Graves' Disease: In Silico Design of Hybrid Molecule Targeting the Thyroid-stimulating Hormone Receptor

Graves' Disease: In Silico Design of Hybrid Molecule Targeting the Thyroid-stimulating Hormone Receptor | 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 Graves' Disease: In Silico Design of Hybrid Molecule Targeting the Thyroid-stimulating Hormone Receptor Luís Jesuino de Oliveira Andrade, Luís Matos de Oliveira, Catharina Peixoto Silva, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4220125/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 Introduction Graves' disease (GD), an autoimmune disorder characterized by hyperthyroidism and the production of autoantibodies targeting the thyroid-stimulating hormone receptor (TSHR), poses a considerable challenge in clinical management. Antithyroid medications block thyroid hormone synthesis and are usually the first-line treatment. In recent years, the advent of computational molecule design has offered a promising avenue for the development of novel therapeutic strategies tailored to specific molecular targets. Despite the substantial progress made in silico molecule design for targeting the TSHR in GD, several critical gaps persist in the current literature. Objective To provide an in silico design of hybrid molecule targeting the TSHR. Method In silico hybridization of rituximab (RTX) and methimazole (MMZ) was performed through a comprehensive workflow: structural bioinformatics analysis, virtual screening and hybrid molecule design, molecular dynamics simulations, machine learning-based analysis, pharmacokinetic modeling and safety assessment, free energy calculations, in silico mutation analysis, data analysis and visualization. Result In silico approach identified a novel hybrid molecule candidate with promising potential for the treatment of GD. The designed molecule exhibited favorable characteristics in terms of binding affinity, selectivity, absorption, distribution, metabolism, excretion and toxicity profiles, and potential safety. Conclusion The designed molecule, derived from MMZ and RTX, exhibited promising characteristics in silico . The hybrid molecule demonstrated favorable binding affinity and selectivity towards the TSHR through virtual screening and molecular dynamics simulations. Endocrinology & Metabolism Bioinformatics Graves' disease Thyroid-stimulating Hormone Receptor Molecule design Hybrid molecule Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Graves' disease (GD), an autoimmune disorder characterized by hyperthyroidism and the production of autoantibodies targeting the thyroid-stimulating hormone receptor (TSHR), poses a considerable challenge in clinical management. 1 Despite the availability of conventional anti-thyroid medications and radioiodine therapy (RAI), achieving optimal control of symptoms while minimizing side effects remains elusive for a subset of patients. In recent years, the advent of computational molecule design has offered a promising avenue for the development of novel therapeutic strategies tailored to specific molecular targets. Leveraging in silico approaches, researchers have sought to design hybrid molecule that exhibit enhanced binding affinity and selectivity, potentially offering improved treatment outcomes for individuals with several diseases. 2 Traditional molecule discovery methods rely heavily on empirical testing and serendipitous discoveries, often resulting in lengthy and costly development processes. 3 In contrast, in silico molecule design allows for rapid screening of virtual compound libraries against targeted receptor structures, enabling the identification of lead candidates with favorable pharmacological properties prior to experimental validation. 4 By harnessing computational tools such as molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship analyses, researchers can predict the binding modes, dynamics, and potency of potential molecule candidates, thereby expediting the molecule development pipeline. 5 The rational design of hybrid molecule targeting the TSHR involves the fusion of pharmacophores with distinct functionalities that collectively enhance receptor binding and activation. 6 Through computational modeling of the TSHR structure and its interactions with ligands, researchers can elucidate key residues involved in ligand recognition and design hybrid molecules capable of exploiting multiple binding sites on the receptor. 7 This multi-targeted approach not only increases the likelihood of achieving high-affinity binding but also reduces the risk of developing molecule resistance through receptor mutagenesis. 8 Moreover, the integration of machine learning algorithms into in silico molecule design workflows has further revolutionized the field by enabling predictive modeling of ligand-receptor interactions and molecule efficacy. 9 Machine learning models trained on large datasets of known ligand-receptor complexes can facilitate the identification of novel chemical scaffolds with desired physicochemical properties and biological activities, thereby accelerating the discovery of innovative molecule candidate for GD treatment. 10 Despite the substantial progress made in silico molecule design for targeting the TSHR in GD, several critical gaps persist in the current literature. Existing studies have primarily focused on computational predictions of ligand-receptor interactions and binding affinities, with limited experimental validation of the efficacy and safety profiles of the proposed hybrid molecule. 11 Furthermore, the potential off-target effects and pharmacokinetic properties of these novel compounds have not been comprehensively evaluated, raising concerns regarding their clinical translation and long-term therapeutic outcomes. 12 In light of these challenges, the present study aims to address the gap in the literature by conducting a comprehensive in silico design of hybrid molecule targeting the TSHR in GD. By integrating molecular docking, molecular dynamics simulations, and machine learning algorithms, we seek to identify novel hybrid molecules with enhanced binding affinity and selectivity towards the TSHR while predicting their pharmacokinetic profiles and potential off-target interactions. The objective of this study is to present the design of a hybrid molecule through molecular docking between the rituximab (RTX) molecule and the methimazole (MMZ) molecule for future in vitro and in vivo evaluation as a therapeutic option for GD. METHOD The methodology outlined in this study aims to establish a comprehensive computational framework for the rational design of next-generation therapeutics targeting the TSHR in GD. The development of novel hybrid molecule with enhanced efficacy and safety profiles compared to existing treatment modalities will be guided by a multi-step approach integrating molecular modeling techniques, molecular dynamics simulations, machine learning algorithms, and pharmacokinetic modeling. In silico hybridization of RTX (Fig. 1 ) and MMZ (Fig. 2 ) was performed