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Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.
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This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research. Furthermore, it emphasizes the need of learning from the SARS-CoV-2 pandemic in order to improve global preparedness for future health crises.
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COVID-19 , Humanos , Poliproteínas/metabolismo , Cisteína Endopeptidasas/metabolismo , SARS-CoV-2/metabolismo , Descubrimiento de Drogas , Simulación del Acoplamiento MolecularRESUMEN
Matrix metalloproteinase 13 plays a central role in osteoarthritis (OA), as its overexpression induces an excessive breakdown of collagen that results in an imbalance between collagen synthesis and degradation in the joint, leading to progressive articular cartilage degradation. Therefore, MMP-13 has been proposed as a key therapeutic target for OA. Here we have developed a virtual screening workflow aimed at identifying selective non-zinc-binding MMP-13 inhibitors by targeting the deep S1' pocket of MMP-13. Three ligands were found to inhibit MMP-13 in the µM range, and one of these showed selectivity over other MMPs. A structure-based analysis guided the chemical optimization of the hit compound, leading to the obtaining of a new N-acyl hydrazone-based derivative with improved inhibitory activity and selectivity for the target enzyme.
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Cartílago Articular , Osteoartritis , Humanos , Metaloproteinasa 13 de la Matriz/metabolismo , Inhibidores de la Metaloproteinasa de la Matriz/química , Cartílago Articular/metabolismo , Osteoartritis/tratamiento farmacológico , Colágeno/uso terapéuticoRESUMEN
The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.
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COVID-19 , Humanos , SARS-CoV-2/metabolismo , Simulación del Acoplamiento Molecular , Reproducibilidad de los Resultados , Inhibidores de Proteasas/química , Antivirales/farmacología , Antivirales/químicaRESUMEN
Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination. C>U mutations are the most common. They are found in the largest number of variants, pangolin lineages, and countries, which indicates that they are a driving force behind the evolution of SARS-CoV-2. Not all SARS-CoV-2 genes have mutated in the same way. Fewer non-synonymous single nucleotide variations are found in genes that encode proteins with a critical role in virus replication than in genes with ancillary roles. Some genes, such as spike (S) and nucleocapsid (N), show more non-synonymous mutations than others. Although the prevalence of mutations in the target regions of COVID-19 diagnostic RT-qPCR tests is generally low, in some cases, such as for some primers that bind to the N gene, it is significant. Therefore, ongoing monitoring of SARS-CoV-2 mutations is crucial. The SARS-CoV-2 Mutation Portal provides access to a database of SARS-CoV-2 mutations.
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COVID-19 , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , COVID-19/genética , Mutación , Nucleótidos , Genoma ViralRESUMEN
Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model's Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.
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COVID-19 , SARS-CoV-2 , Humanos , COVID-19/virología , Mutación , Redes Neurales de la Computación , SARS-CoV-2/genética , ARN Viral/genéticaRESUMEN
This review makes a critical evaluation of 61 peer-reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS-CoV-2 M-pro (M-pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS-CoV-2 M-pro inhibitors extracted from the literature and a second set of 81 M-pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS-CoV-2 M-pro inhibitor. Using two SARS-CoV-2 M-pro structures and five protein-ligand docking programs, we proved that the correlation between the pIC50 and docking scores is not good. Neither was any correlation found between the pIC50 and the ∆G calculated with an MM-GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M-pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M-pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS-CoV-2 M-pro inhibitors presented here could be used for validating future VS protocols which aim to predict M-pro inhibitors.
