Dissolution Profile Similarity Assessment—Best Practices, Decision Trees and Global Harmonization

Abend, Andreas; Hoffelder, Thomas; Cohen, Michael J.; Alstine, Leslie Van; Diaz, Dorys Argelia; Fredro-Kumbaradzi, Emilija; Reynolds, James; Zheng, Yanbing; Witkowski, Krista; Heimbach, Tycho

doi:10.1208/s12248-023-00795-5

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Many statistical approaches have been developed to test the similarity between dissolution curves or profiles, including cluster analysis, decision trees, and linear models; see, e.g. Costa and Sousa Lobo (2001), Maggio et al (2008), Enachescu (2010), Paix ao et al (2017), Abend et al (2023), Pourmohamad and Ng (2023). That also includes nonparametric measures such as the two measures adopted by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA),…”

Section: Detecting Similarity Of Drug Dissolution Profilesmentioning

confidence: 99%

Functional data learning using convolutional neural networks

Galarza,

Oraby

2024

Mach. Learn.: Sci. Technol.

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In this paper, we show how convolutional neural networks (CNN) canbe used in regression and classification learning problems for noisy and non-noisyfunctional data. The main idea is to transform the functional data into a 28 by 28image. We use a specific but typical architecture of a convolutional neural networkto perform all the regression exercises of parameter estimation and functional formclassification. First, we use some functional case studies of functional data with andwithout random noise to showcase the strength of the new method. In particular,we use it to estimate exponential growth and decay rates, the bandwidths of sineand cosine functions, and the magnitudes and widths of curve peaks. We also use itto classify the monotonicity and curvatures of functional data, the algebraic versusexponential growth, and the number of peaks of functional data. Second, we applythe same convolutional neural networks to Lyapunov exponent estimation in noisyand non-noisy chaotic data, in estimating rates of disease transmission from epidemiccurves, and in detecting the similarity of drug dissolution profiles. Finally, we applythe method to real-life data to detect Parkinson’s disease patients in a classificationproblem. We performed ablation analysis and compared the new method with othercommonly used neural networks for functional data and showed that it outperformsthem in all applications. Although simple, the method shows high accuracy and ispromising for future use in engineering and medical applications.

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Section: Detecting Similarity Of Drug Dissolution Profilesmentioning

confidence: 99%

Functional data learning using convolutional neural networks

Galarza,

Oraby

2024

Mach. Learn.: Sci. Technol.

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show abstract

“…Dissolution testing performed under multiple pH conditions or the approved QC method, which may or may not contain surfactants, is not a priori indicative of unacceptable in vivo performance unless these methods are clinically relevant (21). Once a clinically relevant dissolution method (CRDM) has been developed and validated, this method should be used to assess the impact of manufacturing changes as opposed to any dissolution methods with unknown clinical relevance (22). A clinically relevant dissolution specification (CRDS) can be established via traditional bracketing approaches or in silico.…”

Section: Session 2: Basics and Best Practices On Dissolution Testingmentioning

confidence: 99%