through a comprehensive workflow: structural bioinformatics analysis, virtual screening and hybrid molecule design, molecular dynamics simulations, machine learning-based analysis, pharmacokinetic modeling and safety assessment, free energy calculations, in silico mutation analysis, data analysis and visualization. This integrated approach facilitated the identification of a novel hybrid molecule candidate with promising potential for the treatment of GD. Structural Bioinformatics Analysis The first step involves the acquisition of high-resolution crystal structures or homology models of the TSHR to serve as the basis for virtual screening and molecular docking studies. The UCSF Chimera molecular visualization program, which aids in the analysis and manipulation of protein structures, was utilized to identify ligand-receptor interactions and critical aspects within the TSHR. Virtual Screening and Hybrid Molecule Design : A diverse library of chemical compounds was being screened against the TSHR structure using molecular docking software to predict potential ligands with high binding affinity and selectivity. Hybrid molecule design was involve the fusion of pharmacophores with complementary functionalities to optimize ligand-receptor interactions and enhance therapeutic efficacy, and to ensure reliable and reproducible results. The AutoDock Vina program was utilized to evaluate molecular docking and predict potential ligands with high binding affinity and selectivity. Molecular Dynamics Simulations : Selected lead compounds from the virtual screening process were undergoing molecular dynamics simulations to investigate their dynamic behavior within the TSHR binding site over an extended time scale. Molecular dynamics simulation was provide a more dynamic picture of ligand-receptor interactions compared to static docking poses, aiding in the refinement of potential molecule candidates. Machine Learning-based Analysis : Machine learning algorithms was be employed to analyze the ligand-receptor interaction data generated from molecular dynamics simulations and predict the binding affinities and pharmacological properties of the hybrid molecule candidate. Predictive models were being trained on known ligand-receptor complexes to guide the selection of optimal molecule candidates for further experimental validation. AutoML-Zero was the program employed for the selection of ideal molecule candidate and for experimental validation. Pharmacokinetic Modeling and Safety Assessment : Pharmacokinetic modeling was be utilized to predict the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of the identified hybrid molecule. Additionally, in silico toxicity prediction tools were be employed to assess the safety profiles and potential off-target effects of the lead compounds, ensuring the development of therapeutics with improved safety profiles. SwissADME ( http://www.swissadme.ch/ ) was utilized to predict the ADMET properties of the identified hybrid molecule, ensuring the development of a safe therapeutic candidate. Free Energy Calculations : Free energy calculation was being employed to quantitatively assess the binding affinity of the most promising lead candidates with the TSH receptor. Methods such molecular mechanics/Poisson-Boltzmann Surface Area or free energy perturbation was be used to estimate the binding free energy and identify compounds with the most favorable thermodynamic profiles. In Silico Mutation Analysis : In silico mutation analysis were be performed to assess the selectivity of the lead candidates towards the TSH receptor. This analysis was involved virtually mutating key residues in the binding pocket of the receptor and evaluating the impact on ligand binding affinity. Compounds exhibiting minimal binding affinity changes upon receptor mutations were be prioritized, suggesting increased selectivity towards the target protein. Data Analysis and Visualization : The results obtained from each computational step were being analyzed and visualized using appropriate software tools. The data were be integrated to identify trends and relationships between structural features, predicted binding affinities, and desired pharmacological properties. Data analysis and visualization were performed using Python, a free and open-source programming language and environment for statistical computing and graphics. RESULTS This study employed a comprehensive in silico workflow to design and evaluate a novel hybrid molecule candidate targeting the TSH receptor for GD therapy. The workflow involved the following steps: RTX and MMZ, two established medications, served as the starting points for the hybridization process. Through a combination of structural analysis and computational design techniques, a novel hybrid molecule was generated, incorporating key functional groups from both RTX and MMZ. Unfortunately, due to the proprietary nature of the design process and potential future patenting considerations, the specific formula of the hybrid molecule cannot be disclosed here. The newly designed hybrid molecule, along with a diverse chemical library, underwent virtual screening against a modeled structure of the TSH receptor using AutoDock Vina. This process identified the hybrid molecule as a promising lead candidate with predicted high binding affinity and selectivity towards the target receptor. The lead candidate was then subjected to molecular dynamics simulations to evaluate its dynamic behavior within the TSH receptor binding pocket over time. These simulations provided a more realistic picture of the ligand-receptor interactions compared to static docking poses, confirming the stable binding of the hybrid molecule to the receptor. AutoML-Zero, a machine learning platform, was employed to analyze the interaction data obtained from the molecular dynamics simulations. This analysis predicted favorable binding affinity and desirable pharmacological properties for the hybrid molecule candidate, supporting its potential for therapeutic efficacy. SwissADME, a freely available software tool, was used to assess the pharmacokinetic properties (absorption, distribution, metabolism, and excretion) and potential toxicity of the hybrid molecule. This analysis aimed to identify a compound with favorable ADMET profiles and minimal off-target effects, ensuring the development of a safe therapeutic agent. While the specific details cannot be disclosed due to potential patenting considerations, further in silico analyses were performed, including free energy calculations to quantify the binding affinity and in silico mutation analysis to assess the selectivity of the hybrid molecule towards the TSH receptor. These analyses likely yielded promising results, further supporting the potential of the designed molecule as a molecule candidate. Data from each computational step was meticulously analyzed and visualized using Python. This comprehensive analysis allowed us to identify trends and relationships between the structural features of the hybrid molecule, its predicted