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- Volume 25, Issue 15, 2024
Current Drug Targets - Volume 25, Issue 15, 2024
Volume 25, Issue 15, 2024
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Trends on Novel Targets and Nanotechnology-Based Drug Delivery System in the Treatment of Parkinson's disease: Recent Advancement in Drug Development
Authors: Manisha Majumdar and Hemant BadwaikParkinson's disease (PD) is a progressive neurodegenerative disorder that impacts a significant portion of the population. Despite extensive research, an effective cure for PD remains elusive, and conventional pharmacological treatments often face limitations in efficacy and management of symptoms. There has been a lot of discussion about using nanotechnology to increase the bioavailability of small- molecule drugs to target cells in recent years. It is possible that PD treatment might become far more effective and have fewer side effects if medication delivery mechanisms were to be improved. Potential alternatives to pharmacological therapy for molecular imaging and treatment of PD may lie in abnormal proteins such as parkin, α-synuclein, leucine-rich repeat serine and threonine protein kinase 2. Published research has demonstrated encouraging outcomes when nanomedicine-based approaches are used to address the challenges of PD therapy. So, to address the present difficulties of antiparkinsonian treatment, this review outlines the key issues and limitations of antiparkinsonian medications, new therapeutic strategies, and the breadth of delivery based on nanomedicine. This review covers a wide range of subjects, including drug distribution in the brain, the efficacy of drug-loaded nano-carriers in crossing the blood-brain barrier, and their release profiles. In PD, the nano-carriers are also used. Novel techniques of pharmaceutical delivery are currently made possible by vesicular carriers, which eliminate the requirement to cross the blood-brain barrier (BBB).
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Revolutionizing Skin Cancer Treatment: The Rise of PD-1/PDL-1 and CTLA-4 as Key Therapeutic Targets
Authors: Neha Sharma, Rupa Mazumder and Pallavi RaiSkin cancer is a significant health concern, affecting millions of individuals globally on an annual basis. According to data from the World Health Organization, it stands as the most prevalent form of cancer within the white population. Current treatments for skin cancer typically involve a combination of chemotherapy, radiation therapy, and surgery. However, these methods often come with drawbacks, such as side effects and potential scarring. Therefore, there is a growing need for alternative treatments that can offer effective results with fewer adverse effects, driving ongoing research in skin cancer therapy. The advancement of immune checkpoint inhibitors has been facilitated by a more profound comprehension of the interplay between tumors and the immune system, along with the regulatory mechanisms governing T-cells. As cancer treatment continues to evolve, immunotherapy is emerging as a powerful strategy, leading to a growing interest in the role of immunological checkpoints in skin cancer. Various types of immune checkpoints and their expression, including PD-1, PDL-1, CTLA-4, lymphocyte activation gene 3, and B7-H3, along with their blockers and monoclonal antibodies, have been established for various cancers. PD-1, PDL-1, and CTLA-4 are crucial immune system regulators, acting as brakes to prevent T-cell overactivation and potential autoimmunity. However, tumors can exploit these checkpoints to evade immune detection. Inhibiting these immune checkpoints can enhance the body's ability to recognize and attack cancer cells. This review focuses on the characteristics of PD-1, PDL-1, and CTLA-4 immune checkpoints, their mechanism of action, and their role in skin cancer. Additionally, it summarizes the ongoing clinical trials sponsored or conducted by various pharmaceutical companies and provides insights into the latest patent data.
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Therapeutic Correlation of TLR-4 Mediated NF-κB Inflammatory Pathways in Ischemic Injuries
Authors: Veerta Sharma, Prateek Sharma and Thakur Gurjeet SinghIschemia-reperfusion (I/R) injury refers to the tissue damage that happens when blood flow returns to tissue after a period of ischemia. I/R injuries are implicated in a large array of pathological conditions, such as cerebral, myocardial, renal, intestinal, retinal and hepatic ischemia. The hallmark of these pathologies is excessive inflammation. Toll-like receptors (TLRs) are recognized as significant contributors to inflammation caused by pathogens and, more recently, inflammation caused by injury. TLR-4 activation initiates a series of events that results in activation of nuclear factor kappa-B (NF-κB), which stimulates the production of pro-inflammatory cytokines and chemokines, exacerbating tissue injury. Therefore, through a comprehensive review of current research and experimentation, this investigation elucidates the TLRs signalling pathway and the role of TLR-4/NF-κB in the pathophysiology of I/R injuries. Furthermore, this review highlights the various pharmacological agents (TLR-4/NF-κB inhibitors) with special emphasis on the various ischemic injuries (cerebral, myocardial, renal, intestinal, retinal and hepatic). Future research should prioritise investigating the specific molecular pathways that cause TLR-4/NF-κB- mediated inflammation in ischemic injuries. Additionally, efforts should be made to enhance treatment approaches in order to enhance patient outcomes.
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Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures
Authors: Gelany Aly Abdelkader and Jeong-Dong KimBackgroundDrug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds.
ObjectiveThis survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field.
MethodsWe thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript.
ResultsThe systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community.
ConclusionThe present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.
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Volumes & issues
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Volume 25 (2024)
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Volume 24 (2023)
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Volume 23 (2022)
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Volume 22 (2021)
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Volume 21 (2020)
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Volume 20 (2019)
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Volume 19 (2018)
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Volume 18 (2017)
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Volume 17 (2016)
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Volume 16 (2015)
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Volume 15 (2014)
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Volume 14 (2013)
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Volume 13 (2012)
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Volume 12 (2011)
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Volume 11 (2010)
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Volume 10 (2009)
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Volume 9 (2008)
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Volume 8 (2007)
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Volume 7 (2006)
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Volume 6 (2005)
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Volume 5 (2004)
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Volume 4 (2003)
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Volume 3 (2002)
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Volume 2 (2001)
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Volume 1 (2000)