Complex In Vitro Model Characterization for Context of Use in Toxicologic Pathology: Use Cases by Collaborative Teams of Biologists, Bioengineers, and Pathologists.

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.

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.

Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.

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.

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.

Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We hypothesized that artificial intelligence-based deep learning (DL) could provide decision support for the toxicologic pathologist by screening for the proliferative changes, verifying the expected pattern for the positive control groups. Whole slide images (WSIs) of the lungs, thymus, and stomach from positive control groups were used for supervised training of a convolutional neural network (CNN). A single pathologist annotated WSIs of normal and abnormal tissue regions for training the CNN-based supervised classifier using INHAND criteria. The algorithm was evaluated using a subset of tissue regions that were not used for training and then additional tissues were evaluated blindly by 2 independent pathologists. A binary output (proliferative classes present or not) from the pathologists was compared to that of the CNN classifier. The CNN model grouped proliferative lesion positive and negative animals at high concordance with the pathologists. This process simulated a workflow for review of these studies, whereby a DL algorithm could provide decision support for the pathologists in a nonclinical study.

Proposal to Eliminate Urethane-Treated Positive Control Dose Groups in 26-Week Tg.rasH2 Carcinogenicity Studies.

Short-term (26 weeks) Tg.rasH2 mouse carcinogenicity studies have been conducted as an alternative model to the conventional 2-year mouse carcinogenicity studies, using urethane as a positive control material. In these studies, urethane was used at a dose of 1,000 mg/kg/dose, administered intraperitoneally on days 1, 3, and 5. Urethane consistently produces lung adenomas and carcinomas and hemangiosarcomas of the spleen, proving validity of the assay. We conducted 3 pilot studies at 3 different sites of Charles River Laboratories using a lower dose of urethane (500 mg/kg/dose), administered on days 1, 3, and 5, followed by a 12-week observation period. Our results demonstrate that a lower dose can be used successfully with fewer number of animals per sex to prove the validity of the assay. However, based on our cumulative experience with this model, we propose to eliminate positive control dose groups in future Tg.rasH2 carcinogenicity studies.

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology.

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].

The Emergence of Microphysiological Systems (Organs-on-chips) as Paradigm-changing Tools for Toxicologic Pathology.

Microphysiological systems (MPS), commonly known as organs-on-chips, are a rapidly advancing technology that promises to impact many areas of medical and toxicological pathology. In this minireview, the history of MPS and its potential utility in safety assessment are described with the toxicologic pathologist in mind. Several MPS development focus areas are defined, and recent progress in the area is highlighted. MPS will likely become an important tool for the toxicologic pathologist as part of our role in the safety assessment process within the pharmaceutical, biotechnology, medical device, and cosmetic and agrichemical industries.

Interstitial Adipocytes in the Beagle Dog and New Zealand White Rabbit Choroid Plexus.

The choroid plexus (CP) produces cerebrospinal fluid and has epithelial, interstitial, and vascular compartments. The CP is a potential site of toxicity, and recognizing the normal microanatomy in different animal models is important for the pathologist. In preclinical studies with beagle dog and New Zealand white rabbits, we observed variable numbers of adipocytes in the CP interstitium of control and xenobiotic-treated animals. The adipocytes were unilocular and consistent morphologically with white adipose tissue. There was a striking variability in the number of adipocytes; however, the presence of adipocytes was not associated with other microscopic findings that would suggest a pathologic process. The morphology of adipocytes was reminiscent of what is observed normally in the interstitium of other tissues like skeletal muscle, bone marrow, and the subcutis. Therefore, we propose that the interstitial adipocytes not be recorded as a finding in preclinical studies unless the adipocytes are altered spontaneously (ie, lipoma) or after xenobiotic treatment.

Duchenne and Becker Muscular Dystrophies: A Review of Animal Models, Clinical End Points, and Biomarker Quantification.

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are neuromuscular disorders that primarily affect boys due to an X-linked mutation in the DMD gene, resulting in reduced to near absence of dystrophin or expression of truncated forms of dystrophin. Some newer therapeutic interventions aim to increase sarcolemmal dystrophin expression, and accurate dystrophin quantification is critical for demonstrating pharmacodynamic relationships in preclinical studies and clinical trials. Current challenges with measuring dystrophin include the variation in protein expression within individual muscle fibers and across whole muscle samples, the presence of preexisting dystrophin-positive revertant fibers, and trace amounts of residual dystrophin. Immunofluorescence quantification of dystrophin can overcome many of these challenges, but manual quantification of protein expression may be complicated by variations in the collection of images, reproducible scoring of fluorescent intensity, and bias introduced by manual scoring of typically only a few high-power fields. This review highlights the pathology of DMD and BMD, discusses animal models of DMD and BMD, and describes dystrophin biomarker quantitation in DMD and BMD, with several image analysis approaches, including a new automated method that evaluates protein expression of individual muscle fibers.

Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by Eg5 Inhibition.

Modulation of the cell cycle may underlie the toxicologic or pharmacologic responses of a potential therapeutic agent and contributes to decisions on its preclinical and clinical safety and efficacy. The descriptive and quantitative assessment of normal, aberrant, and degenerate mitotic figures in tissue sections is an important end point characterizing the effect of xenobiotics on the cell cycle. Historically, pathologists used manual counting and special staining visualization techniques such as immunohistochemistry for quantification of normal, aberrant, and degenerate mitotic figures. We designed an automated image analysis algorithm for measuring these mitotic figures in hematoxylin and eosin (H&E)-stained sections. Algorithm validation methods used data generated from a subcutaneous human transitional cell carcinoma xenograft model in nude rats treated with the cell cycle inhibitor Eg5. In these studies, we scanned and digitized H&E-stained xenografts and applied a complex ruleset of sequential mathematical filters and shape discriminators for classification of cell populations demonstrating normal, aberrant, or degenerate mitotic figures. The resultant classification system enabled the representations of three identifiable degrees of morphological change associated with tumor differentiation and compound effects. The numbers of mitotic figure variants and mitotic indices data generated corresponded to a manual assessment by a pathologist and supported automated algorithm verification and application for both efficacy and toxicity studies.

Tailored Pig Models for Preclinical Efficacy and Safety Testing of Targeted Therapies.

Despite enormous advances in translational biomedical research, there remains a growing demand for improved animal models of human disease. This is particularly true for diseases where rodent models do not reflect the human disease phenotype. Compared to rodents, pig anatomy and physiology are more similar to humans in cardiovascular, immune, respiratory, skeletal muscle, and metabolic systems. Importantly, efficient and precise techniques for genetic engineering of pigs are now available, facilitating the creation of tailored large animal models that mimic human disease mechanisms at the molecular level. In this article, the benefits of genetically engineered pigs for basic and translational research are exemplified by a novel pig model of Duchenne muscular dystrophy and by porcine models of cystic fibrosis. Particular emphasis is given to potential advantages of using these models for efficacy and safety testing of targeted therapies, such as exon skipping and gene editing, for example, using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated system. In general, genetically tailored pig models have the potential to bridge the gap between proof-of-concept studies in rodents and clinical trials in patients, thus supporting translational medicine.

On-target and off-target-based toxicologic effects.

Adverse toxicologic effects are categorized as chemical-based, on-target, or off-target effects. Chemical-based toxicity is defined as toxicity that is related to the physicochemical characteristics of a compound and its effects on cellular organelles, membranes, and/or metabolic pathways. On-target refers to exaggerated and adverse pharmacologic effects at the target of interest in the test system. Off-target refers to adverse effects as a result of modulation of other targets; these may be related biologically or totally unrelated to the target of interest. Both the risk assessment and development strategies used for xenobiotics are influenced by the understanding of the mechanism of toxicity. It is imperative that the toxicologic pathologist use the toxicologic and biologic data at hand and literature information on the target to form testable hypotheses related to whether a toxicity is chemical-based, on-target, or off-target. The objective of this session at the 2012 Society of Toxicologic Pathologists Symposium in Boston, Massachusetts, was to discuss chemical-based, on-target, and off-target-based effects and the scientific approaches used to aid in their human risk assessment.

High molecular weight polyethylene glycol cellular distribution and PEG-associated cytoplasmic vacuolation is molecular weight dependent and does not require conjugation to proteins.

Conjugation of therapeutic proteins with high molecular weight polyethylene glycols (HMW PEGs) is used to extend the half-life of biologics. To evaluate the effects of HMW PEGs in animals, we used an immunohistochemical procedure to study the tissue distribution and toxicity of unconjugated HMW PEGs in rats given 100 mg/kg (10K)PEG, (20K)PEG, or (40K)PEG intravenously. Both the PEG cellular distribution and the histology were different between groups. In (10K)PEG and (20K)PEG groups, PEG immunoreactivity was most prominent in the renal tubule epithelium and in alveolar macrophages and hepatic Kupffer cells and cellular vacuolation was absent. In contrast, rats given (40K)PEG had strong PEG immunoreactivity in splenic subcapsular red pulp macrophages, renal interstitial macrophages, and choroid plexus epithelial cells that was frequently associated with cytoplasmic vacuolation. While the vacuolation appeared to be an adaptive response, there was focal renal tubular epithelial degeneration associated with strong PEG immunoreactivity in one rat given (40K)PEG. These data indicate that both the tissue distribution and the vacuolation observed with unconjugated HMW PEGs are markedly influenced by the molecular weight of the PEG and that when vacuolation is observed it is likely an adaptive change that is associated with PEG cytoplasmic immunoreactivity.

Proliferative and nonproliferative lesions of the rat and mouse mammary, Zymbal's, preputial, and clitoral glands.

