Skip to main content
NCBI home page
In Silico Pharmacology logoLink to In Silico Pharmacology
. 2022 Nov 18;11(1):1. doi: 10.1007/s40203-022-00137-x

Structural insight and analysis of TLR4 interactions with IAXO-102, TAK-242 and SN-38: an in silico approach

Janine S Y Tam 1,✉, Jinxin V Pei 2, Janet K Coller 3, Clive A Prestidge 4, Joanne M Bowen 1
PMCID: PMC9681971  PMID: 36438853

Abstract

Introduction

Toll-like receptor 4 (TLR4) has attracted interest due to its role in chemotherapy-induced gastrointestinal inflammation. This structural study aimed to provide in silico rational of the recognition and potential binding of TLR4 ligands IAXO-102, TAK-242, and SN-38 (the toxic metabolite of the chemotherapeutic irinotecan hydrochloride), which could contribute to rationale development of therapeutic anti-inflammation drugs targeting TLR4 in the gastrointestinal tract.

Methods

In silico docking was performed between the human TLR4-MD-2 complex and ligands (IAXO-102, TAK-242, SN-38) using Autodock Vina, setting the docking grids to cover either the upper or the lower bound of TLR4. The conformation having the lowest binding energy value (kcal/mol) was processed for post-hoc analysis of the best-fit model. Hydrogen bonding was calculated by using ChimeraX.

Results

Binding energies of IAXO-102, TAK-242 and SN-38 at the upper bound of TLR4-MD-2 ranged between − 3.8 and − 3.1, − 6.9 and − 6.3, and − 9.0 and − 7.0, respectively. Binding energies of IAXO-102, TAK-242 and SN-38 at the lower bound ranged between − 3.9 and − 3.5, − 6.5 and − 5.8, and − 8.2 and − 6.8, respectively. Hydrogen bonding at the upper bound of TLR4/MD-2 with IAXO-102, TAK-242 and SN-38 was to aspartic acid 70, cysteine 133 and serine 120, respectively. Hydrogen bonding at the lower bound of TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 was to serine 528, glycine 480 and glutamine 510, respectively.

Conclusion

The in silico rational presented here supports further investigation of the binding activity of IAXO-102 and TAK-242 for their potential application in the prevention of gastrointestinal inflammation caused by SN-38.

Keywords: TLR4 ligand, In silico molecular docking, Chemotherapy, Gastrointestinal toxicity

Introduction

Gastrointestinal (GI) toll-like receptor 4 (TLR4) promotes mucosal integrity and microbial tolerance while able to rapidly induce an inflammatory response to provide protection from invading bacteria (Park et al. 2009). Lipopolysaccharides (LPS) and other microbial-associated molecular patterns (MAMPs) activate TLR4 expressed on intestinal epithelium as well as immune cells in the lamina propria (Abreu et al. 2002). TLR4 activation controls cellular responses through downstream signaling pathways including NF-κB and IRF3 (Lu et al. 2008). While elimination of noxious stimuli and repair of damaged structures is the ultimate goal in response to TLR4 activation, excessive TLR4 or dysregulated TLR4 signalling is associated with many inflammatory conditions such as cancer treatment-related intestinal inflammation, referred to clinically as mucositis (Wardill et al. 2016).

Chemotherapeutic agents, such as irinotecan hydrochloride (camptothecin-11), cause direct injury to intestinal epithelial cells, allowing luminal antigens to enter the lamina propria (Sonis 2004). The role of TLR4 in intestinal inflammatory conditions has been investigated thoroughly in genetic (knockout and over-expression) mouse models and patient-derived tissue (Wardill et al. 2016; Beilmann-Lehtonen et al. 2020). This previous work has shown strong associations with disease onset, damage severity and even cancer development (Wardill et al. 2016; Beilmann-Lehtonen et al. 2020). A limitation of knockout models is the reliance on TLR4 signaling to repair the colon following inflammatory insult (Abreu et al. 2001). A further barrier is an inability to evaluate the contribution of non-MAMP TLR4 agonists in the development of injury, especially during cancer treatment. Potential agonists include moieties of chemotherapy agents from the taxane and camptothecin classes (Wall and Wani 1996). As such, development of TLR4-targeted pharmacological interventions are required to overcome these limitations.

