Endothelial progenitor cells (EPCs) contribute to ischemic tissue repair by paracrine secretion up-regulated by hypoxia. In this study we use novel nanoparticles (NPs) as carriers for a controlled release of EPC secretome (CM) to improve their angiogenic properties. The in vivo effect in ischemic hindlimb rat model was evaluated, comparing hypoxic EPC-CM-NPs with hypoxic EPC-CM alone. A proteomic characterization of hypoxic CM and the in vitro effect on endothelial cells (HUVECs) were also performed. Up to 647 protein, 17 of which with angiogenic properties, were upregulated by hypoxia. Moreover, hypoxic EPC-CM significantly promoted capillary-like structures on Matrigel. A significant increase of blood perfusion in ischemic limbs at 2 weeks with EPCCM-loaded NPs as compared to both EPC-CM and control and a significant increase of capillary formation were observed. The use of EPC-CM-NPs significantly improved neoangiogenesis in vivo, underlining the advantages of controlled release in regenerative medicine.

The aim was the synthesis of chain-extended poly(ethylene glycol) (PEG) derivatives containing periodic side-chain carboxyl groups. The chain extension of PEG diols with pyromellitic dianhydride was performed in toluene or dimethylformamide solution and in bulk. The degree of chain extension (DoCE) was between 6 and 21, the highest value being recorded when the reaction was performed using low-molecular-weight PEG. The recorded limited increase of molecular weight could be at least partially attributed to the rather low reactivity of the aromatic dianhydride. To overcome this issue, a more reactive aliphatic dianhydride, ethylenediaminetetraacetic dianhydride (EA), was tested. However, the reaction of PEG with EA only afforded a DoCE of 2.5. Appreciably higher DoCE values were obtained when EA was reacted with bisamino-terminated PEG. Independent of prepolymer and dianhydride structure, all chain-extended products displayed less of a tendency to crystallization than the starting prepolymer, very likely due to interference by anhydride residues. The low in vitro cytotoxicity of the chain-extended polymers and the presence of carboxyl groups point to their possible use in biomedical applications, particularly in controlled drug release and tissue engineering. © 2014 Society of Chemical Industry

Multiblock copolymers containing carboxyl groups in the side-chains and at the chain ends were prepared from ABA triblock copolymers of ε-caprolactone, or lactide (as A block), and ethylene glycol (as B block). ABAn multiblock copolymers were prepared after chain-end functionalization and chain extension with pyromellitic dianhydride. A series of polymers were synthesized by varying the poly(ethylene glycol) and polyester molecular weight and the chirality of the lactide. Nuclear magnetic resonance analysis was used to confirm free carboxyl groups in the polymer backbone and at the chain ends. Thermal analysis indicated that the presence of pyromellitic dianhydride residues interfered not only with the formation of crystalline phases but also with the thermal degradation of chain-extended polymers. The biocompatibility of these amphiphilic polymers as evaluated with mouse embryo fibroblasts was acceptable. Both the parent ABA triblock copolymers and the carboxylated polymers were processed into nanoparticles. Depending on the polymer structure and reaction conditions, a narrow size nanoparticle distribution from ~10 to 250 nm was obtained. The nanoparticles were loaded with 60%–90% albumin and released 80%–90% of the albumin absorbed. Overall, this system was found to be well suited for the preparation of high-capacity injectable protein drug delivery.

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure-Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structure-activity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches.

Multiblock copolymers containing periodically spaced side-chain carboxyl groups were obtained by a two-step synthesis involving the preparation of ABA triblock prepolymers of lactic acid (A block) and ethylene glycol (B block) followed by chain extension to (ABA)n multiblock copolymers by reaction with pyromellitic dianhydride (PMDA). A series of polymer grades were synthesized by varying PEG and PLA chain length. NMR analysis demonstrated the incorporation of PMDA in polymer chain and revealed the possibility of PMDA units to exist in two isomers, cisoid and transoid forms. Chain extension resulted in the incorporation of free carboxylic groups in polymer backbone and in a 6-fold increase of molecular weight. Thermal analysis indicated that the extended polymers are more stable and have a Tg of 30−50 °C higher than their prepolymers. The polymers were found to be fast degrading in water following coupled first-order kinetics. Further, the carboxylated polymers can be processed into nanoparticulates by either nanoprecipitation or emulsion−diffusion methods resulting in 50−200 nm sized particles.

We investigated the possibility of modelling structure-toxicity relationships by direct treatment of the molecular structure (without using descriptors) through an adaptive model able to retain the appropriate structural information. With respect to traditional descriptor-based approaches, this provides a more general and flexible way to tackle prediction problems that is particularly suitable when little or no background knowledge is available. Our method employs a tree-structured molecular representation, which is processed by a recursive neural network (RNN). To explore the realization of RNN modelling in toxicological problems, we employed a data set containing growth impairment concentrations (IGC(50)) for Tetrahymena pyriformis.

