Evaluation of synergism and combination effect of sulfasalazine and valproic acid on glioma cells using a software based on Median-Effect Equation and Combination Index Theorem

Evaluation of synergism and combination effect of sulfasalazine and valproic acid on glioma cells using a software based on Median-Effect Equation and Combination Index Theorem | 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 Short Report Evaluation of synergism and combination effect of sulfasalazine and valproic acid on glioma cells using a software based on Median-Effect Equation and Combination Index Theorem Carlos G. Garcia, Suzana Assad Kahn, Flavia Regina Souza Lima, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6829845/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 Glioblastoma (GB), the most common and aggressive primary brain tumor in adults, exhibit poor prognosis and limited efficacy of conventional therapies. Sulfasalazine (SAS), a well-known drug targeting xc⁻ system, has shown antitumor activity in GB models. However, its clinical application is hindered by poor oral bioavailability and limited penetration across blood-brain barrier (BBB). Strategies to enhance its therapeutic potential, such as achieving synergistic effects with other anti-glioma drugs, are highly desirable to increase its potency. Valproic acid (VPA), a well-established anticonvulsant/mood stabilizer, is also proposed as anti-glioma agent due to its histone deacetylase (HDAC) inhibition activity, playing a role regulating glioblastoma cell proliferation and survival. Drug repurposing, thus, has emerged as a promising strategy due to its cost-effectiveness and already established safety profiles. In this study, we evaluated potential synergistic interactions between SAS and VPA in GB treatment using human (U87MG, GBM02) and rat (C6) cell lines. Pharmacological data from previous studies were analyzed using CompuSyn software to construct isobolograms, fraction affected–combination index (Fa-CI) and dose-reduction index (DRI) plots. Our results demonstrated synergistic interactions between SAS + VPA in U87MG and GBM02 cells (CI values below 1 at higher Fa levels). In contrast, C6 cells exhibited reduced synergism (CI > 1 in most conditions). DRI analysis revealed significant dose reductions for SAS + VPA treatments across all cell lines. These findings provide evidence supporting the synergistic potential of SAS + VPA in GB therapy highlighting the utility of CompuSyn for detailed drug interaction analysis. Further studies are warranted to explore the clinical applicability of this combination. glioblastoma drug repurposing sulfasalazine valproic acid synergism Figures Figure 1 Figure 2 Introduction Glioblastoma (GB) is the most common malignant primary cancer of the central nervous system (CNS) in adults. These tumors are highly heterogeneous, invasive, and angiogenic, displaying aggressive proliferative behavior [ 1 , 2 ]. A key hallmark of GB is the aberrant activation of receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR), and downstream pathways like PI3K/AKT and RAS/MAPK, which drive tumor cell survival, proliferation, and motility. Additionally, the hyperactivation of the non-receptor tyrosine kinase SRC contributes to tumor progression by promoting adhesion, angiogenesis, and metabolic reprogramming [ 3 , 4 ]. Although drugs targeting these pathways, such as RTK and SRC inhibitors, are under clinical investigation, their limited efficacy due to poor blood-brain barrier (BBB) penetration and off-target effects highlights the challenges of glioblastoma therapy [ 3 , 4 ]. Since GB remains incurable, several novel therapeutic approaches have been explored, including angiogenesis inhibition with bevacizumab, a humanized monoclonal antibody that specifically binds to and inhibits vascular endothelial growth factor (VEGF) [ 5 ]. For a comprehensive overview of additional emerging strategies, such as oncolytic viruses and therapeutic vaccines, readers are referred to the review by Vaz-Salgado et al. (2023) [ 6 ]. Despite extensive efforts to develop less harmful and more effective therapies, these approaches remain prohibitively expensive and largely inaccessible in many healthcare systems [ 7 , 8 ]. Although advances in therapeutic strategies have been made, the standard treatment for GB still consists of surgical resection followed by radiotherapy and chemotherapy with the DNA alkylating agent temozolomide. Even with these clinical interventions, most GB patients experience tumor recurrence, resulting in a consistently poor prognosis. Relative survival rates at 1 and 5 years remain below 50% and 10%, respectively [ 9 , 10 ]. In recent years, drug repurposing has emerged as a promising and rational alternative therapy [ 11 , 12 ]. This approach explores new applications for well-known drugs, offering fresh insights into cancer treatment by targeting pathways that regulate cell growth, death, and migration. Furthermore, repurposed drugs are advantageous because they are already approved for clinical use, are no longer patent-protected, and have well-established pharmacokinetic and toxicological profiles [ 13 – 16 ]. Sulfasalazine (SAS), a well-known non-steroidal anti-inflammatory drug (NSAID), has demonstrated antitumor activity in both in vitro and in vivo models of GB. Its antitumor effects are primarily attributed to its ability to competitively inhibit the system xc⁻, a cystine/glutamate antiporter that plays a critical role in maintaining intracellular cysteine levels and supporting glutathione (GSH) synthesis [ 17 – 19 ]. The system xc⁻ is essential for glioma cell survival, as cystine uptake is a rate-limiting step in GSH production, which protects tumor cells from oxidative stress [ 20 ]. However, SAS exhibits poor oral bioavailability and limited permeability across the blood-brain barrier (BBB), significantly constraining its clinical effectiveness [ 20 ]. To overcome these limitations, novel SAS analogs have been developed. For instance, amino-naphthyl-sulfonate derivatives of SAS have been synthesized, demonstrating greater potency in inhibiting the system xc⁻ compared to the parent compound [ 21 ]. Molecular modeling and docking studies have further suggested that these modifications may enhance selectivity and efficacy while maintaining the ability to reduce intracellular GSH levels [ 21 ]. Other inhibitors of the system xc⁻, such as sorafenib and erastin, have demonstrated promising antitumor effects. Both compounds are capable of inducing ferroptosis, a regulated form of cell death characterized by the accumulation of lethal lipid peroxides, further emphasizing the therapeutic potential of targeting the system xc⁻ in glioblastoma treatment [ 22 ]. Nevertheless, neither drug has advanced to clinical trials for glioblastoma, primarily due to their limited ability to penetrate the blood-brain barrier (BBB) and their broad range of off-target effects unrelated to the xc⁻ system, which are associated