Animal models often fall short of predicting human outcomes, creating costly translational failures. Human-on-a-Chip systems address this by reproducing key aspects of human physiology to generate clinically relevant insights into the safety and efficacy of therapeutics. Combined with quantitative PK/PD modeling, these platforms deliver human-relevant preclinical insights and enable research into rare diseases where models are limited or non-existent. This talk covers three applications:
First, a cardiac ischemia model that imposes controlled hypoxia and reperfusion within a multi-organ context. Contractile force, electrophysiology, and injury biomarkers quantify damage and recovery enabling evaluation of candidate therapeutics.
Second, a patient-specific neuromuscular junction (NMJ) model for Charcot-Marie-Tooth type 2S. Using iPSC-derived motoneurons and skeletal muscle from the patient alongside healthy controls, we recapitulated NMJ dysfunction and quantified rescue by the client’s therapeutic, as measured by functional readouts. This N-of-1 study supported the FDA’s decision to grant Orphan Drug Designation of a sponsor’s therapeutic illustrating how these platforms can guide therapies for diverse genetic disorders.
Third, we extend our multi-organ PK/PD capabilities to the first published Digital Twin informed by an organ-on-a-chip system, in which longitudinal biological data informed in silico models to predict response and improve translatability.
Together, these studies show how integrated human biology and quantitative modeling can reduce reliance on animal studies, de-risk development, and shorten the path from preclinical insight to clinical decisions.
Learning Objectives:
1. Describe the principles of multi-organ Human-on-a-Chip systems, and list which clinically relevant readouts are used to model cardiac ischemia and recovery.
2. Describe how a Human-on-a-Chip informed Digital Twin is established and explain its potential to improve drug development.
3. Evaluate the integration of organ-on-a-chip data with quantitative PK/PD modeling and digital twin approaches to improve clinical translatability.