Organ-on-a-chip (OOC) technology offers you a transformative approach to better science. By bridging the gaps between traditional cell culture, animal models and the clinic, its complementary use offers the promise of deeper, human-relevant insights and more informed decisions about the right therapeutics to take forward into trials.
The potential of OOC to reduce drug attrition rates, bring novel therapeutics to patients more rapidly and cost-effectively has piqued the interest of early adopters, eager to recreate intricate human physiology in the laboratory. For others, the jury remains out and questions remain. Do transitional challenges outweigh the potential benefits, is the technology ready for primetime?
While there are hurdles associated with adopting any disruptive technology, by considering this upfront it is possible for you to make the complex simple, to ease the transition into OOC, and to reap the benefits faster. In this article, we address some common concerns and present a realistic perspective on the adoption challenges that surround OOC.
1. OOC systems are too complicated to set up
Every myth has a kernel of insight, but OOC systems have come a long way since prototypes with yards of intricately set-up tubing that required an associated bioengineer to make them run.
Whilst commercial solutions are firmly designed for the cell biologist’s hands, it is true, that some systems are trickier than others to set up. Microscale designs operate at extremely low volumes, which is advantageous when working with precious samples, but cell seeding requires steady, experienced hands. Alternative, less-futuristic designs provide a straightforward transition from traditional 2D cell culture via familiar multi-well plate formats. Their higher working volumes can also offer you advantages, a point that will be explored in more detail below.
Rest assured that vendors are not just focused on the technology. Our creative minds have come up with ways to ease adoption through complete solutions that incorporate hardware, off-the-shelf reagents, software, and support packages that shorten the learning curve by essentially guiding you through the process of OOC experimental setup.
2. Primary cells are too challenging to work with
Naturally, primary cells are an important component of OOC models. They are required to accurately recapitulate human organs or disease states in vitro and are, without a doubt, more challenging to work with than immortalized cells. Whilst this challenge isn’t unique to OOC, there is, of course, an extra dimension to consider versus 2D assays! In our experience, primary cells that work well in a standard 2D setting, do not always translate well into 3D culture. Your laboratory could lose valuable time, resources, and budget validating cell lot, after cell lot, to find those that are OOC compatible. To address this issue, we collaborate closely with primary cell providers to supply cells that thrive in 3D, maintaining their function and phenotype for up to 4 weeks when cultured under perfusion. So, rather than spending your time finding needles in haystacks, you can focus on making new discoveries!
Also, don’t forget that OOC vendors like us have had years of experience working with primary cells and will willingly share tips and tricks through protocols, or via our support channels to ensure your assays are both successful and reproducible.
3. OOC offers limited endpoint measurements
As you may expect, approaches that are designed for high throughput screening deliver fewer endpoint measurements from simpler OOC models. However, this statement couldn’t be further from the truth for complementary OOC solutions that sit up and downstream of screening to better understand disease, investigate new targets, confirm drug safety and efficacy, ascertain ADME profiles, or to unlock mechanism of action.
Whilst every OOC system offers you a different mix and breadth of endpoint measurements, there is a rough rule of thumb. The number of possible endpoints roughly correlates to the size of the cultured microtissue and the physiological relevance of the culture conditions. For example, large-scale, horsepower-loaded microtissues that are perfused by finely tuned fluidics to mimic the bloodstream offer ultimate assay sensitivity, culture longevity, and data-richness. High media volumes in these systems enable repeat sampling over multiple weeks for longitudinal metabolomic, proteomic, and clinically translatable biomarker studies, plus, their ample microtissues are recoverable allowing you to perform post-assay microscopic analysis or genomic, transcriptomic, and proteomic profiling.
OOC also enables you to measure new human-relevant endpoints that previously weren’t possible in vitro. For example, their cultures can be maintained up to 4 weeks to study the effects of chronic drug dosing on the liver so that you can identify drug-induced-liver injury liabilities ahead of the clinic. In addition, new, interconnected multi-organ (gut and liver) models recreate human processes in the laboratory such as first-pass metabolism to estimate drug bioavailability, an important parameter currently determined using animal models that offer poor predictability 1, 2, 3.
