Investigating the Selectivity of Allosteric Inhibitors for Mutant T790M EGFR over Wild Type Using Molecular Dynamics and Binding Free Energy Calculations

The recent discovery of the fourth generation EAI045 allosteric inhibitor, which potently and selectively inhibits mutant EGFR, represents an important step forward for the treatment of non-small cell lung cancer. However, the structural determinants of EAI045 selectivity with respect to the wild type (wt) protein have not been fully investigated. To this aim, we performed a comparative analysis of long-scale molecular dynamics simulations and binding free energy calculations on wt and T790M EGFR in complexes with the EAI001 and EAI045 allosteric ligands. Unexpectedly, we found that the observed selectivity for T790M EGFR over wt is not due to more favorable interactions of the two ligands with the mutated gatekeeper residue, as previously suggested. Rather, the allosteric ligands were engaged in a direct hydrogen bond with the Asp855 residue of the DFG motif in mutant T790M but not in wt, in which the hydrogen bond was found to be water-mediated. Per-residue decomposition of binding free energies suggests that the loss of a direct interaction with Asp855 is the main cause of inhibitor selectivity. Moreover, the possibility that the allosteric ligands and adenosine triphosphate may have synergistic binding effects, as previously observed in MEK allosteric inhibitors, was investigated. Altogether, the results suggest that ligand selectivity arises from direct hydrogen bonds with the Asp855 side chain, and that the design of mutant-selective inhibitors should be focused on ligands that form direct hydrogen bonds with Asp855 in T790M EGFR but not in wt EGFR. These results may provide useful hints for future structural design of mutant-selective allosteric inhibitors that spare wt EGFR, which is a highly desirable goal

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms

Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease

The outbreak of a new coronavirus (SARS-CoV-2), which is responsible for the COVID-19 disease and is spreading rapidly around the world, urgently requires effective therapeutic treatments. In this context, drug repurposing represents a valuable strategy, as it enables accelerating the identification of drug candidates with already known safety profiles, possibly aiding in the late stages of clinical evaluation. Moreover, therapeutic treatments based on drugs with beneficial multi-target activities (polypharmacology) may show an increased antiviral activity or help to counteract severe complications concurrently affecting COVID-19 patients. In this study, we present the results of a computational drug repurposing campaign that aimed at identifying potential inhibitors of the main protease (Mpro) of the SARS-CoV-2. The performed in silico screening allowed the identification of 22 candidates with putative SARS-CoV-2 Mpro inhibitory activity. Interestingly, some of the identified compounds have recently entered clinical trials for COVID-19 treatment, albeit not being assayed for their SARS-CoV-2 antiviral activity. Some candidates present a polypharmacology profile that may be beneficial for COVID-19 treatment and, to the best of our knowledge, have never been considered in clinical trials. For each repurposed compound, its therapeutic relevance and potential beneficial polypharmacological effects that may arise due to its original therapeutic indication are thoroughly discussed

Structure-activity exploration of a small-molecule allosteric inhibitor of T790M/L858R double mutant EGFR

EGFR is a protein kinase whose aberrant activity is frequently involved in the development of non-small lung cancer (NSCLC) drug resistant forms. The allosteric inhibition of this enzyme is currently one among the most attractive approaches to design and develop anticancer drugs. In a previous study, we reported the identification of a hit compound acting as type III allosteric inhibitor of the L858R/T790M double mutant EGFR. Herein, we report the design, synthesis and in vitro testing of a series of analogues of the previously identified hit with the aim of exploring the structure-activity relationships (SAR) around this scaffold. The performed analyses allowed us to identify two compounds 15 and 18 showing improved inhibition of double mutant EGFR with respect to the original hit, as well as interesting antiproliferative activity against H1975 NSCLC cancer cells expressing double mutant EGFR. The newly discovered compounds represent promising starting points for further hit-to-lead optimisation

On the integration of in silico drug design methods for drug repurposing

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations

Discovery of a Potent Dual Inhibitor of Aromatase and Aldosterone Synthase.

