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Journal articles on the topic 'Drug disease'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Jyotsna, M., and Y. Hemalatha. "Drug–Drug, Drug–Disease and Disease–Disease Interactions in COVID-19 with Cardiovascular Diseases (CVDs)." Indian Journal of Cardiovascular Disease in Women WINCARS 5, no. 03 (2020): 216–22. http://dx.doi.org/10.1055/s-0040-1716786.

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AbstractCoronaviruses are a large family of single positive-stranded, enveloped RNA viruses that can infect many animal species and humans. Human coronaviruses can be divided based on their pathogenicity. Globally so far, over nine million people have tested COVID-19 positive, of which, 4, 25,000 are in India. The FDA for the prevention or treatment of COVID-19 has approved no drugs or biologics. Numerous other antiviral agents, immunotherapies, and vaccines continue to be investigated and developed as potential therapies. Searching for effective therapies for COVID-19 infection is a complex p

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Yalkızımı, Selcan, and Ümit Şentürk. "Study on Drug Repurposing for ALS Treatment Using Pre-trained Knowledge Graph Embeddings: Methods and Findings." Düzce Üniversitesi Bilim ve Teknoloji Dergisi 13, no. 1 (2025): 317–32. https://doi.org/10.29130/dubited.1507832.

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In this study, research has been conducted using pre-trained knowledge graph embedding for drug repurposing in treating ALS (Amyotrophic Lateral Sclerosis), and its results have been presented. Drug repurposing studies for ALS have been carried out through two main methods: disease-drug relationship and genes-drugs relationship. Drug repurposing recommendations for ALS have been provided by predicting connections between disease and drug entities on the DRKG (Drug Repurposing Knowledge Graph). The findings obtained from the study have been evaluated by comparing them with the list of clinical

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Zhou, Xu, Enyu Dai, Qian Song, et al. "In silico drug repositioning based on drug-miRNA associations." Briefings in Bioinformatics 21, no. 2 (2019): 498–510. http://dx.doi.org/10.1093/bib/bbz012.

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Abstract Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug–drug interaction network was constructed based on curated drug-miRNA associations. Thro

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Xuan, Ping, Yangkun Cao, Tiangang Zhang, Xiao Wang, Shuxiang Pan, and Tonghui Shen. "Drug repositioning through integration of prior knowledge and projections of drugs and diseases." Bioinformatics 35, no. 20 (2019): 4108–19. http://dx.doi.org/10.1093/bioinformatics/btz182.

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Abstract Motivation Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. Results We present a method based on no

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Kim, Yoonbee, and Young-Rae Cho. "Predicting Drug–Gene–Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning." Biomedicines 11, no. 7 (2023): 1998. http://dx.doi.org/10.3390/biomedicines11071998.

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Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug–gene–disease tensor by integrating drug–disease, drug–gene, and disease–gene associations and predict drug–gene–disease tripl

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Jefferson, James W., John H. Greist, Judith Carroll, and Margaret Baudhuin. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 81, no. 5 (1986): 948. http://dx.doi.org/10.1016/0002-9343(86)90382-7.

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Craig Brater, D. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 80, no. 1 (1986): 62–77. http://dx.doi.org/10.1016/0002-9343(86)90933-2.

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Xuan, Ping, Yingying Song, Tiangang Zhang, and Lan Jia. "Prediction of Potential Drug–Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features." International Journal of Molecular Sciences 20, no. 17 (2019): 4102. http://dx.doi.org/10.3390/ijms20174102.

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Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations,

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Kim, Jiwon W., and Paula V. Phongsamran. "Drug-Induced Liver Disease and Drug Use Considerations in Liver Disease." Journal of Pharmacy Practice 22, no. 3 (2009): 278–89. http://dx.doi.org/10.1177/0897190008328696.

