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Vonoprazan, a potassium-competitive acid blocker, is under investigation in the United States and Europe for the treatment of erosive esophagitis and Helicobacter pylori infection. Population pharmacokinetic (popPK) analysis allows the identification of factors that could affect drug exposure in population subgroups. Here, we report a popPK model based on pooled data sets of available pharmacokinetic (PK) studies in healthy volunteers and patients with gastroesophageal reflux disease, including erosive esophagitis, from Asia and Europe. This model was used to evaluate the impact of different covariates, including race and disease status, on vonoprazan exposure. We analyzed PK data from 746 patients and 410 healthy volunteers from 15 clinical trials using a nonlinear mixed-effects approach to develop the popPK model. Model development focused on characterizing and quantifying the effects of clinical covariates of race (Asian vs non-Asian) and disease status (gastroesophageal reflux disease vs healthy volunteers) on vonoprazan exposure. Identified clinical covariates included fed/fasting status, race, sex, disease status, weight, serum creatinine, and age. The impact of variations in these clinical covariates on exposure to vonoprazan was smaller than the effect of halving or doubling the dose. PK parameters were similar in Asian and non-Asian populations. Variations in weight, age, and race are not predicted to have a clinically relevant impact on vonoprazan exposure or safety and require no changes in vonoprazan dosing. The limited impact of race on exposure suggests that efficacy and safety data for vonoprazan in Asian populations are translatable to non-Asian populations.

Asparagus consumption is associated with the production of malodorous urine. Interindividual variability was previously characterized by an American Society for Clinical Pharmacology and Therapeutics crowdsourced study. To further characterize urinary odor kinetics, we conducted a study with consenting participants from Takeda Pharmaceutical International Company. The participants were randomized to consume a specified number of asparagus spears and asked to record urine odor. A kinetic-pharmacodynamic model characterized the data from both the newly conducted Takeda study (N = 42) and the previously analyzed American Society for Clinical Pharmacology and Therapeutics studies (total N = 139). The updated model included the identification of an absorption process with a half-life of 25 minutes. We estimated the elimination half-life of the asparagus effect on malodorous urine to be 7.2 hours, which was 44% longer in our study. We built on previous experience using an improved R-Shiny app for conducting the crowdsourcing experiment, further demonstrating the utility of this population kinetics approach in organizational and educational settings.

Henrik Watz,1 Nitin Bagul,2 Klaus F Rabe,3,4 Stephen Rennard,5,6 Vijay KT Alagappan,7 Jonas Román,8 Axel Facius,9 Peter MA Calverley10 1Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North, German Center for Lung Research, Grosshansdorf, Germany; 2DNA Medical Ltd, Langley, UK; 3Department of Pulmonary Medicine, LungenClinic Grosshansdorf, Airway Research Center North, German Center for Lung Research, Grosshansdorf, 4Department of Medicine, Christian Albrecht University Kiel, Kiel, Germany; 5Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA; 6AstraZeneca, Cambridge, UK; 7AstraZeneca, Gaithersburg, MD, USA; 8AstraZeneca R&D, Gothenburg, Sweden; 9thinkQ2 AG, Baar, Switzerland; 10Department of Clinical Sciences, Institute of Ageing and Chronic Disease, University ofLiverpool, Liverpool, UK Background: The oral selective phosphodiesterase-4 inhibitor roflumilast (ROF) reduces exacerbations in patients with severe COPD. Adverse events (AEs) can cause early ROF discontinuation. Alternative dosing strategies may help patients continue their therapy. Methods: In this multicenter, double-blind trial, 1,321 patients with severe COPD were randomized 1:1:1 to 4weeks’ treatment with ROF 250µg once daily (OD), 500µg every other day (EOD), or 500µg OD, each followed by ROF 500µg OD for 8weeks, plus standard therapy. The primary end point was the percentage of patients prematurely discontinuing study treatment. Results: Patients in the 250µg OD/500µg OD group had significantly fewer treatment discontinuations (odds ratio [OR] 0.66 [95% CI 0.47–0.93], p=0.017) and lower rates of AEs of interest such as diarrhea, nausea, headache, decreased appetite, insomnia and abdominal pain (OR 0.63 [95% CI 0.47–0.83], p=0.001) compared with those in the 500µg OD group. Although rates of discontinuation and AEs of interest were numerically lower with ROF 500µg EOD/500µg OD, the difference was not significant (OR 0.76, p=0.114, and OR 0.78, p=0.091, respectively) compared with ROF 500µg OD. Conclusion: A dose of ROF 250µg OD for 4weeks before escalation to the approved maintenance dose of 500µg OD resulted in reduced treatment discontinuation and improved tolerability. Keywords: roflumilast, COPD, discontinuation, adverse event

