Dementia is a progressive, neurodegenerative disease that involves a
gradual loss of functions of the brain, such as memory, reasoning, and attention. This review
will assess the association between Artificial Intelligence (AI) technologies and the pharmacological
treatment of dementia for diagnosis and treatment.
A narrative review was conducted using literature retrieved from Google Scholar,
ScienceDirect, and PubMed databases. Relevant peer-reviewed studies published between 2000
and 2025 related to dementia, AI, machine learning, neuroimaging, and pharmacotherapy were
analysed. A total of 141 eligible articles were included and thematically reviewed.
Artificial Intelligence methods such as Convolutional Neural Networks (CNNs), Generative
Adversarial Networks (GANs), Support Vector Machines (SVMs), and Natural Language
Processing (NLP) technologies have significantly improved the detection and classification
of dementia using neuroimaging, speech analysis, and cognitive assessment data in very
early or early stages of diagnosis. Newer advanced neuroimaging techniques, such as MRI and
PET, provide clinicians with more accurate test results and allow for more precise measurement.
The continued use of pharmacological approaches, such as donepezil, rivastigmine, galantamine,
memantine, piracetam, and nootropic agents, remains the mainstay of symptomatic treatment
and support for additional cognition. The combination of AI-assisted drug discovery and
predictive modelling showed promise for developing personalised treatment methods while
speeding up the development of new therapies.
The combination of artificial intelligence with traditional dementia treatment methods
enables medical professionals to diagnose patients earlier, make better treatment decisions,
and deliver customised care to individual patients. Medical professionals face major challenges
that prevent them from using the technology due to issues with data protection, system understanding,
and medical validation.
The use of AI in diagnostic and treatment methods is a new development with potential
for improving dementia care. The process of developing dementia care solutions needs
more interdisciplinary research to improve AI models, test their clinical use, and develop successful
personalised treatment methods.
Namrata Bhadauria, A. Singh, Niranjan Kaushik et al.· Current Signal Transduction...· 0 citations
Cancer treatment is difficult, there are many issues regarding cancer treatment because Protein Kinase Inhibitors (PKIs) are unable to control tumors that are resistant to them. Resistance to drugs starts with changes in genetic code and continues with fresh changes, routes and alterations in the tumour. When treating cancer with imatinib or osimertinib, PKIs can correct faulty signals, yet patients generally become resistant swiftly and this resistance can include unpleasant side effects. Reviewing Epidermal Growth Factor Receptor (EGFR) T790M and BCR::ABL1 T315I mutations reveals that the important interactions and binding sites are removed or obstructed with the drug. This is how researchers design second- and third-generation drug inhibitors. In addition, the use of single-cell sequencing, CRISPR screens and liquid biopsies helps researchers and doctors to accurately fight resistance in cancer patients. For cancer treatments to succeed, systems to reduce drug costs and make them available, fresh ideas, global experts and equitable healthcare laws should be implemented.
Mridul Singh Sengar, Ajita Paliwal, Niranjan Kaushik et al.· Journal of chemotherapy· 0 citations
A progressive neurodegenerative disease, Alzheimer's Disease (AD), is typified by cognitive decline, synaptic malfunction, and permanent death of neurons. It has a complicated etiology that includes oxidative stress, neuroinflammatory cascades, and monoamine oxidase dysregulation; therapies are limited in their long-term efficacy. This highlights the necessity for carefully crafted multi-target medicines that can modulate multiple pathogenic pathways at once. The advantageous electronic characteristics and structural flexibility of thiazole and benzothiazole derivatives make them appealing, as they can penetrate the blood-brain barrier and serve as heterocyclic scaffolds in medicinal chemistry. According to recent studies, thiazole-based drugs have a strong inhibitory effect against butyrylcholinesterase and acetylcholinesterase, increasing the availability of acetylcholine in synapses. Monoamine oxidase-B (MAO-B) is also strongly and selectively inhibited by several derivatives, which helps to lower oxidative stress and promote neuroprotection. Significantly affecting enzyme affinity, selectivity, and multitarget engagement are structural alterations such as halogen substitution, methoxy incorporation, hydrazone connections, and sulfonamide or piperazine moieties. Beyond enzyme modulation, thiazole-containing molecules interfere with Aβ aggregation, disrupt β-sheet fibril formation, and demonstrate antioxidant and metal-chelating properties. These combined biological effects position thiazole derivatives as potentially disease-modifying multitarget- directed ligands. According to an analysis of the evaluated research, the most effective anti- Alzheimer effects were found in thiazole and benzothiazole derivatives with halogen, methoxy, hydrazone, piperazine, and sulfonamide substituents. Many substances showed nanomolar to low micromolar inhibition of AChE, BuChE, and MAO-B while concurrently inhibiting oxidative stress and amyloid-β formation. Studies on the structure-activity link have shown how crucial strategic substitution patterns are for improving potency, selectivity, and multitarget engagement. This research highlights the feasibility of using thiazoles as scaffold pharmacophores for developing novel drugs to treat Alzheimer's disease and provides vital guidance on the development of future drugs. This review is a comprehensive compilation of small thiazoles being studied for AD with emphasis on structure-activity relationships, molecular targets, and multitarget therapies. This review provides useful information for the rational design of next-generation anti-Alzheimer drugs. It highlights prospective directions for future drug discovery research by methodically outlining current achievements in thiazole-derived AChE, BuChE, MAO-B, and amyloid-β inhibitors.
Geetanjali Chaudhary, Deepika Paliwal, Aman Thakur et al.· Mini-Reviews in Medical Chem...· 0 citations
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