scholarly journals To explore the pharmacological mechanism of action using digital twin

2022 ◽  
Vol 9 (2) ◽  
pp. 55-62
Author(s):  
Rahman et al. ◽  

With the advent of medical technology and science, the number of animals used in research has increased. For decades, the use of animals in research and product testing has been a point of conflict. Experts and pharmaceutical manufacturers are harming animals worldwide during laboratory research. Animals have also played a significant role in the advancement of science; animal testing has enabled the discovery of various novel drugs. The misery, suffering, and deaths of animals are not worth the potential human benefits. As a result, animals must not be exploited in research to assess the drug mechanism of action (MOA). Apart from the ethical concern, animal testing has a few more downsides, including the requirement for skilled labor, lengthy processes, and cost. Because it is critical to investigate adverse effects and toxicities in the development of potentially viable drugs. Assessment of each target will consume the range of resources as well as disturb living nature. As the digital twin works in an autonomous virtual world without influencing the physical structure and biological system. Our proposed framework suggests that the digital twin is a great reliable model of the physical system that will be beneficial in assessing the possible MOA prior to time without harming animals. The study describes the creation of a digital twin to combine the information and knowledge obtained by studying the different drug targets and diseases. Mechanism of Action using Digital twin (MOA-DT) will enable the experts to use an innovative approach without physical testing to save animals, time, and resources. DT reflects and simulates the actual drug and its relationships with its target, however presenting a more accurate depiction of the drug, which leads to maximize efficacy and decrease the toxicity of a drug. In conclusion, it has been shown that drug discovery and development can be safe, effective, and economical in no time through the combination of the digital and physical models of a pharmaceutical as compared to experimental animals.

2014 ◽  
Vol 12 (02) ◽  
pp. 1441007 ◽  
Author(s):  
Nermin Pinar Karabulut ◽  
Murodzhon Akhmedov ◽  
Murat Cokol

Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action.


Author(s):  
David Edward Jones ◽  
Chris Snider ◽  
Lee Kent ◽  
Ben Hicks

ABSTRACTWhile extensive modelling - both physical and virtual - is imperative to develop right-first-time products, the parallel use of virtual and physical models gives rise to two interrelated issues: the lack of revision control for physical prototypes; and the need for designers to manually inspect, measure, and interpret modifications to either virtual or physical models, for subsequent update of the other. The Digital Twin paradigm addresses similar problems later in the product life-cycle, and while these digital twins, or the “twinning” process, have shown significant value, there is little work to date on their implementation in the earlier design stages. With large prospective benefits in increased product understanding, performance, and reduced design cycle time and cost, this paper explores the concept of using the Digital Twin in early design, including an introduction to digital twinning, examination of opportunities for and challenges of their implementation, a presentation of the structure of Early Stage Twins, and evaluation via two implementation cases.


2018 ◽  
Vol 17 (6) ◽  
pp. 1144-1155 ◽  
Author(s):  
Amir Ata Saei ◽  
Pierre Sabatier ◽  
Ülkü Güler Tokat ◽  
Alexey Chernobrovkin ◽  
Mohammad Pirmoradian ◽  
...  

Chemotherapeutics cause the detachment and death of adherent cancer cells. When studying the proteome changes to determine the protein target and mechanism of action of anticancer drugs, the still-attached cells are normally used, whereas the detached cells are usually ignored. To test the hypothesis that proteomes of detached cells contain valuable information, we separately analyzed the proteomes of detached and attached HCT-116, A375, and RKO cells treated for 48 h with 5-fluorouracil, methotrexate and paclitaxel. Individually, the proteomic data on attached and detached cells had comparable performance in target and drug mechanism deconvolution, whereas the combined data significantly improved the target ranking for paclitaxel. Comparative analysis of attached versus detached proteomes provided further insight into cell life and death decision making. Six proteins consistently up- or downregulated in the detached versus attached cells regardless of the drug and cell type were discovered; their role in cell death/survival was tested by silencing them with siRNA. Knocking down USP11, CTTN, ACAA2, and EIF4H had anti-proliferative effects, affecting UHRF1 additionally sensitized the cells to the anticancer drugs, while knocking down RNF-40 increased cell survival against the treatments. Therefore, adding detached cells to the expression proteomics analysis of drug-treated cells can significantly increase the analytical value of the approach. The data have been deposited to the ProteomeXchange with identifier PXD007686.


2019 ◽  
Vol 36 (5) ◽  
pp. 1607-1613 ◽  
Author(s):  
Joseph C Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter

Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.


Vibration ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 235-265
Author(s):  
Paul Gardner ◽  
Mattia Dal Borgo ◽  
Valentina Ruffini ◽  
Aidan J. Hughes ◽  
Yichen Zhu ◽  
...  

A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase.


2021 ◽  
Author(s):  
qiu tiantian ◽  
Li DongHua ◽  
Liu Yu ◽  
Gao LiFang ◽  
Wei Chao ◽  
...  

Abstract Backgroud: Uterine fibroids (ULs) are the most common benign tumors of the reproductive tract in gynecology and their clinical presentations include menorrhagia, pelvic pressure, dysmenorrhea, and anemia. Surgical resection and the hormonal drug administration are the primary treatment. The plant Astragalus membranaceus (astragalus) has a long history of use in traditional Chinese medicine and studies have shown that it has antitumor effects. However, the role and mechanism of astragalus in ULs are not completely clear. The present study aimed to investigate the astragalus mechanism of action against ULs based on network pharmacology approach, in order to provid insights for the development of a safe and effective drug for the ULs treatment.Methods: The astragalus active ingredients and the potential drug targets were screened by the Traditional Chinese Medicine System Pharmacology Database and Analytical Platform (TCMSP). The gene expression profiles of ULs were obtained from Gene Expression Omnibus (GEO). The intersection of astragalus components target genes and differentially expressed genes between UL and normal patients were obtained using Perl software to provide the astragalus-ULs drug regulatory network. The protein–protein interaction (PPI) network was established using the STRING online database and Cytoscape software, followed by the topological properties analysis of the PPI networks. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were conducted by R software. The KEGG relational network was constructed using Cytoscape software. Results: A total of 21 astragalus active ingredients and 406 drug targets were obtained from the TCMSP. Seventeen of these targets overlap with ULs disease targets and were considered potential targets for the ULs treatment by astragalus. The analysis of the regulatory network showed that the astragalus active components with the most targets are quercetin, kaempferol, mangiferin, tetrodotoxin and isorhamnetin. Target genes with the highest Dgree values obtained from the PPI network analysis are estrogen receptor 1 (ESR1), tumor suppressor factor p53 (TP53), neurotrophic tyrosine kinase receptor 1 (NTRK1) and E3 ubiquitin ligase protein (CUL3). GO and KEGG enrichment analyses indicate that these targets are mainly involved in biological processes related to cellular response to reactive oxygen species, oxidative stress and response to lipopolysaccharides. The main signal transduction pathways involved include the IL-17 and TNF signaling pathways, the AGE-RAGE signaling pathway in diabetic complications and proteoglycans in cancer.Conclusions: The present study demonstrates that the astragalus therapeutic use against ULs have multicomponent and multi-target properties, providing a novel approach to further investigate the astragalus mechanism of action in the treatment of ULs.


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