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1.
J Med Internet Res ; 26: e48572, 2024 May 03.
Article in English | MEDLINE | ID: mdl-38700923

ABSTRACT

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.


Subject(s)
Big Data , Data Mining , Drug-Related Side Effects and Adverse Reactions , Humans , Data Mining/methods , Pharmacovigilance , Models, Theoretical , Adverse Drug Reaction Reporting Systems/statistics & numerical data
2.
Vaccine ; 42(15): 3486-3492, 2024 May 31.
Article in English | MEDLINE | ID: mdl-38704258

ABSTRACT

BACKGROUND: While safety of influenza vaccines is well-established, some studies have suggested potential associations between influenza vaccines and certain adverse events (AEs). This study examined the safety of the 2022-2023 influenza vaccines among U.S. adults ≥ 65 years. METHODS: A self-controlled case series compared incidence rates of anaphylaxis, encephalitis/encephalomyelitis, Guillain-Barré Syndrome (GBS), and transverse myelitis following 2022-2023 seasonal influenza vaccinations (i.e., any, high-dose or adjuvanted) in risk and control intervals among Medicare beneficiaries ≥ 65 years. We used conditional Poisson regression to estimate incidence rate ratios (IRRs) and 95 % confidence intervals (CIs) adjusted for event-dependent observation time and seasonality. Analyses also accounted for uncertainty from outcome misclassification where feasible. For AEs with any statistically significant associations, we stratified results by concomitant vaccination status. RESULTS: Among 12.7 million vaccine recipients, we observed 76 anaphylaxis, 276 encephalitis/encephalomyelitis, 134 GBS and 75 transverse myelitis cases. Only rates of anaphylaxis were elevated in risk compared to control intervals. With all adjustments, an elevated, but non-statistically significant, anaphylaxis rate was observed following any (IRR: 2.40, 95% CI: 0.96-6.03), high-dose (IRR: 2.31, 95% CI: 0.67-7.91), and adjuvanted (IRR: 3.28, 95% CI: 0.71-15.08) influenza vaccination; anaphylaxis IRRs were 2.54 (95% CI: 0.49-13.05) and 1.64 (95% CI: 0.38-7.05) for persons with and without concomitant vaccination, respectively. CONCLUSIONS: Rates of encephalitis/encephalomyelitis, GBS, or transverse myelitis were not elevated following 2022-2023 seasonal influenza vaccinations among U.S. adults ≥ 65 years. There was an increased rate of anaphylaxis following influenza vaccination that may have been influenced by concomitant vaccination.


Subject(s)
Influenza Vaccines , Influenza, Human , Vaccination , Aged , Aged, 80 and over , Female , Humans , Male , Anaphylaxis/epidemiology , Drug-Related Side Effects and Adverse Reactions/epidemiology , Guillain-Barre Syndrome/epidemiology , Guillain-Barre Syndrome/etiology , Guillain-Barre Syndrome/chemically induced , Incidence , Influenza Vaccines/adverse effects , Influenza Vaccines/administration & dosage , Influenza, Human/prevention & control , Influenza, Human/epidemiology , Medicare/statistics & numerical data , Myelitis, Transverse/epidemiology , Myelitis, Transverse/etiology , Seasons , United States/epidemiology , Vaccination/adverse effects
3.
BMC Pediatr ; 24(1): 344, 2024 May 18.
Article in English | MEDLINE | ID: mdl-38760745

