Focused Information Criterion for Restricted Mean Survival Times: Non-Parametric or Parametric Estimators.

Restricted Mean Survival Time (RMST), the average time without an event of interest until a specific time point, is a model-free, easy to interpret statistic. The heavy reliance on non-parametric or semi-parametric methods in the survival analysis has drawn criticism, due to the loss of efficacy compared to parametric methods. This assumes that the parametric family used is the true one, otherwise the gain in efficacy might be lost to interpretability problems due to bias. The Focused Information Criterion (FIC) considers the trade-off between bias and variance and offers an objective framework for the selection of the optimal non-parametric or parametric estimator for scalar statistics. Herein, we present the FIC framework for the selection of the RMST estimator with the best bias-variance trade-off. The aim is not to identify the true underling distribution that generated the data, but to identify families of distributions that best approximate this process. Through simulation studies and theoretical reasoning, we highlight the effect of censoring on the performance of FIC. Applicability is illustrated with a real life example. Censoring has a non-linear effect on FICs performance that can be traced back to the asymptotic relative efficiency of the estimators. FICs performance is sample size dependent; however, with censoring percentages common in practical applications FIC selects the true model at a nominal probability (0.843) even with small or moderate sample sizes.

The novel bronchodilator navafenterol: a phase 2a, multicentre, randomised, double-blind, placebo-controlled crossover trial in COPD.

BACKGROUND: Navafenterol (AZD8871) belongs to a new class of bronchodilator, the single-molecule muscarinic antagonist and ß-agonist, developed for the treatment of COPD. This study aimed to evaluate the efficacy, pharmacokinetics and safety of navafenterol versus placebo and an active comparator treatment for moderate-to-severe COPD. METHODS: This phase 2a, randomised, multicentre (Germany and UK), double-blind, double-dummy, three-way complete crossover study (ClinicalTrials.gov identifier: NCT03645434) compared 2 weeks' treatment of once-daily navafenterol 600 µg via inhalation with placebo and a fixed-dose combination bronchodilator (umeclidinium/vilanterol (UMEC/VI); 62.5 µg/25 µg) in participants with moderate-to-severe COPD. The primary outcome was change from baseline in trough forced expiratory volume in 1 s (FEV1) on day 15. Secondary end-points included change from baseline in peak FEV1; change from baseline in Breathlessness, Cough and Sputum Scale (BCSS); change from baseline in COPD Assessment Tool (CAT); adverse events; and pharmacokinetics. RESULTS: 73 participants were randomised. After 14 days, trough FEV1 was significantly improved with navafenterol compared with placebo (least-squares (LS) mean difference 0.202 L; p<0.0001). There was no significant difference in FEV1 between navafenterol and UMEC/VI (LS mean difference -0.046 L; p=0.075). COPD symptoms (CAT and BCSS) showed significantly greater improvements with both active treatments versus placebo (all p<0.005). Novel objective monitoring (VitaloJAK) showed that cough was reduced with both active treatments compared with placebo. Safety profiles were similar across the treatment groups and no serious adverse events were reported in the navafenterol treatment period. CONCLUSION: Once-daily navafenterol was well tolerated, improved lung function and reduced COPD-related symptoms, similar to an established once-daily fixed-dose combination bronchodilator.

Characterization of Clinical Absorption, Distribution, Metabolism, and Excretion and Pharmacokinetics of Velsecorat Using an Intravenous Microtracer Combined with an Inhaled Dose in Healthy Subjects.