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COVID-19 , Reposicionamiento de Medicamentos , Antivirales/farmacología , Antivirales/uso terapéutico , Proteasas 3C de Coronavirus , Humanos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Reproducibilidad de los Resultados , SARS-CoV-2RESUMEN
Matrix metalloproteinases (MMPs) are the family of proteases that are mainly responsible for degrading extracellular matrix (ECM) components. In the skin, the overexpression of MMPs as a result of ultraviolet radiation triggers an imbalance in the ECM turnover in a process called photoaging, which ultimately results in skin wrinkling and premature skin ageing. Therefore, the inhibition of different enzymes of the MMP family at a topical level could have positive implications for photoaging. Considering that the MMP catalytic region is mostly conserved across different enzymes of the MMP family, in this study we aimed to design a virtual screening (VS) workflow to identify broad-spectrum MMP inhibitors that can be used to delay the development of photoaging. Our in silico approach was validated in vitro with 20 VS hits from the Specs library that were not only structurally different from one another but also from known MMP inhibitors. In this bioactivity assay, 18 of the 20 compounds inhibit at least one of the assayed MMPs at 100 µM (with 5 of them showing around 50% inhibition in all the tested MMPs at this concentration). Finally, this VS was used to identify natural products that have the potential to act as broad-spectrum MMP inhibitors and be used as a treatment for photoaging.
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Inhibidores de la Metaloproteinasa de la Matriz/farmacología , Metaloproteinasas de la Matriz/química , Piel/efectos de los fármacos , Bibliotecas de Moléculas Pequeñas/farmacología , Productos Biológicos/química , Dominio Catalítico , Pruebas de Enzimas , Ensayos Analíticos de Alto Rendimiento , Humanos , Inhibidores de la Metaloproteinasa de la Matriz/química , Metaloproteinasas de la Matriz/metabolismo , Simulación del Acoplamiento Molecular , Unión Proteica , Conformación Proteica , Dominios y Motivos de Interacción de Proteínas , Sensibilidad y Especificidad , Piel/enzimología , Piel/patología , Piel/efectos de la radiación , Envejecimiento de la Piel/efectos de los fármacos , Envejecimiento de la Piel/efectos de la radiación , Bibliotecas de Moléculas Pequeñas/química , Electricidad Estática , Relación Estructura-Actividad , Rayos Ultravioleta/efectos adversos , Interfaz Usuario-ComputadorRESUMEN
Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host-virus relationships.
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In response to foreign or endogenous stimuli, both microglia and astrocytes adopt an activated phenotype that promotes the release of pro-inflammatory mediators. This inflammatory mechanism, known as neuroinflammation, is essential in the defense against foreign invasion and in normal tissue repair; nevertheless, when constantly activated, this process can become detrimental through the release of neurotoxic factors that amplify underlying disease. In consequence, this study presents the anti-inflammatory and immunomodulatory properties of o-orsellinaldehyde, a natural compound found by an in silico approach in the Grifola frondosa mushroom, in astrocytes and microglia cells. For this purpose, primary microglia and astrocytes were isolated from mice brain and cultured in vitro. Subsequently, cells were exposed to LPS in the absence or presence of increasing concentrations of this natural compound. Specifically, the results shown that o-orsellinaldehyde strongly inhibits the LPS-induced inflammatory response in astrocytes and microglia by decreasing nitrite formation and downregulating iNOS and HO-1 expression. Furthermore, in microglia cells o-orsellinaldehyde inhibits NF-κB activation; and potently counteracts LPS-mediated p38 kinase and JNK phosphorylation (MAPK). In this regard, o-orsellinaldehyde treatment also induces a significant cell immunomodulation by repolarizing microglia toward the M2 anti-inflammatory phenotype. Altogether, these results could partially explain the reported beneficial effects of G. frondosa extracts on inflammatory conditions.
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In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.
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Antivirales/química , Proteasas 3C de Coronavirus/antagonistas & inhibidores , Inhibidores de Proteasas/química , SARS-CoV-2/efectos de los fármacos , Antivirales/farmacología , Proteasas 3C de Coronavirus/química , Bases de Datos Farmacéuticas , Pruebas de Enzimas/métodos , Concentración 50 Inhibidora , Inhibidores de Proteasas/farmacología , SARS-CoV-2/enzimología , Replicación Viral/efectos de los fármacosRESUMEN
Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus' replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.
