Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.

Evaluating Drug Risk Using GAN and SMOTE Based on CFDA’s Spontaneous Reporting Data

Adverse drug reactions (ADRs) pose health threats to humans. Therefore, the risk re-evaluation of post-marketing drugs has become an important part of the pharmacovigilance work of various countries. In China, drugs are mainly divided into three categories, from high-risk to low-risk drugs, namely, prescription drugs (Rx), over-the-counter drugs A (OTC-A), and over-the-counter drugs B (OTC-B). Until now, there has been a lack of automated evaluation methods for the three status switch of drugs. Based on China Food and Drug Administration’s (CFDA) spontaneous reporting database (CSRD), we proposed a classification model to predict risk level of drugs by using feature enhancement based on Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE). A total of 985,960 spontaneous reports from 2011 to 2018 were selected from CSRD in Jiangsu Province as experimental data. After data preprocessing, a class-imbalance data set was obtained, which contained 887 Rx (accounting for 84.72%), 113 OTC-A (10.79%), and 47 OTC-B (4.49%). Taking drugs as the samples, ADRs as the features, and signal detection results obtained by proportional reporting ratio (PRR) method as the feature values, we constructed the original data matrix, where the last column represents the category label of each drug. Our proposed model expands the ADR data from both the sample space and the feature space. In terms of feature space, we use feature selection (FS) to screen ADR symptoms with higher importance scores. Then, we use GAN to generate artificial data, which are added to the feature space to achieve feature enhancement. In terms of sample space, we use SMOTE technology to expand the minority samples to balance three categories of drugs and minimize the classification deviation caused by the gap in the sample size. Finally, we use random forest (RF) algorithm to classify the feature-enhanced and balanced data set. The experimental results show that the accuracy of the proposed classification model reaches 98%. Our proposed model can well evaluate drug risk levels and provide automated methods for status switch of post-marketing drugs.

Inflammation as a Risk Factor in Cardiotoxicity: An Important Consideration for Screening During Drug Development

Numerous commonly prescribed drugs, including antiarrhythmics, antihistamines, and antibiotics, carry a proarrhythmic risk and may induce dangerous arrhythmias, including the potentially fatal Torsades de Pointes. For this reason, cardiotoxicity testing has become essential in drug development and a required step in the approval of any medication for use in humans. Blockade of the hERG K+ channel and the consequent prolongation of the QT interval on the ECG have been considered the gold standard to predict the arrhythmogenic risk of drugs. In recent years, however, preclinical safety pharmacology has begun to adopt a more integrative approach that incorporates mathematical modeling and considers the effects of drugs on multiple ion channels. Despite these advances, early stage drug screening research only evaluates QT prolongation in experimental and computational models that represent healthy individuals. We suggest here that integrating disease modeling with cardiotoxicity testing can improve drug risk stratification by predicting how disease processes and additional comorbidities may influence the risks posed by specific drugs. In particular, chronic systemic inflammation, a condition associated with many diseases, affects heart function and can exacerbate medications’ cardiotoxic effects. We discuss emerging research implicating the role of inflammation in cardiac electrophysiology, and we offer a perspective on how in silico modeling of inflammation may lead to improved evaluation of the proarrhythmic risk of drugs at their early stage of development.

Prescription pattern for pregnant and lactating mothers, and attitude towards the safety of medicines in a tertiary hospital in Bangladesh

<p class="abstract"><strong>Background:</strong> Prescription pattern of drugs particularly in the special physiological conditions, e.g., pregnancy and lactation have an impact on the mothers as well as the newborns. More specifically, the evaluation of the pattern can play a pivotal role to minimize their economic burden as well as improving their quality of life by reducing drug related toxicity. This study aims at the evaluation of the prescription pattern for the pregnant and lactating mothers for the first time in one of the largest tertiary hospitals in Bangladesh. Moreover, the perception of the patients about the safety of the medicines has also been enlightened.</p><p class="abstract"><strong>Methods:</strong> Data collected from 500 pregnant women and 335 lactating mothers were analyzed in the context of demographic characteristics, drug use pattern, USFDA drug risk category, and clinical complications to understand their attitude towards the safety of medicines. </p><p class="abstract"><strong>Results:</strong> The study suggests that the majority of the participants were aware of the safety and usage of medications during pregnancy and lactation. Moreover, the linear regression analysis clearly indicates that pregnant women were significantly associated with a higher attitude score compared to that of the lactating mothers. </p><p class="abstract"><strong>Conclusions:</strong> This study necessitates the requirement to implement the relevant WHO recommended core interventions and to develop a healthcare system by incorporating clinical pharmacists about the dispensing and sale of medicines in ensuring the rational use of medicines more efficiently.</p>

