Optimization strategies experimental design

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See also Chemometrics and Statistics Experimental Design Optimization Strategies. 

Initial screens can be distinguished between methods that are used to determine what factors are most important, and follow-up screens that allow optimization and improvement of crystal quality (Table 14.1). In experimental design, this is known as the Box-Wilson strategy (Box et al., 1978). The first group of screens is generally based on a so-called factorial plan which determines the polynomial coefficients of a function with k variables (factors) fitted to the response surface. It can be shown that the number of necessary experiments n increases with 2 if all interactions are taken into account. Instead of running an unrealistic, large number of initial experiments, the full factorial matrix can... 

Multiobjective optimization is an optimization strategy that overcomes the limits of a singleobjective function to optimize preparative chromatography [45]. In the physical programming method of multiobjective optimization, one can specify desirable, tolerable, or undesirable ranges for each design parameter. Optimum experimental conditions are obtained, for instance, using bi-objective (production rate and recovery yield) and tri-objective (production rate, recovery yield. 

The compound selection methods described thus far can be used to select compounds for screening from an in-house collection, or to select which compounds to purchase from an external supplier. In combinatorial library design, however, it is necessary to select subsets of reactants for actual synthesis. The two main strategies for combinatorial library design are reactant-based selection and product-based selection. In reactant-based selection, optimized subsets of reactants are selected without consideration of the products that will result and any of the compound selection methods already identified can be used. An early example of reactant-based design is that already described by Martin and colleagues which is based on experimental design and where diverse subsets of reactants were selected for the synthesis of peptoid libraries [1]. 

The full determinant strategy was introduced by Box and Lucas (1959) and applied sequentially by Box and Hunter (1965) the subset strategy was introduced by Michael Box (1971). Experimental designs of these types are called D-optimal [St. John and Draper (1975)] the literature on them is extensive. 

Franz, R.M. Browne, J. Lewis, A. Experimental design, modeling, and optimization strategies for product and process development. In Pharmaceutical Dosage Forms Disperse Systems, 2nd Ed. Lieberman, H.L., Rieger, M., Banker, G., Eds. Marcel Dekker, Inc. New York, 1996 1, 427-514. 

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