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Betacoronavirus/enzimología , Inhibidores de Proteasas/química , Subtilisinas/antagonistas & inhibidores , Proteínas Virales/antagonistas & inhibidores , Antivirales/química , Antivirales/metabolismo , Sitios de Unión , COVID-19 , Carbazoles/química , Carbazoles/metabolismo , Celecoxib/química , Celecoxib/metabolismo , Infecciones por Coronavirus/patología , Infecciones por Coronavirus/virología , Reposicionamiento de Medicamentos , Humanos , Simulación del Acoplamiento Molecular , Mutación Missense , Pandemias , Neumonía Viral/patología , Neumonía Viral/virología , Inhibidores de Proteasas/metabolismo , Estructura Terciaria de Proteína , SARS-CoV-2 , Subtilisinas/genética , Subtilisinas/metabolismo , Proteínas Virales/genética , Proteínas Virales/metabolismoRESUMEN
Matrix metalloproteinases (MMPs) are a family of proteins involved in a range of pathologies. Given that MMP broad-spectrum inhibition is associated with severe adverse effects, selectivity has become a priority in the design of MMP inhibitors, and is often achieved by targeting the variable S1' pocket. However, the specific characteristics of the S1' pocket that determine inhibitor selectivity are often not described and, in many cases, challenging to identify. In this review, we investigate the variability of the S1' pocket across the MMP family, and propose explanations for the selectivity of previously described inhibitors. These analyses provide valuable insights into how to design novel inhibitors selective for a given MMP.
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Inhibidores de la Metaloproteinasa de la Matriz/farmacología , Metaloproteinasas de la Matriz/metabolismo , Secuencia de Aminoácidos , Animales , Sitios de Unión , Humanos , Metaloproteinasas de la Matriz/químicaRESUMEN
Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.
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Química Computacional/métodos , Bases de Datos de Compuestos Químicos , Dipeptidil Peptidasa 4/química , Inhibidores de la Dipeptidil-Peptidasa IV , Diseño de Fármacos , Animales , Sitios de Unión , Dipeptidil Peptidasa 4/metabolismo , Inhibidores de la Dipeptidil-Peptidasa IV/química , Inhibidores de la Dipeptidil-Peptidasa IV/farmacología , Humanos , Riñón/enzimología , Ligandos , Simulación del Acoplamiento Molecular , Relación Estructura-Actividad , PorcinosRESUMEN
Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.
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Evaluación Preclínica de Medicamentos , Interfaz Usuario-Computador , Ligandos , Simulación del Acoplamiento Molecular , Reproducibilidad de los Resultados , Programas InformáticosRESUMEN
Metabolic syndrome is a cluster of medical conditions that increases the risk of developing cardiovascular disease and type 2 diabetes. Numerous studies have shown that inflammation is directly involved in the onset of metabolic syndrome and related pathologies. In this study, in silico techniques were applied to a natural products database containing molecules isolated from mushrooms from the Catalan forests to predict molecules that can act as human nuclear-factor κß kinase 2 (IKK-2) inhibitors. IKK-2 is the main component responsible for activating the nuclear-factor κß transcription factor (NF-κß). One of these predicted molecules was o-orsellinaldehyde, a molecule present in the mushroom Grifola frondosa. This study shows that o-orsellinaldehyde presents anti-inflammatory and pro-apoptotic properties by acting as IKK-2 inhibitor. Additionally, we suggest that the anti-inflammatory and pro-apoptotic properties of Grifola frondosa mushroom could partially be explained by the presence of o-orsellinaldehyde on its composition.
