Relevant bibliographies by topics / Drug Side Effect Prediction

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Academic literature on the topic 'Drug Side Effect Prediction'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Drug Side Effect Prediction"

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Hu, Baofang, Hong Wang, and Zhenmei Yu. "Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network." Molecules 24, no. 20 (October 11, 2019): 3668. http://dx.doi.org/10.3390/molecules24203668.

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.
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Seo, Sukyung, Taekeon Lee, Mi-hyun Kim, and Youngmi Yoon. "Prediction of Side Effects Using Comprehensive Similarity Measures." BioMed Research International 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1357630.

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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.
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Kim, Jinwoo, and Miyoung Shin. "A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction." Applied Sciences 13, no. 5 (February 22, 2023): 2842. http://dx.doi.org/10.3390/app13052842.

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Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug–protein and protein–protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect.
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Arshed, Muhammad Asad, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, and Saima Abdullah. "A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure." Vol 4 Issue 1 4, no. 1 (January 22, 2022): 19–31. http://dx.doi.org/10.33411/ijist/2022040102.

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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.
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Mohd Ali, Yousoff Effendy, Kiam Heong Kwa, and Kurunathan Ratnavelu. "Predicting new drug indications from network analysis." International Journal of Modern Physics C 28, no. 09 (September 2017): 1750118. http://dx.doi.org/10.1142/s0129183117501182.

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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.
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Zhao, Xian, Lei Chen, Zi-Han Guo, and Tao Liu. "Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks." Current Bioinformatics 14, no. 8 (December 13, 2019): 709–20. http://dx.doi.org/10.2174/1574893614666190220114644.

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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.
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Duffy, Áine, Marie Verbanck, Amanda Dobbyn, Hong-Hee Won, Joshua L. Rein, Iain S. Forrest, Girish Nadkarni, Ghislain Rocheleau, and Ron Do. "Tissue-specific genetic features inform prediction of drug side effects in clinical trials." Science Advances 6, no. 37 (September 2020): eabb6242. http://dx.doi.org/10.1126/sciadv.abb6242.

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Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features—tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint—confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.
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Chen, Lei, Tao Huang, Jian Zhang, Ming-Yue Zheng, Kai-Yan Feng, Yu-Dong Cai, and Kuo-Chen Chou. "Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/485034.

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A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.
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Zhou, Mengshi, Yang Chen, and Rong Xu. "A Drug-Side Effect Context-Sensitive Network approach for drug target prediction." Bioinformatics 35, no. 12 (November 14, 2018): 2100–2107. http://dx.doi.org/10.1093/bioinformatics/bty906.

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Shaked, Itay, Matthew A. Oberhardt, Nir Atias, Roded Sharan, and Eytan Ruppin. "Metabolic Network Prediction of Drug Side Effects." Cell Systems 2, no. 3 (March 2016): 209–13. http://dx.doi.org/10.1016/j.cels.2016.03.001.

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Dissertations / Theses on the topic "Drug Side Effect Prediction"

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Wang, Chen. "High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5509.

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Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.
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Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.

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Les effets indésirables médicamenteux (EIM) ont des répercussions considérables tant sur la santé que sur l'économie. De 1,9% à 2,3% des patients hospitalisés en sont victimes, et leur coût a récemment été estimé aux alentours de 400 millions d'euros pour la seule Allemagne. De plus, les EIM sont fréquemment la cause du retrait d'un médicament du marché, conduisant à des pertes pour l'industrie pharmaceutique se chiffrant parfois en millions d'euros.De multiples études suggèrent que des facteurs génétiques jouent un rôle non négligeable dans la réponse des patients à leur traitement. Cette réponse comprend non seulement les effets thérapeutiques attendus, mais aussi les effets secondaires potentiels. C'est un phénomène complexe, et nous nous tournons vers l'apprentissage statistique pour proposer de nouveaux outils permettant de mieux le comprendre.Nous étudions différents problèmes liés à la prédiction de la réponse d'un patient à son traitement à partir de son profil génétique. Pour ce faire, nous nous plaçons dans le cadre de l'apprentissage statistique multitâche, qui consiste à combiner les données disponibles pour plusieurs problèmes liés afin de les résoudre simultanément.Nous proposons un nouveau modèle linéaire de prédiction multitâche qui s'appuie sur des descripteurs des tâches pour sélectionner les variables pertinentes et améliorer les prédictions obtenues par les algorithmes de l'état de l'art. Enfin, nous étudions comment améliorer la stabilité des variables sélectionnées, afin d'obtenir des modèles interprétables
Adverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications
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Amanzadi, Amirhossein. "Predicting safe drug combinations with Graph Neural Networks (GNN)." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446691.

