Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure.

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.

Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility.

INTRODUCTION: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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 Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk.

INTRODUCTION: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Engaging Patients via Online Healthcare Fora: Three Pharmacovigilance Use Cases.

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.

An automated causality assessment algorithm to detect drug-induced liver injury in electronic medical record data.

PURPOSE: The aim of this study was to develop an automated causality assessment algorithm to identify drug-induced liver injury. METHODS: The Roussel Uclaf Causality Assessment Method (RUCAM) is an algorithm for determining the causal association between a drug and liver injury. In collaboration with hepatology experts, definitions were developed for the RUCAM criteria to operationalize an electronic RUCAM (eRUCAM). The eRUCAM was tested in a population of patients taking 14 drugs with a characteristic phenotype for liver injury. Quality assurance for programming specifications involved comparisons between scores generated by the eRUCAM, for probable and highly probable cases, and expert manual RUCAM (n = 20). Concordance between eRUCAM and manual RUCAM subscores and total score was tested using the Wilcoxon signed rank test. RESULTS: Causality scores were the same for 6 of 20 patients (30%) by manual and eRUCAM algorithms. Analysis of subscores revealed ≥80% concordance between manual and eRUCAM for five of the seven criteria. In general, the total scores tended to be higher for the eRUCAM compared with the manual RUCAM. Programming issues were identified for criterion 5 'non-drug causes of liver injury' where significant differences existed between manual and eRUCAM scoring (p = 0.001). For criterion 5, identical scores occurred in 9 of 20 patients (45%), and manual review identified additional codes, timing criteria, and laboratory results for improving subsequent eRUCAM revisions. CONCLUSION: The eRUCAM had generally good concordance with manual RUCAM scoring. These preliminary findings suggest that the eRUCAM algorithm is feasible and could have application in clinical practice and drug safety surveillance.

Spontaneous adverse event reports of Stevens-Johnson syndrome/toxic epidermal necrolysis: detecting associations with medications.

PURPOSE: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are medically serious skin reactions that are often drug induced. The mainstay of therapy and future prevention is to discontinue and avoid the use of the suspected inducing drug. However, many cases of SJS/TEN occur in patients who are taking multiple medications, and it is often difficult to determine which drug to stop. This analysis was conducted to identify drugs that were most associated with SJS/TEN in the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database and to identify medications that were likely innocent bystanders. METHODS: A Multi-item Gamma Poisson Shrinker value with an EB05 ≥ 2 was considered a disproportional increase in reporting frequency (at least two times higher than expected). The identified drugs with reporting frequency of SJS/TEN in the US FDA AERS database were then compared to the EuroSCAR (European case-control surveillance of severe cutaneous adverse reactions) study results as a reference to define signals. The EB05s were calculated as a cumulative relative reporting frequency from 1968 to 3Q2009. RESULTS: Fifty drugs were identified as being associated with SJS/TEN. This included 12 "highly suspect" drugs and 36 "suspect" drugs. Meloxicam was the only drug that appeared on the "highly suspect" list from EuroSCAR that did not show a disproportional increase in relative reporting frequency (EB05 = 0.734). In addition, several drugs did not have an association with SJS/TEN (EB05 < 2). CONCLUSIONS: There was good concordance between the reporting frequencies observed in the FDA AERS database and the published risk estimation of medications implicated in SJS/TEN.

Children orphaned by AIDS: a global perspective.

Internationally, the orphan crisis caused by the human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) pandemic remains a serious issue with long-term social consequences. At the end of 2001, an estimated 14 million children worldwide had lost their mother or both parents to AIDS or related causes. Sub-Saharan Africa is the most severely affected, accounting for more than 80 percent of those orphaned as a result of AIDS. Without the care of parents or an appointed caregiver, children are likely to face extraordinary risks of malnutrition, poor health, inadequate schooling, migration, homelessness, and abuse. Strengthening existing family and community capacity to assist orphans in Africa should be the first priority. Community support must be coupled with support for education for orphans. Combining local and international responses to deliver protection and services to all orphans and vulnerable children is critical. In addition, saving the lives of parents through access to antiretroviral therapies in resource-poor countries in conjunction with bold support for alleviation of poverty and education must be an integral part of the global response to the orphan crisis in sub-Saharan Africa.