Development of new Catechol-Omethyltransferase inhibitors

The Catechol-O-methyltransferase (COMT, EC 2.2.1.6) is an enzyme responsible for the Omethylation of catechol substrates, such as catecholamines and catechol estrogens. Considering its physiological functions and the existence of polymorphisms, several studies associate COMT with the pathogenesis of several neurological disorders, especially with Parkinson’s Disease (PD) as well as with cardiovascular and hormone-dependent cancers, like breast cancers. Given the important role that COMT has in the catecholamines and catechol estrogens metabolism, COMT has become a relevant therapeutic target. Currently, the most effective and clinically approved by the Federal Drug Administration and the European Medicines Agency for the PD therapy consists of the use of Levodopa, combined with COMT inhibitors. Since the commercially available inhibitors for this enzyme still display a lot of disadvantages, like hepatoxicity, difficulty to reach the brain, among others, the main goal of this work was to develop new COMT inhibitors with potential clinical interest for the PD therapy. For this, triazolopyrimidinics were prepared through the Biginelli reaction, that can be considered catechol bioisosteres, therefore have a higher potential to interact with COMT. This hypothesis was confirmed through molecular docking, being predicted similar interactions as the ones that the catecholic substrates forms with the COMT active site. Their inhibitory properties were evaluated in human recombinant COMT lysates, after the compounds’ incubation at 10 and 100 µM. Contrary to what was expected, the compounds increased the enzyme specific activity, being considered COMT stabilizers. The compounds cytotoxicity was also evaluated in neural dopaminergic rat cells (N27), in the same concentrations. The vast majority of compounds at 10 µM did not exhibited cytotoxicity, being observed similar values to those of the commercial COMT inhibitors, Entacapone and Tolcapone, in the studied cell line. As expected, with the increase in compounds’ concentration (100 µM) a decrease in the relative cell proliferation was observed, reaching values considered to be cytotoxic. Altogether, the synthesized compounds at the concentration of 10 µM stabilized COMT and did not induce cytotoxicity in the N27 cells. In sum, these compounds may be useful for thermal stability assays, crystallography, structure-activity relationship studies and display potential to be studied in specific breast cancers cell lines.A Catecol-O-metiltransferase (COMT, EC 2.2.1.6)) é a enzima responsável pela O-metilação de substratos catecólicos, como as catecolaminas e os estrogénios com estrutura catecólica. Considerando as suas funções fisiológicas e a existência de polimorfismos, vários estudos associam a COMT com a patogénese de várias desordens neurológicas, especialmente com a Doença de Parkinson (DP) e também com doenças cardiovasculares e cancros hormonodependentes, como cancros da mama. Devido à importância que a COMT tem no metabolismo das catecolaminas e dos estrogénios catecólicos, a COMT tornou-se nas últimas décadas num importante alvo terapêutico. Atualmente, a terapia mais eficaz e clinicamente aprovadas pela Federal Drug Administration e European Medicines Agency para a doença de Parkinson consiste no uso de Levodopa, combinada com inibidores da COMT. Uma vez que os inibidores desta enzima comercialmente disponíveis ainda apresentam diversas desvantagens, como hepatotoxicidade, dificuldade em alcançar o cérebro, entre outras, o objetivo principal deste trabalho foi desenvolver novos inibidores da COMT com potencial clínico para a terapia da DP. Para isto, foram preparados triazolopirimidínicos através da reação de Biginelli, os quais podem ser considerados bioisósteros de catecóis, podendo por isso ter potencial para interagir com a COMT. Esta hipótese foi confirmada através de docking molecular, prevendo-se interações moleculares semelhantes às dos substratos catecóis com o centro ativo da COMT. As suas propriedades inibitórias foram avaliadas em lisados recombinantes da enzima, após incubação dos compostos nas concentrações de 10 e 100 µM. Contrariamente ao expectável, os compostos aumentaram a atividade específica da enzima, podendo ser considerados estabilizadores da COMT. Foi ainda avaliada a citoxicidade dos mesmos em células dopaminérgicas neuronais de rato (N27) nas mesmas concentrações. A grande maioria dos compostos a 10 µM não mostrou citotoxicidade, observando-se valores semelhantes aos dos inibidores comerciais da COMT, Entacapone e Tolcapone, na linha celular N27. Como esperado com o aumento da concentração (100 µM) ocorreu um decréscimo na proliferação celular, atingindo valores já considerados citotóxicos. No geral, os compostos sintetizados, na concentração de 10 µM estabilizaram a COMT e não induziram citoxicidade nas células N27. Em suma, as moléculas sintetizadas podem ser úteis para estudos de estabilidade térmica, de cristalografia, de relação estrutura-atividade e apresentam potencialidade para ser estudados em linhas celulares especificas do cancro da mama

Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases.

