Actisomide

Animal species, i.e. rats, are used to evaluate a drugs biol. activity and pharmacokinetics, such as clearance.This study aims to develop a predictive method to evaluate the relationship between the clearance of drugs in rats and humans.Adataset of 395 drugs composed of human and rat clearance data was applied in this study.The drugs, according to the clearance differences, were divided into two groups on the basis of the correlation between humans and rats.Structural parameters were used to develop a model to estimate the class of each drug by binary logistic regression.The results showed that the developed models are a useful way to show which drug have an acceptable correlation between human and rat clearance by using structural features.They can be used to determine the group of drugs with an accuracy of 74%.According to the validation results, the developed method has an acceptable accuracy for checking the relationship between the clearance of drugs in humans and rats.This modeling can be used to exclude the drugs from experiments in rats which reveal a considerable difference between the clearance of humans and that of rats.

We present three in silico volume of distribution at steady state (VDss) models generated on a training set comprising 1096 compounds, which goes well beyond the conventional drug space delineated by the Rule of 5 or similar approaches. We have performed a careful selection of descriptors and kept a homogeneous Molecular Interaction Field-based descriptor set and linear (Partial Least Squares, PLS) and nonlinear (Random Forest, RF) models. We have tested the models, which we deem orthogonal in nature due to different descriptors and statistical approaches, with good results. In particular we tested the RF model, via a leave-class-out approach and by using a set of 34 additional compounds not used for training. We report comparable results against in vivo scaling approaches with geometric mean-fold error at or below 2 (for a set of 60 compounds with animal data available) and discuss the predictive performance based on the ionization states of the compounds. Lastly, we report the findings using a two-tier approach (classification followed by regression) based on VDss ranges, in an attempt to improve the prediction of compounds with very high VDss. We would recommend, overall, the RF model, with 33 descriptors, as the primary choice for VDss prediction in humans.