Liver Cirrhosis Affects the Pharmacokinetics of the Six Substrates of the Basel Phenotyping Cocktail Differently.

BACKGROUND Activities of hepatic cytochrome P450 enzymes (CYPs) are relevant for hepatic clearance of drugs and known to be decreased in patients with liver cirrhosis. Several studies have reported the effect of liver cirrhosis on CYP activity, but the results are partially conflicting and for some CYPs lacking. OBJECTIVE In this study, we aimed to investigate the CYP activity in patients with liver cirrhosis with different Child stages (A-C) using the Basel phenotyping cocktail approach. METHODS We assessed the pharmacokinetics of the six compounds and their CYP-specific metabolites of the Basel phenotyping cocktail (CYP1A2: caffeine, CYP2B6: efavirenz, CYP2C9: flurbiprofen, CYP2C19: omeprazole, CYP2D6: metoprolol, CYP3A: midazolam) in patients with liver cirrhosis (n = 16 Child A cirrhosis, n = 15 Child B cirrhosis, n = 5 Child C cirrhosis) and matched control subjects (n = 12). RESULTS While liver cirrhosis only marginally affected the pharmacokinetics of the low to moderate extraction drugs efavirenz and flurbiprofen, the elimination rate of caffeine was reduced by 51% in patients with Child C cirrhosis. For the moderate to high extraction drugs omeprazole, metoprolol, and midazolam, liver cirrhosis decreased the elimination rate by 75%, 37%, and 60%, respectively, increased exposure, and decreased the apparent systemic clearance (clearance/bioavailability). In patients with Child C cirrhosis, the metabolic ratio (ratio of the area under the plasma concentration-time curve from 0 to 24 h of the metabolite to the parent compound), a marker for CYP activity, decreased by 66%, 47%, 92%, 73%, and 43% for paraxanthine/caffeine (CYP1A2), 8-hydroxyefavirenz/efavirenz (CYP2B6), 5-hydroxyomeprazole/omeprazole (CYP2C19), α-hydroxymetoprolol/metoprolol (CYP2D6), and 1'-hydroxymidazolam/midazolam (CYP3A), respectively. In comparison, the metabolic ratio 4-hydroxyflurbiprofen/flurbiprofen (CYP2C9) remained unchanged. CONCLUSIONS Liver cirrhosis affects the activity of CYP isoforms differently. This variability must be considered for dose adjustment of drugs in patients with liver cirrhosis. CLINICAL TRIAL REGISTRATION NCT03337945

Pre-treatment comorbidities, C-reactive protein and eosinophil count, and immune-related adverse events as predictors of survival with checkpoint inhibition for multiple tumour entities

BACKGROUND The development of immune-related adverse events (irAEs) may be associated with clinical efficacy of checkpoint inhibitors (CPIs) in patients with cancer. We therefore investigated the effect of irAEs and pre-treatment parameters on outcome in a large, real-life patient cohort. METHODS We performed a single-centre, retrospective, observational study including patients who received CPIs from 2011 to 2018 and followed until 2021. The primary outcome was overall survival, and the secondary outcome was the development of irAEs. RESULTS In total, 229 patients with different tumour entities (41% non-small cell lung cancer [NSCLC], 29% melanoma) received a total of 282 CPI treatment courses (ipilimumab, nivolumab, pembrolizumab or atezolizumab). Thirty-four percent of patients developed irAEs (of these 17% had CTCAE Grade ≥3). Factors independently associated with mortality were pre-treatment CRP ≥10 mg/L (hazard ratio [HR] 2.064, p = 0.0003), comorbidity measured by Charlson comorbidity index (HR 1.149, p = 0.014) and irAEs (HR 0.644, p = 0.036) (age-adjusted, n = 216). Baseline eosinophil count ≤0.2 × 10$^{9}$ /L was a further independent predictor of mortality (age-, CRP-, CCI- and irAE-adjusted HR = 2.252, p = 0.002, n = 166). Anti-CTLA-4 use (p < 0.001), and pre-treatment CRP <10 mg/L were independently associated with irAE occurrence (p = 0.037). CONCLUSIONS We found an independent association between irAE occurrence and improved overall survival in a real-life cohort spanning multiple tumour entities and treatment regimens. Pre-treatment comorbidities, CRP and eosinophil count represent potential markers for predicting treatment response

