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Integrating Organ-on-Chips & In Silico Models for Translational Pharmacology Applications

Currently, animal models are the standard for assessing drug toxicity despite their limited ability to predict human toxicity and failure to reduce attrition rates. These models often differ in both morphology and functionality from human organs. Replacing animal studies with more predictive, human-relevant in vitro systems could overcome these challenges.

Organ-on-Chip (OoC; also: tissue chips, microphysiological systems, MPS) technologies are in vitro systems comprising biomaterials, tissue constructs, and specialized microenvironments housed in micro-mesofluidic hardware. These systems aim to recapitulate essential human physiology in vitro and hold the promise to revolutionize drug development. Potential applications include pharmacokinetics (PK), pharmacodynamics (PD), and safety pharmacology. Both industry partners and regulatory agencies are starting to recognize the potential impact these systems may have on drug testing. OoC has the biggest impact in early drug discovery (high-throughput screening) and pre-clinical studies where they could save up to 25% (~700M USD) of total R&D costs (Franzen, Harten, & Loskill, 2019).

Fig. 1: Schematic overview of organ-on-chip technology. These systems aim at recapitulating size, structure, and functionality of human organ in vitro (Edington et al., 2018).

To translate results from bench to bedside, academia, industry partners, and regulatory agencies need to collaborate. These partnerships could prove that OoC systems are better predictors of human outcomes than animal models (or simpler in vitro models) and establish best practices for using commercially available OoC systems.

A framework that integrates both experimental (OoC) data and computational modelling (quantitative systems pharmacology: QSP/physiologically-based pharmacokinetic: PBPK) approaches can inform first-in-human dosing, establish safe dosing regimens in clinical trials, and identify potential drug failures earlier in the development pipeline thus reducing time, cost, and attrition rates.


Case Studies

Translating liver-chip metabolism to clinical pharmacokinetics
A recent study (Tsamandouras et al., 2017) investigated population variability in hepatic metabolism of compounds in vitro using a liver-on-chip system. First, they employed mechanistic modeling using in vitro data to disentangle population-specific clearance of six compounds from the OoC system characteristics. Then, they developed a population PBPK model for one compound (lidocaine) and integrated the intrinsic lidocaine clearance with the virtual population. Using this hybrid in vitro-in silico modeling approach, their predicted plasma pharmacokinetics of lidocaine was reasonably close to measurements from a published clinical trial.

Translation of kidney-chip injury to clinical toxicokinetics (TK)
Another study (Christian Maass et al., 2019) recapitulated drug-induced nephrotoxicity using a kidney-on-chip system. First, they measured the nephrotoxicity biomarker response over time and for different, clinically relevant drug concentrations. Then, they developed a population PBPK model for one compound, cisplatin. Next, they inter-correlated the in silico drug concentrations with the measured in vitro biomarker levels and integrated this in a physiologically-based toxicokinetic (PBTK) model to describe the distribution and production of the measured biomarker in a virtual patient population. Lastly, they predicted plasma biomarker kinetics that matched clinically observed biomarkers levels in acute-kidney injury.

Establishing steady-state operations of Organ-on-chip systems
Cell culture media is an essential factor driving tissue functionality. It is therefore essential to use human-physiologically relevant cell culture medium to provide a more accurate microenvironment.

In yet another study (C. Maass et al., 2018), the authors investigated the metabolome of three different OoC systems and determined the tissue-specific nutrient needs of each OoC system. They then demonstrate how this knowledge can inform using OoC systems at more physiological nutrient levels. The authors developed a partial media change protocol for the gut OoC using model-informed experimental design and mechanistic modeling of nutrient consumption. When supplying glucose and removing ~ 15% of cell culture media daily, the system used physiological glucose and lactate levels throughout a 10-day experiment.


Future Perspectives

Integrating OoC and computational modeling approaches will enable translational pharmacology applications that reduce time, cost, and drug attrition rates. Mechanistic modeling of OoC data will improve our understanding of underlying biological principles and inform drug mechanism of action studies. Single or integrated multi-OoC studies can provide relevant data for pharmacokinetics and toxicology studies in vitro already, and integration with PBPK/QSP modeling approaches will translate those results directly to the bedside.

A multi-disciplinary infrastructure and close collaboration and communication between academic and industry partners is needed to realize the predictive power of Organ-on-chip systems and computational models.

To learn more about how mechanistic modeling can inform understanding of the mechanistic basis of adverse drug reactions to support more predictive and accurate risk assessments, please watch this webinar.



Edington, C. D., Chen, W. L. K., Geishecker, E., Kassis, T., Soenksen, L. R., Bhushan, B. M., Griffith, L. G. (2018). Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-22749-0

Franzen, N., Harten, W. H. Van, & Loskill, P. (2019). Impact of organ-on-a-chip technology on pharmaceutical R&D costs, 00(00). https://doi.org/10.1016/j.drudis.2019.06.003

Maass, C., Dallas, M., Labarge, M. E., Shockley, M., Valdez, J., Geishecker, E., Cirit, M. (2018). Establishing quasi-steady state operations of microphysiological systems (MPS) using tissue-specific metabolic dependencies. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-25971-y

Maass, C., Sorensen, N. B., Himmelfarb, J., Kelly, E. J., Stokes, C. L., & Cirit, M. (2019). Translational assessment of drug-induced proximal tubule injury using a kidney microphysiological system (MPS). CPT: Pharmacometrics & Systems Pharmacology, 1–10. https://doi.org/10.1002/psp4.12400

Tsamandouras, N., Kostrzewski, T., Stokes, C. L., Griffith, L. G., Hughes, D. J., & Cirit, M. (2017). Quantitative Assessment of Population Variability in Hepatic Drug Metabolism Using a Perfused Three-Dimensional Human Liver Microphysiological System. J Pharmacol Exp Ther, 360(1), 95–105. https://doi.org/10.1124/jpet.116.237495

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By: Christian Maass

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