Delivery of the Brainshuttle™ amyloid-beta antibody fusion trontinemab to non-human primate brain and projected efficacious dose regimens in humans.

There are few treatments that slow neurodegeneration in Alzheimer's disease (AD), and while therapeutic antibodies are being investigated in clinical trials for AD treatment, their access to the central nervous system is restricted by the blood-brain barrier. This study investigates a bispecific modular fusion protein composed of gantenerumab, a fully human monoclonal anti- amyloid-beta (Aß) antibody under investigation for AD treatment, with a human transferrin receptor 1-directed Brainshuttle™ module (trontinemab; RG6102, INN trontinemab). In vitro, trontinemab showed a similar binding affinity to fibrillar Aß40 and Aß plaques in human AD brain sections to gantenerumab. A single intravenous administration of trontinemab (10 mg/kg) or gantenerumab (20 mg/kg) to non-human primates (NHPs, Macaca fascicularis), was well tolerated in both groups. Immunohistochemistry indicated increased trontinemab uptake into the brain endothelial cell layer and parenchyma, and more homogeneous distribution, compared with gantenerumab. Brain and plasma pharmacokinetic (PK) parameters for trontinemab were estimated by nonlinear mixed-effects modeling with correction for tissue residual blood, indicating a 4-18-fold increase in brain exposure. A previously developed clinical PK/pharmacodynamic model of gantenerumab was adapted to include a brain compartment as a driver of plaque removal and linked to the allometrically scaled above model from NHP. The new brain exposure-based model was used to predict trontinemab dosing regimens for effective amyloid reduction. Simulations from these models were used to inform dosing of trontinemab in the first-in-human clinical trial.

Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology.

The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain.

Toxicologic Pathology Forum: A Roadmap for Building State-of-the-Art Digital Image Data Resources for Toxicologic Pathology in the Pharmaceutical Industry.

Digitization of histologic slides brings with it the promise of enhanced toxicologic pathology practice through the increased application of computational methods. However, the development of these advanced methods requires access to substrate image data, that is, whole slide images (WSIs). Deep learning methods, in particular, rely on extensive training data to develop robust algorithms. As a result, pharmaceutical companies interested in leveraging computational methods in their digital pathology workflows must first invest in data infrastructure to enable data access for both data scientists and pathologists. The process of building robust image data resources is challenging and includes considerations of generation, curation, and storage of WSI files, and WSI access including via linked metadata. This opinion piece describes the collective experience of building resources for WSI data in the Roche group. We elaborate on the challenges encountered and solutions developed with the goal of providing examples of how to build a data resource for digital pathology analytics in the pharmaceutical industry.

MMO-Net (Multi-Magnification Organ Network): A use case for Organ Identification using Multiple Magnifications in Preclinical Pathology Studies.

Identifying organs within histology images is a fundamental and non-trivial step in toxicological digital pathology workflows as multiple organs often appear on the same whole slide image (WSI). Previous works in automated tissue classification have investigated the use of single magnifications, and demonstrated limitations when attempting to identify small and contiguous organs at low magnifications. In order to overcome these shortcomings, we present a multi-magnification convolutional neural network (CNN), called MMO-Net, which employs context and cellular detail from different magnifications to facilitate the recognition of complex organs. Across N=320 WSI from 3 contract research organization (CRO) laboratories, we demonstrate state-of-the-art organ detection and segmentation performance of 7 rat organs with and without lesions: liver, kidney, thyroid gland, parathyroid gland, urinary bladder, salivary gland, and mandibular lymph node (AUROC=0.99-1.0 for all organs, Dice≥0.9 except parathyroid (0.73)). Evaluation takes place at both inter- and intra CRO levels, suggesting strong generalizability performance. Results are qualitatively reviewed using visualization masks to ensure separation of organs in close proximity (e.g., thyroid vs parathyroid glands). MMO-Net thus offers organ localization that serves as a potential quality control tool to validate WSI metadata and as a preprocessing step for subsequent organ-specific artificial intelligence (AI) use cases. To facilitate research in this area, all associated WSI and metadata used for this study are being made freely available, forming a first of its kind dataset for public use.

