The Use of ROC Analysis for the Qualitative Prediction of Human Oral Bioavailability from Animal Data

PURPOSE: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (F(human)) from animal oral bioavailability (F(animal)) data employing ROC analysis and to identify the optimal thresholds for such predictions. METHODS: A dataset of 184 compounds with known F(human) and F(animal) in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for F(human) was built by setting a threshold for high/low F(human) at 50%. The thresholds for high/low F(animal) were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from ‘cost analysis’ and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. RESULTS: We successfully built ROC curves for the combined dataset and per individual species. Optimal F(animal) thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. CONCLUSIONS: F(animal) can predict high/low F(human) with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-013-1193-2) contains supplementary material, which is available to authorized users

Animal versus human oral drug bioavailability: Do they correlate?

AbstractOral bioavailability is a key consideration in development of drug products, and the use of preclinical species in predicting bioavailability in human has long been debated. In order to clarify whether any correlation between human and animal bioavailability exist, an extensive analysis of the published literature data was conducted. Due to the complex nature of bioavailability calculations inclusion criteria were applied to ensure integrity of the data. A database of 184 compounds was assembled. Linear regression for the reported compounds indicated no strong or predictive correlations to human data for all species, individually and combined.The lack of correlation in this extended dataset highlights that animal bioavailability is not quantitatively predictive of bioavailability in human. Although qualitative (high/low bioavailability) indications might be possible, models taking into account species-specific factors that may affect bioavailability are recommended for developing quantitative prediction

Gut-wall metabolism. Application of pre-clinical models for the prediction of human drug absorption and first-pass elimination

Quantifying the multiple processes which control and modulate the extent of oral bioavailability for drug candidates is critical to accurate projection of human pharmacokinetics (PK). Understanding how gut wall metabolism and hepatic elimination factor into first-pass clearance of drugs has improved enormously. Typically, the cytochrome P450s, uridine 5′-diphosphate-glucuronosyltransferases and sulfotransferases, are the main enzyme classes responsible for drug metabolism. Knowledge of the isoforms functionally expressed within organs of first-pass clearance, their anatomical topology (e.g. zonal distribution), protein homology and relative abundances and how these differ across species is important for building models of human metabolic extraction. The focus of this manuscript is to explore the parameters influencing bioavailability and to consider how well these are predicted in human from animal models or from in vitro to in vivo extrapolation. A unique retrospective analysis of three AstraZeneca molecules progressed to first in human PK studies is used to highlight the impact that species differences in gut wall metabolism can have on predicted human PK. Compared to the liver, pharmaceutical research has further to go in terms of adopting a common approach for characterisation and quantitative prediction of intestinal metabolism. A broad strategy is needed to integrate assessment of intestinal metabolism in the context of typical DMPK activities ongoing within drug discovery programmes up until candidate drug nomination. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1208/s12248-016-9889-y) contains supplementary material, which is available to authorized users

Quantitative ADME Proteomics - CYP and UGT Enzymes in the Beagle Dog Liver and Intestine

International audiencePURPOSE:Beagle dogs are used to study oral pharmacokinetics and guide development of drug formulations for human use. Since mechanistic insight into species differences is needed to translate findings in this species to human, abundances of cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT) drug metabolizing enzymes have been quantified in dog liver and intestine.METHODS:Abundances of enzymes were measured in Beagle dog intestine and liver using selected reaction monitoring mass spectrometry.RESULTS:Seven and two CYPs were present in the liver and intestine, respectively. CYP3A12 was the most abundant CYP in both tissues. Seven UGT enzymes were quantified in the liver and seven in the intestine although UGT1A11 and UGT1A9 were present only in the intestine and UGT1A7 and UGT2B31 were found only in the liver. UGT1A11 and UGT1A2 were the most abundant UGTs in the intestine and UGT2B31 was the most abundant UGT in the liver. Summed abundance of UGT enzymes was similar to the sum of CYP enzymes in the liver whereas intestinal UGTs were up to four times more abundant than CYPs. The estimated coefficients of variation of abundance estimates in the livers of 14 donors were separated into biological and technical components which ranged from 14 to 49% and 20 to 39%, respectively.CONCLUSIONS:Abundances of canine CYP enzymes in liver and intestine have been confirmed in a larger number of dogs and UGT abundances have been quantified for the first time. The biological variability in hepatic CYPs and UGTs has also been estimated

Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin

Drug-induced cardiac arrhythmia, especially occurrence of torsade de pointes (TdP), has been a leading cause of attrition and post-approval re-labeling and withdrawal of many drugs. TdP is a multifactorial event, reflecting more than just drug-induced cardiac ion channel inhibition and QT interval prolongation. This presents a translational gap in extrapolating pre-clinical and clinical cardiac safety assessment to estimate TdP risk reliably, especially when the drug of interest is used in combination with other QT-prolonging drugs for treatment of diseases such as tuberculosis. A multi-scale mechanistic modeling framework consisting of physiologically based pharmacokinetics (PBPK) simulations of clinically relevant drug exposures combined with Quantitative Systems Toxicology (QST) models of cardiac electro-physiology could bridge this gap. We illustrate this PBPK-QST approach in cardiac risk assessment as exemplified by moxifloxacin, an anti-tuberculosis drug with abundant clinical cardiac safety data. PBPK simulations of moxifloxacin concentrations (systemic circulation and estimated in heart tissue) were linked with in vitro measurements of cardiac ion channel inhibition to predict the magnitude of QT prolongation in healthy individuals. Predictions closely reproduced the clinically observed QT interval prolongation, but no arrhythmia was observed, even at ×10 exposure. However, the same exposure levels in presence of physiological risk factors, e.g., hypokalemia and tachycardia, led to arrhythmic event in simulations, consistent with reported moxifloxacin-related TdP events. Application of a progressive PBPK-QST cardiac risk assessment paradigm starting in early development could guide drug development decisions and later define a clinical “safe space” for post-approval risk management to identify high-risk clinical scenarios