identified. Results: Use of generic names, use of abbreviations and incomplete filling up of the details were observed. Options for causality assessment scales, colouring of mandatory details, categorising ADRs as new or old, dates of intake of concomitant drugs were identified as items to be included in the ADR reporting forms in future. Conclusion: As per the study's findings and other similar studies , dates of the concomitant drugs, categorisation of ADRs (as known or new ), different colours for the mandatory fields, options for causality assessment scales , whether the ADR is medically confirmed , exact chronology of clinical events are the items which can be included in ADR reporting forms in future. Need for more training for primary reporters in filling up of the ADR reporting form is recognized in this study.

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“…We detected the signals when the ROR estimated and lower limits of the corresponding 95% confidence interval (CI) were greater than 1, and at least 2 cases were required to define the signal [5, 6]. …”

Section: Methodsmentioning

confidence: 99%

Drug-induced gingival hyperplasia: a retrospective study using spontaneous reporting system databases

Hatahira

Abe

Hane

et al. 2017

J Pharm Health Care Sci

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BackgroundDrug-induced gingival hyperplasia (DIGH) causes problems with chewing, aesthetics, and pronunciation, and leads to the deterioration of the patient’s quality of life (QOL). Thus, the aim of this study was to evaluate the incidence of DIGH using spontaneous reporting system (SRS) databases.MethodsWe analyzed reports of DIGH from SRS databases and calculated the reporting odds ratios (RORs) of suspected drugs (immunosuppressants, calcium channel blockers, and anticonvulsants). The SRS databases used were the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) database. With the data, we evaluated the time-to-onset profile and the hazard type using the Weibull shape parameter (WSP). Furthermore, we used the association rule mining technique to discover undetected relationships such as possible risk factors.ResultsThe FAERS contained 5,821,716 reports. The RORs (95% confidence interval: CI) for cyclosporine, everolimus, sirolimus, mycophenolate mofetil, amlodipine, nifedipine, carbamazepine, clobazam, levetiracetam, phenobarbital, phenytoin, primidone, topiramate, and valproic acid, were 39.4 (95% CI: 30.3–51.2), 4.2 (1.7–10.0), 6.6 (2.5–17.7), 13.1 (7.2–23.2), 94.8 (80.0–112.9), 57.9 (35.7–94.0), 15.1 (10.3–22.3), 65.4 (33.8–126.7), 6.5 (3.6–11.8), 19.7 (8.8–44.0), 65.4 (52.4–82.9), 56.5 (21.1–151.7), 2.9 (1.1–7.7), and 17.5 (12.6–24.4), respectively. The JADER database contained 430,587 reports. The median time-to-onset of gingival hyperplasia values for immunosuppressants, calcium channel blockers, and anticonvulsants use were 71, 262, and 37 days, respectively. Furthermore, the 95% CI of the WSP β for anticonvulsants was over and excluded 1, which meant that they were wear-out failure type.ConclusionsOur results suggest that DIGH monitoring of patients administered immunosuppressants, calcium channel blockers, or anticonvulsants is important. We demonstrated the potential risk of DIGH following the long-term use of calcium channel blocker over approximately 260 days. Based on the results of the association rule mining approach, patients with intellectual disability who are administered phenytoin should be monitored carefully. We recommend that patients who experience symptoms related to DIGH should be closely monitored.

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Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions

Cited by 16 publications

References 18 publications

In Silico Approach for Prediction of Antifungal Peptides

In Silico Approach for Prediction of Antifungal Peptides

A qualitative study to analyse adverse drug reaction reporting forms

Drug-induced gingival hyperplasia: a retrospective study using spontaneous reporting system databases

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