Meteorological radar facility. Part 1: System design

A compilation of information regarding systems design of space shuttles used in meteorological radar probes is presented. Necessary radar equipment is delineated, while space system elements, calibration techniques, antenna systems and other subsystems are reviewed

Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia S

Since the discovery of proprotein convertase subtilisin/kexin type 9 (PCSK9) as an attractive target in the treatment of hypercholesterolemia, multiple anti-PCSK9 therapeutic modalities have been pursued in drug development. The objective of this research is to set the stage for the quantitative benchmarking of two anti-PCSK9 pharmacological modality classes, monoclonal antibodies (mAbs) and small interfering RNA (siRNA). To this end, we developed an integrative mathematical model of lipoprotein homeostasis describing the dynamic interplay between PCSK9, LDL-cholesterol (LDL-C), VLDL-cholesterol, HDL-cholesterol (HDL-C), apoB, lipoprotein a [Lp(a)], and triglycerides (TGs). We demonstrate that LDL-C decreased proportionally to PCSK9 reduction for both mAb and siRNA modalities. At marketed doses, however, treatment with mAbs resulted in an additional similar to 20% LDL-C reduction compared with siRNA. We further used the model as an evaluation tool and determined that no quantitative differences were observed in HDL-C, Lp(a), TG, or apoB responses, suggesting that the disruption of PCSK9 synthesis would provide no additional effects on lipoprotein-related biomarkers in the patient segment investigated. Predictive model simulations further indicate that siRNA therapies may reach reductions in LDL-C levels comparable to those achieved with mAbs if the current threshold of 80% PCSK9 inhibition via siRNA could be overcome

Abstract P014: Clinically Significant Novel Biomarkers for Prediction of First-ever Myocardial Infarction-The Tromsø Study

Background: Identification of individuals with high risk for first-ever myocardial infarction (MI) can be improved. The objectives of the study ere to survey multiple protein biomarkers for association with the 5-year risk of incident MI and identify a clinically significant risk model that adds information to current common risk models. Methods: We employed an immunoassay platform that utilizes a sensitive, sample efficient molecular counting technology to measure 51 proteins in samples from the fourth survey (1994) in the Tromsø Study, a longitudinal study of men and women in Tromsø, Norway. A nested case control design was used with 182 first-ever MI cases (60 females/122 males) and 467 controls (277 females/190 males). Results: Of the proteins measured, 21 were predictors of MI before and after adjustment for traditional risk factors either in men, women or both. In stepwise multivariable analysis with these biomarkers and traditional risk factors, kallikrein; OR 0.58 (95% CI 0.47 - 0.71), matrix metalloproteinase 8; OR 1.41 (1.13 - 1.75), the interaction term CCL5/RANTES*women; OR 0.57 (0.44 - 0.74), the interaction term apolipoprotein B/apolipoprotein A1 ratio*men; OR 1.53 (1.27 - 1.84) and lipoprotein a; OR 1.33 (1.10 - 1.61) added significantly to the model with a net reclassification improvement of 0.10 (p=0.01), while the ROC area increased from 0.77 to 0.83, p&lt;0.001. Conclusion: Novel protein biomarker models improve identification of MI risk above and beyond traditional risk factors with more than 10% better allocation to either high or low risk group than traditional risk factors alone. </jats:p

Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions

Empirical Bayes (“post hoc”) estimates (EBEs) of ηs provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (η-shrinkage, quantified as 1-SD(ηEBE)/ω), IPREDs towards the corresponding observations, and IWRES towards zero (ε-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of η-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values (“ETABAR”) significantly different from zero, even for a correctly specified model; EBE–EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of ε-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial η- or ε-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of η- and ε-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics

A Fast Method for Testing Covariates in Population PK/PD Models

The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate–parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions. Simulated and real datasets were used to compare this method with the conventional nonlinear mixed effect model using both first-order (FO) and FOCE approximations. The methods were mainly assessed in terms of difference in objective function values (ΔOFV) between base and covariate models. The FOCE linearization was superior to the FO linearization and showed a high degree of concordance with corresponding nonlinear models in ΔOFV. The linear and nonlinear FOCE models provided similar coefficient estimates and identified the same covariate–parameter relations as statistically significant or non-significant for the real and simulated datasets. The time required to fit tesaglitazar and docetaxel datasets with 4 and 15 parameter–covariate relations using the linearization method was 5.1 and 0.5 min compared with 152 and 34 h, respectively, with the nonlinear models. The FOCE linearization method allows for a fast estimation of covariate–parameter relations models with good concordance with the nonlinear models. This allows a more efficient model building and may allow the utilization of model building techniques that would otherwise be too time-consuming