The medical dictionary for regulatory activities (MedDRA)

Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities

Objectives

Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs.

Methods

In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs.

Results

Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs.

Conclusions

In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.

Assessment of adverse events related to anti-influenza neuraminidase inhibitors using the FDA adverse event reporting system and online patient reviews

The recommended antiviral drugs available for the treatment and prevention of influenza are neuraminidase inhibitors (NAIs). The aim of this study was to evaluate age-related clinical manifestations of adverse events (AEs) related to NAIs. FAERS and WebMD data were downloaded. The available NAIs selected for the analysis were oseltamivir, peramivir, zanamivir, and laninamivir. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), and the information component (IC) methods. In total, 16729 AEs from 4598 patients and 575 AEs from 440 patients in the FAERS and WebMD, respectively, were included in the analysis. In the FAERS, AEs were more common among those who were younger (<19 years) for zanamivir, while for those who were older (>65 years) for peramivir. A disproportionality analysis showed that signals for vomiting and hallucinations were detected in younger patients given oseltamivir, while an abnormal hepatic function, cardiac failure, shock, and cardio-respiratory arrest were detected in older patients given peramivir. Psychiatric disorders were most common in younger and older patients, while gastrointestinal disorders were most common in adult given oseltamivir in the WebMD. Adverse symptoms related to NAIs varied and depended on the drugs used and the age of the patient.

Combining big data search analytics and the FDA Adverse Event Reporting System database to detect a potential safety signal of mirtazapine abuse

This study sought to detect a potential safety signal of mirtazapine abuse by combining two different sources of surveillance, specifically Google Analytics (Google, Inc., Mountain View, CA, USA) and the FDA Adverse Event Reporting System database. Data from the first quarter of 2004 to the second quarter of 2017 were collected and analysed. The search interest over time, the frequencies of abuse-related terms in the search analytics domain, and the odds ratio of abuse events in FDA Adverse Event Reporting System were determined. Correlations between the two aforementioned domains using quarterly data from the timeline series were also assessed. Our results suggest a positive correlation between abuse-related searches in the Google domain and abuse-related events in FDA Adverse Event Reporting System database. These results indicate that these methods can be used in combination with each other as a pharmacovigilance supplementary tool to detect drug safety signals.

Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics

Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and link them to clinical consequences. Consequently, we developed information retrieval (IR) models to retrieve DDI and drug-gene interaction (DGI) evidence from 25 million PubMed abstracts and distinguish DDI evidence into in vitro pharmacokinetic (PK), clinical PK, and clinical pharmacodynamic (PD) studies for US Food and Drug Administration (FDA) approved and withdrawn drugs. Additionally, information extraction models were developed to extract DDI-pairs and DGI-pairs from the IR-retrieved abstracts. An overlapping analysis identified 986 unique DDI-pairs between all 3 types of evidence. Another 2,157 and 13,012 DDI-pairs and 3,173 DGI-pairs were identified from known clinical PK/PD DDI, clinical PD DDI, and DGI evidence, respectively. By integrating DDI and DGI evidence, we discovered 119 and 18 new pharmacogenetic hypotheses associated with CYP3A and CYP2D6, respectively. Some of these DGI evidence can also aid us in understanding DDI mechanisms.

[Completeness of pharmacovigilance reporting in general medicine in France.]

INTRODUCTION

Spontaneous reporting remains one of the cornerstones of post-marketing drug safety surveillance. One of its main limitations is a lack of completeness.The main aim of this study was to assess the completeness of pharmacovigilance reports sent by general practitioners (GPs) to regional pharmacovigilance centers (RPC) reported in the French pharmacovigilance database (FPVD). Secondary aim was to identify factors associated with complete reports.

METHOD

All adverse drugs reactions (ADRs) sent by GPs in France in 2015 were analyzed. According to information provided in ADR reports (ADR, date of occurrence, clinical description, drugs suspected, etc.), completeness was analyzed from &#8220;mandatory&#8221; criteria (age, gender, ADR&#160;and suspected drug(s)) and &#8220;non-mandatory&#8221; criteria (medical history, concomitant drugs, symptoms evolution and complementary exams) and classified&#160;as&#160;&#8220;well-documented&#8221;, &#8220;slightly-documented&#8221; or &#8220;poorly-documented&#8221;.

RESULTS

In 2015, the FPVD contained 3,020 ADR reports realized by GPs. Only 16.4% of these reports were classified as &#8220;well-documented&#8221;, in accordance with study criteria. The most poorly documented items were concomitant drugs (41.4%) and complementary exams (37.4%). An association between a &#8220;well-documented&#8221; ADR report and its &#8220;seriousness&#8221; (OR = 3,02 [95%&#160;CI&#160;2,44; 3,23], P&#160;&lt;&#160;10&#8211;3) and elderly compared to adults (OR&#160;=&#160;1,76 [95% CI 1,42; 2,18], P&#160;&lt;&#160;10&#8211;3) or children (OR = 4,59 [95% CI 2,51; 8,39], P&#160;&lt;&#160;10&#8211;3).

CONCLUSION

Our study shows that only one out of six ADR reports was &#8220;well-documented&#8221;. It appears to be important to promote pharmacovigilance to improve completeness of ADR reports.

[Evaluation of the Automatic Full Form Retrieval Method from Abbreviation Using Word2vec for Terminology Expansion]

PURPOSES

The purposes of this study were to automatically extract full forms from abbreviations by using Word2vec for terminology expansion and determine the optimal parameters that ensure the highest accuracy.

METHODS

Approximately 300000 English abstracts on "image diagnosis" were collected using PubMed from January 1994 to December 2018. As preprocessing, all uppercase letters in the collected data were converted to lowercase letters, and symbols were deleted. In addition, compound word recognition was performed using RadLex published by the Radiological Society of North America and the abbreviation collection published by the Japanese Society  of  Radiological  Technology.  Next,  distributed  representations  were  generated  by  two  algorithms, continuous bag-of-words (CBOW) and Skip-gram, by using the following parameters: iteration numbers (3-85) and dimensions of word vectors (50-1000). Abbreviations were input to the generated distributed representations, and full forms with the highest cosine similarities with the abbreviations were identified. Then, the rates of the correct answers were calculated by comparing the predicted full forms to 214 gold standards extracted from the abbreviation collection.

RESULTS

The highest correct answer rate was 74.3% by Skip-gram, 200 dimensions and 10 iterations. This rate was higher in Skip-gram than in CBOW for all the tested conditions.

CONCLUSION

The accuracy of extracting the full forms by Word2vec is 74.3%, and this result contributes to the consistency of a terminology and the efficiency of terminology expansion.

Birth Prevalence of Congenital Cytomegalovirus Infection in HIV-Exposed Uninfected Children in the Era of Combination Antiretroviral Therapy

OBJECTIVES

To estimate birth prevalence of congenital cytomegalovirus (cCMV) in HIV-exposed uninfected children born in the current era of combination antiretroviral therapy and describe cCMV-related neurodevelopmental and hearing outcomes.

STUDY DESIGN

The Surveillance Monitoring for ART Toxicities cohort study follows HIV-exposed uninfected children at 22 sites in the US and Puerto Rico. Birth cCMV prevalence was estimated in a subset of participants who had blood pellets collected within three weeks of birth and underwent ≥1 of 6 assessments evaluating cognitive and language development including an audiologic examination between 1 and 5 years of age. Detection of CMV DNA by polymerase chain reaction testing of peripheral blood mononuclear cells was used to diagnose cCMV. Proportions of suboptimal assessment scores were compared by cCMV status using Fisher exact test.

