Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression net-works and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators

Machine learning applications to essential oils and natural extracts

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allow computers to learn without being explicitly programmed. Various are the applications of ML in pharmaceutical sciences, especially for the prediction of chemical bioactivity and physical properties, becoming an integral component of the drug discovery process. ML is characterized by three learning paradigms that differ in the type of task or problem that an algorithm is intended to solve: supervised, unsupervised, and reinforcement learning. In chapter 2, supervised learning methods were applied to extracts of Lycium barbarum L. fruits for the development of a QSPR model to predict zeaxanthin and carotenoids content based on routinely colorimetric analyses performed on homogenized samples, developing a useful tool that could be used in the food industry. In chapters 3 and 4, ML was applied to the chemical composition of essential oils and correlated to the experimentally determined associated biofilm modulation influence that was either positive or negative. In these two studies, it was demonstrated that biofilm growth is influenced by the presence of essential oils extracted from different plants harvested in different seasons. ML classification techniques were used to develop a Quantitative Activity-Composition Relationship (QCAR) to discover the chemical components mainly responsible for the anti-biofilm activity. The derived models demonstrated that machine learning is a valuable tool to investigate complex chemical mixtures, enabling scientists to understand each component's contribution to the activity. Therefore, these classification models can describe and predict the activity of chemical mixtures and guide the composition of artificial essential oils with desired biological activity. In chapter 5, unsupervised learning models were developed and applied to clinical strains of bacteria that cause cystic fibrosis. The most severe infections reoccurring in cystic fibrosis are due to S. aureus and P. aeruginosa. Intensive use of antimicrobial drugs to fight lung infections leads to the development of antibiotic-resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in patients. Sixty-one essential oils were studied against a panel of 40 clinical strains of S. aureus and P. aeruginosa isolated from cystic fibrosis patients, and unsupervised machine learning algorithms were applied to pick-up a small number of representative strains (clusters of strains) among the panel of 40. Thus, rapidly identifying three essential oils that strongly inhibit antibiotic-resistant bacterial growth

Investigation of socio-demographic, clinical and genetic factors associated with blood pressure and glycaemic control among indigenous South African adult patients

Doctor ScientiaeAchieving blood pressure and glycaemic treatment targets remain a major public health challenge in individuals with hypertension and diabetes mellitus (DM). This research project was, therefore, designed to investigate the socio-demographic, clinical and genetic factors associated with blood pressure and glycaemic control among indigenous South African adult patients. The main aims of the project were as follows: (1) To assess the prevalence and socio-demographic factors associated with uncontrolled hypertension, in individuals receiving chronic care in primary healthcare facilities, based in the rural areas of Mkhondo Municipality (Study 1). (2) To investigate the association of nineteen single nucleotide polymorphisms (SNPs) with blood pressure control among adult patients treated with hydrochlorothiazide (Study 2). (3) To assess the level of association between twelve SNPs with uncontrolled blood pressure for adult patients treated with amlodipine (Study 3). (4) To examine the association of five SNPs in selected genes (ABO, VEGFA, BDKRB2, NOS3 and ADRB2) with blood pressure response to enalapril treatment, and further assess interaction patterns that influence blood pressure response (Study 4). (5) To determine the prevalence of poor glycaemic control and its influencing factors among adult patients from Mkhondo Municipality attending chronic care for DM (Study 5). (6) To evaluate the level of association between polymorphisms found in the SLC22A1, SP1, PRPF31, NBEA, SCNN1B, CPA6 and CAPN10 genes, and glycaemic response to metformin and Sulphonylureas (SU) combination therapy among South African adults with DM. Also, to investigate interaction patterns that influence glycaemic control in response to metformin and SU combination therapy (Study 6)

Аntagonists of Estrogen Receptor α: Rational Design of New Breast Cancer Suppressants Based on 3-D QSAR, COMBINEr, and 3-D Pharmacophore Studies

