Approach
ADVANCES in biology necessitate fundamental changes in drug discovery
Sinopia Biosciences is changing how we study diseases and discover novel medicines. In the past three decades, target-based discovery has been the predominant approach for drug discovery. However, since the etiology of many diseases are unknown and pathogenesis is often complex, phenotypic screening has shown higher productivity in discovery of first-in-class medicines. Phenotypic discovery is often a “black-box” approach and focuses on only select pre-defined reporters.
Recent technological progress in measuring biomolecules comprehensively and cost-effectively has transformed biology from a qualitative, data-poor science to a quantitative, data-rich science. Applying these technologies, the phenotype is no longer a readout of a particular protein or growth rate, but instead a quantitative measure of changes in 100s to 100,000s of biomolecules or cellular features, providing deeper insight into pathologies and therapeutics.
Sinopia Biosciences is enabling the promise of data-driven drug discovery (D4) by applying high-throughput multi-omics data, AI/machine learning and network analyses, and disease models (genome engineered in vitro models and relevant in vivo models).
EXPANDING TO Multi-omics data AT SCALE
The vast majority of all omics data used for understanding diseases and therapeutics has been generated by microarray and sequencing technologies for DNA and RNA. Focusing on nucleotides was an obvious choice because the assay technologies in the previous two decades were much more accurate, inexpensive, and comprehensive. However, not measuring other biomolecules at scale limits the ultimate utility of D4.
Combining pioneering advances in metabolomics technologies and computational data analysis is now enabling Sinopia to study diseases and compounds at an unprecedented level of biomolecular detail using signals (i.e. metabolites) that most closely relate to cellular functions. These advances in multi-omics data generation and machine learning analysis are providing superior predictivity.
UNLOCKING COMPLEX MULTI-OMICS DATA WITH THE LEADSTM PLATFORM
The primary challenge of D4 is identifying the key signals in complex data sets that drive traits of interest. The LEADSTM (LEarn And DiScover) platform combines high-throughput screening (HTS), multi-omics data, AI/machine learning, and network analyses to rapidly identify novel targets and mechanisms. LEADSTM has been successfully applied to multiple therapeutic areas. Sinopia is actively pursuing multiple first-in-class programs with high unmet need including its lead program for a novel chemical entity for Parkinson’s disease.
Pipeline
Driven by the LEADSTM platform, we focus on developing first-in-class therapeutics for areas where there exists:
- High clinical unmet need
- LEADSTM signatures that inform discovery and likelihood of success
- Relevant in vitro and in vivo models
Initial areas of focus include treatment-induced pathologies and rare diseases.
Leadership
Founding Team
Aarash Bordbar, PhD
Co-Founder, Chief Technology Officer
Iman Famili, PhD
Chief Executive Officer & President
Bernhard Palsson, PhD
Co-Founder, Board Member
