Molecular dynamics simulations and computational design of inhibitors of protein lysine methyltransferase EZH2

Enhancer of Zeste Homolog 2 (EZH2) je epigenetski enzim koji vrši selektivno metilovanje lizina 27 na histonu H3 (H3K27). Trimetilovani H3K27 je represivan epigenetski signal, cime EZH2 utice na smanjenje transkripcije ciljnih gena. Prekomerna aktivnost EZH2, izazvana povecanom ekspresijom ili mutacijama njegovog katalitickog domena, dovedena je u vezu sa vecim brojem maligniteta kod ljudi, a inhibicija ovog enzima smatra se perspektivnom strategijom u razvoju novih antitumorskih lekova. Prvi selektivni inhibitori EZH2 otkriveni su 2012. godine i njihova biološka karakterizacija potvrdila je terapijski potencijal inhibicije ovog epigenetskog regulatora. Iako postoji potreba za otkricem novih inhibitora, znacajnu prepreku u njihovom dizajnu do sada je predstavljao nedostatak trodimenzionalne strukture kompleksa EZH2 i okarakterisanih liganada. Cilj istraživanja u okviru ove doktorske disertacije bio je da se primenom racunarskih metoda uspostavi i validira model vezivnog mesta EZH2, i da se, zatim, uspostavljeni model upotrebi u dizajnu novih inhibitora kao potencijalnih antitumorskih lekova. U prvoj fazi istraživanja izvršeno je homologo modeliranje katalitickog domena EZH2 koji sadrži vezivno mesto za kofaktor, a za koje se kompetitivno vezuju svi poznati inhibitori. Pocetni model usavršen je primenom simulacija konvencionalne i ubrzane molekularne dinamike (MD), a zatim je daljim simulacijama istraživano kako 13 inhibitora piridonske strukture stupa u interakciju sa vezivnim mestom na enzimu. Korišcenjem prikupljenih trajektorija izvršena je procena slobodne energije vezivanja inhibitora zasnovana na molekularnoj mehanici uz generalizovani Bornov model rastvaraca (MM/GBSA). Izracunate energije upore ene su sa dostupnim eksperimentalnim podacima i utvr eno je dobro slaganje izme u vrednosti koje ukazuje na validnost uspostavljenog modela. Daljom analizom trajektorija i primenom racunarske mutageneze u alanin identifikovane su intermolekulske interakcije koje kljucno doprinose aktivnosti i selektivnosti proucavanih inhibitora. Na ovim osnovama, definisan je farmakoforni model inhibitora EZH2 a njegovom validacijom potvr ena je mogucnost uspešne identifikacije i inhibitora baziranih na drugim osnovnim strukturama. Efikasnost mnogih antitumorskih lekova ogranicava razvoj rezistencije tui morskih celija, pri cemu je opisan veci broj mehanizama koji dovode do pojave rezistencije...methylation of lysine 27 on histone H3. Trimethylated H3K27 constitutes a repressive epigenetic mark, making EZH2 capable of transcriptional silencing of target genes. Aberrant EZH2 activity, caused by either overexpression or point mutations in the catalytic domain, has been related to a number of human malignancies, making EZH2 inhibition a promising strategy in the development of novel anticancer treatments. First disclosure of selective EZH2 inhibitors in 2012 and their biological characterization has confirmed the therapeutic potential of inhibiting this epigenetic regulator. While there is an outstanding need for the discovery of novel EZH2 inhibitors, the lack of a three-dimensional structure of EZH2 complexed with one of its ligands has been a significant obstacle to this end. The aim of research conducted as part of this doctoral dissertation was to establish and validate a model of EZH2 binding site using computational methods, and to subsequently utilize the established model as a foundation in the design of novel potential EZH2 inhibitors. In the first stage of our research, homology modeling of the EZH2 catalytic domain was conducted, which contains the cofactor binding site targeted by all known inhibitors. The initial model was refined through conventional and accelerated molecular dynamics (MD) simulations. Next, structures of EZH2 complexed with 13 different pyridone inhibitors were simulated in order to study the key interactions participating in ligand binding. An assessment of inhibitors’ binding free energy was performed using the molecular mechanics/generalized Born surface area (MM/GBSA) method. The computed energies were correlated to available experimental data and good agreement was found supporting the validity of the established model. Further analysis of MD trajectories and computational alanine scanning facilitated the definition of key intermolecular interactions that contribute to the observed potency and selectivity of the studied inhibitors. On this basis, a pharmacophore model of EZH2 inhibitors was proposed and validated. It demonstrated good capabilities in identifying truly active molecules, including those not based on a pyridone scaffold, in presence of decoys. The efficacy of anticancer drugs can become limited with the onset of resiiv stance that can develop in cancer cells through various mechanisms..

Računarski modeli za predviđanje transporta lekova posredovanog P-glikoproteinom

P-glycoprotein (Pgp) is a transmembrane transporter which can, by transporting structurally diverse compounds, influence the absorption, distribution and efficacy of a number of drugs. Pgp overexpression in cells is a major contributing factor to the development of drug resistance. For these reasons, potential for compound efflux by Pgp should be assessed early on in the drug discovery process, preferably even prior to compound synthesis. To meet this demand, numerous computational models have been developed during the past decade, capable of predicting Pgp-mediated transport based solely on chemical structures. This paper summarizes the various approaches that have been used for model development, discusses their advantages and disadvantages and focuses on key factors that influence model reliability. The promiscuous nature of the transport can be seen as a major challenge for most computational chemistry methods. Nevertheless, the attained level of accuracy of literature models suggests that they can be useful in the drug discovery setting. Greater availability of experimental data and integration of predictions made by different modeling methods has the potential to further improve the reliability of computational predictions.P-glikoprotein (Pgp) je transmembranski transporter koji, transportujući strukturno raznovrsne lekove iz unutrašnjosti ćelije u ekstracelularnu sredinu, može uticati na resorpciju, distribuciju i efikasnost većeg broja lekova. Prekomerna ekspresija Pgp-a u ćelijama predstavlja jedan od mehanizama razvoja rezistencije na lekove. Iz ovih razloga, potrebno je u ranoj fazi otkrića leka predvideti da li je potencijalni lek supstrat za Pgp, idealno i pre same sinteze. U tu svrhu, tokom poslednje decenije razvijen je veliki broj računarskih modela koji omogućavaju predviđanje transporta posredstvom Pgp-a samo na osnovu hemijske strukture. U ovom radu prikazan je pregled različitih pristupa koji su korišćeni u razvoju modela, razmotrene su njihove prednosti i nedostaci, kao i faktori koji u najvećoj meri utiču na pouzdanost predviđanja. Polispecifičnost ovog transportera predstavlja značajan izazov za većinu metoda računarske hemije. Ipak, dostignut nivo tačnosti modela koji su prikazani u litearaturi ukazuje na činjenicu da oni mogu doprineti racionalizaciji procesa dizajniranja novih lekova. Šira dostupnost eksperimentalnih podataka, kao i kombinovanje različitih pristupa modelovanju transporta, mogu dodatno unaprediti postojeće modele