IT/QA and Regulatory Aspects of Digital Pathology: Results of the 8th ESTP International Workshop.

Digital toxicologic histopathology has been broadly adopted in preclinical compound development for informal consultation and peer review. There is now increased interest in implementing the technology for good laboratory practice-regulated study evaluations. However, the implementation is not straightforward because systems and work processes require qualification and validation, with consideration also given to security. As a result of the high-throughput, high-volume nature of safety evaluations, computer performance, ergonomics, efficiency, and integration with laboratory information management systems are further key considerations. The European Society of Toxicologic Pathology organized an international expert workshop with participation by toxicologic pathologists, quality assurance/regulatory experts, and information technology experts to discuss qualification and validation of digital histopathology systems in a good laboratory practice environment, and to share the resulting conclusions broadly in the toxicologic pathology community.

Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies.

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.

Scientific and Regulatory Policy Committee Brief Communication: 2019 Survey on Use of Digital Histopathology Systems in Nonclinical Toxicology Studies.

Histopathologic evaluation and peer review using digital whole-slide images (WSIs) is a relatively new medium for assessing nonclinical toxicology studies in Good Laboratory Practice (GLP) environments. To better understand the present and future use of digital pathology in nonclinical toxicology studies, the Society of Toxicologic Pathology (STP) formed a working group to survey STP members with the goal of creating recommendations for implementation. The survey was administered in December 2019, immediately before the COVID-19 pandemic, and the results suggested that the use of digital histopathology for routine GLP histopathology assessment was not widespread. Subsequently, in follow-up correspondence during the pandemic, many responding institutions either began investigating or adopting digital WSI systems to reduce employee exposure to COVID-19. Therefore, the working group presents the survey results as a pre-pandemic baseline data set. Recommendations for use of WSI systems in GLP environments will be the subject of a separate publication.

The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019).

With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.

Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy.

Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal atrophy, particularly of the outer and inner nuclear layers, in rats. Herein, we report our experience creating and employing a hybrid approach, which combines conventional image processing and DL to quantify rodent retinal atrophy. Utilizing a DL approach based upon the VGG16 model architecture, models were trained, tested, and validated using 10,746 image patches scanned from whole slide images (WSIs) of hematoxylin-eosin stained rodent retina. The accuracy of this computational method was validated using pathologist annotated WSIs throughout and used to separately quantify the thickness of the outer and inner nuclear layers of the retina. Our results show that DL can facilitate the evaluation of therapeutic and/or light-induced atrophy, particularly of the outer retina, efficiently in rodents. In addition, this study provides a template which can be used to train, validate, and analyze the results of toxicologic pathology DL models across different animal species used in preclinical efficacy and safety studies.

Toxicologic Pathology Forum*: Opinion on Integrating Innovative Digital Pathology Tools in the Regulatory Framework.

Digital pathology is defined as the ability to examine digitized microscopic slides and to generate qualitative and quantitative data. The field of digital pathology is rapidly evolving and has the potential to revolutionize toxicologic pathology. Techniques such as automated 2-D image analysis, whole slide imaging, and telepathology are already considered "mature" technologies and have been used for decades in exploratory studies; however, many organizations are reluctant to use digital pathology in regulatory toxicology studies. Innovative technologies using digitized slides including high-content imaging modalities and artificial intelligence are still under development but are increasingly used in toxicologic pathology. While software validation requirements are already described, clear guidance for application of these rules to the digital pathology field are few and the acceptance of these technologies by regulatory authorities remains necessary for successful adoption of digital pathology into the mainstream of toxicologic pathology. This topic was discussed during a roundtable at the 2018 Annual Congress of the French Society of Toxicologic Pathology. This opinion article summarizes the discussion regarding the current questions and challenges on the integration of innovative digital pathology tools within a good laboratory practice framework and is meant to stimulate further discussion among the toxicologic pathology community. *This is an opinion article submitted to the Toxicologic Pathology Forum and does not constitute an official position of the Society of Toxicologic Pathology or the journal Toxicologic Pathology. The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of their respective agencies and organizations. The Toxicologic Forum is designed to stimulate broad discussion of topics relevant to regulatory issues in Toxicologic pathology. Readers of Toxicologic Pathology are encouraged to send their thoughts on these articles or ideas for new topics to toxicologicpathologyforum@toxpath.org .

