Recent advances in nanocellulose technology have revealed the potential of crystalline cellulose nanofibers to reinforce materials which are useful for tissue engineering, among other functions. ...However, the low biodegradability of nanocellulose can possess some problems in biomedical applications. In this work, alginate particles with encapsulated enzyme cellulase extracted from
were prepared for the biodegradation of crystalline cellulose nanofibers, which carrier system could be incorporated in tissue engineering biomaterials to degrade the crystalline cellulose nanoreinforcement in situ and on-demand during tissue regeneration. Both alginate beads and microparticles were processed by extrusion-dropping and inkjet-based methods, respectively. Processing parameters like the alginate concentration, concentration of ionic crosslinker Ca
, hardening time, and ionic strength of the medium were varied. The hydrolytic activity of the free and encapsulated enzyme was evaluated for unmodified (CNFs) and TEMPO-oxidized cellulose nanofibers (TOCNFs) in suspension (heterogeneous conditions); in comparison to solubilized cellulose derivatives (homogeneous conditions). The enzymatic activity was evaluated for temperatures between 25-75 °C, pH range from 3.5 to 8.0 and incubation times until 21 d. Encapsulated cellulase in general displayed higher activity compared to the free enzyme over wider temperature and pH ranges and for longer incubation times. A statistical design allowed optimizing the processing parameters for the preparation of enzyme-encapsulated alginate particles presenting the highest enzymatic activity and sphericity. The statistical analysis yielded the optimum particles characteristics and properties by using a formulation of 2% (w/v) alginate, a coagulation bath of 0.2 M CaCl
and a hardening time of 1 h. In homogeneous conditions the highest catalytic activity was obtained at 55 °C and pH 4.8. These temperature and pH values were considered to study the biodegradation of the crystalline cellulose nanofibers in suspension. The encapsulated cellulase preserved its activity for several weeks over that of the free enzyme, which latter considerably decreased and practically showed deactivation after just 10 d. The alginate microparticles with their high surface area-to-volume ratio effectively allowed the controlled release of the encapsulated enzyme and thereby the sustained hydrolysis of the cellulose nanofibers. The relative activity of cellulase encapsulated in the microparticles leveled-off at around 60% after one day and practically remained at that value for three weeks.
Soft tissues are commonly fiber-reinforced hydrogel composite structures, distinguishable from hard tissues by their low mineral and high water content. In this work, we proposed the development of ...3D printed hydrogel constructs of the biopolymers chitosan (CHI) and cellulose nanofibers (CNFs), both without any chemical modification, which processing did not incorporate any chemical crosslinking. The unique mechanical properties of native cellulose nanofibers offer new strategies for the design of environmentally friendly high mechanical performance composites. In the here proposed 3D printed bioinspired CNF-filled CHI hydrogel biomaterials, the chitosan serves as a biocompatible matrix promoting cell growth with balanced hydrophilic properties, while the CNFs provide mechanical reinforcement to the CHI-based hydrogel. By means of extrusion-based printing (EBB), the design and development of 3D functional hydrogel scaffolds was achieved by using low concentrations of chitosan (2.0-3.0% (
)) and cellulose nanofibers (0.2-0.4% (
)). CHI/CNF printed hydrogels with good mechanical performance (Young's modulus 3.0 MPa, stress at break 1.5 MPa, and strain at break 75%), anisotropic microstructure and suitable biological response, were achieved. The CHI/CNF composition and processing parameters were optimized in terms of 3D printability, resolution, and quality of the constructs (microstructure and mechanical properties), resulting in good cell viability. This work allows expanding the library of the so far used biopolymer compositions for 3D printing of mechanically performant hydrogel constructs, purely based in the natural polymers chitosan and cellulose, offering new perspectives in the engineering of mechanically demanding hydrogel tissues like intervertebral disc (IVD), cartilage, meniscus, among others.
The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which ...represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches.
A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks.
The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools.
The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.
Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic ...regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case–control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.
Antileishmanial activity of 5-nitroindazole derivatives Mollineda-Diogo, Niurka; Chaviano-Montes de Oca, Claudia Sissely; Sifontes-Rodríguez, Sergio ...
Therapeutic advances in infectious disease,
01/2023, Letnik:
10
Journal Article
Recenzirano
Odprti dostop
Background:
Currently, there is no safe and effective vaccine against leishmaniasis and existing therapies are inadequate due to high toxicity, cost and decreased efficacy caused by the emergence of ...resistant parasite strains. Some indazole derivatives have shown in vitro and in vivo activity against Trichomonas vaginalis and Trypanosoma cruzi. On that basis, 20 indazole derivatives were tested in vitro against Leishmania amazonensis.
