Virtual screening of agrochemicals and IC50 prediction for repurposing against Ecdysone receptor of Helicoverpa armigera

Virtual screening of agrochemicals and IC50 prediction for repurposing against Ecdysone receptor of Helicoverpa armigera | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Virtual screening of agrochemicals and IC50 prediction for repurposing against Ecdysone receptor of Helicoverpa armigera Raghunandhan Namachivayam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6608446/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Helicoverpa armigera is a highly destructive agricultural pest known for its resistance to multiple insecticides, necessitating the development of new, sustainable control strategies. In this study, we targeted the ecdysone receptor (EcR), a key regulator of insect moulting, using a drug repurposing approach combined with virtual screening. A library of 1,900 agrochemicals was docked against the ligand-binding domain of HaEcR using AutoDock Vina, with tebufenozide serving as a reference compound. Pyrifluquinazon exhibited strong binding affinity (–10.09 kcal/mol) and emerged as a promising candidate, while herbicidal compounds like Diflufenican and Fluridone, despite high binding energies, were deemed unsuitable. Predicted IC₅₀ values using a Random Forest regression model prioritized Fluxapyroxad and Flusulfamide for further investigation, with the model achieving an R² value of 76%. This integrated computational approach offers a rapid, cost-effective pipeline for identifying novel pest control agents targeting resistant populations of H. armigera. Future in vivo validation is necessary to confirm the efficacy and safety of the identified candidates. Virtual Screening Helicoverpa Docking IC50 prediction repurposing Figures Figure 1 Introduction Helicoverpa armigera , commonly known as the cotton bollworm, is a highly destructive pest that poses a significant threat to various agricultural crops, including cotton, tomato, maize, chickpea, and sorghum [1]. With its high fecundity, polyphagous feeding behavior, and strong migratory ability, H . armigera has become increasingly difficult to manage. The pest has developed resistance to multiple classes of insecticides, including Bacillus thuringiensis (Bt) toxins, complicating pest control efforts [2, 3]. This resistance, combined with the environmental and economic drawbacks of conventional pesticides, has created a pressing need for sustainable pest management strategies. One promising target for such strategies is the ecdysone receptor (EcR) complex, which plays a key role in the physiological and morphological changes associated with insect moulting. This process is regulated by ecdysteroid hormones, such as 20-hydroxyecdysone (20E) and its analogs, which bind to the ligand-binding domain (LBD) of EcR, initiating moulting [4]. As a member of the nuclear hormone receptor superfamily, EcR acts as a ligand-activated transcription factor and consists of five modular domains: A/B (transcriptional activation domain), C (DNA-binding domain), D (hinge region), E (ligand-binding domain), and F (a less defined region) [5]. The receptor forms a heterodimer with the ultraspiracle protein (USP), a homologue of the retinoid X receptor (RXR) [6]. The moulting process is regulated by various transcription factors within the nuclear receptor superfamily, which trigger the up-regulation of genes involved in the hormone pathway, facilitating the moulting process [7]. As these receptors are specific to invertebrates, they present an attractive target for insecticide development [8, 9]. Ecdysteroid agonists, such as tebufenozide (RH-5992), mimic the natural action of 20-hydroxyecdysone by inducing premature lethal moulting in larvae and disrupting reproduction in adult insects, particularly in Lepidoptera and Coleoptera [10]. In the context of pest control, drug repurposing identifying new uses for existing drugs has emerged as a promising strategy. This approach allows researchers to leverage previously approved or clinically tested compounds, accelerating the development of new treatments while significantly reducing the time and cost associated with traditional drug discovery [11, 12]. For pest control, drug repurposing offers the added advantage of bypassing early-phase toxicity testing, thus speeding up the identification of bioactive compounds with potential insecticidal effects. Virtual screening (VS) plays a key role in the drug repurposing pipeline. This computational technique evaluates and predicts the binding affinity of large compound libraries to specific biological targets [13]. Through molecular docking and simulation approaches, VS can identify repurposed drugs that may inhibit essential