ABSTRACT

BACKGROUND: Paediatric patients are especially prone to experiencing adverse drug reactions (ADRs), and the surgical environment gathers many conditions for such reactions to occur. Additionally, little information exists in the literature on ADRs in the paediatric surgical population. We aimed to quantify the ADR frequency in this population, and to investigate the characteristics and risk factors associated with ADR development. METHODS: A prospective observational study was conducted in a cohort of 311 paediatric patients, aged 1-16 years, admitted for surgery at a tertiary referral hospital in Spain (2019-2021). Incidence rates were used to assess ADR frequency. Odds ratios (ORs) were calculated to evaluate the influence of potential risk factors on ADR development. RESULTS: Distinct ADRs (103) were detected in 80 patients (25.7%). The most frequent being hypotension (N = 32; 35%), nausea (N = 16; 15.5%), and emergence delirium (N = 16; 15.5%). Most ADRs occurred because of drug-drug interactions. The combination of sevoflurane and fentanyl was responsible for most of these events (N = 32; 31.1%). The variable most robustly associated to ADR development, was the number of off-label drugs prescribed per patient (OR = 2.99; 95% CI 1.73 to 5.16), followed by the number of drugs prescribed per patient (OR = 1.26, 95% CI 1.13 to 1.41), and older age (OR = 1.26, 95% CI 1.07 to 1.49). The severity of ADRs was assessed according to the criteria of Venulet and the Spanish Pharmacovigilance System. According to both methods, only four ADRs (3.9%) were considered serious. CONCLUSIONS: ADRs have a high incidence rate in the paediatric surgical population. The off-label use of drugs is a key risk factor for ADRs development.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Humans , Prospective Studies , Child , Child, Preschool , Female , Male , Risk Factors , Infant , Adolescent , Drug-Related Side Effects and Adverse Reactions/epidemiology , Spain/epidemiology , Surgical Procedures, Operative/adverse effects , Incidence , Drug Interactions , Off-Label Use , Emergence Delirium/epidemiology , Emergence Delirium/chemically induced
4.
Sci Rep ; 14(1): 11367, 2024 05 18.
Article in English | MEDLINE | ID: mdl-38762547

ABSTRACT

Fulvestrant, as the first selective estrogen receptor degrader, is widely used in the endocrine treatment of breast cancer. However, in the real world, there is a lack of relevant reports on adverse reaction data mining for fulvestrant. To perform data mining on adverse events (AEs) associated with fulvestrant and explore the risk factors contributing to severe AEs, providing a reference for the rational use of fulvestrant in clinical practice. Retrieved adverse event report information associated with fulvestrant from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database, covering the period from market introduction to September 30, 2023. Suspicious AEs were screened using the reporting odds ratio (ROR) and proportional reporting ratio methods based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe AEs to explore the risk factors associated with fulvestrant-induced severe AEs. A total of 6947 reports related to AEs associated with fulvestrant were obtained, including 5924 reports of severe AEs and 1023 reports of non-severe AEs. Using the disproportionality analysis method, a total of 210 valid AEs were identified for fulvestrant, with 45 AEs (21.43%) not listed in the product labeling, involving 11 systems and organs. The AEs associated with fulvestrant were sorted by frequency of occurrence, with neutropenia (325 cases) having the highest number of reports. By signal strength, injection site pruritus showed the strongest signal (ROR = 658.43). The results of the logistic regression analysis showed that concurrent use of medications with extremely high protein binding (≥ 98%) is an independent risk factor for severe AEs associated with fulvestrant. Age served as a protective factor for fulvestrant-related AEs. The co-administration of fulvestrant with CYP3A4 enzyme inhibitors did not show statistically significant correlation with the occurrence of severe AEs. Co-administration of drugs with extremely high protein binding (≥ 98%) may increase the risk of severe adverse reactions of fulvestrant. Meanwhile, age (60-74 years) may reduce the risk of severe AEs of fulvestrant. However, further clinical research is still needed to explore and verify whether there is interaction between fulvestrant and drugs with high protein binding through more clinical studies.


Subject(s)
Adverse Drug Reaction Reporting Systems , Data Mining , Databases, Factual , Fulvestrant , United States Food and Drug Administration , Fulvestrant/adverse effects , Humans , Female , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Middle Aged , Adult , Aged , United States , Breast Neoplasms/drug therapy , Risk Factors , Antineoplastic Agents, Hormonal/adverse effects , Adolescent , Drug-Related Side Effects and Adverse Reactions/epidemiology , Young Adult
5.
JCO Clin Cancer Inform ; 8: e2300159, 2024 May.
Article in English | MEDLINE | ID: mdl-38728613

ABSTRACT

PURPOSE: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets. METHODS: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI. RESULTS: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74. CONCLUSION: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.


