Improving clinical trial efficiency by biomarker-guided patient selection

Background: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit. Methods: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated. Results: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited. Conclusions: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult

Assessment of Determinants of Emission Potentially Affecting the Concentration of Airborne Nano-Objects and Their Agglomerates and Aggregates

Background: Nano-specific inhalation exposure models could potentially be effective tools to assess and control worker exposure to nano-objects, and their aggregates and agglomerates (NOAA). However, due to the lack of reliable and consistent collected NOAA exposure data, the scientific basis for validation of the existing NOAA exposure models is missing or limited. The main objective of this study was to gain more insight into the effect of various determinants underlying the potential on the concentration of airborne NOAA close to the source with the purpose of providing a scientific basis for existing and future exposure inhalation models. Method: Four experimental studies were conducted to investigate the effect of 11 determinants of emission on the concentration airborne NOAA close to the source during dumping of ~100% nanopowders. Determinants under study were: nanomaterial, particle size, dump mass, height, rate, ventilation rate, mixing speed, containment, particle surface coating, moisture content of the powder, and receiving surface. The experiments were conducted in an experimental room (19.5 m3) with well-controlled environmental and ventilation conditions. Particle number concentration and size distribution were measured using real-time measurement devices. Results: Dumping of nanopowders resulted in a higher number concentration and larger particles than dumping their reference microsized powder (P < 0.05). Statistically significant more and larger particles were also found during dumping of SiO2 nanopowder compared to TiO2/Al2O3 nanopowders. Particle surface coating did not affect the number concentration but on average larger particles were found during dumping of coated nanopowders. An increase of the powder's moisture content resulted in less and smaller particles in the air. Furthermore, the results indicate that particle number concentration increases with increasing dump height, rate, and mass and decreases when ventilation is turned on. Discussion: These results give an indication of the direction and magnitude of the effect of the studied determinants on concentrations close to the source and provide a scientific basis for (further) development of existing and future NOAA inhalation exposure models

Assessment of Determinants of Emission Potentially Affecting the Concentration of Airborne Nano-Objects and Their Agglomerates and Aggregates

Background: Nano-specific inhalation exposure models could potentially be effective tools to assess and control worker exposure to nano-objects, and their aggregates and agglomerates (NOAA). However, due to the lack of reliable and consistent collected NOAA exposure data, the scientific basis for validation of the existing NOAA exposure models is missing or limited. The main objective of this study was to gain more insight into the effect of various determinants underlying the potential on the concentration of airborne NOAA close to the source with the purpose of providing a scientific basis for existing and future exposure inhalation models. Method: Four experimental studies were conducted to investigate the effect of 11 determinants of emission on the concentration airborne NOAA close to the source during dumping of ~100% nanopowders. Determinants under study were: nanomaterial, particle size, dump mass, height, rate, ventilation rate, mixing speed, containment, particle surface coating, moisture content of the powder, and receiving surface. The experiments were conducted in an experimental room (19.5 m3) with well-controlled environmental and ventilation conditions. Particle number concentration and size distribution were measured using real-time measurement devices. Results: Dumping of nanopowders resulted in a higher number concentration and larger particles than dumping their reference microsized powder (P < 0.05). Statistically significant more and larger particles were also found during dumping of SiO2 nanopowder compared to TiO2/Al2O3 nanopowders. Particle surface coating did not affect the number concentration but on average larger particles were found during dumping of coated nanopowders. An increase of the powder's moisture content resulted in less and smaller particles in the air. Furthermore, the results indicate that particle number concentration increases with increasing dump height, rate, and mass and decreases when ventilation is turned on. Discussion: These results give an indication of the direction and magnitude of the effect of the studied determinants on concentrations close to the source and provide a scientific basis for (further) development of existing and future NOAA inhalation exposure models

The possibilities of the use of N-of-1 and do-it-yourself trials in nutritional research.

