Approved treatments are changing the course of ATTR-CM, a once-untreatable heart disease. But the thing that matters most is invisible, and a blood measurement of TTR can point two ways at once. So we built a way to quantify what really matters.
Key Takeaways
- We built a mathematical model to describe the central disease process and treatments in ATTR-CM
- The core output of the model, “monomer efflux,” quantifies how quickly dangerous amyloid deposits could form
- Unlike a blood measurement of TTR, it reads the same way for both approved treatment modalities, stabilizers and knockdown agents
- Stabilization and amyloid formation are not linked one-to-one: in our simulations of the age-related form, each additional 1% increase in intact TTR corresponded to roughly a 4.6% further drop in monomer efflux
- Monomer efflux ran nearly twice as high in the inherited form of the disease as in the more common age-related form
- Because approved treatments reduce new amyloid formation rather than clearing existing deposits, how quickly that flow falls may matter, not only whether it falls
ATTR cardiomyopathy is a relentless disease. A protein that is supposed to glide harmlessly through the blood instead falls apart, clumps into stiff deposits, and slowly turns the heart muscle rigid. For a long time, there was little anyone could offer the people living with it. Today there are approved drugs that genuinely change its course and non-invasive diagnosis methods are identifying more patients. Those are the breakthroughs. The puzzle is what comes next: how do we measure what these treatments are actually doing to the disease?
To see why that is such a hard question, start with the protein. Transthyretin, or TTR, normally travels through the blood as a tidy bundle of four identical pieces. In ATTR-CM, the bundle comes apart. The loose pieces misfold, clump together into deposits called amyloid, and pile up in the heart until it can no longer pump the way it should.
How ATTR-CM unfolds, and where each approach intervenes. Stabilizers keep the four-piece bundle clasped shut. Gene knockdown reduces how much TTR the body makes in the first place.
The two approved approaches attack this chain in two very different ways. Stabilizers act like a super-glue that keep the four-piece bundle from coming apart. Gene knockdown approaches turn down how much TTR the body makes to begin with. Both strategies work. But here is the catch that makes them so hard to compare: they push measurements of the central protein in opposite directions. Stabilizers send blood TTR up. Gene knockdown pulls it down. A blood measurement of TTR cannot mean the same thing across both. And the thing you would truly want to measure, the rate at which amyloid is overtaking the heart, is nearly impossible to capture in a living patient. So the field has been working without a common instrument.
So we built a way to see it. Our scientists created a mathematical model of the entire process, from the production of the healthy protein to the moment the bundle splits and amyloid starts to form, and grounded it in laboratory data and results from real clinical trials. Out of that model came a single, unifying measure we call monomer efflux. In plain terms, it captures how fast loose protein pieces are flowing into the amyloid pipeline. It is a model-based proxy for how fast the disease is progressing, measured relative to an untreated patient rather than an absolute amount, and provides a common yardstick to measure the impact of any treatment. For the first time, stabilizers and knockdowns can stand on one scale. Everything that follows comes from that model: these are simulation estimates grounded in laboratory data and published trial results, not direct measurements from clinical trials, and they are hypothesis-generating.
A measure is only worth having if it reflects reality, so we put it to the test. We compared our predictions against how patients fared in trials: how far they could walk, how they rated their own health, and a blood marker of strain on the heart. Across the treated and placebo groups in those trials, higher predicted monomer efflux lined up with worse reported outcomes. That is agreement at the group level, not a prediction for any one person, but it is the check that tells you a new measure is tracking something real.
The most striking thing the scale revealed is that stabilization and amyloid formation are not linked one-to-one. In our simulations of the age-related form of the disease, each additional 1% increase in intact TTR in the blood corresponded to roughly a 4.6% further drop in monomer efflux. The reason is structural. Every loose protein piece that seeds the disease comes from a bundle that has slipped open, so even a modest edge in how completely those bundles are held together translates into a much larger edge in how much amyloid-forming material gets set loose.
Additionally, ATTR-CM builds over time, as amyloid deposits accumulate in the heart. Because approved treatments are designed to reduce new amyloid formation rather than to clear deposits that have already formed, how quickly we can reduce flow to amyloid-forming material may matter. The model tracks the flow as a running total, so within a simulation a reduction that arrives sooner counts for more than the same reduction arriving later. The model does not simulate diagnosing the disease earlier or starting treatment at an earlier stage, so it provides a hypothesis to be clinically confirmed.
The model surfaced other findings worth noting. Monomer efflux ran nearly twice as high in the inherited form of ATTR-CM as in the more common age-related form, consistent with the faster progression reported for the most common disease-causing variant. Genetics can also cut the other way: some TTR variants make the four-piece bundle harder to pull apart, and in the model almost none of the TTR in those carriers left the system as loose monomer at all. The patterns we found proved robust across hundreds of simulations spanning a wide range of biological assumptions. And because we can now place any therapy that acts on this pathway on a single, biology-based scale, we can rigorously evaluate today’s treatments and whatever comes next.
Read the original paper
MAT-US-BBIO-0324