Subject(s)
Algorithms , Immune Checkpoint Inhibitors , Liver Neoplasms , Humans , Immune Checkpoint Inhibitors/adverse effects , Male , Female , Middle Aged , Aged , Liver Neoplasms/drug therapy , Liver Neoplasms/immunology , Retrospective Studies , Phenotype , Chemical and Drug Induced Liver Injury/etiology , Chemical and Drug Induced Liver Injury/diagnosis , Carcinoma, Hepatocellular/drug therapy , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/etiology , Liver/pathology , Liver/drug effects , Liver/immunology
7.
BMC Cancer ; 24(1): 552, 2024 May 02.
Article in English | MEDLINE | ID: mdl-38698336

ABSTRACT

BACKGROUND: Patients with hematological malignancies often require multidrug therapy using a variety of antineoplastic agents and supportive care medications. This increases the risk of drug-related problems (DRPs). Determining DRPs in patients hospitalized in hematology services is important for patients to achieve their drug treatment goals and prevent adverse effects. This study aims to identify DRPs by the clinical pharmacist in the multidisciplinary team in patients hospitalized in the hematology service of a university hospital in Turkey. METHODS: This study was conducted prospectively between December 2022 and May 2023 in the hematology service of Suleyman Demirel University Research and Application Hospital in Isparta, Turkey. DRPs were determined using the Pharmaceutical Care Network Europe (PCNE) 9.1 Turkish version. RESULTS: This study included 140 patients. Older age, longer hospital stay, presence of acute lymphoblastic leukemia, presence of comorbidities, higher number of medications used, and polypharmacy rate were statistically significantly higher in the DRP group than in the non-DRP group (p < 0.05). According to multivariate logistic regression analysis, the probability of DRP in patients with polypharmacy was statistically significant 7.921 times (95% CI: 3.033-20.689) higher than in patients without polypharmacy (p < 0.001).Every 5-day increase in the length of hospital stay increased the likelihood of DRP at a statistically significant level (OR = 1.476, 95% CI: 1.125-1.938 p = 0.005). In this study, at least one DRP was detected in 69 (49.3%) patients and the total number of DRPs was 152. Possible or actual adverse drug events (96.7%) were the most common DRPs. The most important cause of DRPs was drug choice (94.7%), and the highest frequency within its subcategories was the combination of inappropriate drugs (93.4%). CONCLUSIONS: This study shows the importance of including a clinical pharmacist in a multidisciplinary team in identifying and preventing DRPs in the hematology service.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Hematologic Neoplasms , Humans , Male , Female , Prospective Studies , Middle Aged , Hematologic Neoplasms/drug therapy , Hematologic Neoplasms/epidemiology , Aged , Adult , Drug-Related Side Effects and Adverse Reactions/epidemiology , Turkey/epidemiology , Antineoplastic Agents/adverse effects , Antineoplastic Agents/therapeutic use , Polypharmacy , Pharmacists , Hematology , Young Adult , Aged, 80 and over
9.
Zhonghua Yi Xue Za Zhi ; 104(20): 1790-1803, 2024 May 28.
Article in Chinese | MEDLINE | ID: mdl-38782747

ABSTRACT

Immune checkpoint inhibitors (ICIs) have emerged as crucial therapeutic agents for various malignancies by activating the host immune system against tumor cells. However, many different types of skin adverse reactions may occur during its use, including eruption, pruritus, blistering, hypopigmentation, alopecia, and even severe cases, Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). These cutaneous immune-related adverse events (cirAEs) had a high incidence, which seriously affected patients' quality of life and antitumor treatment decisions. Some severe cutaneous adverse reactions (SCARs) even endanger patients' lives. Therefore, the Chinese Society of Dermatology, the Chinese Dermatologist Association of the Chinese Medical Doctor Association, the Dermatology Division of the Chinese Geriatrics Society, and other relevant experts jointly discussed and formulated the 'Chinese Expert Consensus on the Diagnosis and Treatment of Immune Checkpoint Inhibitor-Related Cutaneous Adverse Reactions'. This consensus covers the name, epidemiology, pathogenesis, clinical features, classification and grading of cirAEs, principles of management and the re-initiation of ICIs. It aims to provide a more scientific and authoritative reference for the diagnosis and treatment of cirAEs in China in the future.