Stereohemijski aspekti dejstva i farmakokinetike lekova

The action and fate of drugs in the organism are determined by their interactions with endogenous macromolecules which are predominantly chiral in character. Consideration of the stereochemical aspects of drug action and pharmacokinetics has been intensified since the 1980s and has led to extensive documentation of significant differences existing between stereoisomeric forms of a single drug. Enantiomers can posses different efficacies, exhibit different pharmacological and toxicological effects, and consequently be characterized by different safety profiles. The growing wealth of evidence on the importance of stereochemistry, regulatory incentives, development of asymmetric synthesis methods and analytics - all contributed to fundamental changes in the way new chiral drugs are developed, tested, registered and market-managed. Today, enantiomers need to be treated as separate chemical entities and investigated correspondingly. Development of single enantiomer drugs can facilitate the reduction of total dose of xenobiotic administered to patients; improve the accuracy of doseresponse estimation; simplify pharmacokinetic studies and therapeutic monitoring. The stereochemical characteristics of biologically active compounds should, therefore, be considered from the earliest stages of drug development. Based on the established impact of stereochemistry, a single enantiomer formulation should be favored, whenever justified.Dejstvo i sudbina leka u organizmu zavise od hiralnosti samih lekova kao i endogenih makromolekula sa kojima stupaju u interakciju. Razmatranje stereohemijskih aspekata dejstva i farmakokinetike lekova intenzivirano je početkom 80-tih godina prošlog veka i doprinelo je opsežnom dokumentovanju značajnih razlika između stereoizomernih oblika jednog leka. Enantiomeri tako mogu posedovati različitu efikasnost, mogu ispoljavati drugačije farmakološke i toksikološke efekte i sledstveno mogu imati različite bezbednosne profile. Sve veći broj dokaza o važnosti stereohemije, podsticaji regulatornih agencija, razvoj metoda asimetrične sinteze i analitike - doveli su do suštinskih promena u načinu na koji se novi hiralni lekovi razvijaju, ispituju, registruju i tržišno plasiraju. Enantiomere je danas neophodno tretirati kao zasebne entitete i zasebno ih ispitivati. Razvoj enantiomerno čistih formulacija lekova može omogućiti smanjenje ukupne količine ksenobiotika kojoj se pacijent izlaže, omogućiti precizniju procenu odnosa doze i efekta, pojednostaviti farmakokinetička ispitivanja i terapijski monitoring. Stoga je stereohemijske osobine biološki aktivnih jedinjenja neophodno razmatrati od najranijih faza procesa stvaranja novog leka da bi se blagovremeno napravio racionalan izbor jednog enantiomera za terapijsku primenu, kad god je to opravdano

Računarski modeli za predviđanje rastvorljivosti lekova

Aqueous solubility of a drug is a factor which can significantly influence its oral bioavailability, and can also affect the drug distribution in the body. Consideration of aqueous solubility in early stages of drug discovery and development is vital in reducing the incidence of late-stage drug development failures. The application of computational models for solubility prediction could provide the screening of combinatorial libraries, helping single-out potentially problematic and eliminate compounds with inadequate solubility. In addition to the prediction of solubility from chemical structure, the interpretation of such models can give an insight into structure-solubility relationships and can guide the optimization of structures in order to provide better solubility whilst retaining the activity of the investigated drugs. Development of such models is a complex process that requires consideration of numerous factors which can impact the final model's performance. Different solubility modeling approaches are discussed in this article. Despite intensive research on model development, prediction of the solubility of diverse drugs remains a challenging task. The quality of available experimental data used for modeling of solubility is increasingly recognized as one of the main causes for the limited reliability of many of the proposed models. Therefore, the full potential of the developed modeling methods will only be achieved by greater availability of reliable data obtained by same experimental methodology.Rastvorljivost leka u vodi je faktor koji može značajno da utiče na bioraspoloživost peroralno primenjenog leka, kao i na njegovu raspodelu u organizmu. Razmatranjem rastvorljivosti u ranim fazama otkrića i razvoja leka smanjuje se mogućnost neuspeha u daljem razvoju leka. Računarske metode za predviđanje rastvorljivosti lekova omogućavaju analizu kombinatornih baza podataka, identifikaciju potencijalno problematičnih jedinjenja i isključivanje onih čija je rastvorljivost neadekvatna. Pored predviđanja rastvorljivosti na osnovu hemijske strukture, analizom ovih modela moguće je detaljnije razjasniti odnose hemijske strukture i rastvorljivosti ispitivanih lekova i optimizovati strukture u cilju poboljšanja rastvorljivosti, pri čemu bi njihova aktivnost ostala nepromenjena. Razvoj ovakvih modela je kompleksan proces koji zahteva razmatranje velikog broja faktora koji mogu uticati na uspešnost predviđanja konačnog modela. U ovom radu su prikazani različiti pristupi koji se koriste u razvoju računarskih modela za predviđanje rastvorljivosti. I pored intenzivnog rada na razvoju ovih modela tokom protekle decenije, pouzdanost predviđanja rastvorljivosti lekova različitih struktura još uvek ostaje veliki izazov. Kvalitet dostupnih eksperimentalnih podataka koji se koriste u modelovanju rastvorljivosti se u sve većoj meri prepoznaje kao jedan od glavnih uzroka ograničene pouzdanosti većine do sada predloženih modela. Iskorišćenje punog potencijala razvijenih pristupa modelovanja rastvorljivosti uslovljeno je širom dostupnošću pouzdanih podataka za rastvorljivost određenih pod identičnim eksperimentalnim uslovima

