Improving Pharmacovigilance Signal Detection from Clinical Notes with Locality Sensitive Neural Concept Embeddings.

Although pharmaceutical products undergo clinical trials to profile efficacy and safety, some adverse drug reactions (ADRs) are only discovered after release to market. Post-market drug safety surveillance - pharmacovigilance - leverages information from various sources to proactively identify such ADRs. Clinical notes are one source of observational data that could assist this process, but their inherent complexity can obfuscate possible ADR signals. In previous research, embeddings trained on observational reports have improved detection of such signals over commonly used statistical measures. Moreover, neural embedding methods which further encode juxtapositional information have shown promise on analogical retrieval tasks, suggesting proximity-based alternatives to document-level modeling for signal detection. This work uses natural language processing and locality sensitive neural embeddings to increase ADR signal recovery from clinical notes, with AUCs of ~0.63-0.71. Constituting a ~50% increase over baselines, our method sets the state-of-the-art for these reference standards when solely leveraging clinical notes.

Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.

Adverse event reports (AER) are widely used for post-market drug safety surveillance and drug repurposing, with the assumption that drugs with similar side-effects may have similar therapeutic effects also. In this study, we used distributed representations of drugs derived from the Food and Drug Administration (FDA) AER system using aer2vec, a method of representing AER, with drug embeddings emerging from a neural network trained to predict the probability of adverse drug effects given observed drugs. We combined these representations with molecular features to predict permeability of the blood-brain barrier to drugs, a prerequisite to their application to treat conditions of the central nervous system. Across multiple machine learning classifiers, the addition of distributed representations improved performance over prior methods using drug-drug similarity estimates derived from discrete representations of AER system data. Embedding-based approaches outperformed those using discrete statistics, with improvements in absolute AUC of 5% and 9%, corresponding to improvements of 9% and 13% over performance with molecular features only. Performance was retained when reducing embedding dimensions from 500 to 6, indicating that they are neither attributable to overfitting, nor to a difference in the number of trainable parameters. These results indicate that aer2vec distributed representations carry information that is valuable for drug repurposing.

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

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

Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance.

INTRODUCTION: As a result of the well documented limitations of data collected by spontaneous reporting systems (SRS), such as bias and under-reporting, a number of authors have evaluated the utility of other data sources for the purpose of pharmacovigilance, including the biomedical literature. Previous work has demonstrated the utility of literature-derived distributed representations (concept embeddings) with machine learning for the purpose of drug side-effect prediction. In terms of data sources, these methods are complementary, observing drug safety from two different perspectives (knowledge extracted from the literature and statistics from SRS data). However, the combined utility of these pharmacovigilance methods has yet to be evaluated. OBJECTIVE: This research investigates the utility of directly or indirectly combining an observational signal from SRS with literature-derived distributed representations into a single feature vector or in an ensemble approach for downstream machine learning (logistic regression). METHODS: Leveraging a recently developed representation scheme, concept embeddings were generated from relational connections extracted from the literature and composed to represent drug and associated adverse reactions, as defined by two reference standards of positive (likely causal) and negative (no causal evidence) pairs. Embeddings were presented with and without common measures of observational signal from SRS sources to logistic regressors, and performance was evaluated with the receiver operating characteristic (ROC) area under the curve (AUC) metric. RESULTS: ROC AUC performance with these composite models improves up to ≈ 20% over SRS-based disproportionality metrics alone and exceeds the best prior results reported in the literature when models leverage both sources of information. CONCLUSIONS: Results from this study support the hypothesis that knowledge extracted from the literature can enhance the performance of SRS-based methods (and vice versa). Across reference sets, using literature and SRS information together performed better than using either source alone, providing strong support for the complementary nature of these approaches to post-marketing drug surveillance.

aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations.