4. Throughput is limited
There is no getting away from the fact, that there is an inverse relationship between physiological relevance and throughput capacity. If you need to screen hundreds of thousands of compounds, it won’t be possible to do this using the most “human-like” in vitro models, but there may well be a way to incrementally improve from where you are currently.
1,536-well 3D spheroid assays, for example, offer an incremental improvement in assay performance over traditional 2D assays for yes/no screening. For hit validation, it is possible to incorporate 96/384-well OOC systems that offer a basic (reliant on gravity) method of perfusion to mimic blood flow which improves the human relevance and longevity of 3D cultures over their static counterparts.
From here, chip- and plate-based OOC solutions featuring adjustable “organ-specific” flow rates that recreate in vivo-like biomechanical stimulus, oxygen, and nutrient delivery, enable the most accurate recapitulation of human organs and microtissues in vitro. These significantly more sophisticated systems are indeed relatively low in throughput. Only one replicate can be run per chip or 12 replicates per plate, yet they perfectly complement their higher-throughput siblings by helping to ensure the right lead candidates enter the clinic. Plus, if you were to compare these solutions to in vivo animal studies, it suddenly puts a different perspective on the whole throughput argument.
5. All OOC solutions use Polydimethylsiloxane (PDMS)
PDMS is often used in organ-on-a-chip technologies but by no means all. This gas-permeable polymer is straightforward to manufacture at a small scale, low cost, and transparent, enabling easy imaging. The crucial disadvantage of PDMS is its lipophilicity. Unfortunately, this property causes the non-specific binding of hydrophilic compounds, making it difficult to accurately quantify exposure responses and pharmacokinetics.
PDMS is not the only material currently available to OOC developers. A viable alternative is Cyclic olefin copolymer (COC), an amorphous polymer currently known to be the most inert material available for use in medical devices. Use of COC ensures that non-specific binding is minimized when working with a cross-section of therapeutic modalities, including small and large molecules. We would recommend using COC over PDMS for compound testing purposes to maintain data integrity4.
6. OOC systems lack flexibility
It’s not a fair summary to imply that all OOC systems lack flexibility, or that a lack of flexibility is necessarily a negative. A range of options are available from different vendors that you can match to your circumstance. When making a choice, simply ask the question – can the system grow with me?
Experienced OOC researchers are in short supply. This “experience gap” has led to a prescriptive approach being adopted by some vendors who provide “off-the-shelf” convenience and a fast-track route to adoption. The flip side here is, that you might not be able to adapt or fine-tune OOC models as your experience grows, or your experimental requirements change. Look to the future, are you able to customize the system to develop a new OOC model, or does one size fit all leading to compromise? If so, is the compromise one you want to make?
Similarly, if you have invested time and energy developing an in-house model, it’s important to ensure that the commercially available system you choose is indeed flexible enough to accommodate it via a thorough evaluation before any investment is made.
One final point to consider on this topic relates to the architecture of the system. How open or closed is it? This is a particularly pertinent point to consider when inducing/studying the mechanism of disease, or where the future use of patient biopsies is a consideration. Can you easily access the cultures to change experimental conditions, or manipulate the models once the experiment is set up?
7. The technology is too costly
There is naturally an upfront investment associated with adopting any new technology, however, the initial CAPEX outlay and running costs are probably much smaller than you think when offset against the potential gains. OOC systems fulfill critical unmet needs within R&D and, in the medium, to long term they can enable your organization to realize significant savings. A research publication suggests that incorporating OOC into drug discovery workflows to generate human-relevant data that better predict clinical outcomes, could save companies up to 26% of their R&D costs5.
8. The technology is not ready to deliver regulatory grade results
OOC holds a huge amount of promise to deliver human-predictive information that improves data translatability between the lab and clinic. However, making a strategic shift from entrenched gold standard models to a novel approach is not a straightforward process, especially at the later stages of drug development. So, let’s not jump the gun. Currently, OOC is being used to drive major improvements in the accuracy and efficiency of drug discovery. It represents a concrete approach to reduce, refine, and complement existing tests, right now – rather than a futuristic proposal to sweep away the status quo.