BACKGROUND:N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. OBJECTIVE:To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. METHOD:We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. RESULTS:The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. CONCLUSION:For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference

Effectiveness of a Multidimensional Randomized Control Intervention to Reduce Quartz Exposure Among Construction Workers

There is little evidence with respect to the effectiveness of intervention programs that focus on the reduction of occupational quartz exposure in the construction industry. This article evaluates the effectiveness of a multidimensional intervention which was aimed at reducing occupational quartz exposure among construction workers by increasing the use of technical control measures. Eight companies participating in the cluster randomized controlled trial were randomly allocated to the intervention (four companies) or control condition (four companies). The multidimensional intervention included engineering, organizational, and behavioural elements at both organizational and individual level. Full-shift personal quartz exposure measurements and detailed observations were conducted before and after the intervention among bricklayers, carpenters, concrete drillers, demolishers, and tuck pointers (n = 282). About 59% of these workers measured at baseline were reassessed during follow-up. Bayesian hierarchical models were used to evaluate the intervention effect on exposure levels. Concrete drillers in the intervention group used technical control measures, particularly water suppression, for a significantly greater proportion of the time spent on abrasive tasks during follow-up compared to baseline (93 versus 62%; P < 0.05). A similar effect, although not statistically significant, was observed among demolishers. A substantial overall reduction in quartz exposure (73 versus 40% in the intervention and control group respectively; P < 0.001) was observed for concrete drillers, demolishers, and tuck pointers. The decrease in exposure in the intervention group compared to controls was significantly larger for demolishers and tuck pointers, but not for concrete drillers. The observed effect could at least partly be explained by the introduced interventions; the statistically significant increased use of control measures among concrete drillers explains the observed effect to some extent in this job category only. Sensitivity analyses indicated that the observed decrease in exposure may also partly be attributable to changes in work location and abrasiveness of the tasks performed. Despite the difficulties in assessing the exact magnitude of the intervention, this study showed that the structured intervention approach at least partly contributed to a substantial reduction in quartz exposure among high exposed construction workers

Application of a dynamic population-based model to assess the effect of silica exposure interventions on COPD in Dutch construction workers : results from the 'Relieved Working Study'

OBJECTIVES: A multidimensional intervention aimed at reducing silica exposure in the Dutch construction industry was performed. The objective of this study was to assess the effect of the achieved reduction in exposure on the burden of chronic obstructive pulmonary disease (COPD) in construction workers. METHOD: The intervention aimed at technical, organisational and psycho-social factors and was performed in four construction companies. Pre and post intervention respirable quartz exposure measurements were taken in these and four additional control companies. A mathematical simulation model was used to generate COPD prevalences (GOLD stage ≥1) in response to silica exposure, population characteristics and Dutch trends in smoking behaviour for a population of 20-65 year old construction workers with lifetime silica exposure. RESULTS: Pre-intervention exposure assessment demonstrated highest respirable quartz levels (mg/m(3)) for concrete drillers (GM: 0.20, GSD: 2.75), tuck pointers (GM: 0.18, GSD (2.18) and demolishers (GM: 0.12, GSD: 2.86), exceeding the Dutch occupational exposure limit (OEL) in 71, 92 and 97% of cases, respectively. Preliminary simulations estimated COPD prevalence at 21, 20 and 17% for these groups respectively, as compared to 14% when quartz exposure is reduced to the Dutch OEL and 8% with no exposure. CONCLUSIONS: For several job categories high exposure levels exceeding the Dutch OEL were observed. Reducing these levels to below the OEL would lead to a substantial reduction in the burden of disease. The post intervention exposure levels will become available early 2014. The effect on the burden of disease and economic impact will be assessed with an refined model incorporating population dynamics

Dynamic subcortical blood flow during male sexual activity with ecological validity: A perfusion MRI study

