Zur Optimierung von bioaktiven Leitstrukturen II Versuch einer Systematik

How design statistics concepts can improve experimentation in medicinal chemistry

“…Regarding drug optimization, there are two reasons to improve models of substituent variation which considers both orthogonal and rotatable central-composite designs: 11,15 the advances in theory of D-optimal designs, 20,21 and in rotatable and G-optimal designs. [22][23][24][25][26][27] Unfortunately, such theoretical designs lead generally to scores that are discrete in nature (ϩ1, 0, Ϫ1), and do not reflect real physicochemical features of organic molecules and drugs.…”

“…Before and after this fundamental review, statistics of experimental designing (called briefly design statistics) was introduced in various fields, such as in technological process control and kinetic modeling, 3,7,8 analytical information domain, 9 quantitative structure-activity relationships, (QSARs) 10,11 and planning of substituent variation of closely related drugs. [11][12][13][14][15][16][17][18][19] Unfortunately, "there are diverse notations as to what is the proper list of topics to be included under the response-surface umbrella. It is interesting that all too many subject-matter scientists, many of whom are experienced in the use of statistical tools, are not aware of the term response surface analysis or of the problems it addresses" (Myers et al 6 ).…”