Statistical Tools for Process Control and Quality Improvement in the Pharmaceutical Industry

In Vitro Testing for Orally Inhaled Products: Developments in Science-Based Regulatory Approaches

This article is part of a series of reports from the "Orlando Inhalation Conference-Approaches in International Regulation" which was held in March 2014, and coorganized by the University of Florida and the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS). The goal of the conference was to foster the exchange of ideas and knowledge across the global scientific and regulatory community in order to identify and help move towards strategies for internationally harmonized, science-based regulatory approaches for the development and marketing approval of inhalation medicines, including innovator and second entry products. This article provides an integrated perspective of case studies and discussion related to in vitro testing of orally inhaled products, including in vitro-in vivo correlations and requirements for in vitro data and statistical analysis that support quality or bioequivalence for regulatory applications.

Statistical thinking and knowledge management for quality-driven design and manufacturing in pharmaceuticals

The purpose of this article is to present the evolution of quality principles and how they have been implemented in the pharmaceutical industry. The article discusses the challenges that the FDA PAT Guidance and the ICH Q8, Q9 and Q10 Guidelines present to industry and provides a comprehensive overview of the basic tools that can be used to effectively build quality into products. The principles of the design of experiments, the main tools for statistical process analysis and control, and the requisite culture change necessary to facilitate statistical, knowledge-based management are also addressed.

Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations

This study has investigated the utility and potential advantages of an artificial intelligence technology - neurofuzzy logic - as a modeling tool to study direct compression formulations. The modeling performance was compare with traditional statistical analysis. From results it can be stated that the normalized error obtained from neurofuzzy logic was lower. Compared to the multiple regression analysis neurofuzzy logic showed higher accuracy in prediction for the five outputs studied. Rule sets generated by neurofuzzy logic are completely in agreement with the findings based on statistical analysis and advantageously generate understandable and reusable knowledge. Neurofuzzy logic is easy and rapid to apply and outcomes provided knowledge not revealed via statistical analysis.

Power of experimental design studies for the validation of pharmaceutical processes: case study of a multilayer tablet manufacturing process

Experimental design studies (EDS) are already widely used in the pharmaceutical industry for drug formulation or process optimization. Rare are the situations in which this methodology is applied for validation purposes. The power of this statistical tool, key element of a global validation strategy, is demonstrated for a multilayer tablet manufacturing process. Applied to the Geomatrix system generally composed of one compression and three granulation processes, time and strictness gains are non-negligible. Experimental design studies are not used in this work for modeling. Introduced at each important step of the process development, they allow for the evaluation of process ruggedness at pilot scale and specifications for full production. A demonstration of the complete control of key process parameters is given, identified throughout preliminary studies.

Sequential statistical optimization of a positively-charged submicron emulsion of miconazole

A positively charged oil/water (O/W) emulsion containing an antifungal agent was developed for ophthalmic use. An attempt was made using a sequential statistical methodology to optimize the O/W emulsion by varying both formulation and process parameters to obtain the smallest droplet size emulsion that can remain stable for a long period of time. During the first step of the study, not less than 7 parameters were found to be important--drug content, amount of lipophilic phase, poloxamer concentration, quantity of the phospholipids-stearylamine couple, pH adjustment, time of coarse emulsification and time of high pressure homogenization. A screening approach based on Hadamard's matrix was used to select the parameters displaying the most significant effects on response parameters. A first set of 8 experiments proved efficient enough to define the concentration of poloxamer and the quantity of the couple phospholipids-stearylamine, which confer the overall positive charge to the emulsified droplet, as the most significant parameters affecting the final droplet size of the emulsions formed. A 2k-type experimental design was then built with the two main factors in order to evaluate a first-order polynomial model with interaction. Poor analysis of variance results after an additional center experiment was performed revealed the lack of fit of the linear model as well as the importance of the response surface curvature due to a close optimum location. To find the optimal operating conditions the design was sequentially completed with 4 more experiments according to the Box and Wilson method. The response surfaces in 3-dimensional representation and their corresponding contour plots proved helpful in analyzing the validated models and in highlighting the precise optimum location. The optimized positive submicron emulsion is now under in vivo investigation.