Discovering Macrolide SmTGR Inhibitors via QSAR and Molecular Docking for Schistosomiasis RepurposingAbstract
Macrolides, a class of broad-spectrum antibiotics, are gaining attention for their potential in treating parasitic infections due to their favorable safety profiles, pharmacokinetics, and tissue penetration. This study explores the possibility of repurposing macrolides for the treatment of schistosomiasis, a neglected tropical disease caused by the trematode Schistosoma mansoni. Using computational approaches and molecular docking, including quantitative structure-activity relationship (QSAR) models, we evaluated the predicted binding affinities and inhibition probabilities of macrolides against S. mansoni thioredoxin glutathione reductase (SmTGR), a key enzyme in the parasite redox balance. The virtual screening identified eight promising macrolides, with Sorangicin A emerging as a promising hit compound due to its strong predicted interactions with SmTGR. The results suggest that Sorangicin A and other macrolides may exhibit potential antischistosomal activity, highlighting their viability as repurposed therapeutics. This study underscores the value of leveraging established drug classes for new applications, particularly in addressing neglected diseases. Combining QSAR and molecular docking, we demonstrate a costand time-efficient strategy for identifying novel treatments. It is offered a pathway to combat schistosomiasis and other parasitic infections, which must be validated experimentally.
Keywords:
macrolides; repurposing; schistosomiasis; computational methods; QSAR; molecular docking
Introduction
Macrolides are a class of compounds characterized by a macrocyclic lactone ring linked to one or more amino sugar groups. Initially recognized for their antibacterial properties, macrolides such as erythromycin, clarithromycin, and azithromycin are naturally derived but industrially produced via semi-synthesis. These antibiotics primarily inhibit bacterial protein synthesis by targeting the 50S ribosomal subunit, leading to cell death and making them widely effective against bacterial infections.1,2 However, recent studies have revealed that macrolides exhibit a broad spectrum of biological activities beyond their antibacterial effects, including immunomodulatory, anti-inflammatory, anticancer, antiprotozoal, and antifungal properties.3-9
Of particular interest are the antiprotozoal effects of macrolides, such as azithromycin and erythromycin, which have shown promise in inhibiting the invasion of human erythrocytes by Plasmodium spp., the causative agent of malaria.10 These findings suggest that macrolides could be repurposed to address parasitic infections, offering a costand time-efficient alternative to traditional drug development pathways. By targeting essential parasite enzymes and disrupting metabolic pathways, macrolides may provide a viable strategy for combating neglected tropical diseases, such as schistosomiasis.11
Schistosomiasis, caused by the parasitic trematode Schistosoma mansoni, is a significant public health challenge, particularly in Brazil and other endemic regions.12 According to the World Health Organization (WHO), approximately 264 million people worldwide required treatment for schistosomiasis in 2022, including over 2 million in Brazil.13 The disease manifests with symptoms ranging from anemia, diarrhea, and abdominal pain to severe complications such as gastrointestinal bleeding, malnutrition, and even death.14 Despite its global impact, schistosomiasis remains a neglected tropical disease with limited therapeutic options.
The only drug available for the treatment of schistosomiasis is praziquantel, which is safe and cheap. However, praziquantel has limitations, including hepatic and renal toxicity, limited efficacy against juvenile parasites, and emerging parasitic resistance in endemic areas.15,16 These challenges underscore the urgent need for new antischistosomal strategies. Drug repurposing, which involves identifying new uses for approved drugs, presents a promising approach to accelerating the development of effective treatments for schistosomiasis.17
S. mansoni thioredoxin glutathione reductase (SmTGR) is an enzyme critical for maintaining the oxidative stress balance of the parasite and facilitating electron transport via nicotinamide adenine dinucleotide phosphate (NADPH) reduction, and therefore, is an important target for schistosomiasis drug discovery.18 Inhibiting SmTGR disrupts the redox homeostasis of the parasite, making it a promising therapeutic target. Computational methods, such as quantitative structure-activity relationship (QSAR)19,20 and molecular docking,21 have emerged as powerful tools for identifying potential inhibitors of SmTGR. These approaches offer a more efficient and predictive alternative to traditional high-throughput screening (HTS), with QSAR models achieving prediction rates of 1-40%, compared to the 0.01-0.1% success rate of HTS.20,22-24
This study explores the structural features of macrolides and their potential biological activity against S. mansoni using QSAR and molecular docking against the SmTGR enzyme. Our main goal is to identify novel macrolide derivatives with potential inhibitory activity against SmTGR, providing a foundation for developing new antischistosomal therapies.