This open-label, single-period study describes the human absorption, distribution, metabolism, excretion, and pharmacokinetics of velsecorat (AZD7594). Healthy subjects received inhaled velsecorat (non-radiolabeled; 720 µg) followed by intravenous infusion of carbon 14 (14C)-velsecorat (30 µg). Plasma, urine, and feces were collected up to 168 hours post-dose. Objectives included identification and quantification of velsecorat and its metabolites (i.e., drug-related material) in plasma and excreta, and determining the elimination pathways of velsecorat by measuring the rate and route of excretion, plasma half-life (t1/2), clearance, volume of distribution and mean recovery of radioactivity. On average, 76.0% of administered 14C dose was recovered by the end of the sampling period (urine = 24.4%; feces = 51.6%), with no unchanged compound recovered in excreta, suggesting that biliary excretion is the main elimination route. Compared with intravenous 14C-velsecorat, inhaled velsecorat had a longer t1/2 (27 versus 2 hours), confirming that plasma elimination is absorption-rate-limited from the lungs. Following intravenous administration, t1/2 of 14C-drug-related material was longer than for unchanged velsecorat, and 20% of the 14C plasma content was related to unchanged velsecorat. The geometric mean plasma clearance of velsecorat was high (70.7 l/h) and the geometric mean volume of distribution at steady state was 113 l. Velsecorat was substantially metabolized via O-dealkylation of the indazole ether followed by sulfate conjugation, forming the M1 metabolite, the major metabolite in plasma. There were 15 minor metabolites. Velsecorat was well tolerated, and these results support the progression of velsecorat to phase 3 studies. SIGNIFICANCE STATEMENT: This study describes the human pharmacokinetics and metabolism of velsecorat, a selective glucocorticoid receptor modulator, evaluated via co-administration of a radiolabeled intravenous microtracer dose and a non-radiolabeled inhaled dose. This study provides a comprehensive assessment of the disposition of velsecorat in humans. It also highlights a number of complexities associated with determining human absorption, distribution, metabolism, and excretion for velsecorat, related to the inhaled route, the high metabolic clearance, sequential metabolite formation and the low intravenous dose.

Complexity of design in early phase respiratory clinical trials; evaluating impact of primary endpoints and sponsor size.

Asthma and COPD represent most of the clinical trials in the respiratory area. The Primary Endpoint (PE) defines how trials are conducted. We hypothesised that small and mid-sized pharmaceutical companies may be innovative in the selection of their trial endpoints, to be time- and cost-effective. To test this, a record of industry-sponsored phase II trials in asthma, COPD and Asthma/COPD over 11 years was obtained. The type of PE and the influence these had on length, number of subjects and investigational trial sites were evaluated for the different disease categories. Differences in the type of PE used by large versus small/mid-sized companies were found for both asthma and COPD trials (p = 0.011 and 0.025), with sponsorship influencing the conduction of these. In asthma, studies sponsored by large companies were significantly longer than those from smaller companies (p = 0.0001). Additionally, large companies intended to recruit more subjects (asthma: p = 0.0048, COPD: p ≤ 0.0001) and use more investigational sites (asthma: p = 1 × 10-7, COPD: p = 1 × 10-5) than those from small and mid-size companies. A sub-analysis of the time and subject requirements associated with each type of PE did not provide an explanation for the differences observed. In conclusion, this exploratory analysis indicates differences in study size, duration and type of PE used by small/mid-sized and large companies. For some types of endpoints, differences in length and study size were found. However, it wasn't possible to attribute these differences between sponsors solely to the choice of PE, pointing out to the complexity of running clinical trials.

The cost-effectiveness of prostate cancer screening using the Stockholm3 test.

OBJECTIVES: The European Randomized Study of Screening for Prostate Cancer found that prostate-specific antigen (PSA) screening reduced prostate cancer mortality, however the costs and harms from screening may outweigh any mortality reduction. Compared with screening using the PSA test alone, using the Stockholm3 Model (S3M) as a reflex test for PSA ≥ 1 ng/mL has the same sensitivity for Gleason score ≥ 7 cancers while the relative positive fractions for Gleason score 6 cancers and no cancer were 0.83 and 0.56, respectively. The cost-effectiveness of the S3M test has not previously been assessed. METHODS: We undertook a cost-effectiveness analysis from a lifetime societal perspective. Using a microsimulation model, we simulated for: (i) no prostate cancer screening; (ii) screening using the PSA test; and (iii) screening using the S3M test as a reflex test for PSA values ≥ 1, 1.5 and 2 ng/mL. Screening strategies included quadrennial re-testing for ages 55-69 years performed by a general practitioner. Discounted costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated. RESULTS: Comparing S3M with a reflex threshold of 2 ng/mL with screening using the PSA test, S3M had increased effectiveness, reduced lifetime biopsies by 30%, and increased societal costs by 0.4%. Relative to the PSA test, the S3M reflex thresholds of 1, 1.5 and 2 ng/mL had ICERs of 170,000, 60,000 and 6,000 EUR/QALY, respectively. The S3M test was more cost-effective at higher biopsy costs. CONCLUSIONS: Prostate cancer screening using the S3M test for men with an initial PSA ≥ 2.0 ng/mL was cost-effective compared with screening using the PSA test alone.

