Abstract
Simple SummaryEngineered proteins possess a great therapeutic potential, but the development of such therapies is impeded during preclinical studies by the lack of in vitro models that accurately simulate the human physiology. Animal models, on the other hand, also have difficulties predicting human responses, and are ethically concerning. In this study, we employed a hybrid approach where we combined mathematical modeling with 3D in vitro models that mimic aspects of the tumor microenvironment, in order to simulate the delivery of therapeutic proteins targeting cancer cells and to predict the biological activity. By cross-comparing simulated and experimental data from 3D models, we were able to correctly predict the best dose needed to deliver toxic proteins specifically to tumor cells, while leaving the surrounding non-tumor cells untouched. This study shows the potential of combining computational approaches with novel in vitro models to advance the development of protein therapeutics.To rationally improve targeted drug delivery to tumor cells, new methods combining in silico and physiologically relevant in vitro models are needed. This study combines mathematical modeling with 3D in vitro co-culture models to study the delivery of engineered proteins, called designed ankyrin repeat proteins (DARPins), in biomimetic tumor microenvironments containing fibroblasts and tumor cells overexpressing epithelial cell adhesion molecule (EpCAM) or human epithelial growth factor receptor (HER2). In multicellular tumor spheroids, we observed strong binding-site barriers in combination with low apparent diffusion coefficients of 1 µm2·s−1 and 2 µm2 ·s−1 for EpCAM- and HER2-binding DARPin, respectively. Contrasting this, in a tumor-on-a-chip model for investigating delivery in real-time, transport was characterized by hindered diffusion as a consequence of the lower local tumor cell density. Finally, simulations of the diffusion of an EpCAM-targeting DARPin fused to a fragment of Pseudomonas aeruginosa exotoxin A, which specifically kills tumor cells while leaving fibroblasts untouched, correctly predicted the need for concentrations of 10 nM or higher for extensive tumor cell killing on-chip, whereas in 2D models picomolar concentrations were sufficient. These results illustrate the power of combining in vitro models with mathematical modeling to study and predict the protein activity in complex 3D models.
Highlights
Engineered proteins including antibodies and non-immunoglobulin binding proteins that target cancer-specific features play an increasingly important role in the development of novel cancer therapies [1]
We investigated the specific delivery of designed ankyrin repeat proteins (DARPins) directed against human epidermal growth factor receptor 2 (HER2) or epithelial cell adhesion molecule (EpCAM) in complex 3D tumor microenvironments
We used DARPin 9_26, which binds to HER2 with a a dissociation constant (Kd) of 1.4 nM [35], and EpCAM-binding DARPin Ec1, which binds to EpCAM with a Kd of 68 pM [36], including the non-binding control Off7 (Figure S1A)
Summary
Engineered proteins including antibodies and non-immunoglobulin binding proteins that target cancer-specific features play an increasingly important role in the development of novel cancer therapies [1]. Proteins offer enhanced specificity and/or selective recognition of surface features [2]. Their small size offers benefits with respect to tissue accessibility compared with most nanoparticulate delivery systems [3]. The efficient delivery of therapeutic proteins into tumors can be hindered because of a binding-site barrier [4], a high interstitial pressure as a result of poor tumor vasculature, a defective lymphatic system, and a dense extracellular matrix [5]. The aforementioned barriers to efficient drug delivery are not replicated in 2D culture systems, and in vivo models suffer from a low throughput. There has been growing interest in the utilization of in vitro complex 3D tissues that mimic relevant aspects of human tumor microenvironments for studying drug delivery [11,12]
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