Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models.

Area of Science:

• Pharmacology
• Oncology
• Biostatistics

Background:

• Drug combination therapy shows promise for cancer treatment.
• Preclinical evaluation of drug synergy is crucial but challenging.
• Existing methods for in vivo synergy detection have limitations.

Purpose of the Study:

• To introduce a novel, highly sensitive method for assessing in vivo drug synergy.
• To provide a unified framework for comparing in vitro and in vivo synergy studies.
• To offer an open-source software package for automated analysis of in vivo synergy.

Main Methods:

• Developed a novel approach to estimate combination index and synergy score under Bliss independence and HSA models.
• Validated the method using in vitro synergy data in in vivo xenograft experiments.
• Applied the method to elucidate mechanisms of immune checkpoint inhibitors in syngeneic mouse models.
• Provided guidelines for designing informative in vivo combination studies.

Main Results:

• The novel method accurately estimates in vivo drug synergy with high sensitivity and low false discovery rate.
• Successfully validated in vitro synergy findings in in vivo models.
• Demonstrated utility in understanding immune checkpoint inhibitor mechanisms.
• Established parallelism between in vitro and in vivo synergy analyses.

Conclusions:

• The developed method offers a reliable solution for determining in vivo drug synergy in animal studies.
• The approach enhances the design, analysis, and comparison of combination therapy studies.
• The invivoSyn software package facilitates automated in vivo synergy analysis.