INTRODUCTION
The underlying association between multiple risk factors like age, preexisting diseases, infections, traumatic brain injuries, and certain genetic factors makes the diagnosis and treatment a challenge in AD. Identification of biomarkers and proper diagnosis of AD could make its management easier. Advances in diagnostic techniques like Positron Emission Tomography (PET) imaging have greatly improved the ability to identify, detect early, monitor disease progression, and manage its therapy.
METHODS
The recent therapeutic advancements in treatments encompass immunotherapies aimed at amyloid-beta (Aβ) and tau proteins, novel drug delivery systems, non-pharmacological methodologies, and personalised medicine techniques, combination therapies, and non-invasive brain stimulation are important in the management of AD. Cholinesterase inhibitors, such as galantamine, rivastigmine, and donepezil, are used to enhance cognitive performance and reduce confusion. Moderate to severe AD, memantine, a N-methyl-D-aspartate receptor (NMDA) receptor antagonist, is prescribed. Researchers are looking into both active and passive vaccines to get antibodies to Aβ plaques, and monoclonal antibodies, such as Lecanemab and Donanemab, which are used to treat early-stage AD.
RESULTS
Ongoing research is focused on identifying new biomarkers and therapeutic targets, advancing gene therapy, and refining immunotherapeutic approaches for AD.
DISCUSSION
There is growing interest in comprehending the influence of lifestyle and environmental factors on the mitigation of AD risk. Ongoing research is essential to elucidate the complexity of AD pathophysiology and to create effective therapies.
CONCLUSION
This review highlights the critical need for future AD research to bridge identified gaps in early detection, focus on improving early diagnosis, developing more effective treatments, refining existing drugs and therapies, and understanding the underlying disease progression mechanisms.
Sunita Patnaik, P. K. Patra, C. Patra et al.· Central Nervous System Agent...· 0 citations
The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.
Krishna Chandra Panda, B. R. Ravi Kumar, J. Sruti et al.· Reviews on recent clinical t...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.