Organ-on-Chip

NCATS Organ-on-Chip Technology

NCATS Technology

Microfluidic systems recreating human organ physiology on a chip

Lung

Lung

Liver

Liver

Kidney

Kidney

Heart

Heart

Brain

Brain

GI Tract

GI Tract

Organ-on-chip devices are microfluidic systems that recreate the physiological environment of human organs on a microscale. These platforms enable researchers to study drug effects, toxicity, and disease mechanisms in conditions that closely mimic the human body.

Lung-on-Chip

Recreates the air-blood barrier with breathing motions for inhalation drug testing and respiratory disease modeling.

Liver-on-Chip

Models hepatic metabolism for DILI prediction and drug-drug interaction studies.

Kidney-on-Chip

Simulates nephron function for nephrotoxicity screening and renal drug clearance studies.

Heart-on-Chip

Measures cardiac contractility and electrophysiology for cardiotoxicity assessment.

Organoids

Cellular organoid structures

3D Cell Culture

Self-organizing cellular structures that replicate human organ architecture

Cancer cell structure

Tumor Organoids

Cell microscopy

Cell Imaging

Petri dish culture

Culture Systems

Organoids are self-organizing 3D cellular structures derived from stem cells that replicate the architecture and function of human organs. Patient-derived organoids enable personalized drug testing on an individual's own cells.

Brain Organoids

Model neurological diseases including Alzheimer's, Parkinson's, and brain tumors for CNS drug development.

Intestinal Organoids

Study gut absorption, barrier function, and microbiome interactions for oral drug delivery.

Tumor Organoids

Patient-derived cancer models for personalized oncology treatment selection.

Retinal Organoids

Model retinal degeneration for gene therapy and AMD treatment development.

Digital Twins

Neural network computational biology

Computational Biology

Virtual patient models predicting drug responses before clinical testing

DNA data analysis

Genomic Analysis

Multi-omic Data Integration

AI prediction systems

Machine Learning

AI-Driven Drug Response Prediction

Digital twins are computational models that integrate multi-omic data to create virtual representations of individual patients. These models predict drug responses, optimize dosing, and identify potential adverse effects before clinical testing.

Pharmacokinetic Modeling

Predict drug absorption, distribution, metabolism, and excretion across patient populations.

Systems Biology

Model complex biological networks to identify drug targets and predict pathway effects.

AI-Driven Prediction

Machine learning models trained on clinical data to forecast treatment outcomes.

iPSC Differentiation

Stem cell electron microscopy

Patient-specific stem cells enable personalized medicine at the cellular level

Induced pluripotent stem cells (iPSCs) can be generated from any patient and differentiated into any cell type. This enables disease modeling and drug testing on cells carrying the patient's own genetic background.

200+
Cell Types Achievable
Patient
Specific Genetics
Unlimited
Cell Supply