Mechanistic Studies on the Self-Assembly of PLGA Patchy Particles and Their Potential Applications in Biomedical Imaging

Influence of drug molecular weight on self-assembly and intestinal permeation of polymer-based nanocarriers

For oral delivery, the physicochemical properties of nanocarriers are decisive factors for permeation through the intestinal epithelium. These properties are determined by the composition of the nanocarriers as well as by the process parameters during their self-assembly. For macromolecular drugs, there is still little understanding of the drug-polymer interactions during nanocarrier self-assembly and the effects on carrier properties. In this study, the effect of drug molecular weight on nanocarrier self-assembly, physicochemical properties of nanocarriers as well as their permeation across the intestinal epithelium was investigated. Our results show that the drug molecular weight impacts the physicochemical properties of nanocarriers. Further, the physicochemical properties of the nanocarriers, governed by the molecular weight of the drug, determine their permeation properties across the intestinal epithelium. Comparative in vitro and ex vivo studies revealed that intestinal absorption is dependent on both, the properties of the tissue as well as properties of the carrier system. In conclusion, the molecular weight of drug payload is a key factor determining the physiochemical properties of polymeric nanocarriers and is closely linked to their oral absorption. Using different preclinical models to evaluate intestinal permeation of nanocarriers allows for novel insights into key formulation properties governing oral bioavailability.

A Coarse-Grained Molecular Dynamics Description of Docetaxel-Conjugate Release from PLGA Matrices

Despite the extensive use of poly-lactic-glycolic-acid (PLGA) in biomedical applications, computational research on the mesoscopic characterization of PLGA-based delivery systems is limited. In this study, a computational model for PLGA is proposed, developed, and validated for the reproducibility of transport properties that can influence drug release, the rate of which remains difficult to control. For computational efficiency, coarse-grained (CG) models of the molecular components under consideration were built using the MARTINI force field version 2.2. The translocation free energy barrier ΔG_t^* across the PLGA matrix in the aqueous phase of docetaxel and derivatives of varying sizes and solubilities was predicted via molecular dynamics (MD) simulations and compared with experimental release data. The thermodynamic quantity ΔG_t^* anticipates and can help explain the release kinetics of hydrophobic compounds from the PLGA matrix, albeit within the limit of a drug concentration below a critical aggregation concentration. The proposed computational framework would allow one to predict the pharmacological behavior of polymeric implants loaded with a variety of payloads under different conditions, limiting the experimental workload and associated costs.

Coarse-Grained Molecular Dynamics Simulations of Paclitaxel-Loaded Polymeric Micelles

A continuous manufacturing technology based on coaxial turbulent jet in coflow was previously developed to produce paclitaxel-loaded polymeric micelles. Herein, coarse-grained molecular dynamics (CG-MD) simulations were implemented to better understand the effect of the material attributes (i.e., the drug-polymer ratio and the ethanol concentration) and process parameters (i.e., temperature) on the self-assembly process of polymeric micelles as well as to provide molecular details on micelle instability. An all-atom (AA) poly (ethylene glycol)-poly (lactic acid) (PEG-PLA) polymer model was developed as the reference for parameterizing a coarse-grained (CG) model, and the AA polymer model was further validated with experimental glass transition temperature (T_g). The model transferability was verified by comparing structural properties between the AA and CG models. The CG model was further validated with experimental data, including micelle particle size measurements and drug encapsulation efficiency. Furthermore, the encapsulation of paclitaxel into the polymeric micelles was included in the simulations, taking into consideration the interactions between the paclitaxel and the polymers. The results from various points of view demonstrated a strong dependence of the shape of the micelles on the drug encapsulation, with micelles transitioning from spherical to ellipsoidal structures with an increasing paclitaxel amount. Simulation data were also used to identify the critical aggregation number (i.e., the number of polymer and drug molecules required for transition from one shape to another). Improved micellar structural stability was found with a larger micellar size and less solvent accessibility. Lastly, an evaluation was performed on the micellar dissociation free energy using a steered molecular dynamics simulation over a range of temperatures and ethanol concentrations. These simulations revealed that at higher ethanol and temperature conditions, micelles become destabilized, resulting in greater paclitaxel release. The increased drug release was determined to originate from the solvation of the hydrophobic core, which promoted micellar swelling and an associated reduction in hydrophobic interactions, leading to a loosely packed micellar structure.

Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning

Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer–lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an in silico protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed in silico protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.

Recurrent neural networks as a machine learning tools are gaining popularity in chemical, physical and materials applications searching for viable methods in the structure and energetics analyses of systems ranging from crystals to soft matter.