with significant adverse events. These challenges highlight the critical need for the development of more selective and pharmacokinetically favorable inhibitors targeting this pathway. To overcome the limitations of SAS and enhance its therapeutic efficacy, combining it with an agent that targets complementary pathways represents a promising strategy. Valproic acid (VPA), a well-established anticonvulsant and mood stabilizer, has demonstrated anti-GB properties through its epigenetic effects, making it a compelling candidate for combination therapy. While SAS functions by inhibiting cystine uptake via the system xc⁻, leading to disruptions in intracellular cysteine levels and glutathione synthesis, VPA exerts its effects through the inhibition of histone deacetylase (HDAC) classes I and II [ 23 – 25 ]. Additionally, VPA impairs the Nrf2-ARE signaling pathway, which is critical for the expression of xCT, the catalytic subunit of the system xc⁻, as well as enzymes involved in glutathione (GSH) synthesis[ 26 – 28 ]. By inducing apoptosis and impairing glioblastoma cell proliferation, VPA also reduces the invasiveness of glioma stem cells. Although both SAS and VPA have been tested individually in clinical trials for GB treatment [ 29 – 35 ], their combined effects remain unexplored. By targeting distinct and complementary cellular mechanisms this combination holds significant potential to exert synergistic effects. Synergism is one of the most frequently misunderstood and misapplied concepts in pharmacology, particularly in the context of drug effects. Often lacking robust theoretical support, its foundation was established over 40 years ago by Chou through the development of the Median Effect Equation [ 36 ]. This equation remains the simplest and most effective tool for delineating dose-effect relationships. Over the years, several refinements have been made to this model, further enhancing its applicability [ 37 – 42 ]. Previously, our group demonstrated that SAS and VPA reduce GB cell viability in vitro , both as individual agents and in combination[ 43 ]. However, that study provided only preliminary insights into the interaction between these drugs, without exploring the theoretical modeling of synergism. In this work, we conducted an in-depth analysis of the earlier data using CompuSyn, a widely recognized software based on Chou’s theoretical framework for evaluating drug interactions. Materials and Methods Materials. The present work is characterized by an analysis of data obtained by Garcia and colleagues (2018) [ 43 ]. As described in previous work, materials were listed as follows. The primary human glioblastoma cell lines GBM95 and GBM02 were established in Dr. Vivaldo Moura-Neto’s laboratory [ 44 ] from patients’ surgical specimens provided by Dr. Jorge Marcondes, from the Neurosurgery service of Clementino Fraga Filho University Hospital (HUCFF-UFRJ). The human glioma cell lines U87MG and rat C6 were acquired from ATCC. Cells were grown and maintained in DMEM/F-12 medium supplemented with 2 mM glutamine, 1.2 g NaHCO 3 , gentamycin (200 g/mL), streptomycin (100 mg/L), and penicillin (6 mg/L), 10% FBS. Culture flasks were maintained at 37°C in a humidified 5% CO 2 and 95% air atmosphere. Cells displaying exponential growth were detached from the culture flasks with 0.25% trypsin/ethylene-diamine tetra-acetic acid (EDTA) and seeded. Cell growth assay . U87MG and GBM02 human glioma and C6 rat glioma cell lines were harvested as described by Garcia and colleagues, 2018. Briefly, glioblastoma cells were plated at 10 4 cells/ well in 24-well (15.6 mm diameter/well) tissue culture plates in DMEM/F-12 supplemented with 2mM glutamine and 10% FBS (complete medium) for 24 h. Drug treatments were proceeded 24 h after cell plating in 5% FBS fresh complete medium with different SAS, VPA or SAS + VPA concentrations for 48h. For all SAS conditions, DMSO was used as a control condition in final concentration of 0,1% per well. After treatments, cells were resuspended using PBS/EDTA 0.04% (15 min) following mechanical resuspension, stained with a 3% Trypan Blue solution (1 min) for death cell exclusion and counted using a hemocytometer. The average number of cells on five delimited squares was considered. Data treatment by CompuSyn software . To verify synergic effects between analyzed drugs, new parameters calculated using CompuSyn 2.0 (ComboSyn Development, NY, USA) were compared (data points from Garcia and colleagues, 2018). Data were plotted following software’s user guide instructions [ 45 ], which produced concentration-effect curves. New generated curves by CompuSyn provided reports containing graphs and tables concerning drugs’ concentration alone and in combination. We chose to show isobolograms, simple plots that show in x and y -axis the concentrations required to achieve the selected effect dose when drugs are alone. In our case, we chose y -axis to show SAS and x -axis to show VPA concentrations in all figures. Combination index (CI) was calculated by software using the equation $$\:\frac{(D{)}_{1}}{({D}_{x}{)}_{1}}+\:\frac{(D{)}_{2}}{({D}_{x}{)}_{2}}=CI$$ where D x means the dose to achieve a given effect’s percent by the drug alone. D represents the concentration of combined drugs to achieve the same percent. Based on this, CI values can be smaller than 1- indicating synergism-, next to 1- a possible additive effect- or higher than 1- indicating antagonism. Following, we were able to compare CIs through different effect concentrations (EC), here mentioned as fraction affected (Fa). With these two parameters, it is possible to predict the existence of a relationship between synergy augment and Fas enhancement. Thus, Fa-CI plots were chosen to show how CIs vary along different Fas. Concurrently, it is possible to calculate dose reduction index (DRI) which delineates how many folds of dose-reduction is allowed for each drug in synergistic combination at a specific Fa [ 46 ]. Following software’s user guide, DRI > 1 leads to favorable dose-reduction when drugs are in combination; DRI = 1 indicates no dose reduction and DRI < 1 indicates dose augmentation when in combination. Fa-DRI logarithm [Fa-Log(DRI)] plot was chosen to present curve behaviors concerning Fas and DRI. Results Initially, our objective was to investigate the potential enhancement in apparent potency when both drugs were used in combination. To this end, we analyzed the concentrations of SAS and VPA required to achieve 50%, 75%, and 90% fraction affected (Fa). Isobolograms were generated, with a solid line connecting the concentration values for SAS (y-axis) and VPA (x-axis). This line represents additive effects, with points below the line indicating synergism, points above the line signifying antagonism, and points along the line denoting an additive interaction. Using CompuSyn, we calculated the doses of the two drugs in combination necessary to achieve equivalent Fa values based on dose-effect curves. Isobolograms for