Roflumilast is a selective phosphodiesterase 4 inhibitor (PDE4i) for the treatment of severe chronic obstructive pulmonary disease (COPD). In 2 large phase 3 trials in a broader population of COPD patients (BY217/M2-111, ClinicalTrials.gov: NCT00076089 and BY217/M2-112, ClinicalTrials.gov: NCT00430729), treatment with roflumilast reduced the rate of exacerbations; however, the reduction did not reach statistical significance. Two linked dose-response models for the primary (annualized COPD exacerbation counts) and secondary (change from baseline in forced expiratory volume in 1 second [FEV1 ]) end points were therefore developed to characterize and quantify effect sizes and the patient characteristics influencing them. The models showed that disease severity and bronchitis, particularly the severity of bronchitis expressed in cough-and-sputum scores, were good predictors of exacerbation rates and differential benefit of roflumilast in exacerbation reduction. The models were used to support the rational design of 2 phase 3 randomized, placebo-controlled clinical trials (BY217/M2-124, ClinicalTrials.gov: NCT00297102 and BY217/M2-125, ClinicalTrials.gov: NCT00297115) by identifying the most appropriate patient population using clinical trial simulations. Model predictions for both end points were found to be highly accurate - as confirmed by the results from these trials, which led to the approval of roflumilast as the first oral PDE4i for the treatment of COPD in patients associated with chronic bronchitis and a history of exacerbations.

Background In the OPTIMIZE study, 4 weeks of roflumilast 250 µg once daily before escalation to the approved 500 µg once daily maintenance dose reduced treatment discontinuations and improved tolerability to roflumilast among patients with chronic obstructive pulmonary disease (COPD). In this study, we present the pharmacokinetic (PK) results and PK/pharmacodynamic (PD) modelling data from OPTIMIZE. Methods OPTIMIZE was a multicentre, double-blind, phase III study in which patients with severe COPD were randomized 1:1:1 to receive oral roflumilast 250 μg once daily, 500 μg every other day, or 500 μg once daily for 4 weeks, followed by 500 μg once daily for 8 weeks. A population PK (popPK) model characterized roflumilast exposure levels (total phosphodiesterase-4 inhibition [tPDE4i]). Furthermore, models characterized the percentage of patients with adverse events (AEs) of interest (PK/AE model), and time to discontinuation due to such AEs (PK/time-to-event model). Results The popPK model adequately described average plasma concentrations and variability from 1238 patients. The percentage of patients with AEs of interest increased with predicted tPDE4i exposure (logit scale slope 0.484; confidence interval 0.262–0.706; p = 2 × 10−5). PK/time-to-event model analysis predicted that patients receiving the 250 μg up-titration regimen had significantly lower discontinuation rates and longer time to discontinuation compared with roflumilast 500 μg every other day or 500 μg once daily (p = 0.0014). Conclusions In this PK/PD model, a 4-week up-titration regimen with roflumilast 250 µg once daily was found to reduce discontinuations and improve tolerability, confirming the main clinical findings of the OPTIMIZE study. However, use of this lower dose as long-term maintenance therapy may not induce sufficient phosphodiesterase-4 inhibition to exert clinical efficacy, supporting the approval of 500 µg as maintenance dose. Trial Registration OPTIMIZE: NCT02165826; REACT: NCT01329029. Electronic supplementary material The online version of this article (10.1007/s40262-018-0671-4) contains supplementary material, which is available to authorized users.

Background The development of intranasal fentanyl (INFS) aimed for a rapid treatment of breakthrough pain (BTP) in cancer patients. The pharmacokinetics (PK) of INFS was well characterized in healthy subjects, while PK investigations in cancer patients are limited. Objectives The objective was to develop a population PK model for fentanyl in volunteers and patients following INFS administration, to evaluate the influence of potential covariates and to simulate the exposure of fentanyl after repeated dosing in cancer patients. Methods PK data from ten clinical trials were used for model development. The final model was validated with nonparametric bootstrap and visual predictive check. In addition, the secondary PK parameters (AUC0-tlast, Cmax, tmax) of a separate validation data set of INFS were predicted and compared to noncompartmental analysis results. Afterwards, repeated dose PK profiles in cancer patients were simulated. Results Plasma profiles after INFS administration were best described by a three-compartment model. Significant covariate relationships were identified for naltrexone and oxymetazoline co-treatment. Influences of body weight, BMI, sex and cancer patient as subject type were considered not to be clinically relevant. PK parameters for subpopulations of cancer patients were derived. Steady state simulations revealed that an extension from the current SmPC scenario to 6 pain episodes per day would yield similar Cmax values. Conclusions A robust population PK model for INFS was developed. The model enhances the understanding of fentanyl PK after INFS dosing in cancer patients with BTP, a population for whom real-life data would be very hard to obtain.

