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Structure-Based Virtual Screening of Tumor Necrosis Factor-? Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation.

ABSTRACT: Tumor necrosis factor-? (TNF-?) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-? binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-?/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-? inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-? dimer and effectively block TNF-?/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-?, followed by ligand-TNF-? binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-?; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-?. This study provides valuable insights to design more potent and selective inhibitors of TNF-?, that will help to treat inflammatory disorders.

SUBMITTER: Halim SA

PROVIDER: S-EPMC7926523 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

Project description:ImportanceTumor necrosis factor α (TNF) inhibitor-induced psoriasiform eruption is well recognized in adults, but few reports document this paradoxical effect in children.ObjectiveTo characterize the clinical features and the clinical time course of TNF inhibitor-induced psoriasiform eruptions in children.Design, setting, and participantsA multicenter retrospective case series of children younger than 18 years seen between January 1, 2000, and December 31, 2016, who developed a new-onset psoriasiform eruption while taking a TNF inhibitor for a nondermatologic disorder. Participating sites were members of the Pediatric Dermatology Research Alliance. Data were entered into a Research Electronic Data Capture database at the Mayo Clinic (ie, the coordinating center).ResultsPsoriasiform eruptions were identified in 103 TNF inhibitor-treated patients (median age, 13.8 years [IQR, 11.7-16.4 years]; 52 female patients [50%]; 57 White patients [55%]), with 67 patients (65%) treated with infliximab, 35 (34%) with adalimumab, and 1 (1%) with certolizumab pegol. Most patients had no personal history (101 [98%]) or family history of psoriasis (60 patients [58%]). Inflammatory bowel disease was the most common indication for treatment with TNF inhibitor (94 patients [91%]). The primary extracutaneous disease was under control in 95 patients (92%) who developed the eruption. Most patients (n = 85 [83%]) developed psoriasiform eruptions at multiple anatomic sites, with scalp involvement being most common (65 patients [63%]). Skin disease developed at a median of 14.5 months (IQR, 9-24 months) after TNF inhibitor initiation. To treat the psoriasiform eruption, topical steroidal and nonsteroidal medication was prescribed for all patients. Systemic therapy was added for 30 patients (29%): methotrexate for 24 patients (23%), oral corticosteroids for 8 patients (8%), and azathioprine for 1 patient (1%). For 26 patients (25%), suboptimal effectiveness with topical medications alone prompted discontinuation of the initial TNF inhibitor and a change to a second-line TNF inhibitor with cutaneous improvement in 23 patients (88%) by a median of 3 months (IQR, 2-4 months). Eight patients (31%) who started a second-line TNF inhibitor developed a subsequent TNF inhibitor-induced psoriasiform eruption at a median of 6 months (IQR, 4-8 months). Persistent skin disease in 18 patients (17%) prompted discontinuation of all TNF inhibitors; 11 patients changed to a non-TNF inhibitor systemic therapy, and 7 discontinued all systemic therapy.Conclusions and relevanceIn this case series, paradoxical TNF inhibitor-induced psoriasiform eruptions were seen in children treated with TNF inhibitors for any indication, and there appears to be a class effect among the varying TNF inhibitors. The majority of these children were able to continue TNF inhibitor therapy with adequate skin-directed and other adjuvant therapies.

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Structure-Based Virtual Screening of Tumor Necrosis Factor-? Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation.

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