In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies

Machine Learning for Kinase Drug Discovery

Cancer is one of the major public health issues, causing several million losses every year. Although anti-cancer drugs have been developed and are globally administered, mild to severe side effects are known to occur during treatment. Computer-aided drug discovery has become a cornerstone for unveiling treatments of existing as well as emerging diseases. Computational methods aim to not only speed up the drug design process, but to also reduce time-consuming, costly experiments, as well as in vivo animal testing. In this context, over the last decade especially, deep learning began to play a prominent role in the prediction of molecular activity, property and toxicity. However, there are still major challenges when applying deep learning models in drug discovery. Those challenges include data scarcity for physicochemical tasks, the difficulty of interpreting the prediction made by deep neural networks, and the necessity of open-source and robust workflows to ensure reproducibility and reusability. In this thesis, after reviewing the state-of-the-art in deep learning applied to virtual screening, we address the previously mentioned challenges as follows: Regarding data scarcity in the context of deep learning applied to small molecules, we developed data augmentation techniques based on the SMILES encoding. This linear string notation enumerates the atoms present in a compound by following a path along the molecule graph. Multiplicity of SMILES for a single compound can be reached by traversing the graph using different paths. We applied the developed augmentation techniques to three different deep learning models, including convolutional and recurrent neural networks, and to four property and activity data sets. The results show that augmentation improves the model accuracy independently of the deep learning model, as well as of the data set size. Moreover, we computed the uncertainty of a model by using augmentation at inference time. In this regard, we have shown that the more confident the model is in its prediction, the smaller is the error, implying that a given prediction can be trusted and is close to the target value. The software and associated documentation allows making predictions for novel compounds and have been made freely available. Trusting predictions blindly from algorithms may have serious consequences in areas of healthcare. In this context, better understanding how a neural network classifies a compound based on its input features is highly beneficial by helping to de-risk and optimize compounds. In this research project, we decomposed the inner layers of a deep neural network to identify the toxic substructures, the toxicophores, of a compound that led to the toxicity classification. Using molecular fingerprints —vectors that indicate the presence or absence of a particular atomic environment —we were able to map a toxicity score to each of these substructures. Moreover, we developed a method to visualize in 2D the toxicophores within a compound, the so- called cytotoxicity maps, which could be of great use to medicinal chemists in identifying ways to modify molecules to eliminate toxicity. Not only does the deep learning model reach state-of-the-art results, but the identified toxicophores confirm known toxic substructures, as well as expand new potential candidates. In order to speed up the drug discovery process, the accessibility to robust and modular workflows is extremely advantageous. In this context, the fully open-source TeachOpenCADD project was developed. Significant tasks in both cheminformatics and bioinformatics are implemented in a pedagogical fashion, allowing the material to be used for teaching as well as the starting point for novel research. In this framework, a special pipeline is dedicated to kinases, a family of proteins which are known to be involved in diseases such as cancer. The aim is to gain insights into off-targets, i.e. proteins that are unintentionally affected by a compound, and that can cause adverse effects in treatments. Four measures of kinase similarity are implemented, taking into account sequence, and structural information, as well as protein-ligand interaction, and ligand profiling data. The workflow provides clustering of a set of kinases, which can be further analyzed to understand off-target effects of inhibitors. Results show that analyzing kinases using several perspectives is crucial for the insight into off-target prediction, and gaining a global perspective of the kinome. These novel methods can be exploited in the discovery of new drugs, and more specifically diseases involved in the dysregulation of kinases, such as cancer

Protein kinases: Structure modeling, inhibition, and protein-protein interactions

