Riboflavin-targeted polymers improve tolerance of paclitaxel while maintaining therapeutic efficacy.

Active targeting can enhance precision and efficacy of drug delivery systems (DDS) against cancers. Riboflavin (RF) is a promising ligand for active targeting due to its biocompatibility and high riboflavin-receptor expression in cancers. In this study, RF-targeted 4-arm polyethylene glycol (PEG) stars conjugated with Paclitaxel (PTX), named PEG PTX RF, were evaluated as a targeted DDS. In vitro, PEG PTX RF exhibited higher toxicity against tumor cells compared to the non-targeted counterpart (PEG PTX), while free PTX displayed the highest acute toxicity. In vivo, all treatments were similarly effective, but PEG PTX RF-treated tumors showed fewer proliferating cells, pointing to sustained therapy effects. Moreover, PTX-treated animals' body and liver weights were significantly reduced, whereas both remained stable in PEG PTX and PEG PTX RF-treated animals. Overall, our targeted and non-targeted DDS reduced PTX's adverse effects, with RF targeting promoted drug uptake in cancer cells for sustained therapeutic effect.

Polycaprolactone/polyacrylic acid/graphene oxide composite nanofibers as a highly efficient sorbent to remove lead toxic metal from drinking water and apple juice.

Due to the characteristics of electrospun nanofibers (NFs), they are considered a suitable substrate for the adsorption and removal of heavy metals. Electrospun nanofibers are prepared based on optimized polycaprolactone (PCL, 12 wt%) and polyacrylic acid (PAA, 1 wt%) polymers loaded with graphene oxide nanoparticles (GO NPs, 1 wt%). The morphological, molecular interactions, crystallinity, thermal, hydrophobicity, and biocompatibility properties of NFs are characterized by spectroscopy (scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, Thermogravimetric analysis), contact angle, and MTT tests. Finally, the adsorption efficacy of NFs to remove lead (Pb2+) from water and apple juice samples was determined using inductively coupled plasma optical emission spectroscopy (ICP-OES). The average diameter for PCL, PCL/PAA, and PCL/PAA/GO NFs was 137, 500, and 216 nm, respectively. Additionally, the contact angle for PCL, PCL/PAA, and PCL/PAA/GO NFs was obtained at 74.32º, 91.98º, and 94.59º, respectively. The cytotoxicity test has shown non-toxicity for fabricated NFs against the HUVEC endothelial cell line by more than 80% survival during 72 h. Under optimum conditions including pH (= 6), temperature (25 °C), Pb concentration (25 to 50 mg/L), and time (15 to 30 min), the adsorption efficiency was generally between 80 and 97%. The adsorption isotherm model of PCL/PAA/GO NFs in the adsorption of lead metal follows the Langmuir model, and the reaction kinetics follow the pseudo-second-order. PCL/PA/GO NFs have shown adsorption of over 80% in four consecutive cycles. The adsorption efficacy of NFs to remove Pb in apple juice has reached 76%. It is appropriate and useful to use these nanofibers as a high-efficiency adsorbent in water and food systems based on an analysis of their adsorption properties and how well they work.

Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles.

Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.

3D printing of complicated GelMA-coated Alginate/Tri-calcium silicate scaffold for accelerated bone regeneration.

Polymer-based composite scaffolds are an attractive class of biomaterials due to their suitable physical and mechanical performance as well as appropriate biological properties. When such composites contain osteoinductive ceramic nanopowders, it is possible, in principle, to stimulate the seeded cells to differentiate into osteoblasts. However, reproducibly fabricating and developing an appropriate niche for cells' activities in three-dimensional (3D) scaffolds remains a challenge using conventional fabrication techniques. Additive manufacturing provides a new strategy for the fabrication of complex 3D structures. Here, an extrusion-based 3D printing method was used to fabricate the Alginate (Alg)/Tri-calcium silicate (C3S) bone scaffolds. To improve physical and biological attributes, scaffolds were coated with gelatin methacryloyl (GelMA), a biocompatible viscose hydrogel. Conducting a combination of experimental techniques and molecular dynamics simulations, it is found that the composition ratio of Alg/C3S governs intermolecular interactions among the polymer and ceramic, affecting the product performance. Investigating the effects of various C3S amounts in the bioinks, the 90/10 composition ratio of Alg/C3S is known as the optimum content in developed bioinks. Accordingly, the printability of high-viscosity inks is boosted by improved hierarchical interactions among assemblies, which in turn leads to better nanoscale alignment in extruded macroscopic filaments. Conducting multiple tests on specimens, the GelMA-coated Alg/C3S scaffolds (with a composition ratio of 90/10) were shown to have improved mechanical qualities and cell adhesion, spreading, proliferation, and osteogenic differentiation, compared to the bare scaffolds, making them better candidates for further future research. Overall, the in-silico and in vitro studies of GelMA-coated 3D-printed Alg/C3S scaffolds open new aspects for biomaterials aimed at the regeneration of large- and complicated-bone defects through modifying the extrusion-based 3D-printed constructs.

