Discovery of FIXa inhibitors by combination of pharmacophore modeling, molecular docking, and 3D-QSAR modeling

Fingerprint analysis of dang-gui-Si-Ni decoction and its anticoagulant activity in vivo-in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

Dang-Gui-Si-Ni (DGSN) decoction is a classic prescription in the clinical practice of traditional Chinese Medicine (TCM). DGSN decoction is often used to relieve symptoms of cold coagulation and blood stasis recorded by Treatise on Febrile Diseases (Shang Han Lun) and treat Raynaud's disease, dysmenorrhea, arthritis, migraine in TCM clinic. Accumulated evidences have suggested that this diseases are related to microcirculation disturbance. However, the anticoagulant activity and underlying mechanisms of DGSN decoction responsible for the therapeutic not well understood.

AIM OF THE STUDY

The fingerprint and anticoagulant activity in vivo-in vitro of DGSN decoction were evaluated to strengthen the quality control and activity study of formulas.

MATERIALS AND METHODS

The chemical components of DGSN decoction were analyzed by HPLC and its fingerprint similarity were evaluated by "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation Software (2012 Edition)". The anticoagulant activity of DGSN decoction was assessed by measuring four coagulation factors (PT, TT, APTT, FIB) in vitro. Zebrafish thrombosis model induced by punatinib was established to evaluate the activity of improving microvascular hemodynamics in vivo. Quantitative real-time polymerase chain reaction (q-PCR) were adopted to compare the changes in the RNA expression levels of coagulation factor II (FII), VII (FVII), IX (FIX) and X (FX) in zebrafish thrombosis model.

RESULTS

The fingerprint similarity evaluation method of DGSN decoction was established. The results showed that 18 samples had higher similarity (S1-S18 > 0.878). Pharmacodynamic results showed that DGSN decoction could extend PT, TT and APTT, and reduce FIB content in vitro. Meanwhile, it markedly enhanced the cardiac output and blood flow velocity at low dosage (500 μg mL-1) in vivo. q-PCR data demonstrated that DGSN decoction (500 μg mL-1) could downregulate the RNA expression of FII, FVII, FIX and FX. Interestingly, there were a bidirectional regulation of FII, FIX and FX in a certain concentration range. In general, DGSN decoction can significantly improve hemodynamics and downregulate coagulation factors, and the results were consistent both in vitro - in vivo.

CONCLUSION

The fingerprint study provide a new perspective for improving the quality control of DGSN decoction. DGSN decoction possess anticoagulant activity by regulating multiple coagulation factors simultaneously. Thus, it has the potential to develop into the novel raw material of anticoagulant drugs.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

A Structure Based Study of Selective Inhibition of Factor IXa over Factor Xa

Blood coagulation is an essential physiological process for hemostasis; however, abnormal coagulation can lead to various potentially fatal disorders, generally known as thromboembolic disorders, which are a major cause of mortality in the modern world. Recently, the FDA has approved several anticoagulant drugs for Factor Xa (FXa) which work via the common pathway of the coagulation cascade. A main side effect of these drugs is the potential risk for bleeding in patients. Coagulation Factor IXa (FIXa) has recently emerged as the strategic target to ease these risks as it selectively regulates the intrinsic pathway. These aforementioned coagulation factors are highly similar in structure, functional architecture, and inhibitor binding mode. Therefore, it remains a challenge to design a selective inhibitor which may affect only FIXa. With the availability of a number of X-ray co-crystal structures of these two coagulation factors as protein–ligand complexes, structural alignment, molecular docking, and pharmacophore modeling were employed to derive the relevant criteria for selective inhibition of FIXa over FXa. In this study, six ligands (three potent, two selective, and one inactive) were selected for FIXa inhibition and six potent ligands (four FDA approved drugs) were considered for FXa. The pharmacophore hypotheses provide the distribution patterns for the principal interactions that take place in the binding site. None of the pharmacophoric patterns of the FXa inhibitors matched with any of the patterns of FIXa inhibitors. Based on pharmacophore analysis, a selectivity of a ligand for FIXa over FXa may be defined quantitatively as a docking score of lower than −8.0 kcal/mol in the FIXa-grids and higher than −7.5 kcal/mol in the FXa-grids.

Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs; mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.