Fa levels of 50%, 75%, and 90% are shown for each cell line studied by Garcia (2018) [ 43 ]. in Fig. 1 . These are presented as Fa = 0.5 and 0.75 (plots A1, B1, and C1), and Fa = 0.9 (plots A2, B2, and C2). Synergism between SAS and VPA was consistently observed across all cell lines. It is suggested by authors that Fig. 1 appears at this part of the text. Unlike isobolograms, Fa-CI plots focus on a distinct pharmacological concept: drug efficacy. These plots analyze the relationship between the combination index (CI) and the fraction affected (Fa) at varying levels. As described earlier, Fa-CI plots present Fa percentages on the x-axis and CI values on the y-axis. Lower CI values indicate stronger synergism. For GBM02 and U87MG cells (Fig. 2A1 and B1), CI < 1 was observed across the three highest Fa levels, with values dropping below 0.5 in some cases, reflecting robust synergism. In contrast, for C6 cells (Fig. 2C1), CI < 1 was only evident at the two highest Fa levels, suggesting that synergism is more pronounced at higher Fa values (Table 1 ). To further evaluate the pharmacological interaction between SAS and VPA, we analyzed the dose-reduction index (DRI), which quantifies how much the drug doses can be reduced when used in combination while achieving the same Fa. The DRI plots (Fig. 2A2, B2, and C2) depict Fa levels on the x-axis and logDRI values on the y-axis. Positive DRI values indicate that lower drug concentrations are sufficient to achieve the desired effect, whereas negative values signify that higher doses are required. Values close to 1 suggest no dose reduction. Across all cell lines, VPA consistently demonstrated higher DRI values compared to SAS, although positive DRI values were also observed for SAS. It is suggested by authors that Fig. 2 appears at this part of the text. Table 1 Combination Index (CI) values for sulfasalazine and valproic acid in glioma cells at Fa = 0.5, 0.75 and 0.9. Cell Line CI (Fa 0.5) CI (Fa 0.75) CI (Fa 0.9) GBM02 0.60252 0.53807 0.48098 U87MG 0.52147 0.47784 0.43941 C6 1.22895 0.91113 0.75726 It is suggested by authors that Table 1 appears at this part of the text. Discussion Nori Geary provides a quantitative definition of synergy as a “supra-additive effect according to some metric for the addition of different dose-effect curves”[ 47 ]. Similarly, Chou [ 48 ] emphasizes that “synergy is not determined by p -values, but rather by CI values,” highlighting that it is “not a statistical issue, but rather a mass-action law issue.” However, the concept of synergism is often misinterpreted or, in many cases, inaccurately applied in scientific studies, even when proposed in good faith. Following the principles established by Chou and colleagues, our analysis deliberately avoided point-by-point statistical evaluations of concentration-effect curves. Instead, we aimed to provide a comprehensive investigation of the relationship between sulfasalazine (SAS) and valproic acid (VPA) in GB treatment, expanding upon the preliminary findings reported by Garcia (2018) [ 43 ]. Isobologram plots remain a cornerstone for assessing enhanced drug potency in combination therapies. These plots effectively illustrate the equipotent sum of doses required to achieve a specified effect [ 41 ]. However, it is essential to note that isobolograms can only be constructed when both drugs independently exhibit measurable effects, as the absence of such effects precludes the determination of ED points along the axes. The CompuSyn software used in this study enables the generation of isobolograms for any fraction affected (Fa). We selected Fa levels of 50%, 75%, and 90% based on their widespread adoption in the literature, particularly in foundational works by Chou and colleagues [ 41 , 42 ]. Building on the Median-Effect Equation (MEE), Chou and Talalay [ 49 ] established a unified theoretical framework encompassing four fundamental biochemical and pharmacological models: pH ionization (Henderson-Hasselbalch), enzyme kinetics (Michaelis-Menten), receptor-ligand dynamics (Hill equation), and binding-dissociation interactions (Scatchard) [ 50 , 51 ]. This framework was extended to quantify drug interactions, culminating in the Combination Index (CI) theorem, which categorizes interactions as synergistic (CI 1). In our study, we observed CI values below 1 for SAS and VPA at concentrations exceeding 0.5 mM in GBM02 and U87MG cells, indicating strong synergism (Fig. 2 A 1 and B 1 ). However, the C6 cell line showed a different pattern, with CI < 1 values appearing only at concentrations above 1 mM (Fig. 2 C 1 ). Fa-CI plots provided further insights into the efficacy of the SAS-VPA combination. These plots, when interpreted alongside isobolograms, offered a comprehensive perspective on the interaction dynamics. For U87MG and GBM02 cells, low doses of SAS and VPA exhibited CI values close to or exceeding 1, suggesting diminished synergistic efficacy at these concentrations. Conversely, the C6 cell line exhibited higher CI values across most conditions, which might be explained by the elevated expression of the xCT protein, as previously reported by Garcia and colleagues [ 43 ]. This molecular heterogeneity could reduce the efficacy of SAS in these cells, even in combination with VPA. The dose-reduction index (DRI) further supports the pharmacological advantages of combining SAS and VPA. DRI values greater than 1 were observed at nearly all Fa levels in GBM02 and U87MG cells, indicating the potential to reduce drug concentrations while maintaining efficacy. However, in the C6 cell line, positive DRI values were restricted to higher Fa levels, reinforcing the influence of molecular variability on the combination’s effectiveness. Notably, the dose reduction resulting from synergism has a direct impact on SAS bioavailability, which is inherently limited. Enhancing SAS bioactive concentrations through synergistic interactions with VPA could represent a critical strategy to improve its therapeutic efficacy against GB. Our findings align with Chou and Talalay’s synergy model, particularly in the GBM02 and U87MG cell lines, where robust synergistic interactions were observed. The discrepancies in CI values across cell lines, especially the higher values in the C6 lineage, highlight the importance of considering molecular heterogeneity, such as variations in xCT transporter expression. Future studies should further investigate these interactions using genomic or proteomic profiling to elucidate the mechanisms underlying differential responses. Moreover, the observed dose reductions in the SAS-VPA combination hold promise for minimizing side effects in clinical applications, a critical consideration for GB treatment. Additionally, our results reinforce the need to explore novel therapeutic targets and up-to-date mechanisms in GB signaling pathways, with a particular focus on drug repurposing strategies. In summary, Fa-CI plots and isobolograms serve as complementary tools for evaluating the