While targeting TLR4 is a biologically supported approach to GI inflammatory conditions, there has been little progress in the field which may be due to a lack of specific inhibitors that selectively target the TLR4 protein and/or its associated co-receptors, MD-2 and CD-14. The integration between biological systems and computational techniques provides the possibility to explore drug development opportunities in order to rapidly provide structural, chemical, and biological data to improve understanding of potential drug targets. For example, the interactive association between various methods, such as in silico molecular docking and protein binding studies has been employed by researchers for the development of pharmacologically active drugs (Sharma et al. 2016).

The three ligands of interest are TAK-242 (resatorvid), IAXO-102 and SN-38. TAK-242 is a synthetic cyclohexene derivative and a novel small-molecule compound (Kawamoto et al. 2008), with a proposed selective inhibitory action through MyD88 and TRIF-dependent pathways by binding to cysteine 747 of TLR4. TAK-242 has been studied in a variety of inflammatory models such as sepsis and neuroinflammation (Ciaramelli et al. 2016; Huggins et al. 2015). IAXO-102 is another small-molecule compound classed as a cationic amphiphile (Ciaramelli et al. 2016). The structure of IAXO-102 is based of off the bacterial compound lipopolysaccharide which is generally composed of a hydrophilic domain formed by a glucosamine disaccharide bearing two phosphate groups and a hydrophobic domain formed by linear and branched fatty acid lipid chains which attach to the disaccharide core through amide or ester bonds (Ciaramelli et al. 2016). It was recently developed with proposed inhibitory binding properties against the TLR4-MD-2 complex at both CD-14 and MD-2 sites (Ciaramelli et al. 2016). However, IAXO-102 has only been used in studies with a focus on aortic aneurysms (Huggins et al. 2015). SN-38 is the biologically active metabolite of the anti-cancer drug irinotecan (Wong et al. 2019). It is formed by hydrolysis of irinotecan by carboxylesterases in the liver (Wong et al. 2019). The mechanism of action of SN-38 is the inhibition of topoisomerase I which results in the inhibition of DNA replication (Wong et al. 2019).

These ligands have not been extensively studied in an intestinal inflammation model (Wang et al. 2020). There is also a lack of docking studies for these ligands, especially in regards to SN-38. It is currently unclear if the active metabolite of irinotecan, SN-38, is able to selectively bind to TLR4 and its co-receptors and mimic the agonistic action of LPS (Zhang et al. 2017); or if it poses antagonistic properties at specific concentrations (Wong et al. 2019). Therefore, determining if SN-38 could directly interact with TLR4 and co-receptors will help explain whether it has the capacity to modulate LPS-dependent inflammation.

In the current study, we investigated the interactions of the TLR4 ligands, IAXO-102 and TAK-242, and SN-38 with the TLR4-MD-2 complex by in silico analyses of molecular docking. The purpose of this structural study is to provide a better understanding of the recognition and potential binding of these compounds which may contribute to rationale development of therapeutic anti-inflammation drugs targeting TLR4 in the gastrointestinal tract.

Materials and methods

Protein/chemical structures and docking programs

In silico modelling was performed using similar methods reported previously (Kourghi et al. 2016). The human TLR4-MD-2 protein crystal structure (PDB ID:3FXI) was obtained from the National Institutes of Health NCBI Structure database (https://www.ncbi.nlm.nih.gov/Structure/pdb/3FXI accessed in October 2021). The SMILES codes (Fig. 1A) for the ligands were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov accessed in October 2021) and converted into software-compatible 3D structures in.pdb format using the online SMILES Translator and Structure File Generator (National Cancer Institute, U.S. Department Health and Human, Washington DC).

Fig. 1.

