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Pharmaceuticals
Special Issues
Pharmacovigilance in Drug Therapy: Drug–Drug Interactions and Safety Evaluation
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Pharmacovigilance in Drug Therapy: Drug–Drug Interactions and Safety Evaluation

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 5036

Special Issue Editor

Dr. Jacques Turgeon

E-Mail Website
Guest Editor
1. GalenusRx, Orlando, FL, USA
2. Faculty of Pharmacy, Université de Montréal, Montréal, QC H3C 3J7, Canada
Interests: drug metabolism; multi-drug interactions; clinical decision support systems; drug safety; drug-induced long QT syndrome
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medicines, whether they are small molecules, natural products, or biologics used to treat various diseases and conditions, inherently possess not only desired and expected effects but also, to a certain degree, unexpected and unwanted side-effects. The monitoring of the efficacy and toxicity of medicines within our armamentarium is at the heart of pharmacovigilance programs. Through pharmacovigilance programs, we can detect, assess, understand, and prevent adverse effects related to any medicine. Pharmacovigilance programs monitor medicine effectiveness, efficacy and safety not just under controlled product development conditions, but under real-world situations as well, i.e., in patients within their individualized environment and exposure to different food products, with their various diseases, treated with multiple drugs, history of allergies, ethnicity, preferences, use of recreational drugs, alcohol and tobacco, etc. One of the drawbacks of pharmacovigilance programs is the necessary exposure of patients to medicine and their side-effects. Today, would it be possible to develop new ways to assess medicine safety without exposing many patients to medicines? What are the tools and technologies available such as simulation studies, artificial intelligence, virtual trials, pharmacogenomics, or real-world databases that we could take advantage of and use to better predict drug safety? How could we better predict drug interactions, drug–food interactions, drug–disease interactions, drug–gene interactions, and drug–drug–gene interactions. The objective of this Special Issue, entitled “Pharmacovigilance in Drug-Therapy: Drug–Drug Interactions and Safety Evaluation”, is to collect a series of publications that would better inform the scientific community and the public about medication safety.

As the editor of this Special Issue in Pharmaceutics, I hope that you will accept my invitation to contribute to an original paper.

Sincerely,

Dr. Jacques Turgeon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pharmacovigilance
  • multi-drug interactions
  • clinical decision support systems
  • drug safety
  • drug side-effects
  • drug–drug interactions

Published Papers (3 papers)

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Research

19 pages, 1320 KiB  
Article
An Assessment of Different Decision Support Software from the Perspective of Potential Drug–Drug Interactions in Patients with Chronic Kidney Diseases
by Muhammed Yunus Bektay, Aysun Buker Cakir, Meltem Gursu, Rumeyza Kazancioglu and Fikret Vehbi Izzettin
Pharmaceuticals 2024, 17(5), 562; https://doi.org/10.3390/ph17050562 (registering DOI) - 28 Apr 2024
Viewed by 223
Abstract
Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug–drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD [...] Read more.
Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug–drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD and to ascertain the concordance among different Clinical Decision Support Software (CDSS). A cross-sectional study was conducted in a nephrology outpatient clinic at a university hospital. The pDDIs were identified and evaluated using Lexicomp® and Medscape®. The patients’ characteristics, comorbidities, and medicines used were recorded. The concordance of different CDSS were evaluated using the Kendall W coefficient. An evaluation of 1121 prescribed medications for 137 patients was carried out. The mean age of the patients was 64.80 ± 14.59 years, and 41.60% of them were male. The average year with CKD was 6.48 ± 5.66. The mean number of comorbidities was 2.28 ± 1.14. The most common comorbidities were hypertension, diabetes, and coronary artery disease. According to Medscape, 679 pDDIs were identified; 1 of them was contraindicated (0.14%), 28 (4.12%) were serious-use alternative, and 650 (9.72%) were interventions that required closely monitoring. According to Lexicomp, there were 604 drug–drug interactions. Of these interactions, 9 (1.49%) were in the X category, 60 (9.93%) were in the D category, and 535 (88.57%) were in the C category. Two different CDSS systems exhibited statistically significant concordance with poor agreement (W = 0.073, p < 0.001). Different CDSS systems are commonly used in clinical practice to detect pDDIs. However, various factors such as the operating principles of these programs and patient characteristics can lead to incorrect guidance in clinical decision making. Therefore, instead of solely relying on programs with lower reliability and consistency scores, multidisciplinary healthcare teams, including clinical pharmacists, should take an active role in identifying and preventing pDDIs. Full article
(This article belongs to the Special Issue Pharmacovigilance in Drug Therapy: Drug–Drug Interactions and Safety Evaluation)
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12 pages, 706 KiB  
Article
Serious Safety Signals and Prediction Features Following COVID-19 mRNA Vaccines Using the Vaccine Adverse Event Reporting System
by Jung Yoon Choi, Yongjoon Lee, Nam Gi Park, Mi Sung Kim and Sandy Jeong Rhie
Pharmaceuticals 2024, 17(3), 356; https://doi.org/10.3390/ph17030356 - 10 Mar 2024
Viewed by 3151
Abstract
We aimed to analyze the characteristics of serious adverse events following immunizations (AEFIs) to identify potential safety information and prediction features. We screened the individual case safety reports (ICSRs) in adults who received mRNA-based COVID-19 vaccines using the Vaccine Adverse Event Reporting System [...] Read more.
We aimed to analyze the characteristics of serious adverse events following immunizations (AEFIs) to identify potential safety information and prediction features. We screened the individual case safety reports (ICSRs) in adults who received mRNA-based COVID-19 vaccines using the Vaccine Adverse Event Reporting System until December 2021. We identified the demographic and clinical characteristics of ICSRs and performed signal detection. We developed prediction models for serious AEFIs and identified the prognostic features using logistic regression. Serious ICSRs and serious AEFIs were 51,498 and 271,444, respectively. Hypertension was the most common comorbidity (22%). Signal detection indicated that the reporting odds ratio of acute myocardial infarction (AMI) was more than 10 times. Those who had experienced myocardial infarction (MI) were 5.7 times more likely to suffer from MI as an AEFI (95% CI 5.28–6.71). Moreover, patients who had atrial fibrillation (AF), acute kidney injury (AKI), cardiovascular accident (CVA), or pulmonary embolism (PE) were 7.02 times, 39.09 times, 6.03 times, or 3.97 times more likely to suffer from each AEFI, respectively. Our study suggests that vaccine recipients who had experienced MI, AF, AKI, CVA, or PE could require further evaluation and careful monitoring to prevent those serious AEFIs. Full article
(This article belongs to the Special Issue Pharmacovigilance in Drug Therapy: Drug–Drug Interactions and Safety Evaluation)
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16 pages, 1192 KiB  
Article
Antipsychotics and Risks of Cardiovascular and Cerebrovascular Diseases and Mortality in Dwelling Community Older Adults
by Sylvie Perreault, Laurie-Anne Boivin Proulx, Judith Brouillette, Stéphanie Jarry and Marc Dorais
Pharmaceuticals 2024, 17(2), 178; https://doi.org/10.3390/ph17020178 - 30 Jan 2024
Viewed by 915
Abstract
This study aims to investigate the effect of antipsychotic agents on cardiovascular and cerebrovascular diseases (CVD/CEV) and mortality risks in the older population living in a community. A cohort of 42,650 new users of antipsychotic agents was built using Quebec healthcare databases (1998–2011). [...] Read more.
This study aims to investigate the effect of antipsychotic agents on cardiovascular and cerebrovascular diseases (CVD/CEV) and mortality risks in the older population living in a community. A cohort of 42,650 new users of antipsychotic agents was built using Quebec healthcare databases (1998–2011). The outcomes were CVD/CEV and mortality incidence in 5 years of follow-up in the total cohort, sub-cohort of patients with no schizophrenia/dementia, sub-cohort with schizophrenia, and sub-cohort with dementia. Comparisons were made between the new users who continued the treatment (adherent level ≥ 60%) vs. those ceasing treatment (adherence level < 60%) using inverse probability of treatment weighting and Cox models. Comparing high adherence vs. low levels, CVD/CEV risk was increased by 36% in the sub-cohort with schizophrenia for atypical antipsychotic users and by 25% in the sub-cohort with dementia for typical antipsychotic users. An increasing mortality risk of 2- to 3-fold was linked with the typical antipsychotic use in all cohorts except the sub-cohort with schizophrenia; in addition, mortality risk is linked with the use of high vs. low doses. Antipsychotics were not linked with CVD/CEV risk, except for atypical antipsychotics in patients with schizophrenia and typical antipsychotics in patients with dementia. The mortality risk was linked with the use of typical antipsychotics and the dose used. Full article
(This article belongs to the Special Issue Pharmacovigilance in Drug Therapy: Drug–Drug Interactions and Safety Evaluation)
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