AN OVERVIEW OF EPIGENETIC DRUGS, AND THEIR VIRTUAL SCREENING STUDY RETRIEVED FROM ZINC DATABASE ALONG WITH AN AUTODOCK STUDY OF THE BEST INHIBITOR

Objective: Over the last 30 y cancer epigenetics research has grown extensively. It is note-worthy to recognize that epigenetic misregulation could substantiate the development of cancer and we need to continue to look for anti-neoplastic epi-drugs. Taking into consideration this phenomenon, our first aim is to search for an effective epi-drugs by virtual screening from ZINC database and to explore the validity of the virtual screening. The second aim is to explore a binding conformation of the top affinity ligands against macromolecules, by docking experiment. Methods: The virtual screening was conducted by our Virtual Screening by Docking (VSDK) algorithm and procedure. Small molecules were randomly downloaded by ZINC database. For docking experiment, AutoDock 4.2.6 and AutoDock Tool were used. Results: It took eight to ten hours for the successful virtual screening of the 2778 small compounds retrieved at random from ZINC database. Among histone H2B E76K mutant (HHEM) inhibitors and DNA methyltransferase (DNMT) inhibitors, the first ranked inhibitors were 1H-1,2,4-triazole-3,5-diamine and 2-ethyl-1,3,4-oxadiazole respectively. Conclusion: As for the molecular structures obtained from virtual screening, most of the top ten HHEM and DNMT inhibitors contained 5-member rings. More than two times in affinity difference between the top and bottom ten compounds would indicate a successful virtual screening experiment. The histogram chart of AutoDock4 runs appeared in the lowest affinity region with two or three hydrogen bonds indicating a reliable conformation docking

Strategies for cystic fibrosis transmembrane conductance regulator inhibition: from molecular mechanisms to treatment for secretory diarrhoeas

Cystic fibrosis transmembrane conductance regulator (CFTR) is an unusual ABC transporter. It acts as an anion-selective channel that drives osmotic fluid transport across many epithelia. In the gut, CFTR is crucial for maintaining fluid and acid-base homeostasis, and its activity is tightly controlled by multiple neuro-endocrine factors. However, microbial toxins can disrupt this intricate control mechanism and trigger protracted activation of CFTR. This results in the massive faecal water loss, metabolic acidosis and dehydration that characterize secretory diarrhoeas, a major cause of malnutrition and death of children under 5 years of age. Compounds that inhibit CFTR could improve emergency treatment of diarrhoeal disease. Drawing on recent structural and functional insight, we discuss how existing CFTR inhibitors function at the molecular and cellular level. We compare their mechanisms of action to those of inhibitors of related ABC transporters, revealing some unexpected features of drug action on CFTR. Although challenges remain, especially relating to the practical effectiveness of currently available CFTR inhibitors, we discuss how recent technological advances might help develop therapies to better address this important global health need

Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints

Background Tools to explore large compound databases in search for analogs of query molecules provide a strategically important support in drug discovery to help identify available analogs of any given reference or hit compound by ligand based virtual screening (LBVS). We recently showed that large databases can be formatted for very fast searching with various 2D-fingerprints using the city-block distance as similarity measure, in particular a 2D-atom pair fingerprint (APfp) and the related category extended atom pair fingerprint (Xfp) which efficiently encode molecular shape and pharmacophores, but do not perceive stereochemistry. Here we investigated related 3D-atom pair fingerprints to enable rapid stereoselective searches in the ZINC database (23.2 million 3D structures). Results Molecular fingerprints counting atom pairs at increasing through-space distance intervals were designed using either all atoms (16-bit 3DAPfp) or different atom categories (80-bit 3DXfp). These 3D-fingerprints retrieved molecular shape and pharmacophore analogs (defined by OpenEye ROCS scoring functions) of 110,000 compounds from the Cambridge Structural Database with equal or better accuracy than the 2D-fingerprints APfp and Xfp, and showed comparable performance in recovering actives from decoys in the DUD database. LBVS by 3DXfp or 3DAPfp similarity was stereoselective and gave very different analogs when starting from different diastereomers of the same chiral drug. Results were also different from LBVS with the parent 2D-fingerprints Xfp or APfp. 3D- and 2D-fingerprints also gave very different results in LBVS of folded molecules where through-space distances between atom pairs are much shorter than topological distances. Conclusions 3DAPfp and 3DXfp are suitable for stereoselective searches for shape and pharmacophore analogs of query molecules in large databases. Web-browsers for searching ZINC by 3DAPfp and 3DXfp similarity are accessible at www.gdb.unibe.ch webcite and should provide useful assistance to drug discovery projects

Drug-discovery tools for cystic fibrosis: optical probes to quantify gating and trafficking of ΔF508-CFTR

