SensOoChip

Read the full Challenge Brief here

Background

OoC technologies are microfluidic cell culture devices that recapitulate the structure, function and (patho)physiology of human tissues and organs in vitro. The technology is increasingly delivering key tools that can improve disease modelling, safety and efficacy testing and reduce the reliance on animals by providing faster, cheaper and more physiologically relevant human cell-based models. The OoC field has grown rapidly in the past decade, from chips recapitulating single organ physiology to those modelling clinical responses and disease (1). Multiple OoCs can now be connected  incorporating immune components and vascularisation (2) to study organ cross-talk, such as the effects of drug metabolism on organ toxicity. They have been deployed successfully in a range of applications including safety assessment of an immuno-modulating antibody on a lung-on- and gut-on-chip (3), determining species differences in small molecule induced liver toxicity (4) and study of the gut microbiota (1,5). Despite the progress made, there are still barriers to the wider adoption of OoCs that limit their potential. These include biological, engineering, technical and practical challenges that must be overcome for the full potential of OoCs to be realised.

Addressing the limitations

To fully characterise the local OoC microenvironment, continuous monitoring of dynamic physiological parameters is needed. Current sampling approaches are labour intensive and time consuming with data collection often restricted to single snapshot measurements, and some analyses carried out “off-chip”. The amount of data collected is further limited as the sampling often requires destruction of the chip. The ability to incorporate longitudinal, non-invasive monitoring capabilities through the application of advanced engineering would deliver key benefits including:

• Generation of detailed multiparametric data sets providing the potential for better understanding of the baseline biology of OoCs and their stability over time.
• Collection of temporal data from a single chip allowing OoC experiments to be more scalable and removing chip-to-chip variability.
• Automation of the sampling to streamline workflows and reduce the experimental burden on the user.
• Improved reliability and reproducibility of OoCs through reducing the potential for variability caused by the individual user.

Longer term, OoCs that incorporate longitudinal monitoring could be used to improve absorption, distribution, metabolism and excretion (ADME) and toxicity assessments, and enable more robust studies of disease modelling and efficacy. To achieve the benefits of longitudinal multiparametric monitoring, two fundamental technical hurdles need to be addressed – these are sensors for real-time monitoring and analysis, and interrogation and application of multiparametric OoC datasets.

Sensors for real-time monitoring

Sensors that enable real-time, minimally invasive monitoring are already being integrated into OoC systems (6,7). Electrical sensors, such as transepithelial/transendothelial electrical resistance (TEER), can measure tight junction formation and barrier integrity in real-time for up to 60 days in a human lung airway chip and for up to 12 days in a human gut chip (8). Electrochemical sensors, which incorporate a biological molecule as a response element immobilised on the electrode surface (e.g. an enzyme, antibody, aptamer) can be used to measure analytes and biomarkers. For example, magnetic microbeads coupled to an electrochemical sensor unit have been used to measure transferrin and albumin secretion from hepatic spheroids to monitor the effect of acetaminophen for up to five days (9).  Existing consortia are working to incorporate sensors for real-time monitoring (10), but there is still work to be done to ensure multiparametric data sets can be collected and importantly, shown to improve the quality of data and translational relevance when applied to a defined context of use.

Analysis, interrogation, and application of multiparametric OoC datasets

An improved workflow that delivers large datasets for improved understanding and characterisation of control and treated OoC systems could also enable more accurate and robust comparison with historical compound data and facilitate the identification of novel drug effects previously undetected due to the limited sampling capabilities. In the longer term, advanced computational modelling approaches such as integrated pharmacokinetic (PK) and pharmacodynamic (PD) modelling, quantitative systems pharmacology and toxicology (11), and machine learning (12) could be applied to these datasets to enable robust decisions around compound progression and improve their clinical translation (13).

The Challenge

This Challenge aims to capitalise on the advances already made in the field of OoCs to develop engineering capabilities focused on the:

• Integration of multiparametric, inline monitoring of an established liver OoC system to increase the quantity and quality of data collected.
• Connection and monitoring of a second organ – the heart.
• Demonstration that the multiparametric datasets generated can be interrogated and modelled to improve understanding of the local physiological environment, reproducibility of OoCs and the effect of drug administration.

The Challenge will focus initially on integrating a set of sensors for inline monitoring of key parameters for the liver. Liver-on-chip is arguably the most characterised and biologically understood of current OoC models and has been shown to accurately model parameters such as ADME as well as direct effects such as drug-induced liver injury (14-16).

It is important that sensors for common physiological parameters such as oxygen demand, secretion of proteins, release of biochemical signalling molecules and enzymatic activity can be integrated, with relative ease, into other OoCs, not least as there is an increasing drive to connect multiple OoCs to model more holistic systems and the effects of drug metabolism. The second organ for this Challenge should be a heart-on-chip model as cardiotoxicity is another principal cause of drug failures and recalls and many liver-derived metabolites can cause cardiac safety issues. A connected heart-liver OoC will enable the study of metabolism of drugs by the liver and their effects on the heart (17).
 

The liver and heart, connected in a single OoC experiment, will serve as exemplars to demonstrate that useability, robustness, and reproducibility are improved through continuous inline monitoring, and that the data generated can deliver a step-change in the understanding of the physiology of OoCs and the safety assessment of drug candidates. 

3Rs benefits

The increasing global shift to use new approach methodologies (NAMs) has accelerated the momentum to move to human-based models to improve safety assessments to protect human health. OoCs are key enabling NAM technologies and  increasing their robustness and reliability as set out in this Challenge will reduce barriers to use, permitting more detailed and human relevant studies of physiology and disease. This Challenge has the potential to deliver 3Rs benefits in the pharmaceutical industry by:

• Improving the early identification of drugs with target and/or chemistry-related toxicity and preventing these from progressing into animal studies.
• Ensuring the drugs that do progress to in vivo studies are safer with less potential for toxicity to be identified in the animals.
• Improving mechanistic toxicity and pharmacology (PK/PD) studies in vitro, replacing the need to use animals.

Industry benefits

The Challenge will also deliver additional industry benefits through improved assessment of biological therapeutics where animals are not suitable models, enabling more human-relevant preclinical data to be generated. In the longer term, the approach developed through this Challenge could form part of a suite of in vitro and in silico approaches to provide an absolute replacement of animal studies for a large part of the drug discovery and development process. The approaches developed will also impact across multiple sectors including cosmetics, fast moving consumer goods, food and chemicals.

References

1. Ingber, DE (2022) Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat Rev Genet 23: 467–91 doi: 10.1038/s41576-022-00466-9
    
2. Ronaldson-Bouchard, K et al. (2022) A multi-organ chip with matured tissue niches linked by vascular flow. Nat Biomed Eng 6, 351–71. doi: 10.1038/s41551-022-00882-6
    
3. Kerns S Jordan et al. (2021) Human immunocompetent Organ-on-Chip platforms allow safety profiling of tumor-targeted T-cell bispecific antibodies. eLife 10: e67106: doi: 10.7554/eLife.67106
    
4. Jang KJ et al. (2019) Reproducing human and cross-species drug toxicities using a Liver-Chip. Sci Transl Med 11(517): doi: 10.1126/scitranslmed.aax5516
    
5. Kim HJ et al. (2016) Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip. Proc Natl Acad Sci U S A 113(1):  doi: 10.1073/pnas.1522193112
    
6. Clarke GA et al. (2021) Advancement of Sensor Integrated Organ-on-Chip Devices. Sensors, 21(4): 1367. doi: 10.3390/s21041367
    
7. Ferrari E et al. (2020). Integrating Biosensors in Organs-on-Chip Devices: A Perspective on Current Strategies to Monitor Microphysiological Systems. Biosensors, 10(9): doi: 10.3390/bios10090110
    
8. Henry OYF et al. (2017). Organs-on-chips with integrated electrodes for trans-epithelial electrical resistance (TEER) measurements of human epithelial barrier function. Lab Chip, 17(13): 2264-71.  doi: 10.1039/c7lc00155j
    
9. Riahi R et al, (2016). Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Scientific reports, 6 24598. doi: 10.1038/srep24598
    
10. The SMART Organ-on-Chip consortium Funded by the NWO-TTW Perspective Programme of the Dutch Research Council NWO; project number P19-03
     
11. Bloomingdale P et al. (2017) Quantitative systems toxicology. Curr Opin Toxicol. 4:79-87. doi: 10.1016/j.cotox.2017.07.003
     
12. Li J et al. (2022) An Overview of Organs-on-Chips Based on Deep Learning. Research Wash D C.   doi: 10.34133/2022/9869518
     
13. Wenzel J et al. (2019). Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets. Journal of Chemical Information and Modeling, 59 (3), doi: 10.1021/acs.jcim.8b00785
     
14. Bircsak KM et al. (2021). A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®. Toxicology. 450: doi: 10.1016/j.tox.2020.152667
     
15. 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(3): 1049-61. doi: 10.1111/cts.12969
     
16. Ewart E et al. (2022). Performance assessment and economic analysis of a human liver-chip for predictive toxicology. Commun Med, 2 (1): 154. doi: 10.1038/s43856-022-00209-1
     
17. McAleer et al. (2019) On the potential of in vitro organ-chip models to define temporal pharmacokinetic-pharmacodynamic relationships. Sci Rep 9, 9619. doi: 10.1038/s41598-019-45656-4

Assessment information

Challenge Panel membership