FlowAutoGate™: Validation Methodology and Concordance Results

FlowAutoGate™ is Mount Clinical Research's proprietary AI platform for automated flow cytometry analysis, developed in partnership with Rio Eigen AI. The platform applies an ensemble of machine learning approaches — including FlowSOM, PhenoGraph, UMAP, and supervised classifiers — to identify and gate cell populations from raw FCS data without manual intervention.

To validate the platform for use in sponsor-facing immunogenicity work, gating outputs from FlowAutoGate™ were compared against manual gating performed independently by three trained immunologists across a panel of T-cell, B-cell, NK cell, and myeloid population markers. Concordance exceeded 95% across all validated panels, with the largest gains in consistency observed in polyfunctional T-cell assessments, where manual gating is most prone to inter-operator variability.

Processing time is the other half of the value proposition: FlowAutoGate™ completes gating for a full one-million-event sample in under 30 seconds, compared to the 20-40 minutes a skilled analyst typically requires for a complex multi-parameter panel. At the scale of a multi-site vaccine or immunology trial processing hundreds of samples per week, that difference compounds into a genuinely different turnaround-time offer to sponsors.

Every gating decision FlowAutoGate™ makes is logged with a complete, timestamped audit trail — population statistics, gating rationale, and version history — satisfying 21 CFR Part 11 requirements for electronic records used in regulatory submissions. We view this audit-readiness, not just the speed improvement, as the platform's most important characteristic for sponsor trust.

We are pursuing peer-reviewed publication of the full validation dataset and plan to make FlowAutoGate™ available as a standalone SaaS offering for sponsors and sites with their own internal flow cytometry operations.