Modeling and comparison of dissolution profiles.
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Cites background from "Modeling and comparison of dissolut..."
...f1 0 1⁄4 Pn t1⁄41 Rt Tt j j= Pn t1⁄41 Rt þ Tt ð Þ=2 100 DD_f1cp (13)...
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...# 326 Quadratic F 1⁄4 100 k1 t2 þ k2 t k1, k2 (8,13)...
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...Parameters for Assessing the Difference Between Dissolution Profiles Abbreviation, description Equation Function in DDSolver Reference(s) f1, difference factora f1 ¼ Pn t¼1 Rt Ttj j= Pn t¼1 Rt 100 DD_f1 (6) f2, similarity factor a f2 ¼ 50 log 1þ 1n Pn t¼1 Rt Ttð Þ2 0:5 100 )( DD_f2 (6) f1’, difference factor modified by Costa P.a f1 0 ¼ Pn t¼1 Rt Ttj j= Pn t¼1 Rt þ Ttð Þ=2 100 DD_f1cp (13) ξ1, first-order Rescigno index a, b xj ¼ R t 0 Ri Tij jjdt = R t 0 Ri þ Tij j j dt h i1=j ; j ¼ 1 DD_res1 (7) ξ2, second-order Rescigno indexa, b xj ¼ R t 0 Ri Tij jjdt = R t 0 Ri þ Tij jjdt h i1=j ; j ¼ 2 DD_res2 (7) Sd, difference in similarity c Sd ¼ Pn 1 t¼1 log AUCRt AUCTt n 1 DD_Sd (53) D, sum of squared mean differencesd D ¼ Pp i¼1 yTi yRið Þ2 DD_D (44,54) D1, mean distance d D1 ¼ Pp i¼1 yTi yRij j=p DD_D1 (55,56) D2, mean squared distance d D2 ¼ Pp i¼1 yTi yRið Þ2=p 1=2 DD_D2 (56) DAUC, difference of area under the profilesd,e DAUC ¼ Pp i¼1 yTi þ yT i 1ð Þ yRi þ yR i 1ð Þ h i ti ti 1ð Þ=2 n o DD_DAUC (56) DABC, area between the profiles d,e DABC ¼ Pp i¼1 yTi yRij j tiþ1 þ tið Þ=2 ti þ ti 1ð Þ=2½ f g DD_DABC (56) a Rt, Tt are the percentage dissolved of the reference and test profile, respectively, at time point t; n is the number of sampling points b j is 1 and 2 for the first- (ξ1) and second-order (ξ2) Rescigno indexes, respectively c n is the number of sampling points; AUCRt and AUCTt are the areas under the dissolution curves of the reference and test formulations, respectively, at time t d p is the number of sampling points; yTi and yRi are the mean dissolution values of the test and reference profiles respectively at the ith time point e ti is the ith sampling time point The MSC provided by MicroMath Corporation (38) is another statistical criterion for model selection which is attracting increasing attention in the field of dissolution data modeling (32,39); it is defined as: ¼ ln Pn i¼1 wi yi obs y obsð Þ2 Pn i¼1 wi yi obs yi pre 2 0 BB@ 1 CCA 2pn where wi is the weighting factor, which is usually equal to 1 for fitting dissolution data, yi_obs is the ith observed y value, yi_pre is the ith predicted y value, y obs is the mean of all observed y-data points, p is the number of parameters in the model, and n is the number of data points....
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...# 301 Zero-order F 1⁄4 k0 t k0 (15) # 302 c Zero-order with Tlag F 1⁄4 k0 t Tlag k0, Tlag (16) # 303 d Zero-order with F0 F 1⁄4 F0 þ k0 t k0, F0 (13) # 304 First-order F 1⁄4 100 1 e k1 t k1 (8)...
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...This work was not performed to assess any particular model or to discuss the statistical or mechanical meaning of each model parameter, because these topics have been well reviewed previously (13,14)....
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Cites background from "Modeling and comparison of dissolut..."
...ble drugs and enhancing their bioavailability is an important challenge to pharmaceutical scientists [5,6]....
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References
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"Modeling and comparison of dissolut..." refers background in this paper
...(29) can only be used in systems with a drug diffusion Korsmeyer et al. (1983) developed a simple, semi- coefficient fairly concentration independent....
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4,383 citations
"Modeling and comparison of dissolut..." refers methods in this paper
...Higuchi model Higuchi (1961, 1963) developed several theoretical contact with a perfect sink release media....
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