Subject(s)
Consensus , Immune Checkpoint Inhibitors , Humans , Immune Checkpoint Inhibitors/adverse effects , China , Stevens-Johnson Syndrome/therapy , Stevens-Johnson Syndrome/diagnosis , Stevens-Johnson Syndrome/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/therapy , Quality of Life , Skin/pathology , Neoplasms/drug therapy , Drug Eruptions/diagnosis , Drug Eruptions/therapy , Drug Eruptions/etiology
11.
BMC Bioinformatics ; 25(1): 196, 2024 May 20.
Article in English | MEDLINE | ID: mdl-38769492

ABSTRACT

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Computational Biology/methods , Humans , Algorithms
12.
Cancer Immunol Immunother ; 73(7): 126, 2024 May 11.
Article in English | MEDLINE | ID: mdl-38733406

ABSTRACT

BACKGROUND: Immuno-oncology (IO) drugs are essential for treating various cancer types; however, safety concerns persist in older patients. Although the incidence of immune-related adverse events (irAEs) is similar among age groups, higher rates of hospitalization or discontinuation of IO therapy have been reported in older patients. Limited research exists on IO drug safety and risk factors in older adults. Our investigation aimed to assess the incidence of irAEs and identify the potential risk factors associated with their development. METHODS: This retrospective analysis reviewed the clinical data extracted from the medical records of patients aged > 80 years who underwent IO treatment at our institution. Univariate and multivariate analyses were performed to assess the incidence of irAEs. RESULTS: Our study included 181 patients (median age: 82 years, range: 80-94), mostly men (73%), with a performance status of 0-1 in 87% of the cases; 64% received IO monotherapy. irAEs occurred in 35% of patients, contributing to IO therapy discontinuation in 19%. Our analysis highlighted increased body mass index, eosinophil counts, and albumin levels in patients with irAEs. Eosinophil count emerged as a significant risk factor for any grade irAEs, particularly Grade 3 or higher, with a cutoff of 118 (/µL). The group with eosinophil counts > 118 had a higher frequency of irAEs, and Grade 3 or higher events than the group with counts ≤ 118. CONCLUSION: IO therapy is a safe treatment option for patients > 80 years old. Furthermore, patients with elevated eosinophil counts at treatment initiation should be cautiously managed.


Subject(s)
Immune Checkpoint Inhibitors , Neoplasms , Humans , Retrospective Studies , Male , Female , Aged, 80 and over , Immune Checkpoint Inhibitors/adverse effects , Immune Checkpoint Inhibitors/therapeutic use , Neoplasms/drug therapy , Neoplasms/immunology , Risk Factors , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/etiology , Incidence
13.
BMJ Open ; 14(5): e085115, 2024 May 16.
Article in English | MEDLINE | ID: mdl-38760050

