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.

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).

Hostility Conflict and Reporting of Side Effects by Psychiatric Outpatients

1980 ◽  
Vol 47 (1) ◽  
pp. 319-324 ◽  
Author(s):  
Robert W. Downing ◽  
Karl Rickels
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Self Report ◽  
Partial Replication ◽  
Drug Trials ◽  
Psychiatric Outpatients ◽  
Double Blind ◽  
The Relationship ◽  
Report Measure

The Irritability, Indirect Hostility, Verbal Hostility, and Resentment scales from the Buss-Durkee Hostility Inventory, along with a newly constructed scale intended as a self-report measure of Hostility Conflict, were administered to 84 non-psychotic, primarily anxious psychiatric outpatients receiving an active anxiolytic and participating in one of several 4-wk. double-blind drug trials. Patients who complained of one or more side effects after 2 wk. of treatment were classified as side reactors; the remaining patients, as non-side reactors. Compared to non-side reactors, the side reactors obtained higher hostility conflict scores and lower scores on the Irritability and Indirect Hostility scales. Also, the relationship between side effect status and hostility conflict was stronger in those patients who obtained higher scores on the Irritability, Indirect Hostility, and Verbal Hostility scales and among patients obtaining lower scores on the Resentment scale. Findings were regarded as providing partial replication of and further verification of earlier results.

scholarly journals The relationship between rational drug design and drug side effects

2011 ◽  
Vol 13 (3) ◽  
pp. 377-382 ◽  
Author(s):  
J. Wang ◽  
Z.-x. Li ◽  
C.-x. Qiu ◽  
D. Wang ◽  
Q.-h. Cui
Keyword(s):  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Drug Side Effects ◽  
Rational Drug ◽  
The Relationship

The Initiation of Long-Term Pharmacotherapy in Schizophrenia: Dosage and Side Effect Comparisons Between Oral and Depot Fluphenazine*

1976 ◽  
Vol 09 (04) ◽  
pp. 159-169 ◽  
Author(s):  
Nina R. Schooler ◽  
J. Levine ◽  
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Dosage Forms ◽  
The Other ◽  
Oral Drug ◽  
Double Blind ◽  
Moderate Severity ◽  
Treatment Groups ◽  
The Relationship

SummaryThis report focuses on two comparisons between oral and depot fluphenazine specifically FPZ decanoate: 1) can equivalent dosages for the two drugs be established and do these equivalencies change over six months of treatment; 2) what are the side effects seen with the two drugs during the early weeks of administration.Patients in the study receive either oral or depot FPZ as the active treatment but in order to preserve double blind conditions, they are also given the other treatment in placebo form. No dosage equivalence is established by the protocol, however, if dosage is adjusted, both forms must be changed and in the same direction. During the first weeks of treatment there is a linear relationship between the two dosage forms but a range of relatively low dosages of the oral compound (5-20 mg) is associated with a single dose (25 mg/q 3 weeks) of FPZ decanoate. At higher dosages of the oral drug the relationship is linear. Side effects of some kind are noted in over 60 percent of patients in both treatment groups after four weeks of treatment, while symptoms of at least moderate severity occur in almost 40 percent. Only symptoms involving the extrapyramidal system and sleep disturbance are observed in more than 20 percent of the patients. Benztropine was prescribed only if needed and was administered to 65 percent of patients. In general, those receiving benztropine had more side effects than those who did not. These differences reached significance for extrapyramidal symptoms and depression.Based on these data, we conclude that at the dosages used in this study there are no side effect differences between these two forms of fluphenazine in the early weeks of administration. Dosage equivalence between the two drugs can be set within the range of 5- 60 mg/day oral and 12.5-100 mg/three weeks depot.

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.

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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