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Patient Centricity and Precision Medicine
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Diana Morgenstern, Mina B. Riad, Claudia Zavala, Amrit Ray
To identify the right patient, right medicine at the right time, PM is an approach to the prevention and treatment of disease in an individual patient that takes into consideration the unique influence of genetic, environmental and lifestyle factors (Ray et al., 2019). While its use remains limited, PM is not a new concept in clinical medicine, e.g., a person receiving a blood transfusion is not randomly given another’s blood. Bioinformatics involves the integration of computers, software tools, and databases to address biological questions. These approaches are often used for major initiatives that generate RWD. The exponential growth of data-availability (e.g., digital therapeutics and apps) that considers a person’s uniqueness that confers susceptibility to an illness or benefit from a particular treatment. With PM, treatments are designed for that unique individual.
An Analysis of Protein Interaction and Its Methods, Metabolite Pathway and Drug Discovery
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
Docking use to conform ligand binding to the receptor; usually, the receptor is bigger than the molecules. This information includes the coordination of the ligand atoms, to find the lowest energy binding site of the docking configuration. FLEX and AutoDock example programs will be shown in a later chapter of this chapter. The aim is to confirm the binding affinity and the bind. The overall minimum energy of complex formation can be found with the exact position and direction of the binding ligand that belongs to the interacting molecule activation within that. Various bioinformatics tools are helpful in disease management, diagnosis and drug discovery. Sequencing enables identifying the disease and drug discovery by scientists. Mutation and drug and all identified and experimented by utilizing different computational tools. Drug targets decide the suitable drug entry into the pipeline of drug development with the help of bioinformatics tools. The process of designing a medication with the target molecule is known as drug designing. The smallest molecule is ligand that switches on the biological target molecule output in the therapeutic effect [50]. However, the approach of single regulatory is a difficult task in marketing authorization application in all countries. Figure 13.5 represents the levels of drug discovery with regulations. Figure 13.6 shows the drug designing methods and their types.
Liquid Biopsies for Pancreatic Cancer: A Step Towards Early Detection
Published in Surinder K. Batra, Moorthy P. Ponnusamy, Gene Regulation and Therapeutics for Cancer, 2021
Joseph Carmicheal, Rahat Jahan, Koelina Ganguly, Ashu Shah, Sukhwinder Kaur
The metabolite data acquired is then subjected to a series of bioinformatics and biostatistical platforms in order to normalize the data, remove outliers, and map them to common cancer-associated pathways. This is done in order to glean a possible mechanistic insight or identify an underlying pattern of the analyzed metabolites. Principal component analysis is a commonly used statistical approach to classify the samples based on multiple dimensions. This is followed by the use of various supervised data analysis approaches like partial least squares-discriminate analysis (PLS-DA) and artificial neural networks (ANN). These methods are used in conjunction with histopathological scores, and other “omics” models along with pathological outcome [45, 46]. For verification analysis of metabolic shunts prevalent in the system across a certain period of time, radiolabeled metabolites such as C labeled glucose are widely used [47]. Once the metabolites are identified as potential diagnostic or prognostic markers, validation studies are conducted in independent cohorts with a multi-center approach.
ELANE Promotes M2 Macrophage Polarization by Down-Regulating PTEN and Participates in the Lung Cancer Progression
Published in Immunological Investigations, 2023
Sinuo Song, Yunping Zhao, Tianyu Fu, Yunfei Fan, Jie Tang, Xiaoxing Wang, Chao Liu, Xiaobo Chen
Bioinformatics is a new subject that integrates computer science, statistics and informatics technologies to comprehensively analyze biological data and reveal their possible internal relationships. The TCGA database is an important data source for cancer research. By analyzing the transcriptome expression matrix and clinical information data of patients with LUAD in the TCGA database, we identified 33 candidate genes associated with LUAD development. CIBERSORTx was used to predict the components of immune cells, and eight types of immune cells were obtained. Further evaluation of the relationship between genes and immune cells revealed that the genes most significantly correlated with immune cells were EDN3 and ELANE, among which ELANE was significantly positively correlated with M2 macrophages. In malignant tumors, TAMs are mainly similar to M2 phenotypes (Geng et al. 2019). Studies have shown that M2-like TAMs can promote tumor cell survival, proliferation, invasion and metastasis by driving neovascularization, mediating apoptosis resistance and inhibiting adaptive immune response, and play an important role in extracellular matrix remodeling (Brown et al. 2017). Previous studies have shown that M2-type macrophages can secrete a variety of cytokines (IL-6, IL-10, TGF-β, etc.), chemokines (CCL17, CCL18, CXCL8, CXCL9, CXCL10, etc.) and fibroblast growth factors to inhibit inflammation (Najafi et al. 2019; Shapouri-Moghaddam et al. 2018).
Identifying molecular mechanisms of acute to chronic pain transition and potential drug targets
Published in Expert Opinion on Therapeutic Targets, 2022
Kannan Aravagiri, Adam Ali, Hank C Wang, Kenneth D Candido, Nebojsa Nick Knezevic
We have herein discussed the current clinical trials taking place in order to bring strategies for chronic pain prevention forward. By defining the current research regarding site of effect, we anticipate that future research will incorporate sequential targeting strategies involving each site (location of initial injury/surgery, spinal cord, central and peripheral inflammatory cells, and mood/lifestyle), though again this will be difficult to incorporate. In the future, it may be possible to tailor these strategies to certain patients based on preliminary study data, especially regarding the innovative progress of bioinformatics and technology in personalized medicine. The evolving role of preemptive analgesia in preventing acute trauma-induced (i.e. surgically induced acute pain conditions) pain may hold one key to overall strategies aimed at limiting the development of chronic pain from acute pain. This remains to be seen but appears to be an integral focus of contemporary research. It is therefore our opinion that by allowing further research to be done on the various medications, mechanisms, and strategies put forward, we may be able to provide further possibilities and options for the prevention of chronic pain from acute pain.
Peptidomics and proteogenomics: background, challenges and future needs
Published in Expert Review of Proteomics, 2021
Rui Vitorino, Manisha Choudhury, Sofia Guedes, Rita Ferreira, Visith Thongboonkerd, Lakshya Sharma, Francisco Amado, Sanjeeva Srivastava
The development of different tools is crucial for the growth of bioinformatics. However, running them together on a platform where the output of one tool can become the input for another is a complex process even for computational biologists [5]. The Galaxy framework was developed to overcome these problems and reduce the complexity and time required for the analysis processes. This freely available web-based framework also takes into account the difficulties encountered in proteogenome analysis and provides detailed information about a biological sample using a single platform [5]. This platform was used to evaluate the effects of hibernation on ground squirrel skeletal muscle by examining protein abundance during the annual cycle [77]. The system provided important information on the physiology of ground squirrels. A proteogenomic pipeline has also been developed specifically for bacterial genomes [78]. This pipeline, written in Java, can work on any system and allows the identification of peptides. Splicify, another proteogenomics pipeline for identifying differentially expressed splice variants, was developed using data from the SW480 colon cancer cell line [79]. This pipeline integrates data from massively parallel sequencing of RNA and MS/MS analysis. The tool is publicly available and provides answers to several questions related to the translation or transcription of genes [79].