Drug-induced hypoglycemia: a disproportionality analysis of the FAERS database

BACKGROUND

Hypoglycemia is an adverse event (AE) that cannot be ignored in clinical practice. This study aimed to identify the most common and top drugs associated with the risk of hypoglycemia based on the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.

RESEARCH DESIGN AND METHODS

We used OpenVigil 2.1 pharmacovigilance analytics platform to query FAERS database and data from 2004 to 2023 were retrieved. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify hypoglycemia cases, and DrugBank database was used to determine drug generic names.

RESULTS

A total of 11,155,106 AEs reports were identified, of which 28,443 (0.25%) were related to hypoglycemia. Metformin (6926 cases) was associated with most cases of hypoglycemia. According to the disproportionality analysis, the top five drugs with the highest ROR and PRR were penamecillin, nikethamide, sotagliflozin, norethandrolone, glimepiride/pioglitazone. Nineteen of the top 50 drugs did not have hypoglycemia indicated in the package insert.

CONCLUSIONS

By analyzing the FAERS database, we listed drugs with a strong hypoglycemic signal for which the label does not provide a reminder. Notably, the potential hypoglycemia risks are of great importance and should be closely monitored in medical practice.

Liver injury associated with endothelin receptor antagonists: a pharmacovigilance study based on FDA adverse event reporting system data

BACKGROUND

Endothelin receptor antagonists are commonly used in clinical practice, with concerns about their hepatotoxicity.

AIM

This study aimed to conduct a comprehensive pharmacovigilance study based on FDA adverse event reporting system data to evaluate the possible association between endothelin receptor antagonists and drug-induced liver injury.

METHOD

Adverse event reports from FDA adverse event reporting system between January 2004 and December 2022 were analyzed. Disproportionality algorithms, including reporting odds ratio and information component, were used to evaluate the association between endothelin receptor antagonists and liver injury. Sex- and age-stratified analyses of drug-induced liver injury events were also conducted in relation to endothelin receptor antagonists.

RESULTS

Significant associations between bosentan, macitentan, and liver injury were identified. Bosentan showed a strong link with liver injury, with reporting odds ratios for cholestatic injury at 7.59 (95% confidence interval: 6.90-8.35), hepatocellular injury at 5.63 (5.29-6.00), and serious drug-related hepatic disorders events at 1.33 (1.24-1.43). Drug-induced liver injury signals associated with bosentan were detected in all age groups. Macitentan was associated with liver injury, with reporting odds ratios for hepatic failure at 1.64 (1.39-1.94), cholestatic injury at 1.62 (1.43-1.83), and serious drug-related hepatic disorders events at 1.40 (1.29-1.51). No drug-induced liver injury signal was detected for ambrisentan, and no significant sex differences were observed in drug-induced liver injury events.

CONCLUSION

Both bosentan and macitentan are associated with liver injury. Routine monitoring of serum aminotransferase levels is recommended, especially in patients at higher risk of liver injury. Further research into drug-drug interactions involving endothelin receptor antagonists is warranted.

Use of statins and risks of ovarian, uterine, and cervical diseases: a cohort study in the UK Biobank

PURPOSE

To examine the associations between use of statins and risks of various ovarian, uterine, and cervical diseases, including ovarian cancer, endometrial cancer, cervical cancer, ovarian cyst, polycystic ovarian syndrome, endometriosis, endometrial hyperplasia, endometrial polyp, and cervical polyp.

METHODS

We conducted a cohort study among female participants in the UK Biobank. Information on the use of statins was collected through verbal interview. Outcome information was obtained by linking to national cancer registry data and hospital inpatient data. We used Cox proportional hazards regression to examine the associations.

RESULTS

A total of 180,855 female participants (18,403 statin users and 162,452 non-users) were included. Use of statins was significantly associated with increased risks of cervical cancer (adjusted hazard ratio (HR), 1.55; 95% confidence interval (95% CI), 1.05-2.30) and polycystic ovarian syndrome (adjusted HR, 4.39; 95% CI, 1.68-11.49). However, we observed no significant association between use of statins and risk of ovarian cancer, endometrial cancer, ovarian cyst, endometriosis, endometrial hyperplasia, endometrial polyp, or cervical polyp.

CONCLUSION

Our findings suggest that use of statins is associated with increased risks of cervical cancer and polycystic ovarian syndrome, but is not associated with increased or decreased risk of ovarian cancer, endometrial cancer, ovarian cyst, endometriosis, endometrial polyp, or cervical polyp.

High-Throughput Computing to Automate Population-Based Studies to Detect the 30-Day Risk of Adverse Outcomes After New Outpatient Medication Use in Older Adults with Chronic Kidney Disease: A Clinical Research Protocol

Background

Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription drugs. This protocol describes a new approach that may identify credible drug-safety signals more efficiently using administrative health care data.

Objective

To use high-throughput computing and automation to conduct 700+ drug-safety cohort studies in older adults in Ontario, Canada. Each study will compare 74 acute (30-day) outcomes in patients who start a new prescription drug (new users) to a group of nonusers with similar baseline health characteristics. Risks will be assessed within strata of baseline kidney function.

Design and setting

The studies will be population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). The source population for these studies will be residents of Ontario aged 66 years or older who filled at least one outpatient prescription through the Ontario Drug Benefit (ODB) program during the study period (all residents have universal health care, and those aged 65+ have universal prescription drug coverage through the ODB).

Patients

We identified 3.2 million older adults in the source population during the study period and built 700+ initial medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. Eligibility criteria included a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry date (median time was 71 days before cohort entry in the new user group), no prior receipt of maintenance dialysis or a kidney transplant, and no prior prescriptions for drugs in the same subclass as the study drug. New users and nonusers will be balanced on ~400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: ≥60, 45 to <60, <45 mL/min per 1.73 m2.

Outcomes

We will compare new user and nonuser groups on 74 clinically relevant outcomes (17 composites and 57 individual outcomes) in the 30 days after cohort entry. We used a prespecified approach to identify these 74 outcomes.

Statistical analysis plan

In each cohort, we will obtain eGFR-stratum-specific weighted risk ratios and risk differences using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interaction by eGFR category will be examined. Drug-outcome associations that meet prespecified criteria (identified signals) will be further examined in additional analyses (including survival, negative-control exposure, and E-value analyses) and visualizations.

Results

The initial medication cohorts had a median of 6120 new users per cohort (interquartile range: 1469-38 839) and a median of 1 088 301 nonusers (interquartile range: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR <60 mL/min per 1.73 m2.

Limitations

Despite our use of robust techniques to balance baseline indicators and to control for confounding by indication, residual confounding will remain a possibility. Only acute (30-day) outcomes will be examined. Our data sources do not include nonprescription (over-the-counter) drugs or drugs prescribed in hospitals and do not include outpatient prescription drug use in children or adults <65 years.

Conclusion

This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drug-safety signals in a vulnerable population. The results of this protocol may ultimately help improve medication safety.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Severe cutaneous adverse reactions due to antibiotics therapy: a pharmacovigilance analysis of FDA adverse event reporting system events

BACKGROUND

The aim of this study was to monitor, identify and evaluate severe cutaneous adverse reactions (SCAR) induced by antibiotics in patients.

METHODS

Disproportionality algorithms were performed in data mining to screen suspected SCAR after using nine categories of antibiotics based on the FDA's Adverse Event Reporting System (FAERS) from January 2004 to December 2022. The drug information and demographic characteristics of antibiotic-associated SCAR were also investigated.

