Combinations of drugs which interact as inhibitors of DNA biosynthesis

Abstract

In an attempt to gain further insight into the mechanisms responsible for drug interactions, specific inhibitors of DNA biosynthesis were studied in combination using leukemia L-1210 cells growing in suspension culture. The concentrations of each drug, alone and in selected combinations, required to achieve 50% inhibition of growth were measured using an electronic cell counter. The type of interaction, synergistic, additive or antagonistic effects, were readily interpreted by plotting the data as an isobologram for each pair of inhibitors. The inhibitors selected for study were: 1-β-d-arabinofuranosylcytosine (Ara-C) and 1-β-d-arabinofuranosyladenine (Ara-A), competitive inhibitors of DNA polymerase; 1-formylisoquinoline thiosemicarbazone (IQ-1), a non-competitive inhibitor of ribonucleotide reductase; 5-fluorodeoxyuridine (FUDR), a competitive inhibitor of thymidylate synthetase; and methotrexate (MTX), a stoichiometric inhibitor of dihydrofolate reductase. The observation of the marked antagonism resulting from the combination of MTX with Ara-C led to an acute awareness of the limitations in using the patterns of sequential or concurrent inhibition as a basis for the prediction of antagonism or synergism. Despite the complications introduced by the feedback control of these metabolic pathways, an attempt was made to construct a primitive mathematical model which includes such circuits in an effort to understand this complex phenomenon. Encouraged by reasonably good correlation between predictions based on the mathematical model and the actual data on cell growth, an attempt was made to evaluate the role of feedback loops in determining the type of interaction that results from a combination of inhibitors. The extent to which the primitive model mimics the complex cell culture experiments seems to depend more upon structure and overall shape than on the presence or absence of individual components. Simulation of complex experiments can guide the investment of laboratory effort and additional laboratory data should be used continually to modify or validate the mathematical model. © 1972.