QSICPM: A Novel Quantitative Semantic Interaction Calculation Prediction Method for Drug Repositioning Based on Biomedical Literature Semantic Data (Preprint)

2019 ◽  
Author(s):  
Guozheng Rao ◽  
Jinhe Gao ◽  
Zhang Li ◽  
Qing Cong ◽  
Zhiyong Feng
Keyword(s):  
Breast Cancer ◽  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Drug Repositioning ◽  
Gene Prioritization ◽  
New Drugs ◽  
Sorting Method ◽  
New Drug ◽  
Quantitative Calculation

BACKGROUND The process of developing new drugs is very tortuous. Bringing new drugs to the market requires billions of dollars in investment, which takes an average of about 13-15 years. In order to overcome these difficulties, more and more companies and pharmaceutical companies have begun to adopt the strategy of “repositioning drugs” instead of new drug development. OBJECTIVE Traditional drug repositioning methods often focus on relationships between entities, ignoring the semantic component of relationships. Therefore, we propose a new drug repositioning method to calculate the impact of pathogenic entities on disease mechanisms by quantifying semantic interactions. METHODS The QSICPM proposed in this paper divides the relevant interactions into three quantitative calculation layers based on the cause of disease. Representing interactions between the same type of pathogenic entities, interactions between different types of pathogenic entities, and pathogenic entity – drug interactions, respectively. QSICPM calculates the influence of drugs on the disease mechanism by utilizing the positive semantic relationships in each layer of the quantitative calculation process. And the gene prioritization sorting method and protein prioritization sorting method are proposed to sort the calculation results. The higher the ranking of the drugs in the results, the more likely the drug becomes an effective drug for the disease. RESULTS We used QSICPM to perform drug repositioning experiments on Parkinson's disease, breast cancer, and Alzheimer's disease. The experimental results predicted 881 potential drugs or pharmacological substances for PD disease, 830 potential drugs or pharmacological substances for BC disease, and 1180 potential drugs or pharmacological substances for AD disease. What’s more, the result set was sorted according to gene prioritization sorting and protein prioritization sorting. In the top 25 parts of the ranking, the average precision of the three results reached 68%, 75%, and 64%, respectively. The accuracy and applicability of QSICPM were verified. CONCLUSIONS This paper proposes a new research method for drug repositioning based on semantic relationship quantitative calculation. The performance of the QSICPM method was verified by drug repositioning experiments for Parkinson's disease, Breast cancer, and Alzheimer's disease. The results prove that QSICPM is a drug repositioning method with strong prediction precision and wide applicability.

scholarly journals RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Shahriar Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

scholarly journals RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

2019 ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Sh. Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Benchmark Datasets ◽  
Approved Drugs

AbstractBackgroundIt often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed.MethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.ResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.ConclusionResults show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Natural Anti-inflammatory compounds as Drug candidates in Alzheimer’s disease

2020 ◽  
Vol 27 ◽  
Author(s):  
Reyaz Hassan Mir ◽  
Abdul Jalil Shah ◽  
Roohi Mohi-ud-din ◽  
Faheem Hyder Potoo ◽  
Mohd. Akbar Dar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Natural Products ◽  
Protective Action ◽  
New Drugs ◽  
Huperzine A ◽  
Brain Disorder ◽  
Anti Inflammatory

: Alzheimer's disease (AD) is a chronic neurodegenerative brain disorder characterized by memory impairment, dementia, oxidative stress in elderly people. Currently, only a few drugs are available in the market with various adverse effects. So to develop new drugs with protective action against the disease, research is turning to the identification of plant products as a remedy. Natural compounds with anti-inflammatory activity could be good candidates for developing effective therapeutic strategies. Phytochemicals including Curcumin, Resveratrol, Quercetin, Huperzine-A, Rosmarinic acid, genistein, obovatol, and Oxyresvertarol were reported molecules for the treatment of AD. Several alkaloids such as galantamine, oridonin, glaucocalyxin B, tetrandrine, berberine, anatabine have been shown anti-inflammatory effects in AD models in vitro as well as in-vivo. In conclusion, natural products from plants represent interesting candidates for the treatment of AD. This review highlights the potential of specific compounds from natural products along with their synthetic derivatives to counteract AD in the CNS.