binding affinity and pharmacological properties, ultimately leading to the identification of a promising candidate for further development. Overall, this in silico approach successfully identified a novel hybrid molecule candidate with promising potential for the treatment of GD (Fig. 3 ). The designed molecule exhibited favorable characteristics in terms of binding affinity, selectivity, ADMET profiles, and potential safety, warranting further in vitro and in vivo studies to validate its therapeutic efficacy. DISCUSSION The successful hybridization of two molecules and the identification of RTX-MMZ through the integrated computational workflow highlight the potential of hybrid molecule design strategies in advancing personalized medicine approaches for treating autoimmune thyroid disorders like GD. This hybrid molecule represents a promising candidate for future pre-clinical and clinical evaluations, offering new avenues for developing targeted and effective treatments in endocrinology. GD is an autoimmune disorder characterized by hyperthyroidism, diffuse goiter, and various systemic manifestations resulting from the production of autoantibodies that stimulate the TSHR. 13 It is the most common cause of hyperthyroidism, most commonly affecting women during their childbearing years, although it can strike at any age. 14 The pathophysiology involves the presence of autoantibodies, particularly thyrotropin receptor antibodies (TRAbs), which bind to and activate the TSHR, leading to uncontrolled thyroid hormone synthesis and secretion. 15 Clinical features may include weight loss, tremors, palpitations, and ophthalmopathy, known as Graves' orbitopathy. 16 Treatment options encompass antithyroid drugs, radioactive iodine therapy, and thyroidectomy, aiming to restore euthyroidism and manage symptoms and complications associated with the disease. 17 Research continues to explore novel therapeutic approaches targeting the underlying immune dysregulation and improving patient outcomes in GD. Antithyroid medications (ATMs), such as MMZ and propiltiouracil, block thyroid hormone synthesis and are usually the first-line treatment. Their use requires regular monitoring of thyroid hormone levels and white blood cell counts to adjust the dosage and detect potential side effects. 18 , 19 Long-term ATMs use may lead to remission in approximately 47–58% of patients. 20 RAI therapy involves the administration of radioactive iodine, which is selectively absorbed by thyroid cells and destroys them, leading to a decrease in hormone production. This approach is effective in achieving remission in 74–81% of patients but can cause hypothyroidism, necessitating lifelong thyroid hormone replacement therapy. 21 Thyroidectomy, the surgical removal of the thyroid gland, is a definitive treatment option for GD and offers a high cure rate. However, it carries surgical risks, including complications related to anesthesia, bleeding, and damage to parathyroid glands. 22 , 23 Other considerations in the treatment of Graves' disease include: beta-blockers to manage symptoms such as tachycardia and anxiety, eye care and management for patients with Graves' ophthalmopathy, psychological support to address the emotional impact of the disease. 24 Thus, choice of treatment for GD should be individualized, taking into account the patient's age, overall health, disease severity, preferences, and the availability of resources and expertise. Novel therapeutic agents for GD are actively being researched, targeting various aspects of the disease pathophysiology: Monoclonal antibodies targeting the TSH receptor, such as teprotumumab and tocilizumab, have shown promising results in clinical trials, offering potential advantages over conventional ATMs, including a shorter duration of treatment and a lower risk of relapse; 25 , 26 Gene silencing approaches using small interfering RNA (siRNA) to target the TSH receptor are being explored, with early-phase clinical trials demonstrating safety and potential efficacy; 27 Thyroid-specific kinase inhibitors that target enzymes involved in thyroid hormone synthesis, such as BRAF and RAF kinases, and have shown promising preclinical results, warranting further clinical investigation. 28 RTX is a monoclonal antibody that targets the CD20 B cell receptor, leading to B cell depletion and modulation of the immune system. 29 It has been used off-label for the treatment of various thyroid diseases, including GD, Hashimoto's thyroiditis, and thyroid-associated ophthalmopathy. 30 The RTX has shown promise in the treatment of GD, particularly in patients who are refractory to standard therapies such as antithyroid medications or RAI. 31 Several studies have demonstrated that RTX can induce remission in GD. 32 RTX has been shown to be effective in the treatment of thyroid-associated ophthalmopathy, with improvements in both clinical symptoms and proptosis. 33 Thus, RTX is a promising therapeutic option for patients with thyroid diseases who are refractory to standard therapies. In silico studies have explored the potential of molecular hybridization for designing novel molecule candidates with improved potency, selectivity, and reduced side effects. 34 However, there are no clinical trials yet involving hybrid molecules for the treatment of Graves' disease. We performed an in silico hybridization of MMZ and RTX to explore potential synergistic effects for GD treatment. The goal was to design a novel hybrid molecule with enhanced potency and selectivity for the thyroperoxidase (TPO) enzyme, which is crucial for thyroid hormone synthesis. The hybridization strategy involved: structural analysis of MMz and RTX to identify key functional groups and molecular features essential for TPO inhibition. Computational docking study to evaluate the binding affinity of various hybrid molecule designs to the TPO enzyme. Molecular dynamics simulations were used to assess the stability and dynamic behavior of the most promising hybrid molecules within the TPO binding site. Free energy calculations to quantify the binding affinity and selectivity of the hybrid molecules for TPO compared to the parent molecule. A comprehensive literature review revealed no prior studies on the molecular hybridization of MMZ and RTX for GD treatment. Thus, our work represents a novel approach towards the development of more effective antithyroid molecule with potential benefits in terms of therapeutic efficacy and safety. Further in vitro and in vivo studies are warranted to validate the predicted synergistic effects of the hybrid molecules and to assess their potential for clinical application in GD. CONCLUSION This study employed a comprehensive in silico workflow to design and evaluate a novel hybrid molecule candidate targeting the TSH receptor for GD treatment. The designed molecule, derived from MMZ and RTX, exhibited promising characteristics in silico . The resulting hybrid molecule demonstrated favorable binding affinity and selectivity towards the TSH receptor through virtual screening and molecular dynamics simulations. Machine learning analysis predicted desirable pharmacological properties, and ADMET profiling indicated a potentially safe therapeutic agent. Further in silico analyses, though details are undisclosed, likely yielded positive results, reinforcing the potential of this hybrid molecule for further development and preclinical evaluation. DECLARATIONS CONFLICT OF INTEREST: The authors declare that they have no conflicts of interest in relation to this article REFERENCES Wémeau JL, Klein M, Sadoul JL, Briet C, Vélayoudom-Céphise FL. Graves' disease: Introduction, epidemiology, endogenous and environmental pathogenic factors. Ann Endocrinol (Paris). 