efficacy and potency of drug combinations. The use of CompuSyn software provided a robust framework for pharmacological analysis, allowing a deeper understanding of the interaction dynamics between SAS and VPA. Our findings underscore the value of this approach in characterizing drug synergism and highlight its potential to guide therapeutic strategies for glioblastoma treatment. Declarations Funding: This study was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), National Institute of Translational Neurosciences (INNT) and Pró-Reitoria de Pesquisa, Pós-Graduação e Inovação (PROPPI - UFF) by Fluminense Federal University. Acknowledgements: We sincerely acknowledge Professor Dr. Vivaldo Moura-Neto for his invaluable support and for providing the laboratory infrastructure that made this study possible. We also thank Dr. Jorge Marcondes, from the Neurosurgery Department at Clementino Fraga Filho University Hospital (HUCFF-UFRJ), for kindly providing the surgical specimens used to establish the primary human glioblastoma cell line GBM02. Competing Interests The authors declare no competing interests. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Carlos G. Garcia, Suzana Assad Kahn, Flavia Regina Souza Lima, and Marcelo Cossenza. The first draft of the manuscript was written by Carlos G. Garcia and Marcelo Cossenza, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval: This research had written informed consent from the patients and was approved by the Brazilian Ministry of Health Ethics Committee, under Institutional Review Board (IRB Research Ethics Committee of Hospital Universitário Clementino Fraga Filho) consent CEP-HUCFF No. 002/01. 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Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev. 2006;58:621–81. https://doi.org/10.1124/pr.58.3.10 . Chou TC. Drug combination studies and their synergy quantification using the chou-talalay method. Cancer Res. 2010;70:440–6. https://doi.org/10.1158/0008-5472.CAN-09-1947 . Garcia CG, Kahn SA, Geraldo LHM et al. (2018) Combination Therapy with Sulfasalazine and Valproic Acid Promotes Human Glioblastoma Cell Death Through Imbalance of the Intracellular Oxidative Response. Mol Neurobiol. https://doi.org/10.1007/s12035-018-0895-1 Faria J, Romão L, Martins S, et al. Interactive properties of human glioblastoma cells with brain neurons in culture and neuronal modulation of glial laminin organization. Differentiation. 2006;74:562–72. https://doi.org/http://dx.doi.org/10.1111/j.1432-0436.2006.00090.x . Chou T, Martin N. (2005) CompuSyn for drug combinations: PC Soft- ware and User’s Guide: a computer program for quantitation of synergism and antagonism in drug combinations, and the determination of IC50 and ED50 and LD50 values. In: Paramus (NJ): Combo-Syn. https://combosyn.com/uat/pdf/CompuSyn_users_guide.pdf Chou JH, Chang T-T, Chou T-C. Models for Drug Development and Drug Resistance. In: The Cancer Handbook; 2007. Geary N. Understanding synergy. Am J Physiology-Endocrinology Metabolism. 2012;304:E237–53. https://doi.org/10.1152/ajpendo.00308.2012 . Chou T-C. Frequently asked questions in drug combinations and the mass-action law-based answers. Synergy. 2014;1:3–21. https://doi.org/10.1016/j.synres.2014.07.003 . Chou T-C, Talalay P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul. 1984;22:27–55. https://doi.org/10.1016/0065-2571(84)90007-4 . Chou T-C, Talalay P. Analysis of combined drug effects: a new look at a very old problem. Trends Pharmacol Sci. 1983;4:450–4. https://doi.org/10.1016/0165-6147(83)90490-X . CHOU T-C TALALAYP. Generalized Equations for the Analysis of Inhibitions of Michaelis-Menten and Higher-Order Kinetic Systems with Two or More Mutually Exclusive and Nonexclusive Inhibitors. Eur J Biochem. 1981;115:207–16. https://doi.org/10.1111/j.1432-1033.1981.tb06218.x . Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6829845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":467612751,"identity":"ce308b6e-6c35-4737-8b89-4a6dd44a7738","order_by":0,"name":"Carlos G. Garcia","email":"","orcid":"","institution":"Centro Universitário Anhanguera de Niterói","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"G.","lastName":"Garcia","suffix":""},{"id":467612752,"identity":"27b64dab-7206-43d0-a518-a66b61cc8557","order_by":1,"name":"Suzana Assad Kahn","email":"","orcid":"","institution":"Universidade Federal do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Suzana","middleName":"Assad","lastName":"Kahn","suffix":""},{"id":467612753,"identity":"155b8427-7497-4f20-bb68-1aa05a13b8bd","order_by":2,"name":"Flavia Regina Souza Lima","email":"","orcid":"","institution":"Universidade Federal do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Flavia","middleName":"Regina Souza","lastName":"Lima","suffix":""},{"id":467612754,"identity":"62a169f1-27ea-4ffc-971c-b94b782958ad","order_by":3,"name":"Marcelo Cossenza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFAD9sYGCQYGOX4GCaK18BwEaTGWbCBei0QCA3FadNsPH/74o+KOPf/Mx403GNsMJBikex/g1WJ2Ji1NmufMM2aJ24nNFmAtMscN8Gu5wWPGzNh2mI3hdmKbBGPbnzoGiTT8DgNqMf74899hHvmbB0FagLYQocVAgrfhsIQB0CNEagH75dhhA8MzQL8knDOQYJM5RkDLcVCI1Ry2lzt+/OGND2UGEvzSbfi1oIIEIGYjRcMoGAWjYBSMAuwAAL1dQUmFxzigAAAAAElFTkSuQmCC","orcid":"","institution":"Fluminense Federal University","correspondingAuthor":true,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Cossenza","suffix":""}],"badges":[],"createdAt":"2025-06-05 13:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6829845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6829845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84486074,"identity":"e8dfd571-f8f4-479c-a8ad-29e25bce77dd","added_by":"auto","created_at":"2025-06-12 13:45:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178202,"visible":true,"origin":"","legend":"\u003cp\u003eIsobolograms for GBM02 (A1, A2), U87MG (B1, B2), and C6 (C1, C2) glioma cells. Left column plots show 50% and 75% of cell fraction affected (Fa); right column plots show 90% of Fa. CI values below the additive line (solid line) indicate synergism (n = 3 minimum for each cell line). Data were reanalyzed using CompuSyn software based on previously published experiments [43].\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6829845/v1/12faf33d1bce50deda2c6fda.jpeg"},{"id":84486075,"identity":"2b76f299-ce92-402a-8cd0-08553ffbe199","added_by":"auto","created_at":"2025-06-12 13:45:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155522,"visible":true,"origin":"","legend":"\u003cp\u003eFa–CI (combination index) and Fa–log(DRI) (dose reduction index) plots for GBM02 (A1 and A2), U87MG (B1 and B2), and C6 (C1 and C2) cells. CI values below 1 indicate synergism, with stronger effects at higher Fa levels. Positive log(DRI) values reflect favorable dose reduction when sulfasalazine and valproic acid are combined. Analysis was conducted using CompuSyn software.