Andries van der Meer, scientific lead of the Organ-on-Chip Centre Twente, discusses with us his thoughts on a recent published paper in Nature Biomedical Engineering titled "Towards in vitro models for reducing or replacing the use of animals in drug testing".
"...we envision that [...] a gradual adoption of [microphysiological systems] over the next few decades will increasingly reduce the need for animal studies." This important Comment piece in Nature Biomedical Engineering offers a perspective on the state of the field of microphysiological systems (MPSs), including organs-on-chips.
What makes it particularly important is that it's written by members of the IQ Consortium, who are all affiliated with big pharmaceutical and biotech companies, like Roche, Genentech and AbbVie. Therefore, it offers the "voice-of-customer perspective", which is essential for model developers in academia and high-tech companies to take into account in their future research on innovation of MPS models.
The whole Comment is definitely worth reading, but my main take-aways for innovation were the following:
- Current MPSs don’t fully capture the complexity of human organs. For instance, kidney models often overlook key areas like the glomerulus and distal tubule. To address this, model developers could advance in 3D bioprinting and tissue engineering to create more comprehensive organ models.
- MPSs also face challenges in evaluating the side effects of new therapeutics. Factors such as organ-reserve function and regenerative responses need to be better incorporated. A potential solution could be integrating real-time monitoring systems into MPSs to track organ function and recovery over time.
- Lastly, there’s a need to integrate MPSs with systems toxicology and pharmacology. This would allow us to make more accurate predictions about drug behavior in the human body. To bridge this gap, we could leverage machine learning and AI algorithms to analyze and interpret the complex data generated by MPSs.
We as model developers have a responsibility to keep the momentum in innovation. I strongly believe that 10 years from now, we will look back at the current MPS models as 'first generation' and that we will have made leaps in capturing complexity, multiplexing, long-term culture and integration with computational modeling.
Andries van der Meer
