Distribution of cytochrome P450 2C, 2E1, 3A4, and 3A5 in human colon mucosa

BACKGROUND: Despite the fact that the alimentary tract is part of the body's first line of defense against orally ingested xenobiotica, little is known about the distribution and expression of cytochrome P450 (CYP) enzymes in human colon. Therefore, expression and protein levels of four representative CYPs (CYP2C(8), CYP2E1, CYP3A4, and CYP3A5) were determined in human colon mucosa biopsies obtained from ascending, descending and sigmoid colon. METHODS: Expression of CYP2C, CYP2E1, CYP3A4, and CYP3A5 mRNA in colon mucosa was determined by RT-PCR. Protein concentration of CYPs was determined using Western blot methods. RESULTS: Extensive interindividual variability was found for the expression of most of the genes. However, expression of CYP2C mRNA levels were significantly higher in the ascending colon than in the sigmoid colon. In contrast, mRNA levels of CYP2E1 and CYP3A5 were significantly lower in the ascending colon in comparison to the descending and sigmoid colon. In sigmoid colon protein levels of CYP2C8 were significantly higher by ~73% than in the descending colon. In contrast, protein concentration of CYP2E1 was significantly lower by ~81% in the sigmoid colon in comparison to the descending colon. CONCLUSION: The current data suggest that the expression of CYP2C, CYP2E1, and CYP3A5 varies in different parts of the colon

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies

Fexinidazole – A New Oral Nitroimidazole Drug Candidate Entering Clinical Development for the Treatment of Sleeping Sickness

This article describes the preclinical profile of fexinidazole, a new drug candidate with the potential to become a novel, oral, safe and effective short-course treatment for curing both stage 1 and 2 human African trypanosomiasis and replace the old and highly problematic treatment modalities available today. Fexinidazole is orally available and rapidly metabolized in two metabolites having equivalent biological activity to the parent and contributing significantly to the in vivo efficacy in animal models of both stage 1 and 2 HAT. Animal toxicology studies indicate that fexinidazole has an excellent safety profile, with no particular issues identified. Fexinidazole is a 5-nitroimidazole and, whilst it is Ames-positive, it is devoid of any genetic toxicity in mammalian cells and therefore does not pose a genotoxic risk for use in man. Fexinidazole, which was rediscovered through a process of compound mining, is the first new drug candidate for stage 2 HAT having entered clinical trials in thirty years, and has the potential to revolutionize therapy of this fatal disease at a cost that is acceptable in the endemic regions