proteins in pests like H. armigera, disrupting critical physiological functions. By integrating drug repurposing with virtual screening, researchers can enhance the efficiency of candidate selection and develop targeted, environmentally friendly pest management strategies [14]. Together, these approaches offer a modern, cost-effective path toward innovative control strategies for pesticide-resistant pests like Helicoverpa armigera . Materials and Methods Obtaining and preparing HaEcR protein structure: The three-dimensional structure of the ecdysone receptor (EcR) in complex with the bisacylhydrazine ligand BYI-06830 (PDB ID: 3IXP), resolved at 2.85 Å, was obtained from the Protein Data Bank [15]. Prior to docking, non-essential components such as heteroatoms, ligands, and water molecules were removed from the EcR structure. The protein was then prepared using AutoDock Tools (ADT), which included the addition of polar hydrogen atoms an important step to ensure accurate partial charge calculations. Kollman charges were subsequently assigned to all atoms in the protein [16]. The processed structure was saved in the “PDBQT” format. Ligand collection and structural preparation: A total of 1,900 agrochemical compounds were selected from the "Agrochemical Category" of the PubChem database for virtual screening studies [17]. These compounds were downloaded in 3D SDF format and converted into PDBQT using Open Babel [18]. The 3D structures were then energy minimized using the MMFF94 force field with a convergence criterion of 0.05 [19]. As a reference ligand we used tebufenozide which mimics the natural function of the endogenous insect moulting hormone, 20-hydroxyecdysone (20E), by inducing premature lethal moulting during the larval stages and inhibiting reproduction in adults, particularly in Lepidoptera and Coleoptera. Virtual screening Using AutoDock Vina: Docking simulations were performed using AutoDock Vina [20] on a Linux command-line platform. Docking was carried out using a custom configuration file, which specified the receptor and ligand file paths, as well as the grid box parameters. The grid box dimensions were set to 46 Å × 48 Å × 64 Å with a grid point spacing of 0.375 Å. The center of the grid box was placed at coordinates X = 5.838, Y = 65.407, and Z = 12.076 to cover the active site of the ligand-binding domain (LBD) of chain D [21]. The docking process for multiple ligands was automated using a shell script that iterated over the ligand files. Each ligand was docked independently, and the top-ranking binding poses, based on binding affinity (kcal/mol), were extracted for further analysis. Docking results were recorded in a log file (output.log) and visualized using LigPlus for detailed interaction studies [22]. Prediction of IC50 Values using regression analysis: The IC50 values of the top-performing compounds were predicted using regression analysis based on molecular descriptors. Molecular descriptors were calculated using the PaDEL software [23], which generates a variety of 2D and 3D descriptors for each compound. These descriptors were used as input features for the regression model, which was trained on a dataset of compounds with known IC50 values using Random Forest [24]. The regression model was then applied to predict the IC50 values of the selected agrochemical compounds, providing a quantitative estimate of their potency. The predicted IC50 values were used to prioritize the compounds for further experimental evaluation. Results and Discussion Virtual screening of Agrochemicals: Virtual screening was carried out using Tebufenozide as the reference compound, which exhibited a binding energy of approximately –7.0 kcal/mol, serving as the baseline for evaluating the binding affinities of other compounds. To enhance the selection process for compounds with pesticidal properties, Tice’s Rule was applied as a filtering criterion. Among the tested compounds, Diflufenican demonstrated the highest binding affinity, with a binding energy of –10.72 kcal/mol, forming a hydrogen bond with Tyr408 (bond distance: 3.12 Å). However, Diflufenican is a well-known herbicide that inhibits carotenoid biosynthesis by targeting phytoene desaturase, leading to the accumulation of phytoene and subsequent disruption of photosynthesis in plants [25, 26]. Its herbicidal mode of action and potential phytotoxic effects render it unsuitable for field applications targeting Helicoverpa armigera . Pyrifluquinazon, on the other hand, exhibited a binding energy of –10.09 kcal/mol. It belongs to the pyridine azinomethine chemical class and functions as a chordotonal organ TRPV channel modulator, primarily effective against sap-feeding insects. Although no hydrogen bonds were observed in the docking