RESULTS

The FAERS database received 12,212 antibiotic-associated SCAR cases. Approximately half of the SCAR patients were females, the majority of them were adults aged 18-65 years (48.35%), and 47.68% of SCAR patients required hospitalization. The highest SCAR signals RORs (95% CI) for antibiotics were: sulfonamides 23.30 (22.05-24.62), glycopeptides 21.27 (20.26-22.33), penicillins 16.00 (15.44-16.59), carbapenems 10.46 (9.57-11.44), and cephalosporins 13.27 (12.57-14.00). Cefotaxime, sulfamethoxazole/trimethoprim, cefixime, vancomycin, piperacillin, ceftriaxone, amoxicillin, and meropenem had stronger associations with the SCAR than the other antibiotics. However, sulfonamides-associated SCAR cases had the lowest fatality rate (6.23%), penicillin-associated SCAR cases had the highest hospitalization rate (54.16%), and carbapenem-associated SCAR cases seemingly resulted in the highest risk of death (19.03%).

CONCLUSION

Data mining of FAERS identified 30 antibiotic-associated SCAR signals, and provided a referable evidence of the occurrence, characteristics and prognosis of antibiotic-related SCARs.

Application of machine learning models to animal health pharmacovigilance: A proof-of-concept study

Machine learning (ML) models were applied to pharmacovigilance (PV) data in a two-component proof-of-concept study. PV data were partitioned into Training, Validation, and Holdout datasets for model training and selection. During the first component ML models were challenged to identify factors in individual case safety reports (ICSRs) involving spinosad and neurological and ocular clinical signs. The target feature for the models were these clinical signs that were disproportionately reported for spinosad. The endpoints were normalized coefficient values representing the relationship between the target feature and ICSR free text fields. The deployed model accurately identified the risk factors "demodectic," "demodicosis," and "ivomec." In the second component, the ML models were trained to identify high quality and complete ICSRs free of confounders. The deployed model was presented with an external Test dataset of six ICSRs, one that was complete, of high quality, and devoid of confounders, and five that were not. The endpoints were model-generated probabilities for the ICSRs. The deployed ML model accurately identified the ICSR of interest with a greater than 10-fold higher probability score. Although narrow in scope, the study supports further investigation and potential application of ML models to animal health PV data.

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

A pharmacovigilance study of etoposide in the FDA adverse event reporting system (FAERS) database, what does the real world say?

Introduction: Etoposide is a broad-spectrum antitumor drug that has been extensively studied in clinical trials. However, limited information is available regarding its real-world adverse reactions. Therefore, this study aimed to assess and evaluate etoposide-related adverse events in a real-world setting by using data mining method on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Methods: Through the analysis of 16,134,686 reports in the FAERS database, a total of 9,892 reports of etoposide-related adverse drug events (ADEs) were identified. To determine the significance of these ADEs, various disproportionality analysis algorithms were applied, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) algorithms. Results: As a result, 478 significant disproportionality preferred terms (PTs) that were identified by all four algorithms were retained. These PTs included commonly reported adverse events such as thrombocytopenia, leukopenia, anemia, stomatitis, and pneumonitis, which align with those documented in the drug's instructions and previous clinical trials. However, our analysis also uncovered unexpected and significant ADEs, including thrombotic microangiopathy, ototoxicity, second primary malignancy, nephropathy toxic, and ovarian failure. Furthermore, we examined the time-to-onset (TTO) of these ADEs using the Weibull distribution test and found that the median TTO for etoposide-associated ADEs was 10 days (interquartile range [IQR] 2-32 days). The majority of cases occurred within the first month (73.8%) after etoposide administration. Additionally, our analysis revealed specific high-risk signals for males, such as pneumonia and cardiac infarction, while females showed signals for drug resistance and ototoxicity. Discussion: These findings provide valuable insight into the occurrence of ADEs following etoposide initiation, which can potentially support clinical monitoring and risk identification efforts.

Drug-associated kidney injury in children: a disproportionality analysis of the FDA Adverse Event Reporting System

Drug-associated kidney injury is related to longer hospitalization and increased risk of chronic kidney disease and mortality. However, there is currently a lack of large population studies on drug-associated kidney injury in children. This study aimed to study perform data mining to generate hypotheses on drugs, which may deserve to be assessed as per their potential risk of increasing kidney injury in children. We extracted and analyzed reports on drugs associated with kidney injury in children in the FDA Adverse Event Reporting System (FAERS). We conducted a disproportionality analysis using proportional reporting ratio (PRR) to evaluate the association between drugs and kidney injury in children. Meanwhile, comparisons were performed with drug labels to identify drugs that, despite not having kidney injury currently mentioned in their labels, may potentially be associated with risks of kidney injury in children. A total of 6347 children had drug-associated kidney injury in the FAERS database. The top five drugs with the highest PRR were gentamicin (PRR = 12.28, N = 157 cases, Chi-Squared = 1602.77), piperacillin-tazobactam (PRR = 9.77, N = 129 cases, Chi-Squared = 1003.24), amlodipine (PRR = 8.98, N = 271 cases, Chi-Squared = 1861.46), vancomycin (PRR = 8.91, N = 295 cases, Chi-Squared = 1998.64), and ceftriaxone (PRR = 8.00, N = 251 cases, Chi-Squared = 1494.02). According to drug labels, 9 drugs (9/30) were classified as potential nephrotoxins.

CONCLUSIONS

Approximately one-third of drugs associated with kidney injury in children do not list kidney injury as a side effect in their drug labels. Future studies are therefore warranted to evaluate whether these drugs are associated with such a risk.

WHAT IS KNOWN

• Nephrotoxic drugs are an increasingly common cause of acute kidney injury in hospitalized children. • Currently, no study has systematically combed drugs associated with kidney injury in children.

WHAT IS NEW

• Approximately a third of drugs showing signals for potential kidney injury in children in data mining do not mention this side effect in their drug labels. • This study provides data on drugs needing further study to determine whether they might increase the risk of kidney injury in children.

Cardiovascular Adverse Events Associated With New-Generation Androgen Receptor Pathway Inhibitors (ARPI) for Prostate Cancer: A Disproportionality Analysis Based on the FDA Adverse Event Reporting System (FAERS)

BACKGROUND

The potential cardiovascular adverse events associated with new-generation androgen receptor pathway inhibitors (ARPI) in the treatment of prostate cancer remain unclear. We aimed to assess the pharmacovigilance (PV), reporting rate, severity, and reaction outcomes of major adverse cardiovascular events (MACE) related to new-generation ARPI for prostate cancer reported to the United States Food and Drug Administration Adverse Event Reporting System (FAERS).

METHODS

We analyzed reports of cardiovascular adverse events associated with drug therapy for prostate cancer submitted to FAERS between January 2014 and December 2022. Three primary new-generation ARPIs were identified: abiraterone acetate, enzalutamide, and apalutamide. Our primary composite endpoint was the PV of MACE caused by ARPIs in the treatment of prostate cancer, and the secondary endpoint was PV of other cardiovascular events. The software implemented was STATA 17.0 MP.

RESULTS

A total of 278,031 suspected drug-adverse event pairs related to drug treatment in patients with prostate cancer were identified, of which 10,861 reports were cardiovascular events, including 5800 reports of MACE and 5061 reports of other cardiovascular events. The majority of these cardiovascular adverse event reports came from the United States (36.6%) and were mostly older men (age 76.0 ± 8.6 years). Compared with enzalutamide, the constituent ratio of MACE caused by abiraterone acetate and apalutamide was significantly increased, but the incidence of severe MACE decreased significantly. The PV signal regarding MACE was detected in abiraterone acetate and apalutamide but not in enzalutamide.

