The risk of seizure in offspring following prenatal exposure to levothyroxine is not well investigated. This study aimed to evaluate the association between levothyroxine treatment among pregnant women and the risk of seizure in their offspring. This population-based cohort study included all pregnant women who delivered a live birth between January 2001 to January 2018, with a follow-up to December 2020, using data from the Hong Kong Clinical Data Analysis and Reporting System. Propensity score fine-stratification weighted hazard ratios (wHR) with 95% confidence intervals (CIs) were presented to assess the association between maternal levothyroxine use during pregnancy and seizures in children. Among 528,343 included mother-child pairs, 3044 children were prenatally exposed to levothyroxine at any time during the pregnancy period. A significantly increased risk of seizure was observed in children of the prenatally exposed group compared with the prenatally unexposed group (wHR 1.12, 95% CI 1.02-1.22). An increased risk of seizure was observed when comparing the prenatally exposed group with euthyroid mothers who had no history of thyroid-related diagnosis or prescriptions (wHR 1.12, 95% CI 1.02-1.23). However, no significant difference was observed between the prenatally exposed group and those previously exposed to levothyroxine but had stopped during pregnancy (wHR 0.97, 95% CI 0.66-1.44). No significant difference was observed in the sibling-matched analysis either (wHR 1.23, 95% CI 0.76-2.01). The observed increased risk of seizure in children born from mothers exposed to levothyroxine during pregnancy might be due to residual confounding by maternal thyroid disease. The findings support the current guidelines on the safe use of levothyroxine treatment during pregnancy.

Opioids are commonly used as analgesics; however, like any medicine, they can produce adverse drug reactions (ADRs), including nausea, constipation, dependence, and respiratory depression, that result in harmful and fatal events. Therefore, it is essential to monitor the safety of these drugs in clinical practice. This study aimed to characterize the safety profile of opioids by conducting a descriptive study based on a spontaneous reporting system (SRS) for ADRs in The Netherlands, focusing on abuse, misuse, medication errors, and differences between sexes. Reports submitted to the Netherlands Pharmacovigilance Centre Lareb from January 2003 to December 2021 with an opioid drug as the suspected/interacting medicine were analyzed. Reporting odds ratios (RORs) for drug-ADR combinations were calculated, analyzed, and corrected for sex and drug utilization (expenditure) for the Dutch population. A total of 8769 reports were analyzed. Tramadol was the opioid with the most reports during the period (n=2746), while oxycodone or tramadol had the highest number of reports per year in the study period. The most reported ADRs from opioid use were nausea, followed by dizziness and vomiting, independent of sex, and all of them were more often reported in women. Vomiting associated with tramadol (ROR females/males=2.17) was significantly higher in women. Buprenorphine was responsible for most ADRs when corrected for expenditure, with high RORs observed with application site hypersensitivity, application site reaction, and application site rash. Fentanyl gave rise to most of the reports of ADRs concerning abuse, misuse, and medication errors. Patients treated with opioids experienced ADRs, primarily nausea, dizziness, and vomiting. For those groups of drugs, no significant differences were found between the sexes, except for the vomiting associated with tramadol. In general, ADRs related to opioids presented higher RORs when uncorrected and corrected for sexes and expenditure than other drugs. There was more disproportionate reporting for ADRs concerning abuse, misuse, and medication errors for opioids than other drugs in the Dutch SRS.