Methodology
Database
A database search was conducted using the Cortellis Drug Discovery Intelligence platform available at Clarivate Analytics. Cortellis Drug Discovery Intelligence platform is a comprehensive research tool that provides daily updates on pharmacological, biological, and chemical knowledge for over 420,000 compounds. This database includes chemical structures, pharmacological properties, pharmacokinetics, references, and clinical studies related to the target chemical groups or compounds. A “Quick Search” was conducted using the keyword “Macrolides” under the “Drugs & Biologics” section, yielding 4,883 macrolide compounds in .sdf format.
Dataset preparation
The macrolides were prepared according to the protocol described by Fourches et al.,25 adding hydrogens, normalizing chemotypes, and removing duplicates, salts, and mixtures.
QSAR-based virtual screening
The QSAR classification models were developed and validated by Neves et al.26 were used to predict the probability that the prepared macrolides would be active against SmTGR. The compounds were selected based on their probability of activity greater than 60% and their inclusion within the applicability domain.
Molecular docking with SmTGR
The screened compounds underwent molecular docking with the crystallographic protein of SmTGR (PDB ID: 2X8C) using the program Glide27,28 on the MAESTRO platform29 from Schrödinger. The structure of SmTGR presents a resolution of 3.1 Å. Ligands and the protein were prepared using the LigPrep30 and protein preparation wizard31 available on MAESTRO. The pH was adjusted to 7.5 ± 0.5, the OPLS_200532 force field was applied to the ionization calculation and protein minimization. Crucially, the flavin adenine dinucleotide (FAD) cofactor was retained in the receptor structure to preserve the native electronic and structural environment. The cysteine residues CYS154 and CYS159 were modeled in the oxidized state (forming a disulfide bond), reflecting the conformation of the crystal structure. Hydrogen atoms were added to the protein structure, and water molecules were removed with a distance farther than 5 Å. For all the ligands, 32 conformations were generated.
Then, a grid was constructed to define the docking box, based on the primary amino acid residues of the electron transfer site (K124-K128, R450, C596, and G597) discussed by Souza Neto et al.,33 chosen to accommodate the high molecular weight of the macrolides. The grid box was set to 20 Å, and the X, Y, and Z coordinates were -4.98820572035000, -6.01923446720000, and 4.57170018830000, respectively. Docking was performed using the ligand docking tool with Glide extra precision (XP) software (v. 2018-2).34 The PyMOL software (v.2,5,10)35 was used to visualize interactions and generate images of the docking results.
Results and Discussion
QSAR-based virtual screening
The search yielded 3,965 macrolide compounds. Among these, approximately 839 macrolide molecules are currently under investigation for novel biological activities. Notably, 504 compounds are being studied for anticancer activity, 114 for potential antifungal properties, 131 for antiprotozoal treatment, 56 for hepatitis C therapy, and 34 for arthritis management. Additional biological activities are also under exploration, highlighting the significant repurposing potential of this chemical group in drug discovery. All compounds were previously prepared, with errors in their chemical structures identified and corrected. This includes the removal of mixtures, duplicates, inorganic compounds, and organometallic species; structural standardization; salt elimination; and correction of improper tautomeric forms, resulting in 941 macrolides. The QSAR-based prediction identified 52 macrolides with a probability greater than 50% of exhibiting biological activity against SmTGR. From these, eight macrolides with the highest likelihood of inhibitory activity (> 60%) were visually selected for further analysis, as shown in Figure 1.
Table 1 presents the name of each compound, its 2D chemical structure, and the predicted probability rate as a percentage. Only two selected compounds, 265179 and 108046, are not commercially available but are undergoing clinical trials.
QSAR-based prediction of biological activity for macrolides with their similarities with known SmTGR inhibitors
Among the selected compounds, A-349079-S1 showed the highest predicted probability (66%) for exhibiting biological activity as a SmTGR inhibitor. The remaining seven compounds demonstrated a predicted probability of 66% for biological activity against the SmTGR protein.
Subsequently, those compounds were subjected to molecular docking, and their protein-ligand interactions with SmTGR were predicted.
