Repurposing ciclopirox as a pharmacological chaperone active against congenital erythropoietic porphyria.

138 p.La porfiria eritropoyética congénita es una enfermedad rara autosómica recesiva producida por una actividad deficiente en la uroporfirinógeno III sintasa, la cuarta enzima de la ruta biosintética del grupo hemo. La enfermedad afecta a diversos órganos, llegando a ser potencialmente peligroso para la vida, careciendo actualmente de tratamientos curativos. Bioquímicamente, las mutaciones hereditarias de mayor frecuencia reducen la estabilidad del enzima, alterando su homeostasis, que, en última instancia, reducen la producción de grupo hemo intracelular. Esto da como resultado la acumulación de subproductos de uroporfirina que se distribuyen y depositan por todos los tejidos, agravando la patología con síntomas tales como fotosensibilidad de la piel y lesiones cutáneas fototóxicas desfigurantes. En el presente trabajo, demostramos como el sintético antifúngico y microbiano fármaco comercial denominado ciclopirox, se asocia al enzima estabilizándolo. Ciclopirox asiste al enzima mediante unión alostética, distante del centro activo, sin afectar, por tanto, a su función catalítica. El fármaco es capaz de reestablecer la actividad in vitro, in cellula e in vivo, llegando incluso a aliviar la mayoría de los síntomas clínicos en un modelo de ratón bona fide de la enfermedad, actuando a concentraciones sub-tóxicas, estableciendo una nueva línea de intervención terapéutica contra la porfiria eritropoyética congénita. Aplicable a la mayoría de las mutaciones sin sentido perjudiciales que causan esta devastadora enfermedad.CICbioGUNE. Excelencia Severo Ocho

Drug Repurposing

This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

Repurposing ciclopirox as a pharmacological chaperone active against congenital erythropoietic porphyria.

138 p.La porfiria eritropoyética congénita es una enfermedad rara autosómica recesiva producida por una actividad deficiente en la uroporfirinógeno III sintasa, la cuarta enzima de la ruta biosintética del grupo hemo. La enfermedad afecta a diversos órganos, llegando a ser potencialmente peligroso para la vida, careciendo actualmente de tratamientos curativos. Bioquímicamente, las mutaciones hereditarias de mayor frecuencia reducen la estabilidad del enzima, alterando su homeostasis, que, en última instancia, reducen la producción de grupo hemo intracelular. Esto da como resultado la acumulación de subproductos de uroporfirina que se distribuyen y depositan por todos los tejidos, agravando la patología con síntomas tales como fotosensibilidad de la piel y lesiones cutáneas fototóxicas desfigurantes. En el presente trabajo, demostramos como el sintético antifúngico y microbiano fármaco comercial denominado ciclopirox, se asocia al enzima estabilizándolo. Ciclopirox asiste al enzima mediante unión alostética, distante del centro activo, sin afectar, por tanto, a su función catalítica. El fármaco es capaz de reestablecer la actividad in vitro, in cellula e in vivo, llegando incluso a aliviar la mayoría de los síntomas clínicos en un modelo de ratón bona fide de la enfermedad, actuando a concentraciones sub-tóxicas, estableciendo una nueva línea de intervención terapéutica contra la porfiria eritropoyética congénita. Aplicable a la mayoría de las mutaciones sin sentido perjudiciales que causan esta devastadora enfermedad.CICbioGUNE. Excelencia Severo Ocho

Computational methods in drug repurposing and natural product based drug discovery