Human protein kinases belong to a large and diverse enzyme family that contains more than 500 members. Deregulation of protein kinases is associated with many disorders, and this is why protein kinases are attractive targets for drug discovery. Due to the high conservation of the ATP binding pocket among this family, designing specific and/or selective inhibitors against certain member(s) is challenging. Several studies have been conducted on protein kinases to validate them as suitable drug targets. Although there are numerous target-validated protein kinases, the efforts to develop small molecule inhibitors have so far led to only a limited number of therapeutic agents and drug candidates. In our studies, we tried to understand the basic structural features of protein kinases using available computational tools. There are wide structural variations between different states of the same protein kinase that affect the enzyme specificity and inhibition. Many protein kinases do not yet have an available X-ray crystal structure and have not yet been validated to be drug targets. For these reasons, we developed a new homology modeling approach to facilitate modeling non-crystallized protein kinases and protein kinase states. Our homology modeling approach was able to model proteins having long amino acid sequences and multiple protein domains with reliable model quality and a manageable amount of computational time. Then, we checked the applicability of different docking algorithms (the routinely used computational methodology in virtual screening) in protein kinase studies. After performing the basic study of kinase structure modeling, we focused our research on cyclin dependent kinase 2 (CDK2) and glycogen synthase kinase-3β (GSK-3β). We prepared a non-redundant database from 303 available CDK2 PDB structures. We removed all structural anomalies and proceeded to use the CDK2 database in studying CDK2 structure in its different states, upon ATP, ligand and cyclin binding. We clustered the database based on our findings, and the CDK2 clusters were used to generate protein ligand interaction fingerprints (PLIF). We generated a PLIF-based pharmacophore model which is highly selective for CDK2 ligands. A virtual screening workflow was developed making use of the PLIF-based pharmacophore model, ligand fitting into the CDK2 active site and selective CDK2 shape scoring. We studied the structural basis for selective inhibition of CDK2 and GSK-3β. We compared the amino acid sequence, the 3D features, the binding pockets, contact maps, structural geometry, and Sphoxel maps. From this study we found 1) the ligand structural features that are required for the selective inhibition of CDK2 and GSK-3β, and 2) the amino acid residues which are essential for ligand binding and selective inhibition. We used the findings of this study to design a virtual screening workflow to search for selective inhibitors for CDK2 and GSK-3β. Because protein–protein interactions are essential in the function of protein kinases, and in particular of CDK2, we used protein–protein docking knowledge and binding energy calculations to examine CDK2 and cyclin binding. We applied this study to the voltage dependent calcium channel 1 (VDAC1) binding to Bax. We were able to provide important data relevant to future experimental researchers such as on the possibility of Bax to cross biological membranes and the most relevant amino acid residues in VDAC1 that interact with Bax

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Exploring Molecular Diversity: There is Plenty of Room at Markush's