Objective:
To evaluate the in vitro activity of twenty 2-benzyl-5-nitroindazolin-3-one derivatives against L. amazonensis.
Design:
For the selection of promising compounds, it is necessary to evaluate the indicators for in vitro activity. For this aim, a battery of studies for antileishmanial activity and cytotoxicity were implemented. These results enabled the determination of the substituents in the indazole derivatives responsible for activity and selectivity, through the analysis of the structure–activity relationship (SAR).
Methods:
In vitro cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for 20 compounds. Compounds that showed adequate selectivity were tested against intracellular amastigotes. The SAR from the results in promastigotes was represented using the SARANEA software.
Results:
Eight compounds showed selectivity index >10% and 50% inhibitory concentration <1 µM against the promastigote stage. Against intracellular amastigotes, four were as active as Amphotericin B. The best results were obtained for 2-(benzyl-2,3-dihydro-5-nitro-3-oxoindazol-1-yl) ethyl acetate, with 50% inhibitory concentration of 0.46 ± 0.01 µM against amastigotes and a selectivity index of 875. The SAR study showed the positive effect on the selectivity of the hydrophilic fragments substituted in position 1 of 2-benzyl-5- nitroindazolin-3-one, which played a key role in improving the selectivity profile of this series of compounds.
Conclusion:
2-bencyl-5-nitroindazolin-3-one derivatives showed selective and potent in vitro activity, supporting further investigations on this family of compounds as potential antileishmanial hits.
A2B adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ...ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A2B adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A2B adenosine receptor antagonists, and it can be used to develop other QSAR models.
DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A
2B
2, where GyrB catalyzes the hydrolysis of ATP. Cyclothialidine (Ro 09-1437) has ...been considered as a promising inhibitor whose modifications might lead to more potent compounds against the enzyme. We report here for the first time, QSAR studies regarding to ATPase inhibitors of DNA Gyrase. 1D, 2D and 3D descriptors from DRAGON software were used on a set of 42 cyclothialidine derivatives. Based on the core of the cyclothialidine GR122222X, different conformations were created by using OMEGA. FRED was used to dock these conformers in the cavity of the GyrB subunit to select the best conformations, paying special attention to the 12-membered ring. Three QSAR models were developed considering the dimension of the descriptors. The models were robust, predictive and good in statistical significance, over 70% of the experimental variance was explained. Interpretability of the models was possible by extracting the SAR(s) encoded by these predictive models. Analyzing the compound–enzyme interactions of the complexes obtained by docking allowed us to increase the reliability of the information obtained for the QSAR models.
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► Three QSAR models are made with cyclothialidine-like ATPase DNA GyrB inhibitors. ► 3D structures are created using GR1222222X core as reference. ► Molecular docking is applied to the dataset in the cavity of the enzyme. ► The best conformations achieved are used to calculate 3D descriptors for QSAR model. ► New inhibitors are designed with information from the QSAR and docking models.
Fuzzy clustering as rational partition method for QSAR Pérez-Garrido, Alfonso; Girón-Rodríguez, Francisco; Bueno-Crespo, Andrés ...
Chemometrics and intelligent laboratory systems,
07/2017, Letnik:
166
Journal Article
Recenzirano
Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another ...rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method QLOO2 and then the coefficient of the external test set Qext2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD.
Display omitted
•Fuzzy minimal as partition method for QSAR was compared with classical methods.•Fuzzy minimal partition QSAR models present good predictive performance.•Fuzzy minimal partition for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods.
Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to ...discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease.
In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands.
The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors.
Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments.
There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still ...the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm. The present study proposes two new parameters based on distances in a ROC curve for the selection of classification models with an appropriate balance in both training and test sets, namely the following: the ROC graph Euclidean distance (ROCED) and the ROC graph Euclidean distance corrected with Fitness Function (FIT(λ)) (ROCFIT). The behavior of these indices was observed through the study on the mutagenicity for four genotoxicity end points of a number of nonaromatic halogenated derivatives. It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilk’s lambda, or parameters like the area under the ROC curve. However, when the ROCED parameter was used, the follow-on linear discriminant models showed the lower statistical significance. But the other parameter, ROCFIT, maintains the ROCED capabilities while improving the significance of the models due to the inclusion of FIT(λ).
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