Roflumilast is an oral, selective phosphodiesterase (PDE)-4 inhibitor in development for the treatment of chronic obstructive pulmonary disease (COPD). Both roflumilast and its metabolite roflumilast N-oxide have anti-inflammatory properties that contribute to overall pharmacological activity. To model the pharmacokinetics of roflumilast and roflumilast N-oxide, evaluate the influence of potential covariates, use the total PDE4 inhibitory activity (tPDE4i) concept to estimate the combined inhibition of PDE4 by roflumilast and roflumilast N-oxide, and use individual estimates of tPDE4i to predict the occurrence of adverse events (AEs) in patients with moderate-to-severe COPD. We modelled exposure to roflumilast and roflumilast N-oxide (21 studies provided the index dataset and five separate studies provided the validation dataset), extended the models to COPD (using data from two studies) and assessed the robustness of the parameter estimates. A parametric bootstrap estimation was used to quantify tPDE4i in subpopulations. We established logistic regression models for each AE occurring in >2% of patients in a placebo-controlled trial that achieved a p-value of

Prion diseases are neurodegenerative diseases associated with the accumulation of a pathogenic isoform of the host-encoded prion protein. The cellular responses to prion infection are not well defined. By performing microarray analysis on cultured neuronal cells infected with prion strain 22L, in the group of up-regulated genes we observed predominantly genes of the cholesterol pathway. Increased transcript levels of at least nine enzymes involved in cholesterol synthesis, including the gene for the rate-limiting hydroxymethylglutaryl-CoA reductase, were detected. Up-regulation of cholesterogenic genes was attributable to a prion-dependent increase in the amount and activity of the sterol regulatory element-binding protein Srebp2, resulting in elevated levels of total and free cellular cholesterol. The up-regulation of cholesterol biosynthesis appeared to be a characteristic response of neurons to prion challenge, as cholesterogenic transcripts were also elevated in persistently infected GT-1 cells and prion-exposed primary hippocampal neurons but not in microglial cells and primary astrocytes. These results convincingly demonstrate that prion propagation not only depends on the availability of cholesterol but that neuronal cells themselves respond to prions with specific up-regulation of cholesterol biosynthesis.

OBJECTIVE Teduglutide is a recombinant analogue of human glucagonlike peptide-2 (GLP-2) that was recently approved by the US and European regulatory agencies FDA and EMA for the treatment of short bowel syndrome (SBS). The objectives of this work were, firstly, to develop a population pharmacokinetic (popPK) model based on the available PK data of the entire clinical development program and, secondly, to utilize the model for the justification of the proposed dosing regimen. The exploratory analysis was based on a previously established structural PK model and focused primarily on the investigation of covariate effects. RESULTS The plasma concentrationtime profiles of teduglutide after subcutaneous application were adequately described by a 1-compartment model with first order absorption and elimination. The area under the curve (AUC) was lower for male subjects, for subjects with higher creatinine clearance, for overweight subjects, and for SBS patients. However, except for subjects with severe renal impairment no clinically relevant effects on AUC were identified. CONCLUSION Our model-based analysis supports the approved dose adjustment for SBS patients with and without renal impairments maintaining the exposure in a value range with acceptable variance for the target population.

A little after the genomic revolution had been celebrated, it seemed as if a competition began to found new -omics disciplines that ultimately all have the same goal, the understanding of biological function. There are many similar definitions for proteomics that can be summarized as follows: proteomics is a large-scale study of structure and function of proteins in an organism or cell. Importantly, the proteome is much more variable than the genome through its interactions with the genome and secondary modifications. It differs depending on the tissue and stage in life-cycle. Hence, proteomics is a very diverse discipline that uses a variety of experimental set-ups and targets in order to elucidate function. Its dissociation from other disciplines can only remain artificial. The bioinformatics applied to proteomics are equally varied. In this review we will focus mainly on a few areas of bioinformatics that seem to us as particularly noteworthy or characteristic for proteomics research, for example in 2DE analysis or mass spectrometry. Another important task of bioinformatics is the prediction of functional properties. We will summarize the approaches taken in order to predict protein networks, which are based on the extensive integration of several kinds of -omics data. We will give a short overview of a demanding field in computational biology, the analysis and prediction of protein 3D structures. In order to provide a broader perspective we will close this review with a generalized description of activities and databases in the realm of proteomics.

