Sapient launches next-generation data insights engine for drug discovery and development
DynamiQ™ integrates diverse breadth of multi-omic measures with paired real-world data from serial samples to enable deep analysis of patient journeys, disease drivers, and drug response
2 Dec 2025
Sapient has launched the next generation of its multi-omics and real-world data (RWD) human biology database, now branded as the DynamiQ™ Insights Engine, which increases the speed, versatility, and depth of analyses that Sapient can perform to inform drug discovery and precision drug development strategies.
DynamiQ features an expanded data lakehouse infrastructure, developed in collaboration with Sapient’s partner Rancho Biosciences, that enables rapid curation of Sapient’s unique datasets comprising thousands of protein, metabolite, lipid, and cytokine measures per sample alongside matched genomic and RWD including electronic health records (EHR), lab measures, and clinical outcomes.
This data has been collected and integrated for more than 56,000 samples from diverse individuals across multiple timepoints, making DynamiQ among the most deeply phenotyped datasets available for longitudinal analysis.
Data has been harmonized across conditions, diagnoses, and outcomes, allowing for rapid creation of cohorts in which to discover, validate, and characterize biomarkers or therapeutic targets. It also provides longitudinal views of patient journeys to identify dynamic drivers of disease and drug response and to compare disease progression and treatment-response patterns across individuals and over time.
“With DynamiQ’s next-generation functionality and harmonized datasets, we increase the depth and breadth of analyses we can perform for biomarker discovery, target identification, and clinical insight delivery,” said Dr. Tao Long, Co-Founder and Head of Data Science at Sapient.
“We can readily analyze molecular interactions across omics layers, rapidly build cohorts from standardized patient data, and validate discoveries from client studies in these independent populations. Importantly, because our multi-omics datasets are nontargeted they are ideal for AI and machine learning-based analysis to uncover new subgroups, novel biomarkers, and targets,” she added.