Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

This study was designed to characterize the time course of the effects of dexamethasone (2.5 mg kg-1 week-1, sc) on the renal arachidonate-prostaglandin (PG) system and to define the effect of the steroid on the interstitial cells of the renal inner medulla (RIC). The RIC are rich in triglycerides, which, due to their high content of arachidonic acid, may be a source of arachidonate for PG synthesis during conditions of phospholipase inhibition. After 1 day of dexamethasone treatment, the urinary excretion of PGE2 and PGF2 alpha was reduced to about 50% of the control value (P less than 0.05), and angiotensin II-induced release of arachidonic acid and PGs from renal medulla slices was blunted (P less than 0.05). In contrast, dexamethasone treatment did not affect ionophore A23187-induced release of PGs and arachidonic acid from renal medulla slices. By day 3 of dexamethasone treatment, urinary excretion of PGE2 and PGF2 alpha had returned to control levels, and by days 5 and 14 the excretion rates of both were clearly increased (P less than 0.05). The rise in urinary PG excretion was accompanied by a reduction of renal 15-hydroxyprostaglandin dehydrogenase activity, augmentation of renal medulla microsomal PG synthetase activity, and diminution of renal medulla triglycerides, the latter associated with a reduction in the number of RIC and of RIC osmiophilic granules. This study demonstrates that the effects of dexamethasone on urinary PG are biphasic; this may reflect the changing balance between the opposing actions of the steroid on renal PG-synthesizing and catabolizing enzymes and the inhibitory effect on the synthesis of renal PG that is linked to activation of specific renal lipases by endogenous factors such as angiotensin II.