Exploratory research Preclinical

Organ-On-Chip: Towards totally miniaturized assays?

Before testing a new molecule in Humans, it is necessary to make toxicological and pharmacokinetic predictions with various preclinical models. Researchers try to reconstruct as best as they can what would happen in a specific tissue or organ. Among the most commonly used techniques are cell cultures, which, although effective, cannot fully simulate the dynamics of an organ or a pathology. There are also in vivo models, which are often more relevant, but are not adapted to high-throughput data generation. First, ethically, the models must be sacrificed and what is observed in animals is not always observed in Humans. Of the compounds that fail in the clinic, it is estimated that 60% of the causes are related to lack of efficacy in Humans, and 30% to unexpected toxicity [1]. Clearly, new biological models are needed.


Paradoxically, chemical libraries are growing, but the number of outgoing drugs is thinning. Therefore, the scientific community must rethink its models permanently to generate reliable information quicker. It is from this problem that the genesis of the Organs-On-Chips (OOC) begins. It was in 1995 that Michael L. Schuler was the first to propose a prototype of cell culture analogue, connecting several compartments of different cells 2. It is when these compartments were connected by microchannels that the term “organ-on-a-chip” appeared.

OOCs are devices the size of a USB flash drive. This is made possible thanks to the microchannel technology that harnesses volumes of the order of nanoliter and below. OOCs have three characteristics that allow them to better model a tissue or an organ:

  1. The control of the 3D distribution of cells
  2. The integration of different cell populations
  3. And the possibility of generating and controlling biomechanical forces.

This allows physiological conditions to be transcribed much more faithfully, compared to a static two-dimensional cell culture on a flat surface. There is no single design for OOC, but perhaps the easiest example to visualize is the lung OOC mimicking the air-alveolus interface. (see Figure 1).

Figure 1 : Illustration of an OOC mimicking the air-lung interface. A semi-permeable membrane separates the external environment from the pulmonary cells. The vacuum chamber makes it possible to mimic the diaphragm.

To date, different OOC have been designed, ranging from the liver to chronic obstructive bronchopneumopathy. Riahi et al. have developed a liver OOC, capable of assessing the chronic toxicity of a molecule by quantifying the evolution of certain biomarkers 3. Compared to 2D cultures, the OOC is more sustainable and generates data that could have only been observed in vivo. Another interesting model was developed by Zhang et al. and focuses on the heart and its cardiomyocytes 4. By integrating electrodes on the chip, the researchers were able to assess cell contraction, and evaluate the effectiveness and cardiotoxicity of certain drugs. If the adoption of the technology is successful, the OOC will be used as a complement to cellular tests and animal models, and may completely replace them.

Impressively, the versatility of the concept will allow clinicians to evaluate the response of our own cells to a specific treatment. By implementing, for instance, a tumor extract from a patient in an OOC, it will be possible to observe and optimize the therapeutic response to a molecule X, and transcribe these observations in clinic 5. This is a first step of the OOC towards personalized medicine.


Eventually, the different OOC models can be combined in order to group several organs and simulate an entire organism. This last idea, also known as “body-on-a-chip”, is extremely powerful and could capture both the effect of a drug and its associated toxicity on the various organs. Some models, such as Skardal et al.’, have allowed to study the migration of tumour cells from a colon OOC to a liver OOC 6. Edington et al. were able to connect up to 10 different OOCs, capturing some of their physiological functions. It consisted of the liver, lungs, intestines, endometrium, brain, heart, pancreas, kidneys, skin and skeletal muscles. The system was functional for four weeks 7. Even if such systems are not optimal yet, their exploration will enable the generation of much more relevant data, much faster, to boost Drug Discovery projects.

To go further :

Excellent reviews on the subject are available:


  1. Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).
  2. Sweeney, L. M., Shuler, M. L., Babish, J. G. & Ghanem, A. A cell culture analogue of rodent physiology: Application to naphthalene toxicology. Toxicol. In Vitro 9, 307–316 (1995).
  3. Riahi, R. et al. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Sci. Rep. 6, 24598 (2016).
  4. Zhang, X., Wang, T., Wang, P. & Hu, N. High-Throughput Assessment of Drug Cardiac Safety Using a High-Speed Impedance Detection Technology-Based Heart-on-a-Chip. Micromachines 7, 122 (2016).
  5. Shirure, V. S. et al. Tumor-on-a-chip platform to investigate progression and drug sensitivity in cell lines and patient-derived organoids. Lab. Chip 18, 3687–3702 (2018).
  6. Skardal, A., Devarasetty, M., Forsythe, S., Atala, A. & Soker, S. A Reductionist Metastasis-on-a-Chip Platform for In Vitro Tumor Progression Modeling and Drug Screening. Biotechnol. Bioeng. 113, 2020–2032 (2016).
  7. Edington, C. D. et al. Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies. Sci. Rep. 8, 4530 (2018).

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By Quentin Vicentini

Quentin graduated from Pharmacy School in Lille – France. After various Research experiences in Medicinal Chemistry, he pursued his career abroad and settled in Oxford in 2019. His interest in innovation pushed him to learn more about Machine Learning in Drug Discovery. Today, Quentin is specializing in oligonucleotide chemistry, for therapeutics and diagnostics.