Malignant tumors – as cancer

Bisoyi, Padmini

doi:10.1016/b978-0-323-99883-3.00011-1

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…The process of metastasis is the crucial cause of cancer death. Environmental factors are a significant contributor to cancer initiation [1]. Numerous studies investigate various aspects of cancer, including pathogenesis, prevention, diagnosis, and treatment methods, with the goal of improving patient quality of life and increasing survival rates.…”

Section: Guest Editorial Computational Mathematics Modeling In Cancer...mentioning

confidence: 99%

Guest Editorial Computational Mathematics Modeling in Cancer Analysis

Qin,

Liu,

Zhang

2024

IEEE J. Biomed. Health Inform.

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Section: Guest Editorial Computational Mathematics Modeling In Cancer...mentioning

confidence: 99%

Guest Editorial Computational Mathematics Modeling in Cancer Analysis

Qin,

Liu,

Zhang

2024

IEEE J. Biomed. Health Inform.

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“…In the previous years, oncology has observed a substantial advancement in understanding of the cancer biology 12,13 . With the advent of next generation sequencing (NGS) technology 14–18 such as single cell RNA 19–22 sequencing the ability to model explore the complex phenomenon such as cancer heterogeneity, viral cancers, drug resistance and etiologies have become easily accessible.…”

Section: Introductionmentioning

confidence: 99%

“…11 In the previous years, oncology has observed a substantial advancement in understanding of the cancer biology. 12,13 With the advent of next generation sequencing (NGS) technology [14][15][16][17][18] such as single cell RNA [19][20][21][22] sequencing the ability to model explore the complex phenomenon such as cancer heterogeneity, viral cancers, drug resistance and etiologies have become easily accessible. Additionally, the unprecedented progress in computational power 23 and computational strategies of machine learning (ML) 4,24,25 and deep learning (DL) [26][27][28][29][30] in combination with NGS single-cell RNA technologies have started to yield better results.…”

mentioning

confidence: 99%

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

Ahmed

Kang

Kim

et al. 2023

Journal of Medical Virology

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Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers.Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided.antihepatitis B virus antivirals, antiviral agents, artificial intelligence, drug repurposing, rational drug design | INTRODUCTIONDrug repurposing (also referred to as drug repositioning, drug reprofiling, drug redirecting, drug rescue and more 1 ) is a method for finding new indications beyond the scope of original indications of already approved drugs. 2 A conventional method of drug discovery is quite time taking and expensive task. Time required for a de novo drug is 12-17 years with an estimated expense of $2-$3 billion whereas the success rate is less than 10%. 3 This renders de novo drug discovery a risky process. Drug repurposing is an effective alternative to de novo drug discovery and offers many benefits such as reducing the time and money required to 5-7 years 4,5 and $200-$300 million, respectively, since much of the toxicity and efficacy information is already known. It also increases the success rate in clinical development and testing phases. 6 Success stories of drug repurposing have resulted in serendipity and by physicians' observation. 7 One such example is

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“…Connective tissue, which is made up of fibrous and fatty tissue, holds everything together. Usually, ducts or lobules are places where breast cancer develops [ 3 ]. Blood and lymphatic vessels are two ways that breast cancer can spread to other body areas.…”

Section: Introductionmentioning

confidence: 99%

In Silico Drug Design of Anti-Breast Cancer Agents

Rajagopal,

Kalusalingam,

Bharathidasan

et al. 2023

Molecules

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Cancer is a condition marked by abnormal cell proliferation that has the potential to invade or indicate other health issues. Human beings are affected by more than 100 different types of cancer. Some cancer promotes rapid cell proliferation, whereas others cause cells to divide and develop more slowly. Some cancers, such as leukemia, produce visible tumors, while others, such as breast cancer, do not. In this work, in silico investigations were carried out to investigate the binding mechanisms of four major analogs, which are marine sesquiterpene, sesquiterpene lactone, heteroaromatic chalcones, and benzothiophene against the target estrogen receptor-α for targeting breast cancer using Schrödinger suite 2021-4. The Glide module handled the molecular docking experiments, the QikProp module handled the ADMET screening, and the Prime MM-GB/SA module determined the binding energy of the ligands. The benzothiophene analog BT_ER_15f (G-score −15.922 Kcal/mol) showed the best binding activity against the target protein estrogen receptor-α when compared with the standard drug tamoxifen which has a docking score of −13.560 Kcal/mol. TRP383 (tryptophan) has the highest interaction time with the ligand, and hence it could act for a long time. Based on in silico investigations, the benzothiophene analog BT_ER_15f significantly binds with the active site of the target protein estrogen receptor-α. Similar to the outcomes of molecular docking, the target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system. This study shows that estrogen receptor-α targets inhibitors with better potential and low toxicity when compared to the existing market drugs, which can be made from a benzothiophene derivative. It may result in considerable activity and be applied to more research on breast cancer.

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Malignant tumors – as cancer

Cited by 6 publications

References 27 publications

Guest Editorial Computational Mathematics Modeling in Cancer Analysis

Guest Editorial Computational Mathematics Modeling in Cancer Analysis

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

In Silico Drug Design of Anti-Breast Cancer Agents

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