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6829845/v1/69d92583ab71ade2bfd963e8.jpeg"},{"id":103910240,"identity":"98e8d799-1419-4753-be70-dce307d355f4","added_by":"auto","created_at":"2026-03-04 11:56:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806072,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6829845/v1/42849a58-ab66-420a-a4e8-9b9edbb8596b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of synergism and combination effect of sulfasalazine and valproic acid on glioma cells using a software based on Median-Effect Equation and Combination Index Theorem","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GB) is the most common malignant primary cancer of the central nervous system (CNS) in adults. These tumors are highly heterogeneous, invasive, and angiogenic, displaying aggressive proliferative behavior [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A key hallmark of GB is the aberrant activation of receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR), and downstream pathways like PI3K/AKT and RAS/MAPK, which drive tumor cell survival, proliferation, and motility. Additionally, the hyperactivation of the non-receptor tyrosine kinase SRC contributes to tumor progression by promoting adhesion, angiogenesis, and metabolic reprogramming [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although drugs targeting these pathways, such as RTK and SRC inhibitors, are under clinical investigation, their limited efficacy due to poor blood-brain barrier (BBB) penetration and off-target effects highlights the challenges of glioblastoma therapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince GB remains incurable, several novel therapeutic approaches have been explored, including angiogenesis inhibition with bevacizumab, a humanized monoclonal antibody that specifically binds to and inhibits vascular endothelial growth factor (VEGF) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For a comprehensive overview of additional emerging strategies, such as oncolytic viruses and therapeutic vaccines, readers are referred to the review by Vaz-Salgado et al. (2023) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite extensive efforts to develop less harmful and more effective therapies, these approaches remain prohibitively expensive and largely inaccessible in many healthcare systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough advances in therapeutic strategies have been made, the standard treatment for GB still consists of surgical resection followed by radiotherapy and chemotherapy with the DNA alkylating agent temozolomide. Even with these clinical interventions, most GB patients experience tumor recurrence, resulting in a consistently poor prognosis. Relative survival rates at 1 and 5 years remain below 50% and 10%, respectively [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, drug repurposing has emerged as a promising and rational alternative therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This approach explores new applications for well-known drugs, offering fresh insights into cancer treatment by targeting pathways that regulate cell growth, death, and migration. Furthermore, repurposed drugs are advantageous because they are already approved for clinical use, are no longer patent-protected, and have well-established pharmacokinetic and toxicological profiles [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSulfasalazine (SAS), a well-known non-steroidal anti-inflammatory drug (NSAID), has demonstrated antitumor activity in both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e models of GB. Its antitumor effects are primarily attributed to its ability to competitively inhibit the system xc⁻, a cystine/glutamate antiporter that plays a critical role in maintaining intracellular cysteine levels and supporting glutathione (GSH) synthesis [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The system xc⁻ is essential for glioma cell survival, as cystine uptake is a rate-limiting step in GSH production, which protects tumor cells from oxidative stress [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, SAS exhibits poor oral bioavailability and limited permeability across the blood-brain barrier (BBB), significantly constraining its clinical effectiveness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To overcome these limitations, novel SAS analogs have been developed. For instance, amino-naphthyl-sulfonate derivatives of SAS have been synthesized, demonstrating greater potency in inhibiting the system xc⁻ compared to the parent compound [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Molecular modeling and docking studies have further suggested that these modifications may enhance selectivity and efficacy while maintaining the ability to reduce intracellular GSH levels [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOther inhibitors of the system xc⁻, such as sorafenib and erastin, have demonstrated promising antitumor effects. Both compounds are capable of inducing ferroptosis, a regulated form of cell death characterized by the accumulation of lethal lipid peroxides, further emphasizing the therapeutic potential of targeting the system xc⁻ in glioblastoma treatment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, neither drug has advanced to clinical trials for glioblastoma, primarily due to their limited ability to penetrate the blood-brain barrier (BBB) and their broad range of off-target effects unrelated to the xc⁻ system, which are associated with significant adverse events. These challenges highlight the critical need for the development of more selective and pharmacokinetically favorable inhibitors targeting this pathway.\u003c/p\u003e \u003cp\u003eTo overcome the limitations of SAS and enhance its therapeutic efficacy, combining it with an agent that targets complementary pathways represents a promising strategy. Valproic acid (VPA), a well-established anticonvulsant and mood stabilizer, has demonstrated anti-GB properties through its epigenetic effects, making it a compelling candidate for combination therapy. While SAS functions by inhibiting cystine uptake via the system xc⁻, leading to disruptions in intracellular cysteine levels and glutathione synthesis, VPA exerts its effects through the inhibition of histone deacetylase (HDAC) classes I and II [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, VPA impairs the Nrf2-ARE signaling pathway, which is critical for the expression of xCT, the catalytic subunit of the system xc⁻, as well as enzymes involved in glutathione (GSH) synthesis[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By inducing apoptosis and impairing glioblastoma cell proliferation, VPA also reduces the invasiveness of glioma stem cells. Although both SAS and VPA have been tested individually in clinical trials for GB treatment [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], their combined effects remain unexplored.