SLAS2018 Innovation Award Finalist: Combinatorial Drug Screening, High-Throughput Flow Cytometry, and Agile Integration: a Modern Platform for Personalized Treatment Discovery for Cancer Patients
Recorded On: 02/07/2018
Our mission at Notable Labs is to identify actionable, personalized treatments for cancer patients. To help us achieve this goal, we have developed a platform that combines combinatorial testing, drug repurposing, and several high-throughput technologies to automate our phenotypic screens on primary patient samples. Our current focus is in acute myeloid leukemia (AML) and other hematological malignancies, although the platform and assay can be extended to other indications.
Our automation platform includes our custom-made laboratory information management system (LIMS) working in tandem with our robotic workcell, which handles all screening and assay operation. The architecture of this system is designed to allow for separation between conceptualization and execution; scientists can plan their screens and experiments through the LIMS, then walk to the workcell and start an automated run that executes their plan, with minimal preparation.
The core assay in this platform is a high-throughput flow cytometry assay that generates a wealth of phenotypic data on the primary patient samples that are run through the system. Over the course of the assay, the robotic scheduler serves as a middleman for information flow between the LIMS and the workcell instruments themselves. The LIMS sends relevant data about the screen to the scheduler, which in turn acts on the data and directly controls the instruments to run the assay.
After completion of an assay, raw data flows back from the workcell instruments to the scheduling software, which consolidates the data and uploads it to our cloud-based LIMS. From there, a number of in-house software tools are used to streamline our flow cytometry data analysis, allowing scientists to analyze complex, multi-dimensional flow data across thousands of wells.
The platform has been validated across a number of patients. Our data has led to actionable treatment options for relapsed and refractory patients that has, in some instances, resulted in complete remission in AML patients. The platform and architecture that we have developed brings together our collective hardware, software, and biological knowledge, and demonstrates the predictive power of our data-driven approach to personalized medicine for cancer patients.
Transon Nguyen is the lead automation engineer at Notable Labs, where he develops hardware and software systems to advance Notable's high-throughput screening capabilities. He was previously a biomedical engineer at the Charles Stark Draper Laboratory, developing advanced in vitro organ systems for drug discovery. Transon holds an M.S. in Mechanical Engineering from the Massachusetts Institute of Technology, and an M.S. in Biomedical Engineering from the University of California, Irvine.