ABSTRACT

INTRODUCTION: DNA-informed prescribing (termed pharmacogenomics, PGx) is the epitome of personalised medicine. Despite international guidelines existing, its implementation in paediatric oncology remains sparse. METHODS AND ANALYSIS: Minimising Adverse Drug Reactions and Verifying Economic Legitimacy-Pharmacogenomics Implementation in Children is a national prospective, multicentre, randomised controlled trial assessing the impact of pre-emptive PGx testing for actionable PGx variants on adverse drug reaction (ADR) incidence in patients with a new cancer diagnosis or proceeding to haematopoetic stem cell transplant. All ADRs will be prospectively collected by surveys completed by parents/patients using the National Cancer Institute Pediatric Patient Reported [Ped-PRO]-Common Terminology Criteria for Adverse Events (CTCAE) (weeks 1, 6 and 12). Pharmacist will assess for causality and severity in semistructured interviews using the CTCAE and Liverpool Causality Assessment Tool. The primary outcome is a reduction in ADRs among patients with actionable PGx variants, where an ADR will be considered as any CTCAE grade 2 and above for non-haematological toxicities and any CTCAE grade 3 and above for haematological toxicities Cost-effectiveness of pre-emptive PGx (secondary outcome) will be compared with standard of care using hospital inpatient and outpatient data along with the validated Childhood Health Utility 9D Instrument. Power and statistics considerations: A sample size of 440 patients (220 per arm) will provide 80% power to detect a 24% relative risk reduction in the primary endpoint of ADRs (two-sided α=5%, 80% vs 61%), allowing for 10% drop-out. ETHICS AND DISSEMINATION: The ethics approval of the trial has been obtained from the Royal Children's Hospital Ethics Committee (HREC/89083/RCHM-2022). The ethics committee of each participating centres nationally has undertaken an assessment of the protocol and governance submission. TRIAL REGISTRATION NUMBER: NCT05667766.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Pharmacogenetics , Humans , Child , Drug-Related Side Effects and Adverse Reactions/prevention & control , Prospective Studies , Randomized Controlled Trials as Topic , Neoplasms/drug therapy , Neoplasms/genetics , Multicenter Studies as Topic , Precision Medicine/economics , Hematopoietic Stem Cell Transplantation
14.
Am J Manag Care ; 30(5): e140-e146, 2024 May 01.
Article in English | MEDLINE | ID: mdl-38748914

ABSTRACT

OBJECTIVES: Patients undergoing cardiac surgery are considered at high risk for developing drug-related problems (DRPs) due to comorbidities and complexity of drug treatment. This study aimed to identify DRPs in patients undergoing cardiac surgery and to develop and implement a framework to reduce potential risks associated with drug treatment. STUDY DESIGN: Prospectively designed quasi-experimental study. METHODS: This study consisted of observational (risk assessment and framework development) and interventional (framework implementation) periods and was conducted at a department of cardiovascular surgery in a university hospital. An expert panel evaluated the causes of DRPs. Then a framework was developed in consensus to identify safeguards to be implemented during the interventional period. RESULTS: A total of 200 patients (100 patients per study period) were included. During the observational period, a total of 275 DRPs and 487 causes were identified; 74.5% of DRPs were not solved. For the risk analysis, 487 causes were evaluated and only 32.6% were considered acceptable risk. By implementing the framework in the interventional period, 215 DRPs and 304 causes were identified and 386 interventions were recommended by a clinical pharmacist. A total of 342 (88.6%) interventions were accepted by a health care team, and 128 (59.5%) DRPs were completely solved. For the risk analysis, 304 causes were evaluated and 84.9% were considered acceptable risk ( P < .001 compared with the observational period). CONCLUSIONS: It is possible to reduce risk levels or prevent occurrence of DRPs by implementing a framework for risk management developed by a multidisciplinary care team in areas such as cardiac surgery where time is limited.


Subject(s)
Cardiac Surgical Procedures , Drug-Related Side Effects and Adverse Reactions , Humans , Risk Assessment , Male , Female , Cardiac Surgical Procedures/adverse effects , Prospective Studies , Aged , Drug-Related Side Effects and Adverse Reactions/prevention & control , Middle Aged
15.
Pharmaceut Med ; 38(3): 251-259, 2024 May.
Article in English | MEDLINE | ID: mdl-38705932