Mechanism Understanding of Li-ion Separation Using A Perovskite-Based Membrane.

Lithium ions play a crucial role in the energy storage industry. Finding suitable lithium-ion-conductive membranes is one of the important issues of energy storage studies. Hence, a perovskite-based membrane, Lithium Lanthanum Titanate (LLTO), was innovatively implemented in the presence and absence of solvents to precisely understand the mechanism of lithium ion separation. The ion-selective membrane's mechanism and the perovskite-based membrane's efficiency were investigated using Molecular Dynamic (MD) simulation. The results specified that the change in the ambient condition, pH, and temperature led to a shift in LLTO pore sizes. Based on the results, pH plays an undeniable role in facilitating lithium ion transmission through the membrane. It is noticeable that the hydrogen bond interaction between the ions and membrane led to an expanding pore size, from (1.07 Å) to (1.18-1.20 Å), successfully enriching lithium from seawater. However, this value in the absence of the solvent would have been 1.1 Å at 50 °C. It was found that increasing the temperature slightly impacted lithium extraction. The charge analysis exhibited that the trapping energies applied by the membrane to the first three ions (Li+, K+, and Na+) were more than the ions' hydration energies. Therefore, Li+, K+, and Na+ were fully dehydrated, whereas Mg2+ was partially dehydrated and could not pass through the membrane. Evaluating the membrane window diameter, and the combined effect of the three key parameters (barrier energy, hydration energy, and binding energy) illustrates that the required energy to transport Li ions through the membrane is higher than that for other monovalent cations.

Effect of ligand conjugation site on the micellization of Bio-Targeted PLGA-Based nanohybrids: A computational biology approach.

In this study, the effect of ligand binding position on the polymeric nanoparticles (NPs) is based on poly(lactic-co-glycolic acid) (PLGA) with two different polymer chain length at the atomistic level was presented. We explored the conjugation of riboflavin (RF) ligand from the end of the ribityl chain (N-10) to the polymer strands as well as from the amine group on the isoalloxazine head (N-3). The energy interactions for all samples revealed that the NPs containing ligands from N-10 positions have higher total attraction energies and lower stability in comparison with their peers conjugated from N-3. As NPs containing RF conjugated from N-3 exhibit the lower energy level with 20% and 10% of RF-containing composition for lower and higher. The introduction of RF from the N-10 position in any composition has increased the energy level of nanocarriers. The results of Gibb's free energy confirm the interatomic interaction energies trend where the lowest Gibbs free energy level for N-3 NPs occurs at 20 and 10% of RF-containing polymer content for PLGA10- and PLGA11- based NPs. Furthermore, with N-10 samples based on both polymers, non-targeted models form the stablest particles in each category. These findings are further confirmed with molecular docking analysis which revealed affinity energy of RF toward polymer chain from N-3 and N-10 are -981.57 kJ/mole and -298.23 kJ/mole, respectively. This in-silico study paves the new way for molecular engineering of the bio-responsive PLGA-PEG-RF micelles and can be used to nanoscale tunning of smart carriers used in cancer treatment.Communicated by Ramaswamy H. Sarma.

Engineering of 2D nanomaterials to trap and kill SARS-CoV-2: a new insight from multi-microsecond atomistic simulations.