Data mining, finding and integration of information about proteins of interest, is an essential component in modern biological and biomedical research. Even when focusing on a single organism and on only a small number of proteins, there are often dozens of data sources containing relevant information. We are developing PRIME, a protein information environment, to serve as a virtual central database which integrates distributed heterogeneous information about proteins (linked by a common identifier). PRIME has powerful capabilities to visualize all kinds of protein annotation in specialized views. These views can be displayed side by side at the same time and can be synchronized in order to show simultaneously different aspects of identical proteins. These features allow a quick and comprehensive overview of properties of single proteins or protein sets.

Peptide mass fingerprinting (PMF) is a powerful tool for identification of proteins separated by two-dimensional electrophoresis (2-DE). With the increase in sensitivity of peptide mass determination it becomes obvious that even spots looking well separated on a 2-DE gel may consist of several proteins. As a result the number of mass peaks in PMFs increased dramatically leaving many unassigned after a first database search. A number of these are caused by experiment-specific contaminants or by neighbor spots, as well as by additional proteins or post-translational modifications. To understand the complete protein composition of a spot we suggest an iterative procedure based on large numbers of PMFs, exemplified by PMFs of 480 Helicobacter pylori protein spots. Three key iterations were applied: (1) Elimination of contaminant mass peaks determined by MS-Screener (a software developed for this purpose) followed by reanalysis; (2) neighbor spot mass peak determination by cluster analysis, elimination from the peak list and repeated search; (3) re-evaluation of contaminant peaks. The quality of the identification was improved and spots previously unidentified were assigned to proteins. Eight additional spots were identified with this procedure, increasing the total number of identified spots to 455.

Preconditioned Krylov subspace solvers are an important and frequently used technique for solving large sparse linear systems. There are many advantageous properties concerning convergence rates and error estimates. However, implementing such a solver on a computer, we often observe an unexpected and even contrary behavior.The purpose of this paper is to show that this gap between the theoretical and practical behavior can be narrowed by using a problem-oriented arithmetic. In addition we give rigorous error bounds to our computed results.

OBJECTIVE Roflumilast is the first phosphodiesterase 4 (PDE4) inhibitor approved for the treatment of chronic obstructive pulmonary disease (COPD) associated with chronic bronchitis and history of exacerbations. In clinical practice, side effects such as diarrhea appear to be somewhat more frequent in patients than that observed in clinical trials. We hypothesize that if patients could take a reduced dose for the first few weeks of therapy, the incidence of adverse events (AEs) could be reduced. METHODS We used previously reported population pharmacokinetic/ pharmacodynamic modeling to simulate three dosing scenarios of roflumilast: 500 μ once daily (OD) (approved dose), 250 μ OD and 500 μ every other day (EoD). RESULTS These models predicted that the 250 μ and EoD regimens were associated with lower plasma concentrations, lower total PDE4 inhibition, and lower incidence of diarrhea, nausea, and headache, versus the 500 μ OD dose. CONCLUSIONS Reduction of roflumilast dose in the first few weeks of therapy may be able to reduce the incidence of treatment-related AEs. Clearly, modeling provides only the basis for hypothesis generation, and must be supported with clinical evidence from carefully designed trials.

