Geometry-Dependent Properties of Physically Crosslinked PPy-PVA Hydrogels Enable Tunable Iontophoretic Drug Delivery
Authors: Heather Jin Hee Kim, Mohsen Alishahi, Kyuin Park, Mikaila K. Roncevich, Larissa M. Shepherd, Cara M. Nunez
Organizations: Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853. · Department of Human Centered Design, Cornell University, Ithaca, NY 14853.
Objective: Iontophoretic drug delivery has primarily relied on current modulation to regulate drug transport. Here, we investigate hydrogel geometry, microstructure, and local electric fields as alternative mechanisms for controlling transdermal delivery. Methods: Bovine serum albumin (BSA) was encapsulated in polypyrrole-polyvinyl alcohol (PPy-PVA) hydrogels. Five geometries, including Hilbert curve-based designs, were fabricated to vary pore architecture and local electric field distributions. We optimized PPy content, drug loading, and current density. Electric fields were evaluated using finite element analysis, and microstructures were characterized by scanning electron microscopy. Drug transport was assessed using Franz diffusion cells with artificial membranes. Results: Hydrogel geometries exhibited distinct microstructures and electric field distributions. Iontophoresis substantially enhanced permeability compared with passive diffusion, while individual geometries produced different cumulative release, flux, and permeability profiles. The average iontophoretic permeability was (3.80 +/- 1.93) x 10^-5, exceeding previously reported values using artificial membranes. Conclusion: Geometry-driven fabrication of drug-loaded hydrogels enables tunable transport characteristics under both iontophoretic and passive conditions. Significance: Geometry-mediated control offers an alternative approach to regulating iontophoretic drug delivery without requiring individual current-density modulation.
Figures & tables
Fig. 1 : (a) Exploded view of the device. The device integrates a drug-loaded hydrogel, Ag/AgCl electrodes, and a PDMS gasket. (b) When current is applied, the resulting electric field drives the transport of ionized drug molecules through the skin barrier.
Fig. 2 : The hydrogel can be fabricated in five geometries: (a) Plain , (b) Circle , (c) Interdigital , (d) Hilbert 2nd , and (e) Hilbert 3rd . (f) Photo of the device consisting of a drug-loaded hydrogel and an Ag/AgCl electrode housed within a PDMS gasket. The scale bar represents 1 cm.
Fig. 3 : (a) 4-probe measurement circuit. (b) Separate pairs of electrodes are used for current sourcing and voltage sensing. (c) Schematic of the delivery study setup of the BSA-loaded device.
Fig. 4 : (a) FTIR spectra of PPy, PVA, BSA solution, and the BSA-loaded PPy-PVA gel. (b) Second-derivative FTIR spectra showing the prominent wavenumbers.
Fig. 5 : (a) Gel conductivity by PPy concentration. (b) Released BSA across current density groups. (Mean ± SD).
CBSA (mg mL -1 )
Entrapment Efficiency (%)
5
11.0 ± 22.8
10
83.7 ± 57.1
20
91.2 ± 9.0
30
35.5 ± 6.6
TABLE I : Entrapment efficiency of BSA concentrations. (Mean ± SD).
Fig. 6 : SEM images of BSA-loaded gel samples (500x magnification). Panels (a)-(e) correspond to different geometries, and panels (i)-(ii) represent distinct cross-sectional regions within each geometry. Blue dashed lines denote peripheral gel walls. Note that the scale bar represents 20 μ m.
TABLE II : Summary of microstructure characteristics and electric field strength.
Delivery
Geometry
Q ( μ g cm -2 )
Jss ( μgcm−2h−1 )
τ (min)
Papp (cm s -1 )
ER
R 2
(Mean ± SD)
(Mean ± SD)
(Mean ± SD)
(Mean ± SD)
(Mean ± SD)
IP
Plain
3883±1.8×103
4602±1209
0.209±0.362
6.39×10−5±1.68×10−5
3.52
0.9887±0.0117
IP
Circle
3012±1.7×103
3788±1008
0.074±0.128
5.26×10−5±1.40×10−5
6.98
0.9977±0.0026
IP
Interdigital
1453 ± 1.1 ×103
1346±1146
5.03±6.24
1.87×10−5±1.59×10−5
2.25
0.9890±0.0135
IP
Hilbert 2nd
3576 ± 4.6 ×102
2052±920
1.96±1.75
2.85×10−5±1.28×10−5
2.25
0.9757±0.0046
IP
Hilbert 3rd
2872 ± 1.1 ×103
1891±434
0.214±0.268
2.63×10−5±6.03×10−6
2.90
0.9929±0.0123
TABLE III : Summary table of drug delivery parameters (three samples for each geometry). For all parameters, uncertainties represent the standard deviation (SD) across n=3 independent replicates. For Jss , τ , and Papp , each replicate value was obtained from independent linear regression of its own steady-state region.
Fig. 7 : The plots share the same range of Q (y axis). The endpoint of the delivery study was 180 minutes. Three samples were used for each geometry . Solid lines represent iontophoretic delivery (IP), and dashed lines represent passive delivery (P).
Fig. 8 : Correlation heatmap of geometry-dependent gel properties to drug delivery outcomes.
Fig. S1 : Example of increasing Hilbert curve order: (a) 1st, (b) 2nd, and (c) 3rd order curves. Red dots indicate predicted local “hot spots” where electric field lines concentrate. These hot spots may shift depending on the placement of the other electrode.