The importance of the accuracy of the experimental data for the prediction of solubility

Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets – data set 1, containing solubility data collected from various literature sources using a few criteria (n = 319), data set 2, created by substituting 28 values from data set 1 with uniformly determined experimental data from one laboratory (n = 319), and data set 3, created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the data set 2 (n = 375). The selection of the most significant descriptors was performed by the heuristic method, using one-parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR) for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set

Theoretical Models and QSRR in Retention Modeling of Eight Aminopyridines

In this article, retention modeling of eight aminopyridines (synthesized and characterized at the Faculty of Pharmacy) in reversed-phase high performance liquid chromatography (RP-HPLC) was performed. No data related to their retention in the RP-HPLC system were found. Knowing that, it was recognized as very important to describe their retention behavior. The influences of pH of the mobile phase and the organic modifier content on the retention factors were investigated. Two theoretical models for the dependence of retention factor of organic modifier content were tested. Then, the most reliable and accurate prediction of log k was created, testing multiple linear regression model-quantitative structure-retention relationships (MLR-QSRR) and support vector regression machine-quantitative structure-retention relationships (SVM-QSRR). Initially, 400 descriptors were calculated, but four of them (POM, log D, M-SZX/RZX and m-RPCG) were included in the models. SVM-QSRR performed significantly better than the MLR model. Apart from aminopyridines, four structurally similar substances (indapamide, gliclazide, sulfamethoxazole and furosemide) were followed in the same chromatographic system. They were used as external validation set for the QSRR model (it performed well within its applicability domain, which was defined using a bounding box approach). After having described retention of eight aminopyridines with both theoretical and QSRR models, further investigations in this field can be conducted

Synthesis, cytotoxicity and computational study of novel protoberberine derivatives

A novel and efficient synthetic route was developed for the preparation of protoberberine derivatives. A methodology, designed primarily to control the substitution patterns on the terminal rings, was used to access a small array of these compounds. An initial biological profiling suggested an anticancer potential of the synthesised derivatives, while structure-based target fishing identified their potential targets and established a rational basis for further structural modifications. Although the activities need further improvement, the study demonstrated that the described approach may be useful in the discovery of novel lead compounds

Molecular dynamics simulations and computational design of inhibitors of protein lysine methyltransferase EZH2

