DETERMINATION OF SYNERGISM, ADDITIVITY & ANTAGONISM

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The specific aim of this project is to complete the development of a new unified objective approach to quantitatively assess drug interactions; i.e., to determine synergism, additivity and antagonism among drugs and other agents. The problem which the proposed project is designed to solve is that the assessment of drug interactions: (1) is an important undertaking which is performed very commonly in biomedical research in general and in cancer research specifically; (2) is associated with a wide variety of confusing nomenclature; (3) lacks consistent experimental design approaches; (4) is performed with a wide variety of different data analysis approaches, many of which have underlying flaws; (5) needs standard computer software for its execution; and (6) results in summary information which is oftentimes improperly or suboptimally used. As an attempt to remedy this situation, initial work has been done to develop a universal response surface approach for quantitatively assessing drug interactions. The approach consists of a set of consistent terminology; a set of extendable hierarchical generalized nonlinear models relating measured effects, either continuous or discrete, to applied drug concentrations; a set of model fitting procedures including interactively reweighted nonlinear regression and maximum likelihood procedures including interactively reweighted nonlinear regression and maximum likelihood estimation; and a preliminary software implementation. To accomplish the specific aim of completing the development of the approach: (1) the theoretical and practical properties of the mathematical-statistical models and curve-fitting procedures will be examined; (2) models will be fit to both real and simulated data; (3) deficiencies found in theoretical and/or practical properties will be remedied with changes and/or extensions to the approach; (4) experimental design procedures will be developed; and (5) the library of models, curve- fitting techniques, graphical representations, and experimental design techniques will be implemented in user-friendly computer software for distribution to interested scientists. The complemented approach will (1) aid in the in vitro prediction of in vivo and clinical selective toxicity; (2) aid in the screening of simple tow drug combinations; and (3) aid in the study of complex multidrug combinations. By decreasing the overall usefulness of in vitro drug combination experiments, and by aiding in the efficient design and analysis of in vivo drug combination experiments. The new approach will have extensive applications in experimental cancer research, epidemiology, toxicology, general pharmacology, and other biological and biomedical fields.