"In silico" prediction of drug transport across physiological barriers

Physiological barriers maintain and safeguard homeostasis of certain body compartments by an increased resistance against free diffusion. Distribution and pharmacokinetics of drugs can be altered as well, if they have to cross these barriers in order to reach their target. Knowledge of the physicochemical and structural requirements for drug permeation is a key topic in drug design, development, and clinical application. To assess processes on cellular barriers, in vitro methods are usually applied to elucidate single transport mechanisms or to study isolated transport. As the pharmacokinetics of a living system are often more complex and composed by a concatenation of several barriers, in vivo methods are required. However, this time consuming and expensive testing is not suited to answer the need for high-throughput screening of thousands of compounds in chemical databases. For these purpose in silico methods are ideally suited, which produce computational models to predict pharmacokinetics, drug distribution, or transport across single barriers. The first project of this thesis concerned the modeling of human intestinal absorption. After oral administration and intestinal dissolution, a drug has to cross the gut wall in order to become available for the body. The process is mostly determined by passive diffusion and active transport. Active export and import of molecules on the enterocyte is regulated by a multitude of transport proteins and metabolic enzymes. A dataset of small drug-like compounds, on which information on their human intestinal absorption was available, was collected. Models trained on these data predicted human intestinal absorption with high accuracy. Several machine learning methods were compared as well as different feature sets. The features used to predict intestinal absorption resembled those known from modeling passive diffusion, which are measures of charge and lipophilicity. The models revealed also less commonly used descriptors to model human intestinal absorption, such as gravitational indices and moments of inertia. The aim of the second project was to develop computational models to predict blood brain barrier (BBB) permeation. Development of new central nervous system (CNS) active drugs is hampered by limited brain permeation. As invasive methods have proven themselves to be ineffective and risky for patients, systemic application is the preferred route for drug administration into the brain. Hence, BBB permeability is a feature absolutely mandatory for any drug, which targets the CNS. Limited passive diffusion and active efflux and influx systems account for the complexity of this highly regulated barrier. To establish our models, a database of 163 compounds with information on the in vivo surface permeability product (LogPS) in rats was collected. Decision trees performed with high accuracy (CCR of 90.9 - 93.9%.) and revealed descriptors of lipophilicity and charge, which were yet described in models of passive BBB permeation. However, other descriptors as measures for molecular geometry and connectivity could be related to an active drug transport component. Moreover, a fragment-based approach indicated the involvement of stereochemistry to predict LogPS values. The third project explores the physicochemical and structural requirements for drugs to pass from maternal blood into breast milk. While experimental assessment in humans is limited, computational methods are appropriate to model drug permeation into breast milk. Data preparation for these models was a challenging endeavor. Endpoints were reported in imprecise ways, which asked for a careful selection and binning of the instances. Despite these facts, the 10-fold cross-validated decision trees predicted the endpoint with high accuracy (CCR: 85.3 - 95.3%). Prominent descriptors were measures of molecular size, branching, charge and geometry. Importance of polar fragments was revealed by a fragment-based analysis. The efflux transporter MRP2, a member of the ABC transporter family, was subject of the fourth study. Efflux transporters contribute substantially to barrier function by extruding potentially toxic substances. Three datasets were assembled from literature for MRP2 substrates, inducers, and inhibitors. For inducers and inhibitors, decision trees with high accuracy were grown. However, the substrate dataset did not qualify for decision tree induction, due to an underrepresentation of negative instances. The fifth project deals with an ant colony optimization (ACO) algorithm, which was adapted for fragment based feature selection. The paradigm was tested to predict antimalarial activity of molecules. ACO was able to reveal chemical substructures characterizing antimalarial drug activity, which comprised passive diffusion through the erythrocyte membrane and parasite toxicity. The paradigm outperformed other algorithms such as decision trees or artificial neural networks on the same dataset

A Binary Ant Colony Optimization Classifier for Molecular Activities

Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a <i>Plasmodium falciparum</i> inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information

Development and validation of an LC-MS/MS method for the analysis of ivermectin in plasma, whole blood, and dried blood spots using a fully automatic extraction system.