Estrogen receptor α (ERα) predstavlja transkripcioni regulator čija je fiziološka aktivnost indukovana 17β-estradiolom i koji inicira transkripcionu mašineriju zavisnu od RNA polimeraze II, nakon čega može doći do razvoja kancera dojke direktnim ili indirektnim genomskim putem. Da bi se dizajnirali i sintetisali inovativni ligandi ERα kao supresanti raka dojke, trodimenzionalne studije zavisnosti strukture od aktivnosti (3-D QSAR) bazirane na strukturi molekulske mete (SB), izvedene pomoću 3-D QSAutogrid/R, Py-CoMFA ili PHASE softvera, zatim studije upoređivanja vezivnih energija (COMBINEr), izvedene pomoću Py-ComBinE softvera, kao i 3-D farmakoforne studije, izvedene pomoću PHASE softvera, sprovedene su na osnovu eksperimentalno određenih bioaktivnih konformacija parcijalnih agonista, mešovitih agonista/antagonista (SERMs) i potpunih antagonista (SERDs), ko-kristalizovanih u kompleksu sa prirodnim ili mutiranim receptorima. Takođe, procene sposobnosti reprodukcije bioaktivnih konformacija na osnovu struktura molekulskih meta (SB) kao i bioaktivnih konformacija samih liganada (LB), sprovedene pomoću softvera besplatnih za akdemsku zajednicu ili komercijalnih rešenja, dale su upute kako da se izvrši SB/LB poravnanje netestiranih jedinjenja. 3-D QSAR, COMBINEr i 3-D farmakoforni modeli za ligande ERα, upareni sa pravilima za SB/LB poravnanja, obznanjeni su kao korisni za definisanje molekularnih determinanti za antikancerogenu aktivnost baziranu na antagonizmu ERα kao i za predviđanje aktivnosti odgovarajućih liganada. Ovde razvijeni protokoli verifikovani su kroz dizajn i predviđanje aktivnosti 12 novih SERMs kumarinskog tipa, proisteklih iz 3-D QSAutogrid/R metodologije, nazvanih 3DQ-1a do 3DQ-1e, zatim 6 novih kumarinskih SERMs proisteklih iz Py-CoMFA i PyComBinE metodologija, označenih kao CBE-1 do CBE-6, kao i 12 novih derivata brefeldina A (BFA), kao rezultat aplikacije PHASE 3-D farmakofornih i 3-D QSAR studija, označenih kao 3DPQ-1 do 3DPQ-12, koji su razvijeni nakon virtuelnog skeniranja datasetova iz National Cancer Institute i optimizacije BFA. Svi novi in silico-dizajnirani ERα antagonisti sintetisani su i potvrđeni kao selektivni antagonisti ERα, pokazujući aktivnost u rM do nM opsegu. Jedinjenja su potvrđena kao modulatori ERα i validirana kao antiproliferativni agensi na nivou MCF-7 ćelijskih linija kancera dojke, takođe ispoljavajući rM do nM aktivnost, u isto vreme ne pokazujući agonizam prema endometrijalnim ćelijskim linijama, čime su ispoljili farmakološki profil superiorniji u odnosu na SERMs. Mehanizam delovanja proučavan je na nivou inhibicije Raf-1/MAPK/ERK i p53 puteva signalne transdukcije, sprečavajući hormonski-posredovanu ekspresiju gena na nivou direktnog i indirektnog puta i stopirajući proliferaciju MCF-7 ćelijskih linija u G0/G1 fazi. In vivo eksperimenti u smislu per os administracije na ženkama pacova iz Wistar soja sa indukovanim kancerom dojke, izdvojili su derivate 3DQ-4a, 3DQ-2a, 3DQ-1a, 3DQ1b, 3DQ-2b, 3DQ-3b, CBE-4, CBE-5, CBE-3, 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ2 i 3DPQ-1 kao one sa izvanrednim potencijalom supresije tumora uz ispoljavanje optimalnog farmakokinetičkog profila i bez značajnih histopatoloških posledica. Priloženi podaci ukazuju da navedena jedinjenja trebaju biti podvrgnuta kliničkim studijama u lečenju kancera dojke.The estrogen receptor α (ERα) represents a 17β-estradiol inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e. tethered) pathway. To design and synthesize innovative ligands against ERα, structure-based (SB) three-dimensional quantitative structure-activity relationships (3-D QSAR) studies, conducted either employing 3-D QSAutogrid/R, Py-CoMFA, or PHASE software, SB Comparative Binding Energy (COMBINEr) studies, performed with the aid of Py-ComBinE software, as well as SB 3-D Pharmacophore studies, developed using PHASE software, have been undertaken using experimentally determined bioactive conformations of partial agonists, mixed agonists/antagonists (SERMs), and full antagonists (SERDs) co-crystallized within either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments assessments, performed with the aid of free-for academia or commercial software, ruled out the guidelines for SB/LB alignment of untested compounds. 3-D QSAR, COMBINEr, and 3-D Pharmacophore models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein-developed protocols were verified through the design and potency prediction of new 12 coumarin-based SERMs originating from the 3-D QSAutogrid/R methodology, namely 3DQ-1a to 3DQ-1е, new 6 coumarin-based SERMs originating from the Py-CoMFA and PyComBinE methodologies, namely CBE-1 to CBE-6, as well as 12 new Brefeldin A (BFA)- derivatives, originating from the PHASE 3-D Pharmacophore studies and 3-D QSAR studies, 3DPQ-1 to 3DPQ-12, emerging after the virtual screening of National Cancer Institute datasets and lead optimization of BFA. All new in silico-designed ERα antagonists were synthesized and confirmed as selective ERα antagonists, showing potencies ranging from single-digit nanomolar to picomolar. The hits were confirmed as selective estrogen receptor modulators and validated as antiproliferative agents using MCF-7 breast cancer cell lines exerting picomolar to low nanomolar potency, at the same time showing no agonistic activity within endometrial cell lines thus exerting a superior profile than SERMs. Their mechanism of action was inspected and revealed to be through the inhibition of the Raf-1/MAPK/ERK and p53 signal transduction pathways, preventing hormone-mediated gene expression on either genomic direct or genomic indirect level, and stopping the MCF-7 cells proliferation at G0/G1 phase. In vivo experiments, by means of the per os administration to female Wistar rats with preinduced breast cancer, distinguished the following derivatives, 3DQ-4a, 3DQ-2a, 3DQ-1a, 3DQ1b, 3DQ-2b, 3DQ-3b, CBE-4, CBE-5, CBE-3, 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ2, and 3DPQ-1, showing remarkable potency as tumor suppressors endowing with optimal pharmacokinetic profiles and no significant histopathological profiles. The presented data indicate the new compounds as potential candidates to be submitted to clinical trials for breast cancer therapy