Adeno-associated virus (AAV)-based vectors are the most frequently used platform for retinal gene therapy. Initially explored for the treatment of loss-of-function mutations underpinning many inherited retinal diseases, AAV-based ocular gene therapies are increasingly used to transduce endogenous cells to produce therapeutic proteins, thus producing site-specific biofactories. Relatively invasive ocular routes of administration (ROA) mean prominent procedure-related in-life, and histopathological findings may be observed with some regularity. Test article-related findings may vary with the ROA and cell populations transduced, with retinal pigmented epithelium (RPE) changes prominent (ranging from pigment alteration through degeneration, with or without associated degeneration of the overlying retina) with subretinal ROA, and more anterior changes (iris, ciliary body) generally observed with the intravitreal ROA. Ocular inflammation is the most frequent finding that occurs nonclinically and in patients, and is particularly pronounced with intravitreal administration. Extraocular findings may be observed in extraocular muscles, regional ganglia, or central visual pathways with multiple ocular ROA. Work is still needed to understand the mechanisms underpinning many of these ocular and extraocular findings. Emerging patient data is helping to clarify both the potential for translating nonclinical findings to predict possible human responses and the applicability of nonclinical biomonitoring methods to the clinical setting.

In October 2019, Novartis launched brolucizumab, a single-chain variable fragment molecule targeting vascular endothelial growth factor A, for the treatment of neovascular age-related macular degeneration. In 2020, rare cases of retinal vasculitis and/or retinal vascular occlusion (RV/RO) were reported, often during the first few months after treatment initiation, consistent with a possible immunologic pathobiology. This finding was inconsistent with preclinical studies in cynomolgus monkeys that demonstrated no drug-related intraocular inflammation, or RV/RO, despite the presence of preexisting and treatment-emergent antidrug antibodies (ADAs) in some animals. In this study, the immune response against brolucizumab in humans was assessed using samples from clinical trials and clinical practice. In the brolucizumab-naïve population, anti-brolucizumab ADA responses were detected before any treatment, which was supported by the finding that healthy donors can harbor brolucizumab-specific B cells. This suggested prior exposure of the immune system to proteins with structural similarity. Experiments on samples showed that naïve and brolucizumab-treated ADA-positive patients developed a class-switched, high-affinity immune response, with several linear epitopes being recognized by ADAs. Only patients with RV/RO showed a meaningful T cell response upon recall with brolucizumab. Further studies in cynomolgus monkeys preimmunized against brolucizumab with adjuvant followed by intravitreal brolucizumab challenge demonstrated that high ADA titers were required to generate ocular inflammation and vasculitis/vascular thrombosis, comparable to RV/RO in humans. Immunogenicity therefore seems to be a prerequisite to develop RV/RO. However, because only 2.1% of patients with ADA develop RV/RO, additional factors must play a role in the development of RV/RO.

Pathology scientific reports were first published in the 1960s describing morphometric approaches for quantitative analysis of cytological and histological samples. Digital toxicologic pathology is the use of any digital technology to support slide analysis in toxicologic pathology. Image analysis solutions in these assays can help quantify efficacy endpoints and abnormal responses, refine candidate selection, and help inform later, in vivo studies. A combination of technology advancements, regulatory decisions, and leadership from both the veterinary and medical diagnostic fields has paved the way for advancing digital toxicologic pathology diagnostics in support of discovery and regulatory pathology. It is important for the scientist and stereologist to discuss the meaning of the stereological endpoints in the context of the study and the main question, always keeping that ultimate goal in mind. As one builds digital toxicologic pathology capabilities, it is important to be focused on both the intended use and regulatory requirements.

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.

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 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.