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Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.
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Villafranca, Steven Wayne. "The effect of early psychostimulant treatment on abuse liability and dopamine receptors." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2824.

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Examines whether the reinforcing properties of drugs of abuse were altered in adulthood by methylphenidate, more commonly known as Ritalin. Subjects were 108 rats of Sprague-Dawley descent (Harlan). Methylphenidate, or saline was administered daily to the subjects from the postnatal period (11-20 days old). The rats preference for morphine during early adulthood was measured using conditioned place preference. The number of dopamine D₂ receptors was measured in each rat and the correlation between receptor number and morphine preference was determined. Results indicate that rats pretreated with methylphenidate showed greater preference for morphine than saline pretreated rats and suggests that exposure to methylphenidate during the postnatal period increases the rewarding value of morphine.
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Diaz, Boada Juan Sebastian. "Polypharmacy Side Effect Prediction with Graph Convolutional Neural Network based on Heterogeneous Structural and Biological Data." Thesis, KTH, Numerisk analys, NA, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288537.

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The prediction of polypharmacy side effects is crucial to reduce the mortality and morbidity of patients suffering from complex diseases. However, its experimental prediction is unfeasible due to the many possible drug combinations, leaving in silico tools as the most promising way of addressing this problem. This thesis improves the performance and robustness of a state-of-the-art graph convolutional network designed to predict polypharmacy side effects, by feeding it with complexity properties of the drug-protein network. The modifications also involve the creation of a direct pipeline to reproduce the results and test it with different datasets.
För att minska dödligheten och sjukligheten hos patienter som lider av komplexa sjukdomar är det avgörande att kunna förutsäga biverkningar från polyfarmaci. Att experimentellt förutsäga biverkningarna är dock ogenomförbart på grund av det stora antalet möjliga läkemedelskombinationer, vilket lämnar in silico-verktyg som det mest lovande sättet att lösa detta problem. Detta arbete förbättrar prestandan och robustheten av ett av det senaste grafiska faltningsnätverken som är utformat för att förutsäga biverkningar från polyfarmaci, genom att mata det med läkemedel-protein-nätverkets komplexitetsegenskaper. Ändringarna involverar också skapandet av en direkt pipeline för att återge resultaten och testa den med olika dataset.
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Zayed, Aref. "Development of ICP-MS assays for the study and prediction of the efficacy and side effects of Pt-based drugs in cancer chemotherapy." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9446.

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Pt-based drugs are important cytotoxic agents that are used in the chemotherapeutic regimes of ~50% of all cancer patients. However, the efficacy of these drugs is often limited by drug toxicity and tumour resistance. Determination of the cellular pharmacokinetics and pharmacodynamics of Pt-drugs is important for understanding their molecular mechanisms of action and toxicity, and may be used, therefore, to predict the outcome of the treatment. ICP-MS is the most sensitive technique for the determination of Pt in biological samples and can offer robust, fast and accurate quantitations for studying pharmacokinetics and pharmacodynamics of Pt-drugs in patients. This thesis describes the development of a set of ICP-MS based assays for the determination of Pt-DNA adducts and Pt sub-cellular distribution in leukocytes of cancer patients and human cancer cell lines following treatment with Pt-based chemotherapy. It is ultimately aimed to use these assays in the clinic to predict the effectiveness and toxicity of Pt-based chemotherapy in individual patients, and offer those who would respond to the treatment personalised drug doses. Alternatively, patients who would not benefit from these drugs would be offered other forms of treatment. Pt DNA adduct formation was determined in leukocytes from patients undergoing Pt-based chemotherapy demonstrating significant inter-patient variability and excellent reproducibility of the assay. The sensitivity of the technique enabled quantitation of as little as 0.2 Pt adducts per 106 nucleotides using 10 µg of patient DNA. It was shown that Pt/P ratio was robust against DNA matrix effects, and was considered more reliable approach, with Eu as internal standard, for estimating Pt adducts per nucleotide compared to using Pt data in combination with DNA concentration measured by UV. Comparison of in vivo Pt-DNA adduct formation with the patients clinical notes suggested possible correlation between the adduct formation in leukocytes and toxicity. Speciation methods employing HPLC with complementary ICP-MS and ESI-Ion Trap-MS detection were developed and used for characterisation of oxaliplatin bi-functional adducts with mono-nucleotides and di-nucleotides. Further, a fast and sensitive LC-ICP-MS assay was developed and used for the quantification of oxaliplatin GG intra-strand adducts in human cancer cell lines. The assay, which has a detection limit of 0.22 Pt adduct per 106 nucleotides based on a 10 μg DNA sample, is suitable for in vivo assessment of the adducts in patients undergoing oxaliplatin chemotherapy. Combining the ICP-MS quantitation with a cell fractionation procedure allowed, for the first time, the detailed quantitation of entire sub-cellular Pt-drug partitioning in patient leukocytes in vivo, and in human cancer cell lines in vitro, following exposure to variety of Pt-drugs. The studies showed that Pt broadly follows the total protein content of the individual sub-cellular compartments with the majority being scavenged in the cytosol compartment.
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Gauthier, Kelly J. "Length of Hospital Stay, Delirium and Discharge Status Outcomes Associated With Anticholinergic Drug Use in Elderly Hospitalized Dementia Patients." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1704.

