Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ( 50

Tissue clearing and Light sheet fluorescence microscopy (LSFM) provide spatial information at a subcellular resolution in intact organs and tumors which is a significant advance over tools that limit imaging to a few representative tissue sections. The spatial distribution of drugs, targets, and biomarkers can help inform relationships between exposure at the site of action, efficacy, and safety during drug discovery. We demonstrate the use of LSFM to investigate distribution of an oncolytic virus (OV) and vasculature in xenograft tumors, as well as brain Aβ pathology in an Alzheimer’s disease (AD) mouse model. Machine learning-based image analysis tools developed to segment vasculature in tumors showed that random forest and deep learning methods provided superior segmentation accuracy vs intensity-based thresholding. Sub-cellular resolution enabled detection of punctate and diffuse intracellular OV distribution profiles. LSFM investigation in the brain in a TgCRND8 AD mouse model at 6.5 months of age enabled evaluation of Aβ plaque density in different brain regions. The utility of LSFM data to support quantitative systems pharmacology (QSP) and physiology-based pharmacokinetics (PBPK) modeling to inform drug development are also discussed. In summary, we showcase how LSFM can expand our understanding of macromolecular drug and biomarker distribution to advance drug discovery and development.

We introduce a new strategy for image analysis of inline microscopy monitoring estimate particle size distri-bution using deep learning. The proposed method consists of two major components: First, a novel way to generate training image-label pairs with a high-level of credibility via a Cycle-consistent Generative Adversarial Network (CycleGAN), and second, a Mask-RCNN model trained with the generated data for the particle detection task. The proposed methodology eliminates the need for manual labeling in the training phase which is a labor-intensive step and can result in labeling errors given the fuzziness of these images. We studied the application of this strategy to images acquired with a particle vision and measurement (PVM) probe. The proposed methodology was applied to images of two particle morphologies with different sizes and concentrations. Our results showed that the proposed methodology could be inexpensively used to determine qualitative trends between crystal size distributions. This trend information is a very important aspect of crys-tallization process monitoring and is often enough to determine what is controlling the crystallization. Therefore, we see our approach as a step in the right direction to provide insights into the particularly challenging PVM inline microscopy monitoring process without the need for offline sampling.

Journal Article High-Resolution Ex Vivo Tissue Clearing, Lightsheet Imaging, and Data Analysis to Support Macromolecular Drug and Biomarker Distribution in Whole Organs and Tumors Get access Niyanta Kumar, Niyanta Kumar ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Corresponding author: niyanta.kumar@merck.com Search for other works by this author on: Oxford Academic Google Scholar Petr Hrobař, Petr Hrobař Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Martin Vagenknecht, Martin Vagenknecht Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Jindrich Soukup, Jindrich Soukup Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Peter Bloomingdale, Peter Bloomingdale Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc., Boston, MA, United States Search for other works by this author on: Oxford Academic Google Scholar Tomoko Freshwater, Tomoko Freshwater Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc., Rahway, NJ, United States Search for other works by this author on: Oxford Academic Google Scholar Sophia Bardehle, Sophia Bardehle Neuroscience, Merck & Co. Inc., Boston, MA, United States Search for other works by this author on: Oxford Academic Google Scholar Roman Peter, Roman Peter Data Science & Scientific Informatics, MSD, Prague, Czech Republic Search for other works by this author on: Oxford Academic Google Scholar Nadia Patterson, Nadia Patterson ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Ruban Mangadu, Ruban Mangadu Immuno-Oncology, Merck & Co. Inc., San Francisco, CA, United States Search for other works by this author on: Oxford Academic Google Scholar ... Show more Cinthia Pastuskovas, Cinthia Pastuskovas ADME & Discovery Toxicology, Merck & Co. Inc., San Francisco, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Mark Cancilla Mark Cancilla ADME & Discovery Toxicology, Merck & Co. Inc., West Point, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 1436–1437, https://doi.org/10.1017/S1431927622005840 Published: 01 August 2022