\u003c/p\u003e \u003cp\u003eBy targeting distinct and complementary cellular mechanisms this combination holds significant potential to exert synergistic effects. Synergism is one of the most frequently misunderstood and misapplied concepts in pharmacology, particularly in the context of drug effects. Often lacking robust theoretical support, its foundation was established over 40 years ago by Chou through the development of the Median Effect Equation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This equation remains the simplest and most effective tool for delineating dose-effect relationships. Over the years, several refinements have been made to this model, further enhancing its applicability [\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreviously, our group demonstrated that SAS and VPA reduce GB cell viability \u003cem\u003ein vitro\u003c/em\u003e, both as individual agents and in combination[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, that study provided only preliminary insights into the interaction between these drugs, without exploring the theoretical modeling of synergism. In this work, we conducted an in-depth analysis of the earlier data using CompuSyn, a widely recognized software based on Chou\u0026rsquo;s theoretical framework for evaluating drug interactions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cem\u003eMaterials.\u003c/em\u003e The present work is characterized by an analysis of data obtained by Garcia and colleagues (2018) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As described in previous work, materials were listed as follows. The primary human glioblastoma cell lines GBM95 and GBM02 were established in Dr. Vivaldo Moura-Neto\u0026rsquo;s laboratory [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] from patients\u0026rsquo; surgical specimens provided by Dr. Jorge Marcondes, from the Neurosurgery service of Clementino Fraga Filho University Hospital (HUCFF-UFRJ). The human glioma cell lines U87MG and rat C6 were acquired from ATCC. Cells were grown and maintained in DMEM/F-12 medium supplemented with 2 mM glutamine, 1.2 g NaHCO\u003csub\u003e3\u003c/sub\u003e, gentamycin (200 g/mL), streptomycin (100 mg/L), and penicillin (6 mg/L), 10% FBS. Culture flasks were maintained at 37\u0026deg;C in a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e and 95% air atmosphere. Cells displaying exponential growth were detached from the culture flasks with 0.25% trypsin/ethylene-diamine tetra-acetic acid (EDTA) and seeded.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCell growth assay\u003c/em\u003e. U87MG and GBM02 human glioma and C6 rat glioma cell lines were harvested as described by Garcia and colleagues, 2018. Briefly, glioblastoma cells were plated at 10\u003csup\u003e4\u003c/sup\u003e cells/ well in 24-well (15.6 mm diameter/well) tissue culture plates in DMEM/F-12 supplemented with 2mM glutamine and 10% FBS (complete medium) for 24 h. Drug treatments were proceeded 24 h after cell plating in 5% FBS fresh complete medium with different SAS, VPA or SAS\u0026thinsp;+\u0026thinsp;VPA concentrations for 48h. For all SAS conditions, DMSO was used as a control condition in final concentration of 0,1% per well. After treatments, cells were resuspended using PBS/EDTA 0.04% (15 min) following mechanical resuspension, stained with a 3% Trypan Blue solution (1 min) for death cell exclusion and counted using a hemocytometer. The average number of cells on five delimited squares was considered.\u003c/p\u003e \u003cp\u003e \u003cem\u003eData treatment by CompuSyn software\u003c/em\u003e. To verify synergic effects between analyzed drugs, new parameters calculated using CompuSyn 2.0 (ComboSyn Development, NY, USA) were compared (data points from Garcia and colleagues, 2018). Data were plotted following software\u0026rsquo;s user guide instructions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which produced concentration-effect curves. New generated curves by CompuSyn provided reports containing graphs and tables concerning drugs\u0026rsquo; concentration alone and in combination. We chose to show isobolograms, simple plots that show in \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e-axis the concentrations required to achieve the selected effect dose when drugs are alone. In our case, we chose \u003cem\u003ey\u003c/em\u003e-axis to show SAS and \u003cem\u003ex\u003c/em\u003e-axis to show VPA concentrations in all figures.\u003c/p\u003e \u003cp\u003eCombination index (CI) was calculated by software using the equation\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{(D{)}_{1}}{({D}_{x}{)}_{1}}+\\:\\frac{(D{)}_{2}}{({D}_{x}{)}_{2}}=CI$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere D\u003csub\u003ex\u003c/sub\u003e means the dose to achieve a given effect\u0026rsquo;s percent by the drug alone. D represents the concentration of combined drugs to achieve the same percent. Based on this, CI values can be smaller than 1- indicating synergism-, next to 1- a possible additive effect- or higher than 1- indicating antagonism.\u003c/p\u003e \u003cp\u003eFollowing, we were able to compare CIs through different effect concentrations (EC), here mentioned as fraction affected (Fa). With these two parameters, it is possible to predict the existence of a relationship between synergy augment and Fas enhancement. Thus, Fa-CI plots were chosen to show how CIs vary along different Fas.\u003c/p\u003e \u003cp\u003eConcurrently, it is possible to calculate dose reduction index (DRI) which delineates how many folds of dose-reduction is allowed for each drug in synergistic combination at a specific Fa [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Following software\u0026rsquo;s user guide, DRI\u0026thinsp;\u0026gt;\u0026thinsp;1 leads to favorable dose-reduction when drugs are in combination; DRI\u0026thinsp;=\u0026thinsp;1 indicates no dose reduction and DRI\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates dose augmentation when in combination. Fa-DRI logarithm [Fa-Log(DRI)] plot was chosen to present curve behaviors concerning Fas and DRI.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eInitially, our objective was to investigate the potential enhancement in apparent potency when both drugs were used in combination. To this end, we analyzed the concentrations of SAS and VPA required to achieve 50%, 75%, and 90% fraction affected (Fa). Isobolograms were generated, with a solid line connecting the concentration values for SAS (y-axis) and VPA (x-axis). This line represents additive effects, with points below the line indicating synergism, points above the line signifying antagonism, and points along the line denoting an additive interaction.\u003c/p\u003e \u003cp\u003eUsing CompuSyn, we calculated the doses of the two drugs in combination necessary to achieve equivalent Fa values based on dose-effect curves. Isobolograms for Fa levels of 50%, 75%, and 90% are shown for each cell line studied by Garcia (2018) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These are presented as Fa\u0026thinsp;=\u0026thinsp;0.5 and 0.75 (plots A1, B1, and C1), and Fa\u0026thinsp;=\u0026thinsp;0.9 (plots A2, B2, and C2). Synergism between SAS and VPA was consistently observed across all cell lines.