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Winter, Lara. "Characterisation of the neurosteroid analgesic alphadolone." Monash University, Dept. of Anaesthesia, 2004. http://arrow.monash.edu.au/hdl/1959.1/9669.

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Kucher, Kellie Lynn. "Effect of preweanling methylphenidate exposure on the induction, extinction and reinstatement of morphine-Induced conditioned place preference in rats." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2892.

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This study examined the effect of preweanling methyphenidate exposure on later drug reward. We examined the induction, extinction, and reinstatement of morphine induced conditioned place preference (CPP) in rats that received methylphenidate pretreatment during the preweanling period.
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Gouws, Stephanus Andries. "The impact of hospital surveillance programmes on the incidence of adverse drug reaction reporting in a South African teaching hospital." Master's thesis, University of Cape Town, 1989. http://hdl.handle.net/11427/27186.

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Post-marketing surveillance refers to any non-experimental or observational study, method, or monitoring strategy that is applied to obtain information on drug experience (primarily adverse) after a drug has been approved for clinical use. One of the major problems in post-marketing surveillance studies is the lack or under-reporting of drug experiences by health care professionals. This study was developed to describe the impact of three different prescription event monitoring programmes on the reporting of adverse drug reactions (ADR's) in the hospital situation. The intensive ADR monitoring programme and two voluntary ADR monitoring programmes which followed were conducted in the medical wards of an urban teaching and referral hospital. All patients admitted to the designated wards were monitored by a dedicated pharmacist in the intensive programme, ward pharmacists in the first voluntary programme and by medical and nursing staff in the second voluntary programme. The pharmacist monitored a cohort of patients prospectively in two medical wards for a period of three months. The patient's record was linked with any suspected ADR. All details, i.e. patient drug orders, characteristics and ADR description, were recorded and then reported. From 228 patients monitored, 25 cases have been reported. The impact of the intensive ADR monitoring programme was a reporting rate of 11 percent. Reports were received on ADR's of a particularly mild, common and pharmacologically predictable (type A) nature. The first voluntary ADR monitoring programme comprised the reporting of suspected AD R's and the recording of drug orders for the patients and the patient characteristics. The ward pharmacists monitored for suspected AD R's in all patients during their regular ward rounds. Six cases were reported in a population of 1506 patients monitored during the three months. The reports were mainly on moderate to severe suspected AD R's of pharmacologically unpredictable (type B) nature. The rate of reports received by the surveillance unit in this study was 4 reports per ward pharmacist per annum. The second voluntary ADR monitoring programme comprised the prospective monitoring of 1555 patients by medical and nursing staff during their stay at the designated medical wards during the three month period. Patients were monitored for any ADR and when an ADR was suspected, the patient characteristics and drug orders were recorded and reported to the surveillance unit. Ten cases were reported represented by six reports from doctors and four by sisters. The reporting rate was 2 reports per doctor in four years and 3 reports for each member of the nursing team in 5 years. Reports were mainly received on moderate to severe suspected ADR's of a pharmacologically unpredictable (type B) nature.
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Books on the topic "Drug Side Effect Prediction"

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J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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Roy, Kunal. Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment. Hershey PA: Medical Information Science Reference, an imprint of IGI Global, 2015.