Meeting Report: Tissue-based Image Analysis.

Quantitative image analysis (IA) is a rapidly evolving area of digital pathology. Although not a new concept, the quantification of histological features on photomicrographs used to be cumbersome, resource-intensive, and limited to specialists and specialized laboratories. Recent technological advances like highly efficient automated whole slide digitizer (scanner) systems, innovative IA platforms, and the emergence of pathologist-friendly image annotation and analysis systems mean that quantification of features on histological digital images will become increasingly prominent in pathologists' daily professional lives. The added value of quantitative IA in pathology includes confirmation of equivocal findings noted by a pathologist, increasing the sensitivity of feature detection, quantification of signal intensity, and improving efficiency. There is no denying that quantitative IA is part of the future of pathology; however, there are also several potential pitfalls when trying to estimate volumetric features from limited 2-dimensional sections. This continuing education session on quantitative IA offered a broad overview of the field; a hands-on toxicologic pathologist experience with IA principles, tools, and workflows; a discussion on how to apply basic stereology principles in order to minimize bias in IA; and finally, a reflection on the future of IA in the toxicologic pathology field.

Development and characterization of a largemouth bass cell line.

Abstract The development and characterization of a new cell line, derived from the ovary of Largemouth Bass Micropterus salmoides, is described. Gonad tissue was collected from Largemouth Bass that were electrofished from Oneida Lake, New York. The tissue was processed and grown in culture flasks at approximately 22°C for more than 118 passages during an 8-year period from 2004 to 2011. The identity of these cells as Largemouth Bass origin was confirmed by sequencing a portion of the cytochrome b gene. Growth rate at three different temperatures was documented. The cell line was susceptible to Largemouth Bass virus (LMBV) and its replication was compared with that of Bluegill Lepomis macrochirus fry (BF-2), one of the cell lines recommended for LMBV isolation by the American Fisheries Society Fish Health Section Blue Book. Quantitative PCR results from the replication trial showed the BF-2 cell line produced approximately 10-fold more LMBV copies per cell than the new Largemouth Bass cell line after 6 d, while the titration assay showed similar quantities in each cell line after 1 week. Received February 18, 2014; accepted April 16, 2014.

Bilateral ovarian cystadenoma in a geriatric African elephant (Loxodonta africana).

A 59-yr-old, captive female African elephant (Loxodonta africana) died of a cardiovascular collapse. Necropsy revealed bilateral replacement of the ovarian tissue by multiple cystic formations of up to 10 cm in diameter. The cysts were thin walled and filled with clear watery fluid. Smaller solid masses with an irregular surface projected from the wall of a few of the cysts. Histologically, the cystic structures were characterized by a single layer of well-differentiated cuboidal epithelial cells resting on a basement membrane. Occasionally the cysts contained nodular proliferations of single-layered, well-differentiated cuboidal epithelial cells forming anastomosing tubules and occasional papillary projections. The ovarian neoplasia was diagnosed as a bilateral multilocular serous ovarian cystadenoma. The nulliparous status and the advanced age may have contributed to the ovarian pathology.

Mycobacterium avium Subsp. avium infection in four veal calves: differentiation from intestinal tuberculosis.

Mycobacterium avium subsp. avium (Maa) is an intracellular pathogen belonging to the Mycobacterium avium-intracellulare complex (MAC). Reservoirs of MAC are the natural environment, wildlife and domestic animals. In adult bovine, MAC infections are typically caused by Mycobacterium avium subsp. paratuberculosis (Map). Maa infections in bovine are rarely reported but may cause clinical disease and pathological lesions similar to those observed in paratuberculosis or those induced by members of the Mycobacterium tuberculosis complex (MTBC). Therefore, differentiation of MAC from MTBC infection should be attempted, especially if unusual mycobacterial lesions are encountered. Four veal calves from a fattening farm dying with clinical signs of otitis media, fever, and weight loss were submitted for necropsy. Samples from affected organs were taken for histologic investigation, bacteriologic culture, and bacterial specification using PCR. Macroscopic thickening of the intestinal mucosa was induced by granulomatous enteritis and colitis. Intracytoplasmic acid-fast bacteria were detected by Ziehl-Neelsen stains and PCR revealed positive results for Mycobacterium avium subsp. avium. Clinical and pathological changes of Maa infection in veal calves had features of Mycobacterium avium subsp. paratuberculosis and the MTBC. Therefore, Mycobacterium tuberculosis complex infection should be considered in cases of granulomatous enteritis in calves.