A Working Group of the Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee conducted a technical and scientific review of current practices relating to the fixation, trimming, and sectioning of the nonrodent eye to identify key points and species-specific anatomical landmarks to consider when preparing and evaluating eyes of rabbits, dogs, minipigs, and nonhuman primates from ocular and general toxicity studies. The topics addressed in this Points to Consider article include determination of situations when more comprehensive evaluation of the globe and/or associated extraocular tissues should be implemented (expanded ocular sampling), and what constitutes expanded ocular sampling. In addition, this manuscript highlights the practical aspects of fixing, trimming, and sectioning the eye to ensure adequate histopathological evaluation of all major ocular structures, including the cone-dense areas (visual streak/macula/fovea) of the retina for rabbits, dogs, minipigs, and nonhuman primates, which is a current regulatory expectation for ocular toxicity studies. This Points to Consider article is a product of a Society of Toxicologic Pathology (STP) Working Group commissioned by the Scientific and Regulatory Policy Committee (SRPC) of the STP. It has been reviewed and approved by the SRPC, Executive Committee of the STP, the European Society of Toxicologic Pathology, and the British Society of Toxicological Pathology, but it does not represent a formal Best Practice recommendation of the Society; rather, it is intended to provide key "points to consider" for the toxicologic pathology community. The points expressed in this document are those of the authors and do not reflect views or policies of the employing institutions. Readers of Toxicologic Pathology are encouraged to send their thoughts on this article or ideas for new topics to the editor.

Normal retina and its cell layers are essential for processing visual stimuli, and loss of its integrity has been documented in many disease processes. The numbers and the axonal processes of retinal ganglion cells are reduced substantially in glaucoma, leading to vision loss and blindness. Similarly, selective loss of photoreceptors in age-related macular degeneration and hereditary retinal dystrophies also results in the compromise of visual acuity. Development of genetically modified mice has led to increased understanding of the pathogenesis of many retinal diseases. Similarly, in this digital era, usage of modalities to quantify the retinal cell loss has grown exponentially leading to a better understanding of the suitability of animal models to study human retinal diseases. These quantification modalities provide valuable quantifiable data in studying pathogenesis and disease progression. This review will discuss the immunohistochemical markers for various retinal cells, available automated tools to quantify retinal cells, and present an example of retinal ganglion cell quantification using HALO image analysis platform. Additionally, we briefly review retinal cell types and subtypes, salient features of retina in various laboratory animal species, and a few of the main disease processes that affect retinal cell numbers in humans.

Identification of test article–related microscopic findings in ocular toxicology studies requires a working knowledge of the artifacts and procedure-related or background findings commonly encountered in such studies. The objective of this article is to provide a mini-atlas of the artifacts and procedure-related or spontaneous background findings commonly observed in ocular tissues from animals in toxicology studies of ocular drug candidates. Artifacts in the eye are often related to collection or fixation procedures and include swelling and vacuolation of lens fibers, separation of the neuroretina from the retinal pigment epithelium (RPE), and vacuolation of the optic nerve. Common in-life procedure-related findings include intravitreal injection needle tracks in the sclera and ciliary body pars plana and foci of RPE hypertrophy and/or hyperpigmentation at subretinal injection sites. Common background findings include corneal mineralization, uveal mononuclear cell infiltrates, and peripheral displacement of photoreceptor nuclei in the retina. A few uncommon spontaneous background findings that may be confused with test article–related findings, such as bilateral optic atrophy in macaques, are also included.

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small 224 × 224 pixels images (patches) at 6 different levels of magnification. Using 4 studies as training set and 2 studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each magnification level. Among these model architectures, Inception-v3 and ResNet-50 outperformed VGG-16. Inception-v3 identified the tissue from query images, with an accuracy up to 83.4%. Most misclassifications occurred between histologically similar tissues. Investigation of the features learned by the model (embedding layer) using Uniform Manifold Approximation and Projection revealed not only coherent clusters associated with the individual tissues but also subclusters corresponding to histologically meaningful structures that had not been annotated or trained for. This suggests that the histological representation learned by HistoNet could be useful as the basis of other machine learning algorithms and data mining. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining.

Silicone oil droplets have been reported in the eyes of human patients following intravitreous (IVT) injections with several marketed biotherapeutic products. Intravitreous administration of a novel biotherapeutic in a 14-week cynomolgus monkey study using insulin syringes was associated with 2, non-test-article-related phenomena: “vitreous floater/clear sphere” on indirect ophthalmoscopy and intrascleral “foreign material near injection track” on histopathology. Retrospective analysis of 81 other preclinical studies of IVT administration of novel biotherapeutics found a greater frequency of clear spheres in monkey IVT studies using insulin syringes and formulations containing polysorbate. We were able to correlate microscopic findings of clear circular to oval areas in the sclera near the injection track with an energy-dispersive X-ray spectroscopy (EDS) signal for silicon at the same location in the sclera. These observations provide further evidence that silicone lubricant in insulin syringes/needles is the source of clear spheres noted in the vitreous and foreign material noted near the injection track in the sclera. Although considered inert and toxicologically insignificant, silicone deposition within the eye should form part of the risk–benefit equation in a clinical setting.

