CNS & Neurological Disorders - Drug Targets - Online First
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41 - 42 of 42 results
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An Insight into Medicinal Chemistry and SAR Studies of Cholinesterase and BACE-1 Inhibitors for Alzheimer's Disease
Authors: Abhimannu Shome, Keshav Taruneshwar Jha, Chahat, Viney Chawla and Pooja A. ChawlaAvailable online: 16 October 2024More LessAlzheimer's Disease (AD) is a serious neurodegenerative condition that predominantly impacts the cholinergic neurons of the entorhinal cortex and hippocampal regions, playing a critical role in learning, navigation, and brain processing. This paper aims to discuss the three main hypotheses of Alzheimer's disease, focusing on neurotoxicity and neurodegeneration caused by mitochondrial dysfunction and ROS production, particularly analyzing the susceptibility differences between genders. Our comprehensive review focuses on significant findings from the past five years, particularly on Cholinesterase (ChE) and BACE-1 inhibitors. Researchers have conducted a detailed analysis of in vitro, in silico, and in vivo data, incorporating extensive Structure-Activity Relationship (SAR) studies. The reviewed papers have been sourced from platforms, such as Google Scholar, Semantic Scholar, and ClinicalTrials.gov, and have been selected based on their AChE and BACE-1 inhibitory activity and structural motif similarity. The review identifies the most effective compounds targeting ChE and BACE-1, highlighting acridine, dihydropyridine, and thiazole-coumarin hybrids for ChE inhibition, and oxadiazole, benzofuran, and dihydropyrimidinone for BACE-1 inhibition. This demonstrates a diverse array of potent heterocyclic hybrids. The review presents a varied compilation of scaffolds showing promise in treating Alzheimer's disease, highlighting the potential of specific compounds against ChE and BACE-1. Given the critical insights derived from our analysis, we posit that this compilation will substantially contribute to the ongoing efforts to combat neurodegeneration and prolong dementia, underscoring the importance of continuous research in this domain.
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BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction
Authors: Pawan Kumar, Vandana Saini, Dinesh Gupta, Pooja A. Chawla and Ajit KumarAvailable online: 16 October 2024More LessAimsNeuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB).
BackgroundBBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance.
ObjectiveHere, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool.
MethodA supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results.
ResultBBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools.
ConclusionThe BBBper web tool is freely available at http://bbbper.mdu.ac.in.
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A Retrospective, Multi-Center Cohort Study Evaluating the Severity- Related Effects of Cerebrolysin Treatment on Clinical Outcomes in Traumatic Brain Injury
Authors: Dafin F. Muresanu, Alexandru V. Ciurea, Radu M. Gorgan, Eva Gheorghita, Stefan I. Florian, Horatiu Stan, Alin Blaga, Nicolai Ianovici, Stefan M. Iencean, Dana Turliuc, Horia B. Davidescu, Cornel Mihalache, Felix M. Brehar, Anca . S. Mihaescu, Dinu C. Mardare, Aurelian Anghelescu, Carmen Chiparus, Magdalena Lapadat, Viorel Pruna, Dumitru Mohan, Constantin Costea, Daniel Costea, Claudiu Palade, Narcisa Bucur, Jesus Figueroa and Anton Alvarez
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