Toxicologic Pathology最新文献

The veterinary research community requires statistically robust reference intervals in which to contextualize the results of laboratory tests obtained in research studies. While published reference intervals are available for veterinary clinical practice, they typically do not account for differences in animal husbandry, variations in analytical equipment, and the diverse range of species encountered in a research setting. In addition, existing guidelines for statistical calculation of reference intervals do not address commonly encountered issues with data quality, sample size, research-induced population biases, and other impediments. In this manuscript, we document our pipeline to extract, partition, analyze, and statistically summarize in-house clinical pathology data for developing useful reference intervals to support research at the Integrated Research Facility at Fort Detrick (National Institute of Allergy and Infectious Diseases) and showcase a practical application of statistical methodology that can guide other facilities in their own determination of clinical pathology reference intervals.

Clinical pathology endpoints are routinely assessed in nonclinical toxicity studies and the magnitude of test article-related changes is frequently expressed using quantitative and/or qualitative severity descriptors. Quantitative descriptors (ie, percent or fold change) are easily calculated to express numerical magnitude of a change but may not adequately convey biological relevance. A specific quantitative magnitude may be associated with vastly different levels of pathophysiologic relevance depending on several factors, including the nature of the endpoint, the animal species/strain, and the magnitude and direction of change. Qualitative descriptors (eg, minimal and mild) offer a succinct way to provide additional context to the pathophysiologic relevance but are more challenging to ascribe to a change. The assignment of qualitative descriptors often requires a subjective, comprehensive, and multifaceted approach using various factors in addition to numerical calculation. Because of the subjectivity involved, the qualitative severity descriptor assigned to a specific change may differ among clinical pathology endpoints, species/strain, contributing scientists, and studies/programs. Quantitative and qualitative severity descriptors may provide complementary information and may be used individually or in combination. This opinion piece primarily explains the process and discusses caveats and various factors taken into consideration by clinical pathologists while ascribing qualitative severity descriptors.

This brief communication details the clinical, macroscopic, histological, and immunohistochemical features of a spontaneous case of uveodermatologic syndrome (UDS) in a laboratory beagle dog. A bilateral and symmetrical panuveitis, rich in macrophages and T lymphocytes, with distinctive extracellular clumped or phagocytised melanin pigment granules, and Dalen-Fuchs nodules was diagnosed following occurrence of ocular symptoms and subsequent blindness in a female peripubertal 9.5-month-old beagle dog. Although no macroscopic lesions were visible in the skin, microscopic examination revealed a histiocytic and lymphocytic lichenoid dermatitis with pigmentary incontinence. UDS has not been described as a background finding in laboratory beagle dogs before, although it is a well-known immune-mediated disease in certain canine breeds. Knowledge that UDS can occur in laboratory beagle dogs involved in preclinical studies, especially ocular studies, is essential for toxicologic pathologists.

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.

Advancements in digital pathology and artificial intelligence (AI) have enormous transformative potential for nonclinical toxicologic pathology and are already changing the ways in which pathologists work. However, due to the rapid evolution of digital pathology and AI, the toxicologic pathology community would benefit from an update on these advancements, which can be used to aid drug development. Here we identify key articles published on the use of digital pathology and AI in the field and provide current regulatory statuses and guidelines. For digital pathology, we outline the requirements for equipment, validation processes, workflows, and archiving. Challenges to achieve system interoperability and to establish harmonization through Digital Imaging and Communications in Medicine compatibility are also discussed. For AI, we highlight considerations for model development, including the determination of ground truth, problems that may arise due to bias, and how the accuracy and precision of AI algorithms can be assessed. Finally, we discuss the challenges and potential for AI-assisted toxicologic pathology, picturing a future where technology and scientific expertise work hand-in-hand to improve the quality and efficiency of nonclinical drug safety evaluation. This publication is a deliverable of the European Innovative Medicines Initiative 2 Joint Undertaking, "Bigpicture."