Medication safety problems are common post-hospital discharge, and an important global healthcare improvement target. The Transfers of Care Around Medicines (TCAM) service was launched by a National Health Service Trust in the North-West of England, initially focusing on patients with new or existing Monitored Dosage Systems(MDS). The TCAM service is designed to enable the prompt transfer of medication information, with referrals made by hospitals at discharge to a named community pharmacy. This study aimed to explore the utilisation and impact of the TCAM service on medication safety. The evaluation included a descriptive analysis of 3033 anonymised patient referrals to 71 community pharmacies over a 1-year period alongside an assessment of the impact of the TCAM service on unintentional medication discrepancies and adverse drug events using a retrospective before-and-after study design. Impact data were collected across 18 general practices by 16 trained clinical pharmacists. Most patient referrals (70%, 2126/3033) were marked as 'completed' by community pharmacies, with 15% of completed referrals delayed beyond 30 days. Screening of 411 patient records by clinical pharmacists yielded no statistically significant difference in unintentional medication discrepancies or adverse drug event ratesfollowing TCAM implementation using a multivariable regression analysis (unintentional medication discrepancies adjusted odds ratio = 0.79 [95% confidence interval 0.44-1.44, p = 0.46]; and adverse drug events adjusted odds ratio = 1.19 [95% confidence interval 0.57-2.45, p = 0.63]), although there remained considerable uncertainty. The TCAM service facilitated a number of community pharmacy services offered to patients with monitored dosage systems; but the impact of the intervention on unintentional medication discrepancies and adverse drug event rates post-hospital discharge for this patient group was uncertain. The results of this exploratory study can inform the ongoing implementation of the TCAM service at hospital discharge and highlight the need to understand service implementation in different contexts, which may influence its impact on medication safety.

Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine healthdata. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Adverse drug events (ADEs) are a leading cause of unplanned hospital visits. We designed ActionADE, an online ADE reporting platform, and integrated it with PharmaNet, British Columbia's(BC's) provincial medication dispensing system, to overcome identified barriers in ADE reporting and communicate ADEs to community pharmacies. Our objectives were to characterise ADEs reported in ActionADE, explore associations between patients' age, sex and ADE characteristics, and estimate the re-dispensation rate of culprit medications in community pharmacies. We conducted a prospective observational study of ADE reporting in four BC hospitals between April 1, 2020 and October 31, 2022. We described the characteristics of ADEs reported into ActionADE, used logistic regression modelling to examine associations between age and sex and ADE characteristics, and calculated rates of avoided culprit drug re-dispensations using community pharmacists' responses to ActionADE alerts. In total, 3591 ADE reports were initiated by hospital clinicians, 3174 of which were included in this analysis. Serious or life-threatening ADEs resulting in permanent disability, hospitalisation, extended hospitalisation, and/or death accounted for 28.5% (906/3174; 95% CI 27.0-30.1%) of reports. Males were more likely to have non-adherence reported compared to females and experienced life threatening ADEs at a younger age than females. Of 592 patients who had ≥1 adverse drug reaction or allergy report (a subset of ADEs) transmitted to community pharmacies, 200 subsequently attempted to re-fill the culprit or a same class drug. Community pharmacists responded to preventative alerts by avoiding re-dispensation in 33.0% (66/200; 95% CI 26.5-39.5%). ActionADE is the first interoperable system that communicates ADEs via a central medication database to community pharmacies. Every 10th ADE reported in ActionADEand shared to PharmaNet resulted in community pharmacists' avoiding one culprit or same class drug re-exposure. Further research is needed to understand ActionADE's impact on patient and health system outcomes.