RESULTS

Mothers of 895 eligible HIV-exposed uninfected children delivered between 2007 and 2015. Most (90%) were on combination antiretroviral therapy, 88% had an HIV viral load of ≤400 copies/mL, and 93% had CD4 cell counts of ≥200 cells/μL. Eight infants were diagnosed with cCMV, yielding an estimated prevalence of 0.89% (95% CI, 0.39%-1.75%). After adjusting for a sensitivity of 70%-75% for the testing method, projected prevalence was 1.2%-1.3%. No differences were observed in cognitive, language and hearing assessments by cCMV status.

CONCLUSIONS

Although birth cCMV prevalence in HIV-exposed uninfected children born to women with well-controlled HIV is trending down compared with earlier combination antiretroviral therapy-era estimates, it is above the 0.4% reported for the general US population. HIV-exposed uninfected children remain at increased risk for cCMV.

Abatacept in rheumatoid arthritis and the risk of cancer: a world observational post-marketing study

Objectives

We aimed to investigate whether abatacept used in patients for RA was associated with an increased risk of reporting overall cancer and specific cancers, including breast, lung, lymphoma, melanoma and non-melanoma skin cancer when compared with other biologic DMARDs (bDMARDs).

Methods

We performed an observational study within VigiBase, the World Health Organization’s global database of individual case safety reports, from 2007 to 2017 to compare the cases of cancer reported in RA patients exposed to abatacept with those reported in RA patients exposed to other bDMARDs. We conducted disproportionality analyses allowing the estimation of reporting odds ratios (RORs) with 95% CIs of the exposure odds among spontaneous reporting of cancers to the exposure odds among other reported adverse effects.

Results

We identified 15 846 adverse effects reported in RA patients who received abatacept and 290 568 adverse effects reported in RA patients treated with other bDMARDs. Compared with other bDMARDs, the use of abatacept was not associated with an increased risk of reporting cancer overall [ROR 0.98 (95% CI 0.91, 1.05)]. Analyses by specific cancer sites showed a significantly increased ROR for melanoma [1.58 (95% CI 1.17, 2.08)], but not for other specific cancer sites.

Conclusion

Compared with other bDMARDs, exposure to abatacept in RA patients was only significantly associated with an increased risk of reporting melanoma. This increased risk is consistent with the properties of abatacept (CTLA-4 agonist) since it has an opposite action than ipilimumab, an antibody that blocks CTLA-4 and is approved for the treatment of malignant melanoma.

Trial registration

ClinicalTrials.gov (http://clinicaltrials.gov), NCT03980639.

Database of adverse events associated with drugs and drug combinations

Due to the aging world population and increasing trend in clinical practice to treat patients with multiple drugs, adverse events (AEs) are becoming a major challenge in drug discovery and public health. In particular, identifying AEs caused by drug combinations remains a challenging task. Clinical trials typically focus on individual drugs rather than drug combinations and animal models are unreliable. An added difficulty is the combinatorial explosion in the number of possible combinations that can be made using the increasingly large set of FDA approved chemicals. We present a statistical and computational technique for identifying AEs caused by two-drug combinations. Taking advantage of the large and increasing data deposited in FDA’s postmarketing reports, we demonstrate that the task of predicting AEs for 2-drug combinations is amenable to the Likelihood Ratio Test (LRT). Our pAERS database constructed with LRT contains almost 77 thousand associations between pairs of drugs and corresponding AEs caused solely by drug-drug interactions (DDIs). The DDIs stored in pAERS complement the existing data sets. Due to our stringent statistical test, we expect many of the associations in pAERS to be unrecorded or poorly documented in the literature.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

Background

Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels.

Results

In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively.

Conclusion

Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.

Efficacy and Safety of Avelumab Treatment in Patients With Advanced Unresectable Mesothelioma: Phase 1b Results From the JAVELIN Solid Tumor Trial

Importance

Patients with malignant mesothelioma whose disease has progressed after platinum and pemetrexed treatment have limited options. Anti-programmed cell death 1 (PD-1) antibodies have antitumor activity in this disease, but little is known about the activity of anti-programmed cell death ligand 1 (PD-L1) antibodies in patients with mesothelioma.

Objective

To assess the efficacy and safety of avelumab in a cohort of patients with previously treated mesothelioma.

Design, Setting, and Participants

Phase 1b open-label study (JAVELIN Solid Tumor) in patients with unresectable mesothelioma that progressed after platinum and pemetrexed treatment, enrolled at 25 sites in 3 countries between September 9, 2014, and July 22, 2015.

Interventions

Participants received avelumab, 10 mg/kg, every 2 weeks until disease progression, unacceptable toxic effects, or withdrawal from the study.

Main Outcomes and Measures

Prespecified end points included confirmed best overall response based on Response Evaluation Criteria In Solid Tumors, version 1.1; duration of response; progression-free survival (PFS); overall survival (OS); PD-L1 expression-based analyses; and safety.

Results

Of 53 patients treated with avelumab, the median age was 67 (range, 32-84) years; 32 (60%) were male. As of December 31, 2016, median follow-up was 24.8 (range, 16.8-27.8) months. Twenty patients (38%) had 3 or more previous lines of therapy (median, 2; range, 1-8). The confirmed objective response rate (ORR) was 9% (5 patients; 95% CI, 3.1%-20.7%), with complete response in 1 patient and partial response in 4 patients. Responses were durable (median, 15.2 months; 95% CI, 11.1 to not estimable months) and occurred in patients with PD-L1-positive tumors (3 of 16; ORR, 19%; 95% CI, 4.0%-45.6%) and PD-L1-negative tumors (2 of 27; ORR, 7%; 95% CI, 0.9%-24.3%) based on a 5% or greater PD-L1 cutoff. Disease control rate was 58% (31 patients). Median PFS was 4.1 (95% CI, 1.4-6.2) months, and the 12-month PFS rate was 17.4% (95% CI, 7.7%-30.4%). Median OS was 10.7 (95% CI, 6.4-20.2) months, and the median 12-month OS rate was 43.8% (95% CI, 29.8%-57.0%). Five patients (9%) had a grade 3 or 4 treatment-related adverse event, and 3 (6%) had a grade 3 or 4 immune-related, treatment-related adverse event. There were no treatment-related deaths.

Conclusions and Relevance

Avelumab showed durable antitumor activity and disease control with an acceptable safety profile in a heavily pretreated cohort of patients with mesothelioma.

Trial Registration

ClinicalTrials.gov identifier: NCT01772004.

Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports

Background

Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports.

Methods

In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs.

Results

We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs.

Conclusions

The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time.

Availability

The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.

Drug-induced parkinsonism: Revisiting the epidemiology using the WHO pharmacovigilance database

INTRODUCTION

Drug-Induced Parkinsonism (DIP) is the second most common cause of parkinsonism after idiopathic Parkinson's disease. Little is known about DIP epidemiology. Using VigiBase®, the objective of this study was to assess the main characteristics of DIP reporting around the world.

METHODS

We described reports recorded in the WHO pharmacovigilance database, Vigibase® and classified as "Parkinsonism" between 2000 and 2017. Differences of reporting between geographical locations and characteristics of reports were investigated using disproportionality analysis with calculation of Reporting Odds Ratios (ROR) and its 95% confidence interval.

RESULTS

Among the 9,009,107 reports recorded in VigiBase®, 4565 (0.05%) were DIP. Co reported terms were mainly "tremor" (n = 408, 8.9%), "gait disturbance" (n = 209, 4.6%) and "extrapyramidal disorders" (n = 180, 3.9%). DIP reports were significantly more frequent in men (ROR = 1.4; 95% CI 1.3-1.5) and in patients aged 75 and over (ROR = 2.12; 95% CI 1.98-2.26). Compared to all other continents, risk of reporting drug-induced parkinsonism was higher in Europe (ROR = 2.89; 95% CI 2.73-3.07), Africa (ROR = 1.81; 95% CI 1.46-2.25) and Oceania (ROR = 1.50; 95% CI 1.27-1.77). The risk was lower in Asia (ROR = 0.55; 95% CI 0.51-0.59) and America (ROR = 0.55 95% CI 0.51-0.59). The highest risk of DIP reporting was found with sulpiride and haloperidol followed by risperidone, aripiprazole, paliperidone, metoclopramide, olanzapine, quetiapine and clozapine.

CONCLUSION

Risk of DIP reports was higher in men, in people aged 75 and over and in Europe. Main drugs involved are antipsychotics not only drugs from the first generation but also those from the second one.

Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

Objective

We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.

Materials and Methods

We organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.

Results

Among 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F₁-score) for subtask-1, 0.693 (micro-averaged F₁-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.

Discussion

Among individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).

Conclusions

Data imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).

Development of a neonatal adverse event severity scale through a Delphi consensus approach

BACKGROUND

Assessment of the seriousness, expectedness and causality are necessary for any adverse event (AE) in a clinical trial. In addition, assessing AE severity helps determine the importance of the AE in the clinical setting. Standardisation of AE severity criteria could make safety information more reliable and comparable across trials. Although standardised AE severity scales have been developed in other research fields, they are not suitable for use in neonates. The development of an AE severity scale to facilitate the conduct and interpretation of neonatal clinical trials is therefore urgently needed.

METHODS

A stepwise consensus process was undertaken within the International Neonatal Consortium (INC) with input from all relevant stakeholders. The consensus process included several rounds of surveys (based on a Delphi approach), face-to-face meetings and a pilot validation.

RESULTS

Neonatal AE severity was classified by five grades (mild, moderate, severe, life threatening or death). AE severity in neonates was defined by the effect of the AE on age appropriate behaviour, basal physiological functions and care changes in response to the AE. Pilot validation of the generic criteria revealed κ=0.23 and guided further refinement. This generic scale was applied to 35 typical and common neonatal AEs resulting in the INC neonatal AE severity scale (NAESS) V.1.0, which is now publicly available.

DISCUSSION

The INC NAESS is an ongoing effort that will be continuously updated. Future perspectives include further validation and the development of a training module for users.

Acute kidney injury associated with febuxostat and allopurinol: a post-marketing study

BACKGROUND

For patients with recurrent flares of gout, tophi, urate crystal arthropathy, and renal stones, urate-lowering therapies (ULTs, including allopurinol and febuxostat) are the first-line treatment. Due to the widespread use of these ULTs (especially in patients with impaired renal function), assessment of the associated renal risk is essential. Accordingly, we performed a disproportionality analysis of reported cases of acute renal failure (ARF) associated with allopurinol and febuxostat.

METHODS

We carried out a case/non-case study of the World Health Organization's VigiBase® pharmacovigilance database between January 1, 2008, and December 31, 2018. The frequency of reports of ARF as a standardized Medical Dictionary for Regulatory Activities query for allopurinol and febuxostat was compared with that of all other reports for the two drugs and quoted as the reporting odds ratio (ROR) [95% confidence interval (CI)]. The results' stability was assessed in a series of sensitivity analyses (notably after the exclusion of putative competing drugs).

RESULTS

Among 3509 "suspected drug" notifications for febuxostat and 18,730 for allopurinol, we identified respectively 317 and 1008 cases of ARF. Acute renal failure was reported significantly more frequently for febuxostat and allopurinol than for other drugs (ROR [95%CI] 5.67 [5.05-6.36] and 3.25 [3.05-3.47], respectively). For both drugs, the ROR was higher in women than in men, respectively 11.60 [9.74-13.82] vs. 3.14 [2.69-3.67] for febuxostat and 4.45 [4.04-4.91] vs. 2.29 [2.11-2.50] for allopurinol. The sensitivity analyses confirmed the disproportionality for these two ULTs.

CONCLUSIONS

Acute renal failure was reported respectively 5.7 and 3.3 times more frequently for febuxostat and for allopurinol than for other drugs. Due to the potential consequences of ARF, physicians should take account of this disproportionality signal when prescribing the ULTs febuxostat and allopurinol.

RedMed: Extending drug lexicons for social media applications

Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media. The Reddit comment archive represents an ideal corpus for bridging this gap. We trained a word embedding model, RedMed, to facilitate the identification and retrieval of health entities from Reddit data. We compare the performance of our model trained on a consumer-generated corpus against publicly available models trained on expert-generated corpora. Our automated classification pipeline achieves an accuracy of 0.88 and a specificity of >0.9 across four different term classes. Of all drug mentions, an average of 79% (±0.5%) were exact matches to a generic or trademark drug name, 14% (±0.5%) were misspellings, 6.4% (±0.3%) were synonyms, and 0.13% (±0.05%) were pill marks. We find that our system captures an additional 20% of mentions; these would have been missed by approaches that rely solely on exact string matches. We provide a lexicon of misspellings and synonyms for 2978 drugs and a word embedding model trained on a health-oriented subset of Reddit.

Analgesic Effects of Hydromorphone versus Buprenorphine in Buprenorphine-maintained Individuals

BACKGROUND

Managing acute pain in buprenorphine-maintained individuals in emergency or perioperative settings is a significant challenge. This study compared analgesic and abuse liability effects of adjunct hydromorphone and buprenorphine using quantitative sensory testing, a model of acute clinical pain, in persons maintained on 12 to 16 mg sublingual buprenorphine/naloxone.

METHODS

Participants (N = 13) were enrolled in a randomized within-subject, double-blind, placebo-controlled three-session experiment. Each session used a cumulative dosing design with four IV injections (4, 4, 8, and 16 mg of hydromorphone or 4, 4, 8, and 16 mg of buprenorphine); quantitative sensory testing and abuse liability assessments were measured at baseline and after each injection. The primary analgesia outcome was change from baseline cold pressor testing; secondary outcomes included thermal and pressure pain testing, as well as subjective drug effects and adverse events.

RESULTS

A significant two-way interaction between study drug condition and dose was exhibited in cold pressor threshold (F10,110 = 2.14, P = 0.027) and tolerance (F10,110 = 2.69, P = 0.006). Compared to after placebo, participants displayed increased cold pressor threshold from baseline after cumulative doses of 32 mg of IV hydromorphone (means ± SD) (10 ± 14 s, P = 0.035) and 32 mg of buprenorphine (3 ± 5 s, P = 0.0.39) and in cold pressor tolerance after cumulative doses of 16 mg (18 ± 24 s, P = 0.018) and 32 mg (48 ± 73 s, P = 0.041) IV hydromorphone; cold pressor tolerance scores were not significant for 16 mg (1 ± 15 s, P = 0.619) or 32 mg (7 ± 16 s, P = 0.066) buprenorphine. Hydromorphone and buprenorphine compared with placebo showed greater ratings on subjective measures of high, any drug effects, good effects, and drug liking. Adverse events were more frequent during the hydromorphone compared with buprenorphine and placebo conditions for nausea, pruritus, sedation, and vomiting.

CONCLUSIONS

In this acute clinical pain model, high doses of IV hydromorphone (16 to 32 mg) were most effective in achieving analgesia but also displayed higher abuse liability and more frequent adverse events. Cold pressor testing was the most consistent measure of opioid-related analgesia.