Longitudinal FEV1 and Exacerbation Risk in COPD: Quantifying the Association Using Joint Modelling.

BACKGROUND: Lung function, measured as forced expiratory volume in one second (FEV1), and exacerbations are two endpoints evaluated in chronic obstructive pulmonary disease (COPD) clinical trials. Joint analysis of these endpoints could potentially increase statistical power and enable assessment of efficacy in shorter and smaller clinical trials. OBJECTIVE: To evaluate joint modelling as a tool for analyzing treatment effects in COPD clinical trials by quantifying the association between longitudinal improvements in FEV1 and exacerbation risk reduction. METHODS: A joint model of longitudinal FEV1 and exacerbation risk was developed based on patient-level data from a Phase III clinical study in moderate-to-severe COPD (1740 patients), evaluating efficacy of fixed-dose combinations of a long-acting bronchodilator, formoterol, and an inhaled corticosteroid, budesonide. Two additional studies (1604 and 1042 patients) were used for external model validation and parameter re-estimation. RESULTS: A significant (p<0.0001) association between FEV1 and exacerbation risk was estimated, with an approximate 10% reduction in exacerbation risk per 100 mL improvement in FEV1, consistent across trials and treatment arms. The risk reduction associated with improvements in FEV1 was relatively small compared to the overall exacerbation risk reduction for treatment arms including budesonide (10-15% per 160 µg budesonide). High baseline breathlessness score and previous history of exacerbations also influenced the risk of exacerbation. CONCLUSION: Joint modelling can be used to co-analyze longitudinal FEV1 and exacerbation data in COPD clinical trials. The association between the endpoints was consistent and appeared unrelated to treatment mechanism, suggesting that improved lung function is indicative of an exacerbation risk reduction. The risk reduction associated with improved FEV1 was, however, generally small and no major impact on exacerbation trial design can be expected based on FEV1 alone. Further exploration with other longitudinal endpoints should be considered to further evaluate the use of joint modelling in analyzing COPD clinical trials.

COPDCompEx: A novel composite endpoint for COPD exacerbations to enable faster clinical development.

BACKGROUND: Frequency of moderate and severe chronic obstructive pulmonary disease exacerbations is an important endpoint in clinical trials, but makes them large and lengthy when powered to evaluate it. We aimed to develop a composite endpoint (COPDCompEx) that could predict treatment effect on exacerbations, enabling the design of shorter early phase clinical trials requiring fewer patients. METHODS: In this post hoc analysis, data from 20 randomized controlled trials were used to develop and test COPDCompEx. Diary events were tested against predefined threshold values for peak expiratory flow, reliever medication use, and symptoms. A COPDCompEx event was defined as first occurrence of a diary event, a moderate or severe exacerbation, or a study dropout. Ratios of event frequency, treatment effect and future trial sample size were compared between COPDCompEx and moderate and severe exacerbations. FINDINGS: At 3 months, the proportion of patients experiencing COPDCompEx events increased over 3-fold versus exacerbations alone. All components contributed to COPDCompEx event rate. Treatment effects at 3 months were closely matched between COPDCompEx and exacerbations, and the large net gain in power substantially reduced the required sample size. INTERPRETATION: COPDCompEx may be used to predict treatment effect on moderate and severe exacerbations of chronic obstructive pulmonary disease. This may enable the design of shorter Phase 2 clinical trials requiring fewer patients when compared with current exacerbation studies, with exacerbations as a key Phase 3 endpoint. This would, therefore, allow more efficient decision-making with reduced burden and risk to study participants.

Impact of season and geography on CompEx Asthma: a composite end-point for exacerbations.

BACKGROUND: CompEx Asthma, a novel composite end-point combining severe exacerbations (SevEx) with asthma-worsening events, was recently developed. Further characterisation of CompEx Asthma is needed to illustrate the applicability of this end-point. The objective was to evaluate CompEx Asthma as a rate end-point to determine how seasonal and geographical factors impact this novel outcome. METHODS: Seven 24-56-week randomised controlled trials of budesonide/formoterol (BUD/FORM) and benralizumab were analysed. Annualised event rates (AERs) and treatment effects (hazard ratio (HR)) were analysed with Poisson and Andersen-Gill models, respectively. Seasonality was analysed by month and five geographical regions were evaluated. RESULTS: The studies included 10 815 patients (63% female, mean age 42-49 years). CompEx Asthma AER mirrored seasonal variations in SevEx AER. CompEx Asthma AERs were higher versus SevEx in BUD/FORM and benralizumab trials (range 2.7-4.5-fold and 1.3-2.0-fold increase, respectively) and were less variable versus SevEx between regions (ratios of greatest:smallest AERs: 1.36 for CompEx versus 2.28 for SevEx (BUD/FORM); 1.81 for CompEx versus 2.22 for SevEx (benralizumab)). Treatment effects for CompEx Asthma and SevEx were generally similar across regions and months. However, in Eastern Europe, where SevEx rates were lowest, treatment effect was greater with CompEx Asthma versus SevEx, reaching statistical significance in the benralizumab studies (HR (95% CI): 0.67 (0.53-0.85) versus 0.87 (0.65-1.15)). CONCLUSION: This study confirmed the reliability of CompEx Asthma as a rate end-point and allowed detection of variations in seasonal SevEx rates, reduction of variation in rates across regions and potential greater sensitivity to treatment effects.

Improving the evaluation of COPD exacerbation treatment effects by accounting for early treatment discontinuations: a post-hoc analysis of randomized clinical trials.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) clinical trials aimed at evaluating treatment effects on exacerbations often suffer from early discontinuations of randomized treatment. Treatment discontinuations imply a loss of information and should ideally be considered in the statistical analysis of trial results, particularly if the discontinuations are related to the disease or treatment itself. Here, we explore this issue by investigating (1) whether there exists an association between the risks of exacerbation and treatment discontinuation in COPD clinical trials and (2) whether disregarding this association can cause bias in exacerbation treatment effect estimates. We focus on the hypothetical estimand, i.e. the treatment effect that would have been observed had all subjects completed the trial as planned. METHODS: The association between exacerbation and discontinuation risks was analysed by applying a joint frailty (random effect) model - allowing for the simultaneous analysis of multiple types of correlated events - to data from five Phase III-IV COPD clinical trials. Specifically, the impact of the association on exacerbation treatment effect estimates was assessed by comparing the treatment hazard ratios of the joint frailty model to the rate/hazard ratios of two related statistical models (the negative binomial and shared frailty models), which both assume discontinuations to be unrelated to the trial outcome. The models were also compared using simulated data. RESULTS: A statistically significant (p < 0.0001), positive association between exacerbation and discontinuation risks was found in all trials. Importantly, simulations confirmed that - with such an association - models disregarding the association risk producing biased results (> 5 percentage point difference in hazard/rate ratio). For some treatment comparisons in the clinical trials, the difference in treatment effect estimates between the joint frailty and the other models was as high as 10-15 percentage points. The difference was affected by the strength of the exacerbation-discontinuation association, the population heterogeneity in exacerbation risk, and the difference in discontinuation rates between treatment arms. CONCLUSIONS: We have identified an association between the risks of exacerbation and treatment discontinuation in five COPD clinical trials. We recommend using the joint frailty model to account for this association when estimating exacerbation treatment effects, particularly when targeting the hypothetical estimand.

A natural history model for planning prostate cancer testing: Calibration and validation using Swedish registry data.

Recent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history. An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register. After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC. Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55-69 years was predicted to reduce prostate cancer incidence by 14% and increase prostate cancer mortality by 2%. The model is open source and suitable for planning for effective prostate cancer screening into the future.

Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models.

Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building. Availability and implementation: Source code is freely available at https://github.com/alexjau/uqsa. Supplementary information: Supplementary data are available at Bioinformatics online.

Dipeptidyl Peptidase 1 Inhibitor AZD7986 Induces a Sustained, Exposure-Dependent Reduction in Neutrophil Elastase Activity in Healthy Subjects.

Neutrophil serine proteases (NSPs), such as neutrophil elastase (NE), are activated by dipeptidyl peptidase 1 (DPP1) during neutrophil maturation. High NSP levels can be detrimental, particularly in lung tissue, and inhibition of NSPs is therefore an interesting therapeutic opportunity in multiple lung diseases, including chronic obstructive pulmonary disease (COPD) and bronchiectasis. We conducted a randomized, placebo-controlled, first-in-human study to assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of single and multiple oral doses of the DPP1 inhibitor AZD7986 in healthy subjects. Pharmacokinetic and pharmacodynamic data were analyzed using nonlinear mixed effects modeling and showed that AZD7986 inhibits whole blood NE activity in an exposure-dependent, indirect manner-consistent with in vitro and preclinical predictions. Several dose-dependent, possibly DPP1-related, nonserious skin findings were observed, but these were not considered to prevent further clinical development. Overall, the study results provided confidence to progress AZD7986 to phase II and supported selection of a clinically relevant dose.