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Rachlis, Anita. Anti-retroviral treatment: Side effect management : report. [Ottawa]: Health and Welfare Canada, 1991.

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Drug actions and interactions. New York: McGraw-Hill Medical, 2011.

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Physicians' guide to drug eruptions. New York: Parthenon Pub. Group, 1998.

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Gabriele, Cruciani, ed. Molecular interaction fields: Applications in drug discovery and ADME prediction. Weinheim: Wiley-VCH, 2005.

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Detection of new adverse drug reactions. 3rd ed. Basingstoke: Macmillan, 1992.

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Cutaneous side effects of drugs. Philadelphia: Saunders, 1988.

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C, Talbot J. C., ed. The detection of new adverse drug reactions. 2nd ed. Basingstoke: Macmillan, 1988.

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Book chapters on the topic "Drug Side Effect Prediction"

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Singh, Davinder Paul, Abhishek Gupta, and Baijnath Kaushik. "Anti-Drug Response and Drug Side Effect Prediction Methods: A Review." In Computational Intelligence and Data Analytics, 153–67. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3391-2_11.

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Migeon, Jacques. "Prediction of Side Effects Based on Fingerprint Profiling and Data Mining." In Polypharmacology in Drug Discovery, 111–32. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118098141.ch6.

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Vijayan, Alpha, and B. S. Chandrasekar. "Drug-Drug Interactions and Side Effects Prediction Using Shallow Ensemble Deep Neural Networks." In Lecture Notes in Electrical Engineering, 377–87. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2281-7_36.

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Saad, Abdelrahman, Fahima A. Maghraby, and Yasser M. Omar. "Predicting Drug Target Interaction by Integrating Drug Fingerprint and Drug Side Effect Using Machine Learning." In Advances in Intelligent Systems and Computing, 281–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14118-9_28.

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Vijayan, Alpha, and B. S. Chandrasekar. "An Ensemble BERT CHEM DDI for Prediction of Side Effects in Drug–Drug Interactions." In International Conference on Innovative Computing and Communications, 569–81. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3679-1_47.

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Busatto, Anna, Jonathan Krauß, Evianne Kruithof, Hermenegild Arevalo, and Ilse van Herck. "Electromechanical In Silico Testing Alters Predicted Drug-Induced Risk to Develop Torsade de Pointes." In Computational Physiology, 19–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25374-4_2.

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AbstractTorsade de Pointes (TdP) is a type of ventricular tachycardia that can occur as a side effect of several medications. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a novel testing paradigm that utilizes single cell electrophysiological simulations to predict TdP risk for drugs that could potentially be used clinically. However, the effects on mechanical performance and mechano-electrical feedback are neglected. Here, we demonstrate that including electromechanical simulations in CiPA testing can provide additional insights into the predicted drug-induced TdP risk. In this work, we analyzed six drugs, namely flecainide, ibutilide, metronidazole, mexiletine, quinidine and ranolazine. We compared previously classified risks (low, intermediate, high) with our fully coupled electromechanical simulation results based upon the action potential, the electromechanical window, and the maximum active tension [1]. For ranolazine and metronidazole the predicted risk changed from low to intermediate and intermediate to high, respectively. For the latter, while electrophysiological markers indicated a low risk, the active tension decreased by 58% which can result in a loss of heart function. Therefore, adding mechanics to CiPA testing results in an altered prediction of drug-related TdP risk.
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Kanji, Rakesh, and Ganesh Bagler. "A Generalized Partial Canonical Correlation Model to Measure Contribution of Individual Drug Features Toward Side Effects Prediction." In Advances in Data Science and Management, 159–72. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0978-0_15.

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Wiseman, E. H., and Y. Noguchi. "Limitations of laboratory models in predicting gastrointestinal toleration of oxicams and other anti-inflammatory drugs." In Side-Effects of Anti-Inflammatory Drugs, 41–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-010-9775-8_3.

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Pinto, Diogo, Pedro Costa, Rui Camacho, and Vítor Santos Costa. "Predicting Drugs Adverse Side-Effects Using a Recommender-System." In Discovery Science, 201–8. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24282-8_17.

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Atias, Nir, and Roded Sharan. "An Algorithmic Framework for Predicting Side-Effects of Drugs." In Lecture Notes in Computer Science, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12683-3_1.

