A strategy for population pharmaceutical quality assessment based on quality by design

From a regulatory perspective,drug quality consistency evaluation must concern different processes used for the same drug.In this study,an assessment strategy based on quality by design(QbD)was developed for population pharmaceutical quality evaluation.A descriptive analysis method based on QbD concept was first established to characterize the process by critical evaluation attributes(CEAs).Then quantitative analysis method based on an improved statistical process control(SPC)method was established to investigate the process indicators(PIs)in the process population,such as mean distribution,batch-to-batch difference and abnormal quality probability.After that rules for risk assessment were established based on the SPC limitations and parameters.Both the SPC parameters of the CEAs and the risk of PIs were visualized according to the interaction test results to obtain a better understanding of the population pharmaceutical quality.Finally,an assessment strategy was built and applied to generic drug consistency assessment,process risk assessment and quality trend tracking.The strategy demonstrated in this study could help reveal quality consistency from the perspective of process control and process risk,and further show the recent development status of domestic pharmaceutical production processes.In addition,a process risk assessment and population quality trend tracking provide databased information for approval.Not only can this information serve as a further basis for decisionmaking by the regulatory authority regarding early warnings,but it can also reduce some avoidable adverse reactions.With continuous addition of data,dynamic population pharmaceutical quality is meaningful for emergencies and decision-making regarding drug regulation.

SPC and Kalman filter-based fault detection and diagnosis for an air-cooled chiller

Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major portion of the energy consumption.Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper,the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied.The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load,and the air temperature and flow rate.A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants.The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load.The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control(SPC)for measuring and analyzing their variations for fault detection and diagnosis.The gray-box model in the method,however,requires that the air temperature and flow rate be nearly constant.By introducing two new parameters and deleting data points with low air flow rate,the requirement can be satisfied and the method can then be applicable for an air-cooled chiller.The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis(FDD)performance greatly.It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.