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Functionalized amphiphilic polymers have been widely applied as drug delivery vehicles due to their ability to self-assemble into micelles that enhance the solubility, stability, and bioavailability of poorly water-soluble drugs. Among these, poly-jasmine lactone (PJL), a recently developed amphiphilic copolymer, offers the opportunity to functionalize with versatile functional groups via facile thiol-ene chemistry. In this study, we synthesized and compared anionic functionalized block copolymers of PJL (mPEG-b-PJL-COOH) having varying numbers of the -COOH group to assess the effect on the encapsulation efficiency, particle size, drug release behavior, and cytotoxicity. Our results demonstrate that increasing the number of anionic groups did not improve the encapsulation efficiency of model drugs but sustained the drug release profile. Ex vivo hemolytic studies were also performed to evaluate pH-dependent cell lysis as an indirect indicator of the endosomal escape capability of the prepared micelles. Coarse-grained molecular dynamics simulations also revealed that increasing the number of -COOH groups altered the structural properties of the lipid bilayer. Moreover, the aggregation of -COOH units within the lipid bilayer may represent the molecular mechanism underlying the higher cytotoxicity observed with a greater number of -COOH groups.

Intestinal motility, including peristalsis and segmentation, drives complex fluid movements critical for the oral delivery of biologics and other macromolecules. Despite advances, oral delivery remains commercially limited by low bioavailability, often attributed to poor epithelial permeability. However, variability in motility patterns may also play a critical role, influencing intraluminal distribution and thus absorption, yet this aspect remains underexplored. Here, we combine computational fluid dynamics and machine learning to evaluate how motility type, intensity, pocket size, contractility, and fluid composition affect the delivery of a model macromolecule (insulin) and a permeation enhancer (sodium caprate, C10). We find that segmentation, especially at light intensity, consistently enhances epithelial colocalisation over peristalsis. Under segmentation, smaller pocket sizes (2 mL versus 10 mL) and stronger contractility (occlusion ratio 0.3) yielded optimal performance. Our extreme gradient boosting regression model identified pocket volume, contractility, and motility type as dominant predictors of colocalisation. In a comparative analysis, segmentation led to 128% and 137% higher maximum normalised concentrations of insulin and C10, respectively, than moderate peristalsis with a nutritional drink. Overall, segmentation achieved 6.7-fold and 8.0-fold higher average maximum normalised concentrations for insulin and C10, respectively. These results emphasise segmentation, characteristic of the fed state, as a superior motility pattern for macromolecular absorption compared to peristalsis during the migrating motor complex (MMC). By elucidating the interplay between motility and transport, our findings may guide the design of more effective oral formulations and support personalised strategies for drug delivery based on individual motility profiles.

Liquid crystal nanoparticles (LCNPs), such as hexosomes based on an internal hexagonal phase (H_II), enhance lipid nanoparticle-mediated drug delivery by improving drug solubility, stability and absorption. LCNPs can also be tailored for specific biological environments by incorporating non-ester-linker lipids into the H_II nanostructure. In this study, we developed an H_II model system with a 90:10 phytantriol:farnesol ratio based on experimental data and conducted all-atom molecular dynamics simulations. The model remained stable across various water-to-lipid ratios, and the structural effects observed were consistent with prior experimental data. We used this model to examine the localization and interactions of antibiotics vancomycin and clarithromycin. Clarithromycin, being highly lipophilic, associated mainly with the lipid phase, while vancomycin localized at the water-lipid interface due to its amphiphilic nature. An extended H_II system with repeating units enclosed in Pluronic F127 polymers was also constructed. Simulations showed that hydrogen bonding between Pluronic F127 and water facilitated water influx into the H_II phase, causing interfacial reorganization. To investigate drug release, we performed umbrella sampling simulations. The resulting energy profiles indicated that polymer-water-lipid interactions lowered the energy barrier for vancomycin release compared to clarithromycin. This was confirmed by in vitro release studies, where vancomycin exhibited a higher release rate. Overall, this model provides molecular-level insights into drug loading, partitioning, and release from H_II systems, supporting the design of more effective drug delivery formulations.

