Evaluation of 10 Urinary Biomarkers for Renal Safety With 5 Nephrotoxicants in Nonhuman Primates

Vlasáková, Kateřina; Troth, Sean P.; Sistare, Frank D.; Glaab, Warren E.

doi:10.1177/0192623320932159

Cited by 9 publications

(14 citation statements)

References 33 publications

(44 reference statements)

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“…Also, the methodology presented here could be extended to other biomarkers (eg, cystatin C, β2 microglobulin), a broader spectrum of lesions, other tissues, or even other different contexts, either in toxicology or in general pathology (eg, other tissues, disease markers, genetic abnormalities, etc). 24,25,36…”

Section: Discussionmentioning

confidence: 99%

“…Also, the methodology presented here could be extended to other biomarkers (eg, cystatin C, b2 microglobulin), a broader spectrum of lesions, other tissues, or even other different contexts, either in toxicology or in general pathology (eg, other tissues, disease markers, genetic abnormalities, etc). 24,25,36 The ability of MIL to identify relatively small regions of the slides (in our case squares of 224 mm Â 224 mm) associated with the elevation of a particular biomarker has the potential to facilitate the characterization of precursor lesions. This would apply also to the detection of particular combinations of elementary lesions associated with the elevation of a biomarker, thereby facilitating its phenotypic anchoring.…”

Section: Weakly Supervised Learning Enables Localization and Interpretability At The Tile Levelmentioning

confidence: 99%

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Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

et al. 2021

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Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Weakly Supervised Learning Enables Localization and Interpretability At The Tile Levelmentioning

confidence: 99%

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

et al. 2021

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“…[190][191][192] Numerous rodent DIKI studies demonstrated the utility of urinary KIM-1 for detection of AKI, 47,52,77,[193][194][195][196][197][198][199] but performance of urinary KIM-1 in nonrodent species has been less consistent. Early increases in urine KIM-1 were seen in some AKI studies in monkeys 45,48,196,200 but not in others. 201 In the dog, some authors describe diagnostic utility for KIM-1, 202,203 while others, including the current authors, report poor accuracy of KIM-1 in dog DIKI studies.…”

Section: Kidney Injury Moleculementioning

confidence: 91%

“…58 AKI marker with a large dynamic range in many species. 40,48,59,60 Good at predicting the progression of AKI to CKD. 61 Systemic inflammation or sepsis can cause increased serum levels which can lead to increased urine levels in the absence of renal injury.…”

Section: Estimated Gfrmentioning

confidence: 99%

“…[42][43][44] However, KIM-1, albumin, and NGAL have demonstrated the most translational applicability and appear to be the most commonly used in acute or subacute nonclinical studies. 2,[45][46][47][48][49][50][51][52] The purpose of this manuscript is (1) to provide a comprehensive literature review of current human and animal renal biomarkers of AKI and CKD and how they are being utilized in chronic renal injury/disease (Table 1), (2) to provide a review of alternative assays (Table 2 and Figure 1) that can be utilized in nonclinical safety studies to evaluate chronic renal dysfunction (>4 months duration), and (3) to identify specific situations where these assays could best be utilized. However, only with increased use of these chronic renal injury biomarkers by multiple institutions and in many nonclinical studies will enough information be generated to establish industry-wide confidence in their use and interpretation.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Specific Biomarkers of Chronic Renal Injury and Their Potential Application in Nonclinical Safety Assessment Studies

et al. 2021

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A host of novel renal biomarkers have been developed over the past few decades which have enhanced monitoring of renal disease and drug-induced kidney injury in both preclinical studies and in humans. Since chronic kidney disease (CKD) and acute kidney injury (AKI) share similar underlying mechanisms and the tubulointerstitial compartment has a functional role in the progression of CKD, urinary biomarkers of AKI may provide predictive information in chronic renal disease. Numerous studies have explored whether the recent AKI biomarkers could improve upon the standard clinical biomarkers, estimated glomerular filtration rate (eGFR), and urinary albumin to creatinine ratio, for predicting outcomes in CKD patients. This review is an introduction to alternative assays that can be utilized in chronic (>3 months duration) nonclinical safety studies to provide information on renal dysfunction and to demonstrate specific situations where these assays could be utilized in nonclinical drug development. Novel biomarkers such as symmetrical dimethyl arginine, dickkopf homolog 3, and cystatin C predict chronic renal injury in animals, act as surrogates for GFR, and may predict changes in GFR in patients over time, ultimately providing a bridge from preclinical to clinical renal monitoring.

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Systems analysis of miRNA biomarkers to inform drug safety

Schofield

Brown

et al. 2021

Arch Toxicol

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AbstractmicroRNAs (miRNAs or miRs) are short non-coding RNA molecules which have been shown to be dysregulated and released into the extracellular milieu as a result of many drug and non-drug-induced pathologies in different organ systems. Consequently, circulating miRs have been proposed as useful biomarkers of many disease states, including drug-induced tissue injury. miRs have shown potential to support or even replace the existing traditional biomarkers of drug-induced toxicity in terms of sensitivity and specificity, and there is some evidence for their improved diagnostic and prognostic value. However, several pre-analytical and analytical challenges, mainly associated with assay standardization, require solutions before circulating miRs can be successfully translated into the clinic. This review will consider the value and potential for the use of circulating miRs in drug-safety assessment and describe a systems approach to the analysis of the miRNAome in the discovery setting, as well as highlighting standardization issues that at this stage prevent their clinical use as biomarkers. Highlighting these challenges will hopefully drive future research into finding appropriate solutions, and eventually circulating miRs may be translated to the clinic where their undoubted biomarker potential can be used to benefit patients in rapid, easy to use, point-of-care test systems.

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Evaluation of 10 Urinary Biomarkers for Renal Safety With 5 Nephrotoxicants in Nonhuman Primates

Cited by 9 publications

References 33 publications

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

A Review of Specific Biomarkers of Chronic Renal Injury and Their Potential Application in Nonclinical Safety Assessment Studies

Systems analysis of miRNA biomarkers to inform drug safety

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