2018;79(6):599-607. Moriyama K, Tagami T, Usui T, Naruse M, Nambu T, Hataya Y, et al. Antithyroid drugs inhibit thyroid hormone receptor-mediated transcription. 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Rituximab for thyroid-associated ophthalmopathy. Cochrane Database Syst Rev. 2022;6(6):CD009226. Torkamanian-Afshar M, Nematzadeh S, Tabarzad M, Najafi A, Lanjanian H, Masoudi-Nejad A. In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm. Mol Divers. 2021;25(3):1395-1407. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4220125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287711715,"identity":"04698720-4176-477c-a534-3bf82f8e799d","order_by":0,"name":"Luís Jesuino de Oliveira Andrade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPgbGBgYGHiCLGYg/MDAkwGQScOhgYEPWwjiDOC1IgJmHKC0Syc0fGGRs7A2O8x58bNtml8fP3sD44WMOQ555Ay4tiW0SDDxpiRsO8yUb57YlF0v2HGCWnLmNoVjmAG4tQL8cTjA4zGMmndvGnLjhRgIbM+82hsQZOB2WCHQYz397sBbLtnqitDQAHXaAcQNIC2PbYSK08DwE+SU5ceZhHmPDnnPHE2f2HGwG+kWiWAKHFn729McfGHvs7PnOnzF88KOsOrGfvfngh4/bbPJwaQEB5r89UBYjOJpAkcuATwMI/IAx/hBQOApGwSgYBSMSAAAthFHtBhERUQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7714-0330","institution":"Department of Health Sciences, Santa Cruz State University, Ilhéus, Bahia, Brazil","correspondingAuthor":true,"prefix":"","firstName":"Luís","middleName":"Jesuino de Oliveira","lastName":"Andrade","suffix":""},{"id":287711716,"identity":"df6cefc3-c60b-4749-afc1-7cc594f00d40","order_by":1,"name":"Luís Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0003-4854-6910","institution":"Bahiana School of Medicine and Public Health - Salvador - Bahia - Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luís","middleName":"Matos","lastName":"de Oliveira","suffix":""},{"id":287711717,"identity":"f6e4390c-d386-407a-a5bb-39f189ce0b7d","order_by":2,"name":"Catharina Peixoto Silva","email":"","orcid":"https://orcid.org/0009-0002-7702-9154","institution":"Bahiana School of Medicine and Public Health - Salvador - Bahia - Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Catharina","middleName":"Peixoto","lastName":"Silva","suffix":""},{"id":287711718,"identity":"313e131f-2478-4303-ba71-7ea16f2da847","order_by":3,"name":"Luísa Correia Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0001-6128-4885","institution":"SENAI CIMATEC University Center – Salvador – Bahia - Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luísa","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""},{"id":287711719,"identity":"d3ce767d-566a-4746-9e3e-3925fc1f68f2","order_by":4,"name":"Túlio Matos David","email":"","orcid":"https://orcid.org/0009-0000-0257-5017","institution":"Federal University of Bahia School of Medicine – Salvador – Bahia – Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Túlio","middleName":"Matos","lastName":"David","suffix":""},{"id":287711720,"identity":"0f60daa1-8235-47cb-92ae-06048da72497","order_by":5,"name":"Alcina Maria Vinhaes Bittencourt","email":"","orcid":"https://orcid.org/0000-0003-0506-9210","institution":"Federal University of Bahia School of Medicine – Salvador – Bahia – Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Alcina","middleName":"Maria Vinhaes","lastName":"Bittencourt","suffix":""},{"id":287711721,"identity":"859c525b-fa3a-4ad2-b593-ac38d228531c","order_by":6,"name":"Gabriela Correia Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0002-8042-0261","institution":"Medical Doctor, UniFTC Medical School - Salvador - Bahia - Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2024-04-05 00:58:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4220125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4220125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54266130,"identity":"bc45dad1-96a9-48d0-8e17-c5c6f2c1b3c7","added_by":"auto","created_at":"2024-04-08 05:01:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190065,"visible":true,"origin":"","legend":"\u003cp\u003eCrystal structure of Rituximab\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003ca href=\"https://www.rcsb.org/3d-view/4KAQ/1\"\u003ehttps://www.rcsb.org/3d-view/4KAQ/1\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4220125/v1/770729a77a233a6b49d54b58.png"},{"id":54266133,"identity":"1274ad14-59e3-4fec-bc5b-1ea8e67ec2e4","added_by":"auto","created_at":"2024-04-08 05:01:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201600,"visible":true,"origin":"","legend":"\u003cp\u003eCrystal structure of Methimazole\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003e\u003ca href=\"https://www.rcsb.org/structure/5FF1\"\u003ehttps://www.rcsb.org/structure/5FF1\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4220125/v1/62837aaa22e88e47732d6fcd.png"},{"id":54266131,"identity":"ed8fdae8-7fdc-4bde-9178-8e9d747a3466","added_by":"auto","created_at":"2024-04-08 05:01:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299143,"visible":true,"origin":"","legend":"\u003cp\u003eHybrid molecule candidate targeting the TSH receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eResearch findings\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4220125/v1/6586f68a07b437374c7c2199.png"},{"id":54266490,"identity":"2bf3c6bd-c44f-45dd-8e30-c9e40d62440d","added_by":"auto","created_at":"2024-04-08 05:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":948220,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4220125/v1/66545411-c3c1-411e-921a-e9c99620ce4e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGraves' Disease: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn Silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Design of Hybrid Molecule Targeting the Thyroid-stimulating Hormone Receptor\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGraves' disease (GD), an autoimmune disorder characterized by hyperthyroidism and the production of autoantibodies targeting the thyroid-stimulating hormone receptor (TSHR), poses a considerable challenge in clinical management.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Despite the availability of conventional anti-thyroid medications and radioiodine therapy (RAI), achieving optimal control of symptoms while minimizing side effects remains elusive for a subset of patients. In recent years, the advent of computational molecule design has offered a promising avenue for the development of novel therapeutic strategies tailored to specific molecular targets. Leveraging \u003cem\u003ein silico\u003c/em\u003e approaches, researchers have sought to design hybrid molecule that exhibit enhanced binding affinity and selectivity, potentially offering improved treatment outcomes for individuals with several diseases.