Drug-induced liver injury (DILI) remains a leading cause of clinical drug failure and market withdrawal. At VivoSim, our NAMkind™ Liver Model offers a robust, human-relevant solution for identifying hepatotoxic compounds early in development. With 87.5% sensitivity to known DILI-causing drugs and 100% specificity, the NAMkind™ Liver Model enables researchers to confidently rule out false positives while detecting actual risks, improving decision-making, and reducing late-stage failures.

Engineered with hepatocytes, Kupffer cells, and stellate cells, our model captures complex liver biology. This cellular diversity allows for detecting toxicity through multiple pathways, including inflammation and fibrosis. Kupffer cells reveal immune-mediated hepatotoxicity, while stellate cells highlight fibrosis-related responses—providing a comprehensive view of liver safety.

Liver Toxicity Test:

NAMkind™ TISSUE MODEL

vivosim-tissue-model

Primary Hepatocyte

Liver Endothelial Cell

Primary Kupffer Cell

Liver Stellate Cell

Model Comparison

NAMkind™ Liver Microfluidics
(e.g., Liver-On-Chip)		 2D Cell Models Animal Models
Human Relevance Predictive Accuracy of Human Response
Microenvironment Complexity Long-Term Culture & Viability
3D Tissue Architecture
Multi-Cell Complexity
Functional Zonation
Experimental Utility High-Throughput Compatible
Practical Considerations Cost Effective
Ease of use
NAMkind™ Liver
Human Relevance Predictive Accuracy of Human Response
NAMkind™ Liver
Microenvironment Complexity Long-Term Culture & Viability
Microenvironment Complexity 3D Tissue Architecture
Microenvironment Complexity Multi-Cell Complexity
Microenvironment Complexity Functional Zonation
NAMkind™ Liver
Experimental Utility High-Throughput Compatible
NAMkind™ Liver
Practical Considerations Cost Effective
Practical Considerations Ease of use
Microfluidics
(e.g., Liver-On-Chip)
Human Relevance Predictive Accuracy of Human Response
Microfluidics
(e.g., Liver-On-Chip)
Microenvironment Complexity Long-Term Culture & Viability
Microenvironment Complexity 3D Tissue Architecture
Microenvironment Complexity Multi-Cell Complexity
Microenvironment Complexity Functional Zonation
Microfluidics
(e.g., Liver-On-Chip)
Experimental Utility High-Throughput Compatible
Microfluidics
(e.g., Liver-On-Chip)
Practical Considerations Cost Effective
Practical Considerations Ease of use
2D Cell Models
Human Relevance Predictive Accuracy of Human Response
2D Cell Models
Microenvironment Complexity Long-Term Culture & Viability
Microenvironment Complexity 3D Tissue Architecture
Microenvironment Complexity Multi-Cell Complexity
Microenvironment Complexity Functional Zonation
2D Cell Models
Experimental Utility High-Throughput Compatible
2D Cell Models
Practical Considerations Cost Effective
Practical Considerations Ease of use
Animal Models
Human Relevance Predictive Accuracy of Human Response
Animal Models
Microenvironment Complexity Long-Term Culture & Viability
Microenvironment Complexity 3D Tissue Architecture
Microenvironment Complexity Multi-Cell Complexity
Microenvironment Complexity Functional Zonation
Animal Models
Experimental Utility High-Throughput Compatible
Animal Models
Practical Considerations Cost Effective
Practical Considerations Ease of use

Request Liver Model Case Study and Data Report

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Publications

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Identification of JAK Inhibitors as Potential Therapeutics for Inflammatory Bowel Disease Using Human Primary Cell 3D Models of Crohn’s Disease and Ulcerative Colitis

Dudum R, Payton O, Toohey C, et al. Digestive Disease Week 2025, Abstract No. Tu2040

Development of a Multicellular 3D Intestinal Model Using Human Primary Cells to Identify Novel IBD Therapies

Gervacio S, Dudum R, Aidnick H, et al. Digestive Disease Week 2025, Abstract No. Tu2035

Clinically Benchmarked Multi-Endpoint 3D Liver Spheroid NAM for DILI Prediction and SAR Prioritization 

Rachel Dudum, Mehrsa Mehrabi, Payton Olson, Cole Toohey, Haylee Aidnik, Caridad Rosette, Keith Murphy, Amar A. Sethi

Human 3D Liver Spheroid NAM for ADC DILI Risk Assessment 

Mehrsa Mehrabi, Payton Olson, Cole Toohey, Rachel Dudum, Haylee Aidnik, Caridad Rosette, Keith Murhpy, Amar A. Sethi

A Machine-Learning Enhanced Human Intestinal NAM for Diarrhea Risk Assessment Across Small Molecules and ADCs  

Vivian (Xiao) Han, Haylee Aidnik, Sherwin Gervacio, Cole Toohey, Caridad Rosette, Keith Murphy, Amar A. Sethi

Multicellular Human 3D Liver NAM for DILI Risk Assessment Across Small Molecules and Antibody Drug Conjugates 

Caridad Rosette, Mehrsa Mehrabi, Rachel Dudum, Payton Olson, Cole Toohey, Haylee Aidnik, Philip Tan, Keith Murphy, Amar A. Sethi

Multicellular Human Intestinal NAM for Diarrhea Risk Stratification Across Small Molecules and Antibody Drug Conjugates

Caridad Rosette, Vivian Xiao Han, Sherwin Gervacio, Cole Toohey, Haylee Aidnik, Keith Murphy, Amar A. Sethi

NAMkind™ Liver & Intestine Toxicity Prediction Across Small Molecules and Antibody Drug Conjugates 

Amar A. Sethi, Mehrsa Mehrabi, Vivian Xiao Han, Rachel Dudum, Sherwin Gervacio, Payton Olson, Cole Toohey, Haylee Aidnik, Philip Tan, Caridad Rosette, and Keith Murphy

NAMkind™ Client Study Report

Sample Report

Identify GI risk. Advance with insight.

Brochure

Identify liver risk. Advance with insight.

Brochure

Does your MASH therapy deliver?

Brochure

FDA Alignment

Aligned with FDA’s New Approach Methodology (NAM) Framework
VivoSim is committed to advancing non-animal testing strategies that meet FDA’s NAM guidance for drug safety assessment. Our models and workflows are benchmarked against FDA-referenced hepatotoxins and follow the same standards used in the NIH Tox21 program.

Our mission: Deliver data that regulators can trust — human-relevant, quantitative, and directly comparable to reference datasets.

VivoSim is helping pharma, biotech, and AI-driven discovery teams bridge discovery and safety. By uniting human biology, AI-enabled analytics, and FDA-aligned methodologies, we’re building a faster, more ethical, and more predictive path toward safe therapeutics.