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Aldehídos/química , Antiinflamatorios/química , Catecoles/química , Grifola/química , Inhibidores de Proteínas Quinasas/química , Proteínas Serina-Treonina Quinasas/antagonistas & inhibidores , Aldehídos/metabolismo , Aldehídos/uso terapéutico , Animales , Antiinflamatorios/metabolismo , Antiinflamatorios/uso terapéutico , Apoptosis/efectos de los fármacos , Productos Biológicos/química , Productos Biológicos/metabolismo , Catecoles/metabolismo , Catecoles/uso terapéutico , Supervivencia Celular/efectos de los fármacos , Simulación por Computador , Bases de Datos de Compuestos Químicos , Humanos , Masculino , Ratones , Ratones Endogámicos BALB C , Fosforilación/efectos de los fármacos , Extractos Vegetales/química , Extractos Vegetales/metabolismo , Extractos Vegetales/uso terapéutico , Inhibidores de Proteínas Quinasas/metabolismo , Inhibidores de Proteínas Quinasas/uso terapéutico , Células RAW 264.7 , Transducción de Señal/efectos de los fármacos , Quinasa de Factor Nuclear kappa BRESUMEN
Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for diabetes and obesity. However, the design of PTP1B inhibitors that combine potency and bioavailability is a great challenge, and new leads are needed to circumvent this problem. Virtual screening (VS) workflows can be used to find new PTP1B inhibitors with little chemical similarity to existing inhibitors. Unfortunately, previous VS workflows for the identification of PTP1B inhibitors have several limitations, such as a small number of experimentally tested compounds and the low bioactivity of those compounds. We developed a VS workflow capable of identifying 15 structurally diverse PTP1B inhibitors from 20 compounds, the bioactivity of which was tested in vitro. Moreover, we identified two PTP1B inhibitors with the highest bioactivity reported by any VS campaign (i.e., IC50 values of 1.4 and 2.1â µm), which could be used as new lead compounds.
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Inhibidores Enzimáticos/farmacología , Proteína Tirosina Fosfatasa no Receptora Tipo 1/antagonistas & inhibidores , Relación Dosis-Respuesta a Droga , Evaluación Preclínica de Medicamentos , Inhibidores Enzimáticos/síntesis química , Inhibidores Enzimáticos/química , Humanos , Ligandos , Modelos Moleculares , Estructura Molecular , Proteína Tirosina Fosfatasa no Receptora Tipo 1/metabolismo , Relación Estructura-ActividadRESUMEN
The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC50 ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.
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Inhibidores de la Dipeptidil-Peptidasa IV/química , Inhibidores de la Dipeptidil-Peptidasa IV/farmacología , Evaluación Preclínica de Medicamentos , Interfaz Usuario-Computador , Secuencia de Aminoácidos , Animales , Sitios de Unión , Dipeptidil Peptidasa 4/química , Dipeptidil Peptidasa 4/metabolismo , Inhibidores de la Dipeptidil-Peptidasa IV/análisis , Humanos , Relación Estructura-ActividadRESUMEN
SCOPE: Resveratrol (RSV) has been described as a potent antioxidant, antisteatotic, and antitumor compound, and it has also been identified as a potent autophagy inducer. On the other hand, quercetin (QCT) is a dietary flavonoid with known antitumor, anti-inflammatory, and antidiabetic effects. Additionally, QCT increases autophagy. To study the hypothetical synergistic effect of both compounds, we test the combined effect of QCT and RSV on the autophagy process in HepG2 cells. METHODS AND RESULTS: Autophagy is studied by western blotting, real-time RT-PCR, and cellular staining. Our results clearly indicate a bifunctional molecular effect of RSV. Both polyphenols are individually able to promote autophagy. Strikingly, when RSV is combined with QCT, it promotes a potent reduction of QCT-induced autophagy and influences proapoptotic signaling. CONCLUSION: RSV acts differentially on the autophagic process depending on the cellular energetic state. We further characterize the molecular mechanisms related to this effect, and we observe that AMP-activated protein kinase (AMPK) phosphorylation, heme oxygenase 1 (HO-1) downregulation, lysosomal membrane permeabilization (LMP), and Zinc (Zn2+ ) dynamics could be important modulators of such RSV-related effects and could globally represent a promising strategy to sensitize cancer cells to QCT treatment.