Ivermectin is deployed in mass drug administration (MDA) campaigns to control parasitic diseases in the tropics, with billions of treatments having been administered in the last three decades. Simple blood sampling tools, like the dried blood spots (DBS) technique, are needed to monitor treatments in such challenging settings. Thus, we developed a fully automated method for the analysis of ivermectin in DBS microsamples, including a bioanalytical and clinical validation. Automated extraction was carried out using a DBS-MS 500 autosampler which was coupled to a LC-MS/MS system. DBS were extracted with 20 μL solvent and eluted on a C8 analytical column. Analysis was performed by multiple reaction monitoring in the positive mode. Automated DBS extraction resulted in consistent recoveries (62.8 ± 4.3%) and matrix effects (68.0 ± 8.1%) between different donors and concentration levels. Intra- and inter-day accuracy and precision deviations were ≤15%, while samples with hematocrits from 20 to 60% could be quantified reliably. The achieved sensitivity of 1 ng/mL in DBS samples is sufficient to analyze ivermectin at the dose given (single oral administration of 12 mg) over a period of at least 72 h post treatment. Importantly, DBS samples are stable after one-month storage at room temperature (accuracy: 88.8-96.2%), thus samples collected in the field must not be shipped on dry ice. Ivermectin concentrations in venous and capillary blood agreed strongly, with a mean difference of -4.8%. Moreover, the drying process of DBS did not alter the analysis and importantly plasma concentrations can be estimated from DBS data using the hematocrit and red blood cell partitioning as correction factor. Our method enables uncomplicated sample collection and shipment as well as automated analysis of large amounts of samples, which is key to surveying MDA campaigns in remote settings

Cytokine signaling in the human brain capillary endothelial cell line hCMEC/D3

Brain microvascular endothelial cells are part of the blood-brain barrier and participate actively in immunological processes including cytokine-mediated inflammatory reactions. Using the human brain capillary endothelial cell line hCMEC/D3, activation of JAK/STAT signaling pathways were studied in response to stimulation by cytokines. The phenotype of hCMEC/D3 cells was confirmed by flow cytometry analysis of cell adhesion factors (cluster of differentiation molecules CD31 and CD34) and the von Willebrand factor endothelial marker was detected by immunofluorescence. Strong STAT1, STAT6 and STAT3 activation was observed in response to interferon-gamma (IFN-gamma), interleukin 4 (IL-4) and interleukin 6 (IL-6), respectively. Nuclear translocation of phosphorylated STAT proteins was visualized by confocal microscopy. Treatment of hCMEC/D3 cells with IFN-gamma resulted in interferon-induced upregulation of major histocompatibility complex (MHC) class I within 48h. Interferon-alpha (IFN-alpha) did not activate STAT1 or STAT3 nor did it induce MHC class I upregulation. Therefore, hCMEC/D3 cells were judged to be non-responsive to IFN-alpha. We also observed that hCMEC/D3 cells exhibit functional expression of alternative cytokine signal transduction pathways (i.e. TNF-alpha mediated activation of NF-kappaB). Together these results indicate that human blood-brain barrier hCMEC/D3 cells are responsive towards stimulation with various cytokines. We conclude that this unique cell line can be used to explore in vitro human blood-brain barrier functionality under proinflammatory conditions

Population pharmacokinetics of oral ivermectin in venous plasma and dried blood spots in healthy volunteers

The anthelminthic ivermectin is receiving new attention as it is being repurposed for new indications such as mass drug administrations for the treatment of scabies or in malaria vector control. As its pharmacokinetics are still poorly understood, we aimed to characterize the population pharmacokinetics of ivermectin in plasma and dried blood spots (DBS), a sampling method better suited to field trials, with special focus on the influence of body composition and enterohepatic circulation.; We performed a clinical trial in twelve healthy volunteers who each received a single oral dose of 12 mg ivermectin, and collected peripheral venous and capillary DBS samples. We determined ivermectin concentrations in plasma and DBS by liquid chromatography tandem mass spectrometry using a fully automated and scalable extraction system for DBS sample processing. Pharmacokinetic data were analyzed using non-linear mixed effects modeling.; A two-compartment model with a transit absorption model, first-order elimination, and weight as an influential covariate on central volume of distribution and clearance best described the data. The model estimates (inter-individual variability) for a 70 kg subject were: apparent population clearance 7.7 (25%) L/h, and central and peripheral volumes of distribution 89 (10%) L and 234 (20%) L, respectively. Concentrations obtained from DBS samples were strongly linearly correlated (r; 2; = 0.97) with plasma concentrations, and on average 30% lower.; The model accurately depicts population pharmacokinetics of plasma and DBS concentrations over time for oral ivermectin. The proposed analytical workflow is scalable and applicable to the requirements of mass drug administrations