Analysis of drug interactions

Most of the current therapies against cancer, and also those against immune diseases or viral infections, consist of empirically designed combination strategies, combining a variety of therapeutic agents. Drug combinations are widely used because multiple drugs affect multiple targets and cell subpopulations. The primary aim is a mutual enhancement of the therapeutic effects, while other benefits may include decreased side effects and the delay or prevention of drug resistance. The large majority of combination regimens are being developed empirically and there are few experimental studies designed to explore thoroughly different drug combinations, using appropriate methods of analysis. However, the study of patterns of possible metabolic and biological interactions in preclinical models, as well as scheduling, should improve the development of most drug combinations. The definition of synergism is that the combination is more effective than each agent separately, e.g., one of the agents augments the actions of the second drug. The definition of antagonism is that the combination is less effective than the single agents, e.g. one of the agents counteracts the actions of the other. A combination can be studied by combining the two agents in various different ways, such as simultaneous or sequential combination schedules. It is essential to test the potency of a combination, before evaluation in the clinic, to prevent antagonistic actions. However, one should realize that an antagonistic action may be desired when toxicity is concerned, i.e. one drug decreases the side effects of another drug. Several attempts have been made to quantitatively measure the dose-effect relationship of each drug alone and its combinations and to determine whether a given combination would gain a synergistic effect. One of the most widely used ways to evaluate whether a combination is effective is the median-drug effect analysis method. Using this method, a combination index (CI) is calculated from drug cytotoxicity or growth inhibition curves. To calculate a CI, the computer software Calcusyn can be used, taking the entire shape of the growth inhibition curve into account for calculating whether a combination is synergistic, additive, or antagonistic. Here, we describe how combinations can be designed in vitro and how to analyze them using Calcusyn or Compusyn. Moreover, pitfalls, limitations, and advantages of using these combinations and Calcusyn/Compusyn are described.