Prevalence and Risk Factors Associated with First-Line Anti-Tuberculosis Induced Hepatotoxicity in Suratthani Hospital, Thailand

ackground: Tuberculosis (TB) is a major public health problem, including Thailand. Anti-TB drugs are very effective treatment, but they can cause hepatotoxicity. Data on the prevalence of anti-TB drug-induced hepatotoxicity (DIH), as well as the contributing risk factors, are scarce in Thailand. Objective: To measure the prevalence and identify risk factors associated with first-line drugs (FLD) induced hepatoxicity in TB patients. Materials and Methods: The present study was a retrospective study design in TB clinic of Suratthani Hospital, in Southern Thailand. All patients diagnosed with TB and received FLD between January and December 2017, were eligible for the study. Hepatoxicity defined as the following criteria: serum aspartate aminotransferase (AST) or alanine aminotransferase (ALT) levels >5x upper limit of normal (ULN) without symptoms, or AST or ALT >3x ULN with clinical symptoms. Results: Of all the 198 TB cases, 18 were identified as DIH. Prevalence of DIH was 9.1%. Hepatitis after FLD was independently associated with age>60 years (adjusted OR [aOR] 28.49, 95% CI 2.68 to 302.95, p=0.005) and serum albumin <3.5 g/dL (aOR 20.97, 95% CI 2.11 to 208.51, p=0.009). Conclusion: Age of more than 60 years and low serum albumin of less than 3.5 g/dL were significant risk factors associated with first-line anti-TB drugs induced hepatoxicity. Keywords: Hepatoxicity, Anti-tuberculosis drug, Risk factor, Thailand

Quantifying the severity of adverse drug reactions using social media

Adverse drug reactions (ADRs) impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. The monitoring and analysis of the severity of adverse drug reactions is limited by the current qualitative and categorical system of severity classifications. Previous efforts have generated quantitative estimates for a subset of ADRs, but were limited in scope due to the time and costs associated with the efforts. We present a semi-supervised approach that estimates ADR severity by using a lexical network of ADR word embeddings and label propagation. We use this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from MedDRA. Our Severity of Adverse Events Derived from Reddit (Saedr) scores have good correlations with real-world outcomes. Saedr scores had Spearman correlations with ADR case outcomes in FAERS of 0.595, 0.633, and −0.748 for death, serious outcome, and no outcome, respectively. We investigate different methods for defining initial seed term sets and evaluate their impact on severity estimates. We analyzed severity distributions for ADRs based on their appearance in Boxed Warning drug label sections, as well as ADRs with sex-specific associations. We find that ADRs discovered postmarket have significantly greater severity compared to those discovered in the clinical trial. We create quantitative Drug RIsk Profile (Drip) scores for 968 drugs that have a Spearman correlation of 0.377 with drugs ranked by FAERS cases resulting in death, where the given drug was the primary suspect. We make the Saedr and Drip scores publicly available in order to enable more quantitative analysis of pharmacovigilance data.

Managing a patient exposed to a teratogenic drug at different stages of pregnancy

Pregnancy-related information provided by leaflets usually contains inadequate data to interpret risks of teratogenicity; and in the absence of data, prescribing information often recommends discontinuing use in anticipation of and during pregnancy. In contrast, individual information on drug risk assessment addresses three different clinical perspectives: 1) looking for drugs of choice or planning pregnancy under medication; 2) assessment of drugs’ risk after exposure during an unplanned pregnancy; 3) assessment of causality in cases of adverse pregnancy outcomes in association with drug exposure. Unfortunately, for many women with chronic rheumatic diseases, discontinuation of all medication leaves an unacceptable risk of disease reactivation. Although, for the majority of drugs, human data are still insufficient to rule out developmental risks it is possible to distinguish antirheumatic drugs of choice with apparently low or negligible risks from those with scarce data or controversies on their safety and those with evidenced risk for the unborn.