Fig. 1

Open in a new tab

A Basic chemical and 3D structures of the TLR4 ligands, IAXO-102, TAK-242, and SN-38. B Docking grid of the TLR4-MD-2 complex (grey), which was divided into 2 sections labelled upper (blue) and lower bound (red) Root mean square deviation (RMSD) values measured the average distance between atoms of a position relative to the best fitting position, and were calculated using only movable heavy atoms

Docking estimations

MGLtools was used to prepare both TLR4-MD-2 and ligand docking coordinates. The docking was performed using Autodock Vina (Trott and Olson 2010; Eberhardt et al. 2021), setting the docking grids to cover either the upper (blue) or the lower (red) bound of the TLR4-MD-2 complex (Fig. 1B). Assessment of the top 9 conformations from upper and lower bounds were completed and the conformation having the lowest atomic energy value (kcal/mol) was processed for post-hoc analysis of the best-fit model.

Hydrogen bonding

Hydrogen bonding is critical for the determination of the binding affinity of a ligand (in a specific binding pose) within a specific target protein. Hence the presence of hydrogen bonds between the ligands IAXO-102, TAK-242 and SN-38 with TLR4-MD-2 was investigated using ChimeraX (Goddard et al. 2018; Pettersen et al. 2021).

Results

In silico docking of IAXO-102, TAK-242, and SN-38 with the upper and lower bound of TLR4-MD-2 complex

The docking poses were ranked according to their docking scores and both the ranked list of docked ligands and their corresponding binding poses. This ranking of the compounds was based on their binding energy with TLR4-MD-2. If the binding energy of the ligand was less, then the particular ligand is classified as being more active in nature and has a stronger binding affinity. The binding energies of IAXO-102, TAK-242 and SN-38 with upper bound TLR4-MD-2 ranged between − 3.8 to − 3.1 kcal/mol (Fig. 2A), − 6.9 to − 6.3 kcal/mol (Fig. 3A) and − 9.0 to − 7.0 kcal/mol (Fig. 4A), respectively. Figures 2B, 3B and 4B show docked poses of upper bound TLR4-MD-2 with the ligands IAXO-102, TAK-242 and SN-38, respectively; with the binding positions of the ligands identified. The binding energies of IAXO-102, TAK-242 and SN-38 with lower bound TLR4-MD-2 ranged between − 3.9 to − 3.5 kcal/mol (Fig. 2C), − 6.5 to − 5.8 kcal/mol (Fig. 3C) and − 8.2 to − 6.8 kcal/mol (Fig. 4C), respectively. Figures 2D, 3D and 4D show docked poses of lower bound TLR4-MD-2 with the ligands IAXO-102, TAK-242 and SN-38, respectively.

Fig. 2.

Fig. 2

Open in a new tab

A Energy values describing the affinity of interaction between IAXO-102 with the upper and lower bound TLR4-MD-2. Root mean square deviation (RMSD) values measuring the average distance between atoms of a position relative to the best fitting position (Upper bound: position 1; Lower bound: position 4). Two variants of RMSD metrics are provided, distance from RMSD lower bound (rmsd/lb) and best mode RMSD upper bound (rmsd/ub). B Computer generated views of the predicted binding sites for IAXO-102 on TLR4-MD-2. Panel numbers 1 to 3 shows binding location of IAXO-102 on upper bound TLR4-MD-2. Panel numbers 4 to 6 shows binding location of IAXO-102 on lower bound TLR4-MD-2

Fig. 3.

Fig. 3

Open in a new tab

A Energy values describing the affinity of interaction between TAK-242 with the upper and lower bound TLR4-MD-2. Root mean square deviation (RMSD) values measuring the average distance between atoms of a position relative to the best fitting position (Upper bound: position 1; Lower bound: position 4). Two variants of RMSD metrics are provided, distance from RMSD lower bound (rmsd/lb) and best mode RMSD upper bound (rmsd/ub). B Computer generated views of the predicted binding sites for TAK-242 on TLR4-MD-2. Panel numbers 1 to 3 shows binding location of TAK-242 on upper bound TLR4-MD-2. Panel numbers 4 to 6 shows binding location of TAK-242 on lower bound TLR4-MD-2

Fig. 4.