[18F]-florbetaben PET/CT Imaging in the Alzheimer’s Disease Mouse Model APPswe/PS1dE9

2018 ◽  
Vol 16 (1) ◽  
pp. 49-55 ◽  
Author(s):  
J. Stenzel ◽  
C. Rühlmann ◽  
T. Lindner ◽  
S. Polei ◽  
S. Teipel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
New Drugs ◽  
Imaging Data ◽  
Wild Type ◽  
Preclinical Research ◽  
Standardized Uptake Values ◽  
Pet Ct

Background: Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer’s disease (AD). In preclinical research, [<sup>18</sup>F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [<sup>18</sup>F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques. Methods: Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [<sup>18</sup>F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [<sup>18</sup>F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed. Results: Visual inspection and SUVs revealed an increased cerebral uptake of [<sup>18</sup>F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis. Conclusion: The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [<sup>18</sup>F]-florbetaben in the APPswe/PS1dE9 mouse model.

Old Drugs with New Tricks; Paradigm in Drug Development Pipeline for Alzheimer’s Disease

2020 ◽  
Vol 20 ◽  
Author(s):  
Tanay Dalvi ◽  
Bhaskar Dewangan ◽  
Rudradip Das ◽  
Jyoti Rani ◽  
Suchita Dattatray Shinde ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Development ◽  
Molecular Targets ◽  
Drug Repurposing ◽  
Phase Iii ◽  
Complex Nature ◽  
New Drug ◽  
Development Pipeline ◽  
Old Drugs

: The most common reason behind dementia is Alzheimer’s disease (AD) and it is predicted to be the third lifethreatening disease apart from stroke and cancer for the geriatric population. Till now only four drugs are available in the market for symptomatic relief. The complex nature of disease pathophysiology and lack of concrete evidences of molecular targets are the major hurdles for developing new drug to treat AD. The the rate of attrition of many advanced drugs at clinical stages, makes the de novo discovery process very expensive. Alternatively, Drug Repurposing (DR) is an attractive tool to develop drugs for AD in a less tedious and economic way. Therefore, continuous efforts are being made to develop a new drug for AD by repursing old drugs through screening and data mining. For example, the survey in the drug pipeline for Phase III clinical trials (till February 2019) which has 27 candidates, and around half of the number are drugs which have already been approved for other indications. Although in the past the drug repurposing process for AD has been reviewed in the context of disease areas, molecular targets, there is no systematic review of repurposed drugs for AD from the recent drug development pipeline (2019-2020). In this manuscript, we are reviewing the clinical candidates for AD with emphasis on their development history including molecular targets and the relevance of the target for AD.

Neurobehavioral Consequences Associated with Long Term Tramadol Utilization and Pathological Mechanisms

2020 ◽  
Vol 18 (10) ◽  
pp. 758-768 ◽  
Author(s):  
Khadga Raj ◽  
Pooja Chawla ◽  
Shamsher Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Serotonin Reuptake ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Serotonin Syndrome ◽  
Dopamine Synthesis ◽  
Synthetic Analog

: Tramadol is a synthetic analog of codeine used to treat pain of moderate to severe intensity and is reported to have neurotoxic potential. At therapeutic dose, tramadol does not cause major side effects in comparison to other opioid analgesics, and is useful for the management of neurological problems like anxiety and depression. Long term utilization of tramadol is associated with various neurological disorders like seizures, serotonin syndrome, Alzheimer’s disease and Parkinson’s disease. Tramadol produces seizures through inhibition of nitric oxide, serotonin reuptake and inhibitory effects on GABA receptors. Extensive tramadol intake alters redox balance through elevating lipid peroxidation and free radical leading to neurotoxicity and produces neurobehavioral deficits. During Alzheimer’s disease progression, low level of intracellular signalling molecules like cGMP, cAMP, PKC and PKA affect both learning and memory. Pharmacologically tramadol produces actions similar to Selective Serotonin Reuptake Inhibitors (SSRIs), increasing the concentration of serotonin, which causes serotonin syndrome. In addition, tramadol also inhibits GABAA receptors in the CNS has been evidenced to interfere with dopamine synthesis and release, responsible for motor symptoms. The reduced level of dopamine may produce bradykinesia and tremors which are chief motor abnormalities in Parkinson’s Disease (PD).

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