The mammary gland of laboratory rodents is an important organ for the evaluation of effects of xenobiotics, especially those that perturb hormonal homeostasis or are potentially carcinogenic. Mammary gland cancer is a leading cause of human mortality and morbidity worldwide and is a subject of major research efforts utilizing rodent models. Zymbal's, preputial, and clitoral glands are standard tissues that are evaluated in animal models that enable human risk assessment of xenobiotics. A widely accepted and utilized international harmonization of nomenclature for mammary, Zymbal's, preputial, and clitoral gland lesions in laboratory animals will improve diagnostic alignment among regulatory and scientific research organizations and enrich international exchanges of information among toxicologists and pathologists.

Qualification of cardiac troponin I concentration in mouse serum using isoproterenol and implementation in pharmacology studies to accelerate drug development.

Cardiac troponin I is a useful biomarker of myocardial injury, but its use in mice and application to early drug discovery are not well described. The authors investigated the relationship between cTnI concentration in serum and histologic lesions in heart tissue from mice treated with isoproterenol (ISO). Cardiac TnI concentrations in serum increased in a dose-dependant manner and remained increased twenty-four to forty-eight hours after a single administration of isoproterenol. Increased cTnI concentration was of greater magnitude and longer duration than increased fatty acid binding protein 3 concentration, aspartate aminotransferase activity, and creatine kinase activity in serum. Isoproterenol-induced increases in cTnI concentrations were both greater and more sustained in BALB/c than in CD1 mice and correlated with incidence and severity of lesions observed in heart sections from both strains. In drug development studies in BALB/c mice with novel kinase inhibitors, cTnI concentration was a reliable stand-alone biomarker of cardiac injury and was used in combination with measurements of in vivo target inhibition to demonstrate an off-target contribution to cardiotoxicity. Additional attributes, including low cost and rapid turnaround time, made cTnI concentration in serum invaluable for detecting cardiotoxicity, exploring structure-activity relationships, and prioritizing development of compounds with improved safety profiles early in drug discovery.

Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies.

CONTEXT.­: Duchenne muscular dystrophy is a rare, progressive, and fatal neuromuscular disease caused by dystrophin protein loss. Common investigational treatment approaches aim at increasing dystrophin expression in diseased muscle. Some clinical trials include assessments of novel dystrophin production as a surrogate biomarker of efficacy, which may predict a clinical benefit from treatment. OBJECTIVES.­: To establish an immunofluorescent scanning and digital image analysis workflow that provides an objective approach for staining intensity assessment of the immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in muscle cryosections. DESIGN.­: Optimal and repeatable digital image capture was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap (Flagship Biosciences, Westminster, Colorado) algorithm was validated by comparing high-power microscopic field total and dystrophin-positive fiber counts obtained by trained pathologists to data derived by MuscleMap. Next, the algorithm was tested on whole-slide images of immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy, and control patients. RESULTS.­: When used under the guidance of a trained pathologist, the digital image analysis tool met predefined validation criteria and demonstrated functional and statistical equivalence with manual assessment. This work is the first, to our knowledge, to qualify and validate immunofluorescent scanning and digital tissue image-analysis workflow, respectively, with the rigor required to support the clinical trial environments. CONCLUSIONS.­: MuscleMap enables analysis of all fibers within an entire muscle biopsy section and provides data on a fiber-by-fiber basis. This will allow future clinical trials to objectively investigate myofibers' dystrophin expression at a greater level of consistency and detail.

The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth.

CONTEXT: - Novel therapeutics often target complex cellular mechanisms. Increasingly, quantitative methods like digital tissue image analysis (tIA) are required to evaluate correspondingly complex biomarkers to elucidate subtle phenotypes that can inform treatment decisions with these targeted therapies. These tIA systems need a gold standard, or reference method, to establish analytical validity. Conventional, subjective histopathologic scores assigned by an experienced pathologist are the gold standard in anatomic pathology and are an attractive reference method. The pathologist's score can establish the ground truth to assess a tIA solution's analytical performance. The paradox of this validation strategy, however, is that tIA is often used to assist pathologists to score complex biomarkers because it is more objective and reproducible than manual evaluation alone by overcoming known biases in a human's visual evaluation of tissue, and because it can generate endpoints that cannot be generated by a human observer. OBJECTIVE: - To discuss common visual and cognitive traps known in traditional pathology-based scoring paradigms that may impact characterization of tIA-assisted scoring accuracy, sensitivity, and specificity. DATA SOURCES: - This manuscript reviews the current literature from the past decades available for traditional subjective pathology scoring paradigms and known cognitive and visual traps relevant to these scoring paradigms. CONCLUSIONS: - Awareness of the gold standard paradox is necessary when using traditional pathologist scores to analytically validate a tIA tool because image analysis is used specifically to overcome known sources of bias in visual assessment of tissue sections.