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Top 50 drugs with the most number of cases of TdP. The vertical axis is the name of the drugs, and the horizontal axis is the corresponding number of cases of TdP for each drug. The orange color represents that the drug is included on the QT drug lists of CredibleMeds®. The yellow color represents that the drug is not included on the QT drug lists of CredibleMeds®.
Source publication
Objective
This study aimed to identify the most common and top drugs associated with the risk of torsades de pointes (TdP) based on the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.
Materials and methods
We used OpenVigil 2.1 to query FAERS database and data from the first quarter of 2004 to the...
Citations
... Both exhibit strong agreement when analyzing data on adverse drug reactions and can be used to effectively investigate medications that may cause significant adverse responses. 24 However, data mining is only used as a means of discovering suspicious signals. Further analysis and evaluation through rigorous in vivo and in vitro experiments, as well as thorough medical evaluations, are necessary. ...
Objective
The OpenVigil database can be used to assess medications that may cause supraventricular tachycardia (SVT) and to produce a reference for their safe use in clinical settings.
Methods
We analyzed first-quarter data from 2004 to 2023, obtained by searching the OpenVigil database using the keyword “supraventricular tachycardia.” Trade names and generic names were obtained by querying the RxNav database, and the proportions were summarized. The proportionate reporting ratio (PRR), reporting odds ratio, and chi-square values were also summarized. We created Asahi diagrams and set the screening criteria to drug events ≥30, PRR >2, and chi-square >4. Outcomes were evaluated using the Side Effect Resource database, several scientific literature databases, and the Hangzhou Yiyao Rational Medication System.
Results
A total of 2435 distinct medications were found to induce SVT between the first quarter of 2004 and 2023, leading to 22,375 documented adverse events related to SVT. Further investigation revealed that salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline were most likely to cause SVT.
Conclusion
We conducted signal mining of adverse drug events using the OpenVigil database and evaluated the six drugs most likely to cause SVT. The results of this research can serve as a drug safety reference in the clinic.
... Several studies focused on the role of drug-drug interactions, CYP450 inhibition, and polypharmacy in QTc prolongation [3,4,17,[25][26][27]. As LQTS is one of the critical diseases constituting an augmented risk for sudden cardiac death [28], developing a clinically applicable model to predict the extent of QTc prolongation could be helpful in preventing subsequent TdP, even more so considering that the existing literature agrees on the mortality rate of TdP with values ranging between 15 and 20% [29,30]. ...
There are currently no established methods to predict quantitatively whether the start of a drug with the potential to prolong the QTc interval poses patients at risk for relevant QTc prolongation. Therefore, this retrospective study aimed to pave the way for the development of models for estimating QTc prolongation in patients newly exposed to medications with QTc-prolonging potential. Data of patients with a documented QTc prolongation after initiation of a QTc-prolonging drug were extracted from hospital charts. Using a standard model-building approach, general linear mixed models were identified as the best models for predicting both the extent of QTc prolongation and its absolute value after the start of a QTc-time-prolonging drug. The cohort consisted of 107 adults with a mean age of 64.2 years. Patients were taking an average of 2.4 drugs associated with QTc prolongation, with amiodarone, propofol, pipamperone, ondansetron, and mirtazapine being the most frequently involved. There was a significant but weak correlation between measured and predicted absolute QTc values under medication (r2 = 0.262, p < 0.05), as well as for QTc prolongation (r2 = 0.238, p < 0.05). As the developed models are based on a relatively small number of subjects, further research is necessary to ensure their applicability and reliability in real-world scenarios. Overall, this research contributes to the understanding of QTc prolongation and its association with medications, providing insight into the development of predictive models. With improvements, these models could potentially aid healthcare professionals in assessing the risk of QTc prolongation before adding a new drug and in making informed decisions in clinical settings.
... To the best of our knowledge, this list, which contained 1,088 potential causative drugs of QT prolongation and TdP, is the most comprehensive list summarized using a pharmacovigilance database so far. Although previous studies have tried to use a pharmacovigilance database to explore and summarize high-risk drugs associated with QT prolongation and TdP (Poluzzi et al., 2010;Teng et al., 2019;Cirmi et al., 2020;Ali et al., 2021;He et al., 2021;Wu et al., 2022;Yu and Liao, 2022;Chen et al., 2023), the list provided by these studies is not comprehensive enough. The most related studies only pay attention to a certain drug class and, on this basis, evaluate and compare the risks of limited drugs, such as H1antihistamines (Ali et al., 2021), antifungal triazoles (Yu and Liao, 2022), antibacterial drugs (Teng et al., 2019), antipsychotics (He et al., 2021), tyrosine kinase inhibitors (Cirmi et al., 2020), and antidepressants (Chen et al., 2023). ...