simulations, its high binding affinity indicates promising potential as an ecdysone receptor modulator against H. armigera . Other compounds, such as Flocoumafen (–10.03 kcal/mol), Fluridone (–9.863 kcal/mol), and Fluxapyroxad (–9.815 kcal/mol), also demonstrated notable binding affinities. Among these, Fluridone formed a hydrogen bond with Cys508 (bond distance: 3.34 Å). Nevertheless, Fluridone, like Diflufenican, acts as a herbicide inhibiting carotenoid biosynthesis, thus limiting its practicality for pest control applications in H. armigera management. Flusulfamide and Naptalam showed binding energies of –9.348 kcal/mol and –9.233 kcal/mol, respectively, each forming two hydrogen bonds with critical residues in the receptor’s active site. Despite the presence of multiple hydrogen bonds, their comparatively lower binding affinities suggest they may be less effective as ecdysone receptor modulators relative to Pyrifluquinazon. A summary of the docking results for the top-performing compounds is presented in Table 1 and Figure 1, and the complete docking dataset is provided in Supplementary File 1. Prediction of IC50 for the selected molecules: The predictive model developed for IC₅₀ estimation achieved an R² value of 76%, indicating good correlation between predicted and actual values. Using this model, IC₅₀ values were predicted for eight selected compounds. Among these, Fluxapyroxad showed the lowest predicted IC₅₀ value at 182.872 µM, followed closely by Flusulfamide (184.507 µM) and Diflufenican (196.238 µM). These results suggest that these compounds may exhibit stronger inhibitory activity compared to the others. In contrast, Pyrifluquinazon exhibited the highest predicted IC₅₀ value at 349.104 µM, indicating comparatively lower predicted potency. Other compounds, such as Flocoumafen (252.413 µM), Diphacinone (250.994 µM), Fluridone (204.977 µM), and Naptalam (278.795 µM), displayed intermediate levels of predicted activity. Overall, compounds with lower IC₅₀ values, particularly Fluxapyroxad and Flusulfamide, could be prioritized for further biological testing, while compounds with higher IC₅₀ values may require chemical optimization or could be deprioritized for immediate follow-up studies. The results highlight the usefulness of machine learning models in predicting compound activities and guiding the selection of promising candidates for experimental validation. The relatively high R² value supports the reliability of the model, although experimental assays will be necessary to confirm the predicted inhibitory potentials. Conclusion This study highlights the potential of integrating drug repurposing and virtual screening approaches to identify new insecticidal candidates targeting the ecdysone receptor (EcR) of Helicoverpa armigera , a major agricultural pest. Through docking simulations using AutoDock Vina and predictive IC₅₀ modeling, several agrochemicals demonstrated promising binding affinities and inhibitory potential against the HaEcR protein. Although Diflufenican and Fluridone exhibited strong binding energies, their herbicidal modes of action limit their practical use for pest control. Pyrifluquinazon emerged as a particularly interesting candidate due to its high binding affinity and established insecticidal activity. Furthermore, compounds like Fluxapyroxad and Flusulfamide, with lower predicted IC₅₀ values, offer promising leads for future experimental validation. The combination of computational docking, machine learning-based IC₅₀ prediction, and prior agrochemical knowledge provides a modern, cost-effective framework for accelerating the discovery of novel, environmentally friendly pest management solutions for resistant pests like H. armigera . Future experimental assays are essential to confirm these computational findings and optimize lead compounds for practical field applications. Declarations Funding No fundings were received. Ethics, Consent to Participate, Clinical trial number, and Consent to Publish declarations: not applicable. Data Availability: All data generated or analysed during this study are included in this published article [and its supplementary information files]. References Haile F, Nowatzki T, Storer N. Overview of Pest Status, Potential Risk, and Management Considerations of Helicoverpa armigera (Lepidoptera: Noctuidae) for U.S. Soybean Production. Journal of Integrated Pest Management. 2021;12(1). doi:10.1093/jipm/pmaa030. Zalucki M, Murray D, Gregg P, Fitt G, Twine P, Jones C. Ecology of Helicoverpa-Armigera (Hubner) and Heliothis-Punctigera (Wallengren) in the inland of Australia-larval sampling and host-plant relationships during winter and spring. Australian Journal of Zoology. 