CONCLUSION

Abiraterone acetate and apalutamide presumably are associated with a higher risk of MACE than enzalutamide in new-generation ARPI for prostate cancer. More extensive prospective studies and more extended follow-up periods need to confirm this further.

Atypical femur fracture associated with common anti-osteoporosis drugs in FDA adverse event reporting system

Atypical femur fracture (AFF) is a rare but catastrophic adverse event first reported in the long-term use of alendronate, one of the most commonly used drugs for osteoporosis currently. However, further evidence is needed to learn more regarding other common anti-osteoporosis drugs and the risk for AFF. In this study, reports of AFF were identified from Food and Drug Administration Adverse Event Reporting System database. Disproportionality analyses were performed to examine the reporting odds ratio (ROR), information component (IC) and adjusted ROR (adj. ROR) signals for AFF for common anti-osteoporosis drugs. A total of 1692 unique AFF reports were identified. The disproportionality signals (the lower bound of 95% confidence interval > 1 for ROR and adjusted ROR, and > 0 for IC) were detected for alendronate, denosumab, pamidronate, risedronate, zoledronate, ibandronate, and teriparatide while no signal was detected for raloxifene, abaloparatide, and romosozumab. When restricted in patients with osteoporosis, the disproportionality signals were still detected for alendronate, pamidronate, risedronate, denosumab, and ibandronate. Our results suggest that alendronate has the largest risk signal, while the risks varied among different bisphosphonates. In addition, denosumab was found statistically associated with AFF in both the entire database and patients with osteoporosis.

Improving drug safety with adverse event detection using natural language processing

INTRODUCTION

Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making.

AREAS COVERED

We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic.

EXPERT OPINION

New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.

Bringing FAIRness to FDA data through visualization

Improving representation and inclusion in clinical trials for new medicinal products has been a high priority for federal agencies for over 2 decades, but data to evaluate progress have been difficult to access. In this issue of Patterns, Carmeli et al. provide a novel approach to aggregating and visualizing existing data to improve transparency and research.

Using disproportionality analysis to explore the association between periostitis and triazole antifungals in the FDA Adverse Event Reporting System Database

Though triazole antifungals are the first choice for preventing and treating invasive fungal infections, periostitis caused by voriconazole has been described in emerging case reports; however, no studies exist on this association in real-world clinical settings. Our study aimed to identify the association between periostitis and triazole antifungals by analyzing data from the FDA Adverse Event Reporting System (FAERS). We extracted and analyzed reports on the association between periostitis and triazole antifungals in FAERS from the first quarter of 2004 to the second quarter of 2022 using OpenVigil 2.1. Disproportionality analysis was performed to evaluate the association between periostitis and triazole antifungals, and chi-squared (χ²), relative reporting ratio (RRR), reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural networks (BCPNN) of information components (IC) were reported. In total, 143 patients experienced periostitis while using voriconazole. Disproportionality analysis identified an association between periostitis and voriconazole (χ² = 82,689.0, RRR = 583.6, 95%CI [472.4, 721.1], PRR = 1808.9, 95%CI [1356.0, 2412.9], ROR = 1831.7, 95%CI [1371.6, 2446.3], IC = 9.2, 95%CI [8.6, 9.8]). However, no safety signals were observed between periostitis and other triazole antifungals. When stratified by sex and age, disproportionality analysis identified positive signals between periostitis and voriconazole. The possible association between periostitis and voriconazole should attract sufficient attention in clinical practice. Alternative treatment with other triazole antifungals can be considered, and causality needs to be verified in further prospective studies.

Cardiotoxicity with human epidermal growth factor receptor-2 inhibitors in breast cancer: Disproportionality analysis of the FDA adverse event reporting system

BACKGROUND

The cardiotoxicity induced by human epidermal growth factor receptor-2 (HER-2) inhibitors in patients with breast cancer has been reported widely. However, these data sources were largely limited to fewer patients in clinical trials and case reports, lacking more comprehensive analysis from real-world data.

METHODS

The cases diagnosed with breast cancer from January 2004 to December 2021 were extracted from the FDA adverse event database and further divided into 3 groups (the HER-2 inhibitor group, the positive control group, and the control group). The association between HER-2 inhibitors and cardiovascular adverse events was evaluated using the reporting odds ratio (ROR), a disproportionality method.

RESULTS

A total of 167,639 breast cancer patients were included, including 18,615 cases in the HER-2 inhibitor drug group, 2568 cases in the positive control group, and 146,456 cases in the control group. A total of 2529 cases (13.5%) treated with HER-2 inhibitors experienced cardiovascular adverse events, mainly reported by health professionals (81.5%). The disproportionality analysis showed that cardiomyopathy was observed in all HER-2 inhibitors except trastuzumab deruxtecan. Trastuzumab-related CVAEs were most frequently reported (N =2075), and the median time was 80.50 days (IQR: 8.00 to 206.75 days).

CONCLUSION

Based on real-world data analysis, our study demonstrated a significant association between HER-2 inhibitors and cardiovascular toxicity. Cardiac function in patients with breast cancer should be monitored early during anti-HER therapy, especially within six months.

Analysis of adverse event of interstitial lung disease in men with prostate cancer receiving hormone therapy using the Food and Drug Administration Adverse Event Reporting System

AIMS

To investigate interstitial lung disease (ILD) in men with prostate cancer receiving hormone therapy.

METHODS

We gathered cases diagnosed with prostate cancer based on the United States Food and Drug Administration Adverse Event Reporting System (FAERS) database from 2004 to 2020. We divided the included cases into 3 groups based on the primary suspected drugs: a hormone therapy group, a positive control group (taxanes), and a negative control group. We employed reporting odds ratio, a disproportionality method, to detect the association between ILD events and target drugs.

RESULTS

We finally included a total of 85 403 cases, 69 894 cases (628 ILD event cases) in the hormone therapy group, 2302 cases (158 ILD event cases) in the positive control group and 13 207 cases (72 ILD event cases) in the negative control group. There were 394 ILD event cases (62.74%) in the hormone therapy group in Japan; 78.68% of the ILD events occurred within the first year after hormone treatment. Disproportionality analysis indicated that ILD events were significantly associated with nilutamide, flutamide, bicalutamide, goserelin, degarelix and apalutamide; the reporting odds ratios (95% confidence interval) were 32.14 (11.03-93.63), 9.93 (3.62-27.21), 8.19 (6.01-11.16), 3.74 (2.61-5.37), 2.41 (1.55-3.75) and 1.94 (1.01-3.75), respectively.

CONCLUSION

Based on this FAERS pharmacovigilance analysis, the association between ILD events and hormone therapy drugs, including bicalutamide, flutamide, nilutamide, goserelin, degarelix and apalutamide, should not be ignored, especially in the Japanese population. Lung function of prostate cancer patients should be monitored when receiving the hormone therapy drugs mentioned above, especially for the first year post medication.