Cystic fibrosis (CF) is a debilitating disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which codes for the CFTR anion channel. ΔF508, the most common CF-associated mutation, causes both a gating and a trafficking defect in the CFTR protein. This thesis describes the optimisation and use of two fluorescence assays, capable of measuring the two defects caused by the ΔF508 mutation. Yellow fluorescent protein (YFP)-CFTR, in which halide sensitive YFP is tagged to the Nterminal of the CFTR coding sequence, is a functional assay, used to test for ΔF508-CFTR potentiator activity. CFTR-pHTomato, in which the pH sensor is tagged to the fourth extracellular loop of CFTR, is used to measure membrane density and internal CFTR expression. Human embryonic kidney cells (HEK293 cells), expressing YFP-ΔF508-CFTR were used to screen a pilot library of compounds with some structural similarity to known potentiator VX- 770. Ligand-based virtual screening was then used to construct two further libraries, based on VX-770 and the lead hit compound from the pilot screen. A number of novel ΔF508-CFTR potentiators were identified in each of the screens. Recently published studies suggest that chronic treatment with VX-770 decreases ΔF508- CFTR density at the plasma membrane, potentially limiting its clinical effectiveness. ΔF508- CFTR-pHTomato was used to show that a number of hit compounds from our screens did not decrease membrane density of ΔF508-CFTR. The YFP-CFTR assay was also used for studies investigating the mechanism of potentiation by VX-770. We provide evidence that WT-CFTR does not require phosphorylation for potentiation by VX-770, which has not been reported previously. Additionally, studies using gating mutations do not support the current hypothesis that VX-770 stabilises the posthydrolytic open state in the CFTR gating cycle

Cheminformatics Tools to Explore the Chemical Space of Peptides and Natural Products

Cheminformatics facilitates the analysis, storage, and collection of large quantities of chemical data, such as molecular structures and molecules' properties and biological activity, and it has revolutionized medicinal chemistry for small molecules. However, its application to larger molecules is still underrepresented. This thesis work attempts to fill this gap and extend the cheminformatics approach towards large molecules and peptides. This thesis is divided into two parts. The first part presents the implementation and application of two new molecular descriptors: macromolecule extended atom pair fingerprint (MXFP) and MinHashed atom pair fingerprint of radius 2 (MAP4). MXFP is an atom pair fingerprint suitable for large molecules, and here, it is used to explore the chemical space of non-Lipinski molecules within the widely used PubChem and ChEMBL databases. MAP4 is a MinHashed hybrid of substructure and atom pair fingerprints suitable for encoding small and large molecules. MAP4 is first benchmarked against commonly used atom pairs and substructure fingerprints, and then it is used to investigate the chemical space of microbial and plants natural products with the aid of machine learning and chemical space mapping. The second part of the thesis focuses on peptides, and it is introduced by a review chapter on approaches to discover novel peptide structures and describing the known peptide chemical space. Then, a genetic algorithm that uses MXFP in its fitness function is described and challenged to generate peptide analogs of peptidic or non-peptidic queries. Finally, supervised and unsupervised machine learning is used to generate novel antimicrobial and non-hemolytic peptide sequences

IN SILICO APPROACHES IN DRUG DESIGN AND DEVELOPMENT: APPLICATIONS TO RATIONAL LIGAND DESIGN AND METABOLISM PREDICTION

In the last decades, the applications of computational methods in medicinal chemistry have experienced significant changes which have incredibly expanded their approaches, and more importantly their objectives. The overall aim of the present research project is to explore the different fields of the modelling studies by using well-known computational methods as well as different and innovative techniques. Indeed, computational methods traditionally consisted in ligand-based and the structure-based approaches substantially aimed at optimizing the ligand structure in terms of affinity, potency and selectivity. The studies concerning the muscarinic receptors in the present thesis applied these approaches for the rational design of novel improved bioactive molecules, interacting both in the orthosteric (e.g., 1,4-dioxane agonist) and in the allosteric sites. The research includes also the application of a novel method for target optimization, which consists in the generation of the so-called conformational chimeras to explore the flexibility of the modelled GPCR structures. In parallel, computational methods are finding successful applications in the research phase which precedes the ligand design and which is focused on a detailed validation and characterization of the biological target. A proper example of this kind of studies is given by the study regarding the purinergic receptors, which is aimed at the identification and characterization of potential allosteric binding pockets for the already reported inhibitors, exploiting also innovative approaches for binding site predictions (e.g., PELE, SPILLO-PBSS). Over time, computational applications felt a rich extension of their objectives and one of the clearest examples is represented by the ever increasing attempts to optimize the ADME/Tox profile of the novel compounds, so reducing the marked attrition in drug discovery caused by unsuitable pharmacokinetic profiles. Coherently, the first and main project of the present thesis regards the field of metabolism prediction and is founded on the meta-analysis and the corresponding database called MetaSar, manually collected from the recent specialized literature. This ongoing extended project includes different studies which are overall aimed at developing a comprehensive method for metabolism prediction. In detail, this Thesis reports an interesting application of the database which exploits an innovative predictive technique, the Proteochemometric modelling (PCM). This approach is indeed at the forefront of the latest modelling techniques, as it perfectly fits the growing request of new solutions to deal with the incredibly huge amount of data recently produced by the \u201comics\u201d disciplines. In this context, MetaSar represents an alternative and still appropriate source of data for PCM studies, which also enables the extension of its fields of application to a new avenue, such as the prediction of metabolism biotransformation. In the present thesis, we present the first example of these applications, which involves the building of a classification model for the prediction of the glucuronidation reaction. The field of glucuronidation reactions is exhaustively explored also through an homology modelling study aimed at defining the complete three-dimensional structure of the enzyme UGT2B7, the main isoform of glucuronidation enzymes in humans, in complex with the cofactor UDPGA and a typical substrate, such as Naproxen. The paths of the substrate entering to the binding site and the egress of the product have been investigated by performing Steered Molecular Dynamics (SMD) simulations, which were also useful to gain deeper insights regarding the full mechanism of action and the movements of the cofactor