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. 2013:4:1504.
doi: 10.1038/ncomms2506.

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo

Greg M Thurber  1 , Katy S Yang, Thomas Reiner, Rainer H Kohler, Peter Sorger, Tim Mitchison, Ralph Weissleder
Affiliations

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo

Greg M Thurber et al. Nat Commun. 2013.

Abstract

Pharmacokinetic analysis at the organ level provides insight into how drugs distribute throughout the body, but cannot explain how drugs work at the cellular level. Here we demonstrate in vivo single-cell pharmacokinetic imaging of PARP-1 inhibitors and model drug behaviour under varying conditions. We visualize intracellular kinetics of the PARP-1 inhibitor distribution in real time, showing that PARP-1 inhibitors reach their cellular target compartment, the nucleus, within minutes in vivo both in cancer and normal cells in various cancer models. We also use these data to validate predictive finite element modelling. Our theoretical and experimental data indicate that tumour cells are exposed to sufficiently high PARP-1 inhibitor concentrations in vivo and suggest that drug inefficiency is likely related to proteomic heterogeneity or insensitivity of cancer cells to DNA-repair inhibition. This suggests that single-cell pharmacokinetic imaging and derived modelling improve our understanding of drug action at single-cell resolution in vivo.

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Figures

Figure 1
Figure 1. High spatial and temporal resolution microscopy using companion imaging drugs
Precursor compounds are conjugated with cell permeable, small fluorophores to generate therapeutically active fluorescent companion imaging drugs (See Fig. S1 for structure of parent drug and how piperazine substitutions are tolerated.) Using a window chamber model, in vivo microscopy enabled the detection of drugs (green) with sub-cellular resolution and frame rates of several seconds. Scale bar = 50 μm in an MDA-MB-231 breast cancer cell line expressing a fluorescent H2B protein in the nucleus (red).
Figure 2
Figure 2. Real-Time In Vivo Drug Distribution of a PARP inhibitor
Following bolus intravenous administration, the drug (green) perfused the functional tumor vasculature within seconds and extravasated within minutes (top row). The drug initially distributed non-specifically within H2B-RFP (red) expressing cells (primarily within the endoplasmic reticulum, as determined by in vitro cell imaging; Fig. S4, HT-1080 cell line shown). Rapid target binding (within minutes) combined with clearance of non-specific membrane labeling increased the specificity of target versus non-target uptake within an hour. Specific nuclear PARP targeting was observed (bottom row) and this was maintained for several hours. See Movie 1 for dynamic imaging. Scale bar = 50 μm.
Figure 3
Figure 3. Subcellular spatial resolution of drug distribution
A. A line profile through three cells adjacent to a tumor vessel shows perinuclear signal within seconds after injection (PARPi fluorescence shown in grayscale). Accumulation in the nucleus and diffusion of free drug deeper into the tumor results in nuclear specific staining with the contrast increasing over time. A high signal in the nucleolus can be observed in these HT-1080 cells with similar staining patterns in multiple cell lines. B. High resolution images of intracellular (primarily nuclear) drug distribution in three different cancer models in vivo. PARPi fluorescence shown in green.
Figure 4
Figure 4. Cell population drug kinetics
A. H2B-Apple red fluorescent protein was used to identify the nuclei of individual cancer cells in vivo (model HT-1080 cell line shown). B. The drug (green) accumulates inside cells, specifically in the nuclei. C. To measure the average drug concentration inside each cell, nuclei were segmented (based on A) and outlines were overlaid with the companion imaging drug to yield a mean nuclear concentration (C). Note that the average cellular drug concentration in this in vivo example is 1.2 μM. While virtually all cancer cells accumulated the drug (> 99%), there was still some cell-to-cell variability. D. The average concentration rapidly increases following a bolus dose followed by a slow decay (top). Analyzing the standard deviation of 250 cells over time showed the highest deviation at early time points decreasing to a much lower level as the diffusive gradients dissipated (bottom).
Figure 5
Figure 5. Single cell drug kinetics
Individual cell profiles can be tracked by temporal single cell imaging in vivo. A. Drug concentrations inside cancer cells were plotted as a function of time following intravenous administration and according to their proximity to microvessels. Profiles in the left column are from representative single cancer cells adjacent to microvessels, profiles in the right column are from cells ~200 μm away from the nearest vessel, and the columns in the middle are from cells at intermediate distances. Note that the intracellular drug concentration in cells is similar at 4 hours after administration. B. A histogram of the log-normal population kinetics over time in different cancer cell types. Top: breast cancer (MDA-MB-231), middle: ovarian cancer (OVCA-429), and bottom: fibrosarcoma (HT-1080). Differences in heterogeneity primarily correspond to changes in vascular density.
Figure 6
Figure 6. Mapping drug distribution to host cells
Within a tumor, cancer cells were visualized through expression of H2B-Apple (580 nm, red) and tumor associated macrophages (TAMs) were visualized by a fluorescent nanoparticle (680 nm, blue) internalized into endosomes. The PARPi distribution to cells was visualized at 525 nm (green). The PARPi was seen to accumulate in cancer cells (yellow arrow, HT-1080 cell line) but also in TAMs (as indicated by the white arrowheads). TAM nuclei lack the H2B-apple and are surrounded by CLIO signal. Scale bar = 10 μm
Figure 7
Figure 7. Simulation Development and Validation
Small molecule drugs distribute to cancer cells from functional tumor microvasculature. A. In the model system, tortuous microvessels were mapped using lectins or vascular probes (top left). B. A finite element mesh was generated around the vessel maps. C. Transport equations and boundary conditions were applied to the simulation. D. Simulation results for time and spatially varying drug concentrations could then be compared to the intravital imaging time series (A) for validation or improvement. These simulations could likewise be extrapolated to different species and used to study the effects of other specific variables. See Supplementary Fig. S6 for specific numerical parameters.
Figure 8
Figure 8. Predictive models of PARPi distribution
Vessel heterogeneity can be a major determinant of drug distribution particularly early after administration. In these examples (using HT-1080 xenografts with wide variation in vascular density within the same tumor) drug gradients are modeled as a function of distance from microvessels (white); each oval shape represents a model cell. A. A poorly vascularized region shows a large gradient at early times (15 minutes; left) for both the simulation (top) and the corresponding intravital image (bottom). By 1 hour (right), the gradient is significantly reduced and intracellular drug concentrations reach ~1 μM levels. The largeMander correlation coefficient validates the model predictions based on in vitro experimental measurements and systemic clearance. B. High vessel density reduces the spatial gradients, and the cellular accumulation reaches 2–3 μM following administration of the imaging dose. These types of simulations can be used to determine the impact of changing the drug physiochemical properties on distribution for both therapeutics and imaging agents. See Supplementary Fig. S8 for additional modeling including human extrapolation.
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