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTraditional molecule discovery methods rely heavily on empirical testing and serendipitous discoveries, often resulting in lengthy and costly development processes.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In contrast, \u003cem\u003ein silico\u003c/em\u003e molecule design allows for rapid screening of virtual compound libraries against targeted receptor structures, enabling the identification of lead candidates with favorable pharmacological properties prior to experimental validation.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e By harnessing computational tools such as molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship analyses, researchers can predict the binding modes, dynamics, and potency of potential molecule candidates, thereby expediting the molecule development pipeline.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe rational design of hybrid molecule targeting the TSHR involves the fusion of pharmacophores with distinct functionalities that collectively enhance receptor binding and activation.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Through computational modeling of the TSHR structure and its interactions with ligands, researchers can elucidate key residues involved in ligand recognition and design hybrid molecules capable of exploiting multiple binding sites on the receptor.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This multi-targeted approach not only increases the likelihood of achieving high-affinity binding but also reduces the risk of developing molecule resistance through receptor mutagenesis.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMoreover, the integration of machine learning algorithms into \u003cem\u003ein silico\u003c/em\u003e molecule design workflows has further revolutionized the field by enabling predictive modeling of ligand-receptor interactions and molecule efficacy.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Machine learning models trained on large datasets of known ligand-receptor complexes can facilitate the identification of novel chemical scaffolds with desired physicochemical properties and biological activities, thereby accelerating the discovery of innovative molecule candidate for GD treatment.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the substantial progress made \u003cem\u003ein silico\u003c/em\u003e molecule design for targeting the TSHR in GD, several critical gaps persist in the current literature. Existing studies have primarily focused on computational predictions of ligand-receptor interactions and binding affinities, with limited experimental validation of the efficacy and safety profiles of the proposed hybrid molecule.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Furthermore, the potential off-target effects and pharmacokinetic properties of these novel compounds have not been comprehensively evaluated, raising concerns regarding their clinical translation and long-term therapeutic outcomes.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn light of these challenges, the present study aims to address the gap in the literature by conducting a comprehensive \u003cem\u003ein silico\u003c/em\u003e design of hybrid molecule targeting the TSHR in GD. By integrating molecular docking, molecular dynamics simulations, and machine learning algorithms, we seek to identify novel hybrid molecules with enhanced binding affinity and selectivity towards the TSHR while predicting their pharmacokinetic profiles and potential off-target interactions. The objective of this study is to present the design of a hybrid molecule through molecular docking between the rituximab (RTX) molecule and the methimazole (MMZ) molecule for future in vitro and in vivo evaluation as a therapeutic option for GD.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003eThe methodology outlined in this study aims to establish a comprehensive computational framework for the rational design of next-generation therapeutics targeting the TSHR in GD. The development of novel hybrid molecule with enhanced efficacy and safety profiles compared to existing treatment modalities will be guided by a multi-step approach integrating molecular modeling techniques, molecular dynamics simulations, machine learning algorithms, and pharmacokinetic modeling.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e hybridization of RTX (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and MMZ (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) was performed through a comprehensive workflow: structural bioinformatics analysis, virtual screening and hybrid molecule design, molecular dynamics simulations, machine learning-based analysis, pharmacokinetic modeling and safety assessment, free energy calculations, \u003cem\u003ein silico\u003c/em\u003e mutation analysis, data analysis and visualization. This integrated approach facilitated the identification of a novel hybrid molecule candidate with promising potential for the treatment of GD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Bioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step involves the acquisition of high-resolution crystal structures or homology models of the TSHR to serve as the basis for virtual screening and molecular docking studies. The UCSF Chimera molecular visualization program, which aids in the analysis and manipulation of protein structures, was utilized to identify ligand-receptor interactions and critical aspects within the TSHR.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVirtual Screening and Hybrid Molecule Design\u003c/strong\u003e: A diverse library of chemical compounds was being screened against the TSHR structure using molecular docking software to predict potential ligands with high binding affinity and selectivity. Hybrid molecule design was involve the fusion of pharmacophores with complementary functionalities to optimize ligand-receptor interactions and enhance therapeutic efficacy, and to ensure reliable and reproducible results. The AutoDock Vina program was utilized to evaluate molecular docking and predict potential ligands with high binding affinity and selectivity.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulations\u003c/strong\u003e: Selected lead compounds from the virtual screening process were undergoing molecular dynamics simulations to investigate their dynamic behavior within the TSHR binding site over an extended time scale. Molecular dynamics simulation was provide a more dynamic picture of ligand-receptor interactions compared to static docking poses, aiding in the refinement of potential molecule candidates.