In late 2019, coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spike protein is one of the surface proteins of SARS-CoV-2 that is essential for its infectious function. Therefore, it received lots of attention for the preparation of antiviral drugs, vaccines, and diagnostic tools. In the current study, we use computational methods of chemistry and biology to study the interaction between spike protein and its receptor in the body, angiotensin-I-converting enzyme-2 (ACE2). Additionally, the possible interaction of two-dimensional (2D) nanomaterials, including graphene, bismuthene, phosphorene, p-doped graphene, and functionalized p-doped graphene, with spike protein is investigated. The functionalized p-doped graphene nanomaterials were found to interfere with spike protein better than the other tested nanomaterials. In addition, the interaction of the proposed nanomaterials with the main protease (Mpro) of SARS-CoV-2 was studied. Functionalized p-doped graphene nanomaterials showed more capacity to prevent the activity of Mpro. These 2D nanomaterials efficiently reduce the transmissibility and infectivity of SARS-CoV-2 by both the deformation of the spike protein and inhibiting the Mpro. The results suggest the potential use of 2D nanomaterials in a variety of prophylactic approaches, such as masks or surface coatings, and would deserve further studies in the coming years.

Materials discovery of ion-selective membranes using artificial intelligence.

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Improving the self-assembly of bioresponsive nanocarriers by engineering doped nanocarbons: a computational atomistic insight.

Here, molecular dynamics (MD) simulations were employed to explore the self-assembly of polymers and docetaxel (DTX) as an anticancer drug in the presence of nitrogen, phosphorous, and boron-nitrogen incorporated graphene and fullerene. The electrostatic potential and the Gibbs free energy of the self-assembled materials were used to optimize the atomic doping percentage of the N- and P-doped formulations at 10% and 50%, respectively. Poly lactic-glycolic acid (PLGA)- polyethylene glycol (PEG)-based polymeric nanoparticles were assembled in the presence of nanocarbons in the common (corresponding to the bulk environment) and interface of organic/aqueous solutions (corresponding to the microfluidic environment). Assessment of the modeling results (e.g., size, hydrophobicity, and energy) indicated that among the nanocarbons, the N-doped graphene nanosheet in the interface method created more stable polymeric nanoparticles (PNPs). Energy analysis demonstrated that doping with nanocarbons increased the electrostatic interaction energy in the self-assembly process. On the other hand, the fullerene-based nanocarbons promoted van der Waals intramolecular interactions in the PNPs. Next, the selected N-doped graphene nanosheet was utilized to prepare nanoparticles and explore the physicochemical properties of the nanosheets in the permeation of the resultant nanoparticles through cell-based lipid bilayer membranes. In agreement with the previous results, the N-graphene assisted PNP in the interface method and was translocated into and through the cell membrane with more stable interactions. In summary, the present MD simulation results demonstrated the success of 2D graphene dopants in the nucleation and growth of PLGA-based nanoparticles for improving anticancer drug delivery to cells, establishing new promising materials and a way to assess their performance that should be further studied.

Experimental and Computational Study on the Microfluidic Control of Micellar Nanocarrier Properties.

Microfluidic-based synthesis is a powerful technique to prepare well-defined homogenous nanoparticles (NPs). However, the mechanisms defining NP properties, especially size evolution in a microchannel, are not fully understood. Herein, microfluidic and bulk syntheses of riboflavin (RF)-targeted poly(lactic-co-glycolic acid)-poly(ethylene glycol) (PLGA-PEG-RF) micelles were evaluated experimentally and computationally. Using molecular dynamics (MD), a conventional "random" model for bulk self-assembly of PLGA-PEG-RF was simulated and a conceptual "interface" mechanism was proposed for the microfluidic self-assembly at an atomic scale. The simulation results were in agreement with the observed experimental outcomes. NPs produced by microfluidics were smaller than those prepared by the bulk method. The computational approach suggested that the size-determining factor in microfluidics is the boundary of solvents in the entrance region of the microchannel, explaining the size difference between the two experimental methods. Therefore, this computational approach can be a powerful tool to gain a deeper understanding and optimize NP synthesis.

ß-Amyloid Targeting with Two-Dimensional Covalent Organic Frameworks: Multi-Scale In-Silico Dissection of Nano-Biointerface.