Estrogen deficiency derived from inhibition of estrogen biosynthesis is a typical condition of postmenopausal women and breast cancer (BCs) patients undergoing antihormone therapy. The ensuing increase in aldosterone levels is considered to be the major cause for cardiovascular diseases (CVDs) affecting these patients. Since estrogen biosynthesis is regulated by aromatase (CYP19A1), and aldosterone biosynthesis is modulated by aldosterone synthase (CYP11B2), a dual inhibitor would allow the treatment of BC while reducing the cardiovascular risks typical of these patients. Moreover, this strategy would help overcome some of the disadvantages often observed in single-target or combination therapies. Following an in-depth analysis of a library of synthesized benzylimidazole derivatives, compound was found to be a potent and selective dual inhibitor of aromatase and aldosterone synthase, with IC values of 2.3 and 29 nM, respectively. Remarkably, the compound showed high selectivity with respect to 11β-hydroxylase (CYP11B1), as well as CYP3A4 and CYP1A2. When tested in cells, showed potent antiproliferative activity against BC cell lines, particularly against the ER+ MCF-7 cells (IC of 0.26 ± 0.03 μM at 72 h), and a remarkable pro-apoptotic effect. In addition, the compound significantly inhibited mTOR phosphorylation at its IC concentration, thereby negatively modulating the PI3K/Akt/mTOR axis, which represents an escape for the dependency from ER signaling in BC cells. The compound was further investigated for cytotoxicity on normal cells and potential cardiotoxicity against ERG and Nav1.5 ion channels, demonstrating a safe biological profile. Overall, these assays demonstrated that the compound is potent and safe, thus constituting an excellent candidate for further evaluation. [Abstract copyright: © 2023 The Authors. Published by American Chemical Society.

Strategie di virtual screening per l'identificazione di nuovi inibitori della Lattato Deidrogenasi

La lattato deidrogenasi (LDH) è il principale enzima che interviene nelle cellule eucariote per la metabolizzazione del piruvato in condizioni di ipossia. Risulta però fondamentale per il metabolismo delle cellule cancerose, anche in condizioni di normali pressioni di ossigeno (effetto Warburg). In questo lavoro di tesi è stata effettuata una procedura di virtual screening sul database ENAMINE (circa 2 milioni di composti), tramite due approcci eseguiti in parallelo: 1) costruzione di un modello farmacoforico 2) utilizzo del programma GLIDE SP Le molecole identificate sono state sottoposte a una procedura di consensus docking e di dinamica molecolare

Tecnologie data-driven per il riposizionamento del farmaco: applicazione di protocolli mirati e metodi innovativi