For a few decades now, computation methods have been widely used in drug discovery or drug repurposing process, especially when saving time and money are important factors. Development of bioinformatics, chemoinformatics, molecular modelling techniques and machine or deep learning tools, as well as availability of various biological and chemical databases, have had a significant impact on improving the process of obtaining successful drug candidates. This dissertation describes the role of natural products in drug discovery, as well as presents several computational methods used in drug discovery and drug repurposing. Application of these methods is presented with the example of searching for potential drug treatment options for the COVID-19 disease. The disease is caused by the novel coronavirus SARS-CoV-2, which was first discovered in December 2019 and has caused the death of more than 5.6 million people worldwide (until January 2022). Findings from two research projects, which aimed to identify potential inhibitors of main protease of SARS-CoV-2, are presented in this work. Moreover, a summary on COVID-19 treatment possibilities has been included. In the first project, a ligand-based virtual screening of around 360,000 compounds from natural products databases, as well as approved and withdrawn drugs databases was conducted, followed by molecular docking and molecular dynamics simulations. Moreover, computational predictions of toxicity and cytochrome activity profiles for selected candidates were provided. Twelve candidates as SARS-CoV-2 main protease inhibitors were identified - among them novel drug candidates, as well as existing drugs. The second project was focused on finding potential inhibitors from plants (Reynoutria japonica and Reynoutria sachalinensis) and was based on molecular docking studies, followed by in vitro studies of the activity of selected compounds, extract, and fractions from those plants against the enzyme. Several natural compounds were identified as promising candidates for SARS-CoV-2 main protease inhibitors. Additionally, butanol fraction of Ryenoutria rhizomes extracts also showed inhibitory activity on the enzyme. Suggested drugs, natural compounds and plant extracts should be further investigated to confirm their potential as COVID-19 therapeutic options. Presented workflow could be used for investigation of compounds for other biological targets and different diseases in the future research projects.Seit einigen Jahrzehnten werden bei der Entwicklung und Repositionierung von Arzneimitteln rechenintensive computergestützte Methoden eingesetzt, insbesondere da Zeit- und Kostenersparnis wichtige Faktoren sind. Die Weiterentwicklung der Bioinformatik und Chemoinformatik und die damit einhergehende Optimierung von molekularen Modellierungstechniken und Tools für maschinelles sowie tiefes Lernen ermöglicht die Verarbeitung von großen biologischen und chemischen Datenbanken und hat einen erheblichen Einfluss auf die Verbesserung des Prozesses zur Gewinnung erfolgreicher Arzneimittelkandidaten. In dieser Dissertation wird die Rolle von Naturstoffen bei der Entwicklung von Arzneimitteln beschrieben, und es werden verschiedene computergestützte Methoden vorgestellt, die bei der Entdeckung von Arzneimitteln und der Repositionierung von Arzneimitteln eingesetzt werden. Die Anwendung dieser Methoden wird am Beispiel der Suche nach potenziellen medikamentösen Behandlungsmöglichkeiten für die Krankheit COVID-19 vorgestellt. Die Krankheit wird durch das neuartige Coronavirus SARS-CoV-2 ausgelöst, das erst im Dezember 2019 entdeckt wurde und bisher (bis Januar 2022) weltweit mehr als 5,6 Millionen Menschen das Leben gekostet hat. In dieser Arbeit werden Ergebnisse aus zwei Forschungsprojekten vorgestellt, die darauf abzielten, potenzielle Hemmstoffe der Hauptprotease von SARS-CoV-2 zu identifizieren. Außerdem wird ein Überblick über die Behandlungsmöglichkeiten von COVID-19 gegeben. Im ersten Projekt wurde ein ligandenbasiertes virtuelles Screening von rund 360.000 Kleinstrukturen aus Naturstoffdatenbanken sowie aus Datenbanken für zugelassene und zurückgezogene Arzneimittel durchgeführt, gefolgt von molekularem Docking und Molekulardynamiksimulationen. Darüber hinaus wurden für ausgewählte Kandidaten rechnerische Vorhersagen zur Toxizität und zu Cytochrom-P450-Aktivitätsprofilen erstellt. Es wurden zwölf Kandidaten als SARS-CoV-2-Hauptproteaseinhibitoren identifiziert - darunter sowohl neuartige als auch bereits vorhandene Arzneimittel. Das zweite Projekt konzentrierte sich auf die Suche nach potenziellen Inhibitoren aus Pflanzen (Reynoutria japonica und Reynoutria sachalinensis) und basierte auf molekularen Docking-Studien, gefolgt von In-vitro-Studien der Aktivität ausgewählter Verbindungen, Extrakte und Fraktionen aus diesen Pflanzen gegen das Enzym. Mehrere Naturstoffe wurden als vielversprechende Kandidaten für SARS-CoV-2- Hauptproteaseinhibitoren identifiziert. Außerdem zeigte die Butanolfraktion von Ryenoutria Rhizomextrakten ebenfalls eine hemmende Wirkung auf das Enzym. Die vorgeschlagenen Arzneimittel, Naturstoffe und Pflanzenextrakte sollten weiter untersucht werden, um ihr Potenzial als COVID-19-Therapieoptionen zu bestätigen. Der vorgestellte Arbeitsablauf könnte in zukünftigen Forschungsprojekten zur Untersuchung von Verbindungen für andere biologische Ziele und verschiedene Krankheiten verwendet werden

MOLECULAR DOCKING AND SCREENING OF DRUGS FOR 6LU7 PROTEASE INHIBITOR AS A POTENTIAL TARGET FOR COVID-19

Objective: The aim of present investigation is docking of various existing antiviral, anti-tubercular and anti-malarial drugs on 6LU7 receptor of SARS-CoV-2 in the treatment of COVID-19. Methods: In this study, the structure of coronavirus binding protein and ligands for various drugs were collected from the protein data bank and pub chem. Molecular docking was carried out using Schrodinger 9.0 software. In molecular docking study, 19 different drugs of various categories like antiviral, anti-malarial and anti-tubercular were investigated for analyzing binding to 6LU7 receptors of COVID-19. Results: The docking result showed a high affinity of zanamivir, montelukast, ramdesvir, ritonavir, cobicistat and favipravir to the 6LU7 receptor of novel coronavirus. Thus the combination of these drugs may be useful in preventing further infection and can be used as a potential target for further in vitro and in vivo studies of SARS-CoV-2. Conclusion: Treatment of COVID-19 has been challenge due to the non-availability of effective drug therapy. In this study, we reported drugs for targeting 6LU7 Mpro/3Clpro protein, which showed prominent effects as potential inhibitors of COVID-19 Mpro

Large-scale computational drug repositioning to find treatments for rare diseases

© 2018, The Author(s). Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed eMatchSite, a new computer program to compare drug-binding sites. In this study, eMatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions