Population pharmacokinetics, enzyme occupancy, and pharmacodynamic modeling of soticlestat in patients with developmental and epileptic encephalopathies.

Soticlestat (TAK-935) is a first-in-class, selective inhibitor of cholesterol 24-hydroxylase (CH24H) under phase III development for the treatment of the developmental and epileptic encephalopathies (DEEs), Dravet syndrome (DS), and Lennox-Gastaut syndrome (LGS). A previous model characterized the pharmacokinetics (PKs), CH24H enzyme occupancy (EO), and pharmacodynamics (PDs) of soticlestat in healthy volunteers. The present study extended this original model for patients with DEEs and investigated sources of variability. Model-based simulations were carried out to optimize dosing strategies for use in clinical trials. Data from eight phase I and II trials of healthy volunteers or patients with DEEs receiving oral soticlestat 15-1350 mg were included, encompassing 218 individuals for population PK (PopPK) analyses and 306 individuals for PK/PD analyses. Dosing strategies were identified through model-based simulations. The final mixed-effect PopPK/EO/PD model consisted of a two-compartment PK model and an effect-site compartment in the PK/EO model; soticlestat concentrations at the effect site were linked to 24S-hydroxycholesterol plasma concentrations using a semimechanistic inhibitory indirect response model. Covariates were included to account for sources of variability. Pediatric dosing strategies were developed for four body weight bands (10 to <15, 15 to <30, 30 to <45, and 45-100 kg) to account for covariate effects by body weight. The final PopPK and PK/EO/PD models accurately described PK, EO, and PD profiles of soticlestat in healthy volunteers and patients with DEEs. Covariate analyses and model-based simulations facilitated optimization of phase III trial dosing strategies for patients with DS or LGS.

Population pharmacokinetics, enzyme occupancy, and 24S-hydroxycholesterol modeling of soticlestat, a novel cholesterol 24-hydroxylase inhibitor, in healthy adults.

Soticlestat is a first-in-class, selective inhibitor of cholesterol 24-hydroxylase (CH24H), which catabolizes cholesterol to 24S-hydroxycholesterol (24HC) in the brain, in phase III development for Dravet syndrome and Lennox-Gastaut syndrome treatment. This study aimed to develop a model of soticlestat pharmacokinetics (PKs) and pharmacodynamics (PDs) using 24HC plasma concentrations and CH24H enzyme occupancy (EO) time profiles. Subsequently, model-based simulations were conducted to identify dosing strategies for phase II trials in children and adults with developmental and epileptic encephalopathies (DEEs). Four phase I trials of healthy adults involving oral administration of soticlestat 15-1350 mg were used to develop the mixed-effect population PK/EO/PD model. The population PK analysis utilized 1727 observations (104 individuals), PK/EO analysis utilized 20 observations (11 individuals), and PK/PD analysis utilized 2270 observations (99 individuals). Optimal dosing strategies were identified from model-based PK, EO, and PD simulations. The PK/EO/PD model described the observed data well and comprised a two-compartment model with dose as a covariate on peripheral volume, linear elimination, and intercompartmental clearance. Transit and effect-site compartments were included to accommodate different dosage forms and the delay between plasma drug concentrations and EO. Model-based simulations indicated that soticlestat 100-300 mg twice daily may be an optimal adult dosing regimen with weight-adjusted pediatric dosing strategies identified for evaluation in phase II trials. The population PK/EO/PD model provided understanding of the soticlestat PK/PD relationship with partial delineation of sources of variability, and identified dosing strategies for phase II trials of children and adults with DEEs.

A translational pharmacokinetic/pharmacodynamic approach supports optimal vonoprazan dosing for erosive oesophagitis and Helicobacter pylori infection.

BACKGROUND: Treatment of acid-related disorders relies on gastric acid suppression. The percentage of time intragastric pH is >4 (pH >4 holding time ratio [HTR]) is important for healing erosive oesophagitis; and the pH >6 HTR is critical for eradication of Helicobacter pylori infection, as bacterial replication is active and antibiotic effectiveness is optimised. Vonoprazan, a potassium-competitive acid blocker approved in the USA and other countries, suppresses gastric acid secretion in a predictable, rapid and consistent manner, extended over prolonged periods. AIM: To explore the relationship between vonoprazan exposure and pH HTR through a pharmacokinetic/pharmacodynamic (PK/PD) model. METHODS: We pooled data from Phase 1 studies with intragastric pH measurements. Pharmacokinetic profiles were predicted for study participants using an existing population pharmacokinetic model. Pharmacokinetic and pharmacodynamic data were merged, and three direct-link PK/PD models were derived and used to simulate pH HTRs with between-participant variability for pH >4, >5 and >6, for vonoprazan doses of 20 mg once and twice daily. RESULTS: We used data from five Phase 1 studies to derive the PK/PD model. These included 245 participants (95.1% male, 50.6% Japanese and 49.4% non-Asian). Pre-dose, the mean pH >4 HTR was 6.4%, pH >5 3.2% and pH >6 1.2%. After 7 days of dosing, simulations predicted pH >4 HTRs of 89.7% and 98.1%, and pH >6 HTRs of 53.1% and 75.3%, for vonoprazan 20 mg once and twice daily, respectively. CONCLUSIONS: Vonoprazan 20 mg once- and twice-daily dosing demonstrated high, dose-dependent, 24-hour intragastric acid control in this PK/PD model, supporting clinical efficacy data in patients with acid-related disorders.