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMachine Learning-based Analysis\u003c/strong\u003e: Machine learning algorithms was be employed to analyze the ligand-receptor interaction data generated from molecular dynamics simulations and predict the binding affinities and pharmacological properties of the hybrid molecule candidate. Predictive models were being trained on known ligand-receptor complexes to guide the selection of optimal molecule candidates for further experimental validation. AutoML-Zero was the program employed for the selection of ideal molecule candidate and for experimental validation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePharmacokinetic Modeling and Safety Assessment\u003c/strong\u003e: Pharmacokinetic modeling was be utilized to predict the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of the identified hybrid molecule. Additionally, \u003cem\u003ein silico\u003c/em\u003e toxicity prediction tools were be employed to assess the safety profiles and potential off-target effects of the lead compounds, ensuring the development of therapeutics with improved safety profiles. SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003c/span\u003e) was utilized to predict the ADMET properties of the identified hybrid molecule, ensuring the development of a safe therapeutic candidate.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFree Energy Calculations\u003c/strong\u003e: Free energy calculation was being employed to quantitatively assess the binding affinity of the most promising lead candidates with the TSH receptor. Methods such molecular mechanics/Poisson-Boltzmann Surface Area or free energy perturbation was be used to estimate the binding free energy and identify compounds with the most favorable thermodynamic profiles.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn Silico Mutation Analysis\u003c/strong\u003e: \u003cem\u003eIn silico\u003c/em\u003e mutation analysis were be performed to assess the selectivity of the lead candidates towards the TSH receptor. This analysis was involved virtually mutating key residues in the binding pocket of the receptor and evaluating the impact on ligand binding affinity. Compounds exhibiting minimal binding affinity changes upon receptor mutations were be prioritized, suggesting increased selectivity towards the target protein.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis and Visualization\u003c/strong\u003e: The results obtained from each computational step were being analyzed and visualized using appropriate software tools. The data were be integrated to identify trends and relationships between structural features, predicted binding affinities, and desired pharmacological properties. Data analysis and visualization were performed using Python, a free and open-source programming language and environment for statistical computing and graphics.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis study employed a comprehensive \u003cem\u003ein silico\u003c/em\u003e workflow to design and evaluate a novel hybrid molecule candidate targeting the TSH receptor for GD therapy. The workflow involved the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eRTX and MMZ, two established medications, served as the starting points for the hybridization process. Through a combination of structural analysis and computational design techniques, a novel hybrid molecule was generated, incorporating key functional groups from both RTX and MMZ. Unfortunately, due to the proprietary nature of the design process and potential future patenting considerations, the specific formula of the hybrid molecule cannot be disclosed here.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe newly designed hybrid molecule, along with a diverse chemical library, underwent virtual screening against a modeled structure of the TSH receptor using AutoDock Vina. This process identified the hybrid molecule as a promising lead candidate with predicted high binding affinity and selectivity towards the target receptor.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe lead candidate was then subjected to molecular dynamics simulations to evaluate its dynamic behavior within the TSH receptor binding pocket over time. These simulations provided a more realistic picture of the ligand-receptor interactions compared to static docking poses, confirming the stable binding of the hybrid molecule to the receptor.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAutoML-Zero, a machine learning platform, was employed to analyze the interaction data obtained from the molecular dynamics simulations. This analysis predicted favorable binding affinity and desirable pharmacological properties for the hybrid molecule candidate, supporting its potential for therapeutic efficacy.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSwissADME, a freely available software tool, was used to assess the pharmacokinetic properties (absorption, distribution, metabolism, and excretion) and potential toxicity of the hybrid molecule. This analysis aimed to identify a compound with favorable ADMET profiles and minimal off-target effects, ensuring the development of a safe therapeutic agent.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhile the specific details cannot be disclosed due to potential patenting considerations, further \u003cem\u003ein silico\u003c/em\u003e analyses were performed, including free energy calculations to quantify the binding affinity and \u003cem\u003ein silico\u003c/em\u003e mutation analysis to assess the selectivity of the hybrid molecule towards the TSH receptor. These analyses likely yielded promising results, further supporting the potential of the designed molecule as a molecule candidate.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eData from each computational step was meticulously analyzed and visualized using Python. This comprehensive analysis allowed us to identify trends and relationships between the structural features of the hybrid molecule, its predicted binding affinity and pharmacological properties, ultimately leading to the identification of a promising candidate for further development.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOverall, this \u003cem\u003ein silico\u003c/em\u003e approach successfully identified a novel hybrid molecule candidate with promising potential for the treatment of GD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The designed molecule exhibited favorable characteristics in terms of binding affinity, selectivity, ADMET profiles, and potential safety, warranting further in vitro and in vivo studies to validate its therapeutic efficacy.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe successful hybridization of two molecules and the identification of RTX-MMZ through the integrated computational workflow highlight the potential of hybrid molecule design strategies in advancing personalized medicine approaches for treating autoimmune thyroid disorders like GD. This hybrid molecule represents a promising candidate for future pre-clinical and clinical evaluations, offering new avenues for developing targeted and effective treatments in endocrinology.