Bone Marrow Cell Colonization Of, and Extracellular Matrix Expression On, Biodegradable Polymers

Poly(DL-lactide-co-glycolide)s (PLGAs) have been proposed as substrata for bone tissue engineering. In the experiments reported herein, we sought to identify the optimum lactide to glycolide ratio, from the series 85:15, 75:25, 50:50, or poly-(DL-lactide) (PLA), for the elaboration of bone matrix by cultured rat bone marrow cells (RBMC) on two-dimensional substrates. Having identified PLGA 75:25 as the optimum for bone matrix elaboration by RBMC, we produced three dimensional foams from this copolymer. For the two dimensional substrata, glass coverslips were spin-coated with one of the PLGAs, or PLA. Cultures were maintained for two weeks. We employed a new technique to label the elaborated bone matrix with the fluorescent antibiotic tetracycline. Bone matrix was present to a varying degree dependent on substrate composition: PLGA 75:25 = TCP \u3e PLGA 85:15 \u3e \u3e PLA. No bone matrix was observed on PLGA 50:50 or on uncoated glass coverslips. Cell proliferation was similar on each surface except PLA on which they did not proliferate. Cell morphology was assessed by scanning electron microscopy. Based on these results, three dimensional devices were produced from PLGA 75:25. Our results demonstrate that the copolymer ratios that maximize cell proliferation are not identical to the that optimize bone matrix elaboration. Furthermore, despite the intended use of three dimensional matrices for connective tissue engineering applications, bone marrow-derived cells produced only a superficial matrix layer that did not invade the scaffold, whether produced by either the salt leaching or freeze-drying procedures employed

Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited

Encapsulation-free controlled release: Electrostatic adsorption eliminates the need for protein encapsulation in PLGA nanoparticles

Encapsulation of therapeutic molecules within polymer particles is a well-established method for achieving controlled release, yet challenges such as low loading, poor encapsulation efficiency, and loss of protein activity limit clinical translation. Despite this, the paradigm for the use of polymer particles in drug delivery has remained essentially unchanged for several decades. By taking advantage of the adsorption of protein therapeutics to poly(lactic-co-glycolic acid) (PLGA) nanoparticles, we demonstrate controlled release without encapsulation. In fact, we obtain identical, burst-free, extended-release profiles for three different protein therapeutics with and without encapsulation in PLGA nanoparticles embedded within a hydrogel. Using both positively and negatively charged proteins, we show that short-range electrostatic interactions between the proteins and the PLGA nanoparticles are the underlying mechanism for controlled release. Moreover, we demonstrate tunable release by modifying nanoparticle concentration, nanoparticle size, or environmental pH. These new insights obviate the need for encapsulation and offer promising, translatable strategies for a more effective delivery of therapeutic biomolecules

Structure-Based Discovery of A2A Adenosine Receptor Ligands

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A(2A) adenosine receptor, based on complementarity to its recently determined structure. The A(2A) adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 microM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A(2A) versus the related A(1) and A(3) subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target

Combinatorial Computational Approaches to Identify Tetracycline Derivatives as Flavivirus Inhibitors

Limited structural information of drug targets, cellular toxicity possessed by lead compounds, and large amounts of potential leads are the major issues facing the design-oriented approach of discovering new leads. In an attempt to tackle these issues, we have developed a process of virtual screening based on the observation that conformational rearrangements of the dengue virus envelope protein are essential for the mediation of viral entry into host cells via membrane fusion. Screening was based solely on the structural information of the Dengue virus envelope protein and was focused on a target site that is presumably important for the conformational rearrangements necessary for viral entry. To circumvent the issue of lead compound toxicity, we performed screening based on molecular docking using structural databases of medical compounds. To enhance the identification of hits, we further categorized and selected candidates according to their novel structural characteristics. Finally, the selected candidates were subjected to a biological validation assay to assess inhibition of Dengue virus propagation in mammalian host cells using a plaque formation assay. Among the 10 compounds examined, rolitetracycline and doxycycline significantly inhibited plaque formation, demonstrating their inhibitory effect on dengue virus propagation. Both compounds were tetracycline derivatives with IC(50)s estimated to be 67.1 µM and 55.6 µM, respectively. Their docked conformations displayed common hydrophobic interactions with critical residues that affected membrane fusion during viral entry. These interactions will therefore position the tetracyclic ring moieties of both inhibitors to bind firmly to the target and, subsequently, disrupt conformational rearrangement and block viral entry. This process can be applied to other drug targets in which conformational rearrangement is critical to function

