We expedite biomarker-driven drug development from discovery to commercialization.
- Dataset construction: We help construct large-scale retrospective longitudinal cohorts with our existing data or in tandem with client data. We ensure datasets are representative of target populations and can augment with synthetic data as needed using generative techniques. We help design prospective datasets to ensure sufficient statistical power for subsequent analyses.
dataset creation - cohort design - synthetic data generation - Research and bioninformatics support: We help create a body of evidence supporting the performance of a particular diagnostic or drug with statistical rigor. We conduct retrospective analyses on large-scale longitudinal datasets using statistical and machine learning techniques. We analyze prospective data to quantify the performance and side effects of an intervention.
evidence generation - statistical analyses - bioinformatics - Biomarker creation: Using whole-genome, microarray, or panel data, we can extract and analyze a variety of genomic biomarkers including single nucleotide polymorphisms, insertions and deletions, copy number variants, or even polygenic predictions (e.g., polygenic risk scores or other whole-genome measures constructed using machine learning techniques). We can combine genomic biomarkers with other genetically-derived markers (e.g., ancestry, sex) or other clinical indications (e.g., cholesterol levels, disease history) to create holistic biomarkers for use in drug development and clinical trials.
genomic biomarkers - polygenic risk scores - whole genome analysis - machine-learning-based biomarkers - CDx co-development: Genetic biomarkers are often used as companion diagnostics. We integrate with pharmaceutical companies in their drug development journey to co-create companion diagnostics for a particular therapeutic, helping improve yield and performance for target patients.
companion diagnostics - clinical trials - co-development
