Our Platform
Our product candidates stem from the Axcella Discovery Platform, which combines deep metabolism expertise, biomarkers, computational sciences, machine learning, systems biology and an array of data types – omics, in vitro, preclinical and clinical.
The body is replete with endogenous metabolic modulators (EMMs), which help regulate many pathways and functions. Our process enables us to identify and develop product candidates with multifactorial potential from a vast landscape of possible EMM combinations.
Learn more about the literature on EMMs in disease treatment.
Discover
We begin this process by identifying molecular network models that have the potential to be regulated or signaled by EMMs. We use machine learning and natural language processing to analyze scientific and medical literature and public databases to characterize possible indications for EMM compositions.
Prioritize
We then consider which EMM interventions may have the most beneficial impact on disease or support healthy structures and functions within the body. We use in vitro data, clinical data, primary human cell systems, omics, and biomarker data, such as RNASeq and metabolomics, to rank and select opportunities.
Design
Once we have selected an opportunity for EMM intervention, we design an EMM composition. By analyzing digital health registry data and systems biology networks of disease, we can design a multifactorial combination of EMMs to target the clinically-relevant biological network.
Optimize
Before developing an EMM composition as a potential product candidate, we refine and optimize it for clinical investigation. This step involves validating the product candidate’s effect on biomarkers in preclinical cell systems and translational models and identifying pharmacologically active doses.
Translate
The output of our extensive data science and systems biology process are multi-targeted EMM composition product candidates. We rigorously test safety, tolerability, and impact on biomarkers of disease in Clinical Studies and Trials to translate our platform-designed combinations into potential treatments for complex diseases.
