Monte Carlo Comparison of Rival Experimental Designs For Two-Agent Combined Action Studies

Abstract

OBJECTIVE: The combined action of two or more chemotherapeutic agents and/or biological agents can be quantitatively described wilh empirical multidimensional concenlration-effect response surface models. This intuitive statistical approach provides a framework for suggesting experimental designs for in vitro. in vivo and possibly clinical experiments of agent combinations. Five rival 32-point experimental designs for in vitro continuous response two-agent combined action studies were compared using Monte Carlo simulation.DESIGN: The designs were: factorial; central composite: one-ray in duplicate; four-ray; and D-optimal.SETTING: Datasets were simulated by generating ideal data with the authors’ flagship two-agent combined action model. which includes six parameters: the control sunrival Econ=100 (where Econ is the full range of response that can be affected by the drug); median effective concentrations. IC_50.1=10. IC_50.2= 1 for drug 1 and drug 2, Respectively; slope parameters. m₁ =- 1. m₂=-2 for drug 1 and drug 2. respectively; and the interaction parameter, α=1 or α=5. For each design, for each of four types of error (absolute. relative with 1% coefficient of variation [cv]. relative with 10% cv. and relative with 10% cv plus a noise constant of 1% of Econ) . for each of two values of the true α (1, 5). 500 Monte Carlo datasets were generated. and then flt via weighted nonlinear regression wilh lhe flagship model.MAIN Results: For the α parameter. for relative error-containing datasets. the D-optimal designs had the smallest variances.CONCLUSION: The counterintuitive D-optimal designs may be useful for studies in which the experimental units are relatively precious. and frugal designs are essential. In addition. it may be fruitful to add the D-optimal design points lo standard experimental designs.