Fig. 4

Open in a new tab

A Energy values describing the affinity of interaction between SN-38 with the upper and lower bound TLR4-MD-2. Root mean square deviation (RMSD) values measuring the average distance between atoms of a position relative to the best fitting position (Upper bound: position 1; Lower bound: position 4). Two variants of RMSD metrics are provided, distance from RMSD lower bound (rmsd/lb) and best mode RMSD upper bound (rmsd/ub). B Computer generated views of the predicted binding sites for SN-38 on TLR4-MD-2. Panel numbers 1 to 3 shows binding location of SN-38 on upper bound TLR4-MD-2. Panel numbers 4 to 6 shows binding location of SN-38 on lower bound TLR4-MD-2

Hydrogen bonding of the ligands IAXO-102, TAK-242 and SN-38 with TLR4-MD-2

Figure 5A shows the presence of hydrogen bonding between upper bound TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 at the following amino acid residues: aspartic acid 70, cysteine 133 and serine 120, respectively. While Fig. 5B shows the presence of hydrogen bonding between lower bound TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 at the following amino acid residues: serine 528, glycine 480 and glutamine 510, respectively.

Fig. 5.

Fig. 5

Open in a new tab

Magnified views of hydrogen bonding interactions of IAXO-102, TAK-242 and SN-38 with TLR4-MD-2, calculated by using ChimeraX. A The hydrogen bonding (blue dotted line) between upper bound TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 occurs at the following amino acid residues: aspartic acid 70, cysteine 133 and serine 120, respectively. B The hydrogen bonding (blue dotted line) between lower bound TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 is with serine 528, glycine 480 and glutamine 510, respectively

Discussion

While targeting TLR4 is a biologically supported approach to GI inflammatory conditions, there has been little progress in the field due to lack of specific inhibitors that selectively target the TLR4 protein and its associated co-receptor MD-2. The MD-2 protein is responsible for LPS binding in the TLR4-MD-2 complex (Park et al. 2009). It is characterized by a wide hydrophobic pocket that hosts the fatty acid chains from LPS (Kim et al. 2007). The binding energy between the ligands TAK-242, IAXO-102 and SN-38 to TLR4-MD-2 obtained from analysis of molecular docking supports possible binding at TLR4-MD-2. From the tabulated binding affinities, SN-38 had the strongest binding affinity followed by TAK-242 and IAXO-102, in both upper and lower bound TLR4-MD-2.

Hydrogen bonding plays a crucial role in the determination of protein structure and is equally central in many aspects of biological function (Pantsar and Poso 2018). For this reason, a crucial objective in in silico docking in biological systems is an accurate description of hydrogen bonding. These results may provide further information on the strength and stability of a drug-receptor interaction. An enormous variety of hydrogen bonds, both between various side-chain functional groups and involving the backbone peptide group, are possible. In our study, we found at the upper bound TLR4-MD-2, IAXO-102, TAK-242 and SN-38 forms hydrogen bonds with the amino acids aspartic acid 70, cysteine 133 and serine 120, respectively. While at the lower bound TLR4-MD-2, IAXO-102, TAK-242 and SN-38 forms hydrogen bonds with different amino acids, serine 528, glycine 480 and glutamine 510, respectively.

The TLR4-MD-2 complex is found on the extracellular matrix of cells which means these ligands would not need to cross the cell membrane to become active in comparison to other inflmmatory drugs such as adalimumab and infliximab that inhibit TNF-α receptors (TNFR1 and TNFR2) located intracellularly (Lis et al. 2014). This could be an advantage as there are less anatomical barriers for TLR4-MD-2 ligands which may lead to better therapeutic response. Aditionaly, the hydrogen bonding of all 3 ligands were located on different areas of the TLR4-MD-2 complex. We can therefore infer that these liagnds do not bind in the same positions and would not impede or compete for binding positions.

These in silico docking studies provide rational for the ability of the ligands investigated to bind to TLR4-MD-2 with binding energy values, docking scores, and protein-receptor interactions. Additionally, it supports the hypothesis that SN-38 has a strong binding affinity to TLR4 as previously reported by Wong et al. (2019). However, a limitation of the approach is an inability to determine if this interaction is antagonistic or agonistic. Nonetheless, this strong affinity may explain additional off-target actions of SN-38 due to interaction with TLR4-MD-2 to modulate LPS-dependent inflammation. These findings also further expand the current knowledge about the pathogenesis of intestinal mucositis which was previously thought to occur only as a result of direct DNA damage by SN-38 via it’s topoisomerase I inhibitory activity (Wong et al. 2019). Ultimately, this may support the hypothesis that TLR4 signaling pathways play key roles in the development of irinotecan-induced GI inflmmation.

