Why We Fail to Treat Neurodegeneration: Seven Guiding Principles to Increase Probability of Future Success
Benjamin Stecher, Rune Labs PAB Chair & Brian Pepin, CEO
Imagine your brain as the Amazon Jungle. Try to picture every plant and every tree that lives within. Imagine not only all the plant life, but also every bug and animal that makes the rainforest its home.
Got it? Good.
Now try to imagine what happens when something goes wrong, something that affects multiple levels of the ecosystem. It could be a raging forest fire sparked by a lightning bolt, an earthquake that swallows up hundreds of acres of land, or deforestation caused by humans.
Next, try to think about what comes after all that damage. What mechanism does the forest employ to contain the damage? What are the knock-on effects of the damage and the efforts made to contain it? And, what lasting effects might it have on the underlying structure?[*]
You might be asking, what does any of that have to do with neurodegenerative diseases? Each tiny piece of the Amazon, from its trees, to the creatures that inhabit it, is as good an analogy for the human brain as any we have. It is also a pretty apt way of looking at problems inherent to human attempts to try and contain damage to it.
Embrace complexity. Just as each tree in each forest is unique, we must begin attempts to tackle neurodegenerative diseases with the understanding that each cell in each brain has a specialized role to play, and that trying to pinpoint the moment at which a healthy brain becomes “diseased” is like trying to identify which leaf is responsible for a raging forest fire in the amazon.
Deal with what we know, today. When disaster strikes or damage occurs to places where humans live, do we send scientists to study every microscopic piece possible to figure out their role and only when we have figured out what each granular piece does, do we start efforts to help? Or do we send first responders and rescue engineers to see what can be done today and only afterwards turn things over to academics? Even if we cannot identify yet what is wrong with each individual, we know that people affected by these conditions need researchers to first figure out how to mitigate the damage done and replace what is lost. Thankfully there are such efforts that have begun, some try to replace the cells, others the function of the proteins those cells produce, however, particularly when compared with attempts to get granular, there are still shockingly few dollars allocated to efforts to help people today.
Alzheimer’s and Parkinson’s are not distinct entities. There is no line at which any researcher can point to which differentiates one from the other. Until we stumble across something definitive that allows us to properly demarcate the two, we must assume that they are both under the same umbrella of neurodegeneration, especially since most basic research points to overlapping mechanisms. (1)(2)(3)(4) Primary researchers must start combining resources and stop studying them in vacuums as if they are completely distinct. For all we know, these diseases are simply patterns of degeneration that defy any attempts to put them into nicely labeled boxes. Using labels as silos to understand mechanisms of degeneration obscures our attempts to understand them for what they are. Coming back to the forest – it is as if one group were looking only at wildlife counts, and another only at the changes to biomass, that we would then miss critical interactions between the two pointing to causal mechanisms such as climate change or deforestation.
Remember that: “now is the best time for AD or PD research” is a redundant fallacy. This is a true statement, but it is a description of the Nature of Time. The more time that passes, the closer somebody will be to unlocking something that works in someone. However, that will not come from half-hearted attempts at earlier diagnoses. Instead, the field should focus on developing better tools to predict subpopulations of these diseases so we can appropriately match patients with the therapies we have and those we are developing, while also moving forward our attempts to better predict future brain network outputs based on current readouts.
Study people, not cells. The way research is done today is the functional equivalent of trying to study the entire amazon jungle through a terrarium in your apartment. Investing in large-scale real-world data is a must. The only way to get real-world data is through a patient-centric approach while keeping in mind that in such complex systems, tiny changes to original conditions often lead to immense changes in outputs. Emphasis on collaboration and timely reporting of new results while using real-world data as our end points is the only way forward! Especially since we can now track real-world symptoms.
Doing more research is not the goal of research. The fact that almost all papers end with that notion is an indictment on the way society incentivizes and rewards scientists. They need the intellectual freedom to finish their work without the burdens we place on them to fulfill arbitrary timelines. In that same vein, if people or companies continue to treat findings as if they are a trailer to a movie ‘coming to a conference near you…’, then progress will always be a by-product of research rather than the goal (as recent decisions from the Alzheimer’s Association seem to indicate). If you have something important to share, then any attempt to withhold information delays the progress and prolongs suffering.
Individuals and their symptoms matter. We can no longer afford to talk about diseases without any mention of patients or the real-world symptoms they experience. Progress is dependent on getting individuals diagnosed at the center of these efforts. We need to listen to the individuals affected by these conditions with curiosity and empathy, for life itself. Even beyond a clear moral imperative, involving participants is critical to producing high quality research. We must remember that collecting rich and oftentimes, highly personal data, requires enthusiasm and buy-in from the very individuals we are all trying to help.
The above was written by Benjamin Stecher and Brian Pepin. It was inspired by years of conversations with patients, doctors, nurses, scientists, and caregivers all over the world.We hope it helps others better understand the problem we all face in trying to treat neurodegenerative diseases.
(All photographs were provided courtesy of amateur wildlife/nature photographer Yoram Stecher)
 From the animals and bugs in the Amazon to the sheer variety of different kinds of plant life, each is analogous to something in our brains. To start, it is important to note that the brain has far more than just the two different cell types commonly attributed to it (neurons and glial cells). We know that there are at least 9 different dopaminergic neurons (as just one example of one subclass of cell type), which is likely a gross underestimation given the fact that every neuron has a set of connections unique to it. We’d argue, if accounting for the fact that brain cells have had a much longer runway on which to evolve and change, that the complexity within is immensely greater than anything we see in nature, no matter how big of a forest we wade into.
It is also important to note the relative complexity of the two systems. One way to measure that might be comparing the number of cells in the human brain (roughly 100 billion) with the number of trees in the Amazon (roughly 400 billion). However the complexity of any system does not come from these kinds of raw numbers but rather the connections each node makes over time, and the effects of changes made on those connections. Our best estimates say that each brain cell is connected, on average, to one thousand other cells, giving a total estimate of 100 trillion connections in the average human brain.
(For those wondering how do we have such confidence in these numbers, especially for the human brain where we can’t go in and count how many cells a human has? Well, the methods used to count them are remarkably similar, in both we simply take known quantities, like a certain section of a rat’s basal ganglia or a segment of the Amazon, compare that with what we see in a diseased human or aerial views of the jungle, and then extrapolate. It is an inexact science, but it is the best we have today. For those curious, I’d suggest reading this paper on some of the pitfalls inherent to the latest Stereology techniques from Dr. Samuel Burke et al.)