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Conference papers on the topic "Drug Side Effect Prediction"

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Sun, Chengcheng, Yi Zheng, Yan Jia, and Liang Gan. "Drug Side-effect Prediction based on Comprehensive Drug Similarity." In 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/ifmca-16.2017.28.

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Jahid, Md Jamiul, and Jianhua Ruan. "An ensemble approach for drug side effect prediction." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732532.

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Zheng, Yi, Shameek Ghosh, and Jinyan Li. "An Optimized Drug Similarity Framework for Side-effect Prediction." In 2017 Computing in Cardiology Conference. Computing in Cardiology, 2017. http://dx.doi.org/10.22489/cinc.2017.128-068.

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Luo, Yifu, Qijun Liu, Wenjian Wu, Fei Li, and Xiaochen Bo. "Predicting drug side effects based on link prediction in bipartite network." In 2014 7th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2014. http://dx.doi.org/10.1109/bmei.2014.7002869.

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Zhang, Wen, Yanlin Chen, Shikui Tu, Feng Liu, and Qianlong Qu. "Drug side effect prediction through linear neighborhoods and multiple data source integration." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822555.

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Hu, Pengwei, Keith C. C. Chan, Lun Hu, and Henry Leung. "Discovering second-order sub-structure associations in drug molecules for side-effect prediction." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8218013.

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Etani, Noriko. "Prediction Model of Side Effect in Drug Discovery and its Implementation for Web Application." In Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015). Global Science and Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2382-5669_ict-bdcs15.35.

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Yao, Wenjie, Weizhong Zhao, Xingpeng Jiang, Xianjun Shen, and Tingting He. "MPGNN-DSA: A Meta-path-based Graph Neural Network for drug-side effect association prediction." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995486.

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Hu, Pengwei, Zhu-Hong You, Tiantian He, Shaochun Li, Shuhang Gu, and Keith C. C. Chan. "Learning Latent Patterns in Molecular Data for Explainable Drug Side Effects Prediction." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621121.

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Rozemberczki, Benedek, Stephen Bonner, Andriy Nikolov, Michaël Ughetto, Sebastian Nilsson, and Eliseo Papa. "A Unified View of Relational Deep Learning for Drug Pair Scoring." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/777.

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In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain.
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Reports on the topic "Drug Side Effect Prediction"

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Johnson, Corey, Colton James, Sarah Traughber, and Charles Walker. Postoperative Nausea and Vomiting Implications in Neostigmine versus Sugammadex. University of Tennessee Health Science Center, July 2021. http://dx.doi.org/10.21007/con.dnp.2021.0005.

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Purpose/Background: Postoperative nausea and vomiting (PONV) is a frequent complaint in the postoperative period, which can delay discharge, result in readmission, and increase cost for patients and facilities. Inducing paralysis is common in anesthesia, as is utilizing the drugs neostigmine and sugammadex as reversal agents for non-depolarizing neuromuscular blockers. Many studies are available that compare these two drugs to determine if neostigmine increases the risk of PONV over sugammadex. Sugammadex has a more favorable pharmacologic profile and may improve patient outcomes by reducing PONV. Methods: This review included screening a total of 39 studies and peer-reviewed articles that looked at patients undergoing general anesthesia who received non-depolarizing neuromuscular blockers requiring either neostigmine or sugammadex for reversal, along with their respective PONV rates. 8 articles were included, while 31 articles were removed based on our exclusion criteria. These were published between 2014 and 2020 exclusively. The key words used were “neostigmine”, “sugammadex”, “PONV”, along with combinations “paralytic reversal agents and PONV”. This search was performed on the scholarly database MEDLINE. The data items were PONV rates in neostigmine group, PONV rates in sugammadex group, incidence of postoperative analgesic consumption in neostigmine group, and incidence of postoperative analgesic consumption in sugammadex group. Results: Despite numerical differences being noted in the incidence of PONV with sugammadex over reversal with neostigmine, there did not appear to be any statistically significant data in the multiple peer-reviewed trials included in our review, for not one of the 8 studies concluded that there was a higher incidence of PONV in one drug or the other of an y clinical relevance. Although the side-effect profile tended to be better in the sugammadex group than neostigmine in areas other than PONV, there was not sufficient evidence to conclude that one drug was superior to the other in causing a direct reduction of PONV. Implications for Nursing Practice: There were variable but slight differences noted between both drug groups in PONV rates, but it remained that none of the studies determined it was statically significant or clinically conclusive. This review did, however, note other advantages to sugammadex over neostigmine, including its pharmacologic profile of more efficiently reversing non-depolarizing neuromuscular blocking drugs and its more favorable pharmacokinetics. This lack of statistically significant evidence found within these studies consequentially does not support pharmacologic decision-making of one drug in favor of the other for reducing PONV; therefore, PONV alone is not a sufficient rationale for a provider to justify using one reversal over another at the current time until further research proves otherwise.
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Wideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche, and William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, December 2000. http://dx.doi.org/10.32747/2000.7575287.bard.

