Dataset Information

Torsade de pointes associated with chloroquine, hydroxychloroquine, and azithromycin: a retrospective analysis of individual case safety reports from VigiBase.

ABSTRACT:

Purpose

To analyze the cases of torsade de pointes (TdP) and related symptoms reported in association with chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZT) to the World Health Organization (WHO) global database of individual case safety reports (ICSRs) for drug monitoring (VigiBase) using qualitative and quantitative pharmacovigilance approaches.

Methods

The main characteristics of the ICSRs reporting TdP with CQ, HCQ, and AZT have been summarized. Co-reported drugs with risk to cause QT prolongation have been described. Reporting odds ratios (RORs) as a measure of disproportionality for reported TdP and individual drugs have been calculated.

Results

One hundred seventy ICSRs reporting TdP in association with the drugs of interest were identified (CQ: 11, HCQ: 31, CQ + HCQ: 1, HCQ + AZT: 27, AZT: 100). From these, 41 (24.3%) were received during the pandemic period (December 2019 to February 2021). The median age of the patients was 63, 53, and 63 years old for CQ, HCQ, and AZT, respectively. Reports included concomitant use of other QT-prolonging drugs (CQ 25.0%, HCQ 71.2%, AZT 64.6%). A proportion of the cases were fatal (CQ 25.0%, HCQ 8.6%, AZT 16.1%). Increased disproportionality has been found for the individual drugs and TdP: CQ (ROR: 7.41, 95% confidence interval (CI): 3.82, 12.96), HCQ (ROR: 8.49, 95% CI: 6.57, 10.98), azithromycin (ROR: 8.06, 95% CI: 6.76, 9.61). Disproportionality was also found for other related symptoms, Standardized MedDRA Query for torsade de pointes/QT prolongation (narrow): CQ (ROR: 11.95, 95% CI: 10.04-14.22); HCQ (ROR: 20.43, 95% CI: 19.13, 21.83), AZT (ROR: 7.78, 95% CI: 7.26, 8.34).

Conclusions

The prescription of CQ, HCQ, and AZT should be restricted to therapeutic indications with established positive benefit/risk profile. Doctors and patients should be aware of this potential adverse reaction especially when several risk factors are present.

SUBMITTER: Saint-Gerons DM

PROVIDER: S-EPMC8089128 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS:An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION:A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.