Dataset Information

Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

ABSTRACT: Kidney calcification increases the risk of chronic kidney disease. However, to date, renal calcium phosphate crystallization, a main initiating and driving factor of kidney calcification, has not been explored as a drug target. Pre-clinical drug development is hampered by limited knowledge on the broad range of kidney calcification disorders, characterized by a multifactorial process of disease progression. In this work, we first established an in vitro calcification profiling platform to accelerate pre-clinical drug discovery. The image-based profiling assay allowed the rapid testing of several ionic stimuli and/or inhibitory molecules. We then leveraged a previously established library of inositol hexakisphosphate analogues to identify a renal calcium phosphate inhibitor. A lead compound showed in vitro and in vivo efficacy to prevent calcium phosphate-induced kidney damage. In conclusion, this work reports a renal calcium phosphate inhibitor that could efficiently reduce kidney damage and emphasizes the utility and translational value of the in vitro calcification platform.

SUBMITTER: Kletzmayr A

PROVIDER: S-EPMC7737319 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

Kletzmayr Anna A Bigler Melina M Montanari Elita E Kuro-O Makoto M Hayashi Hirosaka H Ivarsson Mattias E ME Leroux Jean-Christophe JC

ACS pharmacology & translational science 20201123 6

Kidney calcification increases the risk of chronic kidney disease. However, to date, renal calcium phosphate crystallization, a main initiating and driving factor of kidney calcification, has not been explored as a drug target. Pre-clinical drug development is hampered by limited knowledge on the broad range of kidney calcification disorders, characterized by a multifactorial process of disease progression. In this work, we first established an <i>in vitro</i> calcification profiling platform to ...[more]

PMID: 33344907

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Project description:PurposeAutomation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup.MethodsAn automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day's registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance.ResultsFollowing an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.ConclusionIn this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.

Dataset Information

Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

Publications

Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

Similar Datasets

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