RefinedScience uses AI and machine learning to analyze high-fidelity clinical and single-cell omics data, identifying novel drug targets and optimizing clinical trial designs. The platform aims to increase drug development success rates and reduce timelines for pharmaceutical and biotech companies.
Funding
Funding not disclosed
Founders
Product
Problem
The drug development lifecycle is characterized by significant time investment, escalating costs, and a high probability of failure, particularly in oncology. Current clinical trial designs often do not fully integrate evolving scientific understanding of disease heterogeneity and patient diversity, leading to suboptimal outcomes and missed opportunities for therapeutic advancement.
Solution
RefinedScience offers an AI-enabled platform designed to enhance precision drug development by identifying novel drug targets, optimizing clinical trial methodologies, and advancing stalled drug candidates. The platform integrates high-fidelity clinical data, including real-world data and single-cell omics datasets, with advanced analytics powered by machine learning and predictive modeling. This comprehensive approach aims to increase the probability of success for drug candidates while concurrently reducing development timelines and associated expenditures. By leveraging deep data insights and expert scientific knowledge, RefinedScience provides a more refined and data-driven pathway for therapeutic innovation.
Target Audience
The primary customers are pharmaceutical and biotechnology companies engaged in drug discovery and development, particularly those focused on precision medicine and seeking to de-risk their R&D pipelines.
Features
- **High-Fidelity Data Curation:** Ingestion and structuring of diverse real-world clinical data, including dynamic feeds from hospital systems and partner data, accumulating hundreds of data points per patient.
- **Extensive Single-Cell Omics Datasets:** Construction of comprehensive single-cell omics datasets (genomics, transcriptomics, proteomics) across various disease indications, starting with hematological cancers.
- **AI/ML-Powered Analytics:** Application of artificial intelligence, machine learning, and predictive models for data interrogation, pattern discovery, and generation of actionable insights.
- **Novel Target Identification:** Utilization of integrated clinical and biological data to identify and validate new therapeutic targets.
- **Optimized Clinical Trial Design:** Informing clinical trial protocols with deep patient and disease insights to improve success probability and reduce time-to-readout.
- **Advancement of Stalled Candidates:** Re-evaluation and strategic advancement of clinical-stage drug candidates that have encountered development hurdles.
- **S.M.A.R.T. Advantage Framework:** A proprietary methodology encompassing Single Cell Omics, Medical Need focus, Access to diverse data, Research capabilities, and a specialized Team.