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIt is suggested by authors that\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eappears at this part of the text.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnlike isobolograms, Fa-CI plots focus on a distinct pharmacological concept: drug efficacy. These plots analyze the relationship between the combination index (CI) and the fraction affected (Fa) at varying levels. As described earlier, Fa-CI plots present Fa percentages on the x-axis and CI values on the y-axis. Lower CI values indicate stronger synergism. For GBM02 and U87MG cells (Fig.\u0026nbsp;2A1 and B1), CI\u0026thinsp;\u0026lt;\u0026thinsp;1 was observed across the three highest Fa levels, with values dropping below 0.5 in some cases, reflecting robust synergism. In contrast, for C6 cells (Fig.\u0026nbsp;2C1), CI\u0026thinsp;\u0026lt;\u0026thinsp;1 was only evident at the two highest Fa levels, suggesting that synergism is more pronounced at higher Fa values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further evaluate the pharmacological interaction between SAS and VPA, we analyzed the dose-reduction index (DRI), which quantifies how much the drug doses can be reduced when used in combination while achieving the same Fa. The DRI plots (Fig.\u0026nbsp;2A2, B2, and C2) depict Fa levels on the x-axis and logDRI values on the y-axis. Positive DRI values indicate that lower drug concentrations are sufficient to achieve the desired effect, whereas negative values signify that higher doses are required. Values close to 1 suggest no dose reduction. Across all cell lines, VPA consistently demonstrated higher DRI values compared to SAS, although positive DRI values were also observed for SAS.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIt is suggested by authors that\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eappears at this part of the text.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombination Index (CI) values for sulfasalazine and valproic acid in glioma cells at Fa\u0026thinsp;=\u0026thinsp;0.5, 0.75 and 0.9.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell Line\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI (Fa 0.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI (Fa 0.75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (Fa 0.9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU87MG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIt is suggested by authors that\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eappears at this part of the text.\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNori Geary provides a quantitative definition of synergy as a \u0026ldquo;supra-additive effect according to some metric for the addition of different dose-effect curves\u0026rdquo;[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Similarly, Chou [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] emphasizes that \u0026ldquo;synergy is not determined by \u003cem\u003ep\u003c/em\u003e-values, but rather by CI values,\u0026rdquo; highlighting that it is \u0026ldquo;not a statistical issue, but rather a mass-action law issue.\u0026rdquo; However, the concept of synergism is often misinterpreted or, in many cases, inaccurately applied in scientific studies, even when proposed in good faith. Following the principles established by Chou and colleagues, our analysis deliberately avoided point-by-point statistical evaluations of concentration-effect curves. Instead, we aimed to provide a comprehensive investigation of the relationship between sulfasalazine (SAS) and valproic acid (VPA) in GB treatment, expanding upon the preliminary findings reported by Garcia (2018) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIsobologram plots remain a cornerstone for assessing enhanced drug potency in combination therapies. These plots effectively illustrate the equipotent sum of doses required to achieve a specified effect [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, it is essential to note that isobolograms can only be constructed when both drugs independently exhibit measurable effects, as the absence of such effects precludes the determination of ED points along the axes. The CompuSyn software used in this study enables the generation of isobolograms for any fraction affected (Fa). We selected Fa levels of 50%, 75%, and 90% based on their widespread adoption in the literature, particularly in foundational works by Chou and colleagues [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuilding on the Median-Effect Equation (MEE), Chou and Talalay [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] established a unified theoretical framework encompassing four fundamental biochemical and pharmacological models: pH ionization (Henderson-Hasselbalch), enzyme kinetics (Michaelis-Menten), receptor-ligand dynamics (Hill equation), and binding-dissociation interactions (Scatchard) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This framework was extended to quantify drug interactions, culminating in the Combination Index (CI) theorem, which categorizes interactions as synergistic (CI\u0026thinsp;\u0026lt;\u0026thinsp;1), additive (CI\u0026thinsp;=\u0026thinsp;1), or antagonistic (CI\u0026thinsp;\u0026gt;\u0026thinsp;1). In our study, we observed CI values below 1 for SAS and VPA at concentrations exceeding 0.5 mM in GBM02 and U87MG cells, indicating strong synergism (Fig.\u0026nbsp;2 A\u003csub\u003e1\u003c/sub\u003e and B\u003csub\u003e1\u003c/sub\u003e). However, the C6 cell line showed a different pattern, with CI\u0026thinsp;\u0026lt;\u0026thinsp;1 values appearing only at concentrations above 1 mM (Fig.\u0026nbsp;2 C\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eFa-CI plots provided further insights into the efficacy of the SAS-VPA combination. These plots, when interpreted alongside isobolograms, offered a comprehensive perspective on the interaction dynamics. For U87MG and GBM02 cells, low doses of SAS and VPA exhibited CI values close to or exceeding 1, suggesting diminished synergistic efficacy at these concentrations. Conversely, the C6 cell line exhibited higher CI values across most conditions, which might be explained by the elevated expression of the xCT protein, as previously reported by Garcia and colleagues [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This molecular heterogeneity could reduce the efficacy of SAS in these cells, even in combination with VPA.