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Chronic liver disease encompasses a large number of hepatic disorders. One of the most important etiologies of liver disease is drug-induced liver disease, which is the leading cause of liver failure in patients referred for liver transplantation in the United States. Drug-induced liver disease can present in all forms of acute and chronic liver disease with highly variable clinical presentations. There is no effective treatment for most drug-induced liver disease and the recognition and prevention of drug-induced liver disease remain the most important management strategy. Drug dosing in pati

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Le, Duc-Hau, and Duc-Hau Le. "ID:2047 Drug respositioning by integrating known disease-gene and drug-target associations." Biomedical Research and Therapy 4, S (2017): 76. http://dx.doi.org/10.15419/bmrat.v4is.281.

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Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs, diseases and different approaches. Depending on where the discovery of drug-disease relationships comes from, proposed computational methods can be categorized as either ‘drug-based’ or ‘disease-based’. The proposed methods are usually based on an assumption that similar drugs can be used for similar diseases to identify new indications

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Qi, Xiguang, Mingzhe Shen, Peihao Fan, et al. "The Performance of Gene Expression Signature-Guided Drug–Disease Association in Different Categories of Drugs and Diseases." Molecules 25, no. 12 (2020): 2776. http://dx.doi.org/10.3390/molecules25122776.

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A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this s

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Sadeghi, Shaghayegh, Jianguo Lu, and Alioune Ngom. "A network-based drug repurposing method via non-negative matrix factorization." Bioinformatics 38, no. 5 (2021): 1369–77. http://dx.doi.org/10.1093/bioinformatics/btab826.

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Abstract Motivation Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework

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Diniz Gomes, Rita, Elisa Silva, Cátia Fernandes Santos, and Filipa Senos Moutinho. "DRUG-RESISTANT INFESTATION DELUSION IN PARKINSON'S DISEASE." PSYCHIATRIA DANUBINA 34, no. 1 (2022): 94–95. http://dx.doi.org/10.24869/psyd.2022.94.

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Arakelyan, Arsen, Lilit Nersisyan, Maria Nikoghosyan, et al. "Transcriptome-Guided Drug Repositioning." Pharmaceutics 11, no. 12 (2019): 677. http://dx.doi.org/10.3390/pharmaceutics11120677.

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Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of func

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Chen, Lei, Kaiyu Chen, and Bo Zhou. "Inferring drug-disease associations by a deep analysis on drug and disease networks." Mathematical Biosciences and Engineering 20, no. 8 (2023): 14136–57. http://dx.doi.org/10.3934/mbe.2023632.

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<abstract> <p>Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can

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Xuan, Ye, Zhang, Zhao, and Sun. "Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations." Cells 8, no. 7 (2019): 705. http://dx.doi.org/10.3390/cells8070705.

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Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related di

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Dai, Wen, Xi Liu, Yibo Gao, et al. "Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/275045.

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There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space,

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Ovics, Paz, Danielle Regev, Polina Baskin, et al. "Drug Development and the Use of Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Disease Modeling and Drug Toxicity Screening." International Journal of Molecular Sciences 21, no. 19 (2020): 7320. http://dx.doi.org/10.3390/ijms21197320.

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Over the years, numerous groups have employed human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a superb human-compatible model for investigating the function and dysfunction of cardiomyocytes, drug screening and toxicity, disease modeling and for the development of novel drugs for heart diseases. In this review, we discuss the broad use of iPSC-CMs for drug development and disease modeling, in two related themes. In the first theme—drug development, adverse drug reactions, mechanisms of cardiotoxicity and the need for efficient drug screening protocols—we discuss the cr

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Xuan, Ping, Lianfeng Zhao, Tiangang Zhang, Yilin Ye, and Yan Zhang. "Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit." Molecules 24, no. 15 (2019): 2712. http://dx.doi.org/10.3390/molecules24152712.

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Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep

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Zhang, Wenjuan, Hunan Xu, Xiaozhong Li, Qiang Gao, and Lin Wang. "DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion." Bioinformatics 36, no. 9 (2020): 2839–47. http://dx.doi.org/10.1093/bioinformatics/btaa062.

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Abstract Motivation One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using

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Chen, Hailin, and Zuping Zhang. "A miRNA-Driven Inference Model to Construct Potential Drug-Disease Associations for Drug Repositioning." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/406463.

