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Pregled bibliografske jedinice broj: 1219496

Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors


Ramić, Alma; Matošević, Ana; Debanić, Barbara; Mikelić, Ana; Primožič, Ines; Bosak, Anita; Hrenar, Tomica
Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors // Pharmaceuticals, 15 (2022), 10; 1214, 21 doi:10.3390/ph15101214 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1219496 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors

Autori
Ramić, Alma ; Matošević, Ana ; Debanić, Barbara ; Mikelić, Ana ; Primožič, Ines ; Bosak, Anita ; Hrenar, Tomica

Izvornik
Pharmaceuticals (1424-8247) 15 (2022), 10; 1214, 21

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Cinchona alkaloid derivatives ; cholinesterase inhibitors ; multivariate linear regression models

Sažetak
A series of 46 Cinchona alkaloid derivatives that differ in positions of fluorine atom(s) in the molecule were synthesized and tested as human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors. All tested compounds reversibly inhibited AChE and BChE in the nanomolar to micromolar range ; for AChE, the determined enzyme-inhibitor dissociation constants (Ki) ranged from 3.9–80 µM, and 0.075–19 µM for BChE. The most potent AChE inhibitor was N- (para-fluorobenzyl)cinchoninium bromide, while N- (meta-fluorobenzyl)cinchonidinium bromide was the most potent BChE inhibitor with Ki constant in the nanomolar range. Generally, compounds were non- selective or BChE selective cholinesterase inhibitors, where N-(meta- fluorobenzyl)cinchonidinium bromide was the most selective showing 533 times higher preference for BChE. In silico study revealed that twenty-six compounds should be able to cross the blood-brain barrier by passive transport. An extensive machine learning procedure was utilized for the creation of multivariate linear regression models of AChE and BChE inhibition. The best possible models with predicted R2 (CD-derivatives) of 0.9932 and R2(CN- derivatives) of 0.9879 were calculated and cross- validated. From these data, a smart guided search for new potential leads can be performed. These results pointed out that quaternary Cinchona alkaloids are the promising structural base for further development as selective BChE inhibitors which can be used in the central nervous system.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Biologija, Farmacija



POVEZANOST RADA


Projekti:
HRZZ-IP-2016-06-3775 - Aktivnošću i in silico usmjeren dizajn malih bioaktivnih molekula (ADESIRE) (Hrenar, Tomica, HRZZ - 2016-06) ( CroRIS)

Ustanove:
Institut za medicinska istraživanja i medicinu rada, Zagreb,
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Ines Primožič (autor)

Avatar Url Ana Mikelić (autor)

Avatar Url Ana Matošević (autor)

Avatar Url Alma Ramic (autor)

Avatar Url Tomica Hrenar (autor)

Avatar Url Anita Bosak (autor)

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Ramić, Alma; Matošević, Ana; Debanić, Barbara; Mikelić, Ana; Primožič, Ines; Bosak, Anita; Hrenar, Tomica
Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors // Pharmaceuticals, 15 (2022), 10; 1214, 21 doi:10.3390/ph15101214 (međunarodna recenzija, članak, znanstveni)
Ramić, A., Matošević, A., Debanić, B., Mikelić, A., Primožič, I., Bosak, A. & Hrenar, T. (2022) Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors. Pharmaceuticals, 15 (10), 1214, 21 doi:10.3390/ph15101214.
@article{article, author = {Rami\'{c}, Alma and Mato\v{s}evi\'{c}, Ana and Debani\'{c}, Barbara and Mikeli\'{c}, Ana and Primo\v{z}i\v{c}, Ines and Bosak, Anita and Hrenar, Tomica}, year = {2022}, pages = {21}, DOI = {10.3390/ph15101214}, chapter = {1214}, keywords = {Cinchona alkaloid derivatives, cholinesterase inhibitors, multivariate linear regression models}, journal = {Pharmaceuticals}, doi = {10.3390/ph15101214}, volume = {15}, number = {10}, issn = {1424-8247}, title = {Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors}, keyword = {Cinchona alkaloid derivatives, cholinesterase inhibitors, multivariate linear regression models}, chapternumber = {1214} }
@article{article, author = {Rami\'{c}, Alma and Mato\v{s}evi\'{c}, Ana and Debani\'{c}, Barbara and Mikeli\'{c}, Ana and Primo\v{z}i\v{c}, Ines and Bosak, Anita and Hrenar, Tomica}, year = {2022}, pages = {21}, DOI = {10.3390/ph15101214}, chapter = {1214}, keywords = {Cinchona alkaloid derivatives, cholinesterase inhibitors, multivariate linear regression models}, journal = {Pharmaceuticals}, doi = {10.3390/ph15101214}, volume = {15}, number = {10}, issn = {1424-8247}, title = {Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated Cinchona Alkaloid-Based Derivatives as Cholinesterase Inhibitors}, keyword = {Cinchona alkaloid derivatives, cholinesterase inhibitors, multivariate linear regression models}, chapternumber = {1214} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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