BackgroundThe widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.ResultsTree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R-2 and Q(2)-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity.ConclusionsThe proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

Biopurification is a challenging and growing market. Despite great efforts in the past years, current purification strategies still lack specificity, efficiency, and cost-effectiveness. The development of more sustainable functional materials and processes needs to address pressing environmental goals, efficiency, scale-up, and cost. Herein, L-leucine (LEU)-molecularly imprinted polymers (MIPs), LEU-MIPs, are presented as novel biomolecular fishing polymers for affinity sustainable biopurification. Rational design was performed using quantum mechanics calculations and molecular modeling for selecting the most appropriate monomers. LEU-MIPs were synthesized for the first time by two different green approaches, supercritical carbon dioxide (scCO(2)) technology and mechanochemistry. A significant imprinting factor of 12 and a binding capacity of 27 mg LEU/g polymer were obtained for the LEU-MIP synthesized in scCO(2) using 2-vinylpyridine as a functional monomer, while the LEU-MIP using acrylamide as a functional monomer synthesized by mechanochemistry showed an imprinting factor of 1.4 and a binding capacity of 18 mg LEU/g polymer, both systems operating at a low binding concentration (0.5 mg LEU/mL) under physiological conditions. As expected, at a higher concentration (1.5 mg LEU/mL), the binding capacity was considerably increased. Both green technologies show high potential in obtaining ready-to-use, stable, and low-cost polymers with a molecular recognition ability for target biomolecules, being promising materials for biopurification processes.

Background Herein, molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: alpha-CD, beta-CD, and gamma-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications. The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies. Objectives To understand more about the CD docking mechanism and the P-gp. Methods In order to achieve the main goal, the computational docking process was used. The observed docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions,and also hybrid electrostatic/side-chain interactions of the CD-ligands' OH-motifs with acceptor and donor characteristics, which might theoretically cause local perturbations in the TMD/P-gp inter-residues network, influencing ligand extrusion through the blood-brain barrier. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains - TMDs). Result The obtained theoretical docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells). Conclusion Finally, these theoretical results open new horizons for evaluating new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

[No abstract available]

IntroductionMajor depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD.Areas coveredIn the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the preclinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized on the basis of pharmacological targets. Based on these important case studies, the challenges involved in different design strategies were discussed in detail.Expert opinionEven though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modeling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

Regardless of the many common properties shared by ionic liquids (ILs) and deep eutectic solvents (DES), these are two completely different classes of solvents. Unlike ILs, DES are not composed solely of ions, which adds an extra complexity to the systems. Indeed, DES possess a distinct nanostructure organization which is mainly due to the strong hydrogen network present in these solvents. For this reason ILs and DES cannot be treated as similar mixtures, having their own particularities and different promising applications. Nevertheless, both solvents are highly tunable owing to the infinite number of possible combinations of their components and so it is important to tailor their specific properties according to a specific task, by choosing the right components at the right molar ratios. The growing number of applications involving an IL/surface or DES/surface interface is increasing rapidly, but the upscaling of such technologies needs a profound knowledge of chemical compositions, structure and orientational arrangements at these interfaces, also known as the electrical double layer (EDL). In this work, we provide a summary of the progress made in the interfacial area involving ILs and DES, paying special attention to the EDL arrangements and dynamics. It is clear that classical EDL theories do not apply to neither of these solvents, being the one proposed by Kornyshev the most accepted for ILs. Yet, a multilayer organization of the mixtures components at the solvent|electrode interface is recognized by experimental and computational simulation communities for both electrolytes. Each system has its own characteristic differential capacitance curves, which vary greatly due to the different nature of adsorption/desorption species and their reorientation.

Many challenges persist in developing accurate computationalmodelsfor predicting solvation free energy (& UDelta;G (sol)). Despite recent developments in Machine Learning (ML)methodologies that outperformed traditional quantum mechanical models,several issues remain concerning explanatory insights for broad chemicalpredictions with an acceptable speed-accuracy trade-off. Toovercome this, we present a novel supervised ML model to predict the & UDelta;G (sol) for an array of solvent-solutepairs. Using two different ensemble regressor algorithms, we madefast and accurate property predictions using open-source chemicalfeatures, encoding complex electronic, structural, and surface areadescriptors for every solvent and solute. By integrating molecularproperties and chemical interaction features, we have analyzed individualdescriptor importance and optimized our model though explanatory informationform feature groups. On aqueous and organic solvent databases, MLmodels revealed the predictive relevance of solutes with increasingpolar surface area and decreasing polarizability, yielding betterresults than state-of-the-art benchmark Neural Network methods (withoutcomplex quantum mechanical or molecular dynamic simulations). Bothalgorithms successfully outperformed previous & UDelta;G (sol) predictions methods, with a maximum absolute errorof 0.22 & PLUSMN; 0.02 kcal mol(-1), further validatedin an external benchmark database and with solvent hold-out tests.With these explanatory and statistical insights, they allow a thoughtfulapplication of this method for predicting other thermodynamic properties,stressing the relevance of ML modeling for further complex computationalchemistry problems.