Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale

Abstract

The objective of this work was to develop an optimization strategy for the design of pharmaceutical formulations. The mixed strategy was used to optimize a dry powder blend containing 500 mg of alpha methyl dopa to be filled into hard gelatin capsules. The experimental plan consisted of assessing blend flow and dissolution rate using formulations manufactured at small laboratory scale, selecting the optimum formulation, and confirming the data. Two optimization techniques were used in the solid pharmaceutical product design: a genetic algorithm (GA) and a downhill simplex technique. The genetic algorithm used in this work was implemented in an interactive form. Data for each generation of formulations were introduced to the computer with the corresponding values of a fitness function, which was determined in experimental form for each individual formulation. The fitness function used to evaluate product performance (capsule) was defined in terms of the dissolution rate multiplied by a weight function that penalizes those formulations with flow index outside a predefined range. The formulation design contained variable concentrations and types of lubricants/glidants. There were 64 combinations of seven agents with discrete ranges of concentrations codified into a 16-bit chromosome. Crossing and mutation operations were implemented with relatively high probabilities, for generations with a relatively small number of individuals, due to the restrictions imposed by the experimental cost. The mixed formulation strategy based on genetic algorithms and downhill simplex was used to obtain sequentially improved formulations based on two desired targets: in vitro dissolution rate and flow properties. The basic downhill simplex method was used to obtain an optimal formulation on the regression response surface obtained from the genetic algorithm data. The results obtained in this work clearly illustrate the potential of the proposed mixed optimization strategy to obtain optimal formulations.