This study used arterial spin labeling (ASL) fMRI to measure brain perfusion in a group of healthy men under conditions that closely resembled customary sexual behavior. Serial perfusion measures for 30 min during two self-limited periods of partnered penis stimulation, and during post-stimulatory periods, revealed novel sexual activity-related cerebral blood flow (rCBF) changes, mainly in subcortical parts of the brain. Ventral pallidium rCBF was highest during the onset of penile erection, and lowest after the termination of penis stimulation. The perceived level of sexual arousal showed the strongest positive association with rCBF in the right basal forebrain. in addition, our results demonstrate that distinct subregions of the hypothalamus and cingulate cortex subserve opposite functions during human male sexual behavior. The lateral hypothalamus and anterior part of the middle cingulate cortex showed increased rCBF correlated with penile erection. By contrast, the anteroventral hypothalamus and subgenual anterior cingulate cortex exhibited rCBF changes correlated with penile detumescence after penile stimulation. Continuous rapid and high-resolution brain perfusion imaging during normal sexual activity has provided novel insights into the central mechanisms that control male sexual arousal. (C) 2009 Elsevier Inc. All rights reserved

fMRI guided rTMS evidence for reduced left prefrontal involvement after task practice.

INTRODUCTION: Cognitive tasks that do not change the required response for a stimulus over time ('consistent mapping') show dramatically improved performance after relative short periods of practice. This improvement is associated with reduced brain activity in a large network of brain regions, including left prefrontal and parietal cortex. The present study used fMRI-guided repetitive transcranial magnetic stimulation (rTMS), which has been shown to reduce processing efficacy, to examine if the reduced activity in these regions also reflects reduced involvement, or possibly increased efficiency. METHODS: First, subjects performed runs of a Sternberg task in the scanner with novel or practiced target-sets. This data was used to identify individual sites for left prefrontal and parietal peak brain activity, as well as to examine the change in activity related to practice. Outside of the scanner, real and sham rTMS was applied at left prefrontal and parietal cortex to examine their involvement novel and practiced conditions. RESULTS: Prefrontal as well as parietal rTMS significantly reduced target accuracy for novel targets. Prefrontal, but not parietal, rTMS interference was significantly lower for practiced than novel target-sets. rTMS did not affect non-target accuracy, or reaction time in any condition. DISCUSSION: These results show that task practice in a consistent environment reduces involvement of the prefrontal cortex. Our findings suggest that prefrontal cortex is predominantly involved in target maintenance and comparison, as rTMS interference was only detectable for targets. Findings support process switching hypotheses that propose that practice creates the possibility to select a response without the need to compare with target items. Our results also support the notion that practice allows for redistribution of limited maintenance resources

Predictors of diet-induced weight loss in overweight adults with type 2 diabetes

Aims A very low calorie diet improves the metabolic regulation of obesity related type 2 diabetes, but not for all patients, which leads to frustration in patients and professionals alike. The aim of this study was to develop a prediction model of diet-induced weight loss in type 2 diabetes. Methods 192 patients with type 2 diabetes and BMI>27 kg/m2 from the outpatient diabetes clinic of the Erasmus Medical Center underwent an 8-week very low calorie diet. Baseline demographic, psychological and physiological parameters were measured and the C-index was calculated of the model with the largest explained variance of relative weight loss using backward linear regression analysis. The model was internally validated using bootstrapping techniques. Results Weight loss after the diet was 7.8±4.6 kg (95%CI 7.2-8.5;p<0.001) and was independently associated with the baseline variables fasting glucose (B = -0.33 (95%CI -0.49, -0.18), p = 0.001), anxiety (HADS; B = -0.22 (95%CI -0.34, -0.11), p = 0.001), numb feeling in extremities (B = 1.86 (95%CI 0.85, 2.87), p = 0.002), insulin dose (B = 0.01 (95%CI 0.00, 0.02), p = 0.014) and waist-to-hip ratio (B = 6.79 (95%CI 2.10, 11.78), p = 0.003). This model explained 25% of the variance in weight loss. The C-index of this model to predict successful (≥5%) weight loss was 0.74 (95%CI 0.67-0.82), with a sensitivity of 0.93 (95% CI 0.89-0.97) and specificity of 0.29 (95% CI 0.16-0.42). When only the obese T2D patients (BMI≥30 kg/m2 ; n = 181) were considered, age also contributed to the model (B = 0.06 (95%CI 0.02, 0.11), p = 0.008), whereas waist-to-hip ratio did not. Conclusions Diet-induced weight loss in overweight adults with T2D was predicted by five baseline parameters, which were predominantly diabetes related. However, failure seems difficult to predict. We propose to test this prediction model in future prospective diet intervention studies in patients with type 2 diabetes