Biologics have emerged as a powerful and diverse class of molecular and cell-based therapies that are capable of replacing enzymes, editing genomes, targeting tumors, and more. As this complex array of tools arises a distinct set of challenges is rarely encountered in the development of small molecule therapies. Biotherapeutics tend to be big, bulky, polar molecules comprised of protein and/or nucleic acids. Compared to their small molecule counterparts, they are fragile, labile, and heterogeneous. Their biodistribution is often limited by hydrophobic barriers which often restrict their administration to either intravenous or subcutaneous entry routes. Additionally, their potential for immunogenicity has proven to be a challenge to developing safe and reliably efficacious drugs. Our discussion will emphasize immunogenicity in the context of therapeutic proteins, a well-known class of biologics. We set out to describe what is known and unknown about the mechanisms underlying the interplay between antigenicity and immune response and their effect on the safety, efficacy, pharmacokinetics, and pharmacodynamics of these therapeutic agents.

Introduction:We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance.Materials and Methods:A total of 100 regions of interest (ROIs) from a breast biopsy whole-slide images at five levels of JPEG 2000 compression (8:1, 16:1, 32:1, 64:1, and 128:1) plus the uncompressed version were shown to six pathologists to determine benign versus malignant status.Results:There was a significant decrease in performance as a function of compression ratio (F = 14.58, P < 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. Just-noticeable difference (JND) metrics were computed for each image, including the mean, median, ≥90th percentiles, and maximum values. For comparison, PSNR (peak signal-to-noise ratio) and Structural Similarity (SSIM) were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. Observer performance as measured by the Receiver Operating Characteristic area under the curve (ROC Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression levels. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5.Conclusion:Whole-slide images may be compressible to relatively high levels before impacting WSI interpretation performance. The VDM metrics correlated well with artifact conspicuity and human performance.

We aim to improve telepathology images for diagnoses using compression based on information about human visual system. Underlying goal is to demonstrate utility of a visual discrimination model (VDM) for predicting observer performance. 100 ROIs from breast biopsy virtual slides at 5 levels of compression (uncompressed, 8:1, 16:1, 32:1, 64:1, 128:1) were shown to 6 pathologists to determine benign vs malignant. There was a decrease in performance as a function of compression (F = 14.58, p< 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. JND metrics were computed for each image including mean, median, ≥90 th percentiles, and maximum. For comparison PSNR and SSIM were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. The correlation of observer performance in the ROC experiment with image distortion metrics is shown in Figures 3 and 4. Observer performance (Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99 th percentile JND of 6.5. Virtual pathology may be compressible to relatively high levels before impacting diagnostic accuracy and the VDM accurately predicts human performance. PURPOSETelepathology is the practice of pathology at a distance using videomicroscopy at one location, a telecommunications link, and a review workstation for the pathologist at another location.[1] Despite a great amount of research and technological development in the past few years, there are still important technological issues that remain to be resolved. One of the major issues in telepathology is the size of the digitized or "virtual slides". The image files are quite large affecting the transmission rates the rates at which they are retrieved for display from a server or storage device, and the amount of storage space they occupy. The issue is complicated even further depending on the clinical task -some cases require only a low-resolution (40X objective) scan, while others require resolutions significantly higher (80X or 100X objective).[2] Some scanners create even larger images [3] and there is concern in the DICOM (Digital Imaging and Communications in Medicine) Pathology Working Group (WG-26) that DICOM cannot handle images larger than 64,000 pixels and 2GB total size [4]. Compression is one way to deal with this massive amount of data, but it is difficult to define a single "minimum" level of compression (hence image quality) for use across all clinical questions [5].