Toxicologic Pathology最新文献

Small interfering RNAs (siRNAs) have been successfully used as therapeutics to silence disease-causing genes when conjugated to ligands or formulated in lipid nanoparticles to target relevant cell types for efficacy while sparing other cells for safety. To support the development of new methods for delivery of siRNA therapeutics, we developed and characterized a panel of antibodies generated against chemically modified nucleotides used in therapeutic siRNA molecules, identifying a monoclonal antibody that detects a broad range of siRNA representing distinct sequences and modification patterns. By integrating this anti-siRNA antibody with additional reagents, we created a multiplex siRNA immunoassay that simultaneously quantifies siRNA uptake, trafficking, and silencing activity. Using immunohistochemistry (IHC), we applied our method on tissues from mice treated with unconjugated, GalNAc-conjugated, or cholesterol-conjugated siRNAs and quantitatively assessed the biodistribution and activity of siRNAs in various organs. In addition, we used high-content imaging (HCI) and applied our multiplex siRNA immunoassay in tissue culture to enable simultaneous quantification of siRNA uptake, activity, and intracellular colocalization with endosome markers. These methods provide a robust platform for testing nucleic acid delivery methods in vitro and in vivo, allowing precise analysis and visualization of the pharmacokinetics and pharmacodynamics of siRNA therapeutics with cellular and subcellular resolution.

Hematoxylin and eosin (H&E) staining is a suitable approach for detecting substantial structural changes in neural tissues but is less sensitive for identifying subtle alterations to subcellular structures and various chemical constituents, including myelin. Neurohistological methods to better evaluate myelin integrity by light microscopy include acidophilic dyes (eg, eriochrome cyanine R, toluidine blue [used with hard plastic sections]); lipoprotein-binding dyes (eg, Luxol fast blue [LFB], Weil's iron hematoxylin); lipid impregnation with metals (eg, Marchi's, which uses osmium tetroxide for en bloc staining before embedding); and immunohistochemical (IHC) methods to highlight various antigens (eg, myelin basic protein [MBP] and peripheral myelin protein 22 [PMP22]). Some IHC methods reveal enhanced marker expression in damaged myelin (eg, matrix metalloproteinase-9 [MMP9], S100). In neuropathology investigations, H&E is the first-tier screening method, whereas myelin stains (often LFB alone or in combination with dyes that highlight other structural elements) are second-tier procedures performed in combination with other neurohistological procedures to examine neuroaxonal injury and/or glial responses. The choice of myelin method depends on such considerations as cost, institutional preference, the procedure (fixation and embedding medium), and the study objective.

Recent trends in toxicological pathology include implementation of digital platforms that have gained rapid momentum in the field. Are we ready to fully implement this new modality? This opinion piece provides some practical perspectives on digital pathology such as its cost limitations, relative time requirements, and a few technical issues, some of which are encountered for specific lesions, that warrant caution. Although the potential for digital pathology assessment with whole slide images has made great strides, we are of the opinion that it is not yet ready for complete replacement of glass slides in toxicologic pathology safety assessments.

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for agrochemical effects on the thyroid. However, there is a high degree of variability in the appearance of the rat thyroid gland, and toxicologic pathologists often struggle to decide on and consistently apply a threshold for recording low-grade thyroid follicular hypertrophy. This research project developed a deep learning image analysis solution that provides a quantitative score based on the morphological measurements of individual follicles that can be integrated into the standard pathology workflow. To achieve this, a U-Net convolutional deep learning neural network was used that not just identifies the various tissue components but also delineates individual follicles. Further steps to process the raw individual follicle data were developed using empirical models optimized to produce thyroid activity scores that were shown to be superior to the mean epithelial area approach when compared with pathologists' scores. These scores can be used for pathologist decision support using appropriate statistical methods to assess the presence or absence of low-grade thyroid hypertrophy at the group level.

Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and limited distinctive cytologic features of lymphocyte subpopulations as seen with hematoxylin and eosin (H&E) staining. Although immunohistochemistry is necessary for definitive characterization of T- and B-cell compartments, routine toxicologic assessments are based solely on H&E slides. Here, a deep learning (DL) model was developed using normal rats to quantify relevant compartments of the spleen, including periarteriolar lymphoid sheaths, follicles, germinal centers, and marginal zones from H&E slides. Slides were scanned, destained, dual labeled with CD3 and CD79a chromogenic immunohistochemistry, and rescanned to generate exact co-registered images that served as the ground truth for training and validation. The DL model identified individual splenic compartments with high accuracy (97.8% Dice similarity coefficient) directly from H&E-stained tissue. The DL model was utilized to study the normal range of lymphoid compartment area and cellularity. Future implementation of our DL model and expanding this approach to other lymphoid tissues have the potential to improve accuracy and precision in enhanced histopathology evaluation of the immune system with concurrent gains in time efficiency for the pathologist.