1994;42(3):329-46. Tay WT, Soria MF, Walsh T, Thomazoni D, Silvie P, Behere GT et al. A Brave New World for an Old World Pest: Helicoverpa armigera (Lepidoptera: Noctuidae) in Brazil. PLOS ONE. 2013;8(11):e80134. doi:10.1371/journal.pone.0080134. Jayachandran B, Hussain M, Asgari S. Regulation of Helicoverpa armigera ecdysone receptor by miR-14 and its potential link to baculovirus infection. Journal of Invertebrate Pathology. 2013;114(2):151-7. doi:https://doi.org/10.1016/j.jip.2013.07.004. Thummel CS. From embryogenesis to metamorphosis: The regulation and function of drosophila nuclear receptor superfamily members. Cell. 1995;83(6):871-7. doi:https://doi.org/10.1016/0092-8674(95)90203-1. Oro AE, McKeown M, Evans RM. Relationship between the product of the Drosophila ultraspiracle locus and the vertebrate retinoid X receptor. Nature. 1990;347(6290):298-301. doi:10.1038/347298a0. Zheng W-W, Yang D-T, Wang J-X, Song Q-S, Gilbert LI, Zhao X-F. Hsc70 binds to ultraspiracle resulting in the upregulation of 20-hydroxyecdsone-responsive genes in Helicoverpa armigera. Molecular and Cellular Endocrinology. 2010;315(1-2):282-91. Graham LD. Ecdysone-controlled expression of transgenes. Expert Opin Biol Ther. 2002;2(5):525-35. doi:10.1517/14712598.2.5.525. Palli SR, Hormann RE, Schlattner U, Lezzi M. Ecdysteroid receptors and their applications in agriculture and medicine. Vitam Horm. 2005;73:59-100. doi:10.1016/s0083-6729(05)73003-x. Nagata S, Maruyama T, Ohira T, Wataru S, Nagasawa H. Cloning and characterization of ecdysone receptor and ultraspiracle cDNAs from Spodoptera litura. Ann N Y Acad Sci. 2005;1040:417-9. doi:10.1196/annals.1327.078. Wermuth CG, Villoutreix B, Grisoni S, Olivier A, Rocher J-P. Chapter 4 - Strategies in the Search for New Lead Compounds or Original Working Hypotheses. In: Wermuth CG, Aldous D, Raboisson P, Rognan D, editors. The Practice of Medicinal Chemistry (Fourth Edition). San Diego: Academic Press; 2015. p. 73-99. Gan J-h, Liu J-x, Liu Y, Chen S-w, Dai W-t, Xiao Z-X et al. DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacologica Sinica. 2023;44(4):888-96. doi:10.1038/s41401-022-00996-2. Chen YW, Yiu CB, Wong KY. Virtual screening on the web for drug repurposing: a primer. J Biol Methods. 2021;8(2 COVID 19 Spec Iss):e148. doi:10.14440/jbm.2021.351. Jones CM, Parry H, Tay WT, Reynolds DR, Chapman JW. Movement Ecology of Pest Helicoverpa: Implications for Ongoing Spread. Annu Rev Entomol. 2019;64:277-95. doi:10.1146/annurev-ento-011118-111959. Berman HM, Henrick K, Nakamura H, Markley J, Bourne PE, Westbrook J. Realism about PDB. Nat Biotechnol. 2007;25(8):845-6; author reply 6. doi:10.1038/nbt0807-845. Singh UC, Kollman PA. An approach to computing electrostatic charges for molecules. Journal of Computational Chemistry. 1984;5(2):129-45. doi:https://doi.org/10.1002/jcc.540050204. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al. PubChem 2025 update. Nucleic Acids Research. 2024;53(D1):D1516-D25. doi:10.1093/nar/gkae1059. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 2011;3(1):33. doi:10.1186/1758-2946-3-33. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry. 1996;17(5-6):490-519. doi:https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/63.0.CO;2-P. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. 2021;61(8):3891-8. doi:10.1021/acs.jcim.1c00203. Yadav RP, Syed Ibrahim K, Gurusubramanian G, Senthil Kumar N. In silico docking studies of non-azadirachtin limonoids against ecdysone receptor of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae). Medicinal Chemistry Research. 2015;24(6):2621-31. doi:10.1007/s00044-015-1320-1. Laskowski RA, Swindells MB. LigPlot+: Multiple Ligand–Protein Interaction Diagrams for Drug Discovery. Journal of Chemical Information and Modeling. 2011;51(10):2778-86. doi:10.1021/ci200227u. Yap CW. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry. 2011;32(7):1466-74. doi:https://doi.org/10.1002/jcc.21707. Breiman L. Random Forests. Machine Learning. 2001;45(1):5-32. doi:10.1023/A:1010933404324. Barry P, Pallett KE. Herbicidal Inhibition of Carotenogenesis Detected by HPLC. Zeitschrift für Naturforschung C. 1990;45(5):492-7. doi:doi:10.1515/znc-1990-0533. Chen C, Lei Q, Geng W, Wang D, Gan X. Discovery of Novel Pyridazine Herbicides Targeting Phytoene Desaturase with Scaffold Hopping. J Agric Food Chem. 2024;72(22):12425-33. doi:10.1021/acs.jafc.3c09350. Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6608446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459897601,"identity":"a5a33dc2-2833-45d4-8f9a-613dd5b34f51","order_by":0,"name":"Raghunandhan Namachivayam","email":"data:image/png;base64,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","orcid":"","institution":"Tamil Nadu Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Raghunandhan","middleName":"","lastName":"Namachivayam","suffix":""}],"badges":[],"createdAt":"2025-05-07 05:55:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6608446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6608446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83270654,"identity":"25a74cf4-2b59-4a58-8c9b-583ae2b07c2c","added_by":"auto","created_at":"2025-05-22 07:41:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":358747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe interaction of ligands and the receptor active site are illustrated here with LigPlus tool.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between the selected compounds and the HaEcR\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6608446/v1/9719864fc706d794eddade19.png"},{"id":90006932,"identity":"9f83b61c-0309-4c31-b373-d85110b4d2c3","added_by":"auto","created_at":"2025-08-27 09:39:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6608446/v1/a74a25d1-947e-4b71-aae6-e6e1960afc95.pdf"},{"id":83270653,"identity":"0d5b7cee-974b-4d9e-8aea-ecb19cdedefd","added_by":"auto","created_at":"2025-05-22 07:41:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43321,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6608446/v1/26944bc8b6013bea4ed8e315.docx"},{"id":83270656,"identity":"110dde4b-078d-4095-aadb-15aa1c345256","added_by":"auto","created_at":"2025-05-22 07:41:26","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":589970,"visible":true,"origin":"","legend":"","description":"","filename":"results.txt","url":"https://assets-eu.researchsquare.com/files/rs-6608446/v1/0d22efe24d7cc00c5b26031b.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Virtual screening of agrochemicals and IC50 prediction for repurposing against Ecdysone receptor of Helicoverpa armigera","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eHelicoverpa armigera\u003c/em\u003e, commonly known as the cotton bollworm, is a highly destructive pest that poses a significant threat to various agricultural crops, including cotton, tomato, maize, chickpea, and sorghum [1]. With its high fecundity, polyphagous feeding behavior, and strong migratory ability, \u003cem\u003eH\u003c/em\u003e. \u003cem\u003earmigera\u003c/em\u003e has become increasingly difficult to manage. The pest has developed resistance to multiple classes of insecticides, including Bacillus thuringiensis (Bt) toxins, complicating pest control efforts [2, 3]. This resistance, combined with the environmental and economic drawbacks of conventional pesticides, has created a pressing need for sustainable pest management strategies.\u003c/p\u003e\n\u003cp\u003eOne promising target for such strategies is the ecdysone receptor (EcR) complex, which plays a key role in the physiological and morphological changes associated with insect moulting. This process is regulated by ecdysteroid hormones, such as 20-hydroxyecdysone (20E) and its analogs, which bind to the ligand-binding domain (LBD) of EcR, initiating moulting [4]. As a member of the nuclear hormone receptor superfamily, EcR acts as a ligand-activated transcription factor and consists of five modular domains: A/B (transcriptional activation domain), C (DNA-binding domain), D (hinge region), E (ligand-binding domain), and F (a less defined region) [5]. The receptor forms a heterodimer with the ultraspiracle protein (USP), a homologue of the retinoid X receptor (RXR) [6].\u003c/p\u003e\n\u003cp\u003eThe moulting process is regulated by various transcription factors within the nuclear receptor superfamily, which trigger the up-regulation of genes involved in the hormone pathway, facilitating the moulting process [7]. As these receptors are specific to invertebrates, they present an attractive target for insecticide development [8, 9]. Ecdysteroid agonists, such as tebufenozide (RH-5992), mimic the natural action of 20-hydroxyecdysone by inducing premature lethal moulting in larvae and disrupting reproduction in adult insects, particularly in Lepidoptera and Coleoptera [10].\u003c/p\u003e\n\u003cp\u003eIn the context of pest control, drug repurposing identifying new uses for existing drugs has emerged as a promising strategy. This approach allows researchers to leverage previously approved or clinically tested compounds, accelerating the development of new treatments while significantly reducing the time and cost associated with traditional drug discovery [11, 12]. For pest control, drug repurposing offers the added advantage of bypassing early-phase toxicity testing, thus speeding up the identification of bioactive compounds with potential insecticidal effects.