Model-based lamotrigine clearance changes during pregnancy: clinical implication

Objective: The objective of the study was to characterize changes in the oral clearance (CL/F) of lamotrigine (LTG) over the course of pregnancy and the postpartum period through a model-based approach incorporating clinical characteristics that may influence CL/F, in support of developing clinical management guidelines. Methods: Women receiving LTG therapy who were pregnant or planning pregnancy were enrolled. Maternal blood samples were collected at each visit. A pharmacokinetic analysis was performed using a population-based, nonlinear, mixed-effects model. Results: A total of 600 LTG concentrations from 60 women (64 pregnancies) were included. The baseline LTG CL/F was 2.16 L/h with a between-subject variability of 40.6%. The influence of pregnancy on CL/F was described by gestational week. Two subpopulations of women emerged based on the rate of increase in LTG CL/F during pregnancy. The gestational age-associated increase in CL/F displayed a 10-fold higher rate in 77% of the women (0.118 L/h per week) compared to 23% (0.0115 L/h per week). The between-subject variability in these slopes was 43.0%. The increased CL/F at delivery declined to baseline values with a half-life of 0.55 weeks. Interpretation The majority of women had a substantial increase in CL/F from 2.16 to 6.88 L/h by the end of pregnancy, whereas 23% of women had a minimal increase. An increase in CL/F may correspond to decreases in LTG blood concentrations necessitating the need for more frequent dosage adjustments and closer monitoring in some pregnant women with epilepsy. Postpartum doses should be tapered to preconception dose ranges within 3 weeks of delivery

Differential Genotype Dependent Inhibition of CYP2C9 in Humans

ABSTRACT: The effects of genetic polymorphisms in drug-metabolizing enzymes (e.g., CYP2C9*3) on drug clearance have been well characterized but much less is known about whether these polymorphisms alter susceptibility to drug-drug interactions. Previous in vitro work has demonstrated that genotype-dependent inhibition of CYP2C9 mediated flurbiprofen metabolism, suggesting the possibility of genotype-dependent inhibition interactions in vivo. In the current study, flurbiprofen was used as a probe substrate and fluconazole as a prototypical inhibitor to investigate whether genotype-dependent inhibition of CYP2C9 occurs in vivo. From 189 healthy volunteers who were genotyped for CYP2C9 polymorphisms, 11 control subjects (CYP2C9*1/*1), 9 heterozygous and 2 homozygous for the CYP2C9*3 allele participated in the pharmacokinetic drug interaction study. Subjects received a single 50-mg oral dose of flurbiprofen alone or after administration of either 200 or 400 mg of fluconazole for 7 days using an open, randomized, crossover design. Flurbiprofen and fluconazole plasma concentrations along with flurbiprofen and 4-hydroxyflurbiprofen urinary excretion were monitored. Flurbiprofen apparent oral clearance differed significantly among the three genotype groups (p < 0.05) at baseline but not after pretreatment with 400 mg of fluconazole for 7 days. Changes in flurbiprofen apparent oral clearance after fluconazole coadministration were gene dose-dependent, with virtually no change occurring in *3/*3 subjects. Analysis of fractional clearances suggested that the fraction metabolized by CYP2C9, as influenced by genotype, determined the degree of drug interaction observed. In summary, the presence of CYP2C9*3 alleles (either one or two alleles) can alter the degree of drug interaction observed upon coadministration of inhibitors. The cytochrome P450 (P450) superfamily of enzymes plays an important role in the oxidation of numerous xenobiotics, with CYP2C9 accounting for 10 to 20% of the P450 protein content in human liver. CYP2C9 has been reported to catalyze approximately 20% of P450-mediated drug oxidation reactions Certain diseases or the presence of comorbid conditions may necessitate coadministration of multiple medications, increasing the chances of drug-drug interactions. However, genetic polymorphisms in drug-metabolizing enzymes are not routinely evaluated for their impact on drug interactions during clinical studies. Genotype-dependent inhibition has been demonstrated with CYP2D6 and CYP2C19 genetic polymorphism

Early postnatal ozone exposure alters rat nodose and jugular sensory neuron development

Sensory neurons originating in nodose and jugular ganglia that innervate airway epithelium (airway neurons) play a role in inflammation observed following exposure to inhaled environmental irritants such as ozone (O3). Airway neurons can mediate airway inflammation through the release of the neuropeptide substance P (SP). While susceptibility to airway irritants is increased in early life, the developmental dynamics of afferent airway neurons are not well characterized. The hypothesis of this study was that airway neuron number might increase with increasing age, and that an acute, early postnatal O3 exposure might increase both the number of sensory airway neurons as well as the number SP-containing airway neurons. Studies using Fischer 344 rat pups were conducted to determine if age or acute O3 exposure might alter airway neuron number. Airway neurons in nodose and jugular ganglia were retrogradely labeled, removed, dissociated, and counted by means of a novel technique employing flow cytometry. In Study 1, neuron counts were conducted on postnatal days (PD) 6, 10, 15, 21, and 28. Numbers of total and airway neurons increased significantly between PD6 and PD10, then generally stabilized. In Study 2, animals were exposed to O3 (2 ppm) or filtered air (FA) on PD5 and neurons were counted on PD10, 15, 21, and 28. O3-exposed animals displayed significantly less total neurons on PD21 than FA controls. This study shows that age-related changes in neuron number occur, and that an acute, early postnatal O3 exposure significantly alters sensory neuron development

A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000–5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273–283, FIBA 5–16, and LBN 306–313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens

Evaluating the Benefits of Aphasia Intervention Delivered in Virtual Reality: Results of a Quasi-Randomised Study

Introduction This study evaluated an intervention for people with aphasia delivered in a novel virtual reality platform called EVA Park. EVA Park contains a number of functional and fantastic locations and allows for interactive communication between multiple users. Twenty people with aphasia had 5 weeks’ intervention, during which they received daily language stimulation sessions in EVA Park from a support worker. The study employed a quasi randomised design, which compared a group that received immediate intervention with a waitlist control group. Outcome measures explored the effects of intervention on communication and language skills, communicative confidence and feelings of social isolation. Compliance with the intervention was also explored through attrition and usage data. Results There was excellent compliance with the intervention, with no participants lost to follow up and most (18/20) receiving at least 88% of the intended treatment dose. Intervention brought about significant gains on a measure of functional communication. Gains were achieved by both groups of participants, once intervention was received, and were well maintained. Changes on the measures of communicative confidence and feelings of social isolation were not achieved. Results are discussed with reference to previous aphasia therapy findings

The pharmacokinetics of valganciclovir prophylaxis in pediatric solid organ transplant patients at risk for Epstein&amp;ndash;Barr virus disease

Heather&amp;nbsp;E Vezina1,2, Richard C Brundage2, Thomas&amp;nbsp;E Nevins3, Henry&amp;nbsp;H Balfour Jr1,31Department of Laboratory Medicine and Pathology, 2Department of Experimental and Clinical Pharmacology, 3Department of Pediatrics, University of Minnesota, Minneapolis, MN, USAAbstract: Antiviral prophylaxis with valganciclovir is used frequently in pediatric solid organ transplant patients to prevent Epstein&amp;ndash;Barr virus (EBV)-induced infections and tissue-invasive disease including post-transplant lymphoproliferative disorder (PTLD). This approach is untested in clinical trials and valganciclovir dosing strategies in children are highly variable. Our objective was to characterize the pharmacokinetics of ganciclovir in the plasma of pediatric kidney and liver transplant patients taking valganciclovir for EBV prophylaxis. Virologic response was also evaluated. Ganciclovir was measured by liquid chromatography/ultraviolet detection. EBV DNA was quantified by TaqMan&amp;reg; polymerase chain reaction. NONMEM&amp;reg; VI was used for data analysis. Ganciclovir plasma profiles were consistent with a one-compartment model. Final model estimates of apparent oral clearance (L/h), apparent volume of distribution (L), and absorption rate constant were 7.33, 35.1, and 0.85, respectively. There was evidence of lower bioavailability in children younger than three years. All eight subjects achieved ganciclovir plasma concentrations above reported in vitro concentrations needed to inhibit EBV replication by 50%. However, four subjects had detectable EBV DNA with a median (range) of 18,300 (4,400 to 54,900) copies/mL of whole blood. These findings support the need for further studies of the clinical pharmacology and efficacy of valganciclovir for EBV prophylaxis.Keywords: valganciclovir, ganciclovir, pharmacokinetics, Epstein&amp;ndash;Barr virus, pediatrics, solid organ transplantatio

Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis

A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model