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Increasing evidence discovered that the inappropriate expression of microRNAs (miRNAs) will lead to many kinds of complex diseases and drugs can regulate the expression level of miRNAs. Therefore human diseases may be treated by targeting some specific miRNAs with drugs, which provides a new perspective for drug repositioning. However, few studies have attempted to computationally predict associations between drugs and diseases via miRNAs for drug repositioning. In this paper, we developed an inference model to achieve this aim by combining experimentally supported drug-miRNA associations and

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Huang, Weihong, Zhong Li, Yanlei Kang, Xinghuo Ye, and Wenming Feng. "Drug Repositioning Based on the Enhanced Message Passing and Hypergraph Convolutional Networks." Biomolecules 12, no. 11 (2022): 1666. http://dx.doi.org/10.3390/biom12111666.

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Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the

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Rodriguez-Esteban, Raul. "A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease." PLOS Computational Biology 12, no. 4 (2016): e1004852. http://dx.doi.org/10.1371/journal.pcbi.1004852.

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Jadamba, Erkhembayar, and Miyoung Shin. "A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network." BioMed Research International 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/7147039.

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Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge an

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Camus, Ph, P. Foucher, Ph Bonniaud, and K. Ask. "Drug-induced infiltrative lung disease." European Respiratory Journal 18, no. 32 suppl (2001): 93S—100S. http://dx.doi.org/10.1183/09031936.01.18s320093.

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An increasing number of drugs are recognized to induce distinctive patterns of infiltrative lung disease (ILD), ranging from benign infiltrates to life-threatening adult respiratory distress syndromes. In addition to drugs, biomolecules such as proteins and cytokines, and medicinal plants are also capable of inducing respiratory disease, some being severe and/or irreversible.For several reasons it is difficult to estimate the exact frequency of drug-induced infiltrative lung disease (DI‐ILD). The risk for DI‐ILD and the clinical patterns vary depending on a variety of host and drug factors.Alt

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Xuan, Ping, Zixuan Lu, Tiangang Zhang, Yong Liu, and Toshiya Nakaguchi. "Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases." International Journal of Molecular Sciences 23, no. 7 (2022): 3870. http://dx.doi.org/10.3390/ijms23073870.

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Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multip

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Saberian, Nafiseh, Azam Peyvandipour, Michele Donato, Sahar Ansari, and Sorin Draghici. "A new computational drug repurposing method using established disease–drug pair knowledge." Bioinformatics 35, no. 19 (2019): 3672–78. http://dx.doi.org/10.1093/bioinformatics/btz156.

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Abstract Motivation Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a hum

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Wang, Huiqing, Sen Zhao, Jing Zhao, and Zhipeng Feng. "A model for predicting drug-disease associations based on dense convolutional attention network." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7419–39. http://dx.doi.org/10.3934/mbe.2021367.

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<abstract> <p>The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association prediction method ignored the prior knowledge contained in the drug-disease association data, which provided a strong basis for the research. Moreover, the previous methods only paid attention to the high-level features in the network when extracting features, and directly fused or

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Hendyatama, Tenta Hartian, and Nunuk Mardiana. "Calculation of Drug Dosage In Chronic Kidney Disease." Current Internal Medicine Research and Practice Surabaya Journal 1, no. 1 (2020): 21. http://dx.doi.org/10.20473/cimrj.v1i1.16894.

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Kidneys are the main organ in fluid and electrolyte homeostasis. It also have an important role in eliminating various types of drugs. Drug elimination in the kidney is affected by plasma drug concentrations, plasma protein binding, and kidney function. Glomerular filtration rate (GFR) represents the kidney function. Thus by knowing it, drug dosage can be determined.Chronic kidney disease alter the effect of drug, some decrease drug effect but more often increase drug toxicity. Chronic kidney disease affect the pharmacodynamic and pharmacokinetic of drug. Therefore, providing an optimal treatm

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Yan, Ran, Jiahao He, Ge Liu, et al. "Drug Repositioning for Hand, Foot, and Mouth Disease." Viruses 15, no. 1 (2022): 75. http://dx.doi.org/10.3390/v15010075.