L'estratègia de les etapes inicials del descobriment de fàrmacs està normalment basada en un procés anomenat hit-to-lead que implica un extens estudi entorn de la síntesi de derivats d'una molècula original que prèviament hagi mostrat certa activitat biològica davant d'una diana concreta. Per tant, aquest procés comporta la síntesi de molts anàlegs que descriurien una subquimioteca, que generalment evidencia que aquests estudis estan molt focalitzats al voltant de l'espai químic del compost original. Així i tot, quan aquesta molècula és finalment patentada, es descriu un espai químic molt més vast per mitjà d'estructures Markush donant per suposat que alguns dels seus derivats puguin presentar també activitat biològica. Tot i això, la presència d'aquestes estructures no implica la síntesi comprovada de tota la biblioteca molecular sinó només una petita mostra de la mateixa. La nostra hipòtesi és que hi ha una gran part de l’espai químic d’aquestes biblioteques que està sense explorar i pot amagar possibles candidats que poden fins i tot superar l’activitat del hit original. A través d'aquest projecte, es proposa una alternativa que sosté que una selecció racional de poques molècules – basat en l'agrupament segons semblança molecular – pot representar de manera més significativa l'espai químic establert, oferint la possibilitat d'explorar regions desconegudes que podrien amagar més potencial biològic. Després de revisar els darrers fàrmacs aprovats per la FDA en el període del 2008 al 2020 i la base de dades de molècules bioactives de ChEMBL, s'ha dut a terme una exploració de l'ampli espai químic resultant de molècules petites amb propietats similars a les dels medicaments per definir nous espais accessibles que podrien ocultar activitat. Els resultats obtinguts de set casos d'estudis reals han demostrat que tant la selecció racional com l’aleatòria representen més significativament les biblioteques combinatòries declarades a les patents, que les molècules descrites fins ara. S'han realitzat dos estudis pràctics que implementen aquesta metodologia suggerida per descriure millor l'espai químic del fàrmac antipalúdic Tafenoquina i del Dacomitinib, un inhibidor de tirosina cinases de segona generació per al tractament del càncer de pulmó de cèl·lules no petites. L’exploració de l’espai químic d’aquestes dues famílies ha portat a la síntesi racional de set anàlegs antipalúdics i vuit inhibidors de cinases que han mostrat interessants activitats inhibidores. Aquests resultats demostren que l'aplicació de la quimioinformàtica per a la selecció de biblioteques pot millorar la capacitat d'inspeccionar millor els conjunts de dades químiques per identificar nous compostos precandidats i representar grans biblioteques per a posteriors campanyes de reposicionament.La estrategia de las etapas iniciales del descubrimiento de fármacos está normalmente basada en un proceso denominado hit-to-lead que implica un extenso estudio entorno a la síntesis de derivados de una molécula original que previamente haya expresado cierta actividad biológica frente a una diana concreta. Por ende, este proceso conlleva la síntesis de muchos análogos que describirían una sublibrería química, la cual generalmente evidencia que estos estudios están muy focalizados alrededor del espacio químico del compuesto original. Aún y así, cuando esta molécula es finalmente patentada, se describe un espacio químico mucho más vasto por medio de estructuras Markush teorizando que algunos de sus derivados puedan presentar también actividad biológica. Sin embargo, la presencia de estas estructuras no implica la síntesis comprobada de toda la biblioteca molecular sino solo una pequeña muestra de la misma. Nuestra hipótesis es que hay una gran parte del espacio químico de estas bibliotecas que está sin explorar y puede ocultar posibles candidatos que pueden hasta superar la actividad del hit original. A través de este proyecto, se propone una alternativa que sostiene que una selección racional de pocas moléculas – fundada en el agrupamiento según su similitud química – puede representar de manera más significativa el espacio químico establecido, ofreciendo la posibilidad de explorar regiones desconocidas que podrían ocultar más potencial biológico. Después de revisar los últimos fármacos aprobados por la FDA en el período de 2008 a 2020 y la base de datos de moléculas bioactivas de ChEMBL, se ha llevado a cabo una exploración del amplio espacio químico resultante de moléculas pequeñas con propiedades similares a las de los medicamentos para definir nuevos espacios accesible que podrían ocultar actividad. Los resultados obtenidos de siete casos de estudios reales han demostrado que tanto la selección racional como la aleatoria representan más significativamente las bibliotecas combinatorias declaradas en las patentes que las moléculas descritas hasta la fecha. Se han desarrollado dos estudios prácticos que implementan esta metodología sugerida para describir mejor el espacio químico del fármaco antipalúdico Tafenoquina y Dacomitinib, un inhibidor de la tirosina quinasa de segunda generación para el tratamiento del cáncer de pulmón de células no pequeñas. La exploración del espacio químico de estas dos familias ha llevado a la síntesis racional de siete análogos antipalúdicos y ocho inhibidores de quinasas que han mostrado interesantes actividades inhibidoras. Estos resultados demuestran que la aplicación de la quimioinformática para la selección de bibliotecas puede mejorar la capacidad de inspeccionar mejor los conjuntos de datos químicos para identificar nuevos potenciales hits y representar grandes bibliotecas para fines de reposicionamiento.The early Drug Discovery strategy is commonly based on a hit-to-lead process which involves large research on the synthesis of derivatives of an original molecule that had previously shown biological activity against a specific biological target. Therefore, this process implies the synthesis of many analogs leading to the description of a chemical sub-library which generally leads to a highly focused study on the chemical space nearby the hit compound. However, when this drug is finally patented, a wider chemical space derived from a Markush structure is described, theorizing that some analogs within may present biological activity. Nevertheless, this claim involving the Markush structure does not imply the proven synthesis of all the chemical library but just a small population of it. We hypothesize that there is a great part of the chemical space of these libraries that is unexplored and can hide potential lead candidates which may even surpass the activity of the original hit. Through this project, an alternative is proposed claiming that a rational selection of a short sample of small molecules – founded on similarity-based clustering – can represent more significatively the stated chemical space offering the possibility to explore the unknown space that could hide more potential biological activity. After a review on the latest approved drugs by the FDA in the period from 2008 to 2020 and the ChEMBL database of bioactive molecules, an exploration of the resulting wide chemical space of small molecules with drug-like properties has been assessed in order to define accessible spots that might hide biological activity. The obtained results from seven real cases of study have proven that random and rationally selected molecules represent more significantly the combinatorial libraries stated in the patents rather than the reported molecules until date. Furthermore, two practical studies implementing our suggested methodology have been developed to better describe the chemical space of the antimalarial drug Tafenoquine and Dacomitinib, a second-generation tyrosine kinase inhibitor for non-small-cell lung cancer treatment. The assessment driven by a better chemical space exploration of these two families have led to the rational synthesis of seven antimalarial analogs and eight kinase inhibitors which have shown interesting inhibitory activities. Our results evince that the application of cheminformatics for library selection may improve the ability to better inspect chemical datasets in order to identify new potential hits and represent large libraries for further reprofiling purposes

