Biopharmaceutical approaches for improved drug delivery across ocular barriers

The eye is protected from the external environment by various physiological and anatomical barriers. These barriers through there protective actions drastically diminish the ocular bioavailability of drugs. The corneal epithelium acts as a major barrier towards the permeation of hydrophilic agents, whereas poor aqueous solubility presents a formulation challenge for lipophilic compounds. Additionally, P-glycoprotein (P-gp) expressed on the retinal pigmented epithelium (RPE P-gp) limits the penetration of substrates, in therapeutically relevant concentrations, into the back-of-the-eye. In the present research, use of penetration enhancers (chitosan, benzalkonium chloride (BAK) and ethylenediaminetetraacetic acid (EDTA)) and formulation approaches (cyclodextrins and solid lipid nanoparticles (SLNs)) were evaluated in terms of their ability to improve the ocular bioavailability of hydrophilic and lipophilic compounds, respectively. A novel approach, localized modulation of RPE P-gp using topically co-administered P-gp inhibitors, was investigated to improve the back-of-the eye delivery of P-gp substrates. In vitro transcorneal permeation results demonstrated that chitosan brought about a dose dependent increase in the permeability of acyclovir, a model hydrophilic compound. Combination of chitosan, BAK and EDTA resulted in a synergistic effect on the permeation of acyclovir. Dramatic increase in aqueous solubility, stability and in vitro transcorneal permeability of delta-8-tetrahydrocannabinol, a model lipophilic agent, was observed in the presence of 2-Hydroxypropyl-β-cyclodextrin (HPβCD), randomly methylated-β-cyclodextrin and sulfobutylether-β-cyclodextrin. The indomethacin loaded SLN (IN-SLNs) formulation was physically stable following sterilization and on storage. The IN-SLNs formulation increase stability and in vitro corneal permeability of indomethacin in comparison to the solution formulations (cosolvent and HPβCD based) tested. Furthermore, for the first time, studies in anesthetized male New Zealand rabbits demonstrate that topically applied P-gp inhibitors can diffuse to the RPE and alter the elimination kinetics of a systemically or intravitreally administered P-gp substrate, probably through inhibition of the basolateral RPE P-gp. The degree of inhibition was found to be dependent on the physicochemical characteristics of the inhibitor and its affinity for P-gp and the concentration of the therapeutic agent in the plasma or in the vitreous humor. Formulation factors such as inclusion of permeation enhancers may play a major role in yielding effective levels of the inhibitor at the RPE

Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies

Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals. It involves displacing atoms by a small amount in order to calculate the induced forces on all atoms in a supercell for the computation of force constants. Even though these methods are widely in use, to our knowledge, there is no systematic discussion of optimal displacement directions from the crystal's symmetry point of view nor a rigorous error analysis of such methods. Based on the group theory and point group symmetry of a crystal, we propose displacement directions, with an equivalent concept of the group of $k$, deduced directly in the Cartesian coordinates rather than the usual fractional coordinates, that maintain the theoretical maximum for the triple product $V$ spanned by the three displacements to avoid possible severe roundoff errors. The proposed displacement directions are generated from a minimal set of irreducible atomic displacements that keep the required independent force calculations to a minimum. We find the error in the calculated force constant explicitly depends on the inverse of $V$ and inaccuracy of the forces. Test systems such as Si, graphene, and orthorhombic Sb2S3 are used to illustrate the method. Our displacement method is shown to be very robust in treating low-symmetry cells with a large `aspect ratio' due to huge differences in lattice parameters, use of a large vacuum height, or a very oblique unit cell due to unconventional choice of primitive lattice vectors. It is expected that our displacement strategy can be used to address higher-order interatomic interactions to achieve good accuracy and efficiency

Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The first ML model (classification model), accurately classifies samples with a conductivity >~25 to 100 S/cm, achieving a maximum of 100% accuracy rate. For the subset of highly conductive samples, we employed a second ML model (regression model), to predict their conductivities, yielding an impressive test R2 value of 0.984. To validate the approach, we showed that the models, neither trained on the samples with the two highest conductivities of 498 and 506 S/cm, were able to, in an extrapolative manner, correctly classify and predict them at satisfactory levels of errors. The proposed ML workflow results in an improvement in the efficiency of the conductivity measurements by 89% of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.Comment: 33 Pages, 17 figure

Automated Electrokinetic Stretcher for Manipulating Nanomaterials

In this work, we present an automated platform for trapping and stretching individual micro- and nanoscale objects in solution using electrokinetic forces. The platform can trap objects at the stagnation point of a planar elongational electrokinetic field for long time scales, as demonstrated by the trapping of ~100 nanometer polystyrene beads and DNA molecules for minutes, with a standard deviation in displacement from the trap center < 1 micrometer. This capability enables the stretching of deformable nanoscale objects in a high-throughput fashion, as illustrated by the stretching of more than 400 DNA molecules within ~4 hours. The flexibility of the electrokinetic stretcher opens up numerous possibilities for contact-free manipulation, with size-based sorting of DNA molecules performed as an example. The platform described provides an automated, high-throughput method to track and manipulate objects for real-time studies of micro- and nanoscale systems.Comment: 9 pages, 7 figure

Correlating charge and thermoelectric transport to paracrystallinity in conducting polymers.

The conceptual understanding of charge transport in conducting polymers is still ambiguous due to a wide range of paracrystallinity (disorder). Here, we advance this understanding by presenting the relationship between transport, electronic density of states and scattering parameter in conducting polymers. We show that the tail of the density of states possesses a Gaussian form confirmed by two-dimensional tight-binding model supported by Density Functional Theory and Molecular Dynamics simulations. Furthermore, by using the Boltzmann Transport Equation, we find that transport can be understood by the scattering parameter and the effective density of states. Our model aligns well with the experimental transport properties of a variety of conducting polymers; the scattering parameter affects electrical conductivity, carrier mobility, and Seebeck coefficient, while the effective density of states only affects the electrical conductivity. We hope our results advance the fundamental understanding of charge transport in conducting polymers to further enhance their performance in electronic applications