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Automated, Harmonized CRO Data Drives Cost-Effective Collaboration

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ENABLING FAIR ACCESS TO PHARMACOKINETIC AND PHARMACODYNAMIC RESULTS Characterization of absorption, distribution, metabolism, and excretion (ADME) drug parameters inform safety testing and future clinical studies. To augment their current capabilities in a time- and capital-efficient way, pharmaceutical and biotechnology firms often outsource compound synthesis and ADME/DMPK assays to Contract Research Organizations (CROs). Aside from cost savings, the challenges of manually transcribing unformatted or Excel-bound CRO reports dramatically slows the drug discovery and development process. Data availability and quality consumes scientists' cycles, especially when biopharma sponsors work with multiple CROs. Note: most CROs maintain proprietary data formats for standard assays, and these are usually file-based (e.g. Excel or PowerPoint files). SUCCESS METRICS Automated, Harmonized CRO Data Drives Cost- Effective Collaboration CASE STUDY Corporate Headquaters | 177 Huntington Avenue, Suite 1703, Boston, MA 02115 Atlanta | 3280 Peachtree Road NE, 7th Floor, Atlanta, GA 30305 © 2022 TetraScience, Inc. tetrascience.com Who Should Read this Study? Externalized research leaders, life sciences start-up founders, pharmacologists, data scientists & engineers, scientists working in research and development, rare disease specialists, CRO leaders and scientists, R&D IT professionals Customer Profile | Two Boston-area, public, clinical stage start-up biotechnology companies, who specialize in gene therapy and precision medicine, respectively. Both collaborate with a single key CRO for their ADME/PK needs. Product Focus | Small molecule therapeutics, biomarker development, computational screening Client/ Customer | Biopharmaceuticals KPIs / Results | Gene Therapy, Precision Medicine Saved Time Reduced Complexity • Save 2+ hours per week, per scientist of manual file transfer work • Provide structured and clean data integration for other systems to consume • Reduce manual errors; Results are directly calculated and derived from raw data • Automation allows streamlined downstream analysis via data science applications (e.g. Streamlit apps) Accelerated Insights Improved Transparency and Future-Proofing • Improved data-driven decision making (Result is calculated and generated much faster) • Harmonized, vendor-agnostic data accelerates ability to run AI/ML and data science projects ontop of it • Versatile pipelines are configurable and extensible to handle rich set of technologies and formats tetrascience

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