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Hand, foot, and mouth disease (HFMD) is a highly contagious disease in children caused by a group of enteroviruses. HFMD currently presents a major threat to infants and young children because of a lack of antiviral drugs in clinical practice. Drug repositioning is an attractive drug discovery strategy aimed at identifying and developing new drugs for diseases. Notably, repositioning of well-characterized therapeutics, including either approved or investigational drugs, is becoming a potential strategy to identify new treatments for virus infections. Various types of drugs, including antibacte

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Islam, Md Mohaiminul, Yang Wang, and Pingzhao Hu. "A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases." Life 11, no. 11 (2021): 1115. http://dx.doi.org/10.3390/life11111115.

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The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefor

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Jain, Bhushan, Utkarsh Raj, and Pritish Kumar Varadwaj. "Drug Target Interplay: A Network-based Analysis of Human Diseases and the Drug Targets." Current Topics in Medicinal Chemistry 18, no. 13 (2018): 1053–61. http://dx.doi.org/10.2174/1568026618666180719160922.

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Screening and identifying a disease-specific novel drug target is the first step towards a rational drug designing approach. Due to the advent of high throughput data generation techniques, the protein search space has now exceeded 24,500 human protein coding genes, which encodes approximately 1804proteins. This work aims at mining out the relationship between target proteins, drugs, and diseases genes through a network-based systems biology approach. A network of all FDA approved drugs, along with their targets were utilized to construct the proposed Drug Target (DT) network. Further, the exp

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Vijaya, S. "Personalized Drug-Disease prediction using Multiple Linear Regression with ReLU." Journal of Physics: Conference Series 2115, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1742-6596/2115/1/012035.

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Abstract Predicting models for personalized Drugs related to specific disease are essential, as traditional methods are expensive and time consuming. The most challenging task in personalized medicine is predicting the status of disease from high dimensionality data. In the biomedical domain the association between drugs and disease plays a vital role as the same drug may treat similar diseases. For the good adaptability to complex and nonlinear behaviour data, Multiple Linear Regression method with ReLU Activation function is used for calculation and to fit the model with Drug –Disease datase

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Быков, Yuriy Bykov, Бендер, and Tatyana Bender. "PHARMACOLOGICAL METHODS OF THERAPY IN PARKINSON’S DISEASE (LITERATURE REVIEW)." Бюллетень Восточно-Сибирского научного центра Сибирского отделения Российской академии медицинских наук 1, no. 3 (2016): 65–71. http://dx.doi.org/10.12737/21613.

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The review concerns modern pharmacological methods of Parkinson’s disease treatment. The basic drugs used in practice are levodopa, dopamine receptor agonists, MAO-B inhibitors, anticholinergics, СOMT inhibitors, and amantadine drugs. The article considers main features, assessment methods and targets of the differentiated therapy, presents benefits and drawbacks of each drug, and describes the problem of generics. According to the study, there is a preferences to use long-acting drugs. The choice of a drug for Parkinson’s disease treatment depends on such characteristics as the patient’s age,

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Wang, Weiwen, Xiwen Zhang, and Dao-Qing Dai. "springD2A: capturing uncertainty in disease–drug association prediction with model integration." Bioinformatics 38, no. 5 (2021): 1353–60. http://dx.doi.org/10.1093/bioinformatics/btab820.

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Abstract Motivation Drug repositioning that aims to find new indications for existing drugs has been an efficient strategy for drug discovery. In the scenario where we only have confirmed disease–drug associations as positive pairs, a negative set of disease–drug pairs is usually constructed from the unknown disease–drug pairs in previous studies, where we do not know whether drugs and diseases can be associated, to train a model for disease–drug association prediction (drug repositioning). Drugs and diseases in these negative pairs can potentially be associated, but most studies have ignored

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Liu, Xiaoqing, Wenjing Yi, Baohang Xi, and Qi Dai. "Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning." Computational and Mathematical Methods in Medicine 2022 (October 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/7035634.