Stability of Self-Assembled Polymeric Micelles in Serum

The stability of polymeric nanoparticles in serum is critical to their use in drug delivery where dilution after intravenous injection often results in nanoparticle disassembly and drug unloading; however, few investigate this in biologically relevant media. To gain greater insight into nanoparticle stability in blood, the stability of self-assembled polymeric micelles of poly(d,l-lactide-co-2-methyl-2-carboxytrimethylene carbonate)-g-poly(ethylene glycol), P(LA-co-TMCC)-g-PEG, were tested in both serum and individual serum protein solutions. By encapsulating Förster resonance energy transfer pairs and following their release by fluorescence, these micelles demonstrated excellent thermodynamic and kinetic stability in the presence of serum. Further analyses by fast protein liquid chromatography and dynamic light scattering confirmed these data. Moreover, these micelles are compatible with red blood cells, as shown by a hemolysis assay. The stability and compatibility demonstrated in blood suggest that these micelles may be stable in vivo, which is critical for intravenous drug delivery applications. This comprehensive approach to understanding micelle stability and compatibility is broadly applicable

Tunable Growth Factor Delivery from Injectable Hydrogels for Tissue Engineering

Current sustained delivery strategies of protein therapeutics are limited by the fragility of the protein, resulting in minimal quantities of bioactive protein delivered. In order to achieve prolonged release of bioactive protein, an affinity-based approach was designed which exploits the specific binding of the Src homology 3 (SH3) domain with short proline-rich peptides. Specifically, methyl cellulose was modified with SH3-binding peptides (MC-peptide) with either a weak affinity or strong affinity for SH3. The release profile of SH3-rhFGF2 fusion protein from hyaluronan MC-SH3 peptide (HAMC-peptide) hydrogels was investigated and compared to unmodified controls. SH3-rhFGF2 release from HAMC-peptide was extended to 10 days using peptides with different binding affinities compared to the 48 h release from unmodified HAMC. This system is capable of delivering additional proteins with tunable rates of release, while maintaining bioactivity, and thus is broadly applicable

Multilevel Parallelization of AutoDock 4.2

<p>Abstract</p> <p>Background</p> <p>Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4).</p> <p>Results</p> <p>Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers.</p> <p>Conclusions</p> <p>Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.</p

Discovery of novel GPVI receptor antagonists by structure-based repurposing.

Inappropriate platelet aggregation creates a cardiovascular risk that is largely managed with thienopyridines and aspirin. Although effective, these drugs carry risks of increased bleeding and drug 'resistance', underpinning a drive for new antiplatelet agents. To discover such drugs, one strategy is to identify a suitable druggable target and then find small molecules that modulate it. A good and unexploited target is the platelet collagen receptor, GPVI, which promotes thrombus formation. To identify inhibitors of GPVI that are safe and bioavailable, we docked a FDA-approved drug library into the GPVI collagen-binding site in silico. We now report that losartan and cinanserin inhibit GPVI-mediated platelet activation in a selective, competitive and dose-dependent manner. This mechanism of action likely underpins the cardioprotective effects of losartan that could not be ascribed to its antihypertensive effects. We have, therefore, identified small molecule inhibitors of GPVI-mediated platelet activation, and also demonstrated the utility of structure-based repurposing

Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors†

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99 % of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1 % of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone