Felix Hammann

Effect of the inhibition of CYP3A4 or CYP2D6 on the pharmacokinetics and pharmacodynamics of oxycodone

PurposeThe main metabolic pathways of oxycodone, a potent opioid analgetic, are N-demethylation (CYP3A4) to inactive noroxycodone and O-demethylation (CYP2D6) to active oxymorphone. We performed a three-way, placebo-controlled, double-blind cross-over study to assess the pharmacokinetic and pharmacodynamic consequences of drug interactions with oxycodone.MethodsThe 12 participants (CYP2D6 extensive metabolizers) were pre-treated with placebo, ketoconazole or paroxetine before oral oxycodone ingestion (0.2xa0mg/kg).ResultsPre-treatment with ketoconazole increased the AUC for oxycodone 2- to 3-fold compared with placebo or paroxetine. In combination with placebo, oxycodone induced the expected decrease in pupil diameter. This decrease was accentuated in the presence of ketoconazole, but blunted by paroxetine. In comparison to pre-treatment with placebo, ketoconazole increased nausea, drowsiness, and pruritus associated with oxycodone. In contrast, the effect of pre-treatment with paroxetine on the above-mentioned adverse events was not different from that of placebo. Ketoconazole increased the analgetic effect of oxycodone, whereas paroxetine was not different from placebo.ConclusionsInhibition of CYP3A4 by ketoconazole increases the exposure and some pharmacodynamic effects of oxycodone. Paroxetine pretreatment inhibits CYP2D6 without inducing relevant changes in oxycodone exposure, and partially blunts the pharmacodynamic effects of oxycodone due to intrinsic pharmacological activities. Pharmacodynamic changes associated with CYP3A4 inhibition may be clinically important in patients treated with oxycodone.

Prediction of Adverse Drug Reactions Using Decision Tree Modeling

Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure–activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9–90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end‐organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction

Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.

Comparison of the dissolution and pharmacokinetic profiles of two galenical formulations of the endothelin receptor antagonist macitentan

Macitentan (ACT-064992) is an orally active endothelin receptor antagonist. We first compared the in vitro dissolution characteristics of uncoated and film-coated tablets with hard gelatin capsules containing 10mg ACT-064992. Subsequently, we compared the oral pharmacokinetics of ACT-064992 and its active metabolite ACT-132577 of the coated tablet and the gelatin capsule formulation in 11 male volunteers. The dissolution profile showed a rapid disintegration of all formulations with >90% dissolution of ACT-064992 within 45 min. The pharmacokinetics of ACT-064992 and its metabolite ACT-132577 were comparable for the two formulations. ACT-064992 revealed a slow absorption (median t(max) 8h) and a terminal half-life of approximately 13 h. Bioequivalence criteria were met for AUC(0-t) and AUC(0-infinity). Mean C(max) was 19% lower after ingestion of the tablet compared to capsules with its lower 90% confidence limit below the accepted bioequivalence range. The pharmacokinetics of the metabolite ACT-132577, characterized by a t(max) of approximately 48 h and a terminal half-life of approximately 45 h, was not different between the two formulations. We conclude that the absorption profile of the tablet differs from the capsule in peak but not in total exposure, which is not expected to be of clinical significance.

Classification of cytochrome p(450) activities using machine learning methods.

The cytochrome P(450) (CYP) system plays an integral part in the metabolism of drugs and other xenobiotics. Knowledge of the structural features required for interaction with any of the different isoforms of the CYP system is therefore immensely valuable in early drug discovery. In this paper, we focus on three major isoforms (CYP 1A2, CYP 2D6, and CYP 3A4) and present a data set of 335 structurally diverse drug compounds classified for their interaction (as substrate, inhibitor, or any interaction) with these isoforms. We also present machine learning models using a variety of commonly used methods (k-nearest neighbors, decision tree induction using the CHAID and CRT algorithms, random forests, artificial neural networks, and support vector machines using the radial basis function (RBF) and homogeneous polynomials as kernel functions). We discuss the physicochemical features relevant for each end point and compare it to similar studies. Many of these models perform exceptionally well, even with 10-fold cross-validation, yielding corrected classification rates of 81.7 to 91.9% for CYP 1A2, 89.2 to 92.9% for CYP 2D6, and 87.4 to 89.9% for CYP3A4. Our models help in understanding the structural requirements for CYP interactions and can serve as sensitive tools in virtual screenings and lead optimization for toxicological profiles in drug discovery.

Breast Cancer Resistance Protein and P-Glycoprotein Expression in Patients with Newly Diagnosed and Therapy-Refractory Ulcerative Colitis Compared with Healthy Controls

Aims: Efflux transporters such as breast cancer resistance protein (BCRP/ABCG2) and P-glycoprotein (Pgp; MDR1/ABCB1) are protecting the enterocytes from potentially toxic compounds. Both transporters have been reported to be downregulated in patients with active ulcerative colitis (UC). The aim of this study was to evaluate transporter expression in both unaffected and inflamed mucosa of patients with active UC, in drug-naïve and treated patients with UC and compare the results with transporter expression in healthy subjects. Methods: Transporter expression was determined with real-time RT-PCR (TaqMan) in inflamed and unaffected mucosa of newly diagnosed (n = 12) and therapy-refractory (n = 11) patients with UC. Expression levels were compared with UC patients in remission (n = 11) and control subjects (n = 26). BCRP and Pgp expression was evaluated by immunohistochemistry. Results: Compared with unaffected mucosa, BCRP expression was significantly reduced in inflamed mucosa of newly diagnosed drug-naïve patients with UC (expression reduced to 30%) as well as in patients not responding to treatment (reduced to 25%) with either 5-aminosalicylates (n = 7) or prednisone (n = 4). Unaffected mucosa of UC patients showed comparable transporter expression to unaffected mucosa of control subjects. MDR1 expression depicts a similar pattern. Protein staining for Pgp and BCRP was significantly reduced in the inflamed mucosa of patients with active UC. Conclusions: Expression of both efflux transporters BCRP and MDR1 is reduced, but only in inflamed tissue of patients with active UC. Transporter expression in unaffected mucosa of patients with active UC is comparable to healthy controls. The data suggest that the inflammatory process is responsible for the reduced levels. A major role in the pathogenesis of UC is unlikely.

Combinatorial QSAR modeling of human intestinal absorption.

Intestinal drug absorption in humans is a central topic in drug discovery. In this study, we use a broad selection of machine learning and statistical methods for the classification and numerical prediction of this key end point. Our data set is based on a selection of 458 small druglike compounds with FDA approval. Using easily available tools, we calculated one- to three-dimensional physicochemical descriptors and used various methods of feature selection (best-first backward selection, correlation analysis, and decision tree analysis). We then used decision tree induction (DTI), fragment-based lazy-learning (LAZAR), support vector machine classification, multilayer perceptrons, random forests, k-nearest neighbor and Naïve Bayes analysis to model absorption ratios and binary classification (well-absorbed and poorly absorbed compounds). Best performance for classification was seen with DTI using the chi-squared analysis interaction detector (CHAID) algorithm, yielding corrected classification rate of 88% (Matthews correlation coefficient of 75%). In numeric predictions, the multilayer perceptron performed best, achieving a root mean squared error of 25.823 and a coefficient of determination of 0.6. In line with current understanding is the importance of descriptors such as lipophilic partition coefficients (log P) and hydrogen bonding. However, we are able to highlight the utility of gravitational indices and moments of inertia, reflecting the role of structural symmetry in oral absorption. Our models are based on a diverse data set of marketed drugs representing a broad chemical space. These models therefore contribute substantially to the molecular understanding of human intestinal drug absorption and qualify for a generalized use in drug discovery and lead optimization.

Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of P-Glycoprotein

In silico classification of new compounds for certain properties is a useful tool to guide further experiments or compound selection. Interaction of new compounds with the efflux pump P-glycoprotein (P-gp) is an important drug property determining tissue distribution and the potential for drug-drug interactions. We present three datasets on substrate, inhibitor, and inducer activities for P-gp (n = 471) obtained from a literature search which we compared to an existing evaluation of the Prestwick Chemical Library with the calcein-AM assay (retrieved from PubMed). Additionally, we present decision tree models of these activities with predictive accuracies of 77.7 % (substrates), 86.9 % (inhibitors), and 90.3 % (inducers) using three algorithms (CHAID, CART, and C4.5). We also present decision tree models of the calcein-AM assay (79.9 %). Apart from a comprehensive dataset of P-gp interacting compounds, our study provides evidence of the efficacy of logD descriptors and of two algorithms not commonly used in pharmacological QSAR studies (CART and CHAID).

Screening for an ivermectin slow-release formulation suitable for malaria vector control

BackgroundThe prospect of eliminating malaria is challenged by emerging insecticide resistance and vectors with outdoor and/or crepuscular activity. Ivermectin can simultaneously tackle these issues by killing mosquitoes feeding on treated animals and humans. A single oral dose, however, confers only short-lived mosquitocidal plasma levels.MethodsThree different slow-release formulations of ivermectin were screened for their capacity to sustain mosquito-killing levels of ivermectin for months. Thirty rabbits received a dose of one, two or three silicone implants containing different proportions of ivermectin, deoxycholate and sucrose. Animals were checked for toxicity and ivermectin was quantified periodically in blood. Potential impact of corresponding long-lasting formulation was mathematically modelled.ResultsAll combinations of formulation and dose released ivermectin for more than 12xa0weeks; four combinations sustained plasma levels capable of killing 50% of Anopheles gambiae feeding on a treated subject for up to 24xa0weeks. No major adverse effects attributable to the drug were found. Modelling predicts a 98% reduction in infectious vector density by using an ivermectin formulation with a 12-week duration.ConclusionsThese results indicate that relatively stable mosquitocidal plasma levels of ivermectin can be safely sustained in rabbits for up to six months using a silicone-based subcutaneous formulation. Modifying the formulation of ivermectin promises to be a suitable strategy for malaria vector control.

Ivermectin to reduce malaria transmission I. Pharmacokinetic and pharmacodynamic considerations regarding efficacy and safety

Ivermectin is an endectocide that has been used broadly in single dose community campaigns for the control of onchocerciasis and lymphatic filariasis for more than 30xa0years. There is now interest in the potential use of ivermectin regimens to reduce malaria transmission, envisaged as community-wide campaigns tailored to transmission patterns and as complement of the local vector control programme. The development of new ivermectin regimens or other novel endectocides will require integrated development of the drug in the context of traditional entomological tools and endpoints. This document examines the main pharmacokinetic and pharmacodynamic parameters of the medicine and their potential influence on its vector control efficacy and safety at population level. This information could be valuable for trial design and clinical development into regulatory and policy pathways.