... Although those studies highlighted the drugs worthy of attention in the same category, it is difficult to rationally integrate them into a list because of the difference in data time included, inclusion and exclusion criteria of AE reports, and ADR signal detection methods. In order to overcome those limitations, Wu et al. (2022) investigated and evaluated all risky drugs associated with TdP according to the FAERS database with a unified standard. However, it only selected one of the PTs (torsade de pointes, MedDRA code: 10044066) in "torsade de pointes/QT prolongation (SMQs)" to identify target AE reports and only showed the top 50 most frequently reported drugs and the top 50 risky drugs with the ...
... Based on the reasons mentioned above, we introduced the disproportionality analysis method as a uniform standard to evaluate the risk of QT prolongation and TdP of drugs. Although previous studies have used similar ADR signal mining methods to explore the QT prolongation and TdP risk of drugs, the scope of those research studies mainly focused on specific drug classes and specific PTs (Poluzzi et al., 2010;Teng et al., 2019;Cirmi et al., 2020;Ali et al., 2021;He et al., 2021;Wu et al., 2022;Yu and Liao, 2022;Chen et al., 2023). Therefore, it is Table S1), which eliminated the obstacles of cross-drug class and cross-PT risk comparison. ...
Introduction: Drug-induced QT prolongation and (or) Torsade de Pointes (TdP) is a well-known serious adverse reaction (ADR) for some drugs, but the widely recognized comprehensive landscape of culprit-drug of QT prolongation and TdP is currently lacking.
Aim: To identify the top drugs reported in association with QT prolongation and TdP and provide information for clinical practice.
Method: We reviewed the reports related to QT prolongation and TdP in the FDA Adverse Event Reporting System (FAERS) database from January 1, 2004 to December 31, 2022, and summarized a potential causative drug list accordingly. Based on this drug list, the most frequently reported causative drugs and drug classes of QT prolongation and TdP were counted, and the disproportionality analysis for all the drugs was conducted to in detect ADR signal. Furthermore, according to the positive–negative distribution of ADR signal, we integrated the risk characteristic of QT prolongation and TdP in different drugs and drug class.
Results: A total of 42,713 reports in FAERS database were considered to be associated with QT prolongation and TdP from 2004 to 2022, in which 1,088 drugs were reported as potential culprit-drugs, and the largest number of drugs belonged to antineoplastics. On the whole, furosemide was the most frequently reported drugs followed by acetylsalicylic acid, quetiapine, citalopram, metoprolol. In terms of drug classes, psycholeptics was the most frequently reported drug classes followed by psychoanaleptics, analgesics, beta blocking agents, drugs for acid related disorders. In disproportionality analysis, 612 drugs showed at least one positive ADR signals, while citalopram, ondansetron, escitalopram, loperamide, and promethazine were the drug with the maximum number of positive ADR signals. However, the positive-negative distribution of ADR signals between different drug classes showed great differences, representing the overall risk difference of different drug classes.
Conclusion: Our study provided a real-world overview of QT prolongation and TdP to drugs, and the presentation of the potential culprit-drug list, the proportion of reports, the detection results of ADR signals, and the distribution characteristics of ADR signals may help understand the safety profile of drugs and optimize clinical practice.
... An unexpected consequence of the abuse of loperamide is a risk of severe cardiac toxicity consisting of pronounced electrophysiological abnormalities and life-threatening arrhythmias [12,[18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Common attributes of this cardiac toxicity include the prolongation of both the QTc interval (which includes QRS within its measurement) and QRS duration, AV block/Right Bundle Branch Block, with incidences of torsade de pointes (TdP), including Brugada-like syndrome and non-TdP-like forms of ventricular tachycardia (VT) [31][32][33][34][35]. Other potential confounding risk factors were identified in many cases, e.g., the concurrent use of other drugs with known cardiac actions, such as sotalol, amitriptyline, fluoxetine, clonazepam, methadone, and alprazolam, as well as the use of other drugs of abuse, or the use of low serum potassium [18,23,24,36,37], which may also contribute to the cardiac events that occur alongside excessive loperamide ingestion. ...