Bullous pemphigoid associated with dipeptidyl peptidase-4 inhibitors. A case series and analysis of cases reported in the Spanish pharmacovigilance database

BACKGROUND

Bullous pemphigoid (BP) has been associated with dipeptidyl peptidase-4 inhibitors (DPP4i). Clinical features, outcomes, and risk of BP for new DPP4i (linagliptin, saxagliptin, and alopgliptin) are not well established. Comparison of risk of BP appearance for DPP4i and other oral antidiabetic drugs (OADs) such as sodium glucose cotransporter 2 inhibitors has not been studied to date.

OBJECTIVES

To describe the prevalence, sociodemographic, clinical, and histopathological characteristics, and outcome after drug withdrawal in DPP-4i-associated BP cases from our hospital. To review all Spanish spontaneous notifications of BP where DPP4i or OADs were included as suspected drugs and calculate the reporting odds ratios (RORs).

METHODS

A retrospective observational study was performed examining the association between DDP4i and BP. Clinical features and RORs were analyzed. Data from the Spanish Pharmacovigilance System (SEFV) are included.

RESULTS

In our center, 28 of 89 patients with BP (31.5%) were under DPP4i treatment; 53.6% were male, and mean age was 80.8 years. BP debuted the first year after DPP4i in 57.2%. BP control was achieved within 3.7 months after drug withdrawal. Regarding SEFV, 22 of 972 spontaneous notifications were related to BP and DPP4i. RORs were superior for DPP4i compared to other OADs. Vildagliptin had the highest ROR.

CONCLUSIONS

We present the largest DPP4i-induced BP case series in a single center, with a detailed study of the sociodemographic, clinical, and histopathological characteristics of each patient, and their treatment and outcome. Vildagliptin had the highest risk. DPP4i-associated BP does not seem to have specific clinical characteristics.

Ototoxic Adverse Drug Reactions: A Disproportionality Analysis Using the Italian Spontaneous Reporting Database

Introduction: The panorama of drug-induced ototoxicity has widened in the last decades; moreover, post-marketing data are necessary to gain a better insight on ototoxic adverse drug reactions (ADRs). The aim of this study was to perform an analysis of ADR reports describing drug-induced ototoxicity from the Italian spontaneous reporting system (SRS).

Methods: As a measure of disproportionality, we calculated the reporting odds ratios (RORs) and 95% confidence intervals (CIs) with a case/non-case methodology. Cases were all suspected ADR reports regarding drug-induced ototoxicity collected into the Italian SRS from 2001 to 2017. Non-cases included all other ADRs reported in the same period.

Results: Of 325,980 reports, 652 included at least one ototoxic ADR, compared with 325,328 non-cases. Statistically significant adjusted RORs were found for drugs for cardiovascular disorders, urologicals, teriparatide, amikacin, prulifloxacin, rifampicin and isoniazid, cisplatin, hormone antagonists, tacrolimus, pomalidomide, tramadol, and antidepressants. Significant adjusted RORs in relation to tinnitus were also observed for doxazosin (ROR 5.55, 95% CI 2.06–14.93), bisoprolol (4.28, 1.59–11.53), nebivolol (8.06, 3.32–19.56), ramipril (3.96, 2.17–7.23), irbesartan (19.60, 9.19–41.80), betamethasone (4.01, 1.28–12.52), moxifloxacin (4.56, 1.71–12.34), ethambutol (12.25, 3.89–38.57), efavirenz (16.82, 5.34–52.96), sofosbuvir/ledipasvir (5.95, 1.90–18.61), etoposide (7.09, 2.63–19.12), abatacept (6.51, 2.42–17.53), indometacin (6.30, 2.02–19.72), etoricoxib (5.00, 2.23–11.23), tapentadol (4.37, 1.09–17.62), and timolol combinations (23.29, 9.53–56.95). Moreover, significant adjusted RORs for hypoacusis regarded clarithromycin (3.95, 1.86–8.40), azithromycin (10.23, 5.03–20.79), vancomycin (6.72, 2.14–21.11), methotrexate (3.13, 1.00–9.81), pemetrexed (4.38, 1.40–13.76), vincristine (5.93, 1.88–18.70), vinorelbine (21.60, 8.83–52.82), paclitaxel (2.34, 1.03–5.30), rituximab (3.20, 1.19–8.63), interferon alfa-2b (17.44, 8.56–35.53), thalidomide (16.92, 6.92–41.38), and deferasirox (41.06, 20.07–84.01).

Conclusions: This study is largely consistent with results from literature. Nevertheless, propafenone, antituberculars, hormone antagonists, teriparatide, tramadol, and pomalidomide are unknown for being ototoxic. Hypoacusis after the use of vinorelbine, methotrexate, and pemetrexed is unexpected, such as tinnitus related with etoposide, nebivolol, betamethasone, abatacept, sofosbuvir/ledipasvir, and tapentadol, but these considerations require further investigation to better define the risk due to the paucity of data. Moreover, physicians should be aware of the clinical significance of ototoxicity and be conscious about the importance of their contribution to spontaneous reporting.

A pre-specified statistical analysis plan for the VERIFY study: Vildagliptin efficacy in combination with metformin for early treatment of T2DM

AIMS

To ensure the integrity of the planned analyses and maximize the clinical utility of the VERIFY study results by describing the detailed concepts behind its statistical analysis plan (SAP) before completion of data collection and study database lock. The SAP will be adhered to for the final primary data analysis of the VERIFY trial.

MATERIALS AND METHODS

Vildagliptin efficacy in combination with metformin for early treatment of T2DM (VERIFY) is an ongoing, multicentre, randomized controlled trial aiming to demonstrate the clinical benefits of glycaemic durability and glucose control achieved with an early combination therapy in newly-diagnosed type 2 diabetes (T2DM) patients.

RESULTS

The SAP was initially designed at the study protocol conception phase and later modified, as reported here, in collaboration between the steering committee members, statisticians, and the VERIFY study leadership team. All authors were blinded to treatment allocation. An independent statistician has additionally retrieved and presented unblinded data to the independent data safety monitoring committee. An overview of the trial design with a focus on describing the fine-tuning of the analysis plan for the primary efficacy endpoint, risk of initial treatment failure, and secondary, exploratory and pre-specified subgroup analyses is provided here.

CONCLUSION

According to optimal trial practice, the details of the statistical analysis and data-handling plan prior to locking the database are reported here. The SAP accords with high-quality standards of internal validity to minimize analysis bias and will enhance the utility of the reported results for improved outcomes in the management of T2DM.

Multilabel and Missing Label Methods for Binary Quantitative Structure-Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

The prediction of adverse drug reactions in the discovery of new medicines is highly challenging. In the task of predicting the adverse reactions of chemical compounds, information about different targets is often available. Although we can focus on every adverse drug reaction prediction separately, multilabel approaches have been proven useful in many research areas for taking advantage of the relationship among the targets. However, when approaching the prediction problem from a multilabel point of view, we have to deal with the lack of information for some labels. This missing labels problem is a relevant issue in the field of cheminformatics approaches. This paper aims to predict the adverse drug reaction of commercial drugs using a multilabel approach where the possible presence of missing labels is also taken into consideration. We propose the use of multilabel methods to deal with the prediction of a large set of 27 different adverse reaction targets. We also propose the use of multilabel methods specifically designed to deal with the missing labels problem to test their ability to solve this difficult problem. The results show the validity of the proposed approach, demonstrating a superior performance of the multilabel method compared with the single-label approach in addressing the problem of adverse drug reaction prediction.

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA.

Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic).

Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time.

Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

Predicting serious rare adverse reactions of novel chemicals

Motivation

Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world’s economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing.

Results

We present a novel method, based on the theory of ‘compressed sensing’ (CS), which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased and incomplete databases, but our data also demonstrate that the accuracy of CS in predicting a serious ADR for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly.

