Chemists prove chromones are effective against Alzheimer’s disease

RUDN chemists synthesized a range of biologically active molecules called chromones and demonstrated their use in the treatment of Alzheimer’s disease. The results of the work were published in the Bioorganic & Medicinal Chemistry journal.

Alzheimer’s disease is a progredient form of dementia causing irreversible deterioration of cognitive functions (attention, memory, orientation, and thinking) and resulting in complete disintegration of personality. According to the World Health Association, about 6-7 million people are diagnosed with Alzheimer’s disease annually. RUDN chemists with their colleagues from IPAC RAS and Lomonosov MSU synthesized new compounds that are able to stop the progression of this disease and studied their biological activity.

Alzheimer’s disease is associated with the damage of the central or peripheral nervous system. A special role in the work of the nervous system is played by a neurotransmitter called acetylcholine that helps a neural impulse move between neurons and then from neurons to muscles. Reduced levels of acetylcholine are one of the symptoms of Alzheimer’s disease. Today’s treatment methods are reduced to prolonging the activity of the remaining acetylcholine with drugs that slow down its disintegration and partially compensate for its loss.

The disintegration of acetylcholine is affected by several substances. The main role in the process is played by acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). In the course of development of the Alzheimer’s it’s the activity of BChE that increases. By reducing it, one may slow down the disintegration of acetylcholine. RUDN chemists managed to achieve this effect using chromones—biologically active molecules that have been previously successfully used in the treatment of other conditions. In their previous works the authors suggested a new way of synthesizing substituted chromones compounds, and in this research demonstrated their potential as an efficient anti-Alzheimer’s therapy.

“We found chromones interesting because of their pharmacological activity. Their derivatives appeared to have anti-cancer, anti-viral (including anti-HIV), anti-microbial, anti-fungal, anti-inflammatory, anti-diabetic, and antioxidant properties. It was especially important for our studies that chromones and their derivatives played an important role as antioxidants and acceptors of radicals,” said Larisa Kulikova, a candidate of chemistry, and a lecturer of the Faculty of Physics, Mathematics, and Natural Sciences at RUDN.

To evaluate the pharmacological activity of the obtained substances, the scientists used kinetic methods and modeling. The results of screenings showed that the new substances efficiently slowed down the activity of BChE. In the future the team hopes to improve the synthesis method and to obtain chemical compounds with antioxidant as well as BChE-suppressing properties. A substance like that would be able to slow down BChE and at the same time to reduce the so-called oxidative stress—the disbalance between the number of active oxygen or nitrogen compounds and the inability of the body to process them leading to massive cell death.

More information:
Galina F. Makhaeva et al. Synthesis, molecular docking, and biological activity of 2-vinyl chromones: Toward selective butyrylcholinesterase inhibitors for potential Alzheimer’s disease therapeutics, Bioorganic & Medicinal Chemistry (2018). DOI: 10.1016/j.bmc.2018.08.010

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Artificial intelligence predicts Alzheimer’s years before diagnosis

Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer’s disease, according to a study published in the journal Radiology.

Timely diagnosis of Alzheimer’s disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” said study co-author Jae Ho Sohn, M.D., from the Radiology & Biomedical Imaging Department at the University of California in San Francisco (UCSF). “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

The study’s senior author, Benjamin Franc, M.D., from UCSF, approached Dr. Sohn and University of California, Berkeley, undergraduate student Yiming Ding through the Big Data in Radiology (BDRAD) research group, a multidisciplinary team of physicians and engineers focusing on radiological data science. Dr. Franc was interested in applying deep learning, a type of AI in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer’s disease.

The researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance,” Dr. Sohn said. “It was able to predict every single case that advanced to Alzheimer’s disease.”

Although he cautioned that their independent test set was small and needs further validation with a larger multi-institutional prospective study, Dr. Sohn said that the algorithm could be a useful tool to complement the work of radiologists—especially in conjunction with other biochemical and imaging tests—in providing an opportunity for early therapeutic intervention.

“If we diagnose Alzheimer’s disease when all the symptoms have manifested, the brain volume loss is so significant that it’s too late to intervene,” he said. “If we can detect it earlier, that’s an opportunity for investigators to potentially find better ways to slow down or even halt the disease process.”

Future research directions include training the deep learning algorithm to look for patterns associated with the accumulation of beta-amyloid and tau proteins, abnormal protein clumps and tangles in the brain that are markers specific to Alzheimer’s disease, according to UCSF’s Youngho Seo, Ph.D., who served as one of the faculty advisors of the study.

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