Boron in Drug Discovery

The pursuit of novel therapeutics demands a systematic, rational approach to drug design, prioritizing high potency, selectivity, and precise in vivo drug delivery. This work comprises two studies delving into boron-based compound synthesis and their potential in drug discovery. Chapter 2 explores boron-based P2X7 receptor antagonists, vital in immune responses, inflammation, and cell death. It aimed to expand the antagonist library with boron, discovering compounds like 89 (IC50 = 388 ± 68) with high nanomolar range potencies. The study emphasized structural rigidity for effective P2X7 inhibition and highlighted the role of three-dimensional contacts at the receptor site, facilitated by adamantane and closo-carboranes. Chapter 3 employed a fragment-based drug discovery (FBDD) approach to identify boron-based antiviral compounds targeting SARS-CoV-2. Preliminary in vitro evaluations revealed two promising hit fragments, CA87 (nido-carborane; EC50 = 100 µM) and BA34 (ortho-boronic acid; EC50 = 3.13 µM). Assessments against SARS-CoV-2 variants highlighted BA34 isomers' selectivity, notably BA32 (para-substituted phenyl boronic acid), with exceptional delta variant inhibition (EC50 = 6 nM). Expanding the FBDD library yielded 15 additional fragments, aiding structure-activity relationship (SAR) studies. Three potent leads emerged: OX11 (EC50 = 200 nM), BA52 (EC50 = 7.41 nM), and BA49 (EC50 = 390 nM). Unique insights into fragment binding, involving parent compound BA34, revealed direct interaction with the virus spike protein, differentiating it from current COVID-19 agents. In conclusion, this study underscores boron-based compounds' therapeutic potential, offering distinct advantages in drug design. These findings illuminate the design and optimization of boron-containing compounds across various therapeutic applications, including COVID-19 treatment

Anti-angiogenic and toxicity effects of Derris trifoliata extract in zebrafish embryo

Introduction: Derris trifoliata has been traditionally used as folk for the treatment of , rheumatic joints, diarrhoea, and dysmenorrhea, and rotenoids isolated from the plant have shown to exhibit anti-cancer properties. This study aimed to assess the toxicity effects and antiangiogenic activity of extract of Derris trifoliata on zebrafish embryo model. Materials and Methods: Zebrafihs embryos were treated with aqueous extract of Derris Trifoliata to evaluate its effects on angiogenesis and zebrafish-toxicity. Angiogenic response was analyzed using whole-mount alkaline phosphatase (AP) vessel staining on 72 hours post fertilization (hpf) zebrafish embryos. Results: 1.0 mg/ml concentration was toxic to zebrafish embryos and embryos exposed to concentrations at 0.5 mg/ml and below showed some malformations. Derris trifoliata aqueous extract also showed some anti-angiogenic activity in vivo in the zebrafish embryo model wereby at high concentration inhibited vessel formation in zebrafish embryo. Conclusions: The anti-angiogenic response of extract of Derris trifoliata in zebrafish in vivo model suggest its therapeutic potential as anti-cancer agent