Availability and implementation

The program is available at https://github.com/poleksic/side-effects.

Supplementary information

Supplementary data are available at Bioinformatics online.

Development of a neonatal adverse event severity scale through a Delphi consensus approach

BACKGROUND

Assessment of the seriousness, expectedness and causality are necessary for any adverse event (AE) in a clinical trial. In addition, assessing AE severity helps determine the importance of the AE in the clinical setting. Standardisation of AE severity criteria could make safety information more reliable and comparable across trials. Although standardised AE severity scales have been developed in other research fields, they are not suitable for use in neonates. The development of an AE severity scale to facilitate the conduct and interpretation of neonatal clinical trials is therefore urgently needed.

METHODS

A stepwise consensus process was undertaken within the International Neonatal Consortium (INC) with input from all relevant stakeholders. The consensus process included several rounds of surveys (based on a Delphi approach), face-to-face meetings and a pilot validation.

RESULTS

Neonatal AE severity was classified by five grades (mild, moderate, severe, life threatening or death). AE severity in neonates was defined by the effect of the AE on age appropriate behaviour, basal physiological functions and care changes in response to the AE. Pilot validation of the generic criteria revealed κ=0.23 and guided further refinement. This generic scale was applied to 35 typical and common neonatal AEs resulting in the INC neonatal AE severity scale (NAESS) V.1.0, which is now publicly available.

DISCUSSION

The INC NAESS is an ongoing effort that will be continuously updated. Future perspectives include further validation and the development of a training module for users.

Adverse Events and Nocebo Effects in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND AND AIMS

Nocebo effects, adverse outcomes occurring in patients receiving inert therapy, contribute to adverse event [AE] reporting in randomized controlled trials [RCTs]. High placebo AE rates may result in inaccurate estimation of treatment-related AEs. We estimate the pooled rate of AEs in patients randomized to placebo compared to active therapy in inflammatory bowel disease [IBD] RCTs.

METHODS

MEDLINE, EMBASE and CENTRAL were searched to March 1, 2017 for RCTs of conventional medical therapies for Crohn's disease [CD] or ulcerative colitis [UC]. Rates of AEs, serious AEs [SAEs], AE-related trial withdrawal, infections and worsening IBD were pooled using a random-effects model.

RESULTS

We included 124 CD [n = 26 042] and 71 UC RCTs [n = 16 798]. The pooled placebo AE rate was 70.6% (95% confidence interval [CI]: 65.3%, 75.4%) and 54.5% [47.8%, 61.1%] in CD and UC RCTs, respectively. There was no significant risk difference [RD] in AE, SAE or AE-related withdrawal rates between CD patients receiving placebo or active drug. A 1.6% [95% CI: 0.1%, 3.1%] increase in AE rates was observed among UC patients randomized to active therapy. Patients receiving active therapy had a higher risk of infection (RD 1.0% [95% CI: 0.4%, 1.7%] for CD, 2.9% [95% CI: 1.4%, 4.4%] for UC) although a lower risk of worsening CD (RD -3.2% [95% CI: -4.8%, -1.5%]) or UC (RD -3.7% [95% CI: -5.7%, -1.8%]).

CONCLUSIONS

AEs are commonly reported by patients randomized to either placebo or active treatment in IBD RCTs. Clinically relevant differences in AE, SAE and AE-related withdrawal were not observed.

Suspected adverse reactions associated with herbal products used for weight loss: spontaneous reports from the Italian Phytovigilance System

PURPOSE

Overweight and obesity represent worldwide a rising health problem. In this context, dietary supplements and herbal preparations are often used as self-medication for weight loss. The aim of this study was to describe the safety profile of dietary supplements for weight control by analyzing spontaneous reports of suspected adverse reactions (ARs) received by the Italian Phytovigilance System, from July 2010 to October 2017.

METHODS

The suspected ARs were collected using an ad hoc reporting form, registered in a database at the National Institute of Health and evaluated by a multidisciplinary group of experts. The causality assessment was performed using the WHO-UMC system or the CIOMS/RUCAM score. In case of serious adverse reactions, a feedback is provided to the reporter by e-mail.

RESULTS

Sixty-six spontaneous reports were collected. ARs involved cardiovascular system (26%), liver (14%), central nervous system (12%), skin (9%), gastrointestinal system (17%), thyroid (8%), kidney (4%), and other organs/systems (10%). In 64% of cases, the reaction was serious. Dechallenge was positive in 46 cases; three cases of positive rechallenge were reported. After the causality assessment, the association between the product intake and the adverse reaction was judged as possible in the majority of the cases (n = 43; 65%).

CONCLUSIONS

The data collected confirmed the existence of safety concerns on herbal dietary supplements used for body weight control, mainly related to quality of products and their use as self-medication. In this scenario, spontaneous reports represent the only tools available to monitor safety of these products.

Evaluating Patient-Centered Outcomes in Clinical Trials of Procedural Sedation, Part 2 Safety: Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research Recommendations

The Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research, established by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks, a public-private partnership with the US Food and Drug Administration, convened a second meeting of sedation experts from a variety of clinical specialties and research backgrounds to develop recommendations for procedural sedation research. The previous meeting addressed efficacy and patient- and/or family-centered outcomes. This meeting addressed issues of safety, which was defined as "the avoidance of physical or psychological harm." A literature review identified 133 articles addressing safety measures in procedural sedation clinical trials. After basic reporting of vital signs, the most commonly measured safety parameter was oxygen saturation. Adverse events were inconsistently defined throughout the studies. Only 6 of the 133 studies used a previously validated measure of safety. The meeting identified methodological problems associated with measuring infrequent adverse events. With a consensus discussion, a set of core and supplemental measures were recommended to code for safety in future procedural clinical trials. When adopted, these measures should improve the integration of safety data across studies and facilitate comparisons in systematic reviews and meta-analyses.

The use of structured data elements to identify ASCVD patients with statin-associated side effects: Insights from the Department of Veterans Affairs

BACKGROUND

Accurate identification of patients with statin-associated side effects (SASEs) is critical for health care systems to institute strategies to improve guideline-concordant statin use.

OBJECTIVE

The objective of this study was to determine whether adverse drug reaction (ADR) entry by clinicians in the electronic medical record can accurately identify SASEs.

METHODS

We identified 1,248,214 atherosclerotic cardiovascular disease (ASCVD) patients seeking care in the Department of Veterans Affairs. Using an ADR data repository, we identified SASEs in 15 major symptom categories. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed using a chart review of 256 ASCVD patients with identified SASEs, who were not on high-intensity statin therapy.

RESULTS

We identified 171,189 patients (13.71%) with documented SASEs over a 15-year period (9.9%, 2.7%, and 1.1% to 1, 2, or >2 statins, respectively). Statin use, high-intensity statin use, low-density lipoprotein cholesterol, and non-high-density lipoprotein cholesterol levels were 72%, 28.1%, 99 mg/dL, and 129 mg/dL among those with vs 81%, 31.1%, 84 mg/dL, and 111 mg/dL among those without SASEs. Progressively lower statin and high-intensity statin use, and higher low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol levels were noted among those with SASEs to 1, 2, or >2 statins. Two-thirds of SASEs were related to muscle symptoms. Sensitivity, specificity, PPV, NPV compared with manual chart review were 63.4%, 100%, 100%, and 85.3%, respectively.

CONCLUSION

A strategy of using ADR entry in the electronic medical record is feasible to identify SASEs with modest sensitivity and NPV but high specificity and PPV. Health care systems can use this strategy to identify ASCVD patients with SASEs and operationalize efforts to improve guideline-concordant lipid-lowering therapy use in such patients. The sensitivity of this approach can be further enhanced by the use of unstructured text data.

Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.

Surveillance of adverse events following immunisation in Australia annual report, 2017

This report summarises Australian passive surveillance data for adverse events following immunisation (AEFI) for 2017 reported to the Therapeutic Goods Administration and describes reporting trends over the 18-year period 1 January 2000 to 31 December 2017. There were 3,878 AEFI records for vaccines administered in 2017; an annual AEFI reporting rate of 15.8 per 100,000 population. There was a 12% increase in the overall AEFI reporting rate in 2017 compared with 2016. This increase in reported adverse events in 2017 compared to the previous year was likely due to the introduction of the zoster vaccine (Zostavax®) provided free for people aged 70–79 years under the National Immunisation Program (NIP) and also the state- and territory-based meningococcal ACWY conjugate vaccination programs. AEFI reporting rates for most other individual vaccines in 2017 were similar to 2016. The most commonly reported reactions were injection site reaction (34%), pyrexia (17%), rash (15%), vomiting (8%) and pain (7%). The majority of AEFI reports (88%) described non-serious events. Two deaths were reported that were determined to have a causal relationship with vaccination; they occurred in immunocompromised people contraindicated to receive the vaccines.

Adverse events reported for Mirena levonorgestrel-releasing intrauterine device in France and impact of media coverage

AIMS

In 2017, concerns regarding adverse events (AEs) associated with the Mirena levonorgestrel intrauterine device were largely echoed in the media in France. This resulted in a tremendous reporting of AEs to pharmacovigilance centres. The aim of this study was to describe the reporting of AEs regarding Mirena in France and to study the impact of media coverage on this reporting.

METHODS

All cases reports involving Mirena recorded in the French national pharmacovigilance database from marketing (21 July 1995) until 04 August 2017 were extracted. To allow studying the influence of mediatisation, reports were described separately for the periods preceding and following the observed media coverage peak (15 May 2017).

RESULTS

Overall, 3224 reports were considered, 510 (15.8%) recorded before the media coverage peak, and 2714 (84.2%) after. Before the peak, 76.5% of reports originated from health professionals; median time-to-report was of 5.5 months (interquartile range: 1.7-18.6), and median number of AEs per report was 1 (range: 1-17). After the peak, 98.6% originated from patients; median time-to-report was 21 months (interquartile range: 8.1-45.5), and median number of AEs per report was 6 (range: 1-37). After the peak, most reports mentioned anxio-depressive disorders (38.8 vs 10.6% before) or sexual disorders (47.3 vs 6.9%). Other emphasised AEs were weight increase (42.3 vs 10.2%) and pain (gastrointestinal, 19.1 vs 3.5%; musculoskeletal, 22.2 vs 4.5%).

CONCLUSION

This study highlighted the importance of mediatisation impact on spontaneous reporting with changes concerning amounts of reports, type of reporter, and type of reported AEs. For Mirena, this led to generate signals regarding anxio-depressive and sexual disorders.

The drugs that mostly frequently induce gynecomastia: A national case - noncase study

AIMS

Drug-induced gynecomastia accounts for up to 25% of cases of gynecomastia. The objective of the present study was to provide a comprehensive overview of drug-induced gynecomastia on the basis of spontaneously reported adverse drug reactions (ADRs) in the French national pharmacovigilance database (FPVD).

METHODS

We performed a case - noncase study of drug-induced gynecomastia. Cases corresponded to reports of gynecomastia recorded in the FPVD between 1 January 2008 and 31 December 2015. The noncases corresponded to all other spontaneously reported ADRs recorded in the FPVD during the same period. Data were expressed as the reporting odds ratio (ROR) and its 95% confidence interval.

RESULTS

Of the 255,354 ADRs recorded in the FPVD between 1 January 2008 and 31 December 2015, 327 (0.31%) of relevant cases of gynecomastia and 106,800 noncases were analyzed. The RORs were statistically significant for 54 active compounds mentioned 429 times in cases of gynecomastia. A single drug was involved in 59% of cases. The most frequently implicated drug classes were antiretrovirals (23.5%), diuretics (15.5%), proton pump inhibitors (11.9%), HMG-CoA reductase inhibitors (9.1%), neuroleptics and related drugs (6.5%), calcium channel blockers (6.3%), and 5-alpha reductase inhibitors (4%).

CONCLUSIONS

A comprehensive analysis of a national pharmacovigilance database highlighted the main drug classes suspected of inducing gynecomastia. A physiopathological mechanism (a hormone imbalance with elevated estrogen levels) is known or suspected for most of the drugs involved in gynecomastia. However, we noticed a lack of harmonization in the summary of product characteristics for original vs. generic medicines.

Adverse drug event patterns experienced by patients with diabetes: A diary study in primary care

PURPOSE

Little is known about adverse drug events (ADEs) experienced over time during chronic drug use. The purpose of this study was to assess ADE patterns experienced by patients with diabetes.

METHODS

Patients who received an oral glucose-lowering drug completed a daily diary for 13 weeks. The diary asked for experienced symptoms and whether patients related these symptoms to any drug they used. Summaries of Product Characteristics were used to check whether the ADEs were known adverse drug reactions (ADRs) of the drugs used. Patterns of weekly occurring ADEs were assessed with descriptive statistics.

RESULTS

We included 78 patients. Almost half of them reported at least one ADE (N = 36; 46%). In total, 80 ADEs were reported. Of these ADEs, 71 (90%) were known ADRs. ADEs lasted less than 1 week in 27 cases (34%) and between 2 and 12 weeks in 15 cases (19%). The remaining ADEs fluctuated (16 cases; 20%) or persisted (22 cases; 28%) during the entire study period.

CONCLUSIONS

ADEs experienced by patients with diabetes can fluctuate or persist over long periods of drug use.

Time to onset in statistical signal detection revisited: A follow-up study in long-term onset adverse drug reactions

Purpose

In a previous study, we developed a signal detection method using the time to onset (TTO) of adverse drug reactions (ADRs). The aim of the current study was to investigate this method in a subset of ADRs with a longer TTO and to compare its performance with disproportionality analysis.

Methods

Using The Netherlands's spontaneous reporting database, TTO distributions for drug—ADR associations with a median TTO of 7 days or more were compared with other drugs with the same ADR using the two‐sample Anderson–Darling (AD) test. Presence in the Summary of Product Characteristics (SPC) was used as the gold standard for identification of a true ADR. Twelve combinations with different values for the number of reports and median TTO were tested. Performance in terms of sensitivity and positive predictive value (PPV) was compared with disproportionality analysis. A sensitivity analysis was performed to compare the results with those from the previous study.

Results

A total of 38 017 case reports, containing 32 478 unique drug—ADR associations. Sensitivity was lower for the TTO method (range 0.08‐0.34) compared with disproportionality analysis (range 0.60‐0.87), whereas PPV was similar for both methods (range 0.93‐1.0). The results from the sensitivity analysis were similar to the original analysis.

Conclusions

Because of its low sensitivity, the developed TTO method cannot replace disproportionality analysis as a signal detection tool. It may be useful in combination with other methods.

The human gut chemical landscape predicts microbe-mediated biotransformation of foods and drugs

Microbes are nature's chemists, capable of producing and metabolizing a diverse array of compounds. In the human gut, microbial biochemistry can be beneficial, for example vitamin production and complex carbohydrate breakdown; or detrimental, such as the reactivation of an inactive drug metabolite leading to patient toxicity. Identifying clinically relevant microbiome metabolism requires linking microbial biochemistry and ecology with patient outcomes. Here we present MicrobeFDT, a resource which clusters chemically similar drug and food compounds and links these compounds to microbial enzymes and known toxicities. We demonstrate that compound structural similarity can serve as a proxy for toxicity, enzyme sharing, and coarse-grained functional similarity. MicrobeFDT allows users to flexibly interrogate microbial metabolism, compounds of interest, and toxicity profiles to generate novel hypotheses of microbe-diet-drug-phenotype interactions that influence patient outcomes. We validate one such hypothesis experimentally, using MicrobeFDT to reveal unrecognized gut microbiome metabolism of the ovarian cancer drug altretamine.