\u003c/p\u003e\n\u003cp\u003eVirtual screening (VS) plays a key role in the drug repurposing pipeline. This computational technique evaluates and predicts the binding affinity of large compound libraries to specific biological targets [13]. Through molecular docking and simulation approaches, VS can identify repurposed drugs that may inhibit essential proteins in pests like H. armigera, disrupting critical physiological functions. By integrating drug repurposing with virtual screening, researchers can enhance the efficiency of candidate selection and develop targeted, environmentally friendly pest management strategies [14]. Together, these approaches offer a modern, cost-effective path toward innovative control strategies for pesticide-resistant pests like \u003cem\u003eHelicoverpa armigera\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eObtaining and preparing HaEcR protein structure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe three-dimensional structure of the ecdysone receptor (EcR) in complex with the bisacylhydrazine ligand BYI-06830 (PDB ID: 3IXP), resolved at 2.85 \u0026Aring;, was obtained from the Protein Data Bank \u0026nbsp;[15]. Prior to docking, non-essential components such as heteroatoms, ligands, and water molecules were removed from the EcR structure. The protein was then prepared using AutoDock Tools (ADT), which included the addition of polar hydrogen atoms an important step to ensure accurate partial charge calculations. Kollman charges were subsequently assigned to all atoms in the protein \u0026nbsp;[16]. The processed structure was saved in the \u0026ldquo;PDBQT\u0026rdquo; format.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLigand collection and structural preparation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,900 agrochemical compounds were selected from the \u0026quot;Agrochemical Category\u0026quot; of the PubChem database for virtual screening studies [17]. These compounds were downloaded in 3D SDF format and converted into PDBQT using Open Babel [18]. The 3D structures were then energy minimized using the MMFF94 force field with a convergence criterion of 0.05 [19]. As a reference ligand we used tebufenozide which mimics the natural function of the endogenous insect moulting hormone, 20-hydroxyecdysone (20E), by inducing premature lethal moulting during the larval stages and inhibiting reproduction in adults, particularly in Lepidoptera and Coleoptera.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVirtual screening Using AutoDock Vina:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDocking simulations were performed using AutoDock Vina [20] on a Linux command-line platform. Docking was carried out using a custom configuration file, which specified the receptor and ligand file paths, as well as the grid box parameters. The grid box dimensions were set to 46 \u0026Aring; \u0026times; 48 \u0026Aring; \u0026times; 64 \u0026Aring; with a grid point spacing of 0.375 \u0026Aring;. The center of the grid box was placed at coordinates X = 5.838, Y = 65.407, and Z = 12.076 to cover the active site of the ligand-binding domain (LBD) of chain D [21]. The docking process for multiple ligands was automated using a shell script that iterated over the ligand files. Each ligand was docked independently, and the top-ranking binding poses, based on binding affinity (kcal/mol), were extracted for further analysis. Docking results were recorded in a log file (output.log) and visualized using LigPlus for detailed interaction studies [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of IC50 Values using regression analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IC50 values of the top-performing compounds were predicted using regression analysis based on molecular descriptors. Molecular descriptors were calculated using the PaDEL software [23], which generates a variety of 2D and 3D descriptors for each compound. These descriptors were used as input features for the regression model, which was trained on a dataset of compounds with known IC50 values using Random Forest [24]. The regression model was then applied to predict the IC50 values of the selected agrochemical compounds, providing a quantitative estimate of their potency. The predicted IC50 values were used to prioritize the compounds for further experimental evaluation.