Predicting new drug indications from network analysis

2017 ◽  
Vol 28 (09) ◽  
pp. 1750118
Author(s):  
Yousoff Effendy Mohd Ali ◽  
Kiam Heong Kwa ◽  
Kurunathan Ratnavelu
Keyword(s):  
Side Effects ◽  
Network Analysis ◽  
Side Effect ◽  
Drug Repositioning ◽  
Centrality Measures ◽  
Drug Side Effect ◽  
Alternative Approach ◽  
Therapeutic Properties ◽  
Basic Hypothesis ◽  
Optimum Threshold

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

scholarly journals Prediction of Side Effects Using Comprehensive Similarity Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Sukyung Seo ◽  
Taekeon Lee ◽  
Mi-hyun Kim ◽  
Youngmi Yoon
Keyword(s):  
Side Effects ◽  
Drug Interactions ◽  
Side Effect ◽  
Drug Repositioning ◽  
Phenotypic Expression ◽  
Area Under The Curve ◽  
Similarity Measures ◽  
Biological Properties ◽  
Nucleotide Polymorphisms ◽  
Chemical Structures

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

scholarly journals Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning

2021 ◽  
Vol 36 (1) ◽  
pp. 281-289
Author(s):  
D. Mohanapriya ◽  
Dr.R. Beena
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Side Effects ◽  
Transfer Learning ◽  
Language Processing ◽  
Side Effect ◽  
Topic Modeling ◽  
Distance Measure ◽  
Classification And Regression Tree ◽  
Drug Side Effect

In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditional Random Field (BILSTM-CRF). From the sentences in the literature data, a dependency graph was constructed which discovered the relations between gene and drug. The product of the drug on phenotype rule was identified by the Gene Regulation Score (GRS) which creates the drug-topic probability matrix. The probability matrix and a syntactic distance measure was processed in Classification and Regression Tree (CART), Naïve Bayes (NB), logistic regression and Convolutional Neural Network (CNN) classifiers for estimating the drug-gene and drug-side effects. Besides the literature data, social media offers various promising resources with massive volume of data that can be useful in the drug-phenotype and drug-side effect association prediction. So in this paper, drug information with gene, disease and side effects are extracted from different social media such as Twitter, Facebook and LinkedIn and it can be used with the literature data to provide more relevant disease and drug relations. In addition to this, topic modeling with transfer learning is introduced to consider the element categories, probability of overlapping elements and deep contextual significance of a text for better modeling of topics. The topic modeling with transfer learning shares as much knowledge as possible between the literature data and social media information for topic modeling. The topics from social media and literature data are used for creating the drug-topic matrix. The probability matrix and syntactic distance measure are given as input to CART, NB, logistic regression and CNN for estimating the drug-gene and drug-side effect association. This proposed work is named as Enhanced Topic Modeling with Transfer Leaning- IPISTON (ETPTL-IPISTON). The simulation findings exhibit that the efficiency of ETPTL-IPISTON than the traditional methods.

scholarly journals The Impact of Pharmaceutical Side Effects on Audiological and Vestibular Measurements

2019 ◽  
Vol 40 (02) ◽  
pp. 097-103
Author(s):  
Robert DiSogra
Keyword(s):  
Side Effects ◽  
Web Sites ◽  
Case History ◽  
Side Effect ◽  
Communication Strategies ◽  
Test Results ◽  
Drug Side Effect ◽  
History Information ◽  
The Impact ◽  
Cognitive Side Effects

AbstractThere are over 2,000 drugs with a combined total of over 400 side effects that could result in obtaining inaccurate case history information or inaccurate test results that may lead to misdiagnosing the patient's hearing or vestibular problem. The recommendations that are made could be inappropriate and thus can lead to management errors. A review of the auditory, vestibular, and cognitive side effects of many of the drugs patients take regularly (including drugs that can cause tinnitus) is provided. This article offers suggestions to obtain a more accurate case history. A review of preferred Web sites to obtain drug side effect information is included. Suggestions for improved communication strategies between the audiologist, the physician, the patient, and the pharmacist are highlighted.