Exploration of human brain tumour metabolism using pairwise metabolite-metabolite correlation analysis (MMCA) of HR-MAS 1H NMR spectra.

METHODS: We quantified 378 HRMAS 1H NMR spectra of human brain tumours (132 glioblastomas, 101 astrocytomas, 75 meningiomas, 37 oligodendrogliomas and 33 metastases) from the eTumour database and looked for metabolic interactions by metabolite-metabolite correlation analysis (MMCA). RESULTS: All tumour types showed remarkably similar metabolic correlations. Lactate correlated positively with alanine, glutamate with glutamine; creatine + phosphocreatine (tCr) correlated positively with lactate, alanine and choline + phosphocholine + glycerophosphocholine (tCho), and tCho correlated positively with lactate; fatty acids correlated negatively with lactate, glutamate + glutamine (tGlut), tCr and tCho. Oligodendrogliomas had fewer correlations but they still fitted that pattern. CONCLUSIONS: Possible explanations include (i) glycolytic tumour cells (the Warburg effect) generating pyruvate which is converted to lactate, alanine, glutamate and then glutamine; (ii) an association between elevated glycolysis and increased choline turnover in membranes; (iii) an increase in the tCr pool to facilitate phosphocreatine-driven glutamate uptake; (iv) lipid signals come from cytosolic lipid droplets in necrotic or pre-necrotic tumour tissue that has lower concentrations of anabolic and catabolic metabolites. Additional metabolite exchanges with host cells may also be involved. If tumours co-opt a standard set of biochemical mechanisms to grow in the brain, then drugs might be developed to disrupt those mechanisms.

A novel endpoint for exacerbations in asthma to accelerate clinical development: a post-hoc analysis of randomised controlled trials.

BACKGROUND: Occurrence of severe asthma exacerbations are the cornerstone of the evaluation of asthma management, but severe asthma exacerbations are rare events. Therefore, trials that assess drug efficacy on exacerbations are done late in clinical development programmes. We aimed to establish an endpoint capturing clinically relevant deteriorations (diary events) that, when combined with severe exacerbations, create a composite outcome (CompEx). CompEx needs to strongly mirror results seen with the severe exacerbation-validated outcome, to allow the design of clinical trials of shorter duration and that include fewer patients than trials assessing severe exacerbations. METHODS: Data from 12 asthma trials of 6 months or 12 months duration and, with standardised collection of exacerbations and diary card variables, were used to construct and test CompEx. The study populations had a mean age of 35-53 years, 59-69% were female, and had a mean FEV1 percentage of predicted normal of 63-84%. With data from five trials, we established a series of diary events based on peak expiratory flow (P), reliever use (R), symptoms (S), awakenings (A), and threshold values for change from baseline and slopes to assess trends. For the development phase, we evaluated different variable combinations and deterioration criteria to select the most robust algorithm to define a diary event for the composite outcome. We defined a composite outcome, CompEx, as first occurrence of a diary event or a severe exacerbation. We assessed the performance of CompEx in seven trials by comparing the event frequency, treatment effect (hazard ratio; HR), and the sample size needed for future trials for the CompEx versus episodes of severe exacerbations. FINDINGS: CompEx (based on PRS) was the algorithm that best fulfilled our two-set criteria. When censored at 3 months, CompEx resulted in 2·8 times more events than severe exacerbations, and while preserving the treatment effect observed on severe exacerbations (CompEx over severe exacerbation average HR 1·01). The increased number of events, together with the sustained treatment effect, resulted in a large net gain in power, with a 67% mean reduction in the number of patients required in a drug trial for severe exacerbations. In six of seven comparisons tested, CompEx reduced the sample size needed by at least 50%. Validation of independent test populations confirmed the ability of CompEx to increase event frequencies, preserve treatment effect, and reduce the number of patients needed. INTERPRETATION: CompEx is a composite outcome for evaluation of new asthma therapies. CompEx allows design of shorter trials that require fewer patients than studies of severe exacerbations, while preserving the ability to show a treatment effect compared with severe exacerbations. FUNDING: AstraZeneca.

E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.

