scholarly journals Patient Drug Database: Construction of Database for Patient Leading Drug Side Effects Exploration Using Patient Generated Health Data

2021 ◽  
Vol 46 (3) ◽  
pp. 315-325
Author(s):  
Sang Min Lee ◽  
Suehyun Lee ◽  
Jong Yeup Kim
Keyword(s):  
Side Effects ◽  
Drug Safety ◽  
Patient Information ◽  
Side Effect ◽  
Drug Information ◽  
Drug Analysis ◽  
Health Data ◽  
Drug Side Effects ◽  
Basic Drug ◽  
Drug Database

Objectives: This study focuses on building a database for patient-led search on drug side effects using basic drug information, drug analysis results information, patient information, and patient-generated health data (PGHD).Methods: After collecting data from the Health Insurance Review and Assessment Institute, the Korean Pharmaceutical Information Center, the Ministry of Food and Drug Safety, and the Korean Pharmaceutical Association, basic drug information was created. By utilizing the Korea Average Event Reporting System (KAERS) side effect report data provided by the Korea Drug Safety Administration and MetaLAB, a drug side effect detection algorithm applied on the Konyang university hospital’s real data, we designed and built a database using Oracle DB, which contains a table of patient information and PGHD. For drug information, a total of 49,553 drugs were mapped, and drug analysis results used KAERS and MetaLAB.Results: Based on the collected drug information, a total of 15 tables containing basic drug information (7 tables), drug analysis results (2 tables), patient information (1 table), and patient generation information (5 tables) were created using EDI codes, following mapping and normalization. Basic drug information included 49,553 EDI and 2,099 ATC codes. Drug analysis results included 2,046 KAERS ATC codes, 1,701 WHOART-ARRN (PT) that the result of 33 WHOART-SEQ (IT), 15,861 MetaLABEDI codes, and 101ATC codes. TheADR results were constructed using 62 DRUG_IDs and 73 MedDRA_PTI_IDs.Conclusions: The Patient Drug Database (PD2B) in this study was employed to allow patients to voluntarily report on their perception and drug side effects through application tools, which can provide quick measures against drug side effects and assist in the discovery of new ones.

scholarly journals MONITORING OF ANTICONVULSANT DRUG SIDE EFFECTS IN OUTPATIENTS WITH EPILEPSY

2018 ◽  
Vol 10 (1) ◽  
pp. 303
Author(s):  
Santi Purna Sari ◽  
Natasha Kurnia Salma S ◽  
Alfina Rianti
Keyword(s):  
Valproic Acid ◽  
Side Effects ◽  
Medical Records ◽  
Side Effect ◽  
Anticonvulsant Drug ◽  
Secondary Data ◽  
Drug Side Effects ◽  
Inclusion Criteria ◽  
Descriptive Data ◽  
Effect Monitoring

Objective: This study aimed to monitor the side effects of carbamazepine, phenytoin, and valproic acid, and combinations of these drugs in adultpatients with epilepsy, to raise awareness of the importance of drug side effect monitoring in hospitals.Methods: In this prospective study, descriptive data were collected from patients who met the inclusion criteria of complete samples. Primary datawere obtained using questionnaires, secondary data were collected from medical records, and analyses were performed using the Naranjo algorithm.Results: Among the 54 included patients, 38 (70.37%) of them experienced drug side effects, and the most frequently observed side effect occurredin 48.15% of study subjects.Conclusion: No correlation was identified between side effects and age (p=0.903) or gender (p=1.000).

