Our proprietary QuAD³ platform, powered by our AI and ML capabilities, allows us to extract deep phenotypic and genomic data that defines novel biological insights from our patient cohorts. We plan to apply our QuAD3 platform across a range of biologically relevant queries to provide us with additional context around the role the adaptive stress response and innate and adaptive immunity play in treatment resistance, cancer relapse and metastasis. We believe that such insights will, in turn, inform our ongoing efforts in adaptive stress target discovery, patient enrichment, and clinical development of our innovative therapeutic programs.
INSIGHTS INTO ADAPTIVE STRESS MECHANISMS
We are using our QuAD3 platform to understand how patient outcomes correlate with the adaptive stress response. We are pioneering the understanding of the adaptive stress response and we believe that AI/ML will be an important tool in elucidating this complex biology.
INFORMED PATIENT SELECTION
Clinical trials can fail for a variety of reasons. We believe applying our QuAD3 platform can help us to better design our clinical trials by identifying effective biomarkers that can help optimize patient selection and enrichment strategies, and to prioritize clinical indications where stress modulation would have the most meaningful impact.
NOVEL TARGET ID & IN-SILICO VALIDATION
We are also applying our Artificial Intelligence/Machine Learning capabilities to drive novel stress pathway-related target identification and in-silico validation as a means of expanding our clinical pipeline of novel cancer therapies.
We believe it is critical to integrate multi-omic data sets to inform our understanding of the role of adaptive stress in cancer progression.
Despite the complexity of multi-omics data, our QuAD3 platform allows us to integrate and deconvolute genomic and multi-omic data sets, which has allowed us to develop a preliminary 30 gene stress signature, which was supported which was supported by our in vivo signal cell RNA sequencing, or RNA seq, data.
Algorithms utilized to allow us to better project patient response to our therapeutic candidates
qML Computing Strategies
Machine learning strategies capable of superior performance on smaller, Phase 2 clinical trial-sized datasets
Ensemble AI Approach
Multiple algorithms applied in sequence which is an approach that is intended to improve performance and ensure replicability of findings