Media | December 2, 2025
Inside Sapient’s Multi-Omics Revolution: A Deep Dive with Founder Mo Jain
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Read the ArticleMulti-omics leaders are revolutionizing precision medicine by integrating diverse biological data to uncover deeper insights into health and disease. Singular omics approaches may miss critical interactions between genes, proteins, metabolites, and environmental factors, leading to incomplete understanding of disease mechanisms and drug response.
As part of its Viewpoint series, PharmaShots sits down with our Founder and CSO, Dr. Mo Jain, to delve into the science, strategy, and collaborative vision driving Sapient’s mission to be the multi-omics leader that transforms drug development success rates through innovation and world-class partnerships.
Read an excerpt from the full feature below.
PharmaShots: With the launch of the next-generation data insights engine, DynamiQ™, how do you foresee this changing the market landscape and advancing multi-omics and real-world data integration?
Dr. Jain: As with many applications across industries today, having access to data at scale is absolutely critical. Large-scale datasets enable advanced AI modeling and the creation of foundational model systems that allow us to understand disease processes with much greater precision.
That’s exactly why we built the DynamiQ™ database. First, it enables our clients to identify and prioritize new therapeutic targets. Second, it helps bridge a long-standing gap in drug development: the divide between early discovery scientists, who focus on target identification, validation, and chemistry, and the clinical study teams responsible for implementing trials.
DynamiQ™ allows us to connect these two ends of the spectrum by de-risking early biomarkers and targets through translational insights that can be validated in large, independent cohorts. This integration of multi-omics and real-world data creates a clearer path from discovery to clinical application, ultimately accelerating drug development and improving the likelihood of success in clinical studies.
PharmaShots: How do the AI and machine learning tools integrated within DynamiQ™ enhance biomarker discovery and patient stratification?
Dr. Jain: DynamiQ™ is a truly unique database because it is longitudinal, meaning patients are followed over extended periods with serial blood sampling and clinical information, creating a rich, time-based dataset. Data is collected at multiple timepoints, both from biological specimens such as tissue, tumor, and blood samples, together with longitudinal clinical and drug response information.
Within these biological samples, we generate comprehensive multi-omics data, including proteomics, metabolomics, lipidomics, DNA and RNA sequencing, as well as cytokine and chemokine profiling. This creates an exceptionally rich data foundation for applying AI and machine learning approaches.
By leveraging these tools, we can classify patients more effectively, identify subpopulations such as responders versus non-responders, discover and validate ideal therapeutic targets, and better understand patient stratification – essentially determining which individuals are most likely to benefit from a particular therapy.
The integration of advanced AI with DynamiQ’s multi-omics framework is what enables us to be a multi-omics leader, by capturing the biological diversity within disease populations and applying it in a way that advances precision medicine and accelerates drug development.
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