Carrying out in vitro functional protein binding tests would be the next logical step to further define the binding effects of IAXO-102 and TAK-242 compared to the potential agonist effects of SN-38 at TLR4-MD-2. These types of studies may also be able to determine the specificity and selectiveness of TAK-242 and IAXO-102 with TLR4-MD-2 which have not been elucidated using the in silico approach.

If proven to be specific and selective, these ligands may be able to overcome the barriers of other drugs such as naloxone and amitriptyline that have been used previously to target TLR4 in models of mucositis but have failed due to multi-target receptor actions (Coller et al. 2017; Fakiha et al. 2019). In comparison, there is some evidence that TAK-242 has multi-target actions at the cytokine receptor common subunits beta and gamma (Wishart et al. 2018), that functions as a receptor for interleukin-3, interleukin-5 and granulocyte–macrophage colony-stimulating factors, respectively (Wishart et al. 2018). Despite this, TAK-242 has less off-target actions compared to both naloxone and amitriptyline (Wishart et al. 2018). Whilst IAXO-102 has only one reported off-target action at CD-14 (Ciaramelli et al. 2016). CD-14 is another part of the LPS signaling complex with TLR4-MD-2 that is structurally characterized by a bent solenoid typical of leucine-rich repeat proteins with a large hydrophobic pocket and is found on the surface of many TLR4 expressing cells. Both CD-14 and MD-2 pockets share a similar topology in terms of solvent accessible surface area and volume (Cighetti et al. 2014). Since IAXO-102 was designed with a similar structure to LPS as seen by the long phospholipid chains, it is able to bind to the TLR4-MD-2 complex (Cighetti et al. 2014). In addition, due to CD-14’s similarity to MD-2, IAXO-102 is also able to bind via TLR4-CD-14 (Ciaramelli et al. 2016) that is also capable of recognizing other microbial and cellular molecular determinants such as bacteria and glycans, in addition to LPS (Ciaramelli et al. 2016). The major difference between MD-2 and CD-14 is the polarity of the rim which may allow MD-2 to be more selective than CD-14 in the recognition of LPS (Resman et al. 2009), which may suggest that IAXO-102 is similarly more selectively recognized by TLR4-MD-2 compared to TLR4-CD-14. However, since both TLR4-MD-2 and TLR4-CD-14 utilize the same downstream signaling pathway, any off-target action with CD-14 may ultimately be beneficial.

A limitation of our study was that there were no available crystal structure poses and binding scores of the ligands IAXO-102, TAK-242 and SN-38. Therefore comparisons between this study and others could not be made which makes validation of the docking protocol difficult. However, this study is beginning to address this knowledge gap and to allow others to continue with further research.In summary, this study evaluated the potential binding sites and affinity of IAXO-102, TAK-242 and SN-38 to the human TLR4-MD-2 complex, identifying specific amino acid residues of interaction and 3D structural analysis. The evidence presented here supports further investigation of the binding activity of IAXO-102 and TAK-242 for their potential application in the prevention of GI toxicity caused by irinotecan and its toxic metabolite, SN-38.

Acknowledgements

The authors would like to thank Saeed Nourmohammadi for facilitating the meetings that ensured the success of the in silico docking results.

Funding

Janine S.Y. Tam received a scholarship from the University of Adelaide.

Data availability

No associated data.

Declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Abreu MT, Vora P, Faure E, Thomas LS, Arnold ET, Arditi M. Decreased expression of Toll-like receptor-4 and MD-2 correlates with intestinal epithelial cell protection against dysregulated proinflammatory gene expression in response to bacterial lipopolysaccharide. J Immunol. 2001;167(3):1609–1616. doi: 10.4049/jimmunol.167.3.1609. [DOI] [PubMed] [Google Scholar]
  2. Abreu MT, Arnold ET, Thomas LS, Gonsky R, Zhou Y, Hu B, Arditi M. TLR4 and MD-2 expression is regulated by immune-mediated signals in human intestinal epithelial cells. J Biol Chem. 2002;277(23):20431–20437. doi: 10.1074/jbc.M110333200. [DOI] [PubMed] [Google Scholar]
  3. Beilmann-Lehtonen I, Böckelman C, Mustonen H, Koskensalo S, Hagström J, Haglund C. The prognostic role of tissue TLR2 and TLR4 in colorectal cancer. Virchows Arch. 2020;477(5):705–715. doi: 10.1007/s00428-020-02833-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ciaramelli C, Calabrese V, Sestito SE, Pérez-Regidor L, Klett J, Oblak A, Jerala R, Piazza M, Martín-Santamaría S, Peri F. Glycolipid-based TLR4 modulators and fluorescent probes: rational design, synthesis, and biological properties. Chem Biol Drug Des. 2016;88(2):217–229. doi: 10.1111/cbdd.12749. [DOI] [PubMed] [Google Scholar]
  5. Cighetti R, Ciaramelli C, Sestito SE, Zanoni I, Kubik Ł, Ardá-Freire A, Calabrese V, Granucci F, Jerala R, Martín-Santamaría S, Jiménez-Barbero J, Peri F. Modulation of CD14 and TLR4·MD-2 activities by a synthetic lipid A mimetic. Chembiochem Eur J Chem Biol. 2014;15(2):250–258. doi: 10.1002/cbic.201300588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Coller JK, Bowen JM, Ball IA, Wardill HR, van Sebille YZ, Stansborough RL, Lightwala Z, Wignall A, Shirren J, Secombe K, Gibson RJ. Potential safety concerns of TLR4 antagonism with irinotecan: a preclinical observational report. Cancer Chemother Pharmacol. 2017;79(2):431–434. doi: 10.1007/s00280-016-3223-3. [DOI] [PubMed] [Google Scholar]
  7. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61(8):3891–3898. doi: 10.1021/acs.jcim.1c00203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Fakiha K, Coller JK, Logan RM, Gibson RJ, Bowen JM. Amitriptyline prevents CPT-11-induced early-onset diarrhea and colonic apoptosis without reducing overall gastrointestinal damage in a rat model of mucositis. Support Care Cancer. 2019;27(6):2313–2320. doi: 10.1007/s00520-018-4511-8. [DOI] [PubMed] [Google Scholar]
  9. Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, Ferrin TE. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 2018;27(1):14–25. doi: 10.1002/pro.3235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Huggins C, Pearce S, Peri F, Neumann F, Cockerill G, Pirianov G. A novel small molecule TLR4 antagonist (IAXO-102) negatively regulates non-hematopoietic toll like receptor 4 signalling and inhibits aortic aneurysms development. Atherosclerosis. 2015;242(2):563–570. doi: 10.1016/j.atherosclerosis.2015.08.010. [DOI] [PubMed] [Google Scholar]
  11. Kawamoto T, Ii M, Kitazaki T, Iizawa Y, Kimura H. TAK-242 selectively suppresses Toll-like receptor 4-signaling mediated by the intracellular domain. Eur J Pharmacol. 2008;584(1):40–48. doi: 10.1016/j.ejphar.2008.01.026. [DOI] [PubMed] [Google Scholar]
  12. Kim HM, Park BS, Kim JI, Kim SE, Lee J, Oh SC, Enkhbayar P, Matsushima N, Lee H, Yoo OJ, Lee JO. Crystal structure of the TLR4-MD-2 complex with bound endotoxin antagonist Eritoran. Cell. 2007;130(5):906–917. doi: 10.1016/j.cell.2007.08.002. [DOI] [PubMed] [Google Scholar]