Background Intranasal Fentanyl Spray (INFS) was developed for the treatment of breakthrough pain (BTP) in cancer patients using a new route of administration. Dose strengths of 50, 100, and 200 μg INFS (Instanyl®) are currently on the market, however, some adult cancer patients with BTP may require higher doses up to 400 μg INFS. Objective As pharmacokinetic (PK) samples from cancer patients with BTP are hard to obtain, PK of 400 μg INFS was investigated in healthy volunteers. Using prior knowledge from an available population PK (PopPK) model, a PK trial design was derived which aimed for short study duration and reduced trial costs without jeopardizing trial readout. Methods Different trial designs to investigate the systemic exposure of 400 μg INFS were simulated using the available PopPK model. Parameters with strong influence on Cmax and AUC, i.e., clearance (CL), absorption rate constant (KA), central volume (V2) and bioavailability (F1), were estimated, while other parameters were fixed to previous model estimates. The concentration-time data obtained from the applied trial design was subjected to a PopPK analysis. From the final individual parameter estimates, single-dose concentration-time profiles with wash-out were simulated, and AUC and Cmax values were calculated as for a classical trial design. Results The final trial design was a two-sequence, three period, and three-treatment cross-over design with no wash-out intervals between treatments. 20 subjects received three doses of INFS. Four hours after a single dose of 200 μg INFS (Treatment A), subjects received either a single dose of 400 μg INFS (Treatment B) or two single doses (10 minutes apart) of 400 μg INFS (Treatment C). At t = 24 hours subjects received either Treatment B or Treatment C as cross-over. Plasma samples were taken up to 72 hours. The study duration per subject was less than 4 days. PopPK analysis and validation were performed successfully. The estimated primary PK parameters were F1 = 59%, CL=33.5 l/h, V2 = 68.8 l and KA = 12.8 1/h. The ratio analysis of the least square geometric means of dose normalized AUC∞ values resulted in point estimates of 97 - 104%, indicating dose proportionality in the investigated dose range of 200 μg - 2 × 400 μg. Conclusion The implementation of a PopPK approach in the planning and analysis of this trial yielded an innovative, cost- and time-saving trial design that successfully delivered the required information about the PK of the 400 μg dose strength within this small clinical study.

Background Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods. Results Our work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results for the MIPS FunCat 1.3 annotation of four bacterial genomes, Bacillus subtilis, Helicobacter pylori, Listeria innocua and Listeria monocytogenes. However, the gSPC covered about 12% more sequences compared to the other methods. The SPC algorithm was programmed in house using C++ and it is available at http://mips.gsf.de/proj/spc. The FunCat annotation is available at http://mips.gsf.de. Conclusion The gSPC calculated to a higher accuracy or covered a larger number of sequences than the TRIBE-MCL algorithm. Thus it is a useful approach for automatic detection of protein families and unsupervised annotation of full genomes.

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, naïve Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.

Proving Conjectures by Use of Interval Arithmetic.- 1 Computer Assisted Proofs in Analysis.- 2 The Kepler Conjecture.- 3 The Double Bubble Conjecture.- 4 The Dirac-Schwinger Conjecture.- 5 'Chaos conjectures'.- References.- Advanced Arithmetic for the Digital Computer Interval Arithmetic Revisited.- 1 Introduction and Historical Remarks.- 2 Interval Arithmetic, a Powerful Calculus to Deal with Inequalities.- 3 Interval Arithmetic as Executable Set Operations.- 4 Enclosing the Range of Function Values.- 5 The Interval Newton Method.- 6 Extended Interval Arithmetic.- 7 The Extended Interval Newton Method.- 8 Differentiation Arithmetic, Enclosures of Derivatives.- 9 Interval Arithmetic on the Computer.- 10 Hardware Support for Interval Arithmetic.- References.- Highly Accurate Verified Error Bounds for Krylov Type Linear System Solvers.- 1 Introduction.- 2 Iterative Solvers and Finite Precision.- 3 Krylov Subspace Methods.- 4 Improved Arithmetic.- 5 Verified Error Bounds.- 6 Computational Results.- References.- Elements of Scientific Computing.- 1 Hardware Requirements.- 2 Software Requirements.- 3 Modelling Requirements.- 4 Conclusion.- References.- Biography.- The Mainstreaming of Interval Arithmetic.- 1 Introduction.- 2 Moore's Law and Precision.- 3 Interval Physics.- 4 Summary.- References.- Bounds for Eigenvalues with the Use of Finite Elements.- 1 Introduction.- 2 Setting for the Problem.- 3 Calculation of Bounds.- 4 Verified Computation.- 5 Application: the Membrane Problem.- 6 Numerical Examples.- References.- Algorithmic Differencing.- 1 Algorithmic Representation of Functions.- 2 Transformation of Algorithms.- 3 Finite Precision Calculations.- 4 First Order Difference Operators.- 5 Differences of Inverse Functions.- 6 Higher Order Divided Differences.- References.- A Comparison of Techniques for Evaluating Centered Forms.- 1 Introduction.- 2 Methods for Computing Slope Vectors.- 3 A Numerical Example.- 4 Summary and Recommendations.- References.- On the Limit of the Total Step Method in Interval Analysis.- 1 Introduction.- 2 Notations.- 3 Results.- References.- How Fast can Moore' Interval Integration Method Really be?.- 1 Introduction.- 2 Moore's Algorithm.- 3 Estimation of the Integration Error.- 4 Conclusions.- References.- Numerical Verification and Validation of Kinematics and Dynamical Models for Flexible Robots in Complex Environments.- 1 Introduction.- 2 Error Propagation Control and Reliable Numerical Algorithms in MOBILE.- 3 Verified Calculation of the Solution of Discrete-Time Algebraic Riccati Equation.- 4 Accurate Distance Calculation Algorithms.- 5 Accurate Robot Reliability Estimation.- 6 Further Work.- 7 Acknowledgement.- References.- On the Ubiquity of the Wrapping Effect in the Computation of Error Bounds.- 1 Introduction or What is the Wrapping Effect?.- 2 Where does the Wrapping Effect appear?.- 3 How can we Reduce the Wrapping Effect?.- 4 Conclusion.- References.- A New Perspective on the Wrapping Effect in Interval Methods for Initial Value Problems for Ordinary Differential Equations.- 1 Introduction.- 2 Preliminaries.- 3 How the Wrapping Effect Arises in Interval Methods for IVPs for ODEs: A Traditional Explanation.- 4 The Wrapping Effect as a Source of Instability in Interval Methods for IVPs for ODEs.- 5 The Parallelepiped and Lohner's QR-Factorization Methods.- 6 Why the Parallelepiped Method Often Fails.- 7 When Does the Parallelepiped Method Work Well.- 8 How the QR Method Improves Stability.- 9 Conclusions.- A Lemmas.- References.- A Guaranteed Bound of the Optimal Constant in the Error Estimates for Linear Triangular Elements.- 1 Introduction.- 2 Strategy.- 3 The Method to Calculate a Rigorous Solution.- 4 Checking the Condition.- 5 Some Computational Techniques for Efficient Enclosure Methods.- 6 Numerical Results.- References.- Nonsmooth Global Optimization.- 1 Introduction.- 2 Preliminaries.- 3 A Pruning Technique for Global Optimizationx.- 4 Multidimensional Pruning Techniques for Global Optimization.- References.

