Biochemical and antitumor activity of tiazofurin and its selenium analog (2-beta-D-ribofuranosyl-4-selenazolecarboxamide)

2-beta-D-Ribofuranosyl-4-selenazolecarboxamide (selenazofurin, CI-935), the selenium analog of tiazofurin (CI-909), was 3- to 10-fold more cytotoxic to murine or human tumor cells in vitro than tiazofurin and was also more active against P388 mouse leukemia in vivo. In vitro cytotoxicity could be reversed by guanosine or guanine but not by other purine nucleosides or bases. Three human tumor cell lines selected for selenazofurin or tiazofurin resistance showed cross resistance between selenazofurin and tiazofurin. Treatment with tiazofurin, selenazofurin, or mycophenolic acid decreased guanylate pools and caused an accumulation of IMP in WIL2 human lymphoma cells. The decrease in guanylate pools was accompanied by inhibition of RNA and DNA synthesis. The NAD analogs of tiazofurin and selenazofurin were inhibitors of L1210 IMP dehydrogenase (IMP:NAD oxidoreductase, EC 1.2.1.14), and both showed uncompetitive inhibition with respect to NAD having Kii values of 5.7 X 10(-8)M and 3.3 X 10(-8)M respectively.

Assessing differential drug effect

The regression effect gives a misleading impression of the relationship between drug or treatment effect and baseline measurement. We propose a method of adjusting for the regression effect; we also suggest a corresponding test for the differential drug effect. The likelihood ratio test for no differential drug effect is equivalent to a test for equality of variances, suggested by Pitman (1939, Biometrika 31, 9-12) and Morgan (1939, Biometrika 31, 13-19). The proposed adjustment and test are applied to an example of blood pressure data.

Application of second-order polynomial equations to the study of pharmacodynamic interactions: the effect of flecainide acetate and propranolol on cardiac output and vascular resistance

We have examined the analysis of pharmacodynamic data in normal subjects receiving propranolol, flecainide or the two together. We find that a second-order polynomial describes accurately the response of the left ventricular ejection fraction, cardiac output and vascular resistance of the subjects to these drugs. Both single dose and multiple doses of propranolol (80 mg 3 times a day) decreased the left ventricular ejection fraction and cardiac output, but increased the vascular resistance. On the other hand, single and multiple doses of flecainide (200 mg twice a day) decreased the left ventricular ejection fraction but did not affect either the cardiac output or vascular resistance. These data demonstrate the power of the polynomial formulation in analyzing pharmacodynamic data.

Regression analysis applied to PVC histories: a statistical procedure for evaluating antiarrhythmic drug efficacy

Suppression of premature ventricular contractions (PVCs) is one of the goals of antiarrhythmic therapy. In a clinical trial, however, it may be difficult to distinguish antiarrhythmic drug effect from spontaneous variation in PVCs. We propose the application of linear regression to PVC histories to ascertain drug effect in individual patients. The model determines which variables are important in explaining a patient's PVCs. One such variable indicates the presence or absence of the drug; the model determines whether the drug has an effect on the patient's PVCs, while compensating for the other explanatory variables. In addition to determining the statistical significance of any drug effect, the model estimates the strength of the effect for each patient. We demonstrate the method with data from a three-day clinical trial which used 24-hour Holter monitoring. The method is flexible and can be modified to apply to any clinical study design. It allows for inferences concerning populations and subpopulations of patients.

Statistical inference and the design of clinical trials

According to the likelihood principle of statistics, a decision to stop or otherwise alter a clinical trial can be made on the basis of accumulating information without losing the ability to draw inferences from the results of the trial. In particular, balanced, randomized designs are not necessary. The probability that a particular treatment is the best among those in the trial can be calculated after each patient response, and may suggest that the treatment should be used predominantly in the next stage of the trial. Doing so results in more effective treatment of the patients in the trial while sacrifacing some of the information on the other treatments. Not doing so results in equal information on the treatments but sacrifices effective treatment. There are many trials in which compromise is possible between these antagonistic consequences.