Biosimilar artificial mucus models that mimic native mucus facilitate efficient, lab-based drug diffusion studies, addressing the costly and challenging preclinical phase of drug development, especially for nano- and micro-scale particle-based colonic drug delivery. This study presents a machine-learning-driven framework that integrates microrheological features into diffusional fingerprinting to characterize nano- and micro-scale particle diffusion patterns in mucus and assess the effect of mucus microrheology on such movements. We investigated the diffusion of fluorescent-labeled polystyrene particles in native pig mucus and two artificial mucus models. Particles (100, 200, and 1000 nm in diameter) with carboxylate- or amine-modified surfaces were tracked during passive diffusion. From each particle trajectory, 20 features -including microrheology-based parameters- were extracted. Based on these features, seven supervised machine learning models were applied to classify or identify similarities among mucus hydrogels. Of these, gradient boosting achieved the highest accuracy. SHapley Additive exPlanations analysis identified creep compliance as the most influential feature in distinguishing the mucus models. In native mucus, smaller negatively charged nanoparticles exhibited the highest mobility, with fewer particles being in the immobile and subdiffusive states. Microrheology data further indicated that larger particles experienced greater restriction owing to the elastic properties of native mucus. In contrast, smaller particles interacted more with the viscous liquid phase. A comprehensive feature-wide analysis revealed that hydroxyethyl cellulose (HEC)-based artificial mucus more closely resembled native pig mucus than the polyacrylic acid-based model. In conclusion, the machine-learning-driven fingerprinting approach, incorporating microrheological features, successfully differentiated the microstructural characteristics and rheological properties of the three mucus models. It also supported the selection of HEC-based artificial mucus as a viable substitute for native colonic mucus.

The stratum corneum (SC) plays the most important role in the absorption of topical and transdermal drugs. In this study, we developed a multi-layered SC model using coarse-grained molecular dynamics (CGMD) simulations of ceramides, cholesterol, and fatty acids in equimolar proportions, starting from two different initial configurations. In the first approach, all ceramide molecules were initially in the hairpin conformation, and the membrane bilayers were pre-formed. In the second approach, ceramide molecules were introduced in either the hairpin or splayed conformation, with the lipid molecules randomly oriented at the start of the simulation. The aim was to evaluate the effects of lipid chain length on the structural and dynamic properties of SC. By incorporating ceramides and fatty acids of different chain lengths, we simulated the SC membrane in healthy and diseased states. We calculated key structural properties including the thickness, normalized lipid area, lipid tail order parameters, and spatial ordering of the lipids from each system. The results showed that systems with higher ordering and structural integrity contained an equimolar ratio of ceramides (chain length of 24 carbon atoms), fatty acids with chain lengths ≥ of 20 carbon atoms, and cholesterol. In these systems, strong apolar interactions between the ceramide and fatty acid long acyl chains restricted the mobility of the lipid molecules, thereby maintaining a compact lipid headgroup region and high order in the lipid tail region. The simulations also revealed distinct flip-flop mechanisms for cholesterol and fatty acid within the multi-layered membrane. Cholesterol is mostly diffused through the tail-tail interface region of the membrane and could flip-flop in the same bilayer. In contrast, fatty acids flip-flopped between adjacent leaflets of two bilayers in which the tails crossed the thinner headgroup region of the membrane. To conclude, our SC model provides mechanistic insights into lipid mobility and is flexible in its design and composition of different lipids, enabling studies of varying skin conditions.Keywords: Coarse-grained molecular dynamics; Lipid flip-flop; Membrane structural properties; Skin membrane; k-means clustering.

This chapter aims to explore the use of molecular dynamics (MD) simulations to investigate the solubilization of drugs by various surfactants and excipients. Examples from the literature are presented to demonstrate that MD simulations provide valuable insights into the solubilization mechanisms, and several metrics for predicting drug solubility in complex formulations are also presented and discussed. We also indicate the potential that is to be found in this area by the combination of MD simulations with machine learning methods.