Enhancer of Zeste Homolog 2 (EZH2) je epigenetski enzim koji vrši selektivno metilovanje lizina 27 na histonu H3 (H3K27). Trimetilovani H3K27 je represivan epigenetski signal, cime EZH2 utice na smanjenje transkripcije ciljnih gena. Prekomerna aktivnost EZH2, izazvana povecanom ekspresijom ili mutacijama njegovog katalitickog domena, dovedena je u vezu sa vecim brojem maligniteta kod ljudi, a inhibicija ovog enzima smatra se perspektivnom strategijom u razvoju novih antitumorskih lekova. Prvi selektivni inhibitori EZH2 otkriveni su 2012. godine i njihova biološka karakterizacija potvrdila je terapijski potencijal inhibicije ovog epigenetskog regulatora. Iako postoji potreba za otkricem novih inhibitora, znacajnu prepreku u njihovom dizajnu do sada je predstavljao nedostatak trodimenzionalne strukture kompleksa EZH2 i okarakterisanih liganada. Cilj istraživanja u okviru ove doktorske disertacije bio je da se primenom racunarskih metoda uspostavi i validira model vezivnog mesta EZH2, i da se, zatim, uspostavljeni model upotrebi u dizajnu novih inhibitora kao potencijalnih antitumorskih lekova. U prvoj fazi istraživanja izvršeno je homologo modeliranje katalitickog domena EZH2 koji sadrži vezivno mesto za kofaktor, a za koje se kompetitivno vezuju svi poznati inhibitori. Pocetni model usavršen je primenom simulacija konvencionalne i ubrzane molekularne dinamike (MD), a zatim je daljim simulacijama istraživano kako 13 inhibitora piridonske strukture stupa u interakciju sa vezivnim mestom na enzimu. Korišcenjem prikupljenih trajektorija izvršena je procena slobodne energije vezivanja inhibitora zasnovana na molekularnoj mehanici uz generalizovani Bornov model rastvaraca (MM/GBSA). Izracunate energije upore ene su sa dostupnim eksperimentalnim podacima i utvr eno je dobro slaganje izme u vrednosti koje ukazuje na validnost uspostavljenog modela. Daljom analizom trajektorija i primenom racunarske mutageneze u alanin identifikovane su intermolekulske interakcije koje kljucno doprinose aktivnosti i selektivnosti proucavanih inhibitora. Na ovim osnovama, definisan je farmakoforni model inhibitora EZH2 a njegovom validacijom potvr ena je mogucnost uspešne identifikacije i inhibitora baziranih na drugim osnovnim strukturama. Efikasnost mnogih antitumorskih lekova ogranicava razvoj rezistencije tui morskih celija, pri cemu je opisan veci broj mehanizama koji dovode do pojave rezistencije...methylation of lysine 27 on histone H3. Trimethylated H3K27 constitutes a repressive epigenetic mark, making EZH2 capable of transcriptional silencing of target genes. Aberrant EZH2 activity, caused by either overexpression or point mutations in the catalytic domain, has been related to a number of human malignancies, making EZH2 inhibition a promising strategy in the development of novel anticancer treatments. First disclosure of selective EZH2 inhibitors in 2012 and their biological characterization has confirmed the therapeutic potential of inhibiting this epigenetic regulator. While there is an outstanding need for the discovery of novel EZH2 inhibitors, the lack of a three-dimensional structure of EZH2 complexed with one of its ligands has been a significant obstacle to this end. The aim of research conducted as part of this doctoral dissertation was to establish and validate a model of EZH2 binding site using computational methods, and to subsequently utilize the established model as a foundation in the design of novel potential EZH2 inhibitors. In the first stage of our research, homology modeling of the EZH2 catalytic domain was conducted, which contains the cofactor binding site targeted by all known inhibitors. The initial model was refined through conventional and accelerated molecular dynamics (MD) simulations. Next, structures of EZH2 complexed with 13 different pyridone inhibitors were simulated in order to study the key interactions participating in ligand binding. An assessment of inhibitors’ binding free energy was performed using the molecular mechanics/generalized Born surface area (MM/GBSA) method. The computed energies were correlated to available experimental data and good agreement was found supporting the validity of the established model. Further analysis of MD trajectories and computational alanine scanning facilitated the definition of key intermolecular interactions that contribute to the observed potency and selectivity of the studied inhibitors. On this basis, a pharmacophore model of EZH2 inhibitors was proposed and validated. It demonstrated good capabilities in identifying truly active molecules, including those not based on a pyridone scaffold, in presence of decoys. The efficacy of anticancer drugs can become limited with the onset of resiiv stance that can develop in cancer cells through various mechanisms..

Computational models for predicting drug transport mediated by P-glycoprotein

P-glycoprotein (Pgp) is a transmembrane transporter which can, by transporting structurally diverse compounds, influence the absorption, distribution and efficacy of a number of drugs. Pgp overexpression in cells is a major contributing factor to the development of drug resistance. For these reasons, potential for compound efflux by Pgp should be assessed early on in the drug discovery process, preferably even prior to compound synthesis. To meet this demand, numerous computational models have been developed during the past decade, capable of predicting Pgp-mediated transport based solely on chemical structures. This paper summarizes the various approaches that have been used for model development, discusses their advantages and disadvantages and focuses on key factors that influence model reliability. The promiscuous nature of the transport can be seen as a major challenge for most computational chemistry methods. Nevertheless, the attained level of accuracy of literature models suggests that they can be useful in the drug discovery setting. Greater availability of experimental data and integration of predictions made by different modeling methods has the potential to further improve the reliability of computational predictions