Impact of Microdevice Geometry on Transit and Retention in the Murine Gastrointestinal Tract

Abstract

Oral protein delivery technologies often depend on encapsulating or enclosing the protein cargo to protect it against pH-driven degradation in the stomach or enzymatic digestion in the small intestine. An emergent methodology is to encapsulate therapeutics in microscale, asymmetric, planar microparticles, referred to as microdevices. Previous work has shown that, compared to spherical particles, planar microdevices have longer residence times in the GI tract, but it remains unclear how specific design choices (e.g., material selection, particle diameter) impact microdevice behavior in vivo. Recent advances in microdevice fabrication through picoliter printing have expanded the range of device sizes that can be fabricated in a rapid manner. However, relatively little work has explored how device size governs their behavior in the intestinal environment. In this study, we probe the impact of geometry of planar microdevices on their transit and accumulation in the murine GI tract. Additionally, we present a strategy to label, image, and quantify these distributions in intact tissue in a continuous manner, enabling a more detailed understanding of device distribution and transit kinetics than previously possible. We show that smaller particles (194.6 ± 7 μm.diameter) tend to empty from the stomach faster than midsize (293.2 ± 7 μm.diameter) and larger devices (440.9 ± 9 μm.diameter) and that larger devices distribute more broadly in the GI tract and exit slower than other geometries. In general, we observed an inverse correlation between device diameter and GI transit rate. These results inform the future design of drug delivery systems, using particle geometry as an engineering design parameter to control device accumulation and distribution in the GI tract. Additionally, our image analysis process provides greater insight into the tissue level distribution and transit of particle populations. Using this technique, we demonstrate that microdevices act and translocate independently, as opposed to transiting in one homogeneous mass, meaning that target sites will likely be exposed to devices multiple times over the course of hours post administration. This imaging technique and associated findings enable data-informed design of future particle delivery systems, allowing orthogonal control of transit and distribution kinetics in vivo independent of material and cargo selection.

Keywords: additive manufacturing; enteric materials; fluorescent tracking; gastrointestinal distribution; microdevices.

Figure 1.

Schematic of device fabrication and device characterization. (A) (1) Silicon wafer silanized to enable fluid beading on the surface. (2) Programmed volume of polymer solution was dispensed onto the surface. (3) Polymer solution was allowed to fully dry, forming the device reservoir. (4) Devices were removed from the wafer surface using a razorblade. (4) Prior to oral dosing, devices were dispersed in a simulated gastric fluid solution containing 1% (w/v) poly(vinyl alcohol). (B) Light microscopy images of representative microdevices. Device measurements can be found in Table 1. Scale bars represent 200 μm. (C) Scanning electron microscopy image of devices mechanically removed from silicon wafer; scale bar represents 100 μm.

Figure 2.

Fluorescent polymer conjugate was stable throughout the anticipated use case. (A) Representative fluorescent images of devices after exposure to simulated gastric fluid (SGF) and simulated intestinal fluid adjusted to pH 5 (SIF). (B) Comparison of maximum pixel intensity between fluorescently labeled devices exposed to SGF for 30 min, SIF adjusted to pH 5 for 60 min or 4 h. Data represents mean ±1 SD of n = 5 devices per group. No differences were detected between groups at α = 0.05, using a one-way ANOVA followed by a Tukey-Kramer correction for multiple comparisons.

Figure 3.

Experimental process schematic. (A) Microdevices were dosed to animals via oral gavage, directly into the stomach at time = 0. (B) At each time point (30 min, 60 min, and 4 h) animals were sacrificed, their gastrointestinal tract from the stomach to the anus was removed and imaged (representative images of 300 μm device group shown). Images and image meta-data were processed using two independent methods: (C) using preset ROIs, total fluorescent signal from major compartments was captured (in order: stomach, upper small intestine, lower small intestine, cecum, and colon). Fluorescent signal was collected as total photon counts. (D) In parallel, images were processed to extract device distribution information in a continuous manner. Data shown were extracted from images in B. Signal was normalized to the total amount of signal in each sample, and distance was normalized to the total distance of the GI tract sample. Data are presented in a semicompressed format, where each bar represents the total signal from the preceding 1% of the GI tract. More information on the image processing pipeline in Figure S3.

Figure 4.

Fluorescent signal from discrete tissue regions demonstrates device transit through tissue. Signals divided by device group (rows) and time (columns). Data were normalized to the total amount of signal in each sample and expressed as a percentage. From the left to right in each plot bars represent signal from the stomach (st), upper small intestine (U.si), lower small intestine (L.si), cecum (ce), and colon (co). Data were expressed as the sample mean ±1 SD, n = 3 measurements per group for the 200 μm devices (green) and 450 μm devices (purple), n = 2 measurements per groups for 300 μm devices (orange). Statistical difference between groups was determined by one-way ANOVA followed by a Tukey-Kramer correction for multiple comparisons. * = p < 0.05, ** = p < 0.01. ns = no significant differences measured.

Figure 5.

Continuous distributions show device translocation through the GI tract over time. Signal data were normalized to the total amount of signal in each sample, and expressed as a percentage. Distance was expressed as a percentage of the total distance along the GI tract. Each bar represents signal summed over the preceding 1% of the length of the GI tract. Data represent the mean of n = 3 separate measurements for the 200 and 450 μm groups, and n = 2 separate measurements for the 300 μm group.

Figure 6.

Comparison of distribution signals by-time indicate group stratification. (A–C) Overlays of each device group at (A, D) 30 min, (B, E) 60 min, and (C, F) 4 h presented after gastric emptying (15–100% total distance). Signal data are normalized to the total signal present in each sample, and distance is normalized to the total distance along the GI tract. Each bar represents the arithmetic mean of the cumulative signal from the preceding 1% of total distance from n = 3 animals for the 200 and 450 μm groups, and n = 2 animals for the 300 μm group. (D–F) Cross correlation results for pairwise comparisons between groups. p-values less than p = 0.05 represent a statistically significant correlation between the indicated signals, whereas larger p values fail to reject the null-hypothesis of no correlation. In the 30 min group (D), there was insufficient signal from the 300 μm group for comparison. Family-wise error rate was limited to α = 0.05 using a Bonferroni correction for multiple comparisons. * = p < 0.05 and indicates a statistically significant correlation.

Figure 7.

Signal lag shows devices accumulate differentially along the GI tract over time. The presented biased correlation lag analysis results account for shifts along the entirety of the analyzed portion of the GI tract (excluding the stomach). Signals were normalized to the maximum in each group. Peaks at positive lag values represent the second term needing to shift in the positive direction to match the first term, whereas negative lag values indicate the second term moving in the negative direction. Because of an insufficient signal, no comparisons to the 300 μm group were possible at the 30 min time point.