Distinctive Formation of PEG-Lipid Nanopatches onto Solid Polymer Surfaces Interfacing Solvents from Atomistic Simulation

The interface between solid poly(lactic acid-co-glycolic acid), PLGA, and solvents is described by large-scale atomistic simulations for water, ethyl acetate, and the mixture of them at ambient conditions. Interactions at the interface are dominated by Coulomb forces for water and become overwhelmingly dispersive for the other two solvents. This effect drives a neat liquid-phase separation of the mixed solvent, with ethyl acetate covering the PLGA surface and water being segregated away from it. We explore with all-atom Molecular Dynamics the formation of macromolecular assemblies on the surface of the PLGA-solvent interface when DSPE-PEG, 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-(polyethylene glycol)_n amine, is added to the solvent. By following in time the deposition of the DSPE-PEG macromolecules onto the PLGA surface, the mechanism of how nanopatches remain adsorbed to the surface despite the presence of the solvent is probed. These patches have a droplet-like aspect when formed at the PLGA-water interface that flatten in the PLGA-ethyl acetate interface case. Dispersive forces are dominant for the nanopatch adhesion to the surface, while electrostatic forces are dominant for keeping the solvent around the new formations. Considering the droplet-like patches as wetting the PLGA surface, we predict an effective wetting behavior at the water interface that fades significantly at the ethyl acetate interface. The predicted mechanism of PEG-lipid nanopatch formation may be generally applicable for tailoring the synthesis of asymmetric PLGA nanoparticles for specific drug delivery conditions.

Solutions and Condensed Phases of PEG2000 from All-Atom Molecular Dynamics

Extensive all-atom molecular dynamics studies of polyethylene glycol (PEG₂₀₀₀) when solvated and in the polymer bulk condensed phases were performed across a wide temperature range. We proposed two modified all-atom force field and observed the fate of the PEG₂₀₀₀ macromolecule when solvated in water, water with 4% ethanol, and ethyl acetate. In aqueous solutions, the macromolecule collapsed into a prolate spheroidal ball-like structure while adopting a rather elongated coiled structure in ethyl acetate. Inspection of the polymer-condensed phases across the 150-340 K temperature range enabled the atomistic view of the solid glass below the glass transition temperature of 230 K < T_g < 250 K and the rubber behavior above T_g. Predicted properties include the enthalpy, density, and cohesive energy temperature behavior, the specific heat, thermal expansivity, thermal compressibility, bulk modulus, and Hildebrand solubility parameter both below and above T_g. Within the polymer matrix, the PEG₂₀₀₀ macromolecules were entangled displaying a wide distribution of sizes that persisted when transitioning from the glass to the rubbery phases. Calculated properties agree very well with experiments when available or stand as crucial predictions while awaiting experimental measurement. Understanding the thermodynamics and structure of this useful polymer enables the efficient prediction of its behavior when building novel composite materials for nanomedicine and nanotherapeutics.

Strategies for Preparing Different Types of Lipid Polymer Hybrid Nanoparticles in Targeted Tumor Therapy

At present, cancer is one of the most common diseases in the world, causing a large number of deaths and seriously affecting people's health. The traditional treatment of cancer is mainly surgery, radiotherapy or chemotherapy. Conventional chemotherapy is still an important treatment, but it has some shortcomings, such as poor cell selectivity, serious side effects, drug resistance and so on. Nanoparticle administration can improve drug stability, reduce toxicity, prolong drug release time, prolong system half-life, and bring broad prospects for tumor therapy. Lipid polymer hybrid nanoparticles (LPNs), which combine the advantages of polymer core and phospholipid shell to form a single platform, have become multi-functional drug delivery platforms. This review introduces the basic characteristics, structure and preparation methods of LPNs, and discusses targeting strategies of LPNs in tumor therapy in order to overcome the defects of traditional drug therapy.

Integration of Multitargeted Polymer-Based Contrast Agents with Photoacoustic Computed Tomography: An Imaging Technique to Visualize Breast Cancer Intratumor Heterogeneity

One of the primary challenges in breast cancer diagnosis and treatment is intratumor heterogeneity (ITH), i.e., the coexistence of different genetically and epigenetically distinct malignant cells within the same tumor. Thus, the identification of ITH is critical for designing better treatments and hence to increase patient survival rates. Herein, we report a noninvasive hybrid imaging technology that integrates multitargeted and multiplexed patchy polymeric photoacoustic contrast agents (MTMPPPCAs) with single-impulse panoramic photoacoustic computed tomography (SIP-PACT). The target specificity ability of MTMPPPCAs to distinguish estrogen and progesterone receptor-positive breast tumors was demonstrated through both fluorescence and photoacoustic measurements and validated by tissue pathology analysis. This work provides the proof-of-concept of the MTMPPPCAs/SIP-PACT system to identify ITH in nonmetastatic tumors, with both high molecular specificity and real-time detection capability.

