A dual-application poly (DL-lactic-co-glycolic) acid (PLGA)-chitosan composite scaffold for potential use in bone tissue engineering

The development of patient-friendly alternatives to bone-graft procedures is the driving force for new frontiers in bone tissue engineering. Poly (DL-lactic-co-glycolic acid), (PLGA) and chitosan are well-studied and easy-to-process polymers from which scaffolds can be fabricated. In this study, a novel dual-application scaffold system was formulated from porous PLGA and protein-loaded PLGA/chitosan microspheres. Physicochemical and in vitro protein release attributes were established. The therapeutic relevance, cytocompatibility with primary human mesenchymal stem cells (hMSCs) and osteogenic properties were tested. There was a significant reduction in burst release from the composite PLGA/chitosan microspheres compared with PLGA alone. Scaffolds sintered from porous microspheres at 37°C were significantly stronger than the PLGA control, with compressive strengths of 0.846 ± 0.272 MPa and 0.406 ± 0.265 MPa, respectively (p < 0.05). The formulation also sintered at 37°C following injection through a needle, demonstrating its injectable potential. The scaffolds demonstrated cytocompatibility, with increased cell numbers observed over an 8-day study period. Von Kossa and immunostaining of the hMSC-scaffolds confirmed their osteogenic potential with the ability to sinter at 37°C in situ

Molecular Dynamics Simulations of the Adenosine A2a Receptor in POPC and POPE Lipid Bilayers: Effects of Membrane on Protein Behavior

Analysis of 300 ns (ns) molecular dynamics (MD) simulations of an adenosine A2a receptor (A2a AR) model, conducted in triplicate, in 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and 1-palmitoyl-2-oleoylphosphatidylethanolamine (POPE) bilayers reveals significantly different protein dynamical behavior. Principal component analysis (PCA) shows that the dissimilarities stem from interhelical rather than intrahelical motions. The difference in the hydrophobic thicknesses of these simulated lipid bilayers is potentially a significant reason for the observed difference in results. The distinct lipid headgroups might also lead to different molecular interactions and hence different protein loop motions. Overall, the A2a AR shows higher mobility and flexibility in POPC as compared to POPE

Molecular Dynamics Simulations of the Adenosine A2a Receptor: Structural Stability, Sampling, and Convergence

Molecular dynamics (MD) simulations of membrane-embedded G-protein coupled receptors (GPCRs) have rapidly gained popularity among the molecular simulation community in recent years, a trend which has an obvious link to the tremendous pharmaceutical importance of this group of receptors and the increasing availability of crystal structures. In view of the widespread use of this technique, it is of fundamental importance to ensure the reliability and robustness of the methodologies so they yield valid results and enable sufficiently accurate predictions to be made. In this work, 200 ns simulations of the A2a adenosine receptor (A2a AR) have been produced and evaluated in the light of these requirements. The conformational dynamics of the target protein, as obtained from replicate simulations in both the presence and absence of an inverse agonist ligand (ZM241385), have been investigated and compared using principal component analysis (PCA). Results show that, on this time scale, convergence of the replicates is not readily evident and dependent on the types of the protein motions considered. Thus rates of inter- as opposed to intrahelical relaxation and sampling can be different. When studied individually, we find that helices III and IV have noticeably greater stability than helices I, II, V, VI, and VII in the apo form. The addition of the inverse agonist ligand greatly improves the stability of all helices

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration’s Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency’s ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼70–89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals