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Updated: Sep 26, 2025
An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
Published on: December 3, 2020
Application of Micro-Engineered Kidney, Liver, and Respiratory System Models to Accelerate Preclinical Drug Testing
Hanieh Gholizadeh1,2,3, Shaokoon Cheng3, Agisilaos Kourmatzis4
1Macquarie Medical School, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Ryde, NSW 2109, Australia.
Abstract:
Developing novel drug formulations and progressing them to the clinical environment relies on preclinical in vitro studies and animal tests to evaluate efficacy and toxicity. However, these current techniques have failed to accurately predict the clinical success of new therapies with a high degree of certainty. The main reason for this failure is that conventional in vitro tissue models lack numerous physiological characteristics of human organs, such as biomechanical forces and biofluid flow. Moreover, animal models often fail to recapitulate the physiology, anatomy, and mechanisms of disease development in human. These shortfalls often lead to failure in drug development, with substantial time and money spent. To tackle this issue, organ-on-chip technology offers realistic in vitro human organ models that mimic the physiology of tissues, including biomechanical forces, stress, strain, cellular heterogeneity, and the interaction between multiple tissues and their simultaneous responses to a therapy. For the latter, complex networks of multiple-organ models are constructed together, known as multiple-organs-on-chip. Numerous studies have demonstrated successful application of organ-on-chips for drug testing, with results comparable to clinical outcomes. This review will summarize and critically evaluate these studies, with a focus on kidney, liver, and respiratory system-on-chip models, and will discuss their progress in their application as a preclinical drug-testing platform to determine in vitro drug toxicology, metabolism, and transport. Further, the advances in the design of these models for improving preclinical drug testing as well as the opportunities for future work will be discussed.
Insights
Organ-on-chip technology provides realistic human organ models, overcoming limitations of traditional preclinical drug testing. These advanced models improve prediction of drug efficacy and toxicity, saving time and resources in drug development.
Area of Science:
- Biomedical Engineering
- Drug Development
- Toxicology
Background:
- Current preclinical drug testing methods (in vitro and animal models) often fail to predict clinical outcomes due to lack of human physiological relevance.
- Conventional models lack biomechanical forces, biofluid flow, and accurate human disease mechanisms, leading to high drug development failure rates.
Purpose of the Study:
- To review and critically evaluate organ-on-chip technology for preclinical drug testing.
- To focus on kidney, liver, and respiratory system-on-chip models and their application in assessing drug toxicology, metabolism, and transport.
- To discuss advancements and future opportunities in organ-on-chip technology for drug development.
Main Methods:
- Review of existing studies on organ-on-chip and multiple-organs-on-chip models.
- Critical evaluation of their application in preclinical drug testing.
- Focus on specific organ systems: kidney, liver, and respiratory.
Main Results:
- Organ-on-chip models accurately mimic human organ physiology, including biomechanical forces and cellular heterogeneity.
- Studies show comparable results between organ-on-chip drug testing and clinical outcomes.
- Multiple-organs-on-chip models enable assessment of complex inter-tissue responses to therapies.
Conclusions:
- Organ-on-chip technology offers a more predictive preclinical drug testing platform than conventional methods.
- These models show significant promise for determining in vitro drug toxicology, metabolism, and transport.
- Further advancements in organ-on-chip design will enhance preclinical drug testing efficiency and success rates.