The (-)-A-26771B is a macrolide antibiotic derived from Penicillium turbatum,36 which demonstrates broad-spectrum antimicrobial activity. It exhibits efficacy against methicillin-resistant Staphylococcus aureus (MRSA) strains, with minimum inhibitory concentrations (MICs) ranging from 6 to 16 μg mL-1.37 Antifungal activity is observed against Candida tropicalis, Trichophyto mentagrophytes, Botrytis cinerea, Ceratocystis ulmi, and Verticillium albo atrum, with MIC values of 6.25 100 μg mL 1.36 Additionally, a 26771B has been shown to inhibit Stromelysin-1 (matrix metalloproteinase-3, MMP 3), an enzyme encoded by the MMP3 gene in humans,37 and disrupts K+-dependent ATPase activity in rat liver mitochondria.36 These findings underscore its dual pharmacological potential as both an antimicrobial agent and an enzymatic modulator.
Molecular docking with SmTGR
The C-terminal site of SmTGR, essential for electron transport via NADPH reduction, is highly conserved and critical for maintaining oxidative homeostasis in S. mansoni. Inhibiting this site can disrupt electron transfer, thereby disrupting the redox balance and life cycle of the parasite.38
The SmTGR C-terminal region contains positively charged residues, including K124, K128, R450, and R454, that help shape the electrostatic environment of the site. In addition to these residues, this region contains key electron-transporting residues, namely Gly595 Cys596 Cys597 Gly598 (GCCG), and nearby residues, and is located in a flexible loop.39 In contrast, mammalian thioredoxin reductases, including the human enzyme, contain a related but distinct C-terminal redox motif Gly-Cys-Sec-Gly (GCUG) on a flexible C-terminal arm, which fulfills an analogous electron-transport role but differs in sequence composition, length, and dynamics. These local structural differences between SmTGR and mammalian TrxRs (thioredoxin reductases) offer opportunities to achieve parasite selectivity at this site.23
With a detailed study of the binding site and structural understanding of the macrolides, it is feasible to block electron transport by interacting with both carrier and transport residues, thereby preventing electron transfer within the protein. Consequently, molecular docking was performed with eight macrolides at the electron transfer site of SmTGR. In addition, the known SmTGR inhibitor WNN0397-C010 and the clinically used SmTGR-targeting drug Auranofin were included as reference compounds.
The docking study revealed vital distinctions among these macrolides based on binding affinities (docking scores) and their specific interactions with amino acid residues on the target protein (Table 2). Note that molecular docking provides binding pose predictions and relative affinity ranking but does not estimate absolute binding free energies. Molecular dynamics simulations and free-energy calculations will be required to optimize the lead candidates identified here. The 2D protein-interaction figures are shown in Figure S1 (Supplementary Information (SI) section).
Identification, docking scores, and protein-ligand interactions obtained from molecular docking with SmTGR for the eight repurposed macrolides and two reference inhibitors (WNN0397-, C010, and Auranofin)
The molecular docking simulations revealed a broad spectrum of binding affinities among the investigated ligands, with docking scores ranging from -8.148 to -3.734 kcal mol-1. The polyether macrolide 100415-25 6 (Sorangincin A) presented the highest affinity, with a docking score of -8.148 kcal mol-1. It forms hydrogen bonds with residues R450A, R454A, H582B, and K124A, forming a salt bridge. This higher score suggests stronger, potentially more stable binding, likely due to multiple key interactions, especially with charged residues (K124A and R450A) (Figure 2).
Molecular interactions between the SmTGR protein and 100415-25-6 were predicted by molecular docking.
Regarding pharmacokinetics, 100415-25-6 has a worse profile, presenting only moderate interaction with CYP3A4. While this macrolide performed well in rat models (half-life of 4.4 min and CLint (intrinsic clearance) 239 µL mg-1 min 1), it lacked efficacy in mice (half-life of 3.4 min and Clint 557 µL mg-1 min-1 in mouse liver microsomes) due to rapid plasma degradation (half-life of 17.5 min) and low systemic exposure. In contrast, zebrafish embryos tolerated 100415-25-6 up to high concentrations.40
100415-25-6 is a macrolide originally extracted from Sorangium cellulosum,41 and is currently produced through semi-synthesis. This macrolide demonstrates significant bioactivity against Gram-positive and Gram negative bacteria, owing to its unique ability to inhibit transcription initiation by targeting bacterial RNA polymerase (RNAP).42,43 This mechanism distinguishes it from most macrolide antibiotics, which typically target transcription elongation by binding the bacterial ribosome.44 Recently, 100415-25-6 has also shown notable potency against resistant strains of Mycobacterium tuberculosis45 and Chlamydia trachomatis,46 highlighting its potential for repurposing for various infectious diseases.
