Ari Gesher, the new Head of Technology at Parallel Bio, may be a newcomer to biotech, but he’s already making waves with his ambitious vision for disrupting biological research. The startup is banking on a combination of automation and organoid technology to streamline experiments, making them faster, more efficient, and highly reliable. The aim? Freeing scientists to focus on designing innovative experiments while robots handle the repetitive tasks.
Gesher believes this approach could solve the longstanding biotech dilemma of having to choose between speed, quality, and cost. “There’s this old adage: cheaper, faster, better—pick two,” he said. “Our approach asks, why not have all three?”
At the heart of Parallel Bio’s innovation is their “Clinical Trial in a Dish” platform, which combines immune organoid technology with robotic automation. This service-based model promises to deliver faster results, reduce costs, and provide higher-quality data than traditional animal models. Pharmaceutical companies are already taking note, seeking to test drug candidates on these human organoid models much earlier in the development process. Moving away from animal testing has not only ethical advantages but also leads to cleaner, faster, and more reliable results. “Using human models instead of animal models is better in almost every way,” Gesher explained, adding that as costs come down, there will likely be a wholesale shift toward these methods.
The federal government is also pushing for alternatives to animal testing. In December 2022, President Biden signed the FDA Modernization Act 2.0 into law, removing the requirement for animal testing in drug development, opening the door for companies like Parallel Bio to drive change.
The company’s immune organoids—lab-grown, miniature versions of human immune systems—are derived from the same donor, allowing for controlled, repeatable experiments that eliminate much of the variability seen in animal models or even different human donors. “We can hold the biology itself constant,” Gesher said, emphasizing that this consistency gives researchers unprecedented control over their experiments.
However, organoids aren’t without their challenges. Traditionally, issues like variability between samples and the lack of complex features such as vascularization have limited their ability to fully replicate in vivo conditions. Parallel Bio aims to overcome these obstacles with automation. By incorporating robots to handle tasks like pipetting and data collection, they can run experiments continuously, reducing human error and boosting the reproducibility of results. “Automation allows researchers to focus on the bigger questions and do biology at a higher level of abstraction,” Gesher noted.
One of the biggest challenges in science today is the reproducibility crisis, where many experiments cannot be consistently replicated. Gesher believes automation offers a solution. “Automation is a quality ratchet,” he explained. “When every operation is controlled, you can continuously improve over time, minimizing errors and ensuring consistent results.” By removing the variability introduced by manual setups and human oversight, researchers can rerun experiments years later with the same precision and accuracy.
Looking to the future, Gesher envisions an “industrialization” of scientific research, where automation and organoid technology become standard. He likens this transformation to the cloud revolution in computing, where companies no longer need to manage data centers or infrastructure but instead outsource to service providers. “In the same way, with our product, researchers can simply ask how the immune system reacts to a compound without having to set up a lab,” he said. “We’ll provide the answer, and this model can address many more questions in the realm of biology.”
Gesher’s vision for Parallel Bio could mark a turning point in biotech, with automation, organoids, and a service-based model redefining the landscape of biological research—faster, better, and more efficient than ever before.
By Impact Lab
