A newfangled study using risk silhouette and uncertainty approximation for quantification of acyclovir in diverse formulation

Risk assessment and uncertainty approximation are two major and important parameters that need to be adopted for the development of pharmaceutical process to ensure reliable results.Additionally,there is a need to switch from the traditional method validation checklist to provide a high level of assurance of method reliability to measure quality attribute of a drug product.In the present work,evaluation of risk profile,combined standard uncertainty and expanded uncertainty in the analysis of acyclovir were studied.Uncertainty was calculated using cause-effect approach,and to make it more accurately applicable a method was validated in our laboratory as per the ICH guidelines.While assessing the results of validation,the calibration model was justified by the lack of fit and Levene＇s test.Risk profile represents the future applications of this method.In uncertainty the major contribution is due to sample concentration and mass.This work demonstrates the application of theoretical concepts of calibration model tests,relative bias,risk profile and uncertainty in routine methods used for analysis in pharmaceutical field.

Uncertainty analysis for integrated water system simulations using GLUE with different acceptability thresholds

Integrated water quantity and quality simulations have become a popular tool in investigations on global water crisis.For integrated and complex models,conventional uncertainty estimations focus on the uncertainties of individual modules,e.g.,module parameters and structures,and do not consider the uncertainties propagated from interconnected modules.Therefore,this study investigated all the uncertainties of integrated water system simulations using the GLUE(i.e.,generalized likelihood uncertainty estimation)method,including uncertainties associated with individual modules,propagated uncertainties associated with interconnected modules,and their combinations.The changes in both acceptability thresholds of GLUE and the uncertainty estimation results were also investigated for different fixed percentages of total number of iterations(100000).Water quantity and quality variables(i.e.,runoff and ammonium nitrogen)were selected for the case study.The results showed that module uncertainty did not affect the runoff simulation performance,but remarkably weakened the water quality responses as the fixed percentage increased during calibration and validation periods.The propagated uncertainty from hydrological modules could not be ignored for water quality simulations,particularly during validation.The combination of module and propagated uncertainties further weakened the water quality simulation performance.The uncertainty intervals became wider owing to an increase in the fixed percentages and introduction of more uncertainty sources.Moreover,the acceptability threshold had a negative nonlinear relationship with the fixed percentage.The fixed percentages(20.0%-30.0%)were proposed as the acceptability thresholds owing to the satisfactory simulation performance and noticeably reduced uncertainty intervals they produced.This study provided methodological foundations for estimating multiple uncertainty sources of integrated water system models.

Good deal hedging and valuation under combined uncertainty about drift and volatility

We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices.Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good,by restricting instantaneous Sharpe ratios.A non-dominated multiple priors approach to model uncertainty(ambiguity)leads to worst-case good-deal bounds.Corresponding hedging strategies arise as minimizers of a suitable coherent risk measure.Good-deal bounds and hedges for measurable claims are characterized by solutions to secondorder backward stochastic differential equations whose generators are non-convex in the volatility.These hedging strategies are robust with respect to uncertainty in the sense that their tracking errors satisfy a supermartingale property under all a-priori valuation measures,uniformly over all priors.