The high-affinity group also includes A-349079-S1 and WNN0397-C010, yielding scores of -7.357 and -6.007 kcal mol-1, respectively. Interestingly, while WNN0397-C010, an SmTGR inhibitor with an half maximal inhibitory concentration (IC50) of 0.862 µmol L-1, displayed a more extensive electrostatic profile, forming four salt bridges (R450A, K124A, E125A, R454A) and four hydrogen bonds, its overall docking score was lower than that of A-349079-S1. This suggests that, for A-349079-S1, the combination of hydrogen bonds at K124A and H582B, with a favorable hydrophobic fit at A215A and I592B, may provide superior energetic stabilization compared to the highly charged but potentially constrained binding of WNN0397-C010.
Compounds with intermediate affinities, such as 134781-24-1 (-5.786 kcal mol-1) and 205111-00-8 (-5.369 kcal mol-1), consistently targeted residues Y212A and V593B. However, their lower scores relative to the lead compounds are likely due to the absence of key salt bridges and fewer simultaneous contacts with the K124A/R450A cluster. As affinity decreased further in compounds such as 481694-30-8 and 134781-23-0, the interaction maps showed a shift toward more peripheral residues (e.g., D565B, N219A), suggesting a loss of the anchoring effect provided by the central catalytic or structural residues.
Another compound targeting SmTGR is Auranofin, with an experimental IC50 of 0.007 µmol L-1.47 This drug contains a gold atom and is used to treat rheumatoid arthritis. It presented a lower affinity docking score, forming hydrogen bonds with N219A(P) and K124A(+) and salt bridges with R450A(+) and K124A(+). Higher docking scores generally correlate with extensive interactions involving key residues, such as K124A and R450A, which may stabilize ligand binding. The charged and hydrophobic interactions particularly reinforce strong binding. In contrast, weaker binding ligands may lack sufficient interactions at these critical sites, underscoring the importance of amino acid composition in ligand affinity and binding stability.
Physicochemical and ADMET profiles
Combining experimental and computational absorption, distribution, metabolism, excretion, toxicity (ADMET) profiling is vital for prioritizing drug candidates. In this discussion, we evaluate eight compounds based on data from the Cortellis platform (Table 3) and predicted values from the ADMET-AI platform48 (Figure 3) to determine their suitability for oral drug development. Because complete experimental ADMET profiles were not available for all compounds, in silico predictions were used to complement the available data and to guide hit-to-lead optimization toward SmTGR inhibition.
Computational ADMET predictions for repurposed compounds. Predicted ADMET profiles generated by the ADMET-AI platform, including human intestinal absorption (HIA), Caco2 permeability, and toxicity endpoints (DILI, hERG inhibition), are compared to the Cortellis data (Table 3).
The aqueous solubility (LogS) values range from -7.93 (100415-25-6) to -2.91 (481694-30-8), reflecting poor to moderate solubility. Compound 481694-30-8 (LogS = -2.91) shows favorable solubility, attributed to its lower molecular weight (MW = 394.42) and moderate LogP (2.62). Conversely, 100415-25-6 (LogS = -7.93) presents significant formulation challenges due to its high MW (807.03) and LogP (6.68). Regarding pKa values, compounds such as 134781-24-1 (pKa = 12.4) and 134781-23-0 (pKa = 12.06) are basic and fully ionized at physiological pH, possibly limiting passive absorption unless actively transported. On the other hand, 95152 88 8 (pKa = 1.88) is acidic and may exhibit pH-dependent solubility.
The distribution coefficient at pH 7.4 (LogD) ranges from 1.17 (56448-20-5) to 6.68 (100415-25-6). Both 205111-00-8 (LogD = 5.98) and 95152-88-8 (LogD = 7.3) exhibit high lipophilicity, which enhances membrane permeability; however, their extreme LogP values (6.24 and 7.3, respectively) raise concerns about off-target toxicity and metabolic stability.
All compounds exhibit high human intestinal absorption (HIA > 0.7), with A-349079-S1, 205111-00-8, 134781 23 0, 134781-24-1, and 481694-30-8 ranging from 0.95 to 0.99, indicating robust passive absorption. Nevertheless, Caco 2 permeability values are consistently low (-5.11 to -6.19 log(106 cm s-1)), aligning with predictions of limited transcellular transport. This paradox of high HIA yet low permeability may indicate that active transport mechanisms are compensating for poor passive diffusion particularly for 205111-00-8 and 134781-23-0, which demonstrate potent P-glycoprotein (Pgp) inhibition (0.94 and 0.85), potentially diminishing efflux and optimizing absorption. ADMET-AI predictions (Figure 3) correlated well with experimental HIA but overestimated permeability, highlighting the need for refining transporter-mediated processes in the model.