La tradizionale scoperta di farmaci è un processo lungo e costoso, spesso ostacolato da alti tassi di fallimento. Una valida alternativa è il riposizionamento dei farmaci (drug repurposing), definito come l'identificazione di nuove indicazioni terapeutiche per farmaci già noti o candidati farmaci, prodotti sintetici e naturali. Il riposizionamento dei farmaci permette di ridurre i tempi, i rischi e i costi associati alle tradizionali procedure di scoperta, poiché la maggior parte dei composti ha in molti casi già superato studi di sicurezza e tossicità. Inoltre, l'aumento dei dati biologici, clinici e chimici ha creato nuove opportunità per il riposizionamento dei farmaci. Perciò, l'uso su larga scala di approcci in silico ha dimostrato di essere una strategia efficiente e conveniente. Tuttavia, ad oggi rimane un forte bisogno di protocolli razionali e nuove metodologie per aiutare i ricercatori in questo campo. Sulla base di queste premesse, l'obiettivo del progetto di dottorato è stato focalizzato su due aree principali del riposizionamento dei farmaci in silico: i) l'applicazione di protocolli su misura per specifiche campagne di riposizionamento; e ii) lo sviluppo di nuovi metodi e approcci generali. Durante il corso di dottorato, sono state messe a punto molteplici applicazioni di protocolli integranti diversi approcci computazionali per il riposizionamento di prodotti di origine sia naturale che sintetica. Il data mining da noti database pubblici ha permesso di eseguire valutazioni della similarità 2D e 3D, e di selezionare target per eseguire studi di docking molecolare. Ogni protocollo è stato personalizzato tenendo conto delle caratteristiche delle molecole sotto studio. Infine, i test in vitro su proteine o cellule isolate hanno permesso di convalidare sperimentalmente le previsioni. Parallelamente, il progetto di dottorato è stato incentrato sullo sviluppo di protocolli innovativi in grado di fornire nuove risorse. Per esempio, è stata sviluppata una piattaforma basata sulle tecniche di machine learning (ML) per prevedere il profilo di selettività tra diverse isoforme enzimatiche. Inoltre, è stato realizzato il sito web LigAdvisor, una piattaforma integrata per il repurposing e la polifarmacologia. I progetti implementati hanno fornito risultati molto soddisfacenti. Infatti, nel corso dei tre anni è stato possibile: riposizionare una libreria di composti di origine sintetica, identificando un potente inibitore della Anidrasi Carbonica (hCA) II, e poi progettare suoi derivati con attività duale su hCA e sui Recettori degli Estrogeni (ER); riposizionare prodotti naturali su ERβ; identificare candidati per l'inibizione della proteasi principale (Mpro) di SARS-CoV-2. La piattaforma di screening tramite machine learning ha fornito eccellenti prestazioni predittive, migliori di quelle ottenute con altri approcci tradizionali. Infine, lo sviluppo del sito web LigAdvisor, liberamente accessibile, permette anche ai non esperti del settore di reperire una grande quantità di dati di alta qualità e di eseguire una varietà di ricerche. In conclusione, i risultati riportati in questa tesi dimostrano come l'uso di approcci computazionali, intelligenza artificiale e tecniche di data mining sia realmente utile nella progettazione di campagne di riposizionamento. Uno degli aspetti innovativi dei progetti realizzati è infatti rappresentato dall'integrazione di diversi metodi consolidati in nuovi protocolli e piattaforme, aumentando così la loro usabilità e migliorando le possibilità di sviluppare campagne di riposizionamento di successo. I dati qui presentati sono stati oggetto di molteplici pubblicazioni su riviste internazionali, e le nuove piattaforme proposte sono state rese disponibili al pubblico.Traditional de novo drug discovery is a long, expensive process that is often hampered by high failure rates. A viable alternative strategy is drug repurposing (or drug repositioning), defined as the identification of novel therapeutic indications for already known drugs or drug candidates, as well as synthetic and natural products. Drug repurposing allows to reduce times, risks and costs associated with traditional de novo discovery pipelines, as most compounds have in many cases already passed safety and toxicity studies. Moreover, in recent times the increase in biological, clinical and chemical data has enabled the progress of novel attractive drug repurposing opportunities. Accordingly, the large-scale use of integrated in silico approaches has proven to be an efficient and cost-effective strategy. However, to date there is still a large need for rational protocols and new methodologies to help researchers in this field. Based on these premises, the aim of the PhD project was focused on two main areas of in silico drug repurposing: i) the application of tailored protocols for specific repositioning campaigns; and ii) the development of novel methods and general approaches. During the PhD course, several applications of integrated protocols using different computational approaches were developed for the repositioning of products of both natural and synthetic origin. Data mining from well-known public databases allowed to perform 2D and 3D similarity estimations, and to select appropriate targets to perform in-depth molecular docking studies. Each protocol was customized taking into account the specific characteristics of the molecules under study. Finally, in vitro testing on isolated proteins or cells allowed to experimentally validate the predictions. At the same time, the PhD project focused on the development of innovative protocols capable of providing new assets to researchers working with drug repurposing. For instance, a machine learning (ML) based platform was developed to predict selectivity profiles across different enzyme isoforms. Moreover, the development of the LigAdvisor website, an integrated platform for repurposing and polypharmacology, was also carried out. The implemented projects provided highly satisfactory results. Indeed, over the three years it was possible to: reposition a library of compounds of synthetic origin, by identifying a potent human Carbonic Anhydrase (hCA) II inhibitor, and then design derivatives with dual activity on hCA and the Estrogen Receptors (ER); to reposition natural products on ERβ; to identify candidates for the inhibition of the SARS-CoV-2 main protease (Mpro). The machine learning hCA screening platform provided excellent predictive performances, which remarkably proved to be better than those obtained by other traditional approaches. Finally, the development of the freely accessible LigAdvisor website provides also non-experts in the field to retrieve a large amount of high quality data and perform a variety of different queries. In conclusion, the results reported in this dissertation demonstrate how the use of computational approaches, artificial intelligence and data mining techniques is indeed of great help in the rational design of repurposing campaigns and useful resources. One of the innovative aspects of the projects carried out is indeed represented by the integration of different established methods in new protocols and platforms, thus increasing their usability and improving the chances of developing successful repositioning campaigns. The data featured here were the subject of multiple publications in international journals, and the novel proposed platforms were made available to the public