Background

Despite over a century of dedicated research, amyotrophic lateral sclerosis (ALS) remains one of the most devastating and least tractable neurological diseases. The condition was first described in 1869 by Jean-Martin Charcot [1], yet no curative therapy exists today. Once symptoms appear, median survival is only 2 to 5 years, and fewer than 10% of patients live beyond a decade [2]. Each year, around 5,000 new cases are diagnosed in the United States alone, and the global prevalence continues to rise with aging populations and improved diagnostics [3].

From a therapeutic standpoint, progress has been painfully slow. Across nearly one thousand clinical trials, over 95% of ALS drug candidates have failed [4], and only four drugs: Riluzole (1995), Nuedexta (2010), Edaravone (2017), and Tofersen (2023) have achieved U.S. FDA approval [5]. Notably, they offer limited benefit, typically extending survival by just 3 to 6 months [3], and are not compatible treatment options for all those diagnosed with ALS. For instance, Tofersen is only suitable for around 2% of the diagnosed population [5]. Moreover, estimates of median survival from diagnosis remain in the range of 2 to 3 years, underscoring the urgency for novel therapeutic options for patients alive today. [6]

Despite decades of global investment, ALS research has seen limited therapeutic progress relative to the scale of funding. Since the 2014 Ice Bucket Challenge alone, [7] the ALS Association has committed over $160 million to more than 560 projects worldwide [8], while U.S. federal funding has grown to roughly $143 million annually through the NIH [9], and nearly $200 million through broader congressional allocations in 2022 [10]. Altogether, this represents billions of dollars invested over the past two decades by public, private, and philanthropic sources yet only a handful of modestly effective drugs have emerged, with average survival after diagnosis remaining at two to five years [3]. The challenge is not insufficient effort or resources, but rather the lack of dynamic, systems-level data capable of decoding ALS biology in patients and rapidly testing on those very same patients in a timeframe that can benefit them directly.

Unravel’s Predictable Medicine™ framework offers a new path forward: by using dynamic RNA-based network modeling, we can move beyond static, single-gene models of disease and instead map how ALS disrupts the entire biological system across tissues, time, and disease stages.

This whitepaper outlines how Unravel’s BioNAV™ systems biology platform can:

1. Stratify patients into specific treatable subgroups even if the cause is unknown.

2. Outline therapeutic prediction for ALS by reframing the disease as a biological network imbalance, not an isolated neuronal failure.

3. Accelerate drug development to get to the clinic faster.

First, understanding the core challenges in ALS research and why progress has lagged is crucial to identifying a solution. Cancer, a similarly devastating disease, has seen transformative advances over the past 20 years by embracing molecular profiling, patient stratification, and biomarker-driven precision medicine. Therapeutic development for ALS, however, has remained constrained by technical barriers that make real-time, patient-specific biology difficult to access and interpret. These limitations have slowed translation from biological insight to clinical impact. Understanding these gaps highlights where new approaches like Unravel’s systems-biology model can finally move the sector forward from a field rich in hypotheses to one rich in clinically validated solutions.

ALS Needs

Resulting Limitations

Unravel addresses these challenges with its network-based approach [11-13]. By analyzing RNA signatures from non-invasively collected samples like nasal swabs, Unravel’s AI platform builds living molecular twins of patients. These living molecular twins are used to rapidly screen thousands of molecules that restore systems-level balance, uncover entirely new biology with the potential to transform ALS treatment, and validate the discoveries using readily-available drugs in the very same patients – all in 2-3 months. Target-centric research on SOD1, C9ORF72, NEK [14], and others remains essential for understanding mechanisms but is inherently time-consuming, sometimes focused only on a subset of patients, and historically hard to translate into the clinic. To translate insight into benefit for patients sooner, mechanistic discovery must be paired with clinical validation methods like Unravel’s that prioritize speed, actionability, and efficacy in living patients. In practice, Unravel’s “living molecular twins” operate symbiotically with academic research: our patient-level predictions guide what to test first, creating real-time clinical validation opportunities for patients in the present, while mechanistic studies explain why and how those interventions work and can be improved in the future.

Given the following problem: ALS research studies fragments of a system, one piece at a time, stopping and examining every piece to understand where it fits in the puzzle.

Unravel presents this solution: Unravel models the system itself, focusing on high level function and working towards getting the system back to equilibrium.