bioactive packaging
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Zein is renewable plant protein with valuable film-forming properties that can be used as a packaging material. It is known that the addition of natural cross-linkers can enhance a film's tensile properties. In this study, we aimed to prepare antimicrobial zein-based films enriched with monolaurin, eugenol, oregano, and thyme essential oil. Films were prepared using the solvent casting technique from ethanol solution. Their physicochemical properties were investigated using structural, morphological, and thermal techniques. Polar and dispersive components were analyzed using two models to evaluate the effects on the surface free energy values. The antimicrobial activity was proven using a disk diffusion method and the suppression of bacterial growth was confirmed via a growth kinetics study with the Gompertz function. The films' morphological characteristics led to systems with uniform distribution of essential oils or eugenol droplets combined with a flat-plated structure of monolaurin. A unique combination of polyphenolic eugenol and amphiphilic monoglyceride provided highly stretchable films with enhanced barrier properties and efficiency against Gram-positive and Gram-negative bacteria, yeasts, and molds. The prepared zein-based films with tunable surface properties represent an alternative to non-renewable resources with a potential application as active packaging materials.
- MeSH
- antibakteriální látky farmakologie MeSH
- antifungální látky farmakologie MeSH
- biomechanika účinky léků MeSH
- diferenciální skenovací kalorimetrie MeSH
- Escherichia coli účinky léků MeSH
- eugenol farmakologie MeSH
- laurany farmakologie MeSH
- mikroskopie atomárních sil MeSH
- monoglyceridy farmakologie MeSH
- obaly potravin * MeSH
- oleje prchavé farmakologie MeSH
- pára MeSH
- permeabilita MeSH
- povrchové vlastnosti MeSH
- smáčivost MeSH
- spektroskopie infračervená s Fourierovou transformací MeSH
- Staphylococcus aureus účinky léků MeSH
- zein farmakologie MeSH
- Publikační typ
- časopisecké články MeSH
Popularity of natural-based preparations supporting the sexual potency significantly increased in recent years, which also led to the increase of illegal use of phosphodiesterase type 5 inhibitor (PDE-5) in sexual performance enhancement products. In this study, a rapid U-HPLC‒HRMS/MS method has been developed to simultaneously determine 59 PDE-5 inhibitors and their analogues. Within the development of sensitive method for analysis of 59 PDE-5 inhibitors and their analogues, both sample preparation procedure, as well as separation / detection conditions have been optimized. Extraction efficiency of particular extraction solvents, influence of different mobile phase additives on target analytes separation, as well as impact of various settings of mass analyzer on sensitivity of detection were examined. Data were collected in the 'full MS/data dependent MS/MS' acquisition mode (full MS-dd-MS/MS). Before the U-HPLC‒HRMS/MS method was used for analysis of real samples, proper validation had been conducted. The precision of the method expressed as the relative standard deviation (RSD) was ≤4.2% and ≤5.2% at spiking concentrations 5 μg/g and 0.25 μg/g, respectively. The limits of quantification were in the range 0.25 - 0.05 μg/g and the recovery ranged between 71 and 90%. The optimized method was successfully applied for analysis of 64 real samples, and 10 of them were proved to contain both registered or unregistered synthetic PDE-5 inhibitors. Additionally, the acquired U-HPLC‒HRMS/MS fingerprints were demonstrated to serve as an efficient tool for revealing of other type of possible fraud in products labeling. Retrospective mining of markers of herbs declared on dietary supplements packaging allowed to assess the trueness / untruth in the declaration of medical herbs composition.
- MeSH
- fytonutrienty analýza normy MeSH
- inhibitory fosfodiesterasy 5 analýza MeSH
- kontaminace léku prevence a kontrola MeSH
- limita detekce MeSH
- padělané léky analýza MeSH
- podvod prevence a kontrola MeSH
- potravní doplňky analýza normy MeSH
- tandemová hmotnostní spektrometrie metody MeSH
- vysokoúčinná kapalinová chromatografie metody MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
Monoclonal antibodies are leading agents for therapeutic treatment of human diseases, but are limited in use by the paucity of clinically relevant models for validation. Sporadic canine tumours mimic the features of some human equivalents. Developing canine immunotherapeutics can be an approach for modeling human disease responses. Rituximab is a pioneering agent used to treat human hematological malignancies. Biologic mimics that target canine CD20 are just being developed by the biotechnology industry. Towards a comparative canine-human model system, we have developed a novel anti-CD20 monoclonal antibody (NCD1.2) that binds both human and canine CD20. NCD1.2 has a sub-nanomolar Kd as defined by an octet red binding assay. Using FACS, NCD1.2 binds to clinically derived canine cells including B-cells in peripheral blood and in different histotypes of B-cell lymphoma. Immunohistochemical staining of canine tissues indicates that the NCD1.2 binds to membrane localized cells in Diffuse Large B-cell lymphoma, Marginal Zone Lymphoma, and other canine B-cell lymphomas. We cloned the heavy and light chains of NCD1.2 from hybridomas to determine whether active scaffolds can be acquired as future biologics tools. The VH and VL genes from the hybridomas were cloned using degenerate primers and packaged as single chains (scFv) into a phage-display library. Surprisingly, we identified two scFv (scFv-3 and scFv-7) isolated from the hybridoma with bioactivity towards CD20. The two scFv had identical VH genes but different VL genes and identical CDR3s, indicating that at least two light chain mRNAs are encoded by NCD1.2 hybridoma cells. Both scFv-3 and scFv-7 were cloned into mammalian vectors for secretion in CHO cells and the antibodies were bioactive towards recombinant CD20 protein or peptide. The scFv-3 and scFv-7 were cloned into an ADEPT-CPG2 bioconjugate vector where bioactivity was retained when expressed in bacterial systems. These data identify a recombinant anti-CD20 scFv that might form a useful tool for evaluation in bioconjugate-directed anti-CD20 immunotherapies in comparative medicine.
- MeSH
- antigeny CD20 * chemie genetika imunologie metabolismus MeSH
- buněčné linie MeSH
- epitopy imunologie MeSH
- exprese genu MeSH
- hybridomy imunologie metabolismus MeSH
- jednořetězcové protilátky imunologie farmakologie MeSH
- klonování DNA MeSH
- lehké řetězce imunoglobulinů genetika MeSH
- lidé MeSH
- molekulární sekvence - údaje MeSH
- myši MeSH
- peptidová knihovna MeSH
- peptidy chemie metabolismus MeSH
- psi MeSH
- rekombinantní fúzní proteiny farmakologie MeSH
- sekvence aminokyselin MeSH
- sekvenční seřazení MeSH
- specificita protilátek imunologie MeSH
- těžké řetězce imunoglobulinů genetika MeSH
- tvorba protilátek imunologie MeSH
- vazba proteinů imunologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- psi MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.
- Publikační typ
- časopisecké články MeSH
Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.