The Design of Poly(lactide-co-glycolide) Nanocarriers for Medical Applications

Polymeric biomaterials have found widespread applications in nanomedicine, and poly(lactide-co-glycolide), (PLGA) in particular has been successfully implemented in numerous drug delivery formulations due to its synthetic malleability and biocompatibility. However, the need for preconception in these formulations is increasing, and this can be achieved by selection and elimination of design variables in order for these systems to be tailored for their specific applications. The starting materials and preparation methods have been shown to influence various parameters of PLGA-based nanocarriers and their implementation in drug delivery systems, while the implementation of computational simulations as a component of formulation studies can provide valuable information on their characteristics. This review provides a critical summary of the synthesis and applications of PLGA-based systems in bio-medicine and outlines experimental and computational design considerations of these systems.

Exploring with Molecular Dynamics the Structural Fate of PLGA Oligomers in Various Solvents

This study focuses on the solvent effects that promote preferred solvated structures of polylactic-co-glycolic acid (PLGA) oligomers of molecular weight 278, 668, and 1449 u in ethyl acetate, water, and a mixture of both solvents. Our methodology consists of all-atom, explicit solvent molecular dynamics simulations for inspection of the solvated oligomer structures at ambient conditions. Parameters for the generalized Amber force field are developed in this work for the ethyl acetate liquid and the PLGA oligomers. Energetics, oligomer radius of gyration, end-to-end distance, orientational order parameter, flexibility coefficient, and backbone dihedral angles are reported along with a size scaling property yielding a power law for PLGA oligomers in each of the three solvents considered. It is found that the PLGA oligomer has two characteristic states identified by a set of extended structures and a set of collapsed structures, the former being energetically preferred in ethyl acetate and its mixture with water. The two types of PLGA structures occur in the three solvents and although they flip from one to the other in a sporadic fashion, in ethyl acetate, the extended structures may persist for more than 20 ns. The collapsed structures are significantly more frequent in water, occurring seldom in the mixed ethyl acetate-water solvent. PLGA is a biodegradable polymer approved for use in pharmaceutical and biomedical applications. Insights provided therein are of importance for the polymer aggregation process and its glassy state in condensed phases.

Microfluidic gradient device for simultaneously preparing four distinct types of microparticles

Preparing monodisperse, reproducible and functionally sophisticated microparticles is challenging but important for biomedical applications. Droplet microfluidics is a microparticle generation method that can precisely control the geometry and composition of microparticles. Droplet microfluidics generally produces only one type of microparticle at a time. Here, we report a simple and controllable method to simultaneously prepare four distinct types of microparticles by combining droplet generation with a gradient generator. The method can be more widely applied and with higher productivity than other microparticle generation methods due to the integration of dispersed phases which paves the way for the application to regenerative medicine. Different sizes, heterogenous and anisotropic microparticles are generated by manipulating the poly(lactic-co-glycolic acid) concentration gradient, the poly(ε-caprolactone)/poly(lactic-co-glycolic acid) ratio gradient, and the dimethyl carbonate/dichloromethane ratio gradient. This straightforward preparation of microparticles will promote their application in drug delivery agents, identifiers for biological assays, microsensors and tissue engineering.

Preparing monodisperse, reproducible and functionally sophisticated microparticles is challenging but important for biomedical applications.

Lipid-polymer hybrid nanocarriers for delivering cancer therapeutics

Cancer remained a major cause of death providing diversified challenges in terms of treatment including non-specific toxicity, chemoresistance and relapse. Nanotechnology- based delivery systems grabbed tremendous attention for delivering cancer therapeutics as they provide benefits including controlled drug release, improved biological half-life, reduced toxicity and targeted delivery. Majority of the nanocarriers consists of either a polymer or a lipid component along with other excipients to stabilize the colloidal system. Lipid-based systems provide advantages like better entrapment efficiency, scalability and low- cost raw materials, however, suffer from limitations including instability, a burst release of the drug, and limited surface functionalization. On the other hand, polymeric systems provide an excellent diversity of chemical modifications, stability, controlled release, however limited drug loading capacities and scale up limit their use. Hybrid nanocarriers consisting of lipid and polymer were able to overcome some of these disadvantages while retaining the advantages of both the systems. Designing a stable lipid-polymer hybrid system requires a thorough understanding of the material properties and their behavior in in vitro and in vivo environments. This review highlights the current status and future prospects of lipid-polymer hybrid systems with a particular focus on cancer nanotherapeutics.