Identification of the human P450 enzymes involved in the in vitro metabolism of the synthetic steroidal hormones Org 4060 and Org 30659

Metabolism of 17alpha-hydroxyprogesterone caproate by hepatic and placental microsomes of human and baboons

Recent data from our laboratory revealed the formation of an unknown metabolite of 17 hydroxyprogesterone caproate (17-HPC), used for treatment of preterm deliveries, during its perfusion across the dually perfused human placental lobule. Previously, we demonstrated that the drug is not hydrolyzed, neither in vivo nor in vitro, to progesterone and caproate. Therefore, the hypothesis for this investigation is that 17-HPC is actively metabolized by human and baboon (Papio cynocephalus) hepatic and placental microsomes. Baboon hepatic and placental microsomes were investigated to validate the nonhuman primate as an animal model for drug use during pregnancy. Data presented here indicate that human and baboon hepatic microsomes formed several mono-, di-, and tri-hydroxylated derivatives of 17-HPC. However, microsomes of human and baboon placentas metabolized 17-HPC to its mono-hydroxylated derivatives only in quantities that were a fraction of those formed by their respective livers, except for two metabolites (M16' and M17') that are unique for placenta and contributed to 25% and 75% of the total metabolites formed by human and baboon, respectively. The amounts of metabolites formed, relative to each other, by human and baboon microsomes were different suggesting that the affinity of 17-HPC to CYP enzymes and their activity could be species-dependent.

Computational models to assign biopharmaceutics drug disposition classification from molecular structure

PURPOSE

We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS).

MATERIALS AND METHODS

Models were developed using machine learning methods including recursive partitioning (RP), random forest (RF) and support vector machine (SVM) algorithms with ChemDraw, clogP, polar surface area, VolSurf and MolConnZ descriptors. The dataset consisted of 165 training and 56 test set molecules.

RESULTS

RF model 3, RP model 1, and SVM model 1 can correctly predict 73.1, 63.6 and 78.6% test compounds in classes 1, 2 and 3, respectively. Both RP and SVM models can be used for class 4 prediction. The inclusion of consensus analysis resulted in improved test set predictions for class 2 and 4 drugs.

CONCLUSIONS

The models can be used to predict BDDCS class for new compounds from molecular structure using readily available molecular descriptors and software, representing an area where in silico approaches could aid the pharmaceutical industry in speeding drugs to the patient and reducing costs. This could have significant applications in drug discovery to identify molecules that may have future developability issues.