Hyperuricaemia, gout and related adverse events associated with antihypertensive drugs: A real-world analysis using the FDA adverse event reporting system

Background: The role of antihypertensive drugs in inducing hyperuricaemia and gout has been a long-term concern in clinical practice. However, clinical studies regarding this issue are limited in number and have yielded inconsistent results. We comprehensively evaluated the association between various antihypertensive drugs and the occurrences of hyperuricaemia, gout and related adverse events (AEs) using the FDA Adverse Event Reporting System (FAERS), aiming to guide the selection of antihypertensive drugs with a goal of minimizing the risk of hyperuricaemia, gout and related AEs. Methods: We used OpenVigil 2.1 to query the FAERS database. Hyperuricaemia, gout and related AEs were defined by 5 Preferred Terms: hyperuricaemia, gout, gouty arthritis, gouty tophus and urate nephropathy. Disproportionality analysis was performed, and a positive signal indicated an association between AEs and antihypertensive drugs. Results: The numbers of antihypertensive drugs with positive signals for hyperuricaemia, gout, gouty arthritis, gouty tophus and urate nephropathy were 46, 66, 27, 8 and 6, respectively. These drugs included diuretics, antihypertensive drugs with central action, α blockers, β blockers, α and β blockers, calcium channel blockers, angiotensin converting enzyme inhibitors, angiotensin II receptor blockers, renin inhibitors, vasodilators, and compound preparations. Furthermore, 42 antihypertensive drugs had positive signal for more than one AEs. Conclusion: Our study suggests that some potassium-sparing diuretics, calcium channel blockers and losartan may be associated with increased risk of hyperuricaemia, gout or related AEs, which is inconsistent with most previous studies. Moreover, Our study also suggests that some antihypertensive drugs with central action, α and β blockers, renin inhibitors and vasodilators may be associated with increased risk of hyperuricaemia, gout or related AEs, which has not been reported in previous studies. These findings complement real-world evidence on the potential risks of hyperuricaemia, gout and related AEs associated with antihypertensive drugs.

Cardiac disorder-related adverse events for aryl hydrocarbon receptor agonists: a safety review

BACKGROUND

Although cardiac disorder-related adverse events (AEs) have been reported in patients treated with aryl hydrocarbon receptor (AHR) agonists, their safety profiles remain unknown. Here, we identified significant cardiac disorders associated with AHR agonists and further evaluated their relevance.

RESEARCH DESIGN AND METHODS

Database queries were performed using OpenVigil 2.1 and AEs voluntarily submitted to Food and Drug Administration Adverse Event Reporting System (FAERS) between 2004 and 2020 were included. This study based on the Medical Dictionary for Regulatory Activities and the standardized MedDRA Queries to define the preferred terms, and we used reporting odd ratio to detect signals.

RESULTS

In the FAERS database, 14,078 cardiac disorder-related AEs were identified in patients receiving AHR agonists. Among all AHR agonists, the number of cardiac disorder-related PTs with positive signals for AHR agonists was 93. Peripheral swelling (n = 1572) and atrial fibrillation (n = 1277) were the most reported cardiac disorder-related AEs among AHR agonists in disproportionately reported PTs. Moreover, several AHR agonists were highly associated with tachyarrhythmia.

CONCLUSIONS

By mining the FAERS database, we provided more information on the association between AHR agonist use and cardiac disorder-related AEs.

Post-marketing surveillance study on influenza vaccine in South Korea using a nationwide spontaneous reporting database with multiple data mining methods

Safety profiles of the influenza vaccine and its subtypes are still limited. We aimed to address this knowledge gap using multiple data mining methods and calculated performance measurements to evaluate the precision of different detection methods. We conducted a post-marketing surveillance study between 2005 and 2019 using the Korea Adverse Event Reporting System database. Three data mining methods were applied: (a) proportional reporting ratio, (b) information component, and (c) tree-based scan statistics. We evaluated the performance of each method in comparison with the known adverse events (AEs) described in the labeling information. Compared to other vaccines, we identified 36 safety signals for the influenza vaccine, and 7 safety signals were unlabeled. In subtype-stratified analyses, application site disorders were reported more frequently with quadrivalent and cell-based vaccines, while a wide range of AEs were noted for trivalent and egg-based vaccines. Tree-based scan statistics showed well-balanced performance. Among the detected signals of influenza vaccines, narcolepsy requires special attention. A wider range of AEs were detected as signals for trivalent and egg-based vaccines. Although tree-based scan statistics showed balanced performance, complementary use of other techniques would be beneficial when large noise due to false positives is expected.

Drug-induced torsades de pointes: Disproportionality analysis of the United States Food and Drug Administration adverse event reporting system

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 third quarter of 2021 were retrieved. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify TdP cases. We listed the most common drugs associated with the reported TdP cases. Then, the reporting odds ratio (ROR) and the proportional reporting ratio (PRR) for the reporting association between different drugs and TdP risk were calculated. Meanwhile, comparisons were conducted with the QT drug lists of CredibleMeds^® in an attempt to identify drugs with a potential risk of TdP that were not on the list.

Results

A total of 9,217,181 adverse event reports were identified, of which 3,807 (0.04%) were related to TdP. TdP was more likely to occur in the elderly and females. Amiodarone (464 cases) was associated with most cases of TdP. According to the disproportionality analysis, the top five drugs with the highest ROR and PRR were tolazoline (ROR 1615.11, 95% confidence interval [CI] 455.59–5725.75, PRR 969.46, χ² 2960.10), levomethadyl (ROR 1211.01, 95% CI 302.75–4844.04, PRR 807.67, χ² 1677.03), ibutilide (ROR 1118.74, 95% CI 425.00–2944.91, PRR 765.77, χ² 3845.27), halofantrine (ROR 660.55, 95% CI 184.21–2368.69, PRR 519.22, χ² 1076.31), and isoproterenol (ROR 352.20, 95% CI 227.19–546.00, PRR 307.82, χ² 6692.53). Approximately half of the top 50 drugs (22 for ROR, 30 for PRR) were not outlined on the QT drug lists of CredibleMeds^®.

Conclusion

Approximately half of the top risk drugs (22 for ROR, 30 for PRR) were not outlined in the QT drug lists of CredibleMeds^®. Notably, potential risks are of great importance and should be closely monitored in clinical practice. Also, further research is needed to investigate the association between these drugs and TdP.

Characterization and corroboration of safety signals identified from the US Food and Drug Administration Adverse Event Reporting System, 2008-19: cross sectional study

OBJECTIVE

To characterize potential drug safety signals identified from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), from 2008 to 2019, to determine how often these signals resulted in regulatory action by the FDA and whether these actions were corroborated by published research findings or public assessments by the Sentinel Initiative.

DESIGN

Cross sectional study.

SETTING

USA.

POPULATION

Safety signals identified from the FAERS and publicly reported by the FDA between 2008 and 2019; and review of the relevant literature published before and after safety signals were reported in 2014-15. Literature searches were performed in November 2019, Sentinel Initiative assessments were searched in December 2021, and data analysis was finalized in December 2021.

MAIN OUTCOME MEASURES

Safety signals and resulting regulatory actions; number and characteristics of published studies, including corroboration of regulatory action as evidenced by significant associations (or no associations) between the drug related to the signal and the adverse event.

RESULTS

From 2008 to 2019, 603 potential safety signals identified from the FAERS were reported by the FDA (median 48 annually, interquartile range 41-61), of which 413 (68.5%) were resolved as of December 2021 (372 of 399 (93.2%) signals ≥3 years old were resolved). Among the resolved safety signals, 91 (22.0%) led to no regulatory action and 322 (78.0%) resulted in regulatory action, including 319 (77.2%) changes to drug labeling and 59 (14.3%) drug safety communications or other public communications from the FDA. For a subset of 82 potential safety signals reported in 2014-15, a literature search identified 1712 relevant publications; 1201 (70.2%) were case reports or case series. Among these 82 safety signals, 76 (92.7%) were resolved, of which relevant published research was identified for 57 (75.0%) signals and relevant Sentinel Initiative assessments for four (5.3%) signals. Regulatory actions by the FDA were corroborated by at least one relevant published research study for 17 of the 57 (29.8%) resolved safety signals; none of the relevant Sentinel Initiative assessments corroborated FDA regulatory action.