OBJECTIVE: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. MATERIALS AND METHODS: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences. RESULTS: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform. DISCUSSION AND CONCLUSION: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

COPD biomarkers as tools for decision making in early clinical drug development.

In this perspective article, we discuss, from a statistician's perspective, how biomarkers can be useful in decision making in drug development with emphasis on early clinical development (Phase I and II) in chronic obstructive pulmonary disease. We illustrate with examples of how biomarkers can affect the very choice of treatment strategy: for example, targeting patients in early versus late phases of the disease or patients with particular extrapulmonary manifestations of chronic obstructive pulmonary disease. We also illustrate the use of biomarkers for establishing proof of mechanism in Phase I trials and how surrogate biomarkers can be used as end points in Phase II leading to shorter and more efficient proof-of-principle and dose-finding trials.

HSP90 inhibition blocks ERBB3 and RET phosphorylation in myxoid/round cell liposarcoma and causes massive cell death in vitro and in vivo.

Myxoid sarcoma (MLS) is one of the most common types of malignant soft tissue tumors. MLS is characterized by the FUS-DDIT3 or EWSR1-DDIT3 fusion oncogenes that encode abnormal transcription factors. The receptor tyrosine kinase (RTK) encoding RET was previously identified as a putative downstream target gene to FUS-DDIT3 and here we show that cultured MLS cells expressed phosphorylated RET together with its ligand Persephin. Treatment with RET specific kinase inhibitor Vandetanib failed to reduce RET phosphorylation and inhibit cell growth, suggesting that other RTKs may phosphorylate RET. A screening pointed out EGFR and ERBB3 as the strongest expressed phosphorylated RTKs in MLS cells. We show that ERBB3 formed nuclear and cytoplasmic complexes with RET and both RTKs were previously reported to form complexes with EGFR. The formation of RTK hetero complexes could explain the observed Vandetanib resistence in MLS. EGFR and ERBB3 are clients of HSP90 that help complex formation and RTK activation. Treatment of cultured MLS cells with HSP90 inhibitor 17-DMAG, caused loss of RET and ERBB3 phosphorylation and lead to rapid cell death. Treatment of MLS xenograft carrying Nude mice resulted in massive necrosis, rupture of capillaries and hemorrhages in tumor tissues. We conclude that complex formation between RET and other RTKs may cause RTK inhibitor resistance. HSP90 inhibitors can overcome this resistance and are thus promising drugs for treatment of MLS/RCLS.

Metabolomic changes during cellular transformation monitored by metabolite-metabolite correlation analysis and correlated with gene expression.

To investigate metabolic changes during cellular transformation, we used a 1H NMR based metabolite-metabolite correlation analysis (MMCA) method, which permits analysis of homeostatic mechanisms in cells at the steady state, in an inducible cell transformation model. Transcriptomic data were used to further explain the results. Transformed cells showed many more metabolite-metabolite correlations than control cells. Some had intuitively plausible explanations: a shift from glycolysis to amino acid oxidation after transformation was accompanied by a strongly positive correlation between glucose and glutamine and a strongly negative one between lactate and glutamate; there were also many correlations between the branched chain amino acids and the aromatic amino acids. Others remain puzzling: after transformation strong positive correlations developed between choline and a group of five amino acids, whereas the same amino acids showed negative correlations with phosphocholine, a membrane phospholipid precursor. MMCA in conjunction with transcriptome analysis has opened a new window into the metabolome.

Normalization of metabolomics data with applications to correlation maps.

MOTIVATION: In metabolomics, the goal is to identify and measure the concentrations of different metabolites (small molecules) in a cell or a biological system. The metabolites form an important layer in the complex metabolic network, and the interactions between different metabolites are often of interest. It is crucial to perform proper normalization of metabolomics data, but current methods may not be applicable when estimating interactions in the form of correlations between metabolites. We propose a normalization approach based on a mixed model, with simultaneous estimation of a correlation matrix. We also investigate how the common use of a calibration standard in nuclear magnetic resonance (NMR) experiments affects the estimation of correlations. RESULTS: We show with both real and simulated data that our proposed normalization method is robust and has good performance when discovering true correlations between metabolites. The standardization of NMR data is shown in simulation studies to affect our ability to discover true correlations to a small extent. However, comparing standardized and non-standardized real data does not result in any large differences in correlation estimates. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://sourceforge.net/projects/metabnorm/ CONTACT: alexandra.jauhiainen@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.