A digital point-of-care solution was implemented to test the feasibility of near-real-time bi-directional communication between pharmacovigilance experts (PVEs) and healthcare professionals (HCPs) for exchanging unique and informative adverse event (AE) information. The solution was implemented in a commercially available electronic health record (EHR) system/platform, no direct contact between PVEs and the HCPs was possible. The Clinical Affairs team of the EHR vendor was used as an intermediary to ensure appropriate information was exchanged while protecting HCP and patient privacy. The study yielded 9 drug-event pairs of interest (AEI), 2 of which were confirmed as AEs by the HCP. On average it took 20.6 h to receive initial AEI information and 58.8 h to receive follow-up information, which represents a 96

INTRODUCTION:Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases. OBJECTIVE:This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis. METHODS:The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression). RESULTS:Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency's (EMA's) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs. CONCLUSIONS:Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployments in high-risk and safety-critical domains pose unique challenges, notably the issue of “hallucination,” where LLMs can generate fabricated information. This is particularly concerning in settings such as drug safety, where inaccuracies could lead to patient harm. To mitigate these risks, we have developed and demonstrated a proof of concept suite of guardrails specifically designed to mitigate certain types of hallucinations and errors for drug safety, and potentially applicable to other medical safety-critical contexts. These guardrails include mechanisms to detect anomalous documents to prevent the ingestion of inappropriate data, identify incorrect drug names or adverse event terms, and convey uncertainty in generated content. We integrated these guardrails with an LLM fine-tuned for a text-to-text task, which involves converting both structured and unstructured data within adverse event reports into natural language. This method was applied to translate individual case safety reports, demonstrating effective application in a pharmacovigilance processing task. Our guardrail framework offers a set of tools with broad applicability across various domains, ensuring LLMs can be safely used in high-risk situations by eliminating the occurrence of key errors, including the generation of incorrect pharmacovigilance-related terms, thus adhering to stringent regulatory and quality standards in medical safety-critical environments.

Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review. Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large. The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK’s products. The mean CUSP score for ‘key cases’ and ‘cases of low utility’ was 19.7 (median: 21; range: 7–27) and 17.3 (median: 19; range: 4–27), respectively. CUSP distribution for ‘key cases’ were skewed toward the higher range of scores compared with ‘all cases’. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for ‘key cases’ for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5–23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5–20.7). Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review.

Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit–risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Twenty-seven PRAC subgroup examples representing 1719 subgroup drug–event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Background: Enrollment in pregnancy registries is challenging despite substantial awareness-raising activities, generally resulting in low recruitment owing to limited safety data. Understanding patient and physician awareness of and attitudes toward pregnancy registries is needed to facilitate enrollment. Crowdsourcing, in which services, ideas, or content are obtained by soliciting contributions from a large group of people using web-based platforms, has shown promise for improving patient engagement and obtaining patient insights. Objective: This study aimed to use web-based crowdsourcing platforms to evaluate Belimumab Pregnancy Registry (BPR) awareness among patients and physicians and to identify potential barriers to pregnancy registry enrollment with the BPR as a case study. Methods: We conducted 2 surveys using separate web-based crowdsourcing platforms: Amazon Mechanical Turk (a 14-question patient survey) and Sermo RealTime (a 11-question rheumatologist survey). Eligible patients were women, aged 18-55 years; diagnosed with systemic lupus erythematosus (SLE); and pregnant, recently pregnant (within 2 years), or planning pregnancy. Eligible rheumatologists had prescribed belimumab and treated pregnant women. Responses were descriptively analyzed. Results: Of 151 patient respondents over a 3-month period (n=88, 58.3% aged 26-35 years; n=149, 98.7% with mild or moderate SLE; and n=148, 98% from the United States), 51% (77/151) were currently or recently pregnant. Overall, 169 rheumatologists completed the survey within 48 hours, and 59.2% (100/169) were based in the United States. Belimumab exposure was reported by 41.7% (63/151) patients, whereas 51.7% (75/145) rheumatologists had prescribed belimumab to <5 patients, 25.5% (37/145) had prescribed to 5-10 patients, and 22.8% (33/145) had prescribed to >10 patients who were pregnant or trying to conceive. Of the patients exposed to belimumab, 51% (32/63) were BPR-aware, and 45.5% (77/169) of the rheumatologists were BPR-aware. Overall, 60% (38/63) of patients reported belimumab discontinuation because of pregnancy or planned pregnancy. Among the 77 BPR-aware rheumatologists, 70 (91%) referred patients to the registry. Concerns among rheumatologists who did not prescribe belimumab during pregnancy included unknown pregnancy safety profile (119/169, 70.4%), and 61.5% (104/169) reported their patients' concerns about the unknown pregnancy safety profile. Belimumab exposure during or recently after pregnancy or while trying to conceive was reported in patients with mild (6/64, 9%), moderate (22/85, 26%), or severe (1/2, 50%) SLE. Rheumatologists more commonly recommended belimumab for moderate (84/169, 49.7%) and severe (123/169, 72.8%) SLE than for mild SLE (36/169, 21.3%) for patients trying to conceive recently or currently pregnant. Overall, 81.6% (138/169) of the rheumatologists suggested a belimumab washout period before pregnancy of 0-30 days (44/138, 31.9%), 30-60 days (64/138, 46.4%), or >60 days (30/138, 21.7%). Conclusions: In this case, crowdsourcing efficiently obtained patient and rheumatologist input, with some patients with SLE continuing to use belimumab during or while planning a pregnancy. There was moderate awareness of the BPR among patients and physicians.