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Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used

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Savva, Kyriaki, Margarita Zachariou, Marilena M. Bourdakou, Nikolas Dietis, and George M. Spyrou. "DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing." International Journal of Molecular Sciences 25, no. 10 (2024): 5319. http://dx.doi.org/10.3390/ijms25105319.

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In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, nam

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Rai, Parul, and Kenneth I. Ataga. "Using disease-modifying therapies in sickle cell disease." Hematology 2023, no. 1 (2023): 519–31. http://dx.doi.org/10.1182/hematology.2023000485.

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Abstract As curative therapy using allogeneic hematopoietic stem cell transplantation as well as gene therapy and gene editing remains inaccessible to most patients with sickle cell disease, the availability of drug therapies that are safe, efficacious, and affordable is highly desirable. Increasing progress is being made in developing drug therapies based on our understanding of disease pathophysiology. Four drugs, hydroxyurea, L-glutamine, crizanlizumab, and voxelotor, are currently approved by the US Food and Drug Administration, with multiple others at various stages of testing. With the l

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Yang, Mengyun, Huimin Luo, Yaohang Li, and Jianxin Wang. "Drug repositioning based on bounded nuclear norm regularization." Bioinformatics 35, no. 14 (2019): i455—i463. http://dx.doi.org/10.1093/bioinformatics/btz331.

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Abstract Motivation Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent

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López-Rodríguez, Irene, Cesár F. Reyes-Manzano, Ariel Guzmán-Vargas, and Lev Guzmán-Vargas. "The Complex Structure of the Pharmacological Drug–Disease Network." Entropy 23, no. 9 (2021): 1139. http://dx.doi.org/10.3390/e23091139.

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The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with diffe

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Gupta, Urvashi, Ashwin Kamath, and Priyanka Kamath. "A descriptive study of new drug approvals during 2017–2021 and disease morbidity and mortality patterns in India." Perspectives in Clinical Research 15, no. 2 (2023): 66–72. http://dx.doi.org/10.4103/picr.picr_109_23.

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Abstract Aim: Studies show the presence of a mismatch between drug research and disease burden. A study conducted in the European Union found that new drug development was restricted to certain diseases. A study of biosimilar approvals in India found that 87% of drugs were for treating noncommunicable diseases. This study aimed to determine the new drugs approved in India from 2017 to 2021 and the top ten causes of morbidity and mortality and detect the presence of any discordance between these. Methods: A descriptive study was conducted using data on new drug approvals accessed from the Centr

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Georgopapadakou, N. "Infectious disease 2001: drug resistance, new drugs." Drug Resistance Updates 5, no. 5 (2002): 181–91. http://dx.doi.org/10.1016/s1368-7646(02)00088-2.

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Waddington, James C., Xiaoli Meng, Dean J. Naisbitt, and B. Kevin Park. "Immune drug-induced liver disease and drugs." Current Opinion in Toxicology 10 (August 2018): 46–53. http://dx.doi.org/10.1016/j.cotox.2017.12.006.

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Perangin-angin, Resianta, Ika Yusnita Sari, Elvika Rahmi, and Roni Jhonson Simamora. "SIMULASI MONTE CARLO DALAM MEMPREDIKSI PEMAKAIAN OBAT PENYAKIT GIGI DAN MULUT PADA RUMAH SAKIT." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 6, no. 6 (2022): 239–43. http://dx.doi.org/10.46880/jmika.vol6no2.pp239-243.

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The use of drugs in patients with dental disease is a necessity that needs to be considered by the hospital in providing medical services to patients. Adequate and well-managed drug supply prevents shortages or excess drug stocks. So it needs good planning in managing and monitoring drug stocks appropriately. This study aims to make predictions in the use of dental disease drugs by using a monte carlo simulation. The data used is data on the use of drugs for dental diseases from 2020 to 2022. The data on drug use processed were 12 types of drugs. The data will be processed based on the Monte C

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Vasic, Nada, Branislava Milenkovic, Dragica Pesut, Ruza Stevic, and Dragana Jovanovic. "Drug induced lung disease - amiodarone in focus." Medical review 67, no. 9-10 (2014): 334–37. http://dx.doi.org/10.2298/mpns1410334v.