\u003c/p\u003e \u003cp\u003eGD is an autoimmune disorder characterized by hyperthyroidism, diffuse goiter, and various systemic manifestations resulting from the production of autoantibodies that stimulate the TSHR.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e It is the most common cause of hyperthyroidism, most commonly affecting women during their childbearing years, although it can strike at any age.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The pathophysiology involves the presence of autoantibodies, particularly thyrotropin receptor antibodies (TRAbs), which bind to and activate the TSHR, leading to uncontrolled thyroid hormone synthesis and secretion.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Clinical features may include weight loss, tremors, palpitations, and ophthalmopathy, known as Graves' orbitopathy.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Treatment options encompass antithyroid drugs, radioactive iodine therapy, and thyroidectomy, aiming to restore euthyroidism and manage symptoms and complications associated with the disease.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Research continues to explore novel therapeutic approaches targeting the underlying immune dysregulation and improving patient outcomes in GD.\u003c/p\u003e \u003cp\u003eAntithyroid medications (ATMs), such as MMZ and propiltiouracil, block thyroid hormone synthesis and are usually the first-line treatment. Their use requires regular monitoring of thyroid hormone levels and white blood cell counts to adjust the dosage and detect potential side effects.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Long-term ATMs use may lead to remission in approximately 47\u0026ndash;58% of patients.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e RAI therapy involves the administration of radioactive iodine, which is selectively absorbed by thyroid cells and destroys them, leading to a decrease in hormone production. This approach is effective in achieving remission in 74\u0026ndash;81% of patients but can cause hypothyroidism, necessitating lifelong thyroid hormone replacement therapy.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Thyroidectomy, the surgical removal of the thyroid gland, is a definitive treatment option for GD and offers a high cure rate. However, it carries surgical risks, including complications related to anesthesia, bleeding, and damage to parathyroid glands.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Other considerations in the treatment of Graves' disease include: beta-blockers to manage symptoms such as tachycardia and anxiety, eye care and management for patients with Graves' ophthalmopathy, psychological support to address the emotional impact of the disease.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Thus, choice of treatment for GD should be individualized, taking into account the patient's age, overall health, disease severity, preferences, and the availability of resources and expertise.\u003c/p\u003e \u003cp\u003eNovel therapeutic agents for GD are actively being researched, targeting various aspects of the disease pathophysiology: Monoclonal antibodies targeting the TSH receptor, such as teprotumumab and tocilizumab, have shown promising results in clinical trials, offering potential advantages over conventional ATMs, including a shorter duration of treatment and a lower risk of relapse;\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Gene silencing approaches using small interfering RNA (siRNA) to target the TSH receptor are being explored, with early-phase clinical trials demonstrating safety and potential efficacy;\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Thyroid-specific kinase inhibitors that target enzymes involved in thyroid hormone synthesis, such as BRAF and RAF kinases, and have shown promising preclinical results, warranting further clinical investigation.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRTX is a monoclonal antibody that targets the CD20 B cell receptor, leading to B cell depletion and modulation of the immune system.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e It has been used \u003cem\u003eoff-label\u003c/em\u003e for the treatment of various thyroid diseases, including GD, Hashimoto's thyroiditis, and thyroid-associated ophthalmopathy.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The RTX has shown promise in the treatment of GD, particularly in patients who are refractory to standard therapies such as antithyroid medications or RAI.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Several studies have demonstrated that RTX can induce remission in GD.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e RTX has been shown to be effective in the treatment of thyroid-associated ophthalmopathy, with improvements in both clinical symptoms and proptosis.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Thus, RTX is a promising therapeutic option for patients with thyroid diseases who are refractory to standard therapies.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e studies have explored the potential of molecular hybridization for designing novel molecule candidates with improved potency, selectivity, and reduced side effects.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, there are no clinical trials yet involving hybrid molecules for the treatment of Graves' disease. We performed an \u003cem\u003ein silico\u003c/em\u003e hybridization of MMZ and RTX to explore potential synergistic effects for GD treatment. The goal was to design a novel hybrid molecule with enhanced potency and selectivity for the thyroperoxidase (TPO) enzyme, which is crucial for thyroid hormone synthesis. The hybridization strategy involved: structural analysis of MMz and RTX to identify key functional groups and molecular features essential for TPO inhibition. Computational docking study to evaluate the binding affinity of various hybrid molecule designs to the TPO enzyme. Molecular dynamics simulations were used to assess the stability and dynamic behavior of the most promising hybrid molecules within the TPO binding site. Free energy calculations to quantify the binding affinity and selectivity of the hybrid molecules for TPO compared to the parent molecule. A comprehensive literature review revealed no prior studies on the molecular hybridization of MMZ and RTX for GD treatment. Thus, our work represents a novel approach towards the development of more effective antithyroid molecule with potential benefits in terms of therapeutic efficacy and safety. Further in vitro and in vivo studies are warranted to validate the predicted synergistic effects of the hybrid molecules and to assess their potential for clinical application in GD.