Is biotech ready for the lecanemab opportunity?

The US Veterans Health Administration recently announced that it will cover the cost of Biogen’s lecanemab for veterans suffering from early stages of Alzheimer’s disease. This decision is a massive endorsement of the impact and value of novel therapeutics for patients. This is not trivial, and we should be excited about this positive news for patients and their families, who lose so much and have so few options. 

As an industry we are all hopeful that lecanemab is just the first of many new drugs to hit the market. However, there are some systemic challenges for both new drugs generally and lecanemab’s own widespread adoption. One of the biggest challenges for adoption is proving efficacy and safety outside of clinical studies. 

Elusive diagnosis and evaluation

Diagnosis and evaluation of disease progression in Alzheimer’s remains elusive. The core issue is the absence of objective and sensitive disease measures, which renders evaluating the efficacy of novel drugs and patient subtyping extremely difficult, compromising the development of precision medicine for Alzheimer’s - namely, the introduction of novel patient-tailored therapeutics. 

In the lecanemab confirmatory phase 3 clinical study, the drug was shown to reduce clinical decline on the relevant cognitive scale (CDR-SB) by 27% compared to placebo. However, as noted in a Lancet Editorial (Dec, 2022), this statistical significance may or may not be clinically meaningful. More specifically, the 27% reduction corresponds to a 0.45 point difference on the CDR-SB 18-point scale, whereas the Lancet Editorial goes on to explain that “[a] 2019 study suggested that the minimal clinically important difference for the CDR-SB was 0.98 for people with mild cognitive impairment and presumed Alzheimer's aetiology, and 1.63 for those with mild Alzheimer's disease”. 

We’re faced with a perplexing state of affairs. The drug is clearly biologically effective, but its clinical efficacy is unclear, most likely because our measures aren’t accurate and sensitive enough. It’s also entirely possible that the drug is extremely effective, but works best on specific sub-populations of patients. 

Novel biotech measures for neurology

In this regard, we believe that there’s a huge opportunity for biotech start-ups to introduce novel measures for Alzheimer’s, and more generally neurodegenerative diseases. Over the last two decades, oncology-focused biotech start-ups, such as Foundation Medicine, Guardant Health, and Flatiron Health, helped usher in precision medicine for cancer with novel biomarkers, assays, and data-driven insights and technologies to predict which drugs would work on which patient phenotypes and subpopulations. In much the same way, CNS-focused start-ups could do the same now for neurology.

Today, an opportunity exists for pharma to seek out and partner with smaller companies building out these technologies. With massive spending on neurology R&D, and as novel therapies are developed and introduced, incentives are finally aligning between diagnostics, monitoring, and therapeutics, which will likely spark the creation of a multitude of novel digital biomarker technologies.

Start-up ecosystems have the agility and innovation to solve the challenges that have held neurology back, creating a tremendous opportunity for biotech start-ups to use machine learning to produce insights at an accelerated rate. As noted, oncology is an excellent example of this. Many brilliant startups have worked collaboratively with pharma companies to benefit both R&D teams and patients by improving diagnosis, monitoring, and stratification of patients into sub-populations, and driving forward more tailored drug development.

The importance of objective measures

In neurology, we have largely not made use of predictive tools – yet. Currently available measures are far too subjective to model progression and make accurate predictions. For example, Parkinson’s Disease is measured using the highly subjective scale of UPDRS (Universal Parkinson’s Disease Rating Scale), which suffers from 20-25% inter-rater variability. Meanwhile, Multiple Sclerosis (MS) is determined by lesions observed in MRIs, but the acuity of the disease is again measured by subjective assessments, such as the Expanded Disability Status Scale (EDSS). 

Objective measures are essential for any predictive algorithm to be applied, which in turn can pave a path for precision medicine, as they have in other areas of medicine. Without objective measures, neither modelling nor precision medicine are possible. 

The potential of precision medicine is especially pronounced in neurology, where disease aetiology and pathophysiology are still not well understood, so empirically-defining clinically meaningful subpopulations responding differently to drugs could prove game-changing. Further, some of these novel objective measures can help us diagnose disease earlier, which has been shown to improve prognosis across a host of neurodegenerative diseases, including Alzheimer’s, Parkinson’s, and MS. Earlier diagnosis may also prove critical for the adoption of lecanemab, as the phase 3 trial was conducted on patients with early stages of Alzheimer’s. 

Now, we will see if the Centers for Medicare & Medicaid Services (CMS) will approve lecanemab. We also have a few months until July, when the FDA will potentially approve lecanemab based on the confirmatory trial details. 

Lecanemab is an opportunity for biotech companies that can help determine the efficacy of drugs, diagnose patients earlier, and make sure the patients that will most benefit from the drug receive it. Are we, as a field, ready to rise to the challenge? If not, we need to throw our weight behind developing objective measures for neurology, so that we’re not only prepared for the next drug, but actually play a role in its development.

About the author

Micha Breakstone is the CEO and co-founder of NeuraLight, an AI platform that measures eye movements to track and diagnose neurological diseases like Parkinson’s Disease, ALS, Alzheimer’s, and MS. Breakstone holds an MScasters in Mathematics and a PhD in Cognitive Science, and is a repeat entrepreneur who previously co-founded Chorus.ai (acquired for $575m by ZoomInfo).