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More than 380 medications are known to cause pulmonary toxicity. Selected drugs that are important causes of pulmonary toxicity fall into the following classes: cytotoxic, cardiovascular, anti-inflammatory, antimicrobial, illicit drugs, miscellaneous. The adverse reactions can involve the pulmonary parenchyma, pleura, the airways, pulmonary vascular system, and mediastinum. Drug-induced lung diseases have no pathognomonic clinical, laboratory, physical, radiographic or histological findings. A drug-induced lung disease is usually considered a diagnosis of exclusion of other diseases. The diagn

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Dongre, Kanchan, Anja Jungo, Selina Späni, Yvonne Zysset, and Anne Leuppi-Taegtmeyer. "Disease-Drug Interactions Requiring Special Attention." Praxis 111, no. 12 (2022): 700–705. http://dx.doi.org/10.1024/1661-8157/a003923.

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Abstract. This short review addresses disease-drug interactions requiring special attention, namely interactions between common conditions and over-the-counter medication and interactions between rare conditions and drugs that are absolutely contraindicated. We specifically examine over-the-counter analgesics, antiemetics and drugs used to treat allergy symptoms and underlying disease conditions they can exacerbate. Resources for avoiding disease-drug interactions in patients with rare conditions, such as myasthenia gravis, glucose-6-phosphate deficiency, mitochondriopathies and long QT-syndro

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Muhammad Arfat Yameen, Mubashra Tafseer, Warda Khan, Sanaa Anjum, Raza-E-Mustafa, and Ossam Chohan. "Trends in Prescribing Patterns and Drug Related Problems of Kidney Disease Patients." Journal of the Pakistan Medical Association 71, no. 11 (2021): 2629–36. http://dx.doi.org/10.47391/jpma.01816.

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Objective: The aim of the study was the evaluation of drug-related problems, including drug-drug interactions, dose error, use of nephrotoxic drugs and polypharmacy with special emphasis on kidney disease patients. Methods: Descriptive cross-sectional study from January to April 2019 was carried out in nephrology ward of Ayub teaching hospital, Abbottabad, Pakistan to review patient’s medication orders for evaluation of drug-related problems. Doses of medicine and drug-drug interactions were evaluated by comparing it with standard protocols given in BNF and Lexicomp software. Prescriptions wer

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Jamali, Ali Akbar, Yuting Tan, Anthony Kusalik, and Fang-Xiang Wu. "NTD-DR: Nonnegative tensor decomposition for drug repositioning." PLOS ONE 17, no. 7 (2022): e0270852. http://dx.doi.org/10.1371/journal.pone.0270852.

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Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and targ

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Joy, Melanie S., Mary La, and Bo Xiao. "Individualizing Therapy in Patients With Chronic Kidney Disease." Journal of Pharmacy Practice 21, no. 3 (2008): 225–36. http://dx.doi.org/10.1177/0897190008315907.

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Patients with chronic kidney diseases have multiple clinical abnormalities that may affect disposition of drugs, including alterations in glomerular filtration rate, excretion of plasma proteins, reductions in serum albumin, and reductions in drug metabolizing enzyme activity. Inflammation may also influence the previous factors. Concomitant drug therapies can lead to drug— drug interactions that may affect the pharmacokinetics of administered drugs. Pharmacogenomics has begun to be evaluated for effects of genotype and haplotype of drug metabolizing enzymes and transporters on drug dispositio

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Martínez-Morales, Patricia L., and Isabel Liste. "Stem Cells asIn VitroModel of Parkinson's Disease." Stem Cells International 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/980941.

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Progress in understanding neurodegenerative cell biology in Parkinson's disease (PD) has been hampered by a lack of predictive and relevant cellular models. In addition, the lack of an adequatein vitrohuman neuron cell-based model has been an obstacle for the uncover of new drugs for treating PD. The ability to generate induced pluripotent stem cells (iPSCs) from PD patients and a refined capacity to differentiate these iPSCs into DA neurons, the relevant disease cell type, promises a new paradigm in drug development that positions human disease pathophysiology at the core of preclinical drug

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