  13. Kourghi M, Pei JV, De Ieso ML, Flynn G, Yool AJ. Bumetanide derivatives AqB007 and AqB011 selectively block the aquaporin-1 ion channel conductance and slow cancer cell migration. Mol Pharmacol. 2016;89(1):133–140. doi: 10.1124/mol.115.101618. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Lis K, Kuzawińska O, Bałkowiec-Iskra E. Tumor necrosis factor inhibitors—state of knowledge. Arch Med Sci AMS. 2014;10(6):1175–1185. doi: 10.5114/aoms.2014.47827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Lu Y-C, Yeh W-C, Ohashi PS. LPS/TLR4 signal transduction pathway. Cytokine. 2008;42(2):145–151. doi: 10.1016/j.cyto.2008.01.006. [DOI] [PubMed] [Google Scholar]
  16. Pantsar T, Poso A. Binding affinity via docking: fact and fiction. Molecules (basel, Switzerland) 2018;23(8):1899. doi: 10.3390/molecules23081899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Park BS, Song DH, Kim HM, Choi B-S, Lee H, Lee J-O. The structural basis of lipopolysaccharide recognition by the TLR4–MD-2 complex. Nature. 2009;458:1191–1195. doi: 10.1038/nature07830. [DOI] [PubMed] [Google Scholar]
  18. Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI, Morris JH, Ferrin TE. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 2021;30(1):70–82. doi: 10.1002/pro.3943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Resman N, Vasl J, Oblak A, Pristovsek P, Gioannini T, Weiss J, Jerala R. Essential roles of hydrophobic residues in both MD-2 and toll-like receptor 4 in activation by endotoxin. J Biol Chem. 2009;284:15052–15060. doi: 10.1074/jbc.M901429200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Sharma V, Sharma PC, Kumar V. In silico molecular docking analysis of natural pyridoacridines as anticancer agents. Advances in Chemistry. 2016;2016:5409387. doi: 10.1155/2016/5409387. [DOI] [Google Scholar]
  21. Sonis ST. The pathobiology of mucositis. Nat Rev Cancer. 2004;4(4):277–284. doi: 10.1038/nrc1318. [DOI] [PubMed] [Google Scholar]
  22. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–461. doi: 10.1002/jcc.21334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Wall ME, Wani MC. Camptothecin and taxol: from discovery to clinic. J Ethnopharmacol. 1996;51(1):239–254. doi: 10.1016/0378-8741(95)01367-9. [DOI] [PubMed] [Google Scholar]
  24. Wang J, Zhu G, Sun C, Xiong K, Yao T, Su Y, Fang H. TAK-242 ameliorates DSS-induced colitis by regulating the gut microbiota and the JAK2/STAT3 signaling pathway. Microb Cell Fact. 2020;19(1):158. doi: 10.1186/s12934-020-01417-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Wardill HR, Gibson RJ, Van Sebille YZA, Secombe KR, Coller JK, White IA, Manavis J, Hutchinson MR, Staikopoulos V, Logan RM, Bowen JM. Irinotecan-induced gastrointestinal dysfunction and pain are mediated by common TLR4-dependent mechanisms. Mol Cancer Ther. 2016;15(6):1376–1386. doi: 10.1158/1535-7163.MCT-15-0990. [DOI] [PubMed] [Google Scholar]
  26. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 50: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(1):D1074–d1082. doi: 10.1093/nar/gkx1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Wong DVT, Ribeiro-Filho HV, Wanderley CWS, Leite C, Lima JB, Assef ANB, Cajado AG, Batista GLP, González RH, Silva KO, Borges LPC, Alencar NMN, Wilke DV, Cunha TM, Figueira ACM, Cunha FQ, Lima-Júnior RCP. SN-38, the active metabolite of irinotecan, inhibits the acute inflammatory response by targeting toll-like receptor 4. Cancer Chemother Pharmacol. 2019;84(2):287–298. doi: 10.1007/s00280-019-03844-z. [DOI] [PubMed] [Google Scholar]
  28. Zhang L, Lou W-H, Xu X-F, Wu W, Rong Y-F, Jin D-Y. SN38 increases IL-8 expression through the MAPK pathways in HCT8 cells. Int J Mol Med. 2017;39(1):217–222. doi: 10.3892/ijmm.2016.2810. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No associated data.

Articles from In Silico Pharmacology are provided here courtesy of Springer

RESOURCES