Developing ‘mini-brains’ With the Help of Machine Learning

Producing novel treatments for psychological disorders with the help of machine learning

November 1, 2018, by Stacy Stanford — last updated May 23, 2020

System1 Biosciences uses machine learning, neuroscience, robotic automation, and cerebral organoid science [3] to discover deep characterizations of the disorders to develop novel therapeutic targets and drug treatments.

Designed by Machine Learning Memoirs Inc. | Image Labeled for Reuse with Proper Credits

What if we told you that there is a company that grows “mini-brains,” doses them with research drugs, and then reads their data outputs with the help of machine learning and robotic automation?

System1 Biosciences, a Silicon Valley-based neuro-therapeutics company that combines cerebral organoid science, systems neuroscience, robotic automation, and machine learning, is working to discover characterizations of disease never before achievable, in which these “deep phenotypes” are exploited to identify novel therapeutic targets and drug treatments [4]. The startup announced this past September that it raised a $25 million Series A round [7] co-led by CRV and Pfizer Ventures, as well as several other investors participated in the deal. The deal brings the company’s total funding to USD 30 million.

To make things clear, System1 does not grow full brains; it grows so-called “cerebral organoids,” which Harvard’s Stem Cell Institute defines as [1] “tiny, self-organized three-dimensional disuse cultures, which are derived from stem cells. Such cultures are crafted to replicate much of the complexity of an organ, and/or to express selected aspects of such.”

Such organisms are grown from the stem cells of patients with autism, epilepsy, and schizophrenia; the cerebral organoids help reveal to a team of researchers some fundamental signs of those diseases which can then be treated with novel drugs and treatment.

The company mentions that analysis of the resulting data streaming off its organoids “yield systems-level characterizations of disease never before achievable.” These characterizations, which the company refers to as “deep phenotypes,” can be used to find targets for new treatments for neurological and psychiatric diseases.

System1, CEO and Co-Founder, Sean Escola mentions that the traditional target-based drug discovery approach is broken, making people suffering from such to be hungry for innovation. The technique may sound like a futuristic science fiction novel, but these data-intensive, highly-parallel machine learning methods are a big part of the future of drug discovery. It just so happens that this one company grows tiny brains to do it.

A section of a brain organoid after three months of culture. The different colors mark distinct types of cells, highlighting the organoid’s structural complexity. (Image courtesy of Harvard’s Arlotta Laboratory [2])

For decades, researchers who study psychiatric diseases have been coming up with new drugs by homing in on what they believe is broken or sick molecules or cells that may be causing the symptoms of the disease. However, in some 90% of cases, some part of their thesis about those molecules was wrong in the first place, and large amounts of time and billions of dollars are wasted, in which most of the current drugs on the market are still a product of this model.

For instance, the most popular drugs for schizophrenia, target dopamine — a molecule that is thought to play a critical role in memory and pleasure. But these drugs fail to help many people. One reason is that many schizophrenia symptoms, from hallucinations to memory loss, could be the result of a much more fundamental set of cross-brain issues.

However, current drug development approaches in neuroscience don’t probe these system-level problems. Instead, they focus on one or two isolated components of the disease. Escola, who is also an assistant professor of psychiatry at Columbia University, believes that’s a tragic oversight. He thinks it could also explain why so many medications fail to help patients. Which could mean that many of our previous attempts at creating drugs for psychiatric diseases like epilepsy and schizophrenia have been “missing the forest for the trees.”

To address that problem, Escola and his co-founder Saul Kato, a professor of neurology at the University of California in San Francisco, aim to mine the activity across their mini-brains for signs of disease that could be happening at a much more foundational level of the illness.

The hope is to make a promising discovery in their work with the cerebral organoids, in which Escola and Kato’s researchers will need to further test their idea and eventually run clinical trials in people. The researchers at System1 are spending months after months of thoroughly studying the cerebral organoids, using software powered by machine learning. They hope to end up with several of what Escola calls “deep phenotypes,” or characteristics of the disease that can be seen across entire systems of activity as opposed to just inside specific molecules or cells.

Several startups in the drug discovery and development field are using a similar approach to System1’s machine learning component [6]. Silicon Valley-based startup Numerate is using machine learning to optimize small molecule drug discovery and better predict toxic side-effects. InSilico, a startup out of Baltimore, is using deep learning to assess data on the genome, epigenome, and microbiome-level. And London-based startup BenevolentBio uses datasets [5] from clinical trials, and research papers into artificial intelligence powered algorithms that help reveal deeper relationships between diseases and drug candidates. Nevertheless, System1 is the only company doing such research with its source of biological data — the cerebral organoids.

References

[1] Organoids: A new window into disease, development, and discovery | Harvard Stem Cell Institute | https://hsci.harvard.edu/organoids

[2] Arlotta Laboratory | Harvard University, Department of Stem Cell and Regenerative Biology | https://hscrb.harvard.edu/res-fl-arlotta

[3] Development and Characterization of Human Cerebral Organoids: An Optimized Protocol | NCBI | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038047/pdf/10.1177_0963689717752946.pdf

[4] About System1 Biosciences Inc. | System1 Biosciences | https://system1.bio/press/#about

[5]How artificial intelligence is changing drug discovery | Nature |https://www.nature.com/articles/d41586-018-05267-x

[6] full-force: A Target-based Method for Training Recurrent Networks | Arxiv | https://arxiv.org/pdf/1710.03070.pdf

[7] System1 Biosciences | Crunchbase | https://www.crunchbase.com/organization/system1-biosciences