With an eye on the future, regulatory authorities have recognized OOC’s potential and are investing in collaborative initiatives to help underpin the use of OOC and fast-track its adoption. In a recent publication with CN Bio, the FDA sought to address the lack of available quality control and performance criteria for consistent use of OOC devices and reproducibility of results2. This publication demonstrates the robustness, reliability, and superior performance of our PhysioMimix™ liver-on-a-chip (LOAC) for drug evaluation purposes versus standard technology. We continue to collaborate closely with the FDA whose focus has shifted towards the evaluation of our lung models for inhaled therapeutic drug applications. Additionally, consortiums such as the IQ-MPS Affiliative, made up of pharmaceutical and biotechnology companies plus leading academics, meet regularly to address challenges and support the implementation of OOC in drug development.
So, although OOC is not currently delivering regulatory accepted data, there is a strong foundation in place that, we foresee, will lead to regulatory acceptance of OOC data within IND submissions in the not-too-distant future. For now, this should not be a reason to hold back from adopting a technique that has been proven to enable better science.
9. We cannot match the complexity of a human
The saying ‘Don’t let perfect be the enemy of the good’ is highly relevant here. A test model is, as its name suggests, a model and not real! However, the unique advantage of single-and multi-OOC models is that they mimic a well-defined phenotype, function, or process that is associated with a specific human organ, or organs. Their performance is validated against published in-human data and once translatability has been established, you can explore a much wider range of scientific questions than via traditional 2D cell culture.
While animal models provide a dynamic, whole system with all essential cues present, there are significant cross-species differences that can mislead. OOC has been proven to predict clinical outcomes that animal models cannot7 so, why not run OOC alongside for a sanity check? And, whilst on the topic, an area of high potential for OOC lies within new modality development, such as cell or gene therapies, which rely on human-specific modes of action for which animals are unsuited.
So, although OOC models are not real, a lack of human-relevant biology for potential therapeutics against is contributing to the high attrition rates we see. OOC offers a solution that is available right now to bridge gaps between the preclinical and clinical phases of medicines discovery. Simply by complementing the insights derived using traditional approaches with those from OOC, more informed decisions can be made about which drugs to take forward into the clinic.
Going forward, we will continue to invest in OOC technology, the path to adoption, and regulatory acceptance. We hope that by ironing out the misunderstandings, misconceptions, and outright myths that surround OOC you may join us so that we can focus on advancing science and creating a brighter future together.
For more information on the PhysioMimix OOC range, including the PhysioMimix Single-Organ and PhysioMimix Multi-Organ Systems visit:
Atefeh Mobasseri, MSc, PhD
Field Application Scientist, CN Bio
- Tsamandouras, N., et al. (2017). Integrated Gut and Liver Microphysiological Systems for Quantitative In Vitro Pharmacokinetic Studies. AAPS J 19: 1499–1512 https://doi.org/10.1208/s12248-017-0122-4
- Rubiano, A., et al. (2021). Characterizing the reproducibility in using a liver microphysiological system for assaying drug toxicity, metabolism, and accumulation. Clin Transl Sci, 14: 1049-1061. https://doi.org/10.1111/cts.12969
- Yassen Abbas et al. (2021). Drug metabolism in a gut-liver microphysiological system
- Paul M. van Midwoud, et al. (2012). Comparison of Biocompatibility and Adsorption Properties of Different Plastics for Advanced Microfluidic Cell and Tissue Culture Models. Analytical Chemistry, 84 (9): 3938-3944. https://pubs.acs.org/doi/10.1021/ac300771z
- Franzen, N et al. (2019). Impact of organ-on-a-chip technology on pharmaceutical R&D costs. Drug Discovery Today, 24, ( 9): 1720-1724. https://doi.org/10.1016/j.drudis.2019.06.003
- MEPs demand EU action plan to end the use of animals in research and testing
- Rowe, C et al. (2018). Perfused human hepatocyte microtissues identify reactive metabolite-forming and mitochondria-perturbing hepatotoxins. Toxicol In Vitro, 46:29-38. https://doi.org/10.1016/j.tiv.2017.09.012