No abstract

There are currently more than 560 therapeutic monoclonal antibodies (mAbs) at various stages of research and clinical testing, including candidates for administration by subcutaneous (SC) injection. Preclinical studies based on in vitro measurements of high molecular weight proteins within simulated SC matrices are assisting laboratory studies of interactions of injectable biotherapeutic proteins within the SC environment in relation to bioavailability. We report a new method for directly measuring diffusion of unlabeled, high molecular weight proteins injected into an in vitro matrix that simulates the negatively charged environment of the SC. The matrix consists of 10 mg/ml HA in a repurposed cell culture chamber. The measurement consists of pipetting triplicate 20 μl protein samples into the matrix, placing the chamber in a laboratory scanner, activating tryptophan residues in the protein at 280 nm, and imaging the resulting protein fluorescence at 384 nm over a 0.5–4 h time period thus tracking protein movement. This facile approach enables mapping of protein concentration as a function of time and distance within the matrix, and determination of diffusion coefficients, D, within ±10%. Bovine IgG and BSA gave D = 2.3 ± 0.2*10−7 and 4.6 ± 0.2*10−7 cm2/s at 24°C, respectively, for initial protein concentrations of 21 mg/mL.

No abstract

We introduce a new method to measure the concentration-dependent diffusion coefficient from a sequence of images of molecules diffusion from a source towards a sink. Most approaches such as Fluorescence Recovery After Photobleaching (FRAP), assume the diffusion coefficient is constant. Hence, they cannot capture the concentration dependence of the diffusion coefficient. Other approaches measure the concentration-dependent diffusion from an instantaneous concentration profile and lose the temporal information. These methods make unrealistic assumptions and lead to 100% error. <p>We introduce a framework that utilizes spatial and temporal information in a sequence of images and numerically solves the general form of Fick's second law using Radial Basis Functions to measure the concentration-dependent diffusion coefficient. We term this approach Concentration Image Diffusimetry (CID).</p> <p>Our method makes no assumptions about the sink and source size and the diffusion dependence on concentration. CID is superior to existing methods in estimating spatiotemporal changes and concentration-dependent diffusion. CID also provides a statistical uncertainty quantification on the measurements using bootstrapping.</p> <p>We assessed CID's performance using synthetic images. Our analysis suggests that CID accurately measures the diffusion coefficient with less than 2% error for most cases. We validated CID with FRAP images and showed agreement with established FRAP algorithms for samples with a constant diffusion.</p> <p>Finally, we demonstrate the application of CID to experimental datasets of protein diffusion into tissue. In conclusion, this work presents the first of its kind image-based methodology that uses the spatial and temporal changes of concentration fields to measure the concentration-dependent diffusion coefficient.

No abstract