... The recent increases in the abuse and intentional overdose of the antidiarrheal drug, loperamide, which acts via an opiate mechanism of action, has been accompanied, in many cases, by severe cardiac toxicity consisting of pronounced QT prolongation, QRS widening, AV blockade, and associated life-threatening ventricular arrhythmia, such as TdP and other forms of ventricular tachycardia and ventricular fibrillation [14,34,35]. The present nonclinical in vitro, in vivo, and in silico studies with loperamide provide a mechanistic basis for the cardiac electrophysiological toxicity of loperamide at extreme overdoses associated with abuse. ...
Loperamide has been a safe and effective treatment for diarrhea for many years. However, many cases of cardiotoxicity with intentional abuse of loperamide ingestion have recently been reported. We evaluated loperamide in in vitro and in vivo cardiac safety models to understand the mechanisms for this cardiotoxicity. Loperamide slowed conduction (QRS-duration) starting at 0.3 µM [~1200-fold (×) its human Free Therapeutic Plasma Concentration; FTPC] and reduced the QT-interval and caused cardiac arrhythmias starting at 3 µM (~12,000× FTPC) in an isolated rabbit ventricular-wedge model. Loperamide also slowed conduction and elicited Type II/III A-V block in anesthetized guinea pigs at overdose exposures of 879× and 3802× FTPC. In ion-channel studies, loperamide inhibited hERG (IKr), INa, and ICa currents with IC50 values of 0.390 µM, 0.526 µM, and 4.091 µM, respectively (i.e., >1560× FTPC). Additionally, in silico trials in human ventricular action potential models based on these IC50s confirmed that loperamide has large safety margins at therapeutic exposures (≤600× FTPC) and confirmed repolarization abnormalities in the case of extreme doses of loperamide. The studies confirmed the large safety margin for the therapeutic use of loperamide but revealed that at the extreme exposure levels observed in human overdose, loperamide can cause a combination of conduction slowing and alterations in repolarization time, resulting in cardiac proarrhythmia. Loperamide’s inhibition of the INa channel and hERG-mediated IKr are the most likely basis for this cardiac electrophysiological toxicity at overdose exposures. The cardiac toxic effects of loperamide at the overdoses could be aggravated by co-medication with other drug(s) causing ion channel inhibition.
... In the disproportionate analysis of arrhythmias at HLT level, ibutilide monotherapy presented no signal in three specific arrhythmias except for ventricular arrhythmias and cardiac arrest, while mexiletine, dofetilide and dronedarone monotherapy demonstrated negative signal in supraventricular arrhythmias, cardiac conduction disorders, and ventricular arrhythmias and cardiac arrest, respectively. Notably, the risk of ventricular arrhythmia/TdP of dronedarone varied in different literatures, some of which showed a lower risk of dronedarone (Lafuente-Lafuente et al., 2012;Friberg, 2018;Tisdale et al., 2020), while others showed the opposite (Kao et al., 2012;Wu et al., 2022). Previous study reported 138 cases of ventricular arrhythmia associated with dronedarone between July 2009 and June 2011 (Kao et al., 2012), while our research identified only 19 reports during January 2016-June 2022. ...
... Frontiers in Pharmacology frontiersin.org Additionally, the FAERS database recorded 61 cases of TdP related to dronedarone from the first quarter of 2009 to the fourth quarter of 2015 but only 2 reports between January 2016 and June 2022, resulting in the positive signal of TdP in dronedarone after incorporating data before 2016 (Kao et al., 2012;Wu et al., 2022). The higher reports of ventricular arrhythmia/TdP before 2016 and the lower cases after 2016 may be related to the early nonstandard use of dronedarone, as it clearly worsens outcomes in patients with decompensated heart failure (Kober et al., 2008) and permanent atrial fibrillation (Rosenstein and Woods, 2012). ...
Objective: This study aimed to identify the different associations between antiarrhythmic drugs (AADs) and arrhythmias, and to determine whether pharmacokinetic drug interactions involving AADs increase the risk of AAD-related arrhythmias compared to using AADs alone.
Materials and methods: The disproportionality analysis of AAD-associated cardiac arrhythmias, including AAD monotherapies and concomitant use of pharmacokinetic interacting agents involving AADs, was conducted by using reporting odds ratio (ROR) and information component (IC) as detection of potential safety signals based on FAERS data from January 2016 to June 2022. We compared the clinical features of patients reported with AAD–associated arrhythmias between fatal and non-fatal groups, and further investigated the onset time (TTO) following different AAD regimens.
