A New Tool to Find New Drugs

At NeuroInitiative we are driven by the idea that new inventions are the way to have the biggest impact on civilization. Look back at human history, and it’s the inventions that stand out — controlled fire, cooking, wheels, engines, flight, microscopes, computers. If there’s even a chance you can create something to advance life, why would you do anything else?

Figure 1. Inventions which have changed the course of humanity. From left to right, an early microscope, the Wright brothers’ first flight, and the first transistor.
Figure 2. Penicillin

Problems in Drug Discovery

Unfortunately the process of drug invention is fraught with peril. It currently takes over 20 years and $2 billion to get a single drug to market, with 90% of drugs entering clinical trial failing to reach approval. Most of these failures are due to lack of efficacy — drugs safely do something, but fail to impact disease.

Figure 3. Cause of clinical trial failures (Source: https://www.nature.com/articles/nrd.2016.184)

Why Neuro?

The complexity of neuronal cells and disease process present an incredible challenge — one which I believe is especially well suited to computational assistance. The puzzle pieces are there but there are too many moving parts to manually make sense of. Also, these diseases are a huge unmet need and getting worse, contrary to some of the more “popular” research areas.

Figure 4. Change in causes of death.

Recipe for a Successful New Medicine.

For a new drug to be successful, it needs to meet a handful of factors. You need some molecule that effectively engages a target, where that target engagement safely alters disease progression or symptom. It needs to solve a problem for which there are patients, and you have to be able to identify for which patients to prescribe the medicine. As mentioned above, about half of drugs fail for lack of efficacy, which means the drug did something, but that target didn’t impact disease, or it wasn’t tested on the right patients. The software platform I describe below directly aims to improve visibility into validating why targets should work, and for whom they should work.

Figure 5. Recipe for successful new drug. Target and Patient selection primary cause of efficacy failure.

NeuroInitiative’s Simulation Platform

These are solvable problems with the right data, tools, and talent. Luckily the world’s scientists have been churning out data at an incredible rate with 29M manuscripts on PubMed growing at over 1 million per year. Since the human genome project we know the parts, and largely what they look like, which I’ve previously described if you are interested in diving into some of the data. Now I’ll dive into a little more detail on our patented simulation platform and how we bring that data together for in silico cells. Because we wanted this to be used by a broad base of biologists, rather than just computational scientists we put some effort up front into developing a user interface for building models, running & visualizing simulations, and analyzing results — many projects can be completed out of the box with no new code. To not block the power users we built a REST api to expose data and logic, which plug in nicely for R or Python scripting. The heart of the system is a custom C++ simulation engine built on Nvidia CUDA toolkit, and hosted on Microsoft Azure. Virtualized GPUs on Azure have been great, allowing us to scale in minutes to levels which just a few years ago would have required access to a handful of supercomputers around the world.

Figure 6. High-level system architecture of NeuroInitiative’s SEED simulation platform.
Figure 7. Screenshot of virtual cell visualization from SEED user interface
Figure 8. Mapping cellular spatial layout from data model to visualization.
Figure 9. Protein Protein Interaction as listed in IntAct at top, followed by one of the variations in SEED.
Figure 10. Simulation timestep loop.
Figure 11. Math used to simulate Newtonian physics.
Figure 12. Comparison of simulation results to biological studies.
Figure 13. Overlap between genetic and sporadic forms of disease provide interesting hypotheses.
Figure 14. Results of G2019S knock-in simulation.
Figure 15. Source of human patient transcriptomic data.
Figure 16. Results of Sporadic patient simulation.
Figure 17. Levels of active RAB3A over time on left. Schematic of synaptic vesicle cycle on right.
Figure 18. Change in ratio of inactive Rab3A after treatment in simulation.

Update:

Since publishing this story, NeuroInitiative has spawned Vincere Biosciences to develop small molecule therapeutics for Parkinson’s disease. We are hopeful that this unique blend of technology and science will accelerate new options into the clinic for the 10 million Parkinson’s patients worldwide.

Co-founder of Vincere Bio where we use tech from NeuroInitiative to find drugs to stop Parkinson’s disease

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