Pathology, a fundamental discipline that bridges basic scientific discovery to the clinic, is integral to successful drug development. Intrinsically multimodal and multidimensional, anatomic pathology continues to be empowered by advancements in molecular and digital technologies enabling the spatial tissue detection of biomolecules such as genes, transcripts, and proteins. Over the past two decades, breakthroughs in spatial molecular biology technologies and advancements in automation and digitization of laboratory processes have enabled the implementation of higher throughput assays and the generation of extensive molecular data sets from tissue sections in biopharmaceutical research and development research units. It is our goal to provide readers with some rationale, advice, and ideas to help establish a modern molecular pathology laboratory to meet the emerging needs of biopharmaceutical research. This manuscript provides (1) a high-level overview of the current state and future vision for excellence in research pathology practice and (2) shared perspectives on how to optimally leverage the expertise of discovery, toxicologic, and translational pathologists to provide effective spatial, molecular, and digital pathology data to support modern drug discovery. It captures insights from the experiences, challenges, and solutions from pathology laboratories of various biopharmaceutical organizations, including their approaches to troubleshooting and adopting new technologies.

Before the COVID-19 pandemic, digital pathology was increasingly used in veterinary education, diagnostics, and research. The pandemic accelerated this adoption as institutions needed to maintain operations amidst lockdowns. It also enabled pharmaceutical companies to conduct peer reviews digitally, circumventing travel restrictions. At the 2023 Society of Toxicologic Pathology Annual Symposium, a Town Hall Meeting highlighted the current use of digital pathology. A majority of the respondents viewed whole slide images (WSI) favorably. Many institutions use digital pathology primarily for non-GLP and GLP conforming primary reads and peer reviews. Takeda has long utilized digital pathology, incorporating scanners and an image management repository, and recently adopted a cloud-based platform tailored for toxicologic pathology, enhancing efficiency and collaboration. Digital pathology not only saves time but also reduces travel needs and environmental impact. Technological advancements and wider adoption are expected to further enhance the field, promising significant benefits for the overall digital pathology infrastructure.

Mass spectrometry imaging (MSI) was used to investigate and provide insights into observed biliary pathology found in dogs and rats after administration of two different compounds. Both compounds were associated with peribiliary inflammatory infiltrates and proliferation of the bile duct epithelium. However, MSI revealed very different spatial distribution profiles for the two compounds: Compound A showed significant accumulation within the bile duct epithelium with a much higher concentration than in the parenchymal hepatocytes, while Compound T exhibited only a slight increase in the bile duct epithelium compared to parenchymal hepatocytes. These findings implicate cholangiocyte uptake and accumulation as a key step in the mechanism of biliary toxicity. In both cases, compounds are shown at the site of toxicity in support of a direct mechanism of toxicity on the biliary epithelium. MSI is a powerful tool for localizing small molecules within tissue sections and improvements in sensitivity have enabled localization down to the cellular level in some cases. MSI was also able to identify biomarker candidates of toxicity by differential analysis of ion profiles comparing treated and control cholangiocytes from tissue sections.

The virtual control group (VCG) concept was originally developed in the IMI2 project eTRANSAFE, using data of control animals which pharmaceutical companies have accrued over decades from animal toxicity studies. This control data could be repurposed to create virtual control animals to reduce or replace concurrent controls in animal studies. Initial work demonstrated the general feasibility of the VCG concept, but implementation requires significant further collaborative efforts. The new Innovative Health Initiative (IHI) project VICT3R aims to address these challenges and to obtain regulatory acceptance for the VCG concept. To achieve these goals, VICT3R will build a database comprising high-quality, standardized, and duly annotated control animal data from past and forthcoming toxicity studies. The VICT3R project will create workflows and computational tools to generate adequate VCGs based on statistical and artificial intelligence (AI) approaches. The validity, reproducibility, and robustness of the resulting VCGs will be assessed by comparing the performance of their use with that of real control groups.

The Golden Syrian hamster is a well-characterized rodent model for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-associated pneumonia. We sought to characterize the pulmonary disease course during SARS-CoV-2 infection (strain USA-WA1/2020) in the hamster model using micro-computed tomography (micro-CT) and compare radiologic observations with histopathologic findings. We observed a range of radiologic abnormalities, including ground glass opacities (GGOs), consolidations, air bronchograms, and pneumomediastinum. The appearance, distribution, and progression of these abnormalities in hamsters were similar to those observed in the lungs of coronavirus disease 2019 (COVID-19) patients by clinical CT and chest X-rays, and correlated with clinical signs and weight loss during the course of disease. Histopathological analysis of infected hamsters revealed lung pathology characteristic of COVID-19 pneumonia, and we observed a strong association between CT and histopathologic scorings. We also analyzed accumulation of air in the thoracic cavity by both manual and automated threshold-based segmentation and found that automated analysis significantly decreases the time needed for data analysis. Data presented here demonstrate that micro-CT imaging can be a major tool in preclinical investigative studies using animal models by providing early and detailed assessment of disease severity and outcomes.