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i n ce the int rod u ction of the f2 m e t ric in 1994 and its subsequent inclusion in va rious guidance s, the use of the f2 m e t ri c for dissolution profile co m p a rison has be come co m m o n p l a ce in the industry.Ot h e r m e t rics have been co n s i d e red and co m p a red with f2 ( 1 ) .The f2 m e t ric has been studied exte n s i vely fo r its stat i s t i cal at t ri b u tes (2),and the shortcomings of this metric have been po i nted out (3).Howeve r, most of the lite rat u re re g a rding dissolution pro f i l e co m p a rison has focused on the re g u l ato ry applications of these metri c s.For the pharm a ce u t i ca l fo rm u l ato r,the benefit of dissolution pro f i l e co m p a rison lies in the ability to accurately distinguish be tween fo rm u l at i o n s.Du ring deve l o p m e nt, the fo rm u l ator has to eva l u ate the effe ct of va ri o u s fo rm u l ation and process va riables on the dissolution pro f i l e.The effe ct of these va riables on dissolution is difficult to dete rmine by inspe ct i o n ,so the dissolution metric po te ntially could be come a va l uable tool for this purpo s e.Howeve r, the suitability of f2 and other metrics as re s ponse va riables in stat i s t ically designed ex pe ri m e nts has not been co n s i de red thoro u g h l y. An ideal re s ponse va riable should provide a re p roducible and object i ve means of assessing the e f fe ct of changing the co nt rolled va riable leve l s.I n a d d i t i o n ,the re s ponse va riable should have stat i st i cal pro pe rties that pe rmit it to be modeled approp ri ately by the empiri cal functions norm a l l y a s s oc i ated with stat i s t i cally designed ex pe ri m e nt s. If the metrics have appro p ri ate stat i s t i cal pro pe rt i e s, it will be possible to estimate the model coe f f ic i e nts without bias. If they do not,some of the m odel te rms will not re f l e ct the cause and effe ct re l ationships be tween the co nt rolled va riables and re ponses essential to efficient prod u ct deve l o pm e nt .I n d e e d, re s e a rch co n d u cted at Sh i re Labo rato ri e s,I n c. (SLI) using small facto rial designs (4-8 design po i nts) has shown that f1 and f2 be h ave d i f fe re ntly when used as re s ponse va ri a b l e s.Ofte n , s i g n i f i ca nt factors and inte ra ctions for the be s t f i t m at h e m at i cal models of the two metrics in the same ex pe ri m e nt are quite diffe re nt ,and R2 va l u e s and lack-of-fit statistics va ry widely be tween them. Due to the small size of the ex pe ri m e nts run at SLI the impo rt a n ce of these observed diffe re n ce s cannot be studied thoro u g h l y. Th e re fo re,it is the object of this paper to inve s t ig ate the use of f1 and f2 as re s ponse va riables in a l a rger stat i s t i cally designed ex pe ri m e nt .The significa nt factors and inte ra ctions as well as the mat h em at i cal models deve l o ped from the design will be co m p a re d.Mat h e m at i cal models deri ved for bo t h m e t rics will be co m p a red and analyzed for ro b u s tness and precision by examining f2, lack of fit, p re d i ction accura cy,and re s i d u a l s.

Dissolution Fit Factors as Response Variables in Statistically Designed Experiments