Absolute bioavailability of cefprozil after oral administration in beagles

Examination of Urinary Excretion of Unchanged Drug in Humans and Preclinical Animal Models: Increasing the Predictability of Poor Metabolism in Humans

PURPOSE

A dataset of fraction excreted unchanged in the urine (fe) values was developed and used to evaluate the ability of preclinical animal species to predict high urinary excretion, and corresponding poor metabolism, in humans.

METHODS

A literature review of fe values in rats, dogs, and monkeys was conducted for all Biopharmaceutics Drug Disposition Classification System (BDDCS) Class 3 and 4 drugs (n=352) and a set of Class 1 and 2 drugs (n=80). The final dataset consisted of 202 total fe values for 135 unique drugs. Human and animal data were compared through correlations, two-fold analysis, and binary classifications of high (fe ≥30%) versus low (<30%) urinary excretion in humans. Receiver Operating Characteristic curves were plotted to optimize animal fe thresholds.

RESULTS

Significant correlations were found between fe values for each animal species and human fe (p<0.05). Sixty-five percent of all fe values were within two-fold of human fe with animals more likely to underpredict human urinary excretion as opposed to overpredict. Dogs were the most reliable predictors of human fe of the three animal species examined with 72% of fe values within two-fold of human fe and the greatest accuracy in predicting human fe ≥30%. ROC determined thresholds of ≥25% in rats, ≥19% in dogs, and ≥10% in monkeys had improved accuracies in predicting human fe of ≥30%.

CONCLUSIONS

Drugs with high urinary excretion in animals are likely to have high urinary excretion in humans. Animal models tend to underpredict the urinary excretion of unchanged drug in humans.

Correlation between in silico physicochemical characteristics of drugs and their mean residence time in human and dog

The correlation between 52 physicochemical parameters and mean residence time (MRT) for 27 drugs used in human and dog were investigated. The physicochemical parameter values calculated provided a basis for deriving a series of arithmetic expressions, which were used to build a mathematical model describing the relationship between them and the MRT values. From the entire set of analyzed parameters, a subset of 14 was identified that contributed to the derivation of an arithmetic expression: Log(PSA - WPSA + ACID) x [XlogP - (LogKp - EAxLn(Caco2 + AMINE + SAF))] + (AMIDE + IP - FG) - Ln(MW + PISA) the value of which is highly correlated with the MRT value in dogs (P < .001) and allowed prediction of the MRT predicted (MRT(pred)). In humans, no correlation was found that allowed the calculation of MRT(pred). These results indicate that predicting the pharmacokinetics of any specific drug for humans based on pharmacokinetic data obtained in the dog should be undertaken with knowledge of the inherent limitations.