Vivid Genomics

Drug development in neurodegenerative disease is challenging, and Alzheimer’s disease specifically is notorious for failures; there have been ~150 failed attempts in the last 20 years, at an estimated cost of $5.5B per successful drug from pre-clinical to phase 3. Yet academic researchers, governments, and pharmaceutical companies continue to innovate and invest in development, because the need is so great.

So why have all of these drugs failed, and what can we do about it?

We believe a large part of the problem is that there is a significant amount of variation between patients; they often have multiple forms of dementia, differing rates of disease progression, or different subtypes of disease.  All of these factors can bias or negatively impact clinical trial outcomes, and yet this variation is challenging to characterize.   

Vivid Genomics is developing tools to identify this variation to support and improve clinical trial design and analysis. 

Precision medicine has become the standard approach in Oncology and cancer drug development over the last ~20 years, and with the availability of large neurogenetic data sets, proprietary samples, and new sophisticated AI and machine learning tools, Vivid Genomics is enabling precision medicine for neurodegenerative disease.

Vivid Genomics enables pharma companies to select and stratify the right patients for their clinical trials, increasing the probability of trial success and drug approval.

We use machine learning and genomics to develop blood-based, genetic tests (“Genomic Biopsy^TM”) for neurodegenerative diseases.

Our first two tests will address features of Alzheimer’s disease:

AmyloidGB™

Predicts the presence of amyloid plaques, a hallmark of Alzheimer’s disease for precision patient enrollment, enriching the subset of patients who will be positive in Amyloid PET.

ProgressionGB™

Provides information on predicted Alzheimer’s disease progression rate for precision balancing of the trial arms. It can be used for retrospective analysis to determine if a trial would have been successful by balancing for progression rate or removal of non-progressors.