Design of active compounds against neurodegenerative diseases
Kuupäev
2020-07-14
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Neurodegeneratiivsed haigused on tänapäeval kujunenud keskseks meditsiiniliseks ja sotsiaalseks probleemiks. Ühest küljest on selle põhjuseks haigustega kaasnevad rasked füüsilised ja vaimsed puuded ning mõjusate ravimeetodite puudumine. Teisalt on valdav enamus neurodegeneratiivseid haigusi seotud vananemisega. Oodatava eluea märkimisväärne pikenemine on põhjustanud patsientide arvu olulist kasvu. Üks peamisi takistusi neurodegeneratiivsete häirete jaoks radikaalsete ravimeetodite leidmisel on uute ravimite väljatöötamise protsessi pikkus ja kulukus. Viimase kümnendi jooksul on aga molekulaarse modelleerimise ja tehisintellekti arvutusmeetodite kaasamine võimaldanud märkimisväärselt lühendada nii uute ravimite väljatöötamiseks kuluvat aega kui ka maksumust.
Käesolevas väitekirjas rakendati mitmesuguseid arvutipõhiseid ravimite otsimise meetodeid uute potentsiaalsete aktiivsete keemiliste ühendite väljatöötamiseks neurodegeneratiivsete haiguste raviks. Kaasaegsete arvutikeemia molekulaarsildamise ja molekulaardünaamika meetodite abil sõeluti virtuaalselt suuri keemiliste ühendite andmebaase, leidmaks neurodegeneratiivsete haigustega seotud valkudele toimivaid aineid. Nii tehti kindlaks rida uusi looduslikke ühendeid, mis toimivad erinevate ensüümvalkude inhibiitoritena, aga ka uudne ühend, mis toimib efektiivselt samaaegselt kahele Alzheimeri tõvega seotud valgule.
Üks peamisi neurodegeneratiivsete haiguste tekke põhjusi on nn närvikasvufaktorite puudulikkus neuronites. Seetõttu on väga huvitavaks ja perspektiivseks suunaks keemiliste ühendite leidmine, mis käituksid analoogselt nende faktoritega ning kaitseks närvirakke suremise eest. Käesoleva töö käigus uuriti erinevate arvutusmeetodite abil põhjalikult ühe taolise närvikasvufaktori (gliia närvikasvufaktor GDNF) toimemehhanismi ning ennustati seda faktorit imiteeriv aktiivne ühend. Kuigi selle eksperimentaalselt mõõdetud neuroneid kaitsev toime ei vasta veel ravimitele esitatavatele nõutele, on siiski tegemist esimese sellelaadse ühendiga maailmas, mille alusel oleks võimalik välja arendada täiesti uut tüüpi ravimeid nii Parkinsoni kui ka Huntingtoni tõve raviks
Today, neurodegenerative diseases are one of the most acute medical and social problems. This is due to both severe physical and mental disabilities resulting from the constant progression of the process, and the age-dependent nature of the vast majority of neurodegenerative diseases. The current accelerating increase in life expectancy inevitably leads to a significant increase in the number of such patients. There is currently no radical treatment for neurodegenerative disorders. One of the main obstacles to finding effective drugs is the length and cost of the process of developing a new drug. However, the development of modern molecular modelling and artificial intelligence methods has substantially shortened the time to dispense new medicines and reduced their cost. This dissertation provides examples of the use of various methods of computer-aided drug design such as molecular docking and molecular dynamics to develop new potential candidates against neurodegenerative diseases. The high-throughput virtual screening of large molecular libraries enabled to identify effective compounds against target proteins related to neurodegenerative diseases. In result, a series of natural compounds acting as inhibitors to enzymes related to different diseases was established. Notably, a fully novel compound acting against two proteins related to Alzheimer’s disease was predicted and experimentally verified. One of the main causes of the neurodegeneration is the mostly age-related deficiency of so called neurotrophic factors. Small molecules that can mimic the activity of these factors in cells would be thus very attractive novel drug candidates. In the present thesis, the computational modelling was used for detailed study of the mechanism of action of one of the most important neurotrophic factors (glial cell-derived neurotrophic factor GDNF). The results of this study enabled to develop first time a compound that acted similarly to this factor itself. Whereas the experimentally measured activity of this compound was moderate, it creates a basis for the development of fully new type of drugs against Parkinson’s and Huntington’s diseases.
Today, neurodegenerative diseases are one of the most acute medical and social problems. This is due to both severe physical and mental disabilities resulting from the constant progression of the process, and the age-dependent nature of the vast majority of neurodegenerative diseases. The current accelerating increase in life expectancy inevitably leads to a significant increase in the number of such patients. There is currently no radical treatment for neurodegenerative disorders. One of the main obstacles to finding effective drugs is the length and cost of the process of developing a new drug. However, the development of modern molecular modelling and artificial intelligence methods has substantially shortened the time to dispense new medicines and reduced their cost. This dissertation provides examples of the use of various methods of computer-aided drug design such as molecular docking and molecular dynamics to develop new potential candidates against neurodegenerative diseases. The high-throughput virtual screening of large molecular libraries enabled to identify effective compounds against target proteins related to neurodegenerative diseases. In result, a series of natural compounds acting as inhibitors to enzymes related to different diseases was established. Notably, a fully novel compound acting against two proteins related to Alzheimer’s disease was predicted and experimentally verified. One of the main causes of the neurodegeneration is the mostly age-related deficiency of so called neurotrophic factors. Small molecules that can mimic the activity of these factors in cells would be thus very attractive novel drug candidates. In the present thesis, the computational modelling was used for detailed study of the mechanism of action of one of the most important neurotrophic factors (glial cell-derived neurotrophic factor GDNF). The results of this study enabled to develop first time a compound that acted similarly to this factor itself. Whereas the experimentally measured activity of this compound was moderate, it creates a basis for the development of fully new type of drugs against Parkinson’s and Huntington’s diseases.
Kirjeldus
Väitekirja elektrooniline versioon ei sisalda publikatsioone
Märksõnad
neurodegenerative diseases, neuroglia, growth factors, receptors, ligands, molecular dynamics, artificial neural networks, drug design