Fig. S2 : (a) BSA and (b) PPy calibration results. UV-Vis absorbance spectra (left) and NNLS-derived amplitudes aB(278),aP plotted as a function of concentration with corresponding linear fits (95% CI bands are shaded) and R2 values (right).
Fig. S3 : (a) PPy was synthesized by chemical oxidation. (b) A viscous PVA solution was prepared for physical crosslinking and mixed with water-soluble PPy. (c) BSA was added to form the precursor mixture. (d) The solution is freeze-thawed and assembled with an Ag/AgCl electrode.
Delivery
Geometry
Jss ( μgcm−2h−1 )
τ (min)
Papp (cm s -1 )
(Mean ± SE)
(Mean ± SE)
(Mean ± SE)
IP
Plain
5200±500
0.85±1.7
7.22×10−5±6.50×10−6
IP
Circle
4480±200
0
6.22×10−5±3.10×10−6
IP
Interdigital
2150±60
9.88±0.86
2.98×10−5±8.10×10−7
IP
Hilbert 2nd
555±70
0
7.71×10−6±9.70×10−7
IP
Hilbert 3rd
3160±700
2.00±3.8
4.39×10−5±9.20×10−6
TABLE S1 : Summary table of drug delivery parameters. (Three samples for each geometry.) For Jss , τ , and Papp , the reported uncertainties represent the standard error of the linear regression used to determine the steady-state region or the propagated standard error of regression-derived quantities.
Streaming-potential-mediated transport of viscoelastic fluids has attracted research attention owing to its applications in electrokinetic energy conversion and microfluidic transport. Existing analytical and semi-analytical models in published literature provide valuable physical insights, but require repeated numerical evaluations for exploring large design spaces and identifying the optimal operating conditions. In this work, a surrogate-assisted framework is developed for rapid design optimization of pressure-driven electrokinetic transport of simplified Phan-Thien-Tanner fluids in a slit microchannel. A high-fidelity numerical database is generated over a broad range of governing dimensionless parameters, which includes the zeta potential, the Debye parameter, the Dukhin number, and the viscoelastic parameter. A Machine Learning surrogate model is subsequently trained to accurately approximate the nonlinear relationship between the governing parameters and the streaming potential, while the volumetric flow rate and hydroelectric energy conversion efficiency were calculated from closed form equation by using the streaming potential predicted by the surrogate. This is coupled with a multi-objective optimization strategy to identify operating conditions that simultaneously maximize energy conversion efficiency and volumetric flow rate. The proposed methodology can significantly accelerate parametric exploration compared with repeated numerical simulations across different parameters and provides practical design guidelines for electrokinetic microfluidic devices. The study demonstrates the potential of combining computational fluid mechanics with data-driven surrogate modeling for efficient engineering design and optimization.
Ankan Basu, Sumanta Banerjee
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. · 2*Department of Mechanical Engineering, Heritage Institute ofAug Technology, Kolkata, India.
Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other objectives. To navigate this multidimensional space, Corbion and Intrepid combined Corbion's diverse PURASORB bioresorbable polymer library with Intrepid Labs' proprietary AI algorithm (ANDROMEDA 1) to develop in situ forming depots for a therapeutic peptide. Over approximately 15 weeks, 181 unique formulations spanning drug loadings of 6-12% w/w were prepared and characterized through broad design-space mapping and targeted multi-objective optimization. Four lead candidate formulations were identified at 6%, 9%, and 12% w/w drug loading. Each met the predefined viscosity and injectability criteria while providing distinct 30-day in vitro release profiles. The study evaluated polymers spanning a broad range of molecular weights, including commercially available PURASORB grades and new polymers under development by Corbion to expand its polymer toolbox. ANDROMEDA 1 identified that polymers with intermediate molecular weights provided a favorable balance between sustained release and solution viscosity. Together, these findings demonstrate how integrated polymer expertise and AI-driven optimization can rapidly identify differentiated formulation candidates, focus the development space, and establish a strong data-driven foundation for further optimization and in vivo evaluation.
Pauric Bannigan, Siddarth Chandrasekaran, Brigitte A. G. Lamers +6
Intrepid Labs Inc., 140 - 101 College St, Toronto, ON M5G 1L7. · Corbion N.V., Arkelsedijk 46, Gorinchem, 4206 AC
The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI), to minor changes in process parameters. Factors like formulation concentration, flow rates, and mixing ratios can significantly influence clinical efficacy and therapeutic outcomes. The absence of predictive mathematical frameworks has made iterative experimental screening necessary, increasing both costs and development time. This study introduces and validates a predictive modeling approach based on shape constraints, aiming to enhance the estimation of nanoparticle characteristics across various process conditions. Using controlled microfluidic methods, liposomes and lipid nanoparticles were systematically prepared under varying lipid concentrations, flow rates, and aqueous-to-organic mixing ratios. The shape-constrained model, informed by both experimental data and expert knowledge, was subsequently validated for a pharmaceutical application using minimal empirical data. Results reveal that shape-constrained modeling facilitates accurate prediction of nanoparticle size and dispersity, reducing the need for extensive experimental workflows. This framework supports rational and efficient process development for manufacturing nanomedicine systems.
Kai Dahms, Eilien Heinrich, Jochen Schmid +3
Fraunhofer Institute for Microengineering and Microsystems (IMM), 55129 Mainz, Germany · Fraunhofer Institute for Industrial Mathematics (ITWM), 67663 Kaiserslautern, Germany