CONCLUSIONS

Most potential safety signals identified from the FAERS led to regulatory action by the FDA. Only a third of regulatory actions were corroborated by published research, however, and none by public assessments from the Sentinel Initiative. These findings suggest that either the FDA is taking regulatory actions based on evidence not made publicly available or more comprehensive safety evaluations might be needed when potential safety signals are identified.

Text Mining Approaches for Postmarket Food Safety Surveillance Using Online Media

Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers' interactions with hazardous food products. We compare our methods to traditional sentiment-based text mining. We assess performance in a high-volume setting, utilizing a data set of over 4 million online reviews. Our methods were 77-90% accurate in top-ranking reviews, while sentiment analysis was just 11-26% accurate. Moreover, we aggregate review-level results to make product-level risk assessments. A panel of 21 food safety experts assessed our model's hazard-flagged products to exhibit substantially higher risk than baseline products. We suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. Our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time.

Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports

Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.

A pharmacovigilance study of the association between tetracyclines and hepatotoxicity based on Food and Drug Administration adverse event reporting system data

Background While tetracycline antibiotics are commonly prescribed in practice, the risk of drug-induced liver injury (DILI) remains controversial. Aim To evaluate the association of DILI with tetracycline antibiotics. Method All DILI cases of tetracycline antibiotics as primary suspected drugs were extracted from the US Food and Drug Administration adverse event reporting system (FAERS). The outcomes included severe DILI, hepatocellular injury, cholestatic injury, and liver failure. Disproportionality analyses were conducted by estimating the reporting odds ratio (ROR) and the information component (IC). Results A total of 1,435 liver injury cases associated with tetracycline antibiotics were identified. The DILI signal was detected in tigecycline, minocycline, and doxycycline. The RORs and the 95% confidence intervals (95% CI) of tigecycline, minocycline, and doxycycline were (ROR 5.85, 95% CI 4.96-6.91), (ROR 6.4, 95% CI 5.76-7.11), and (ROR 2.07, 95% CI 1.86-2.31), respectively. Compared to minocycline (ROR 5.5, 95% CI 4.94-6.12; IC 2.35, 95% CI 1.98-2.68) and doxycycline (ROR 1.91, 95% CI 1.71-2.12; IC 0.91, 95% CI 0.55-1.26), tigecycline showed a stronger association with hepatocellular injury (ROR 7.11, 95% CI 6.13-8.23; IC 2.68, 95% CI 2.16-3.13). Tigecycline also showed a stronger association with cholestatic injury (ROR 12.16, 95% CI 10.13-14.61; IC 3.51, 95% CI 2.79-4) than minocycline (ROR 3.23, 95% CI 2.59-4.04; IC 1.67, 95% CI 0.9-2.37) or doxycycline (ROR 2.86, 95% CI 2.47-3.31; IC 1.5, 95% CI 1-1.97). Tigecycline (ROR 6.56, 95% CI 4.57-9.41; IC 2.69, 95% CI 1.28-3.64) and minocycline (ROR 4.22, 95% CI 3.14-5.66; IC 2.06, 95% CI 1-2.93) showed a significant association with liver failure. Conclusion The data mining of FAERS suggested an association between DILI and tigecycline, minocycline, and doxycycline.

Adverse Events During Pregnancy Associated With Entecavir and Adefovir: New Insights From a Real-World Analysis of Cases Reported to FDA Adverse Event Reporting System

Background: Due to the embryotoxicity found in animal studies and scarce clinical data in pregnant women, it is still controversial whether entecavir (ETV) and adefovir dipivoxil (ADV) are safe during human pregnancy. This is of paramount importance when counseling pregnant women with hepatitis B virus (HBV) on risks and benefits to their offspring.

Objective: To quantify the association between administration of ETV and ADV in pregnant women and occurrence of adverse events (AEs) during pregnancy (AEDP).

Methods: Pregnancy reports from the FDA Adverse Event Reporting System (FAERS) were used to perform a retrospective analysis of AEDP associated with ETV or ADV. Disproportionality analysis estimating the reporting odds ratio (ROR) was conducted to identify the risk signals. A signal was defined as ROR value >2, and lower limit of 95% confidence interval (CI)> 1.

Results: A total of 1,286,367 reports involving AEDP were submitted to FAERS by healthcare professionals. Of these, there were 547 cases reporting ETV and 242 cases reporting ADV as primary suspected drugs. We found a moderate or strong signal for increased risk of spontaneous abortion when comparing ETV with tenofovir disoproxil fumarate (TDF) and telbivudine (LdT), with RORs equal to 1.58 (95% CI, 1.09–2.30) and 2.13 (95% CI, 1.04–4.36), respectively. However, when the included reports were limited to indication containing HBV infection, no signals for increased AEDP were detected. Futhermore, a strong signal for increased risk of spontaneous abortion was identified in patients with HBV infection when comparing ETV or ADV with lamivudine (LAM), with RORs of 3.55 (95% CI, 1.54–8.18) and 2.85 (95% CI, 1.15–7.08), respectively.

Conclusion: We found a strong signal for increased risk of spontaneous abortion in patients with HBV infection taking ETV or ADV, in comparison with those prescribed with LAM. Moreover, no obvious signal association of human teratogenicity with exposure to ETV or ADV was identified in fetuses during pregnancy. Nevertheless, owing to the limitations of a spontaneous reporting database, which inevitably contains potential biases, there is a pressing need for well-designed comparative safety studies to validate these results in clinical practice.

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

Potential kidney damage associated with the use of remdesivir for COVID-19: analysis of a pharmacovigilance database

The U.S. Food and Drug Administration (FDA) has stated that the prescription of remdesivir should be cautious for patients with estimated glomerular filtration rate (eGFR) < 30 and some studies reported risk of adverse renal events. The available information on the renal safety profile for remdesivir is limited, thus we analyzed the renal and urinary adverse reactions attributed to remdesivir reported in a large open pharmacovigilance database. We obtained reports of remdesivir and other drugs used to treat COVID-19 (tocilizumab, hydroxychloroquine, lopinavir/ritonavir) registered by September 30 2020, from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). We analyzed the reporting odds ratios (RORs) for reports of adverse renal and urinary events for remdesivir and other drugs. We found 2,922 reports with remdesivir registered in FAERS for COVID-19. Among these, 493 renal and urinary adverse effects (16.9%) were reported. The most frequent events were acute kidney injury (338; 11.6%), renal impairment (86; 2.9%), and renal failure (53; 1.8%). Versus hydroxychloroquine, lopinavir/ritonavir, or tocilizumab, the use of remdesivir was associated with an increased chance of reporting renal and urinary disorders regardless of gender and age of patients (2.53; 95%CI: 2.10-3.06). The ROR remained significant when we restricted the analysis to hydroxychloroquine (4.31; 95%CI: 3.25-5.71) or tocilizumab (3.92; 95%CI: 2.51-6.12). Our results reinforce this already reported signal, emphasizing that it could be extremely useful for health professionals who prescribe this new antiviral to treat COVID-19, mainly knowing its low efficacy.

Adverse Events Related to Off-Label Drugs Using Spontaneous Adverse Event Reporting Systems

Purpose

The purpose of this study was to investigate the adverse events (AEs) related to the use of off-label drugs.

