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Humbert, Henri; Cabiac, M. D.; Barradas, J.; Gerbeau, C

doi:10.1023/a:1016088609005

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The USP Level A correlation "...represents a point-to-point relationship between in vitro dissolution and...in vivo dissolution." [1][2][3][4][5][6][7][8][9][10][11][12] Drug products which are strongly permeation-rate-limited (R , 1) will yield a "reverse L" correlation plot and may be the case for very rapidly dissolving immediate release products. Not surprisingly, based upon this definition for Level A correlation, "successful" correlation attempts in the literature have focused on extended release products.…”

Section: Resultsmentioning

confidence: 99%

“…1,2,[4][5][6][7][8]10,11 Points from eq 15 were generated by varying R between 1000 and 0.001 and fa between 0.1 and 1. A USP Level A correlation approach was pursued.…”

Section: Methodsmentioning

confidence: 99%

“…Dissolution-rate-limited absorption (e.g., absorption from extended release products [1][2][3][4][5][6][7][8][9][10][11][12] in particular has been emphasized in the literature and has shown high in vitro-in vivo correlation. For orally-administered drugs, dissolution and intestinal permeation have long been recognized as two possible rate-limiting phenomena in the absorption process.…”

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confidence: 99%

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Novel Approach to the Analysis of in Vitro–in Vivo Relationships**Presented in part at the Ninth Annual Meeting and Exposition of the American Association of Pharmaceutical Scientists, San Diego, CA, November 1994.

Polli

Crison

Amidon

1996

Journal of Pharmaceutical Sciences

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The objective of this study was to quantify the dependence of degree of in vitro-in vivo correlation on the relative rates of dissolution and intestinal permeation and on the fraction of dose absorbed. The following equation was derived assuming first-order dissolution and permeation after oral drug administration: Fa = fa-1(1 - alpha(alpha - 1)-1 (1 - Fd) + (alpha - 1)-1(1 - Fd)alpha), where Fa is the fraction of the total amount of drug absorbed at time t, fa the fraction of the dose absorbed at t = infinitive, alpha is the ratio of the first-order permeation rate constant to the first-order dissolution rate constant, and Fd is the fraction of dose dissolved in vitro at time t. This equation was examined in order to pursue a theoretical treatment of in vitro-in vivo correlation. The degree of in vitro-in vivo correlation between Fa and Fd was measured by r2. alpha was varied between 1000 and 0.001. fa was varied between 0.1 and 1.0. Points employed in the linear regression were geometrically balanced about the derived equation. r2 values decreased as alpha decreased for all values of fa. r2 values were virtually independent of fa for all values of alpha, except for 0.01 < alpha < 1.0. The slope of the regression was modulated by both alpha and fa; larger alpha and smaller fa each increased slope. Application of the equation to a piroxicam data set demonstrated the equation's utility relative to the USP Level A correlation approach. It is concluded that the degree of in vitro-in vivo correlation depends on the relative rates of dissolution and intestinal permeation and on the fraction of dose absorbed and that the derived model merits further study.

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Section: Resultsmentioning

confidence: 99%

“…1,2,[4][5][6][7][8]10,11 Points from eq 15 were generated by varying R between 1000 and 0.001 and fa between 0.1 and 1. A USP Level A correlation approach was pursued.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Novel Approach to the Analysis of in Vitro–in Vivo Relationships**Presented in part at the Ninth Annual Meeting and Exposition of the American Association of Pharmaceutical Scientists, San Diego, CA, November 1994.

Polli

Crison

Amidon

1996

Journal of Pharmaceutical Sciences

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“…The author herein provides arguments for provision and acceptance of such data in many circumstances. While this issue has been addressed previously in somewhat similar vein (3), it seems appropriate to revisit at the present time in view of the persistence of this analytical dilemma and the advancement of pharmacometric concepts and methods for dealing with low exposure data.…”

Section: Introductionmentioning

confidence: 92%

Use of Pharmacokinetic Data Below Lower Limit of Quantitation Values

Jusko

2012

Pharm Res

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Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Tessier

Bertrand

Chenel

et al. 2015

AAPS J

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Abstract. Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H 1 ), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H 1 . We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.

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Untitled

Cited by 4 publications

References 11 publications

Novel Approach to the Analysis of in Vitro–in Vivo Relationships**Presented in part at the Ninth Annual Meeting and Exposition of the American Association of Pharmaceutical Scientists, San Diego, CA, November 1994.

Novel Approach to the Analysis of in Vitro–in Vivo Relationships**Presented in part at the Ninth Annual Meeting and Exposition of the American Association of Pharmaceutical Scientists, San Diego, CA, November 1994.

Use of Pharmacokinetic Data Below Lower Limit of Quantitation Values

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

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