AIMC Topic: Hepatocytes

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Recent advances in measurement of metabolic clearance, metabolite profile and reaction phenotyping of low clearance compounds.

Expert opinion on drug discovery
INTRODUCTION: Low metabolic clearance is usually a highly desirable property of drug candidates in order to reduce dose and dosing frequency. However, measurement of low clearance can be challenging in drug discovery. A number of new tools have recen...

Predicting prime editing efficiency and product purity by deep learning.

Nature biotechnology
Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423...

Profiling epigenetic age in single cells.

Nature aging
DNA methylation dynamics emerged as a promising biomarker of mammalian aging, with multivariate machine learning models ('epigenetic clocks') enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized m...

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells.

Stem cell reports
Efficient translation of human induced pluripotent stem cells (hiPSCs) requires scalable cell manufacturing strategies for optimal self-renewal and functional differentiation. Traditional manual cell culture is variable and labor intensive, posing ch...

Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens.

Diagnostic pathology
BACKGROUND: The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of...

Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes.

PloS one
In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to r...

Cross-species regulatory sequence activity prediction.

PLoS computational biology
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.

Molecular pharmaceutics
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descrip...

A Network Pharmacology-Based Study on the Hepatoprotective Effect of Fructus Schisandrae.

Molecules (Basel, Switzerland)
(Wuweizi in Chinese), a common traditional Chinese herbal medicine, has been used for centuries to treat chronic liver disease. The therapeutic efficacy of Wuweizi has also been validated in clinical practice. In this study, molecular docking and ne...