In previous studies, cystic fibrosis (CF) fibroblasts were demonstrated to be resistant to the cytotoxic effects of ouabain, dexamethasone, and the sex hormones, dihydrotestosterone, 17beta-estradiol, and progesterone. We now show that CF fibroblasts also exhibit greatly increased resistance to the cytotoxic effects of exogenous dibutyryl cyclic AMP (cAMP), as well as to isoproterenol and theophylline, drugs which are known to increase endogenous levels of cAMP. CF cells were also shown to have normal amounts of (3H)cAMP binding to protein kinase as well as normal amounts of cAMP-stimulated protein kinase activity. Phosphodiesterase in CF cells was also found to be stimulated by cAMP to the same degree as in normal cells. These findings suggest that there is no detectable protein kinase deficiency in CF cells. cf cells thus appear to be unlike some cAMP-resistant mutants described by others which are defective in protein kinase activity and cAMP regulation of phosphodiesterase levels. The cross-resistance of CF fibroblasts to ouabain, steroid hormones, and cAMP may provide a unique opportunity to study the biochemical events involved in the metabolism of these drugs as well as the basic biochemical defect in a common human genetic disease.

The Submission Data Standards Group of the Clinical Data Interchange Standards Consortium (CDISC) has developed metadata standards for electronic submissions. These standards have been defined to guide the organization, content, and form of submission datasets for the 12 safety-related domains specified in the Food and Drug Administration (FDA) guidance documents for electronic submissions. Although the CDISC model provides consistency for variable names and data structures, it is also flexible, allowing for differences in what data sponsors collect and how they collect them.This paper is divided into two major parts. The first includes the background of CDISC and an overview of the CDISC model. The scope of the current CDISC effort, the elements of the metadata model, and the variables common to all domains are discussed. using the "Demographics" domain as an example. The second part uses the "Physical Exam" domain to illustrate the model? flexibility, as well as to show the practical application of the model in a clinical data management system. The benefits and disadvantages of several modeling approaches are discussed. While much of the focus is on modeling the "Physical Exam" domain in Oracle Clinical, the ultimate solution described herein could be implemented in any clinical data management system.