Results: A total of 11754 AAD–associated cardiac arrhythmias reports were identified, which was more likely to occur in the elderly (52.17%). Significant signals were detected between cardiac arrhythmia and all AAD monotherapies, with ROR ranging from 4.86 with mexiletine to 11.07 with flecainide. Regarding four specific arrhythmias in High Level Term (HLT) level, the AAD monotherapies with the highest ROR were flecainide in cardiac conduction disorders (ROR025 = 21.18), propafenone in rate and rhythm disorders (ROR025 = 10.36), dofetilide in supraventricular arrhythmias (ROR025 = 17.61), and ibutilide in ventricular arrhythmias (ROR025 = 4.91). Dofetilide/ibutilide, ibutilide, mexiletine/ibutilide and dronedarone presented no signal in the above four specific arrhythmias respectively. Compared with amiodarone monotherapy, sofosbuvir plus amiodarone detected the most significantly increased ROR in arrhythmias.
Conclusion: The investigation showed the spectrum and risk of AAD–associated cardiac arrhythmias varied among different AAD therapies. The early identification and management of AAD-associated arrhythmias are of great importance in clinical practice.
... The pharmacological mechanisms of DDIs were due to synergistic actions on biological pathways (PD-DDIs) and to a lesser extent due to modulation of ADME processes (PK-DDIs). The majority of observed PD-DDIs involved elevation of QT-prolongation risk due to the co-administration of drugs that influence the repolarization phase of cardiac myocytes such as quinolones (ATC-J01), selective β-2-adrenoreceptor agonists (ATC-R03), and selective serotonin reuptake inhibitors (ATC-N06) (Table 4, Figure 4) [37][38][39][40]. The associated clinical risk for arrhythmias from co-administered drugs with QT-prolonging effects might be unpredictable: thus, when combining drugs with potentiating effect on QT interval, a risk-benefit analysis considering individual patient status along with each drug's risk for QT-prolongation and monitoring for potential signs of arrhythmias is suggested [41][42][43]. ...
Background: Patients with respiratory disorders often have additional diseases and are usually treated with more than one medication to manage their respiratory conditions as well as additional comorbidities. Thus, they are frequently exposed to polypharmacy (≥5 drugs), which raises the risk for drug–drug interactions (DDIs) and adverse drug reactions (ADRs). In this work, we present the results regarding the prevalence of DDIs in hospitalized patients with respiratory disorders in Greece. Methods: A 6-month descriptive single-center retrospective observational study enrolled 102 patients with acute or chronic respiratory disorders. Clinical characteristics and medication regimens were recorded upon admission, hospitalization, and discharge. The prevalence of DDIs and their clinical significance was recorded and analyzed. Results: Unspecified acute lower respiratory tract infection (25%), exacerbations of chronic obstructive pulmonary disease (12%) and pneumonia (8%) were the most frequent reasons for admission. Cardiovascular disorders (46%), co-existing respiratory disorders (32%), and diabetes (25%) were the most prevalent comorbidities. Polypharmacy was noted in 61% of patients upon admission, 98% during hospitalization, and 63% upon discharge. Associated DDIs were estimated to be 55% upon admission, 96% throughout hospitalization, and 63% on discharge. Pharmacodynamic (PD) DDIs were the most prevalent cases (81%) and referred mostly to potential risk for QT-prolongation (31.4% of PD-DDIs) or modulation of coagulation process as expressed through the international normalized ratio (INR) (29.0% of DDIs). Pharmacokinetic (PK) DDIs (19% of DDIs) were due to inhibition of Cytochrome P450 mediated metabolism that could lead to elevated systemic drug concentrations. Clinically significant DDIs characterized as “serious-use alternative” related to 7% of cases while 59% of DDIs referred to combinations that could be characterized as “use with caution—monitor”. Clinically significant DDIs mostly referred to medication regimens upon admission and discharge and were associated with outpatient prescriptions. Conclusions: Hospitalized patients with respiratory disorders often experience multimorbidity and polypharmacy that raise the risk of DDIs. Clinicians should be conscious especially if any occurring arrhythmias, INR modulations, and prolonged or increased drug action is associated with DDIs.
Amiodarone (AMIO) is an antiarrhythmic drug with the pKa in the physiological range. Here, we explored how mild extracellular pH (pHe) changes shape the interaction of AMIO with atrial tissue and impact its pharmacological properties in the classical model of sea anemone sodium channel neurotoxin type 2 (ATX) induced late sodium current (INa−Late) and arrhythmias.