Sulawesi tortoise adenovirus-1 in two impressed tortoises (Manouria impressa) and a Burmese star tortoise (Geochelone platynota).

Sulawesi tortoise adenovirus-1 (STAdV-1) is a newly discovered virus infecting endangered and threatened tortoises. It was initially described from a confiscated group of 105 Sulawesi tortoises (Indotestudo forsteni) obtained by the Turtle Survival Alliance and distributed to five sites with available veterinary care across the United States. In a 3-yr period from the initial outbreak, one multi-species collection that rehabilitated and housed adenovirus-infected Sulawesi tortoises experienced deaths in impressed tortoises (Manouria impressa) and a Burmese star tortoise (Geochelone platynota). Impressed tortoises that died had evidence of systemic viral infection with histopathologic features of adenovirus. Adenovirus was identified by consensus nested polymerase chain reaction (PCR) testing and subsequent sequencing of PCR products. Sequencing indicated that the adenovirus infecting these impressed tortoises and Burmese star tortoise was STAdV-1. In one impressed tortoise, viral infection was confirmed using transmission electron microscopy. In situ hybridization using a semiautomated protocol and fluorescein-labeled riboprobe identified STAdV-1 inclusions in spleen, liver, kidney, and testis of one impressed tortoise. The impact of this virus on captive and wild populations of tortoises is unknown; however, these findings indicate that STAdV-1 can be transmitted to and can infect other tortoise species, the impressed tortoise and Burmese star tortoise, when cohabitated with infected Sulawesi tortoises.

Meningitis caused by Mycoplasma mycoides subspecies capri in a goat.

A 2-year-old, female goat from Connecticut was submitted for necropsy with a 5-day history of pyrexia and intermittent neurologic signs, including nystagmus, seizures, and circling. Postmortem examination revealed suppurative meningitis. Histologic examination of the brain revealed that the meninges were diffusely infiltrated by moderate numbers of lymphocytes, macrophages, and fibrin, with scattered foci of dense neutrophilic infiltrate. Culture of pus and brainstem yielded typical mycoplasma colonies. DNA sequencing of the 16S ribosomal RNA gene revealed 99% sequence homology with Mycoplasma mycoides subspecies capri and Mycoplasma mycoides subspecies mycoides Large Colony biotype, which are genetically indistinguishable and likely to be combined as a single subspecies labeled M. mycoides subsp. capri. The present case is unusual in that not only are mycoplasma an uncommon cause of meningitis in animals, but additionally, in that all other reported cases of mycoplasma meningitis in goats, systemic lesions were also present. In the present case, meningitis was the only lesion, thus illustrating the need to consider mycoplasma as a differential diagnosis for meningitis in goats.

Actinomyces hyovaginalis-associated lymphadenitis in a Nubian goat.

A 6-year-old Nubian goat with a history of progressive weight loss and cough was presented for necropsy. The goat tested negative for antibodies to caseous lymphadenitis and caprine arthritis and encephalitis by hemagglutination inhibition assay and enzyme-linked immunosorbent assay, respectively. Postmortem examination revealed marked enlargement and, with histopathology, a fibrinopurulent necrotizing lymphadenitis of a tracheobronchial lymph node, with an appearance similar to that reported in cases of caseous lymphadenitis. An organism characterized by molecular methods as Actinomyces hyovaginalis was isolated together with Staphylococcus spp. and Streptococcus spp. from the lesion. No Corynebacterium pseudotuberculosis was recovered. To the authors' knowledge, this is the first isolation of A. hyovaginalis from a goat. Although the exact contribution of A. hyovaginalis to the lesion remains to be established, this case demonstrates that A. hyovaginalis should be considered in cases of caseous lymphadenitis-type lesions, especially when C. pseudotuberculosis has been excluded.