bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.04.13.648585

PLGA Soya Lecithin Based Hybrid Nanocomposite for Targeted Topical Delivery of Resveratrol in Psoriasis Management

keshari, R.·Bagale, R.·Rath, S.·Raut, S.·Bhatt, L. K.·De, A.·Sen, S.·Srivastava, R.
View original source ↗Export metadata ↓

Abstract

Psoriasis is a recurring autoimmune-driven inflammatory skin disorder characterised by red, scaly, dry skin patches, keratinocyte hyperproliferation and infiltration of inflammatory cells, impacting millions globally. Patients with psoriasis often require repeated treatments with corticosteroids, which are often associated with significant side effects and comorbidities. Therefore, there is a growing need for advanced nanomedicine-based therapeutic approaches. Resveratrol (RSV), a plant-derived polyphenol, has shown promising anti-inflammatory and antioxidant properties for the treatment and management of psoriasis. However, its therapeutic potential is limited due to its poor solubility, low stability, and reduced skin penetration. To overcome these challenges, we have developed dual-carrier conjugated nanoparticles (RSVNPL) composed of soya lecithin and poly(lactic-co-glycolic acid) (PLGA). To further enhance its ease of application and prolong skin residence time, RSVNPL was incorporated into a carbomer 974P-based hydrogel system, forming RSVNPGel. Our formulation demonstrated improved physicochemical properties, including enhanced spreadability, superior skin adhesiveness and stability. Ex vivo skin permeation studies confirmed a significantly higher accumulation of RSV in the epidermis and dermis compared to free drugs. In vivo, efficacy was investigated using imiquimod (IMQ)-induced psoriatic mouse model, where RSVNPGel treatment led to a notable reduction in epidermal thickness, inflammatory cell infiltration, and key histopathological features associated with psoriasis. Additionally, serum biochemical analysis and organ histology established the biocompatibility and safety of the formulation. Overall, RSVNPGel presents a promising nanocarrier-based strategy for the effective topical management of psoriasis, enhancing RSV delivery and therapeutic efficacy while minimizing systemic exposure. Research HighlightsO_LIHybrid nanoparticles composite of soya lecithin and PLGA were developed for RSV delivery. C_LIO_LIThe formulation enhanced skin penetration, retention and created a better skin adhesive profile in psoriatic lesions. C_LIO_LISignificant anti-psoriatic effects were achieved in a preclinical model. C_LIO_LIThe system showed excellent safety and biocompatibility. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/648585v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@e28c4corg.highwire.dtl.DTLVardef@1690c80org.highwire.dtl.DTLVardef@1b3e4bforg.highwire.dtl.DTLVardef@16ec0ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

keshari, R., Bagale, R., Rath, S., Raut, S., Bhatt, L. K., De, A., Sen, S., Srivastava, R.. 2025-04-19. PLGA Soya Lecithin Based Hybrid Nanocomposite for Targeted Topical Delivery of Resveratrol in Psoriasis Management. https://doi.org/10.1101/2025.04.13.648585

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗
bioRxiv2026

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

bioengineering↗
bioRxiv2026

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