Introduction: Flow cytometric-based pharmacodynamic (PD) assessments are increasingly incorporated into dose optimization efforts as they provide evidence of direct target engagement and downstream bioactivity. With the expansion in parameters evaluated by conventional flow cytometry there is a concomitant increase in data volume, leading to an unmet need to streamline data analysis.
Objective: To overcome data volume burdens, a bioinformatics approach was used to expedite data analysis generated from preclinical cynomolgus monkey studies.
Methods: Target engagement of a T cell agonist, PD-1-GITRL bispecific monoclonal antibody (bsAb), on CD4+ central memory T cells (Tcm) and downstream immune cell activation were evaluated in cynomolgus whole blood using a 16-color 4-Tube flow cytometric assay during two in vivo preclinical studies. flowDensity, flowType and a statistical filter were encoded into a bioinformatics pipeline to capture immune cell populations and identify novel pharmacodynamic effects observed post-dosing.
Results: Automated gating accurately identified immune populations despite biological variation with similar precision to manually gated data. Computational analysis quickly recapitulated pharmacodynamic effects, such as target engagement of PD-1 and GITR by PD-1-GITRL bsAb and TIGIT upregulation on CD4+ central memory T cells, observed by manual gating. The addition of flowType and a statistical filter elucidated over 600 additional PD biomarkers that demonstrated significant change compared to baseline data points and between dosing groups.
Conclusion: Inclusion of bioinformatic scripts not only reduced analysis time, but also revealed novel pharmacodynamic biomarkers. The efficiency of automated gating to streamline data enables improved refinement of dosing decisions for optimal drug development.