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools

Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis

The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health and defines a need to develop new, effective and inexpensive anti-tubercular agents. Previously we developed a chemical systems biology approach to identify off-targets of major pharmaceuticals on a proteome-wide scale. In this paper we further demonstrate the value of this approach through the discovery that existing commercially available drugs, prescribed for the treatment of Parkinson's disease, have the potential to treat MDR and XDR tuberculosis. These drugs, entacapone and tolcapone, are predicted to bind to the enzyme InhA and directly inhibit substrate binding. The prediction is validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone. The minimal inhibition concentration (MIC99) of entacapone for Mycobacterium tuberculosis (M.tuberculosis) is approximately 260.0 µM, well below the toxicity concentration determined by an in vitro cytotoxicity model using a human neuroblastoma cell line. Moreover, kinetic assays indicate that Comtan inhibits InhA activity by 47.0% at an entacapone concentration of approximately 80 µM. Thus the active component in Comtan represents a promising lead compound for developing a new class of anti-tubercular therapeutics with excellent safety profiles. More generally, the protocol described in this paper can be included in a drug discovery pipeline in an effort to discover novel drug leads with desired safety profiles, and therefore accelerate the development of new drugs

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

Optical substrates for drug-metabolizing enzymes : Recent advances and future perspectives

Drug-metabolizing enzymes (DMEs), a diverse group of enzymes responsible for the metabolic elimination of drugs and other xenobiotics, have been recognized as the critical determinants to drug safety and efficacy. Deciphering and understanding the key roles of individual DMEs in drug metabolism and toxicity, as well as characterizing the interactions of central DMEs with xenobiotics require reliable, practical and highly specific tools for sensing the activities of these enzymes in biological systems. In the last few decades, the scientists have developed a variety of optical substrates for sensing human DMEs, parts of them have been successfully used for studying target enzyme(s) in tissue preparations and living systems. Herein, molecular design principals and recent advances in the development and applications of optical substrates for human DMEs have been reviewed systematically. Furthermore, the challenges and future perspectives in this field are also highlighted. The presented information offers a group of practical approaches and imaging tools for sensing DMEs activities in complex biological systems, which strongly facilitates high-throughput screening the modulators of target DMEs and studies on drug/herb-drug interactions, as well as promotes the fundamental researches for exploring the relevance of DMEs to human diseases and drug treatment outcomes. (C) 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.Peer reviewe

Molecular Determinants of Juvenile Hormone Action as Revealed by 3D QSAR Analysis in Drosophila

BACKGROUND:Postembryonic development, including metamorphosis, of many animals is under control of hormones. In Drosophila and other insects these developmental transitions are regulated by the coordinate action of two principal hormones, the steroid ecdysone and the sesquiterpenoid juvenile hormone (JH). While the mode of ecdysone action is relatively well understood, the molecular mode of JH action remains elusive. METHODOLOGY/PRINCIPAL FINDINGS:To gain more insights into the molecular mechanism of JH action, we have tested the biological activity of 86 structurally diverse JH agonists in Drosophila melanogaster. The results were evaluated using 3D QSAR analyses involving CoMFA and CoMSIA procedures. Using this approach we have generated both computer-aided and species-specific pharmacophore fingerprints of JH and its agonists, which revealed that the most active compounds must possess an electronegative atom (oxygen or nitrogen) at both ends of the molecule. When either of these electronegative atoms are replaced by carbon or the distance between them is shorter than 11.5 A or longer than 13.5 A, their biological activity is dramatically decreased. The presence of an electron-deficient moiety in the middle of the JH agonist is also essential for high activity. CONCLUSIONS/SIGNIFICANCE:The information from 3D QSAR provides guidelines and mechanistic scope for identification of steric and electrostatic properties as well as donor and acceptor hydrogen-bonding that are important features of the ligand-binding cavity of a JH target protein. In order to refine the pharmacophore analysis and evaluate the outcomes of the CoMFA and CoMSIA study we used pseudoreceptor modeling software PrGen to generate a putative binding site surrogate that is composed of eight amino acid residues corresponding to the defined molecular interactions

Computer-Aided Drug Design of Neuraminidase Inhibitors and MCL-1 Specific Drugs

Ph.DDOCTOR OF PHILOSOPH