A Population Pharmacokinetic Model of Vonoprazan: Evaluating the Effects of Race, Disease Status, and Other Covariates on Exposure.

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.

What Can Be Learned From Crowdsourced Population Asparagus Urinary Odor Kinetics?

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.

Pharmacokinetic and Pharmacodynamic Modelling to Characterize the Tolerability of Alternative Up-Titration Regimens of Roflumilast in Patients with Chronic Obstructive Pulmonary Disease.

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.

Use of a 4-week up-titration regimen of roflumilast in patients with severe COPD.

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 4 weeks' treatment with ROF 250 µg once daily (OD), 500 µg every other day (EOD), or 500 µg OD, each followed by ROF 500 µg OD for 8 weeks, plus standard therapy. The primary end point was the percentage of patients prematurely discontinuing study treatment. Results: Patients in the 250 µg OD/500 µg 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 500 µg OD group. Although rates of discontinuation and AEs of interest were numerically lower with ROF 500 µg EOD/500 µg OD, the difference was not significant (OR 0.76, p=0.114, and OR 0.78, p=0.091, respectively) compared with ROF 500 µg OD. Conclusion: A dose of ROF 250 µg OD for 4 weeks before escalation to the approved maintenance dose of 500 µg OD resulted in reduced treatment discontinuation and improved tolerability.

Modeling and Simulation of Pivotal Clinical Trials Using Linked Models for Multiple Endpoints in Chronic Obstructive Pulmonary Disease With Roflumilast.

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.

Population pharmacokinetic meta-analysis to bridge ferumoxytol plasma pharmacokinetics across populations.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 (Cmax) 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. CONCLUSIONS: 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.

Utility of a population pharmacokinetic meta analysis during the approval process of teduglutide for the treatment of short bowel syndrome.

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.

Application of population pharmacokinetic modeling to explore the impact of alternative roflumilast dosing regimens on tolerability.

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.

An innovative phase I population pharmacokinetic approach to investigate the pharmacokinetics of an intranasal fentanyl spray in healthy subjects.

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.

Population pharmacokinetic meta-analysis of intranasal fentanyl spray as a means to enrich pharmacokinetic information for patients with cancer breakthrough pain.

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.

Population pharmacokinetic modelling of roflumilast and roflumilast N-oxide by total phosphodiesterase-4 inhibitory activity and development of a population pharmacodynamic-adverse event model.

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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 <0.2 in a permutation test. The exposure variables were the area under the plasma concentration-time curve (AUC) of roflumilast, the AUC of roflumilast N-oxide and tPDE4i. Individual AUC values were estimated from population models. RESULTS: Roflumilast pharmacokinetics were modelled with a two-compartment model with first-order absorption including a lag time. A one-compartment model with zero-order absorption was used for roflumilast N-oxide. The final models displayed good descriptive and predictive performance with no appreciable systematic trends versus time, dose or study. Posterior predictive checks and robustness analysis showed that the models adequately described the pharmacokinetic parameters and the covariate effects on disposition. For roflumilast, the covariates of sex, smoking and race influenced clearance; and food influenced the absorption rate constant and lag time. For roflumilast N-oxide, age, sex and smoking influenced clearance; age, sex and race influenced the fraction metabolized; bodyweight influenced the apparent volume of distribution; and food influenced the apparent duration of formation. The COPD covariate increased the central volume of distribution of roflumilast by 184% and reduced its clearance by 39%; it also reduced the estimated volume of distribution of roflumilast N-oxide by 21% and reduced its clearance by 7.9%. Compared with the reference population (male, non-smoking, White, healthy, 40-year-old subjects), the relative geometric mean [95% CI] tPDE4i was higher in patients with COPD (12.6% [-6.6, 35.6]), women (19.3% [8.2, 31.6]), Black subjects (42.1% [16.4, 73.4]), Hispanic subjects (28.2% [4.1, 57.9]) and older subjects (e.g. 8.3% [-11.2, 32.2] in 60-year-olds), and was lower in smokers (-19.1% [-34.0, -0.7]). Among all possible subgroups in this analysis, the subgroup with maximal tPDE4i comprised elderly, Black, female, non-smoking, COPD patients (tPDE4i 217% [95% CI 107, 437] compared with the value in the reference population). The probability of a patient with tPDE4i at the population geometric mean [95% CI] was 13.0% [7.5, 18.5] for developing diarrhoea, 6.0% [2.6, 9.4] for nausea and 5.1% [1.9, 8.6] for headache. CONCLUSIONS: Covariate effects have a limited impact on tPDE4i. There was a general association between tPDE4i and the occurrence of common AEs in patients with COPD.

Prion-induced activation of cholesterogenic gene expression by Srebp2 in neuronal cells.

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.

Super paramagnetic clustering of protein sequences.

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.

PRIME: a graphical interface for integrating genomic/proteomic databases.

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 only on a small number of proteins, there are often dozens fo 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 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.

Gene selection from microarray data for cancer classification--a machine learning approach.

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.

Bioinformatics challenges in proteomics.

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.

Iterative data analysis is the key for exhaustive analysis of peptide mass fingerprints from proteins separated by two-dimensional electrophoresis.

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.