Blood-brain barrier (BBB) penetration is low (0.07 0.48), except for 56448-20-5 (0.48) and 95152 88 8 (0.34), likely due to high plasma protein binding (PPB ≥ 82.08%), which limits free drug availability. Notably, A-349079-S1, 95152 88-8, and 481694-30-8 exhibit PPB > 98%, suggesting prolonged half-lives but limited tissue penetration. The ADMET-AI predictions reflect these trends, indicating low BBB penetration for most compounds.
With respect to safety, all compounds demonstrate low risk (probability <) of hERG (human Ether-à-go-go-Related Gene) blockage, except 481694 30 8 (0.38), which necessitates further cardiac safety investigations. Regarding mutagenicity (Ames predictor), 205111-00-8 (0.57) and 481694-30-8 (0.43) show a moderate risk, which differs from predictions (Figure 3), which highlighted 205111 00 8 as high-risk, suggesting model sensitivity to structural alerts. The drug-induced liver injury (DILI) indicates severe hepatotoxicity risks for A-349079-S1 (0.96) and 205111-00-8 (0.91), consistent with their high clearance and CYP inhibition. ADMET-AI predictions aligned with experimental DILI scores, demonstrating its utility for early toxicity screening. Most compounds demonstrate negligible binding to androgen (≤ 0.12) and estrogen (≤ 0.28) receptors, except 95152-88-8 (estrogen receptor = 0.28), which may require studies on endocrine disruption.
Overall, 56448-20-5 and 481694-30-8 emerge as the most promising candidates from a developability standpoint, combining more balanced solubility, permeability, and compliance with rule-based filters. In contrast, 205111 00-8 and, in particular, 100415-25-6 should be viewed as potent SmTGR-binding hits rather than immediately developable oral leads, given their excessive molecular weight, high lipophilicity, and, in the case of 100415-25-6, very low aqueous solubility. Future optimization of these hits will likely require reducing molecular weight and LogP and/or exploring prodrug and advanced formulation strategies. The integration of physicochemical profiling with ADMET data (Table 3 and Figure 3) therefore reinforces the importance of multi-parametric optimization in early SmTGR-oriented drug discovery.
Conclusions
This study underscores the potential of macrolides as starting points (hits) for antischistosomal therapies through computational repurposing. Using QSAR modeling and molecular docking against SmTGR, we identified eight macrolides with promising predicted binding affinities. Among these, Sorangicin A (100415-25-6) emerged as the strongest SmTGR binder (docking score: -8.148 kcal mol 1), while compounds 56448-20-5 and 481694-30-8 exhibited the most favorable physicochemical and ADMET profiles for oral development. Although Sorangicin A displays exceptional target affinity, its high molecular weight (807 Da), elevated lipophilicity (LogP 6.68), poor aqueous solubility (LogS -7.93), and Lipinski violations indicate substantial developability challenges that preclude its immediate consideration as an oral lead candidate. In contrast, 56448-20-5 and 481694-30-8 better balance target engagement with drug-like properties, positioning them as more immediately actionable hits for future optimization. Drug repurposing of macrolides leverages their established safety profiles while computational screening accelerates hit identification for neglected diseases like schistosomiasis. Importantly, this work establishes SmTGR-binding macrolides as a novel chemical series warranting experimental validation, rather than presenting immediately developable candidates. Future studies should prioritize the synthesis/optimization of the most balanced hits (particularly 56448-20-5 and 481694-30-8) alongside targeted medicinal chemistry efforts to address the developability limitations of high-affinity compounds such as Sorangicin A.
Supplementary Information
Additional figure (2D protein-ligand interactions) is available free of charge at http://jbcs.sbq.org.br as PDF file.
Supplementary material 1
Acknowledgments
The authors gratefully acknowledge the collaboration between Universidade Federal do Oeste do Pará and Universidade Federal de Goiás. The authors also thank the financial support provided by CAPES (financing code 001), CNPq (Grant No. 483659/2013-4), FAPEG (Grant No. 202010267000272), BRICS STI COVID-19 (Grant No. 441038/2020-4), National Temporary External Academic Mobility Program of the Undergraduate Education Pro-rectorate (Grant No. 82/2018-PROEN), Program to Promote Course Completion Works (Grant No. 10/2018/PROPPIT PROTCC), and CAPES for granting access to private platforms that contributed to advancing knowledge.
Data Availability Statement
The models used in this article is available in Neves et al.26 work.
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Edited by
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Editor handled this article:
Paulo Augusto Netz (Executive)