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

The RNA-binding protein HuD (a.k.a., ELAVL4) is involved in neuronal development and synaptic plasticity mechanisms, including addiction-related processes such as cocaine conditioned-place preference (CPP) and food reward. The most studied function of this protein is mRNA stabilization; however, we have recently shown that HuD also regulates the levels of circular RNAs (circRNAs) in neurons. To examine the role of HuD in the control of coding and non-coding RNA networks associated with substance use, we identified sets of differentially expressed mRNAs, circRNAs and miRNAs in the striatum of HuD knockout (KO) mice. Our findings indicate that significantly downregulated mRNAs are enriched in biological pathways related to cell morphology and behavior. Furthermore, deletion of HuD altered the levels of 15 miRNAs associated with drug seeking. Using these sets of data, we predicted that a large number of upregulated miRNAs form competing endogenous RNA (ceRNA) networks with circRNAs and mRNAs associated with the neuronal development and synaptic plasticity proteins LSAMP and MARK3. Additionally, several downregulated miRNAs form ceRNA networks with mRNAs and circRNAs from MEF2D, PIK3R3, PTRPM and other neuronal proteins. Together, our results indicate that HuD regulates ceRNA networks controlling the levels of mRNAs associated with neuronal differentiation and synaptic physiology.

INTRODUCTION:Machine learning offers an alluring solution to developing automated approaches to the increasing individual case safety report burden being placed upon pharmacovigilance. Leveraging crowdsourcing to annotate unstructured data may provide accurate, efficient, and contemporaneous training data sets in support of machine learning.OBJECTIVE:The objective of this study was to evaluate whether crowdsourcing can be used to accurately and efficiently develop training data sets in support of pharmacovigilance automation.MATERIALS AND METHODS:Pharmacovigilance experts created a reference dataset by reviewing 15,490 de-identified social media posts of narratives pertaining to 15 drugs and 22 medically relevant topics. A random sampling of posts from the reference dataset was published on Amazon Turk and its users (Turkers) were asked a series of questions about those same medical concepts. Accuracy, price elasticity, and time efficiency were evaluated.RESULTS:Accuracy of crowdsourced curation exceeded 90% when compared to the reference dataset and was completed in about 5% of the time. There was an increase in time efficiency with higher pay, but there was no significant difference in accuracy. Additionally, having a social media post reviewed by more than one Turker (using a voting system) did not offer significant improvements in terms of accuracy.CONCLUSIONS:Crowdsourcing is an accurate and efficient method that can be used to develop training data sets in support of pharmacovigilance automation. More research is needed to better understand the breadth and depth of possible uses as well as strengths, limitations, and generalizability of results.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection. Many patients and clinicians have taken to social media to discuss their positive and negative experiences of medications, creating a source of publicly available information that has the potential to provide insights into medicinal product safety concerns. The WEB-RADR project has developed a collaborative English language workspace for visualising and analysing social media data for a number of medicinal products. Further, novel text and data mining methods for social media analysis have been developed and evaluated. From this original research, several recommendations are presented with supporting rationale and consideration of the limitations. Recommendations for further research that extend beyond the scope of the current project are also presented.