Cytotoxic aggregation of misfolded ß-amyloid (Aß) proteins is the main culprit suspected to be behind the development of Alzheimer's disease (AD). In this study, Aß interactions with the novel two-dimensional (2D) covalent organic frameworks (COFs) as therapeutic options for avoiding ß-amyloid aggregation have been investigated. The results from multi-scale atomistic simulations suggest that amine-functionalized COFs with a large surface area (more than 1000 m2 /gr) have the potential to prevent Aß aggregation. Gibb's free energy analysis confirmed that COFs could prevent protofibril self-assembly in addition to inhibiting ß-amyloid aggregation. Additionally, it was observed that the amine functional group and high contact area could improve the inhibitory effect of COFs on Aß aggregation and enhance the diffusivity of COFs through the blood-brain barrier (BBB). In addition, microsecond coarse-grained (CG) simulations with three hundred amyloids reveal that the presence of COFs creates instability in the structure of amyloids and consequently prevents the fibrillation. These results suggest promising applications of engineered COFs in the treatment of AD and provide a new perspective on future experimental research.

An insight into the role of riboflavin ligand in the self-assembly of poly(lactic-co-glycolic acid)-based nanoparticles - a molecular simulation and experimental approach.

Nanoparticles (NPs) used for targeted delivery purposes are rapidly gaining importance in diagnostic and therapeutic fields. These agents have been studied extensively so far to reveal their optimal physicochemical properties including the effects of ligands and their density on the surface of NPs. This article was conducted through a computational approach (all-atom molecular dynamics simulations) to predict the stability of NPs based on a poly-lactic-co-glycolic acid (PLGA) hydrophobic core with a poly-ethylene glycol (PEG) hydrophilic shell and varying numbers of riboflavin (RF) molecules as ligands. Depending on the molecular weight of the polymers, the most stable composition of NPs was achieved at 20 wt% and 10 wt% PLGA-PEG-RF for PLGA3kDa-PEG2kDa and PLGA4.5kDa-PEG2kDa polymers, respectively. According to the simulations, riboflavin molecules were located on the surface of the NPs, which would indicate that riboflavin-bound PLGA-PEG NPs could be efficiently utilized for active targeting purposes. To scrutinize the simulation results, NPs with riboflavin ligands were synthesized and put into in vitro experiments. Outstandingly, the empirical outcomes revealed that the hydrodynamic sizes of NPs also met minimum points at 20 and 10 wt% for PLGA3kDa-PEG2kDa and PLGA4.5kDa-PEG2kDa, respectively. Moreover, similar trends in the gyration radius as a function of riboflavin content were observed in the simulation analysis and the experimental results, which would indicate that the method of molecular dynamics (MD) simulation is a reliable mathematical technique and could be applied for predicting the physicochemical properties of NPs.

Microfluidic assisted synthesis of PLGA drug delivery systems.

Poly(lactic-co-glycolic acid) (PLGA) is a biocompatible and biodegradable polymer that recently attracted attention for use as part of drug delivery systems (DDS). In this context, there is an emerging need for a rapid, reliable and reproducible method of synthesis. Here, microfluidic systems provide great opportunities for synthesizing carriers in a tightly controlled manner and with low consumption of materials, energy and time. These miniature devices have been the focus of recent research since they can address the challenges inherent to the bulk system, e.g. low drug loading efficiency and encapsulation, broad size distribution and burst initial release. In this article, we provide an overview of current microfluidic systems used in drug delivery production, with a special focus on PLGA-based DDS. In this context, we highlight the advantages associated with the use of microchip systems in the fabrication of nanoparticles (NPs) and microparticles (MPs), e.g. in achieving complex morphologies. Furthermore, we discuss the challenges for selecting proper microfluidics for targeted DDS production in a translational setting and introduce strategies that are used to overcome microfluidics shortcomings, like low throughput for production.

PLGA-Based Nanoparticles in Cancer Treatment.

Nanomedicines can be used for a variety of cancer therapies including tumor-targeted drug delivery, hyperthermia, and photodynamic therapy. Poly (lactic-co-glycolic acid) (PLGA)-based materials are frequently used in such setups. This review article gives an overview of the properties of previously reported PLGA nanoparticles (NPs), their behavior in biological systems, and their use for cancer therapy. Strategies are emphasized to target PLGA NPs to the tumor site passively and actively. Furthermore, combination therapies are introduced that enhance the accumulation of NPs and, thereby, their therapeutic efficacy. In this context, the huge number of reports on PLGA NPs used as drug delivery systems in cancer treatment highlight the potential of PLGA NPs as drug carriers for cancer therapeutics and encourage further translational research.