The self-aggregation and gelation of an amphiphilic peptide (C17H35CONH–A4G3ERGD, peptide amphiphile) were studied by light scattering, viscometry, nuclear magnetic resonance diffusometry, and atomic force microscopy. The peptide amphiphile critical aggregation concentration was evaluated to be 16 and 60 µM by light scattering and viscometry, respectively. The observed difference was attributed to the larger sensitivity of the latter technique to the presence of long fibrils. The addition of one equivalent or more of divalent cations (Ca2+ and Mg2+) to peptide amphiphile formed dense incoherent hydrogels. Based on the atomic force microscopy and nanoindentation data, both the hydrogel morphology and stiffness were independent of the cation type and peptide amphiphile concentration. However, gel stiffness increased on increasing Ca2+/peptide amphiphile molar ratio while a parallel decrease in the apparent water diffusion rate was observed by nuclear magnetic resonance diffusometry. The dispersions of endothelial progenitor cells in the peptide amphiphile hydrogels were evaluated in vivo on a rat tissue hypoxia model. Significant capillary formation at the injection site was observed by tissue appearance and histological examination, which indicated endothelial progenitor cell/peptide amphiphile hydrogel-enhanced angiogenesis in ischemic tissue.

The glass transition temperature (T-g) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing pre-defined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275 copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9 K, a standard error of prediction (S) of 6.1 K and a squared correlation coefficient (R-2) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting.

We investigated the self-aggregation of 12 short ionic oligopeptides constituted by 4-7 amino acid residues to establish useful structure-property relationships that might be exploited in the biomedical field by using the concept of molecular Lego. We show that the critical aggregation concentration (CAC) of tetrapeptides decreases with increasing hydrophobicity of neutral residues. Additionally, the dependence of the CAC of isomeric oligopeptides on the distribution of amino acid residues confirms the high tendency to self-organization of molecules with alternating ionic and neutral residues. Indeed, atomic force microscopy (AFM) images recorded on oligopeptide solutions above the CAC show the presence of either fibrillar or spherical aggregates depending on oligopeptide structure and concentration, steric hindrance, solution pH, and time. The potential of the investigated oligopeptides in tissue engineering applications is supported by their in vitro cytocompatibility. (C) 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 48: 889897, 2010

Recent advances in biotechnology demonstrate that peptides and proteins are the basis of a new generation of drugs. However, the transportation of protein drugs in the body is limited by their high molecular weight, which prevents the crossing of tissue barriers, and by their short lifetime due to immuno response and enzymatic degradation. Moreover, the ability to selectively deliver drugs to target organs, tissues or cells is a major challenge in the treatment of several human diseases, including cancer. Indeed, targeted delivery can be much more efficient than systemic application, while improving bioavailability and limiting undesirable side effects. This review describes how the use of targeted nanocarriers such as nanoparticles and liposomes can improve the pharmacokinetic properties of protein drugs, thus increasing their safety and maximizing the therapeutic effect.

RWDW, a complementary ionic tetrapeptide spontaneously self-assembles in cell culture medium to form thick hydrogels constituted by interwound nanofibers. When inoculated with HepG2 cells, RWDW hydrogels support cell adhesion and proliferation, as demonstrated by viability tests. To the best of our knowledge, RWDW is the shortest ionic oligopeptide that can be used as injectable scaffold for tissue engineering applications.

Free accessAbstractFirst published online July/September 2009Commercially Available Polymeric Materials as Substrates for the Fabrication of Cancer Polymarker BiochipsAntonella Battisti and Roberto SolaroVolume 24, Issue 3https://doi.org/10.1177/172460080902400324

Free accessAbstractFirst published online July/September 2009Novel Polymeric Materials for the Fabrication of BiochipsRoberto SolaroVolume 24, Issue 3https://doi.org/10.1177/172460080902400311

We propose a new method based on a Recursive Neural Network (RecNN) for predicting polymer properties from their structured molecular representations. RecNN allows for a completely novel approach to QSPR analysis by direct adaptive processing of molecular graphs. This model joins the representational power of structured domains with Neural Network ability to capture underlying complex relationships in the data by a process of training from examples. To this aim, a structured representation was designed for the modelling of polymer structures. The adopted representation can account also for average macromolecule characteristics, such as degree of polymerization, stereoregularity, comonomer distribution. To begin with, this model was applied to the prediction of the glass transition temperature of (meth)acrylic polymers with different degree of main chain tacticity. The results so far obtained indicate that the proposed representation of polymer structure can convey information on both the repeating unit structure and average polymer features. The ability of the proposed RecNN method of treating this structured representation makes this method more general and flexible with respect to standard literature methods. Moreover, the same model can handle at the same time the Tg of polymer samples present in only one tacticity form together with that of polymer with different stereoregularity.