ABSTRACT

INTRODUCTION: Spontaneous reporting of adverse events (AEs) is a mainstay of pharmacovigilance, and an ongoing challenge is how to ensure that more high-quality reports are collected for comprehensive information provision. The Med Safety App, a smartphone-based application, was launched in Nigeria in November 2020 to provide an electronic platform for users to seamlessly report AEs. There has been a paucity of evidence on the use of this application or other mobile applications for reporting adverse drug reactions/AEs following immunization in the Nigerian environment. OBJECTIVE: The aim of this study was to evaluate the trends in adverse event reporting before and after the introduction of the Med Safety App in Nigeria. METHODS: This was a retrospective, observational study using data from the VigiFlow database to compare adverse event reporting in Nigeria before and after the deployment of the Med Safety App. The baseline period was 1st April 2019 to 30th October 2020 and the comparison period was 1st November 2020 to 31st May 2022. We used Vigilance Hub, the back-end system for the Med Safety App, to extract data on App downloads and de-identified user statistics. Data were summarized using descriptive statistics, frequencies and proportions. Quality was assessed by assigning a completeness score to each individual case safety report. The Kruskal-Wallis test was used to test for differences in medians between groups. RESULTS: Following deployment of the App, the Nigerian National Pharmacovigilance Centre recorded an increase in the total number of adverse event reports received in VigiFlow, from 2051 in the baseline period to 18,995 following deployment of the App, with 81.7% of those reported via the Med Safety App. There was a reduction in the proportion of paper-based reporting from 98.4 to 15.7% post-deployment, and direct reporting by consumers increased from 2.7 to 17.6%. Of the 15,526 reports submitted via the App, 15,111 (97.3%) had a completeness score above 70% and 6993 (45%) had a completeness score of 100%. The median completeness score of adverse event reports on the Med Safety App was 6 out of 7. On bivariate analysis using the Kruskal-Wallis test, there was an association between means of reporting and completeness score, and this association was significant, with a p value of 0.0001, which may reflect the validation rules that are applied within the App. CONCLUSION: Deployment of the Med Safety App increased both the number and quality of adverse event reports; however, more awareness and capacity building are needed to strengthen and sustain reporting on the tool by all categories of healthcare professionals and consumers/patients.


Subject(s)
Adverse Drug Reaction Reporting Systems , Drug-Related Side Effects and Adverse Reactions , Mobile Applications , Pharmacovigilance , Humans , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Nigeria , Retrospective Studies , Drug-Related Side Effects and Adverse Reactions/epidemiology , Smartphone , Databases, Factual
16.
J Med Syst ; 48(1): 51, 2024 May 16.
Article in English | MEDLINE | ID: mdl-38753223

ABSTRACT

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.


Subject(s)
Adverse Drug Reaction Reporting Systems , Electronic Health Records , Pharmacovigilance , Electronic Health Records/organization & administration , Humans , Adverse Drug Reaction Reporting Systems/organization & administration , Netherlands , Natural Language Processing , Drug-Related Side Effects and Adverse Reactions/prevention & control , Data Mining/methods
17.
Medicine (Baltimore) ; 103(20): e38273, 2024 May 17.
Article in English | MEDLINE | ID: mdl-38758847

ABSTRACT

The study aims to estimate the incidence and risk factors of adverse drug reactions (ADRs) induced by anti-tuberculosis (TB) drugs. A single center retrospective analysis of patients taking anti-TB therapy from January 2016 to December 2018 in the hospital was conducted. Univariate and multivariate logistic regression analysis were used to identify these risk factors of ADRs induced by anti-TB drugs. Among 1430 patients receiving anti-TB therapy, 440 (30.77%) patients showed at least 1 ADR induced by anti-TB drugs. Hyperuricemia was the most common ADR, followed by hepatic function test abnormality, liver damage and gastrointestinal reactions. Significant differences (P < .05) were also seen in diabetes, age, treatment duration, type of TB (extrapulmonary) and some therapeutic regimens between ADR group and non-ADR group, respectively. Multivariate logistic regression analysis showed that treatment duration (OR = 1.029, 95%CI[1.018-1.040], P = .000), type of TB (extrapulmonary, OR = 1.487, 95%CI[1.134-1.952], P = .004) and some therapeutic regimens (HREZ, OR = 1.425, 95%CI[0.922-2.903], P = .001; HRZS, OR = 2.063, 95% CI[1.234-3.449], P = .006; HRZ, OR = 3.623, 95%CI[2.289-5.736], P = .000) were risk factors for ADRs induced by anti-TB drugs. Anti-TB drugs usually induced the occurrence of severe and frequent adverse effects, such as hyperuricemia. Treatment duration, HREZ, HRZS and HRZ regimens, and type of TB (extrapulmonary) should be considered as high-risk factors. Thus, it should be recommended to consider optimum management during anti-TB therapy, particularly hyperuricemia monitoring and hepatic function test.