In many research areas like structural mechanics, economics, meteorology, and fluid dynamics, problems are mapped to large sparse linear systems via discretization. The resulting matrices are often ill-conditioned with condition numbers of about 1016 and higher. Usually these systems are preconditioned before they are fed to an iterative solver.

Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods. Our work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results for the MIPS FunCat 1.3 annotation of four bacterial genomes, Bacillus subtilis, Helicobacter pylori, Listeria innocua and Listeria monocytogenes. However, the gSPC covered about 12% more sequences compared to the other methods. The SPC algorithm was programmed in house using C++ and it is available at http://mips.gsf.de/proj/spc . The FunCat annotation is available at http://mips.gsf.de . The gSPC calculated to a higher accuracy or covered a larger number of sequences than the TRIBE-MCL algorithm. Thus it is a useful approach for automatic detection of protein families and unsupervised annotation of full genomes.

Ferumoxytol is approved for the treatment of iron-deficiency anaemia (IDA) in adult patients with chronic kidney disease (CKD). Ferumoxytol has recently been investigated for use in all-cause IDA. This analysis was employed to bridge ferumoxytol pharmacokinetics (PK) across populations of healthy subjects and patients with CKD on haemodialysis, and to then make informed inferences regarding the PK behaviour of ferumoxytol in the all-cause IDA population. The data analysis was performed using NONMEM. Selected parameters were included for covariate testing. Investigations to determine if changes in volume of distribution during haemodialysis improved the model fit were also conducted. The final model was used to simulate PK in healthy volunteers (HVs) and CKD patients with and without haemodialysis. The final model was a two-compartment model with non-linear elimination. During haemodialysis, the central volume V1 was estimated to be reduced by 0.198 L/h. A positive relationship was identified between initial V1 and observed weight loss during haemodialysis. V1 increased by 0.614 % per kilogram of body weight, and females had an 18.3 % lower V1 than males. Differences between simulated profiles for different populations were marginal: maximum concentration (C max) of 209 vs. 204 ng/mL and area under the curve from time zero to infinity (AUCinf) of 5,980 vs. 5,920 ng·h/mL in HVs and CKD non-haemodialysis patients, respectively, for a single dose of 510 mg. The results indicate that ferumoxytol PK are comparable between HVs and CKD patients. Furthermore, the results are representative of the PK in other populations and can be used to bridge to subjects with general IDA.