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eVirtual screening of Agrochemicals:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVirtual screening was carried out using Tebufenozide as the reference compound, which exhibited a binding energy of approximately \u0026ndash;7.0 kcal/mol, serving as the baseline for evaluating the binding affinities of other compounds. To enhance the selection process for compounds with pesticidal properties, Tice\u0026rsquo;s Rule was applied as a filtering criterion. Among the tested compounds, Diflufenican demonstrated the highest binding affinity, with a binding energy of \u0026ndash;10.72 kcal/mol, forming a hydrogen bond with Tyr408 (bond distance: 3.12 \u0026Aring;). However, Diflufenican is a well-known herbicide that inhibits carotenoid biosynthesis by targeting phytoene desaturase, leading to the accumulation of phytoene and subsequent disruption of photosynthesis in plants [25, 26]. Its herbicidal mode of action and potential phytotoxic effects render it unsuitable for field applications targeting \u003cem\u003eHelicoverpa armigera\u003c/em\u003e. Pyrifluquinazon, on the other hand, exhibited a binding energy of \u0026ndash;10.09 kcal/mol. It belongs to the pyridine azinomethine chemical class and functions as a chordotonal organ TRPV channel modulator, primarily effective against sap-feeding insects. Although no hydrogen bonds were observed in the docking simulations, its high binding affinity indicates promising potential as an ecdysone receptor modulator against \u003cem\u003eH. armigera\u003c/em\u003e. Other compounds, such as Flocoumafen (\u0026ndash;10.03 kcal/mol), Fluridone (\u0026ndash;9.863 kcal/mol), and Fluxapyroxad (\u0026ndash;9.815 kcal/mol), also demonstrated notable binding affinities. Among these, Fluridone formed a hydrogen bond with Cys508 (bond distance: 3.34 \u0026Aring;). Nevertheless, Fluridone, like Diflufenican, acts as a herbicide inhibiting carotenoid biosynthesis, thus limiting its practicality for pest control applications in \u003cem\u003eH. armigera\u003c/em\u003e management. Flusulfamide and Naptalam showed binding energies of \u0026ndash;9.348 kcal/mol and \u0026ndash;9.233 kcal/mol, respectively, each forming two hydrogen bonds with critical residues in the receptor\u0026rsquo;s active site. Despite the presence of multiple hydrogen bonds, their comparatively lower binding affinities suggest they may be less effective as ecdysone receptor modulators relative to Pyrifluquinazon. A summary of the docking results for the top-performing compounds is presented in Table 1 and Figure 1, and the complete docking dataset is provided in Supplementary File 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of IC50 for the selected molecules:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive model developed for IC₅₀ estimation achieved an R\u0026sup2; value of 76%, indicating good correlation between predicted and actual values. Using this model, IC₅₀ values were predicted for eight selected compounds. Among these, Fluxapyroxad showed the lowest predicted IC₅₀ value at 182.872 \u0026micro;M, followed closely by Flusulfamide (184.507 \u0026micro;M) and Diflufenican (196.238 \u0026micro;M). These results suggest that these compounds may exhibit stronger inhibitory activity compared to the others. In contrast, Pyrifluquinazon exhibited the highest predicted IC₅₀ value at 349.104 \u0026micro;M, indicating comparatively lower predicted potency. Other compounds, such as Flocoumafen (252.413 \u0026micro;M), Diphacinone (250.994 \u0026micro;M), Fluridone (204.977 \u0026micro;M), and Naptalam (278.795 \u0026micro;M), displayed intermediate levels of predicted activity. Overall, compounds with lower IC₅₀ values, particularly Fluxapyroxad and Flusulfamide, could be prioritized for further biological testing, while compounds with higher IC₅₀ values may require chemical optimization or could be deprioritized for immediate follow-up studies. The results highlight the usefulness of machine learning models in predicting compound activities and guiding the selection of promising candidates for experimental validation. The relatively high R\u0026sup2; value supports the reliability of the model, although experimental assays will be necessary to confirm the predicted inhibitory potentials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the potential of integrating drug repurposing and virtual screening approaches to identify new insecticidal candidates targeting the ecdysone receptor (EcR) of \u003cem\u003eHelicoverpa armigera\u003c/em\u003e, a major agricultural pest. Through docking simulations using AutoDock Vina and predictive IC₅₀ modeling, several agrochemicals demonstrated promising binding affinities and inhibitory potential against the HaEcR protein. Although Diflufenican and Fluridone exhibited strong binding energies, their herbicidal modes of action limit their practical use for pest control. Pyrifluquinazon emerged as a particularly interesting candidate due to its high binding affinity and established insecticidal activity. Furthermore, compounds like Fluxapyroxad and Flusulfamide, with lower predicted IC₅₀ values, offer promising leads for future experimental validation. The combination of computational docking, machine learning-based IC₅₀ prediction, and prior agrochemical knowledge provides a modern, cost-effective framework for accelerating the discovery of novel, environmentally friendly pest management solutions for resistant pests like \u003cem\u003eH. armigera\u003c/em\u003e. Future experimental assays are essential to confirm these computational findings and optimize lead compounds for practical field applications.