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 Computational drug repositioning based on side-effects mined from social media

2016 ◽  
Vol 2 ◽  
pp. e46 ◽  
Author(s):  
Timothy Nugent ◽  
Vassilis Plachouras ◽  
Jochen L. Leidner
Keyword(s):  
Social Media ◽  
Side Effects ◽  
Graphical Models ◽  
Side Effect ◽  
Graphical Model ◽  
Drug Repositioning ◽  
Computational Method ◽  
Common Mechanism ◽  
Therapeutic Effects ◽  
Effect Data

Drug repositioning methods attempt to identify novel therapeutic indications for marketed drugs. Strategies include the use of side-effects to assign new disease indications, based on the premise that both therapeutic effects and side-effects are measurable physiological changes resulting from drug intervention. Drugs with similar side-effects might share a common mechanism of action linking side-effects with disease treatment, or may serve as a treatment by “rescuing” a disease phenotype on the basis of their side-effects; therefore it may be possible to infer new indications based on the similarity of side-effect profiles. While existing methods leverage side-effect data from clinical studies and drug labels, evidence suggests this information is often incomplete due to under-reporting. Here, we describe a novel computational method that uses side-effect data mined from social media to generate a sparse undirected graphical model using inverse covariance estimation with ℓ1-norm regularization. Results show that known indications are well recovered while current trial indications can also be identified, suggesting that sparse graphical models generated using side-effect data mined from social media may be useful for computational drug repositioning.

Cutaneous ulcers following hydroxyurea therapy

Phlebologie ◽  
2004 ◽  
Vol 33 (06) ◽  
pp. 202-205 ◽  
Author(s):  
K. Hartmann ◽  
S. Nagel ◽  
T. Erichsen ◽  
E. Rabe ◽  
K. H. Grips ◽  
...  
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Antineoplastic Agent ◽  
Limited Time ◽  
Myeloproliferative Diseases ◽  
Dermatological Side Effects ◽  
Chronic Myeloproliferative Diseases ◽  
Hydroxyurea Therapy

SummaryHydroxyurea (HU) is usually a well tolerated antineoplastic agent and is commonly used in the treatment of chronic myeloproliferative diseases. Dermatological side effects are frequently seen in patients receiving longterm HU therapy. Cutaneous ulcers have been reported occasionally.We report on four patients with cutaneous ulcers whilst on long-term hydroxyurea therapy for myeloproliferative diseases. In all patients we were able to reduce the dose, or stop HU altogether and their ulcers markedly improved. Our observations suggest that cutaneous ulcers should be considered as possible side effect of long-term HU therapy and healing of the ulcers can be achieved not only by cessation of the HU treatment, but also by reducing the dose of hydroxyurea for a limited time.

Anti-Diarrheal Drug Repositioning in Tumour Cell Cytotoxicity

2019 ◽  
Vol 19 (8) ◽  
pp. 1037-1047 ◽  
Author(s):  
Jihene Elloumi-Mseddi ◽  
Dhouha Msalbi ◽  
Raouia Fakhfakh ◽  
Sami Aifa
Keyword(s):  
Side Effects ◽  
Anticancer Drugs ◽  
Tumour Cell ◽  
Caspase 3 ◽  
Drug Repositioning ◽  
Brdu Incorporation ◽  
Drug Concentrations ◽  
Significant Difference ◽  
Ic50 Values ◽  
Mcf 7

Background:Drug repositioning is becoming an ideal strategy to select new anticancer drugs. In particular, drugs treating the side effects of chemotherapy are the best candidates.Objective:In this present work, we undertook the evaluation of anti-tumour activity of two anti-diarrheal drugs (nifuroxazide and rifaximin).Methods:Anti-proliferative effect against breast cancer cells (MDA-MB-231, MCF-7 and T47D) was assessed by MTT analysis, the Brdu incorporation, mitochondrial permeability and caspase-3 activity.Results:Both the drugs displayed cytotoxic effects on MCF-7, T47D and MDA-MB-231 cells. The lowest IC50 values were obtained on MCF-7 cells after 24, 48 and 72 hours of treatment while T47D and MDA-MB-231 were more resistant. The IC50 values on T47D and MDA-MB-231 cells became significantly low after 72 hours of treatment showing a late cytotoxicity effect especially of nifuroxazide but still less important than that of MCF-7 cells. According to the IC50 values, the non-tumour cell line HEK293 seems to be less sensitive to cytotoxicity especially against rifaximin. Both the drugs have shown an accumulation of rhodamine 123 as a function of the rise of their concentrations while the Brdu incorporation decreased. Despite the absence of a significant difference in the cell cycle between the treated and non-treated MCF-7 cells, the caspase-3 activity increased with the drug concentrations rise suggesting an apoptotic effect.Conclusion:Nifuroxazide and rifaximin are used to overcome the diarrheal side effect of anticancer drugs. However, they have shown to be anti-tumour drugs which make them potential dual effective drugs against cancer and the side effects of chemotherapy.

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