Isolated atrial cardiomyocytes from male Wistar rats and human embryonic kidney cells expressing SCN5A Na+ channels were used for patch-clamp experiments. Isolated right atria (RA) and left atria (LA) tissue were used for bath organ experiments.
A more acidophilic pHe caused negative inotropic effects on isolated RA and LA atrial tissue, without modification of the pharmacological properties of AMIO. A pHe of 7.0 changed the sodium current (INa) related components of the action potential (AP), which was enhanced in the presence of AMIO. ATXinduced arrhythmias in isolated RA and LA. Also, ATX prolonged the AP duration and enhanced repolarization dispersion in isolated cardiomyocytes in both pHe 7.4 and pHe 7.0. Pre-incubation of the isolated RA and LA and isolated atrial cardiomyocytes with AMIO prevented arrhythmias induced by ATX only at a pHe of 7.0. Moreover, AMIO was able to block INa−Late induced by ATX only at a pHe of 7.0.
The pharmacological properties of AMIO concerning healthy rat atrial tissue are not dependent on pHe. However, the prevention of arrhythmias induced by INa−Late is pHe-dependent. The development of drugs analogous to AMIO with charge stabilization may help to create more effective drugs to treat arrhythmias related to the INa−Late.
Background
Selective serotonin reuptake inhibitors (SSRIs) are the most frequently prescribed agents to treat depression. Considering the growth in antidepressant prescription rates, SSRI-induced adverse events (AEs) need to be comprehensively clarified.
Objective
This study was to investigate safety profiles and potential AEs associated with SSRIs using the Food and Drug Administration Adverse Event Reporting System (FAERS).
Methods
A retrospective pharmacovigilance analysis was conducted using the FAERS database, with Open Vigil 2.1 used for data extraction. The study included cases from the marketing date of each SSRI (ie, citalopram, escitalopram, fluoxetine, paroxetine, fluvoxamine, and sertraline) to April 30, 2023. We employed the reporting odds ratio and Bayesian confidence propagation neural network as analytical tools to assess the association between SSRIs and AEs. The Medical Dictionary for Regulatory Activities was used to standardize the definition of AEs. AE classification was achieved using system organ classes (SOCs).
Results
Overall, 427 655 AE reports were identified for the 6 SSRIs, primarily associated with 25 SOCs, including psychiatric, nervous system, congenital, familial, genetic, cardiac, and reproductive disorders. Notably, sertraline ( n = 967) and fluvoxamine ( n = 169) exhibited the highest and lowest signal frequencies, respectively. All SSRIs had relatively strong signals related to congenital, psychiatric, and nervous disorders.
Conclusions and relevance
Most of our findings are consistent with those reported previously, but some AEs were not previously identified. However, AEs attributed to SSRIs remain ambiguous, warranting further validation. Applying data-mining methods to the FAERS database can provide additional insights that can assist in appropriately utilizing SSRIs.
Drug-drug interactions (DDIs) pose a significant issue in modern healthcare, potentially compromising treatment efficacy and patient safety. DDIs arise when significant alterations occur in the pharmacological action of a drug due to its co-administration with another drug, leading to potential adverse drug reactions (ADRs), toxicity or diminished therapeutic efficacy. Apart from the obvious cases of drug combinations that should be avoided, there are instances where risk-benefit analysis may allow co-administration. Hence, DDIs may represent clinically significant cases depending on the clinical outcome, time point of administration, etc. The issue is especially critical in cases of patients with multimorbidity and
complex therapeutic regimens with different time points in drug administrations. This work employs a graph-based approach
aimed at optimizing therapeutic regiments while considering the minimization of DDIs potential. In this approach each drug is
represented as a node, and edges represent the clinical significance of DDIs. We aim to identify sets of drugs that either
have no DDIs or exhibit minor to moderate clinical significance (referred to as Maximal Independent Sets), indicating that they can be taken together. In practice, we solved the complementary problem, which is finding Maximal Cliques. Both problems are NP-hard, but for small graphs, they can be solved exactly. From all the cliques we identify, those selected to be a part of each proposed therapeutic regimen must consist of nodes that appear only once. This problem is once again reduced to clique finding. The above approach is demonstrated using two clinical scenarios involving two patients who are experiencing
polypharmacy and are at risk for ADRs due to potential DDIs of varying clinical significance. By applying our approach, the
therapeutic schemes are optimized towards minimizing the risk of ADRs.