Adverse event report (AER) data are a key source of signal for post marketing drug surveillance. The standard methodology to analyze AER data applies disproportionality metrics, which estimate the strength of drug/side-effect associations from discrete counts of their occurrence at report level. However, in other domains, improvements in predictive modeling accuracy have been obtained through representation learning, where discrete features are replaced by distributed representations learned from unlabeled data. This paper describes aer2vec, a novel representational approach for AER data in which concept embeddings emerge from neural networks trained to predict drug/side-effect co-occurrence. Trained models are evaluated for their utility in identifying drug/side-effect relationships, with improvements over disproportionality metrics in most cases. In addition, we evaluate the utility of an otherwise-untapped resource in the Food and Drug Administration (FDA) AER system - reporter designations of suspected causality - and find that incorporating this information enhances performance of all models evaluated.

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications

The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform better, while being faster to train, more re-usable, and significantly simpler.

Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Objective: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring. Methods: Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug/side-effect reference set were evaluated against a list of ≈1100 drugs from an online database. Results: The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.95; F1 0.84 across the two sets), outperforming methods utilizing literature and/or spontaneous reporting system data. Examination of predictions for unseen drug/side-effect pairs indicates the ability of these methods to generalize, with over tenfold label support enrichment in the top 100 predictions versus the bottom 100 predictions. Discussion and Conclusion: Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.

Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.

An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associations be manually reviewed - a human-intensive process that scales poorly with large numbers of possibly dangerous associations and rapid growth of biomedical literature. Recent work has employed Literature Based Discovery methods that exploit implicit relationships between biomedical entities within the literature to estimate the plausibility of drug/ADE connections. We extend this work by evaluating machine learning classifiers applied to high-dimensional vector representations of relationships extracted from the literature as a means to identify substantiated drug/ADE connections. Using a curated reference standard, we show applying classifiers to such representations improves performance (+≈37%AUC) over previous approaches. These trained systems reproduce outcomes of the manual literature review process used to create the reference standard, but further research is required to establish their generalizability.

Chitosan-heparin polyelectrolyte multilayers on cortical bone: periosteum-mimetic, cytophilic, antibacterial coatings.

Cortical bone allografts suffer from high rates of failure due to poor integration with host tissue, leading to non-union, fracture, and infection following secondary procedures. Here, we report a method for modifying the surfaces of cortical bone with coatings that have biological functions that may help overcome these challenges. These chitosan-heparin coatings promote mesenchymal stem cell attachment and have significant antibacterial activity against both S. aureus and E. coli. Furthermore, their chemistry is similar to coatings we have reported on previously, which effectively stabilize and deliver heparin-binding growth factors. These coatings have potential as synthetic periosteum for improving bone allograft outcomes.

Dopaminergic amacrine cells express opioid receptors in the mouse retina.

The presence of opioid receptors has been confirmed by a variety of techniques in vertebrate retinas including those of mammals; however, in most reports, the location of these receptors has been limited to retinal regions rather than specific cell types. Concurrently, our knowledge of the physiological functions of opioid signaling in the retina is based on only a handful of studies. To date, the best-documented opioid effect is the modulation of retinal dopamine release, which has been shown in a variety of vertebrate species. Nonetheless, it is not known if opioids can affect dopaminergic amacrine cells (DACs) directly, via opioid receptors expressed by DACs. This study, using immunohistochemical methods, sought to determine whether (1) µ- and δ-opioid receptors (MORs and DORs, respectively) are present in the mouse retina, and if present, (2) are they expressed by DACs. We found that MOR and DOR immunolabeling were associated with multiple cell types in the inner retina, suggesting that opioids might influence visual information processing at multiple sites within the mammalian retinal circuitry. Specifically, colabeling studies with the DAC molecular marker anti-tyrosine hydroxylase antibody showed that both MOR and DOR immunolabeling localize to DACs. These findings predict that opioids can affect DACs in the mouse retina directly, via MOR and DOR signaling, and might modulate dopamine release as reported in other mammalian and nonmammalian retinas.