\u003cp\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo fundings were received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, Clinical trial number, and Consent to Publish declarations:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHaile F, Nowatzki T, Storer N. Overview of Pest Status, Potential Risk, and Management Considerations of Helicoverpa armigera (Lepidoptera: Noctuidae) for U.S. Soybean Production. 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Nucleic Acids Research. 2024;53(D1):D1516-D25. doi:10.1093/nar/gkae1059.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 2011;3(1):33. doi:10.1186/1758-2946-3-33.\u003c/li\u003e\n\u003cli\u003eHalgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry. 1996;17(5-6):490-519. doi:https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6\u0026lt;490::AID-JCC1\u0026gt;3.0.CO;2-P.\u003c/li\u003e\n\u003cli\u003eEberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. 2021;61(8):3891-8. doi:10.1021/acs.jcim.1c00203.\u003c/li\u003e\n\u003cli\u003eYadav RP, Syed Ibrahim K, Gurusubramanian G, Senthil Kumar N. In silico docking studies of non-azadirachtin limonoids against ecdysone receptor of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae). Medicinal Chemistry Research. 2015;24(6):2621-31. doi:10.1007/s00044-015-1320-1.\u003c/li\u003e\n\u003cli\u003eLaskowski RA, Swindells MB. LigPlot+: Multiple Ligand\u0026ndash;Protein Interaction Diagrams for Drug Discovery. Journal of Chemical Information and Modeling. 2011;51(10):2778-86. doi:10.1021/ci200227u.\u003c/li\u003e\n\u003cli\u003eYap CW. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry. 2011;32(7):1466-74. doi:https://doi.org/10.1002/jcc.21707.\u003c/li\u003e\n\u003cli\u003eBreiman L. Random Forests. Machine Learning. 2001;45(1):5-32. doi:10.1023/A:1010933404324.\u003c/li\u003e\n\u003cli\u003eBarry P, Pallett KE. Herbicidal Inhibition of Carotenogenesis Detected by HPLC. Zeitschrift f\u0026uuml;r Naturforschung C. 1990;45(5):492-7. doi:doi:10.1515/znc-1990-0533.\u003c/li\u003e\n\u003cli\u003eChen C, Lei Q, Geng W, Wang D, Gan X. Discovery of Novel Pyridazine Herbicides Targeting Phytoene Desaturase with Scaffold Hopping. J Agric Food Chem. 2024;72(22):12425-33. doi:10.1021/acs.jafc.3c09350.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Virtual Screening, Helicoverpa, Docking, IC50 prediction, repurposing ","lastPublishedDoi":"10.21203/rs.3.rs-6608446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6608446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Helicoverpa armigera is a highly destructive agricultural pest known for its resistance to multiple insecticides, necessitating the development of new, sustainable control strategies. In this study, we targeted the ecdysone receptor (EcR), a key regulator of insect moulting, using a drug repurposing approach combined with virtual screening. A library of 1,900 agrochemicals was docked against the ligand-binding domain of HaEcR using AutoDock Vina, with tebufenozide serving as a reference compound. Pyrifluquinazon exhibited strong binding affinity (–10.09 kcal/mol) and emerged as a promising candidate, while herbicidal compounds like Diflufenican and Fluridone, despite high binding energies, were deemed unsuitable. Predicted IC₅₀ values using a Random Forest regression model prioritized Fluxapyroxad and Flusulfamide for further investigation, with the model achieving an R² value of 76%. This integrated computational approach offers a rapid, cost-effective pipeline for identifying novel pest control agents targeting resistant populations of H. armigera. Future in vivo validation is necessary to confirm the efficacy and safety of the identified candidates.","manuscriptTitle":"Virtual screening of agrochemicals and IC50 prediction for repurposing against Ecdysone receptor of Helicoverpa armigera","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 07:41:22","doi":"10.21203/rs.3.rs-6608446/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a5b1d81-6cfd-484c-a4eb-e105df1e8c93","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T09:39:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 07:41:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6608446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6608446","identity":"rs-6608446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}