Data from Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models

Abstract

<div>Abstract<p>Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess <i>in vivo</i> drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate <i>in vitro</i> drug synergy discovered from cell line assays in <i>in vivo</i> xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of <i>in vitro</i> and <i>in vivo</i> synergy by presenting a parallelism between the fixed-dose <i>in vitro</i> and the 4-group <i>in vivo</i> combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative <i>in vivo</i> combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of <i>in vivo</i> synergy using our method and several other existing methods.</p>Significance:<p>This work presents a general solution to reliably determine <i>in vivo</i> drug synergy in single-dose 4-group animal combination studies.</p></div>