Side Effects of Side Effect Information in Drug Information Leaflets

1994 ◽  
Vol 24 (4) ◽  
pp. 389-404 ◽  
Author(s):  
H. Pander Maat ◽  
R. Klaassen
Keyword(s):  
Side Effects ◽  
Patient Information ◽  
Drug Information ◽  
The Other ◽  
Package Inserts ◽  
Patient Information Leaflets ◽  
Information Leaflets ◽  
Dependent Variables ◽  
Preliminary Study ◽  
Therapy Compliance

This study examines the way side effects information is presented in patient information leaflets about drugs. In a field experiment, we tested the effects of two attempts to improve a side effects paragraph in a leaflet about a nonsteroid anti-inflammatory drug. First, a short introductory passage on the nature of side effects was added. Second and more importantly, we changed the frequency descriptors (FDs) for the side effects. A preliminary study had shown that the frequencies associated with common Dutch FDs are much higher than the writers of patient information leaflets and package inserts mean to convey. In our experiment we replaced the original FDs by lower-assessed FDs. For instance, soms (sometimes) was replaced by zelden (seldomly). Replacing FDs led to lower recall for the side effects mentioned in the leaflet. It also decreased the number of side effects experienced. Contrary to our expectations, lower FDs did not significantly increase the confidence in the safety and effectiveness of the drug, nor did they increase therapy compliance; incompliance was extremely rare in our sample of patients. Adding an introductory passage on the nature of side effects lowered FD interpretations. It did not significantly affect any of the other dependent variables.

scholarly journals Impact of Pharmacist Counselling on Clozapine Knowledge

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ciara Ní Dhubhlaing ◽  
Ailish Young ◽  
Laura J. Sahm
Keyword(s):  
Side Effects ◽  
Patient Information ◽  
Side Effect ◽  
Information Sources ◽  
Knowledge Score ◽  
University Hospital ◽  
Patient Knowledge ◽  
Specific Knowledge ◽  
The Impact ◽  
Improve Patient

Clozapine is the only antipsychotic with evidence for efficacy in treatment of resistant schizophrenia but it carries a high side effect burden. Patient information is provided but may be poorly retained. This study aims to examine the impact of pharmacist counselling upon patient knowledge of clozapine. Outpatients, aged 18 years and over, attending St. Patrick’s University Hospital, Dublin, participated in this study between June and August 2015. The intervention consisted of pharmacist counselling on two occasions one month apart. Knowledge was assessed using a 28-point checklist devised from the currently available clozapine patient information sources, at baseline and after each counselling session. Ethics approval was obtained. Twenty-five participants (40% female; mean age 45.1 years, SD 9.82; 64% unemployed, 28% smokers) showed an improvement in knowledge scores of clozapine from baseline to postcounselling on each occasion with an overall improvement in knowledge score, from baseline to postcounselling at one month, of 39.43%; p<0.001. This study adds to the evidence that interventions involving pharmacist counselling can improve patient knowledge, whilst the specific knowledge gained relating to recognition of side effects may help patients towards more empowerment regarding their treatment.

scholarly journals Upaya Peningkatan Pengetahuan tentang Efek Samping Obat pada Warga Dasa Wisma dalam Upaya Penerapan Farmakovigilans

2021 ◽  
Vol 1 (2) ◽  
pp. 149-153
Author(s):  
Inayatush Sholihah ◽  
Joko Santoso
Keyword(s):  
Social Media ◽  
Side Effects ◽  
Community Service ◽  
Measurement Method ◽  
Drug Information ◽  
Public Understanding ◽  
Public Knowledge ◽  
Drug Side Effects ◽  
Know How ◽  
Naranjo Algorithm

The lack of public understanding and awareness about safe drug information and misleading social media news related to drug use is a new challenge that cannot be underestimated. The purpose of this community service was to overcome the problem of the lack of public knowledge about side effects, especially as an effort to implement pharmacovigilance in the community. The method started from intervention to the community included providing education (socialization) on the dangers of drug side effects and workshops on the use of the method of measuring drug side effects using the Naranjo algorithm. The target of this activity was the residents of the Mertoudan, Mojosongo, Surakarta would get additional knowledge about drug side effects and know how to monitor it, also creating people who are aware of drug side effects. This activity increased the knowledge of residents about drug side effects and the ability to measure the probability of the occurrence of drug side effects. The implementation of this activity received a good response from the Chairperson of Dasa Wisma and the participants, who were very enthusiastic to increase their knowledge about drug side effects and the measurement method with the Naranjo algorithm.