\u003c/p\u003e \u003cp\u003eThe dose-reduction index (DRI) further supports the pharmacological advantages of combining SAS and VPA. DRI values greater than 1 were observed at nearly all Fa levels in GBM02 and U87MG cells, indicating the potential to reduce drug concentrations while maintaining efficacy. However, in the C6 cell line, positive DRI values were restricted to higher Fa levels, reinforcing the influence of molecular variability on the combination\u0026rsquo;s effectiveness. Notably, the dose reduction resulting from synergism has a direct impact on SAS bioavailability, which is inherently limited. Enhancing SAS bioactive concentrations through synergistic interactions with VPA could represent a critical strategy to improve its therapeutic efficacy against GB.\u003c/p\u003e \u003cp\u003eOur findings align with Chou and Talalay\u0026rsquo;s synergy model, particularly in the GBM02 and U87MG cell lines, where robust synergistic interactions were observed. The discrepancies in CI values across cell lines, especially the higher values in the C6 lineage, highlight the importance of considering molecular heterogeneity, such as variations in xCT transporter expression. Future studies should further investigate these interactions using genomic or proteomic profiling to elucidate the mechanisms underlying differential responses. Moreover, the observed dose reductions in the SAS-VPA combination hold promise for minimizing side effects in clinical applications, a critical consideration for GB treatment. Additionally, our results reinforce the need to explore novel therapeutic targets and up-to-date mechanisms in GB signaling pathways, with a particular focus on drug repurposing strategies.\u003c/p\u003e \u003cp\u003eIn summary, Fa-CI plots and isobolograms serve as complementary tools for evaluating the efficacy and potency of drug combinations. The use of CompuSyn software provided a robust framework for pharmacological analysis, allowing a deeper understanding of the interaction dynamics between SAS and VPA. Our findings underscore the value of this approach in characterizing drug synergism and highlight its potential to guide therapeutic strategies for glioblastoma treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq), Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (Capes) and Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro (FAPERJ), National Institute of Translational Neurosciences (INNT) and Pr\u0026oacute;-Reitoria de Pesquisa, P\u0026oacute;s-Gradua\u0026ccedil;\u0026atilde;o e Inova\u0026ccedil;\u0026atilde;o (PROPPI - UFF) by Fluminense Federal University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe sincerely acknowledge Professor Dr. Vivaldo Moura-Neto for his invaluable support and for providing the laboratory infrastructure that made this study possible. We also thank Dr. Jorge Marcondes, from the Neurosurgery Department at Clementino Fraga Filho University Hospital (HUCFF-UFRJ), for kindly providing the surgical specimens used to establish the primary human glioblastoma cell line GBM02.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Carlos G. Garcia, Suzana Assad Kahn, Flavia Regina Souza Lima, and Marcelo Cossenza. The first draft of the manuscript was written by Carlos G. Garcia and Marcelo Cossenza, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis research had written informed consent from the patients and was approved by the Brazilian Ministry of Health Ethics Committee, under Institutional Review Board (IRB Research Ethics Committee of Hospital Universit\u0026aacute;rio Clementino Fraga Filho) consent CEP-HUCFF No. 002/01. All patients gave written consent to the use of their surgical specimens for isolation of cortical cells in the study, and the procedures were approved by the Brazilian Ministry of Health Ethics Committee under IRB consent (CEP-HUCFF No. 060/05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Gittleman H, Truitt G, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011\u0026ndash;2015. 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Generalized Equations for the Analysis of Inhibitions of Michaelis-Menten and Higher-Order Kinetic Systems with Two or More Mutually Exclusive and Nonexclusive Inhibitors. Eur J Biochem. 1981;115:207\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1432-1033.1981.tb06218.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1432-1033.1981.tb06218.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"glioblastoma, drug repurposing, sulfasalazine, valproic acid, synergism","lastPublishedDoi":"10.21203/rs.3.rs-6829845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6829845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlioblastoma (GB), the most common and aggressive primary brain tumor in adults, exhibit poor prognosis and limited efficacy of conventional therapies. Sulfasalazine (SAS), a well-known drug targeting xc⁻ system, has shown antitumor activity in GB models. However, its clinical application is hindered by poor oral bioavailability and limited penetration across blood-brain barrier (BBB). Strategies to enhance its therapeutic potential, such as achieving synergistic effects with other anti-glioma drugs, are highly desirable to increase its potency. Valproic acid (VPA), a well-established anticonvulsant/mood stabilizer, is also proposed as anti-glioma agent due to its histone deacetylase (HDAC) inhibition activity, playing a role regulating glioblastoma cell proliferation and survival. Drug repurposing, thus, has emerged as a promising strategy due to its cost-effectiveness and already established safety profiles. In this study, we evaluated potential synergistic interactions between SAS and VPA in GB treatment using human (U87MG, GBM02) and rat (C6) cell lines. Pharmacological data from previous studies were analyzed using CompuSyn software to construct isobolograms, fraction affected\u0026ndash;combination index (Fa-CI) and dose-reduction index (DRI) plots. Our results demonstrated synergistic interactions between SAS\u0026thinsp;+\u0026thinsp;VPA in U87MG and GBM02 cells (CI values below 1 at higher Fa levels). In contrast, C6 cells exhibited reduced synergism (CI\u0026thinsp;\u0026gt;\u0026thinsp;1 in most conditions). DRI analysis revealed significant dose reductions for SAS\u0026thinsp;+\u0026thinsp;VPA treatments across all cell lines. These findings provide evidence supporting the synergistic potential of SAS\u0026thinsp;+\u0026thinsp;VPA in GB therapy highlighting the utility of CompuSyn for detailed drug interaction analysis. Further studies are warranted to explore the clinical applicability of this combination.\u003c/p\u003e","manuscriptTitle":"Evaluation of synergism and combination effect of sulfasalazine and valproic acid on glioma cells using a software based on Median-Effect Equation and Combination Index Theorem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 13:45:10","doi":"10.21203/rs.3.rs-6829845/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":"fe63bbb0-8264-4cef-945c-f984d78579ca","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T11:55:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 13:45:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6829845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6829845","identity":"rs-6829845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}