Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

A Simulation-based Comparison of Drug-Drug Interaction Signal Detection Methods

Many studies have proposed methods to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance. Post-market drug safety surveillance relies on spontaneous reporting data including ADR reports and prescription information. Most previous studies have applied statistical models to real world data and compared the performance. In this article, we assess the performance of various detection methods by implementing simulations under various conditions. This allows us to determine which situation each of the methods is most useful for. In addition, we summarize and generalize the characteristics of each method. 많은 선행연구에서 두 가지 약물을 함께 복용함으로 인해 발생하는 약물 부작용을 탐지하는 방법을 연구하였다. 약물 부작용으로 의심되는 신호는 시판 후 의약품 안전 감시를 통하여 발견될 수 있다. 시판 후 의약품 안전 감시는 부작용 보고와 의약품 처방 정보에 대한 자발적 보고 데이터에 기반한다. 약물간 상호작용 신호 탐지방법에 대한 대부분의 선행 연구는 이러한 자발적 보고 데이터에 각 방법들을 적용하고 각 방법들 간의 성능을 비교하였다. 본 논문에서는 다양한 조건하에서 시물레이션을 수행하여 여러 방법들 간의 성능을 평가한다. 이를 통해 각 방법의 특성을 요약하고 어떤 상황에서 유용한지 살펴보고자 한다.open석

Medication-Wide Association Studies

Undiscovered side effects of drugs can have a profound effect on the health of the nation, and electronic health-care databases offer opportunities to speed up the discovery of these side effects. We applied a “medication-wide association study” approach that combined multivariate analysis with exploratory visualization to study four health outcomes of interest in an administrative claims database of 46 million patients and a clinical database of 11 million patients. The technique had good predictive value, but there was no threshold high enough to eliminate false-positive findings. The visualization not only highlighted the class effects that strengthened the review of specific products but also underscored the challenges in confounding. These findings suggest that observational databases are useful for identifying potential associations that warrant further consideration but are unlikely to provide definitive evidence of causal effects

Potential drug-drug interactions in paediatric outpatient prescriptions in Nigeria and implications for the future

BACKGROUND: Information regarding the incidence of drug-drug interactions (DDIs) and adverse drug events (ADEs) among paediatric patients in Nigeria is limited. METHODS: Prospective clinical audit among paediatric outpatients in four general hospitals in Nigeria over a 3-month period. Details of ADEs documented in case files was extracted. RESULTS: Among 1233 eligible patients, 208 (16.9%) received prescriptions with at least one potential DDI. Seven drug classes were implicated with antimalarial combination therapies predominating. Exposure mostly to a single potential DDI, commonly involved promethazine, artemether/lumefantrine, ciprofloxacin and artemether/lumefantrine. Exposure mostly to major and serious, and moderate and clinically significant, potential DDIs. Overall exposure similar across all age groups and across genders. A significant association was seen between severity of potential DDIs and age. Only 48 (23.1%) of these patients presented at follow-up clinics with only 15 reporting ADEs. CONCLUSION: There was exposure to potential DDIs in this population. However, potential DDIs were associated with only a few reported ADEs

Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes

BACKGROUND: Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS: The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION: Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development

Identifying Unexpected Inflammation Resulting from Drug-Drug Interactions

Adverse drug events result in nearly 1.3 million emergency room visits per year in the United States of America. As much as 30% of these adverse events are a result of drug-drug interactions (DDI’s). There is a gap in knowledge concerning these DDI outcomes especially when it comes to inflammation. Inflammation is linked to a variety of chronic health conditions and non-infectious diseases such as cancer. Purpose: The goal of this study was to examine how many active drug ingredients from FDA’s Adverse Event Reporting System (FAERS), when combined (in groups of two or more), elicit an inflammatory response and if drug pairs were identified as causing inflammation what were the symptoms? Methods: In this study secondary data was analyzed from the publicly available FAERS database. Cases that involved any of the five symptoms of inflammation (swelling, pain, chills, headache, red coloration) ranging from 1980 to August 5, 2022, were retrieved (n= 23,964). Followed by the removal of all cases not reported by healthcare professionals and those that only occurred once (n= 11,957) (See figure 1). Results: The data suggested that all these cases were related to 549 different drugs and 37 different drug combinations. Out of these combinations, three-drug combinations did not contain an active ingredient that elicited an effect alone (See Table 1). Discussion: Sense inflammation is associated with long-term disorders; Betamethasone Sodium Phosphate and Betamethasone Acetate which are used to treat long-term disorders, may lead to cancer