Here, we characterize FDA-approved drugs commonly used to treat GWI symptoms to find those that have the highest chance of interfering with TNF-and IL-2 cytokines, representing the stress, male sex, and immune components of our previous models, respectively

Here, we characterize FDA-approved drugs commonly used to treat GWI symptoms to find those that have the highest chance of interfering with TNF-and IL-2 cytokines, representing the stress, male sex, and immune components of our previous models, respectively. 2. drug treatment course development, for the repositioning of United States Food and Drug Administration (FDA)-approved drugs, and for the identification of ADRs. As such, combination treatment design by the clinician should take all precautions to minimize ADRs and off-target interactions, whether for the treatment of a single illness or for the treatment of an illness with comorbid conditions. Here, we characterize FDA-approved drugs commonly used to treat GWI symptoms to find those that have the highest chance of interfering with TNF-and IL-2 cytokines, representing the stress, male sex, and immune components of our previous models, respectively. 2. Results 2.1. Validation of Docking Accuracy The ability of the 43 FDA-approved small molecule drugs commonly used to treat GWI symptoms (see Table 1) to interfere with a proposed multidrug GWI treatment course [13] was determined through virtual docking to multiple crystal structures of the GCR, AR, and the TNF-and IL-2 cytokines. As the GCR and AR both have agonistic and antagonistic forms, we evaluated each of these Rabbit Polyclonal to Sumo1 separately to remove any bias towards a given mode of action in order judge which form MF-438 of the receptor may be more affected by the 43 GWI symptom-treating drugs. No such difference in forms was available for the TNF-and IL-2 cytokines. Additionally, we only chose structures that were in complex with a small molecule binder (except 1TNF, see Section 2.3.4 below for clarification); this allowed us to re-dock the known binder using each of the three programs to ensure accuracy. For each target, we only computed results from programs which docked known binders to within a root mean square deviation (RMSD) of 2.0 MF-438 ? of the crystallographic pose, a value known to reliably identify correctly docked ligands [30]. Table 2 MF-438 provides a summary of the successes and failures of each program to dock known binders to within the 2 2.0 ? RMSD cutoff. Note that the crystal structure identifiers refer to targets from the RCSB Protein Data Bank (PDB) [31,32]. Table 2 Docking programs that succeeded/failed to produce poses within the root mean square deviation (RMSD) cutoff range of 2.0 ?. * signifies docking programs which succeeded. # indicates supplementary docking runs to support statistical analysis. (4TWT)**#TNF-(1TNF)## Open in a separate window AD4 and VINA were excluded from AR 2PNU and 2AX6 because their predicted poses for the known binders were above the 2 2.0 ? RMSD cutoff range. Similar to AD4 and VINA, GLIDE was excluded from AR 2AMB and 1Z95 because it exceeded the RMSD cutoff score. GLIDE failed to predict a pose for TNF-4TWTs known binder altogether. Figure 2 displays the alpha helices and beta sheets of each targets binding pocket, along with their known binders. The predicted poses from each docking program are shown as well for comparison. Note that all images were created using PyMOL version 1.8.6.2 [33]. Open in a separate window Figure 2 Docked poses of known binders to their targets. Known binder (red) compared to AD4 (yellow), VINA (blue), and GLIDE (green). Note that Residues 636C652 of GCR, and the hydrogen atoms on each ligand, are not shown for clarity. 2.2. Statistical Accuracy The docking of a ligand MF-438 with the structure program combinations given in Table 2 yields a distribution of results for each ligandCtarget interaction. To determine if a given ligand binds to a given target, the results from the various crystal structureCprogram combinations for the ligand were compared from the distributed results to that of a known binder for the target (i.e., testosterone for AR agonist, hydroxyflutamide for AR antagonist, dexamethasone for GCR agonist, mifepristone for GCR antagonist, and suramin for IL-2 and TNF- 0.02. These three drugs include trazodone, an oral antidepressant used to treat major depressive disorder; carbamazepine, used primarily in the treatment of neuropathic pain; and buspirone, an anxiolytic drug that is primarily used to treat generalized anxiety disorder. Additionally, both suramin and mifepristone were found to be statistically similar to testosterone; however, their.