Advancing Alzheimer's Therapy: Computational strategies and treatment innovations
Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlightin...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | IBRO Neuroscience Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266724212500020X |
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| author | Jibon Kumar Paul Abbeha Malik Mahir Azmal Tooba Gulzar Muhammad Talal Rahim Afghan Omar Faruk Talukder Samar Shahzadi Ajit Ghosh |
| author_facet | Jibon Kumar Paul Abbeha Malik Mahir Azmal Tooba Gulzar Muhammad Talal Rahim Afghan Omar Faruk Talukder Samar Shahzadi Ajit Ghosh |
| author_sort | Jibon Kumar Paul |
| collection | DOAJ |
| description | Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment. |
| format | Article |
| id | doaj-art-443bef41066549e4b5e3f4f91dcbea76 |
| institution | Kabale University |
| issn | 2667-2421 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IBRO Neuroscience Reports |
| spelling | doaj-art-443bef41066549e4b5e3f4f91dcbea762025-02-08T05:01:36ZengElsevierIBRO Neuroscience Reports2667-24212025-06-0118270282Advancing Alzheimer's Therapy: Computational strategies and treatment innovationsJibon Kumar Paul0Abbeha Malik1Mahir Azmal2Tooba Gulzar3Muhammad Talal Rahim Afghan4Omar Faruk Talukder5Samar Shahzadi6Ajit Ghosh7Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, PakistanDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, PakistanDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, PakistanDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, BangladeshDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Pakistan; Corresponding authors.Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh; Corresponding authors.Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment.http://www.sciencedirect.com/science/article/pii/S266724212500020XComputational techniquesAlzheimer therapyMachine learningArtificial intelligenceNeurodegenerative diseaseDrug design |
| spellingShingle | Jibon Kumar Paul Abbeha Malik Mahir Azmal Tooba Gulzar Muhammad Talal Rahim Afghan Omar Faruk Talukder Samar Shahzadi Ajit Ghosh Advancing Alzheimer's Therapy: Computational strategies and treatment innovations IBRO Neuroscience Reports Computational techniques Alzheimer therapy Machine learning Artificial intelligence Neurodegenerative disease Drug design |
| title | Advancing Alzheimer's Therapy: Computational strategies and treatment innovations |
| title_full | Advancing Alzheimer's Therapy: Computational strategies and treatment innovations |
| title_fullStr | Advancing Alzheimer's Therapy: Computational strategies and treatment innovations |
| title_full_unstemmed | Advancing Alzheimer's Therapy: Computational strategies and treatment innovations |
| title_short | Advancing Alzheimer's Therapy: Computational strategies and treatment innovations |
| title_sort | advancing alzheimer s therapy computational strategies and treatment innovations |
| topic | Computational techniques Alzheimer therapy Machine learning Artificial intelligence Neurodegenerative disease Drug design |
| url | http://www.sciencedirect.com/science/article/pii/S266724212500020X |
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