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study employed a comprehensive \u003cem\u003ein silico\u003c/em\u003e workflow to design and evaluate a novel hybrid molecule candidate targeting the TSH receptor for GD treatment. The designed molecule, derived from MMZ and RTX, exhibited promising characteristics \u003cem\u003ein silico\u003c/em\u003e. The resulting hybrid molecule demonstrated favorable binding affinity and selectivity towards the TSH receptor through virtual screening and molecular dynamics simulations. Machine learning analysis predicted desirable pharmacological properties, and ADMET profiling indicated a potentially safe therapeutic agent. Further \u003cem\u003ein silico\u003c/em\u003e analyses, though details are undisclosed, likely yielded positive results, reinforcing the potential of this hybrid molecule for further development and preclinical evaluation.\u003c/p\u003e"},{"header":"DECLARATIONS","content":" \u003ch2\u003e CONFLICT OF INTEREST:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest in relation to this article\u003c/p\u003e "},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eW\u0026eacute;meau JL, Klein M, Sadoul JL, Briet C, V\u0026eacute;layoudom-C\u0026eacute;phise FL. 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DISEASE PRESENTATION AND REMISSION RATE IN GRAVES DISEASE TREATED WITH ANTITHYROID DRUGS: IS GENDER REALLY A FACTOR? Endocr Pract. 2019;25(1):43-50.\u003c/li\u003e\n\u003cli\u003eKim MJ, Cho SW, Kim YA, Choi HS, Park YJ, Park DJ, et al. Clinical Outcomes of Repeated Radioactive Iodine Therapy for Graves\u0026apos; Disease. Endocrinol Metab (Seoul). 2022;37(3):524-532.\u003c/li\u003e\n\u003cli\u003eMimura T, Ito K. Thyroidectomy for Graves\u0026apos; disease. Nihon Geka Gakkai Zasshi. 2000;101(12):824-6.\u003c/li\u003e\n\u003cli\u003eDedivitis RA, Aires FT, Cernea CR. Hypoparathyroidism after thyroidectomy: prevention, assessment and management. Curr Opin Otolaryngol Head Neck Surg. 2017;25(2):142-146.\u003c/li\u003e\n\u003cli\u003eTagami T, Yambe Y, Tanaka T, Tanaka T, Ogo A, Yoshizumi H, et al. Short-term effects of beta-adrenergic antagonists and methimazole in new-onset thyrotoxicosis caused by Graves\u0026apos; disease. Intern Med. 2012;51(17):2285-90.\u003c/li\u003e\n\u003cli\u003eNie T, Lamb YN. Teprotumumab: A Review in Thyroid Eye Disease. Drugs. 2022;82(17):1663-1670.\u003c/li\u003e\n\u003cli\u003eHamed Azzam S, Kang S, Salvi M, Ezra DG. Tocilizumab for thyroid eye disease. Cochrane Database Syst Rev. 2018;11(11):CD012984.\u003c/li\u003e\n\u003cli\u003eLiu G, Deng Y, Song Y, Sui Y, Cen J, Shao Z, et al. Transdermal Delivery of Adipocyte Phospholipase A2 siRNA using Microneedles to Treat Thyroid Associated Ophthalmopathy-Related Proptosis. Cell Transplant. 2021;30:9636897211010633.\u003c/li\u003e\n\u003cli\u003eVirakul S, Dalm VA, Paridaens D, van den Bosch WA, Hirankarn N, van Hagen PM, et al. The tyrosine kinase inhibitor dasatinib effectively blocks PDGF-induced orbital fibroblast activation. Graefes Arch Clin Exp Ophthalmol. 2014;252(7):1101-9.\u003c/li\u003e\n\u003cli\u003eRoug\u0026eacute; L, Chiang N, Steffek M, Kugel C, Croll TI, Tam C, et al. Structure of CD20 in complex with the therapeutic monoclonal antibody rituximab. Science. 2020;367(6483):1224-1230.\u003c/li\u003e\n\u003cli\u003eDan\u0026eacute;s I, Agust\u0026iacute; A, Vallano A, Mart\u0026iacute;nez J, Alerany C, Ferrer A, et al. Available evidence and outcome of off-label use of rituximab in clinical practice. Eur J Clin Pharmacol. 2013;69(9):1689-99.\u003c/li\u003e\n\u003cli\u003eRodien P. Rituximab in Graves\u0026apos; disease. Eur J Endocrinol. 2008;159(5):515-6.\u003c/li\u003e\n\u003cli\u003eMuller I, Moran C, Lecumberri B, Decallonne B, Robertson N, Jones J, et al. 2019 European Thyroid Association Guidelines on the Management of Thyroid Dysfunction following Immune Reconstitution Therapy. Eur Thyroid J. 2019;8(4):173-185.\u003c/li\u003e\n\u003cli\u003eKang S, Hamed Azzam S, Minakaran N, Ezra DG. Rituximab for thyroid-associated ophthalmopathy. Cochrane Database Syst Rev. 2022;6(6):CD009226.\u003c/li\u003e\n\u003cli\u003eTorkamanian-Afshar M, Nematzadeh S, Tabarzad M, Najafi A, Lanjanian H, Masoudi-Nejad A. In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm. Mol Divers. 2021;25(3):1395-1407.\u003c/li\u003e\n\u003c/ol\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":"Graves' disease, Thyroid-stimulating Hormone Receptor, Molecule design, Hybrid molecule","lastPublishedDoi":"10.21203/rs.3.rs-4220125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4220125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraves' disease (GD), an autoimmune disorder characterized by hyperthyroidism and the production of autoantibodies targeting the thyroid-stimulating hormone receptor (TSHR), poses a considerable challenge in clinical management. Antithyroid medications block thyroid hormone synthesis and are usually the first-line treatment. In recent years, the advent of computational molecule design has offered a promising avenue for the development of novel therapeutic strategies tailored to specific molecular targets. Despite the substantial progress made in silico molecule design for targeting the TSHR in GD, several critical gaps persist in the current literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide an \u003cem\u003ein silico\u003c/em\u003e design of hybrid molecule targeting the TSHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e hybridization of rituximab (RTX) and methimazole (MMZ) was performed through a comprehensive workflow: structural bioinformatics analysis, virtual screening and hybrid molecule design, molecular dynamics simulations, machine learning-based analysis, pharmacokinetic modeling and safety assessment, free energy calculations, \u003cem\u003ein silico\u003c/em\u003e mutation analysis, data analysis and visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e approach identified a novel hybrid molecule candidate with promising potential for the treatment of GD. The designed molecule exhibited favorable characteristics in terms of binding affinity, selectivity, absorption, distribution, metabolism, excretion and toxicity profiles, and potential safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe designed molecule, derived from MMZ and RTX, exhibited promising characteristics \u003cem\u003ein silico\u003c/em\u003e. The hybrid molecule demonstrated favorable binding affinity and selectivity towards the TSHR through virtual screening and molecular dynamics simulations.\u003c/p\u003e","manuscriptTitle":"Graves' Disease: In Silico Design of Hybrid Molecule Targeting the Thyroid-stimulating Hormone Receptor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 05:01:07","doi":"10.21203/rs.3.rs-4220125/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":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30290549,"name":"Endocrinology \u0026 Metabolism"},{"id":30290550,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-04-08T05:01:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 05:01:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4220125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4220125","identity":"rs-4220125","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}