Leveraging genetic interactions for adverse drug-drug interaction prediction

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power—including the prediction of 432 novel DDIs—our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.

Adverse drug-drug interactions are adverse side effects caused by taking two or more drugs together. As co-prescription of multiple drugs becomes an increasingly prevalent practice, affecting 42.2% of Americans over 65 years old, adverse drug-drug interactions have become a serious safety concern, accounting for over 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone. Since experimental determination of adverse drug-drug interactions is labor-intensive and time-consuming, various machine learning-based computational approaches have been developed for predicting drug-drug interactions. Considering the fact that drugs effect through binding and modulating the function of their targets, we have explored whether drug-drug interactions can be predicted from the genetic interaction between the gene targets of two drugs, which characterizes the unexpected fitness effect when two genes are simultaneously knocked out. Furthermore, we have built a fast and robust classifier that achieves accurate prediction of adverse drug-drug interactions by incorporating genetic interaction and several other types of widely used features. Our analyses suggest that genetic interaction is an important feature for our prediction model, and that it provides mechanistic insight into the mode of action of drugs leading to drug-drug interactions.

Are Patients With Schizophrenia Spectrum Disorders More Prone to Manifest Nocebo-Like-Effects? A Meta-Analysis of Adverse Events in Placebo Groups of Double-Blind Antipsychotic Trials

Background: Antipsychotic clinical trials use to present adverse events (AEs) for the drug under evaluation to treat schizophrenia. Interestingly, patients who receive the placebo during antipsychotic trials often report several AEs, but little is known about the essence of these negative effects in patients with schizophrenia spectrum disorders (SCD). In the present meta-analysis, we evaluated the relationship between the level of psychiatric symptomatology expressed as Positive and Negative Syndrome Scale (PANSS) scores and the rates of AEs reported in the placebo arms of double-blind clinical trials, for commonly prescribed atypical antipsychotic medications.

Methods: We selected 58 clinical trials describing AEs in SCD placebo groups, which compared atypical antipsychotic medications with placebo. A total of 6,301 placebo-treated patients were considered. AE profiles of the class were clusterized using MedDRA classification and analysed using a meta-regression approach.

Results: In the placebo arms the proportions of patients with any AE was 66.3% (95% CI: 62.7–69.8%). The proportion of withdrawal of patients treated with placebo because of AEs was 7.2% (95% CI: 5.9–8.4%). Interestingly, the AEs in the placebo arms corresponded to those of the antipsychotic-atypical-medication-class against which the placebo was compared. Namely, using meta-regression analysis we found an association between the level of psychiatric symptomatology measured with PANSS scores and higher AEs reported as nervous system (p = 0.020) and gastrintestinal disorders (p = 0.004). Moreover, the level of a higher psychiatric symptomatology expressed with PANSS scores was also related with higher AEs associated with psychiatric symptoms (p = 0.017).

Conclusion: These findings emphasise that the AEs in placebo arms of clinical trials of antipsychotic medications were substantial. Importantly, a higher level of psychiatric symptomatology makes SCD patients more prone to express AEs, thus contributing to possible drop-outs and to a lower adherence to treatments. These results are consistent with the expectation theory of placebo and nocebo effects.

Disruption of podocyte cytoskeletal biomechanics by dasatinib leads to nephrotoxicity

Nephrotoxicity is a critical adverse event that leads to discontinuation of kinase inhibitor (KI) treatment. Here we show, through meta-analyses of FDA Adverse Event Reporting System, that dasatinib is associated with high risk for glomerular toxicity that is uncoupled from hypertension, suggesting a direct link between dasatinib and podocytes. We further investigate the cellular effects of dasatinib and other comparable KIs with varying risks of nephrotoxicity. Dasatinib treated podocytes show significant changes in focal adhesions, actin cytoskeleton, and morphology that are not observed with other KIs. We use phosphoproteomics and kinome profiling to identify the molecular mechanisms of dasatinib-induced injury to the actin cytoskeleton, and atomic force microscopy to quantify impairment to cellular biomechanics. Furthermore, chronic administration of dasatinib in mice causes reversible glomerular dysfunction, loss of stress fibers, and foot process effacement. We conclude that dasatinib induces nephrotoxicity through altered podocyte actin cytoskeleton, leading to injurious cellular biomechanics.

Preventable Cases of Oral Anticoagulant-Induced Bleeding: Data From the Spontaneous Reporting System

Background

Despite the risk of bleeding is a well-known adverse effect of oral anticoagulants, there is scarce evidence on the preventability of oral anticoagulant-induced bleedings. Therefore, we investigated the potential risk factors related to preventable cases of oral anticoagulant-induced bleedings.

Methods

We performed a study using Individual Case Safety Reports (ICSRs) with an oral anticoagulant as suspected drug among those reported through the spontaneous reporting system of Campania Region from 1 July 2012 to 31 December 2017. The P-method was used for the preventability assessment of all cases of bleeding.

Results

In total, 58 cases out of 253 (22.9%) were preventable, and the most reported suspected drug was an indirect oral anticoagulant (warfarin). Sixty-eight critical criteria for preventability were identified, all related to healthcare professionals' practices. The most detected risk factor related to healthcare professionals' practices was the labeled drug-drug interaction for both direct and indirect oral anticoagulants.

Conclusion

Our findings describe the most reported risk factors for preventability of oral anticoagulant-induced bleedings. These factors may be useful for targeting interventions to improve pharmacovigilance activities in our regional territory and to reduce the burden of medication errors and inappropriate prescription.

Characterization and preventability of adverse drug events as cause of emergency department visits: a prospective 1-year observational study

BACKGROUND

Adverse drug events (ADEs) are a significant cause of emergency department (ED) visits, with a major impact on healthcare resource utilization. A multicentre observational study, aimed to describe frequency, seriousness and preventability of ADEs reported in four EDs, was performed in Sicily (Italy) over a 1-year period.

METHODS

Two trained monitors for each ED supported clinicians in identifying ADEs of patients admitted to EDs between June 1st, 2013 and May 31st, 2014 through a systematic interview of patients or their caregivers and with an additional record review. A research team analyzed each case of suspected ADE, to make a causality assessment applying the Naranjo algorithm and a preventability assessment using Schumock and Thornton criteria. Absolute and percentage frequencies with 95% confidence interval (CI) and medians with interquartile ranges (IQR) were estimated. Logistic regression models were used to evaluate independent predictors of serious and certainly preventable ADEs.

RESULTS

Out of 16,963 ED visits, 575 (3.4%) were associated to ADEs, of which 15.1% resulted in hospitalization. ADEs were classified as probable in 45.9%, possible in 51.7% and definite in 2.4% of the cases. Moreover, ADEs were considered certainly preventable in 12.3%, probably preventable in 58.4%, and not preventable in 29.2% of the cases. Polytherapy influenced the risk to experience a serious, as well as a certainly preventable ADE. Whilst, older age resulted an independent predictor only of serious events. The most common implicated drug classes were antibiotics (34.4%) and anti-inflammatory drugs (22.6%). ADEs due to psycholeptics and antiepileptics resulted preventable in 62.7 and 54.5% of the cases, respectively. Allergic reactions (64%) were the most frequent cause of ADE-related ED visits, followed by neurological effects (10.2%) that resulted preventable in 1.9 and 37.3% of the cases, respectively.