In this work, simulations of intestinal peristalsis are performed to investigate the intraluminal transport of macromolecules (MMs) and permeation enhancers (PEs). Properties of insulin and sodium caprate (C10) are used to represent the general class of MM and PE molecules. Nuclear magnetic resonance spectroscopy was used to obtain the diffusivity of C10, and coarse-grain molecular dynamics simulations were carried out to estimate the concentration-dependent diffusivity of C10. A segment of the small intestine with the length of 29.75 cm was modeled. Peristaltic speed, pocket size, release location, and occlusion ratio of the peristaltic wave were varied to study the effect on drug transport. It was observed that the maximum concentration at the epithelial surface for the PE and the MM increased by 397 % and 380 %, respectively, when the peristaltic wave speed was decreased from 1.5 to 0.5 cm s−1. At this wave speed, physiologically relevant concentrations of PE were found at the epithelial surface. However, when the occlusion ratio is increased from 0.3 to 0.7, the concentration approaches zero. These results suggest that a slower-moving and more contracted peristaltic wave leads to higher efficiency in transporting mass to the epithelial wall during the peristalsis phases of the migrating motor complex.

Permeability enhancer-based formulations offer a promising approach to enhance the oral bioavailability of peptides. We used all-atom molecular dynamics simulations to investigate the interaction between two permeability enhancers (sodium caprate, and SNAC), and four different peptides (octreotide, hexarelin, degarelix, and insulin), in the presence of taurocholate, an intestinal bile salt. The permeability enhancers exhibited distinct effects on peptide release based on their properties, promoting hydrophobic peptide release while inhibiting water-soluble peptide release. Lowering peptide concentrations in the simulations reduced peptide-peptide interactions but increased their interactions with the enhancers and taurocholates. Introducing peptides randomly with enhancer and taurocholate molecules yielded dynamic molecular aggregation, and reduced peptide-peptide interactions and hydrogen bond formation compared to peptide-only systems. The simulations provided insights into molecular-level interactions, highlighting the specific contacts between peptide residues responsible for aggregation, and the interactions between peptide residues and permeability enhancers/taurocholates that are crucial within the mixed colloids. Therefore, our results can provide insights into how modifications of these critical contacts can be made to alter drug release profiles from peptide-only or mixed peptide-PE-taurocholate aggregates. To further probe the molecular nature of permeability enhancers and peptide interactions, we also analyzed insulin secondary structures using Fourier transform infrared spectroscopy. The presence of SNAC led to an increase in beta-sheet formation in insulin. In contrast, both in the absence and presence of caprate, alpha-helices, and random structures dominated. These molecular-level insights can guide the design of improved permeability enhancer-based dosage forms, allowing for precise control of peptide release profiles near the intended absorption site. Molecular-level insights can guide the design of improved permeability enhancer-based dosage forms, allowing for precise control of peptide release profiles near the intended absorption site.

Understanding of peptide aggregation propensity is an important aspect in pharmaceutical development of peptide drugs. In this work, methodologies based on all-atom molecular dynamics (AA-MD) simulations and H-1 NMR (in neat H2O) were evaluated as tools for identification and investigation of peptide aggregation. A series of structurally similar, pharmaceutically relevant peptides with known differences in aggregation behavior (D-Phe(6)-GnRH, ozarelix, cetrorelix, and degarelix) were investigated. The H-1 NMR methodology was used to systematically investigate variations in aggregation with peptide concentration and time. Results show that H-1 NMR can be used to detect the presence of coexisting classes of aggregates and the inclusion or exclusion of counterions in peptide aggregates. Interestingly, results suggest that the acetate counterions are included in aggregates of ozarelix and cetrorelix but not in aggregates of degarelix. The peptides investigated in AA-MD simulations (D-Phe(6)-GnRH, ozarelix, and cetrorelix) showed the same rank order of aggregation propensity as in the NMR experiments. The AA-MD simulations also provided molecular-level insights into aggregation dynamics, aggregation pathways, and the influence of different structural elements on peptide aggregation propensity and intermolecular interactions within the aggregates. Taken together, the findings from this study illustrate that H-1 NMR and AA-MD simulations can be useful, complementary tools in early evaluation of aggregation propensity and formulation development for peptide drugs.