Materials and Methods

A cross-sectional study was carried out using available data pertaining to off-label drug were sourced from U.S. FDA spontaneous adverse drug reaction reporting database (FAERS) and Korea Adverse Event Reporting System database (KIDS-KD) for the years 2014 to 2018. The number and frequencies of AE cases were calculated. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM) methods.

Results

The reported AEs associated with off-label drug use were more common among older patients compared with younger patients. Gastric nonspecific symptoms and therapeutic procedure (4.16–4.57%) and haemorrage term (4.16–5.29%) were the most common AE symptoms and antithrombotic agents and immunosuppressants were the drugs most commonly reported to cause AEs in FAERS. Secondary term events (43.45–48.62%) including inappropriate schedule of drug administration and medication error were the most common AEs, and immunosuppressants and antipsychotics were the most common AE-related drugs from KIDS-KD. The numbers of reported AEs in new drug categories such as other antineoplastic agents trended to increase from 2014 to 2018 in both datasets.

Conclusion

The numbers of reported AEs with off-label drug increased annually. AEs associated with off-label drugs may have a significant impact on older patients. Healthcare experts should be concerned about prescriptions of off-label drugs, especially anticoagulants and newly developed drugs such as immunosuppressants and antineoplastic agents.

Hyponatremia Following Antipsychotic Treatment: In Silico Pharmacodynamics Analysis of Spontaneous Reports From the US Food and Drug Administration Adverse Event Reporting System Database and an Updated Systematic Review

BACKGROUND

Hyponatremia associated with antipsychotic drugs is a rare but potentially life-threatening adverse drug reaction; the underlying pharmacological mechanism has not yet been explained.

METHODS

We investigated the relationship between pharmacological targets of antipsychotic drugs and the occurrence of hyponatremia by conducting a nested case-control study using the Food and Drug Administration Adverse Event Reporting System database. Multiple logistic regression was used to determine the associations between antipsychotics receptor occupancy and hyponatremia. We also performed a systematic review of clinical studies on this association.

RESULTS

Of 139 816 reports involving at least 1 antipsychotic, 1.1% reported hyponatremia. Olanzapine was the most frequently suspected drug (27%). A significant positive association was found between dopamine D3, D4, and hyponatremia, while adrenergic α 1, serotonin 5-HT1A, and 5-HT2A receptor occupancies were negatively associated. A multivariable stepwise regression model showed that dopamine D3 (adj. odds ratio = 1.21; 95% CI = 1.09-1.34; P < .05) predicted the risk for hyponatremia (P < .05), while serotonin 5-HT2A occupancy (Adj. odds ratio = 0.78; 95% CI = 0.68-0.90; P < .01) exhibited a protective effect against hyponatremia. Among the 11 studies included in the systematic review, incidence rates of hyponatremia diverged between 0.003% and 86%, whereas the odds of developing hyponatremia from effect studies ranged between 0.83 and 3.47.

CONCLUSIONS

Antipsychotic drugs having a combined modest occupancy for D3 and 5-HT2A receptors and higher levels of D3 receptor occupancy correspond to different degrees of risk for hyponatremia. Based on the few, relatively large-scale available studies, atypical antipsychotics have a more attenuated risk profile for hyponatremia.

Nonmedical Use of Xtampza® ER and Other Oxycodone Medications in Adults Evaluated for Substance Abuse Treatment: Real-World Data from the Addiction Severity Index-Multimedia Version (ASI-MV®)

Purpose

The purpose of this study was to evaluate real-world data related to past 30-day nonmedical use (NMU) and routes of administration of Xtampza^® ER and comparator oxycodone medications in the US as captured within the Addiction Severity Index-Multimedia Version^® (ASI-MV^®).

Methods

Data were collected from July 2016 through December 2019 from 647 centers located in 44 states using the ASI-MV, a clinical instrument used to evaluate substance use and treatment planning. Demographic characteristics were assessed using Pearson's chi-square test for categorical data and quarterly NMU rates were calculated. Distribution of route of administration was studied using a proportional reporting ratio (PRR) analysis.

Results

Of 192,810 assessments, 42,279 (21.9%) indicated past 30-day NMU of at least one prescription opioid, including Xtampza ER (N=73, 0.2%), other oxycodone ER (n=3802, 9.0%) and oxycodone IR (n=14,579, 34.5%). All quarterly Xtampza ER NMU rates per 100 ASI-MV assessments were significantly lower than those for other oxycodone ER and oxycodone IR. Overall, quarterly Xtampza ER NMU drug utilization adjusted rates were significantly lower than quarterly rates observed for other oxycodone ER NMU but not consistently significantly lower than oxycodone IR NMU. Although not all statistically significant, all ratios from the PRR analysis were less than 1.0, indicating that rates of use of any alternate route, any non-oral route, snorting, and injecting were higher for other oxycodone ER and oxycodone IR than for Xtampza ER.

Conclusion

Xtampza ER had significantly lower rates of NMU than other oxycodone ER products and oxycodone IR products, as well as significantly lower rates of non-oral NMU than oxycodone IR products, in a population of individuals seeking substance abuse treatment. Understanding risks associated with different opioid medications is important for prescribers as they manage risks of opioid misuse and abuse with effective pain therapy.

Augmenting aer2vec: Enriching distributed representations of adverse event report data with orthographic and lexical information

Adverse Drug Events (ADEs) are prevalent, costly, and sometimes preventable. Post-marketing drug surveillance aims to monitor ADEs that occur after a drug is released to market. Reports of such ADEs are aggregated by reporting systems, such as the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). In this paper, we consider the topic of how best to represent data derived from reports in FAERS for the purpose of detecting post-marketing surveillance signals, in order to inform regulatory decision making. In our previous work, we developed aer2vec, a method for deriving distributed representations (concept embeddings) of drugs and side effects from ADE reports, establishing the utility of distributional information for pharmacovigilance signal detection. In this paper, we advance this line of research further by evaluating the utility of encoding orthographic and lexical information. We do so by adapting two Natural Language Processing methods, subword embedding and vector retrofitting, which were developed to encode such information into word embeddings. Models were compared for their ability to distinguish between positive and negative examples in a set of manually curated drug/ADE relationships, with both aer2vec enhancements offering advantages in performances over baseline models, and best performance obtained when retrofitting and subword embeddings were applied in concert. In addition, this work demonstrates that models leveraging distributed representations do not require extensive manual preprocessing to perform well on this pharmacovigilance signal detection task, and may even benefit from information that would otherwise be lost during the normalization and standardization process.

A pharmacovigilance study of association between proton pump inhibitor and dementia event based on FDA adverse event reporting system data

Proton pump inhibitor (PPI) was widely used around the world. Studies suggested conflicting results between PPI treatment and dementia event. This study examined the association between six PPI agents and dementia event by mining the US FDA Adverse Event Reporting System (FAERS) database from 2004 to 2020. We employed proportional reporting ratio (PRR) and information element (IC) methods to detect the signals of dementia relevant to PPI. We also analyzed characteristics of PPI and positive control reports, compared dementia event between long- and short-duration of PPI treatment. Finally, we identified 2396 dementia cases with PPI treatment. We did not detect significant signal between PPI and dementia event: PRR = 0.98, 95%CI 0.94 to 1.02, IC = −0.03, 95%CI − 0.17 to 0.10, even in gastroesophageal reflux disease cases: PRR = 0.65, 95%CI 0.59 to 0.72, IC = −0.62, 95%CI − 0.97 to − 0.27. No significant differences of dementia event were detected between long- and short- duration groups, the OR (95%CI) of the 3 years, 5 years and 10 years comparison were 0.70 (0.48 to 1.02), 0.72 (0.45 to 1.15) and 1.65 (0.75 to 3.63), respectively. Based on the current FAERS data mining, we discovered no association between PPI use and dementia event, even in long-term PPI therapy case.