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Background PHS (pulmonary hypertension syndrome, ascites syndrome) is a serious cause of loss in the broiler industry, and is a prime example of an undesirable side effect of successful genetic development that may be deleteriously manifested by factors in the environment of growing broilers. Basically, continuous and pinpointed selection for rapid growth in broilers has led to higher oxygen demand and consequently to more frequent manifestation of an inherent potential cardiopulmonary incapability to sufficiently oxygenate the arterial blood. The multifaceted causes and modifiers of PHS make research into finding solutions to the syndrome a complex and multi threaded challenge. This research used several directions to better understand the development of PHS and to probe possible means of achieving a goal of monitoring and increasing resistance to the syndrome. Research Objectives (1) To evaluate the growth dynamics of individuals within breeding stocks and their correlation with individual susceptibility or resistance to PHS; (2) To compile data on diagnostic indices found in this work to be predictive for PHS, during exposure to experimental protocols known to trigger PHS; (3) To conduct detailed physiological evaluations of cardiopulmonary function in broilers; (4) To compile data on growth dynamics and other diagnostic indices in existing lines selected for susceptibility or resistance to PHS; (5) To integrate growth dynamics and other diagnostic data within appropriate statistical procedures to provide geneticists with predictive indices that characterize resistance or susceptibility to PHS. Revisions In the first year, the US team acquired the costly Peckode weigh platform / individual bird I.D. system that was to provide the continuous (several times each day), automated weighing of birds, for a comprehensive monitoring of growth dynamics. However, data generated were found to be inaccurate and irreproducible, so making its use implausible. Henceforth, weighing was manual, this highly labor intensive work precluding some of the original objectives of using such a strategy of growth dynamics in selection procedures involving thousands of birds. Major conclusions, solutions, achievements 1. Healthy broilers were found to have greater oscillations in growth velocity and acceleration than PHS susceptible birds. This proved the scientific validity of our original hypothesis that such differences occur. 2. Growth rate in the first week is higher in PHS-susceptible than in PHS-resistant chicks. Artificial neural network accurately distinguished differences between the two groups based on growth patterns in this period. 3. In the US, the unilateral pulmonary occlusion technique was used in collaboration with a major broiler breeding company to create a commercial broiler line that is highly resistant to PHS induced by fast growth and low ambient temperatures. 4. In Israel, lines were obtained by genetic selection on PHS mortality after cold exposure in a dam-line population comprising of 85 sire families. The wide range of PHS incidence per family (0-50%), high heritability (about 0.6), and the results in cold challenged progeny, suggested a highly effective and relatively easy means for selection for PHS resistance 5. The best minimally-invasive diagnostic indices for prediction of PHS resistance were found to be oximetry, hematocrit values, heart rate and electrocardiographic (ECG) lead II waves. Some differences in results were found between the US and Israeli teams, probably reflecting genetic differences in the broiler strains used in the two countries. For instance the US team found the S wave amplitude to predict PHS susceptibility well, whereas the Israeli team found the P wave amplitude to be a better valid predictor. 6. Comprehensive physiological studies further increased knowledge on the development of PHS cardiopulmonary characteristics of pre-ascitic birds, pulmonary arterial wedge pressures, hypotension/kidney response, pulmonary hemodynamic responses to vasoactive mediators were all examined in depth. Implications, scientific and agricultural Substantial progress has been made in understanding the genetic and environmental factors involved in PHS, and their interaction. The two teams each successfully developed different selection programs, by surgical means and by divergent selection under cold challenge. Monitoring of the progress and success of the programs was done be using the in-depth estimations that this research engendered on the reliability and value of non-invasive predictive parameters. These findings helped corroborate the validity of practical means to improve PHT resistance by research-based programs of selection.
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