Whereas some guidelines recommend statin use to achieve low-density lipoprotein cholesterol (LDL-C) goal < 70 mg/dL for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in patients at higher risk, others recommend against a target LDL-C level. Achieving a target level < 70 mg/dL commonly requires the use of high intensity statins, which has been associated with higher risk of diabetes progression. The objective of this study is to assess the association of strict (≤ 70 mg/dL) versus lenient (> 70 to100 mg/dL) LDL-C lowering on major adverse cardiovascular events (MACE), diabetes progression, diabetes microvascular complications, and total mortality in patients with diabetes. This was a retrospective propensity score (PS)-matched study from a national cohort of, predominantly male, veterans diagnosed with diabetes without prior cardiovascular disease (from fiscal years 2003-2015), who were initiated on a statin. We created PS to match strict (mean LDL-C during follow-up ≤ 70 mg/dL) versus lenient (mean LDL-C during follow up > 70-100 mg/dL) using 65 baseline characteristics including comorbidities, risk scores, medication classes usage, vital signs, and laboratory data. Outcomes included MACE, diabetes progression, microvascular diabetes complications, and total mortality. From 80,110 eligible patients, we PS-matched 21,294 pairs of statin initiators with strict or lenient LDL-C lowering. The mean (SD) age was 64 (9.5) years and mean (SD) duration of follow-up was 6 (3) years. MACE was similar in the PS-matched groups [6.1% in strict versus 5.8% in lenient; odds ratio (OR): 1.06; 95% confidence interval (95% CI) 0.98-1.15, P = 0.17]. Diabetes progression was higher among the strict vs lenient group (66.7% in strict versus 64.1% in lenient; OR 1.12; 95% CI 1.08-1.17, P < 0.001). There was no difference in microvascular diabetes complications (22.3% in strict versus 21.9% in lenient; OR 1.02; 95% CI 0.98-1.07, P = 0.31) and no difference in total mortality (14.6% in strict versus 15% in lenient; OR 0.97; 95% CI 0.92-1.02, P = 0.20). Strict compared with lenient lowering of LDL-C with statins in men with diabetes without preexisting ASCVD did not decrease the risk of MACE but was associated with an increased diabetes progression. Clinicians should monitor their patients for diabetes progression upon escalating statins to achieve LDL-C levels ≤ 70 mg/dL.

Substandard medicines can lead to serious safety issues affecting public health; however, the nature of such issues can be widely heterogeneous. Health product regulators seek to prioritise critical product quality defects for review to ensure that prompt risk mitigation measures are taken. This study aims to classify the nature of issues for substandard medicines using machine learning to augment a risk-based and timely review of cases. A combined machine learning algorithm with a keyword-based model was developed to classify quality issues using text relating to substandard medicines (CISTERM). The nature of issues for product defect cases were classified based on Medical Dictionary for Regulatory Activities-Health Sciences Authority (MedDRA-HSA) lowest-level terms. Product defect cases received from January 2010 to December 2021 were used for training (n = 11,082) and for testing (n = 2771). The machine learning model achieved a good recall (precision) of 92% (96%) for 'Product adulterated and/or contains prohibited substance', 86% (90%) for 'Out of specification or out of trend test result' and 90% (91%) for 'Manufacturing non-compliance'. Post-market surveillance of substandard medicines remains a key activity for drug regulatory authorities. A combined machine learning algorithm with keyword-based model can help to prioritise the review of product quality defect issues in a timely manner.

Postmarketing drug safety surveillance research has focused on the product-patient interaction as the primary source of variability in clinical outcomes. However, the inherent complexity of pharmaceutical manufacturing and distribution, especially of biologic drugs, also underscores the importance of risks related to variability in manufacturing and supply chain conditions that could potentially impact clinical outcomes. We propose a data-driven signal detection method called HMMScan to monitor for manufacturing lot-dependent changes in adverse event (AE) rates, and herein apply it to a biologic drug. The HMMScan method chooses the best-fitting candidate from a family of probabilistic Hidden Markov Models to detect temporal correlations in per lot AE rates that could signal clinically relevant variability in manufacturing and supply chain conditions. Additionally, HMMScan indicates the particular lots most likely to be related to risky states of the manufacturing or supply chain condition. The HMMScan method was validated on extensive simulated data and applied to three actual lot sequences of a major biologic drug by combining lot metadata from the manufacturer with AE reports from the US FDA Adverse Event Reporting System (FAERS). Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. Applying the HMMScan method to FAERS data, two of the three actual lot sequences examined exhibited evidence of potential manufacturing or supply chain-related variability. HMMScan could be utilized by both manufacturers and regulators to automate lot variability monitoring and inform targeted root-cause analysis. Broad application of HMMScan would rely on a well-developed data input pipeline. The proposed method is implemented in an open-source GitHub repository.

