Determinants of immune complex-mediated glomerulonephritis

Determinants of immune complex-mediated glomerulonephritis. We have studied the influence of steric factors on the clinico-pathologic expression of immune complex-mediated glomerular diseases, utilizing ferritin as an exogenous antigen. The tracer was planted in the left kidney either in the subepithelial layer of the glomerular capillary wall or on the endothelium and lamina rara interna. Subepithelial immune complex formation resulted in non-inflammatory injury with heterologous-and autologous proteinuric phases (115 ± 16 mg/24 hrs on day 2; 183 ± 16 mg/24 hrs on day 9) lasting four to five weeks. The glomerular filtration rate of the experimental left kidney was reduced by 19% at day 3, and was increased by 20% at day 12 over right kidney values. Immune complexes persisted for more than seven weeks in the lamina rara externa. In contrast, immune complex deposition on the endothelium and in the lamina rara interna led to acute transient anuria, with a 38% drop in glomerular filtration rate at one hour, massive platelet accumulation, followed by a strong inflammatory response. Proteinuria did not develop. Functional and structural integrity was restored within 24 hours, with complete clearing of immune deposits. We conclude that the distribution of exogenous antigens within the capillary wall determines the structural and functional expression of immune-mediated glomerular diseases

Protecting Human and Animal Health: The Road from Animal Models to New Approach Methods

Animals and animal models have been invaluable for our current understanding of human and animal biology, including physiology, pharmacology, biochemistry, and disease pathology. However, there are increasing concerns with continued use of animals in basic biomedical, pharmacological, and regulatory research to provide safety assessments for drugs and chemicals. There are concerns that animals do not provide sufficient information on toxicity and/or efficacy to protect the target population, so scientists are utilizing the principles of replacement, reduction, and refinement (the 3Rs) and increasing the development and application of new approach methods (NAMs). NAMs are any technology, methodology, approach, or assay used to understand the effects and mechanisms of drugs or chemicals, with specific focus on applying the 3Rs. Although progress has been made in several areas with NAMs, complete replacement of animal models with NAMs is not yet attainable. The road to NAMs requires additional development, increased use, and, for regulatory decision making, usually formal validation. Moreover, it is likely that replacement of animal models with NAMs will require multiple assays to ensure sufficient biologic coverage. The purpose of this manuscript is to provide a balanced view of the current state of the use of animal models and NAMs as approaches to development, safety, efficacy, and toxicity testing of drugs and chemicals. Animals do not provide all needed information nor do NAMs, but each can elucidate key pieces of the puzzle of human and animal biology and contribute to the goal of protecting human and animal health. SIGNIFICANCE STATEMENT: Data from traditional animal studies have predominantly been used to inform human health safety and efficacy. Although it is unlikely that all animal studies will be able to be replaced, with the continued advancement in new approach methods (NAMs), it is possible that sometime in the future, NAMs will likely be an important component by which the discovery, efficacy, and toxicity testing of drugs and chemicals is conducted and regulatory decisions are made

Machine Learning Methods for Predicting HLA-Peptide Binding Activity

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis