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.

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).

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.

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.

scholarly journals Pemanfaatan Obat Tradisional Dengan Pertimbangan Manfaat Dan Keamanannya

2006 ◽  
Vol 3 (1) ◽  
pp. 1-7
Author(s):  
Lusia Oktora Ruma Kumala Sari ◽  
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Herbal Medicines ◽  
Drug Side Effects ◽  
Modern Drug ◽  
Expected Information ◽  
Traditional Drug

Herbal medicines in general are safer than modern drug. This matter is caused by the less side effect of herbal medicines than modern drug. Side effects of herbal medicines can be reduced with the used of right materials, accurat dose, accurat usage time, accurat way of usage, accurat analyze information, and without abusing of herbal medicines itself. Accuracy of materials determine the effect of herbal medicines. Dose measuring in set of gram can lessen possibility the happening of effect which do not be expected. Information which is not supported by adequate basic knowledges and enough study can make traditional drug return to endangering.

Precipitating factors for falls among patients with dementia on a psychogeriatric ward

2010 ◽  
Vol 22 (4) ◽  
pp. 641-649 ◽  
Author(s):  
Sofie Tängman ◽  
Staffan Eriksson ◽  
Yngve Gustafson ◽  
Lillemor Lundin-Olsson
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Hospital Ward ◽  
Acute Disease ◽  
Drug Side Effects ◽  
Precipitating Factors ◽  
Drug Side Effect ◽  
The North ◽  
People With Dementia ◽  
Relevant Factors

ABSTRACTBackground:Predisposing factors alone explain only a limited proportion of the variation in fall events, especially in people with dementia. The aim of this study was to identify precipitating factors for falls among people with dementia.Methods:We examined prospective fall registrations over a two-year period on a psychogeriatric hospital ward in the north of Sweden. Circumstances associated with each fall event were analyzed by independent reviewers, possible precipitating factors were documented, evaluated and the most likely precipitating factors were identified. In total, 223 patients with any type of diagnosed dementia were admitted to the ward and 91 fell at least once. Of these, 46 were women and 45 were men (mean age 80.3 years, range 60–94).Results:A total of 298 falls were registered, 62% of which were sustained by men. The most likely factor or combination of factors could be ascertained in 247 falls (83%). Falls took place at all hours but almost half of the falls (44%) occurred during the nightshift (between 9pm and 7am). Acute disease or symptoms of disease and/or acute drug side-effects were, alone or in combination with other factors, judged to precipitate more than three out of four falls.Conclusion:It is possible to identify many precipitating factors that may contribute to a fall. Falls in people with dementia should be regarded as a symptom of acute disease or as a drug side-effect until proven otherwise. Prompt detection of these relevant factors is, therefore, essential.

scholarly journals Learning important features from multi-view data to predict drug side effects

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xujun Liang ◽  
Pengfei Zhang ◽  
Jun Li ◽  
Ying Fu ◽  
Lingzhi Qu ◽  
...  
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Graph Model ◽  
Graph Laplacian ◽  
Heterogeneous Data ◽  
Drug Side Effects ◽  
Computational Framework ◽  
Single View ◽  
Different Types ◽  
Laplacian Regularization

AbstractThe problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.

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.

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