scholarly journals The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

2019 ◽  
Author(s):  
Diego Galeano ◽  
Alberto Paccanaro
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Side Effect ◽  
Large Scale ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Optimal Solution ◽  
Drug Side Effects ◽  
Chemical Structures

AbstractPair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

scholarly journals Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions

2020 ◽  
Vol 23 (4) ◽  
pp. 285-294 ◽  
Author(s):  
Bo Zhou ◽  
Xian Zhao ◽  
Jing Lu ◽  
Zuntao Sun ◽  
Min Liu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Computational Methods ◽  
Side Effect ◽  
Permutation Test ◽  
Human Life ◽  
Statistical Significance ◽  
Drug Side Effects ◽  
Computational Investigation ◽  
The Relationship

Background:Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.Method:In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.Results and Conclusion:A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

2019 ◽  
Vol 14 (8) ◽  
pp. 709-720 ◽  
Author(s):  
Xian Zhao ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Tao Liu
Keyword(s):  
Side Effects ◽  
Network Model ◽  
Heterogeneous Networks ◽  
Side Effect ◽  
Correlation Coefficients ◽  
Machine Learning Algorithms ◽  
Drug Side Effects ◽  
Essential Information ◽  
Approved Drugs ◽  
Feature Dimension

Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

scholarly journals Advances in Understanding the Initial Steps of Pruritoceptive Itch: How the Itch Hits the Switch

2020 ◽  
Vol 21 (14) ◽  
pp. 4883
Author(s):  
Shirin Kahremany ◽  
Lukas Hofmann ◽  
Arie Gruzman ◽  
Guy Cohen
Keyword(s):  
Side Effects ◽  
Molecular Mechanism ◽  
Side Effect ◽  
Drug Side Effects ◽  
Stinging Nettle ◽  
Comprehensive View ◽  
Itch Sensation ◽  
Critical Aspects

Pruritoceptive (dermal) itch was long considered an accompanying symptom of diseases, a side effect of drug applications, or a temporary sensation induced by invading pruritogens, as produced by the stinging nettle. Due to extensive research in recent years, it was possible to provide detailed insights into the mechanism of itch mediation and modulation. Hence, it became apparent that pruritus is a complex symptom or disease in itself, which requires particular attention to improve patients’ health. Here, we summarize recent findings in pruritoceptive itch, including how this sensation is triggered and modulated by diverse endogenous and exogenous pruritogens and their receptors. A differentiation between mediating pruritogen and modulating pruritogen seems to be of great advantage to understand and decipher the molecular mechanism of itch perception. Only a comprehensive view on itch sensation will provide a solid basis for targeting this long-neglected adverse sensation accompanying numerous diseases and many drug side effects. Finally, we identify critical aspects of itch perception that require future investigation.

Investigating side effect modules in the interactome and their use in drug adverse effect discovery

2016 ◽  
Author(s):  
Emre Guney
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Drug Targets ◽  
Large Scale ◽  
New Drugs ◽  
Drug Side Effects ◽  
Data Set ◽  
Drug Adverse Effect ◽  
Phase Ii And Iii

One of the biggest challenges in drug development is increasing costs of bringing new drugs to the market. Many candidate drugs fail during phase II and III trials due to unexpected side effects and experimental methods remain cost ineffective for large scale discovery of adverse effects. Alternatively, computational methods are used to characterize drug side effects, but they often rely on training predictors based on drug and side effect similarity. Moreover, these methods are typically tailored to the underlying data set and provide little mechanistic insights on the predicted associations. In this study, we investigate the role of network topology in explaining observed side effects of drugs. We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects. We show that the interactome based proximity can be used to identify side effects and we highlight a use case in which interactome-based side effect prediction can give insights on drug side effects observed in the clinic.

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