Summary of adverse drug events for hydroxychloroquine, azithromycin, and chloroquine during the COVID-19 pandemic

OBJECTIVE

Given the increased use of hydroxychloroquine (HCQ), chloroquine (CQ), and azithromycin (AZM) during the early months of the coronavirus disease 2019 (COVID-19) pandemic, there is a need to evaluate the associated safety concerns. The objective of this study was to summarize the adverse drug events (ADEs) associated with HCQ, CQ, and AZM use during the national COVID-19 emergency and compare the results with known adverse reactions listed in the drugs' package inserts.

METHODS

A cross-sectional study design was used. The publicly available Food and Drug Administration Adverse Event Reporting System quarterly data extract files from January 1, 2020 to June 30, 2020 were downloaded. A disproportionality analysis was conducted using the proportional reporting ratio to identify possible ADE signals. A Poisson regression was used to assess if the number of ADE reports for the 3 drugs increased over time.

RESULTS

There was a statistically significant increasing trend in the reported ADEs for both HCQ (P < 0.001) and AZM (P < 0.001). Before the declaration of the national emergency, there were 592 reported drug-ADE pairs for the 3 drugs compared with 2492 drug-ADE pairs reported after March 13, 2020. These 2492 drug-ADE pairs represented 848 ADEs across the 3 drugs, of which 114 (13.4%) were identified as potential signals including 55 (48.2%) that were not listed in the prescribing information.

CONCLUSIONS

Our results showed that the reported ADEs for HCQ and AZM have increased during the COVID-19 pandemic. Differences were observed in both the type of and frequency of the highest reported ADEs for the 3 selected drugs before and after the national emergency declaration. Although causation cannot be determined from ADE reports, further investigation of some reports may be warranted. Our results highlight the need for pharmacovigilance and education of health care professionals on the safety of these drugs when being used for COVID-19 prophylaxis or treatment.

Exploring a Safety Signal of Antipsychotic-Associated Pneumonia: A Pharmacovigilance-Pharmacodynamic Study

An association between antipsychotic drugs and pneumonia has been demonstrated in several studies; however, the risk for pneumonia caused by specific antipsychotics has not been extensively studied. The underlying mechanism is still unknown, and several receptor mechanisms have been proposed. Therefore, using a combined pharmacovigilance-pharmacodynamic approach, we aimed to investigate safety signals of US Food and Drug Administration (FDA)-approved antipsychotics for reporting pneumonia and the potential receptor mechanisms involved. A disproportionality analysis was performed to detect a signal for reporting "infective-pneumonia" and "pneumonia-aspiration" and antipsychotics using reports submitted between 2004 and 2019 to the FDA adverse events spontaneous reporting system (FAERS) database. Disproportionality was estimated using the crude and the adjusted reporting odds ratio (aROR) and its 95% confidence interval (CI) in a multivariable logistic regression. Linear regressions investigated the relationship between aROR and receptor occupancy, which was estimated using in vitro receptor-binding profiles. Safety signals for reporting infective-pneumonia were identified for clozapine (LL = 95% 3.4, n = 546 [aROR: 4.8]) as well as olanzapine (LL = 95% 1.5, n = 250 [aROR: 2.1]) compared with haloperidol, while aRORs were associated with higher occupancies of muscarinic receptors (beta = .125, P-value = .016), yet other anti-muscarinic drugs were not included as potential confounders. No safety signals for reporting pneumonia-aspiration were detected for individual antipsychotics. Multiple antipsychotic use was associated with both reporting infective-pneumonia (LL 95%: 1.1, n = 369 [aROR:1.2]) and pneumonia-aspiration (LL 95%: 1.7, n = 194 [aROR: 2.0]). Considering the limitations of disproportionality analysis, further pharmacovigilance data and clinical causality assessment are needed to validate this safety signal.

Data Sharing and Privacy in Pharmaceutical Studies

Adverse drug events have been a long-standing concern for the wide-ranging harms to public health, and the substantial disease burden. The key to diminish or eliminate the impacts is to build a comprehensive pharmacovigilance system. Application of the "big data" approach has been proved to assist the detection of adverse drug events by involving previously unavailable data sources and promoting health information exchange. Even though challenges and potential risks still remain. The lack of effective privacy-preserving measures in the flow of medical data is the most important Accepted: one, where urgent actions are required to prevent the threats and facilitate the construction of pharmacovigilance systems. Several privacy protection methods are reviewed in this article, which may be helpful to break the barrier.

Applying regulatory science in traditional chinese medicines for improving public safety and facilitating innovation in China: a scoping review and regulatory implications

BACKGROUND

The National Medical Products Administration (NMPA) in China has set to advance the regulatory capacity of traditional Chinese medicines (TCMs) with the adoption of regulatory science (RS). However, the priority of actions at the interface of RS and TCMs were yet to be defined. This research aims to identify the priority areas and summarize core actions for advancing RS for traditional medicines in China.

METHODS

A mixed approach of documentary analysis of government policies, regulations and official information about TCMs regulation in China, and a scoping review of literature using 4 databases (PubMed, ScienceDirect, Scopus and CNKI) on major concerns in TCMs regulation was employed.

RESULTS

Ten priority areas in the development of TCM-related regulatory science in China have been identified, including: (1) modernizing the regulatory system with a holistic approach; (2) advancing the methodology for the quality control of TCMs; (3) fostering the control mechanism of TCMs manufacturing process; (4) improving clinical evaluation of TCMs and leveraging real world data; (5) re-evaluation of TCMs injection; (6) developing evaluation standards for classic TCMs formula; (7) harnessing diverse data to improve pharmacovigilance of TCMs; (8) evaluating the value of integrative medicine in clinical practice with scientific research; (9) advancing the regulatory capacity to encourage innovation in TCMs; and (10) advancing a vision of collaboration for RS development in TCMs.

CONCLUSIONS

RS for TCMs in China encompasses revolution of operational procedures, advancement in science and technology, and cross-disciplinary collaborations. Such experiences could be integrated in the communications among drug regulatory authorities to promote standardized and scientific regulation of traditional medicines.

Proton pump inhibitors associated acute kidney injury and chronic kidney disease: data mining of US FDA adverse event reporting system

Proton pump inhibitors (PPIs) were widely used. Observational studies suggested increasing risk of kidney injury in patients with PPIs treatment. We gathered six PPI regimens and adverse reports of acute kidney injury (AKI) and chronic kidney disease (CKD) based on US FDA Adverse Event Reporting System (FAERS) database from 2004 to 2019. We employed reporting odds ratio (ROR) to detect signals. Finally, we identified 3187 PPIs-associated AKI cases and 3457 PPIs-associated CKD cases. We detected significant signals between PPIs and AKI as well as CKD. The signal strength was stronger for CKD (ROR = 8.80, 95% CI 8.49–9.13) than AKI (ROR = 3.95, 95% CI 3.81–4.10), while dexlansoprazole performed stronger association for CKD (ROR = 34.94, 95% CI 30.89–39.53) and AKI (ROR = 8.18, 95% CI 7.04–9.51) than the other five PPIs. The median time from PPIs use to event occurrence was 23 days for AKI and 177 days for CKD. PPIs-associated AKI resulted larger proportion of death, life-threatening, hospitalization and disability events than PPIs-associated CKD. By mining the FAERS big data, we provided more information between PPIs use and the AKI and CKD events. PPIs rational use should be repeatedly stressed.

