The use of emerging safety biomarkers in nonclinical and clinical safety assessment - The current and future state: An IQ DruSafe industry survey.

Pharmaceutical and biotechnology companies rarely disclose their use of translational emerging safety biomarkers (ESBs) during drug development, and the impact of ESB use on the speed of drug development remains unclear. A cross-industry survey of 20 companies of varying size was conducted to understand current trends in ESB use and future use prospects. The objectives were to: (1) determine current ESB use in nonclinical and clinical drug development and impact on asset advancement; (2) identify opportunities, gaps, and challenges to greater ESB implementation; and (3) benchmark perspectives on regulatory acceptance. Although ESBs were employed in only 5-50% of studies/programs, most companies used ESBs to some extent, with larger companies demonstrating greater nonclinical use. Inclusion of ESBs in investigational new drug applications (INDs) was similar across all companies; however, differences in clinical trial usage could vary among the prevailing health authority (HA). Broader implementation of ESBs requires resource support, cross-industry partnerships, and collaboration with HAs. This includes generating sufficient foundational data, demonstrating nonclinical to clinical translatability and practical utility, and clearly written criteria by HAs to enable qualification. If achieved, ESBs will play a critical role in the development of next-generation, translationally-tailored standard laboratory tests for drug development.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment.

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics.

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Rat Urinary Osteopontin and Neutrophil Gelatinase-Associated Lipocalin Improve Certainty of Detecting Drug-Induced Kidney Injury.

Traditional kidney biomarkers are insensitive indicators of acute kidney injury, with meaningful changes occurring late in the course of injury. The aim of this work was to demonstrate the diagnostic potential of urinary osteopontin (OPN) and neutrophil gelatinase-associated lipocalin (NGAL) for drug-induced kidney injury (DIKI) in rats using data from a recent regulatory qualification submission of translational DIKI biomarkers and to compare performance of NGAL and OPN to five previously qualified DIKI urinary biomarkers. Data were compiled from 15 studies of 11 different pharmaceuticals contributed by Critical Path Institute's Predictive Safety Testing Consortium (PSTC) Nephrotoxicity Working Group (NWG). Rats were given doses known to cause DIKI or other target organ toxicity, and urinary levels of the candidate biomarkers were assessed relative to kidney histopathology and serum creatinine (sCr) and blood urea nitrogen (BUN).OPN and NGAL outperformed sCr and BUN in identifying DIKI manifested as renal tubular epithelial degeneration or necrosis. In addition, urinary OPN and NGAL, when used with sCr and BUN, increased the ability to detect renal tubular epithelial degeneration or necrosis. NGAL and OPN had comparable or improved performance relative to Kim-1, clusterin, albumin, total protein, and beta-2 microglobulin. Given these data, both urinary OPN and NGAL are appropriate for use with current methods for assessing nephrotoxicity to identify and monitor DIKI in regulatory toxicology studies in rats. These data also support exploratory use of urinary OPN and NGAL in safety monitoring strategies of early clinical trials to aid in the assurance of patient safety.

Detection of cytokine protein expression in mouse lung homogenates using suspension bead array.

BACKGROUND: The objective for this present study was to determine whether or not suspension bead array is a feasible method to detect changes in cytokine protein expression in mouse lung tissue homogenates. Here, we report on suspension bead array as a feasible method for detection of lipopolysaccharide (LPS)-evoked changes in cytokine protein expression in mouse lung tissue homogenates. MATERIALS AND METHODS: Mice were treated (0.2 ml, intraperitoneal, i.p.) with phosphate buffered saline (PBS) or LPS (0.25 mg/ml) and sacrificed at either 2- or 24-hours post treatment. Formalin-fixed and paraffin-embedded lung sections were evaluated by light microscopy. Flash frozen lung tissues were homogenized for measurement of various cytokine protein expression levels using suspension bead array, antibody array and ELISA. Comparison between groups was performed using a Wilcoxon signed rank test. RESULTS: Pulmonary perivascular edema and an accumulation of mixed cell infiltrates within blood and lymphatic vessels, as well as in the adjacent interstitium, were present at both 2- and 24-hours following LPS treatment. A minimal increase in the number of alveolar macrophages was also observed in the 24-hour LPS-treated mice only. The suspension bead array assay revealed statistically significant increases in mouse lung tissue homogenate levels of interleukin-6 (IL-6) and granulocyte/macrophage colony-stimulating factor (GM-CSF) proteins and a decrease in IL-2 protein at 24-hours post LPS-treatment only. Similar cytokine protein expression patterns were observed using antibody array. Significantly increased IL-6 protein expression levels were also detected using ELISA, which correlated with the suspension bead array data. CONCLUSION: The present study shows that suspension bead array is a feasible method to detect changes in cytokine protein expression in mouse lung tissue homogenates.