The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxicologic pathology would improve the daily workflow and future impact of toxicologic pathologists globally. Now 2 years later, the authors of this review consider whether, in their opinion, there is any evidence that supports that thesis. Specifically, we consider the opportunities and challenges for applying ML (the study of computer algorithms that are able to learn from example data and extrapolate the learned information to unseen data) algorithms in toxicologic pathology and how regulatory bodies are navigating this rapidly evolving field. Although we see similarities with the "Last Mile" metaphor, the weight of evidence suggests that toxicologic pathologists should approach ML with an equal dose of skepticism and enthusiasm. There are increasing opportunities for impact in our field that leave the authors cautiously excited and optimistic. Toxicologic pathologists have the opportunity to critically evaluate ML applications with a "call-to-arms" mentality. Why should we be late adopters? There is ample evidence to encourage engagement, growth, and leadership in this field.

Biomedical research involving animal models continues to provide important insights into disease pathogenesis and treatment of diseases that impact human health. In particular, nonhuman primates (NHPs) have been used extensively in translational research due to their phylogenetic proximity to humans and similarities to disease pathogenesis and treatment responses as assessed in clinical trials. Microscopic changes in tissues remain a significant endpoint in studies involving these models. Spontaneous, expected (ie, incidental or background) histopathologic changes are commonly encountered and influenced by species, genetic variations, age, and geographical origin of animals, including exposure to infectious or parasitic agents. Often, the background findings confound study-related changes, because numbers of NHPs used in research are limited by animal welfare and other considerations. Moreover, background findings in NHPs can be exacerbated by experimental conditions such as treatment with xenobiotics (eg, infectious morphological changes related to immunosuppressive therapy). This review and summary of research-relevant conditions and pathology in rhesus and cynomolgus macaques, baboons, African green monkeys, common marmosets, tamarins, and squirrel and owl monkeys aims to improve the interpretation and validity of NHP studies.

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]

Toxicologic pathology is one of the most valuable fields contributing to the advancement of animal and human health. With the ever-changing technological and economic environment, the basic skill set that pathologists are equipped with may require refinement to address the current and future needs. Periodically, pathologists must add relevant, new skills to their toolbox. The Career Development and Outreach Committee of the Society of Toxicologic Pathology (STP) sponsored a career development workshop entitled “Looking Forward: Cutting-edge Technologies and Skills for Pathologists in the Future” in conjunction with the STP 38th Annual Symposium. Experts were chosen to speak on artificial intelligence, clustered regularly interspaced short palindromic repeats technology, microRNAs, and next-generation sequencing. This article provides a summary of the talks presented at the workshop.

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Information on background changes in the ocular tissues of rabbits ( Oryctolagus cuniculus), a common species employed in ophthalmic toxicology studies, is sparse. This complicates interpretation of changes in light of small sample sizes on any single study. The purpose of this publication is to document the interstudy incidence of spontaneous or iatrogenic changes occurring in eyes of control rabbits. Photomicrographs of select lesions are provided. The data set was derived from a total of 54 studies conducted over an eleven-year period at Alcon Research Ltd., a Novartis Division, which featured topical ocular and contact lens routes of administration. It includes a total of 1,222 pigmented and albino New Zealand rabbits and a total of 2,084 eyes which were either untreated or treated with innocuous control articles. There were no noteworthy differences across routes of administration. Changes in anterior segment ocular and adnexal tissues were more common than in posterior segment ocular tissues. Overall, mononuclear cell infiltration was the most common finding. The retina was the posterior tissue most commonly observed with spontaneous changes, with folds and rosettes being the most common retinal finding. Retinal changes were more common in albino as compared to pigmented rabbits. Understanding the incidence and characteristics of spontaneous ocular lesions facilitates accurate and consistent diagnosis and data interpretation.