Dissolution Curve Comparisons Through theF2Parameter, a Bayesian Extension of thef2Statistic

Novick, Steven; Shen, Yan; Yang, Harry; Peterson, John J.; LeBlond, Dave; Altan, Stan

doi:10.1080/10543406.2014.971175

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…To calculate the

f_{2}

statistic in the CTGP model, we can use

f_{2} = 50 \log_{10} [100, {(1 + \frac{1}{p + r} | | μ_{R, scriptA} - μ_{T, scriptA} {false| false|}^{2})}^{- 1 false/ 2}] .

Posterior samples of

f_{2}

can be obtained be making draws of

{bold-italicμ}_{R, A}

and

{bold-italicμ}_{T, A}

over the course of the MCMC sampling algorithm to generate a full posterior distribution for

f_{2}

. Likewise, the δ test statistic introduced in Novick et al (2015) can also be calculated via posterior draws of

{bold-italicμ}_{R, A}

and

{bold-italicμ}_{T, A}

using the following formula,

δ = \max (false| μ_{R, scriptA} - μ_{T, scriptA} false|) .

…”

Section: Methodsmentioning

confidence: 99%

“…Similarly for the test product, Y T ∼ N(𝝁 T , 𝚺 T ) and a Jeffrey's prior is once again used resulting in the same forms for the posterior distributions. Novick et al (2015) goes beyond the typical case of solely using f 2 statistic for dissolution curve comparison by adding another condition for declaring comparability. Let 𝛿 = max |𝝁 R − 𝝁 T |.…”

Section: 11mentioning

confidence: 99%

“…Posterior samples of f 2 can be obtained be making draws of 𝝁 R, and 𝝁 T, over the course of the MCMC sampling algorithm to generate a full posterior distribution for f 2 . Likewise, the 𝛿 test statistic introduced in Novick et al (2015) can also be calculated via posterior draws of 𝝁 R, and 𝝁 T, using the following formula,…”

Section: 13mentioning

confidence: 99%

“…In addition, violating this assumption also guarantees that no correlation information across time points is taken into account in the uncertainty quantification. Novick et al (2015) took a fully Bayesian approach by quantifying dissolution curve similarity through probabilistic statements based on posterior samples of the

f_{2}

statistic, for example,

\Pr (f_{2} > 50)

. More recently, Ng et al (2018) took advantage of the expected monotonicity of the dissolution curves and treated the sets of dissolution curves as arising from a gamma process model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian Process Modeling for Dissolution Curve Comparisons

Pourmohamad¹,

Avilés²,

Richardson

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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Dissolution studies are an integral part of pharmaceutical drug development, yet standard methods for analysing dissolution data are inadequate for capturing the true underlying shapes of the dissolution curves. Methods based on similarity factors, such as the f 2 statistic, have been developed to demonstrate comparability of dissolution curves, however, this inability to capture the shapes of the dissolution curves can lead to substantial bias in comparability estimators. In this article, we propose two novel semi-parametric dissolution curve modeling strategies for establishing the comparability of dissolution curves. The first method relies upon hierarchical Gaussian process regression models to construct an f 2 statistic based on continuous time modeling that results in significant bias reduction. The second method uses a Bayesian model selection approach for creating a framework that does not suffer from the limitations of the f 2 statistic. Overall, these two methods are shown to be superior to their comparator methods and provide feasible alternatives for similarity assessment under practical limitations. Illustrations highlighting the success of our methods are provided for two motivating real dissolution data sets from the literature, as well as extensive simulation studies.

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“…To calculate the

f_{2}

statistic in the CTGP model, we can use

f_{2} = 50 \log_{10} [100, {(1 + \frac{1}{p + r} | | μ_{R, scriptA} - μ_{T, scriptA} {false| false|}^{2})}^{- 1 false/ 2}] .

Posterior samples of

f_{2}

can be obtained be making draws of

{bold-italicμ}_{R, A}

and

{bold-italicμ}_{T, A}

over the course of the MCMC sampling algorithm to generate a full posterior distribution for

f_{2}

. Likewise, the δ test statistic introduced in Novick et al (2015) can also be calculated via posterior draws of

{bold-italicμ}_{R, A}

and

{bold-italicμ}_{T, A}

using the following formula,

δ = \max (false| μ_{R, scriptA} - μ_{T, scriptA} false|) .

…”

Section: Methodsmentioning

confidence: 99%

Section: 11mentioning

confidence: 99%

Section: 13mentioning

confidence: 99%

f_{2}

statistic, for example,

\Pr (f_{2} > 50)

. More recently, Ng et al (2018) took advantage of the expected monotonicity of the dissolution curves and treated the sets of dissolution curves as arising from a gamma process model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gaussian Process Modeling for Dissolution Curve Comparisons

Pourmohamad¹,

Avilés²,

Richardson

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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show abstract

“…For these reasons, we believe Bayesian methodology has the potential of overcoming many of the issues with f 2 and MSD while maintaining a link to established criteria. Such methodologies are the subject of a separate article by the authors of this review (40). Other papers expanding on the topic are under discussion.…”

Section: A Bayesian Perspective To the In Vitro Dissolution Comparisomentioning

confidence: 99%

In Vitro Dissolution Curve Comparisons: A Critique of Current Practice

LeBlond¹,

Altan²,

Novick³

et al. 2016

Dissolution Technol.

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Many pharmacologically active molecules are formulated as solid dosage form drug products. Following oral administration, the diffusion of an active molecule from the gastrointestinal tract into systemic distribution requires the disintegration of the dosage form followed by the dissolution of the molecule in the stomach lumen. Its dissolution properties may have a direct impact on its bioavailability and subsequent therapeutic effect. Consequently, dissolution (or in vitro release) testing has been the subject of intense scientific and regulatory interest over the past several decades. Much interest has focused on models describing in vitro release profiles over a time scale, and a number of methods have been proposed for testing similarity of profiles. In this article, we review previously published work on dissolution profile similarity testing and provide a detailed critique of current methods in order to set the stage for a Bayesian approach. KEY WORDS:Dissolution profile similarity; in vitro release; f 2 statistic; Bayesian model.

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The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

Faya

Sondag

Novick

et al. 2020

Pharmaceutical Statistics

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Summary The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA‐BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.

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Dissolution Curve Comparisons Through theF₂Parameter, a Bayesian Extension of thef₂Statistic

Cited by 12 publications

References 19 publications

Gaussian Process Modeling for Dissolution Curve Comparisons

Gaussian Process Modeling for Dissolution Curve Comparisons

In Vitro Dissolution Curve Comparisons: A Critique of Current Practice

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

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