Surveys are commonly used to assess effectiveness of FDA-required risk evaluation and mitigation strategies (REMS) for drugs and biologics in the United States. The aim of this study was to assess the scientific rigor of REMS knowledge survey protocols submitted to FDA and compare protocols before and after FDA's 2012 public workshop and 2019 draft guidance. A content analysis of index survey protocols submitted to FDA (2007-2020) for single-product REMS with elements to assure safe use (39 programs, 78 protocols) was conducted. Each protocol was scored against 52 core essential elements (CEE), abstracted from FDA's guidance and grouped into six domains: study objective (n=5), study design (n=18), survey instrument (n=9), participant recruitment (n=7), survey administration (n=9), and statistical analysis plan (n=4). Scores were collected by time periods: (A) Oct 2007 to Jul 2012; (B) Aug 2012 to Feb 2019; (C) Mar 2019 to Dec 2020; and compared using logistic generalized linear mixed models adjusting for domain, survey population, vendor, program, and protocol. There were 30 (38.5%), 40 (51.3%), and 8 (10.3%) protocols submitted in time period A, B, and C, respectively. Adjusted marginal means of elements present (on the probability scale) by time period were 0.5816 (SE = 0.0242), 0.6429 (SE = 0.0229), and 0.7543 (SE = 0.0394). The likelihood of missing a CEE declined over time (adjusted p-value = 0.0094, time period A vs C). The statistical analysis plan domain had the most improvement; study design remained the weakest domain with the scientific justification CEE particularly underrepresented. The rigor of REMS knowledge survey protocols improved over time consistent with FDA's efforts to advance regulatory science, but gaps remain.

Biosimilar CT-P13 was approved with limited data from clinical trials compared to the originator infliximab in biologic-naïve patients with rheumatoid arthritis. Three prospective post-marketing surveillance studies have been conducted in Japanese biologic-naïve patients and switched patients from biologics including the originator infliximab. We performed an integrated analysis of final data from three post-marketing studies to provide long-term safety and efficacy data of CT-P13 in a real-world clinical setting. A total of 1816 patients consisting of 987 patients with rheumatoid arthritis, 342 patients with Crohn's disease, 322 patients with ulcerative colitis, and 165 patients with psoriasis were evaluated for safety. Efficacy was assessed in 1150 patients whose disease parameter values were serially collected. Adverse drug reactions were reported in 24.2% of all patients. The incidence of adverse drug reactions differed by the prior treatment status with biologics: 30.5% in patients naïve to biologics, 17.0% in patients switched from the originator infliximab, and 33.5% in patients switched from other biologics. Infusion reactions were the most frequent adverse drug reactions (8.2%), and its incidence was significantly higher in patients with ulcerative colitis and an allergy history in a multivariable Cox regression analysis. Infection was the second most frequent (6.1%), but tuberculosis only occurred in four patients (0.2%). The incidence of infection was low in patients with Crohn's disease and psoriasis, and significant risk factors were an allergy history, comorbidities, and concomitant steroid use. Interstitial lung disease occurred in 16 patients (0.9%), including 11 patients with rheumatoid arthritis. With CT-P13 therapy, disease activity parameters decreased similarly in all four diseases, although long-term drug discontinuation rates because of inefficacy varied by disease. In naïve patients, the disease activity parameters decreased rapidly and the proportion of patients in remission increased. Patients switched from infliximab maintained lowered parameter levels with infliximab pretreatment. Decreases were also observed in patients switched from other biologics, but discontinuations were most often because of insufficient efficacy. The integrated analysis of a large number of patients detected no new safety signals with long-term CT-P13 treatment. Efficacy in rheumatoid arthritis, psoriasis, Crohn's disease, and ulcerative colitis cases was confirmed in biologic-naïve patients and switched patients from the originator infliximab or other biologics.