Safety of antipsychotic drugs: A systematic review of disproportionality analysis studies

Antipsychotic drugs are commonly prescribed, mainly for the treatment of schizophrenia and other psychotic disorders. Disproportionality analysis of pharmacovigilance data from national and international databases have been recently utilized to investigate the side-effect profiles of antipsychotics and have provided unique insights of their safety. Among several national and international spontaneous reporting databases the databases of the World Health Organization (VigiBase), of the European Medicines Agency (EudraVigilance) and the US Food and Drug Administration (FAERS) incorporate millions of Individual Case Safety Reports. The aim of our study was to systematically review published disproportionality analyses on antipsychotic drugs, in order to summarize the current state of methodology and potential strengths of this analysis while highlighting safety signal generated for these pharmacological group. PubMed was searched using a search algorithm combining terms for antipsychotic drugs and disproportionality analysis. A total of 39 articles were found to be eligible corresponding to 38 original disproportionality studies. Different measures of disproportionality were used in each study: reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM) and the information component (IC). Despite the inherent limitations of the pharmacovigilance databases disproportionality analysis provides complemented evidence from RCTs on the safety of antipsychotics, especially regarding participants often excluded from RCTs, such as pregnant and breastfeeding women, children and participants with drug abuse, comorbidities or concomitant medications.

No population left behind: Improving paediatric drug safety using informatics and systems biology

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Evaluation of signal detection algorithms within the Elanco Animal Health Pharmacovigilance database

Statistical algorithms for detecting safety signals are beginning to be applied to Animal Health Pharmacovigilance (PV) databases. How these signal detection algorithms (SDAs) perform in an animal health PV database is the subject of this report. Statistical methods and SDAs were assessed against a set of known signals in order to identify which SDAs were most appropriate for signal detection using the Elanco Animal Health PV database. A reference set of adverse events that should signal was created for 31 products across four species. Nine SDAs based on five disproportionality statistical methods were evaluated against the reference set. The performance metrics were sensitivity, precision, specificity, accuracy, and F score. For bovine and porcine products, the Observed-to-Expected (O/E) SDA was the closest in terms of geometric distance to 100% sensitivity and 100% precision. For canine and feline products, the Information Component (IC) SDA was geometrically closest to 100% sensitivity and 100% precision. Principal Component Analysis confirmed that the O/E and IC SDAs were unique performers with respect to one another and other SDAs. The performance of the SDAs was dependent on the choice of the statistical method with differences seen between animal species.

Angioedema with sacubitril/valsartan: Trial-level meta-analysis of over 14,000 patients and real-world evidence to date

BACKGROUND

Sacubitril/valsartan reduces the risk of hospitalizations and death among patients with heart failure (HF) with reduced ejection fraction; its use is poised to increase worldwide. As bradykinin is a substrate of neprilysin, angioedema was a theoretical concern potentiated by neprilysin inhibition.

METHODS

We explored angioedema in clinical trials and real-world pharmacovigilance data. We conducted a trial-level random-effects meta-analysis of 5 RCTs studying the effects of sacubitril/valsartan in heart failure. FDA Adverse Event Reporting System (FAERS) provided real-world pharmacovigilance data in the US.

RESULTS

The 5 trials enrolled 14,841 patients with follow-up ranging from 2 to 27 months. The collective rate of angioedema in RCTs was 0.5% in sacubitril/valsartan arms vs. 0.3% in control arms (pooled odds ratio of 1.35; 95% confidence interval - 0.45 to 4.1; P = .59) with moderate heterogeneity (I² 55.2.%). These relative effects were driven by the larger PARADIGM-HF and PARAGON-HF experiences. FAERS pharmacovigilance data identified 426 angioedema cases over the last 5 years out of 40,559 adverse events reported related to sacubitril/valsartan.

CONCLUSIONS

Rates of angioedema with sacubitril/valsartan are reported to be low in RCTs and real-world clinical practice.

Ovary and uterus related adverse events associated with statin use: an analysis of the FDA Adverse Event Reporting System

Experimental studies have demonstrated statin-induced toxicity for ovary and uterus. However, the safety of statins on the functions of ovary and uterus in real-world clinical settings remains unknown. The aim of this study was to identify ovary and uterus related adverse events (AEs) associated with statin use by analyzing data from FDA Adverse Event Reporting System (FAERS). We used OpenVigil 2.1 to query FAERS database. Ovary and uterus related AEs were defined by 383 Preferred Terms, which could be classified into ten aspects. Disproportionality analysis was performed to assess the association between AEs and statin use. Our results suggest that statin use may be associated with a series of ovary and uterus related AEs. These AEs are involved in ovarian cysts and neoplasms, uterine neoplasms, cervix neoplasms, uterine disorders (excl neoplasms), cervix disorders (excl neoplasms), endocrine disorders of gonadal function, menstrual cycle and uterine bleeding disorders, menopause related conditions, and sexual function disorders. Moreover, there are variabilities in the types and signal strengths of ovary and uterus related AEs across individual statins. According to our findings, the potential ovary and uterus related AEs of statins should attract enough attention and be closely monitored in future clinical practice.

Pharmacovigilance of anti-cancer medicines: opportunities and challenges

INTRODUCTION

The foundations of pharmacovigilance are the monitoring of drug safety in real-world medicine, and identification of new adverse effects, unknown at the time of market approval. Cancer patients are prone to adverse drug reactions due to the complexity of the neoplastic disease and its treatment. Pharmacovigilance of anti-cancer medicines is further complicated because patients have comorbidities, as for elderly patients. It is even more challenging when complete safety and risk data for a drug are lacking, as may occur for new molecules or when it comes to drugs for children.

AREAS COVERED

This article introduces the field of pharmacovigilance of anti-cancer drugs, describing the various layers of complexity that make the recognition of adverse drug events in oncology particularly problematic, including the type of medicines, the phenomenon of underreporting and polypharmacy. Finally, it reviews new digital tools to help pharmacovigilance activities in oncology.

EXPERT OPINION

The authors outline some crucial challenges and opportunities that can be useful for pharmacovigilance to keep up with the times and follow the current technological and scientific progress. In addition to the evaluations made by researchers, it will, of course, be necessary to have an equality important concrete response from the institutions and regulatory bodies.

Evaluating medical device adverse event signals using a likelihood ratio test method

Signal detection methods have been used extensively in post-market surveillance to identify elevated risks of adverse events. However, these statistical methods have not been widely used in detecting AE signals for medical devices. In this paper, we focused on the use of a likelihood ratio test (LRT)-based method in identifying adverse event (AE) signals associated with left ventricular assist devices (LVADs) using Medical Device Reporting (MDR) data. Among 110,927 adverse event entries identified in MDR data for LVADs, the LRT method detected 18 AE signals which included seven bleeding-related AEs such as hemolysis, thrombosis, hematuria, thrombus, blood loss, and hemorrhage. The LRT method was also applied to longitudinal data from 2007 to 2019 where a monotone alpha-spending function was used to ensure the control of type I error at each look and overall for trend analysis. Furthermore, the LRT method was compared to proportional reporting ratios (PRRs), Bayesian confidence propagation neural network (BCPNN), and simplified Bayes methods and found to be the most conservative method when examining the total number of detected signals, given its ability to control type-I error and the false discovery rate.