Subject(s)
Antitubercular Agents , Humans , Retrospective Studies , Antitubercular Agents/adverse effects , Male , Female , China/epidemiology , Middle Aged , Risk Factors , Adult , Aged , Incidence , Hyperuricemia/drug therapy , Hyperuricemia/epidemiology , Tuberculosis/drug therapy , Tuberculosis/epidemiology , Hospitalization/statistics & numerical data , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/etiology
18.
Methods ; 226: 164-175, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38702021

ABSTRACT

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.


Subject(s)
Deep Learning , Humans , Drug Discovery/methods , Animals , Drug-Related Side Effects and Adverse Reactions , Cardiotoxicity/etiology
19.
Front Public Health ; 12: 1392180, 2024.
Article in English | MEDLINE | ID: mdl-38716250

ABSTRACT

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Natural Language Processing , Pharmacovigilance , Social Media , Humans , Adverse Drug Reaction Reporting Systems
20.
Front Immunol ; 15: 1396752, 2024.
Article in English | MEDLINE | ID: mdl-38745663

ABSTRACT

Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, the application of ICIs can also cause treatment-related adverse events (trAEs) and immune-related adverse events (irAEs). This study was to evaluate both the irAEs and trAEs of different ICI strategies for NSCLC based on randomized clinical trials (RCTs). The study also examined real-world pharmacovigilance data from the Food and Drug Administration Adverse Event Reporting System (FAERS) regarding claimed ICI-associated AEs in clinical practice. Methods: Based on Pubmed, Embase, Medline, and the Cochrane CENTRAL, we retrieved RCTs comparing ICIs with chemotherapy drugs or with different ICI regimens for the treatment of NSCLC up to October 20, 2023. Bayesian network meta-analysis (NMA) was performed using odds ratios (ORs) with 95% credible intervals (95%CrI). Separately, a retrospective pharmacovigilance study was performed based on FAERS database, extracting ICI-associated AEs in NSCLC patients between the first quarter (Q1) of 2004 and Q4 of 2023. The proportional reports reporting odds ratio was calculated to analyze the disproportionality. Results: The NMA included 51 RCTs that involved a total of 26,958 patients with NSCLC. Based on the lowest risk of any trAEs, cemiplimab, tislelizumab, and durvalumab were ranked as the best. Among the agents associated with the lowest risk of grades 3-5 trAEs, tislelizumab, avelumab, and nivolumab were most likely to rank highest. As far as any or grades 3-5 irAEs are concerned, atezolizumab plus bevacizumab plus chemotherapy is considered the most safety option. However, it is associated with a high risk of grades 3-5 trAEs. As a result of FAERS pharmacovigilance data analysis, 9,420 AEs cases have been identified in 7,339 NSCLC patients treated with ICIs, and ICIs were related to statistically significant positive signal with 311 preferred terms (PTs), and comprehensively investigated and identified those AEs highly associated with ICIs. In total, 152 significant signals were associated with Nivolumab, with malignant neoplasm progression, death, and hypothyroidism being the most frequent PTs. Conclusion: These findings revealed that ICIs differed in their safety profile. ICI treatment strategies can be improved and preventive methods can be developed for NSCLC patients based on our results.


Subject(s)
Carcinoma, Non-Small-Cell Lung , Immune Checkpoint Inhibitors , Lung Neoplasms , Pharmacovigilance , United States Food and Drug Administration , Humans , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/immunology , Lung Neoplasms/drug therapy , Immune Checkpoint Inhibitors/adverse effects , Immune Checkpoint Inhibitors/therapeutic use , United States , Randomized Controlled Trials as Topic , Drug-Related Side Effects and Adverse Reactions/etiology , Drug-Related Side Effects and Adverse Reactions/epidemiology , Adverse Drug Reaction Reporting Systems , Bayes Theorem , Retrospective Studies
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