CONCLUSION

ADEs are a frequent cause of ED visits. The commonly used antibiotics and anti-inflammatory drugs should be carefully managed, as they are widely involved in mild to severe ADEs. Polytherapy is associated with the occurrence of serious, as well as certainly preventable ADEs, while older age only with serious events. A greater sensitivity to drug monitoring programs among health professionals is needed.

Improvements in Lung Function with Nebulized Revefenacin in the Treatment of Patients with Moderate to Very Severe COPD: Results from Two Replicate Phase III Clinical Trials

Background

Revefenacin, a novel, lung-selective, long-acting muscarinic antagonist, has been developed for nebulized therapy for chronic obstructive pulmonary disease (COPD). We present the results of replicate Phase III efficacy and safety studies of revefenacin in patients with moderate to very severe COPD.

Methods

In 2 double-blind, parallel-group studies, (Study 0126 and Study 0127), patients ≥ 40 years old were randomized to revefenacin 88 μg, revefenacin 175 μg or placebo administered once daily by standard jet nebulizer for 12 weeks. The primary endpoint was 24-hour trough forced expiratory volume in 1 second (FEV₁) on day 85. Secondary efficacy endpoints included overall treatment effect (OTE) on trough FEV₁ and peak FEV₁ (0-2 hours after first dose). Safety assessments included treatment-emergent adverse events.

Results

At day 85, revefenacin 88 µg and 175 µg improved trough FEV₁ versus placebo in Study 0126 (by 79 mL [p=0.0003] and 146 mL [p<0.0001]) and Study 0127 (by 160 mL and 147 mL; both p<0.0001). Compared with placebo, pooled data of revefenacin 88 µg and 175 µg increased OTE trough FEV₁ by 115 mL and 142 mL (both p<0.001) and increased peak FEV₁ by 127 mL and 129 mL (both p<0.0001). Revefenacin 175 µg demonstrated greater improvements in FEV₁ in concomitant long-acting beta2-agonist patients and in more severe patients than revefenacin 88 µg. Adverse events were minor.

Conclusion

Revefenacin, administered once daily for 12 weeks to patients with moderate to very severe COPD, demonstrated clinically significant improvements in trough FEV₁ and OTE FEV₁. Revefenacin was generally well tolerated with no major safety concerns.

Recommendations for the Reporting of Harms in Manuscripts on Clinical Trials Assessing Osteoarthritis Drugs: A Consensus Statement from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)

BACKGROUND

There is strong evidence of under-reporting of harms in manuscripts on randomized controlled trials (RCTs) compared with the volume of raw data retrieved from these trials. Many guidelines have been developed to tackle this, but they have failed to address some important issues that would allow for standardization and transparency. As a consequence, harms reporting in manuscripts remains suboptimal.

OBJECTIVE

The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) aimed to deliver accurate recommendations for better reporting of harms in clinical trials manuscripts on anti-osteoarthritis (OA) drugs. These could help to better inform clinicians on harms recorded in RCTs and further help researchers conducting meta-analyses.

METHODS

Using the outcomes of several systematic reviews on the safety of anti-OA drugs, we summarized the ways in which harms have been reported in OA RCT manuscripts to date. Next, we drafted some recommendations and initiated a modified Delphi process that involved a panel of clinicians and clinical researchers to build an expert consensus on recommendations from the ESCEO for the reporting of harms in future manuscripts on RCTs assessing anti-OA drugs.

RESULTS

These recommendations emphasize that all treatment-emergent adverse events (AEs) should always be taken into account for harms reporting, with no frequency threshold, and describe how specific AEs should be reported; they also provide a list of the most relevant organ systems to be considered according to each class of drug for reporting of harms within the results section of a manuscript. Irrespective of the drug, the ESCEO recommends that total, severe and serious AEs and withdrawals due to AEs should always be reported; guidance on the reporting of specific events pertaining to each category is provided. The ESCEO also recommends the reporting of information on drug effect on biological parameters, with specific guidance.

CONCLUSIONS

These recommendations may contribute to improve transparency in the field of safety of anti-OA medications. Pharmaceutical companies developing drugs for OA, and researchers conducting clinical trials, are encouraged to comply with them when reporting harms-related results in manuscripts on RCTs. The ESCEO also encourages journals to refer to the ESCEO recommendations in their instructions to authors for the publication of manuscripts on trials of anti-OA medications.

Linking entities through an ontology using word embeddings and syntactic re-ranking

BACKGROUND

Although there is an enormous number of textual resources in the biomedical domain, currently, manually curated resources cover only a small part of the existing knowledge. The vast majority of these information is in unstructured form which contain nonstandard naming conventions. The task of named entity recognition, which is the identification of entity names from text, is not adequate without a standardization step. Linking each identified entity mention in text to an ontology/dictionary concept is an essential task to make sense of the identified entities. This paper presents an unsupervised approach for the linking of named entities to concepts in an ontology/dictionary. We propose an approach for the normalization of biomedical entities through an ontology/dictionary by using word embeddings to represent semantic spaces, and a syntactic parser to give higher weight to the most informative word in the named entity mentions.

RESULTS

We applied the proposed method to two different normalization tasks: the normalization of bacteria biotope entities through the Onto-Biotope ontology and the normalization of adverse drug reaction entities through the Medical Dictionary for Regulatory Activities (MedDRA). The proposed method achieved a precision score of 65.9%, which is 2.9 percentage points above the state-of-the-art result on the BioNLP Shared Task 2016 Bacteria Biotope test data and a macro-averaged precision score of 68.7% on the Text Analysis Conference 2017 Adverse Drug Reaction test data.

CONCLUSIONS

The core contribution of this paper is a syntax-based way of combining the individual word vectors to form vectors for the named entity mentions and ontology concepts, which can then be used to measure the similarity between them. The proposed approach is unsupervised and does not require labeled data, making it easily applicable to different domains.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA

BACKGROUND

Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern.

RESULTS

This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression).

CONCLUSIONS

This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Safety profile of human papilloma virus vaccines: an analysis of the US Vaccine Adverse Event Reporting System from 2007 to 2017

AIMS

Human papilloma virus (HPV) is the cause of different types of carcinoma. Despite the remarkable effectiveness of the HPV vaccines, there have been many complaints about their risk-benefit profile due to adverse events following immunization (AEFI). The purpose of this study is to analyse the safety profile of the HPV vaccine basing on real-life data derived from reports of suspected AEFIs collected in the US Vaccine Adverse Events Reporting System (VAERS) and assess if the searches on Google overlap with spontaneous reporting.

METHODS

We collected all the reports in VAERS between January 2007 to December 2017 related to the HPV vaccines. A disproportionality analysis using reporting odds ratio (ROR) with 95% confidence interval was performed.

RESULTS

Over the 10-year period, 55 356 reports of AEFI related to HPV vaccines were retrieved in VAERS, corresponding to 224 863 vaccine-event pairs. The highest number of reports was related to Gardasil (n = 42 244). The two events more frequently reported and statistically significant for HPV vaccines were dizziness (n = 6259; ROR = 2.60; 95% confidence interval 2.53-2.66) and syncope (n = 6004; ROR = 6.28; 95% confidence interval 6.12-6.44). The trends of spontaneous reporting and Google searches overlap.

CONCLUSION

The AEFI analysis showed that the events most frequently reported were non-serious and listed in the corresponding summary of product characteristics. Potential safety signals arose regarding less frequent AEFIs that would deserve further investigation. It is extremely important to disseminate correct and evidence-based scientific information.