Mixture Experimentation in Pharmaceutical Formulations: A Tutorial.

Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.

Assessing analytical variability of measurement of vitamin A in corn-soy blend.

Two multilaboratory investigations were conducted by SUSTAIN to assess variability in the measurement of vitamin A, the marker used to verify levels of vitamin premix addition to enriched/fortified food aid products, including the widely distributed corn-soy blend (CSB). CSB specifications identify AACC Approved Method 86-06 or equivalent methods for vitamin A analysis, however there is no requirement to demonstrate equivalency. CSB samples with known and blinded levels of vitamin A and a reference standard were analyzed by 16 laboratories using their respective methods. Calculated coefficients of variation across all laboratories and methods for unknown samples and reference standard were 35 and 7.1%, respectively, suggesting the largest source of variation is the vitamin extraction procedure. Laboratories generally overestimated low levels and underestimated high levels of vitamin A within the range of 6000 and 16 000 IU/lb. Only two laboratories demonstrated excellent internal precision (+/- 300 IU vitamin A/lb) and reported values within 95% confidence interval for all blinded samples. Results of this study have implications both for quality control in food aid products (due to the use of vitamin A as a marker) and for regulatory oversight of vitamin A content in commercial food products.