Manufacturing process of liposomal Formation: A coarse-grained molecular dynamics simulation.

A method of producing liposomes has been previously developed using a continuous manufacturing technology that involves a co-axial turbulent jet in co-flow. In this study, coarse-grained molecular dynamics (CG-MD) simulations were used to gain a deeper understanding of how the self-assembly process of liposomes is affected by the material attributes (such as the concentration of ethanol) and the process parameters (such as temperature), while also providing detailed information on a nano-scale molecular level. Specifically, the CG-MD simulations yield a comprehensive internal view of the structure and formation mechanisms of liposomes containing DPPC, DPPG, and cholesterol molecules. The importance of this work is that structural details on the molecular level are proposed, and such detail is not possible to obtain through experimental studies alone. The assessment of structural properties, including the area per lipid, diffusion coefficient, and order parameters, indicated that a thicker bilayer was observed at higher ethanol concentrations, while a thinner bilayer was present at higher temperatures. These conditions led to more water penetrating the interior of the bilayer and an unstable structure, as indicated by a larger contact area between lipids and water, and a higher coefficient of lipid lateral diffusion. However, stable liposomes were found through these evaluations at lower ethanol concentrations and/or lower process temperatures. Furthermore, the CG-MD model was further compared and validated with experimental and computational data including liposomal bilayer thickness and area per lipid measurements.

Studying the ssDNA loaded adeno-associated virus aggregation using coarse-grained molecular dynamics simulations.

The aggregation of adeno-associated viral (AAV) capsids in an aqueous environment was investigated via coarse-grained molecular dynamics (CG-MD) simulations. The primary driving force and mechanism of the aggregation were investigated with or without single-strand DNA (ssDNA) loaded at various process temperatures. Capsid aggregation appeared to involve multiple residue interactions (i.e., hydrophobic, polar and charged residues) leading to complex protein aggregation. In addition, two aggregation mechanisms (i.e., the fivefold face-to-face contact and the edge-to-edge contact) were identified from this study. The ssDNA with its asymmetric structure could be the reason for destabilizing protein subunits and enhancing the interaction between the charged residues, and further result in the non-reversible face-to-face contact. At higher temperature, the capsid structure was found to be unstable with the significant size expansion of the loaded ssDNA which could be attributed to reduced number of intramolecular hydrogen bonds, the increased conformational deviations of protein subunits and the higher residue fluctuations. The CG-MD model was further validated with previous experimental and simulation data, including the full capsid size measurement and the capsid internal pressure. Thus, a good understanding of AAV capsid aggregation, instability and the role of ssDNA were revealed by applying the developed computational model.

Head-to-head comparison of in vitro and in vivo efficacy of pHLIP-conjugated anti-seed gamma peptide nucleic acids.

Gamma peptide nucleic acids (γPNAs) have recently garnered attention in diverse therapeutic and diagnostic applications. Serine and diethylene-glycol-containing γPNAs have been tested for numerous RNA-targeting purposes. Here, we comprehensively evaluated the in vitro and in vivo efficacy of pH-low insertion peptide (pHLIP)-conjugated serine and diethylene-based γPNAs. pHLIP targets only the acidic tumor microenvironment and not the normal cells. We synthesized and parallelly tested pHLIP-serine γPNAs and pHLIP-diethylene glycol γPNAs that target the seed region of microRNA-155, a microRNA that is upregulated in various cancers. We performed an all-atom molecular dynamics simulation-based computational study to elucidate the interaction of pHLIP-γPNA constructs with the lipid bilayer. We also determined the biodistribution and efficacy of the pHLIP constructs in the U2932-derived xenograft model. Overall, we established that the pHLIP-serine γPNAs show superior results in vivo compared with the pHLIP-diethylene glycol-based γPNA.

Mitigation of Tribocharging in Pharmaceutical Powders using Surface Modified V-Blenders.

INTRODUCTION: The pharmaceutical industry involves handling of powders on a large scale for manufacturing of solid dosage forms such as tablets and capsules constituting about 85% of the dosage forms. During this manufacturing process, powders get electrostatically charged due to numerous particle-particle and particle-equipment wall collisions. Most of the pharmaceutical powders are insulators in nature and they accumulate charge for longer durations making it difficult to dissipate the generated charge. In this study, a surface modified blender has been used to analyze tribocharging in pharmaceutical powders. METHODS: The surface modified blender has been fabricated using two types of materials, an insulator, and a conductor. The conductor or the metal arm induces charge of opposite polarity to that of the charge induced by the insulator arm and the overall charge on the powder decreases during the tumbling motion of the blender. Ibuprofen was used as the model drug and processed in aluminum, polyvinyl chloride (PVC), stainless steel, surface modified aluminum-PVC (Al-PVC) and surface modified stainless steel- PVC (SS-PVC) blender at 20% RH for different blending times such as 2, 10, 20, 30 and 40 min. To better understand the tribocharging phenomenon in surface modified V blenders, an experimentally validated computational model was developed using Discrete Element Method (DEM) modeling. RESULTS: Significant reduction (> 50%) in electrostatic charge was observed for Ibuprofen using surface modified blenders in comparison to metal only and insulator only V blenders. Additionally, an identical charging trend was observed between the simulation and experimental data.  CONCLUSION: It was established that careful selection of equipment materials could significantly reduce the electrostatic charging of pharmaceutical powders and DEM model could be a really useful tool in assessing the applicability of the modified V blenders.

On the Applicability of the Coarse Grained Coupled CFD-DEM Model to Predict the Heat Transfer During the Fluidized Bed Drying of Pharmaceutical Granules.

Fluidized bed dryer often used in the pharmaceutical industry for drying of wet granules. Coupled computational fluid dynamics (CFD) - discrete element method (DEM) is frequently used to model the drying process because of its ability to obtain the relevant information at the particle level. However, it becomes almost impossible to model the industrial scale fluidized bed dryer using the coupled CFD-DEM method because of the presence of large number of particles [Formula: see text]. To reduce the number of particles to be tracked in the simulation, coarse grained coupled CFD-DEM method was developed by researchers where a certain number of particles of the original system was represented by a relatively bigger particle in the coarse-grained system. The appropriate scaling of the particle-particle and particle-fluid interaction forces is necessary to make sure that the dynamics of the coarse-grained particles/parcels accurately represent the dynamics of the original particles. The coarse-graining of the drying process of pharmaceutical granules during fluidization needs systematic coarse-graining of the momentum, heat, and solvent vapor transfer process. A coarse grained coupled CFD-DEM method was used to model the momentum and heat transfer during the fluidization of pharmaceutical granules. It was shown that the heat transfer during the fluidization of large number of particles could be predicted by simulating a smaller number of bigger particles with appropriate scaling of particle-particle heat and momentum transfer, and particle-fluid heat and momentum transfer at significantly smaller computational time. This model can be further extended by including a coarse-grained moisture transport model in future.

Molecular Dynamics Modeling Based Investigation of the Effect of Freezing Rate on Lysozyme Stability.

PURPOSE: The stability of protein drug products frozen during fill finish operations is greatly affected by the freezing rate applied. Non-optimal freezing rates may lead to the denaturation of protein's complex macromolecular conformation. However, limited work has been done to address the effect of different freezing rates on protein stability at nano-scale level. METHODS: The stability of a model protein, lysozyme, was investigated at atomic and molecular scale under varying freezing rates and moving ice-water interface. Ice seeding approach was adopted to initiate ice formation in this present simulation. RESULTS: The faster freezing rate (11-12 K/490 ns) applied resulted in overall smaller ice fraction within the simulation box with a larger freeze-concentrated liquid (FCL) region. Consequently, the faster freezing rate better maintained protein stability with less secondary structure deviations, higher hydration level and structural compactness, and less fluctuations at individual residues than observed following slow (5-6 K/490 ns) and medium (7-8 K/490 ns) freezing rates. The present study also identified the residues near and within helices 3, 6, 7, and 8 dominate the structural instability of the lysozyme at 247 K freezing temperature. CONCLUSIONS: For the first time, ice formation in therapeutic protein solution was studied "non-isothermally" at different freezing rates using molecular dynamics simulations. Thus, a good understanding of freezing rates on protein instability was revealed by applying the developed computational model.

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 (Tg). 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.

Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing.

The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homogeneous sizes. The input features of the ANN included critical material attributes (CMAs) (e.g., hydrocarbon tail length, cholesterol percent, and buffer type) and critical process parameters (CPPs) (e.g., solvent temperature and flow rate), while the ANN outputs included critical quality attributes (CQAs) of liposomes (i.e., particle size and polydispersity index (PDI)). Two common ANN architectures, multiple-input-multiple-output (MIMO) models and multiple-input-single-output (MISO) models, were evaluated in this study, where the MISO outperformed MIMO with improved accuracy. Molecular descriptors, obtained from PaDEL-Descriptor software, were used to capture the physicochemical properties of the lipids and used in training of the ANN. The combination of CMAs, CPPs, and molecular descriptors as inputs to the MISO ANN model reduced the training and testing mean relative error. Additionally, a graphic user interface (GUI) was successfully developed to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions.

Molecular Dynamics Simulation to Uncover the Mechanisms of Protein Instability During Freezing.

Freezing is a common process applied in the pharmaceutical industry to store and transport biotherapeutics. Herewith, multi-scale molecular dynamics simulations of Lactate dehydrogenase (LDH) protein in phosphate buffer with/without ice formation performed to uncover the still poorly understood mechanisms and molecular details of protein destabilization upon freezing. Both fast and slow ice growing conditions were simulated at 243 K from one or two-side of the simulation box, respectively. The rate of ice formation at all-atom simulations was crucial to LDH stability, as faster freezing rates resulted in enhanced structural stability maintained by a higher number of intramolecular hydrogen bonds, less flexible protein's residues, lower solvent accessibility and greater structural compactness. Further, protein aggregation investigated by coarse-grained simulations was verified to be initiated by extended protein structures and retained by electrostatic interactions of the salt bridges between charged residues and hydrogen bonds between polar residues of the protein. Lastly, the study of free energy of dissociation through steered molecular dynamics simulation revealed LDH was destabilized by the solvation of the hydrophobic core and the loss of hydrophobic interactions. For the first time, experimentally validated molecular simulations revealed the detailed mechanisms of LDH destabilization upon ice formation and cryoconcentration of solutes.