Histopathologic evaluation plays a crucial role in assessing morphological tissue alterations in disease models and toxicology studies. Identifying small quantitative shifts in specific substructures of organs can be challenging due to the subjective nature of visual assessment and the pathologist's reliance on categorical measurements rather than continuous ones. The emergence of digital pathology and artificial intelligence (AI) provides the ability to quantify different organ substructures using automated methods. Here, we employed a deep learning method to integrate normal pancreatic substructures into an algorithm. We also included areas of abnormal pancreas in the deep learning model. Once the image analysis pipeline was developed, we tested its effectiveness on a disease model and a toxicity study. The quantitative measurements clearly differentiated between control animals and those in the disease model or treated with a test article. In the toxicity study, we observed a distinct dose-dependent change. This approach could be applied to other organs and different species.

Toxicologic pathologists assess large data sets from nonclinical studies to identify treatment-related effects to assist in predicting human safety hazards. Statistical testing can facilitate data interpretation by highlighting group differences that have a low probability of random occurrence based on a pre-determined P-value cut-off (eg, P < .05). While this method has been used in the interpretation of pathology data for decades, the appropriateness of utilizing statistical testing in this way has been challenged. Here, we discuss common statistical pitfalls in the analysis of toxicologic pathology data, with emphasis on clinical pathology, reaffirming that appropriate use of statistical analysis requires an understanding of (1) the parameters assessed; (2) the inherent strengths and weaknesses of the statistical method used; and (3) that appropriate interpretation of pathology data is based on the pathologist's expertise. The presence or absence of statistical significance should not supersede expert judgment but should be one of many tools used to reach a conclusion.

This article is a summary of a half-day continuing education course jointly sponsored by the Society for Toxicologic Pathology (STP) and the Health and Environmental Sciences Institute (HESI) at the annual meeting of the STP in Baltimore, MD, held June 16-19, 2024. Presenters discussed pathology, toxicology, immunotoxicology, and regulatory implications of findings in the immune system in context of development of immunomodulatory therapeutics. Interpretation of pathology findings requires knowledge of immune system morphology and function including species-specific differences and spontaneous findings in animal model systems. A weight of evidence (WoE) approach is required to integrate pathology findings and immunotoxicology assay results to assess translatability to humans. Communication and collaboration among scientists of various disciplines can be instrumental in optimal generation and interpretation of appropriate data for development of immunomodulatory therapeutics.

Historically, safety and efficacy assessment of medical devices began and has continued as standards under the International Organization for Standardization (ISO) rather than under regulatory agency guidelines applied to developing other biomedical product classes. These parallel and unequal pathways have led to multiple and substantive differences in methods and endpoints to determine adverse biological responses among therapeutic classes. Toxicologic pathologists with medical device experience consider standardized nomenclature and diagnostic criteria for medical devices and device-containing combination products as a critical unmet need for nonclinical pathology evaluations. The International Harmonization of Nomenclature and Diagnostic Criteria for Lesions (INHAND) initiative has established globally accepted terminology for proliferative and nonproliferative lesions in various laboratory animal species. Experienced pathologists have identified that some existing INHAND terms for rodents and particularly nonrodents are already used or can be modified for use in medical device studies, but new terms for diagnostic features unique to medical device studies are needed to close gaps in existing INHAND nomenclature. The best approach to establishing appropriate INHAND terms for medical devices (and by extension, device-containing combination products) will be to develop and implement suitable terminology (modified and new, as warranted) to address unmet needs for this distinctive therapeutic class.

The pace of technological innovation in the pharmaceutical industry, like in many other sectors, is accelerating rapidly. This is not only reshaping how pharmaceutical Research and Development (R&D) is conducted (e.g., introduction of novel models, endpoints, and instrumentation) but also influencing the types of therapeutic modalities being developed. In addition, societal and regulatory expectations have evolved to emphasize approaches that align with the 4Rs principles (Replacement, Reduction, Refinement, and Responsibility) and to encourage the replacement of animal testing with new approach methods (NAMs) through the FDA Modernization Act 2.0. While innovation, societal changes, and regulatory evolution are not new, what stands out is the unprecedented speed and scale at which these transformations are occurring. This acceleration is fueled predominantly by groundbreaking technological advancements (e.g., artificial intelligence, deep learning, communication tools, and digital pathology) in the context of rapidly changing societal dynamics such as globalization, social networking, and the increase in remote working. Given these potentially disruptive changes, it is essential to consider how toxicologic pathologists need to adapt. More importantly, how can they leverage these advancements to contribute even more significantly to the discovery and development of novel, safe, and effective medicines? In essence, what types of toxicologic pathologists will the pharmaceutical industry require in the future?