Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis

Mercuri, Annalisa; Pagliari, M; Baxevanis, Fotios; Fares, R.; Fotaki, Nikoletta

doi:10.1016/j.ijpharm.2016.12.034

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…Dissolution profiles can be analysed through different approaches: using model-dependent methods where experimental data are fitted using mathematical equations, model-independent methods (single values such as mean dissolution time and area under the dissolution curve (AUC) are used for data evaluation) and/or statistical methods ( e.g. ANOVA and multivariate analysis) ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

In Vivo Predictive Dissolution Testing of Montelukast Sodium Formulations Administered with Drinks and Soft Foods to Infants

et al. 2020

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In vitro dissolution testing conditions that reflect and predict in vivo drug product performance are advantageous, especially for the development of paediatric medicines, as clinical testing in this population is hindered by ethical and technical considerations. The aim of this study was to develop an in vivo predictive dissolution test in order to investigate the impact of medicine co-administration with soft food and drinks on the dissolution performance of a poorly soluble compound. Relevant in vitro dissolution conditions simulating the in vivo gastrointestinal environment of infants were used to establish in vitro-in vivo relationships with corresponding in vivo data. Dissolution studies of montelukast formulations were conducted with mini-paddle apparatus on a two-stage approach: infant fasted-state simulated gastric fluid (Pi-FaSSGF; for 1 h) followed by either infant fasted-state or infant fed-state simulated intestinal fluid (FaSSIF-V2 or Pi-FeSSIF, respectively; for 3 h). The dosing scenarios tested reflected in vivo paediatric administration practices: (i.) direct administration of formulation; (ii.) formulation co-administered with vehicles (formula, milk or applesauce). Drug dissolution was significantly affected by co-administration of the formulation with vehicles compared with after direct administration of the formulation. Montelukast dissolution from the granules was significantly higher under fed-state simulated intestinal conditions in comparison with the fasted state and was predictive of the in vivo performance when the granules are co-administered with milk. This study supports the potential utility of the in vitro biorelevant dissolution approach proposed to predict in vivo formulation performance after co-administration with vehicles, in the paediatric population.

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Section: Introductionmentioning

confidence: 99%

In Vivo Predictive Dissolution Testing of Montelukast Sodium Formulations Administered with Drinks and Soft Foods to Infants

et al. 2020

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“…With this, if DoE utilized random forest to fit the data, the result should be the same as with the ML method. A successful case of the utilization of the DoE method to optimize biopredictive dissolution conditions and to build in vitro–in vivo correlations for an IR capsule was presented by Mercuri et al [ 46 ]. Moreover, Carapeto et al utilized DoE and physiologically based biopharmaceutics modeling to develop a biopredictive dissolution method [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

A Rational Approach to Predicting Immediate Release Formulation Behavior in Multiple Gastric Motility Patterns: A Combination of a Biorelevant Apparatus, Design of Experiments, and Machine Learning

Staniszewska,

Romański,

Polak

et al. 2023

Pharmaceutics

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Gastric mechanical stress often impacts drug dissolution from solid oral dosage forms, but in vitro experiments cannot recreate the substantial variability of gastric motility in a reasonable time. This study, for the first time, combines a novel dissolution apparatus with the design of experiments (DoE) and machine learning (ML) to overcome this obstacle. The workflow involves the testing of soft gelatin capsules in a set of fasted-state biorelevant dissolution experiments created with DoE. The dissolution results are used by an ML algorithm to build the classification model of the capsule’s opening in response to intragastric stress (IS) within the physiological space of timing and magnitude. Next, a random forest algorithm is used to model the further drug dissolution. The predictive power of the two ML models is verified with independent dissolution tests, and they outperform a polynomial-based DoE model. Moreover, the developed tool reasonably simulates over 50 dissolution profiles under varying IS conditions. Hence, we prove that our method can be utilized for the simulation of dissolution profiles related to the multiplicity of individual gastric motility patterns. In perspective, the developed workflow can improve virtual bioequivalence trials and the patient-centric development of immediate-release oral dosage forms.

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“…It is known that model dependent approaches can identify differences between dissolution curves that model independent approaches cannot distinguish (Mercuri et al 2017;Paixão et al 2017), and the two approaches can be complementary. The differences seen here are not large by any of the techniques.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to formulation performance

Tobyn

Ferreira

Lightfoot

et al. 2018

Pharmaceutical Development and Technology

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A key part of the Risk Assessment of excipients is to understand how raw material variability could (or does) contribute to differences in performance of the drug product. Here we demonstrate an approach which achieves the necessary understanding for a complex, functional, excipient. Multivariate analysis (MVA) of the certificates of analysis of an ethylcellulose aqueous dispersion (Surelease) formulation revealed low overall variability of the properties of the systems. Review of the scores plot to highlight batches manufactured using the same ethylcellulose raw material in the formulation, indicated that these batches tend to be more closely related than other randomly selected batches. This variability could result in potential differences in the quality of drug product lots made from these batches. Manufacture of a model drug product from Surelease batches coated using different lots of starting material revealed small differences in the release of a model drug, which could be detected by certain model dependent dissolution modelling techniques, but they were not observed when using model-independent techniques. This illustrates that the techniques are suitable for detecting and understanding excipient variability, but that, in this case, the product was still robust.

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Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis

Cited by 10 publications

References 33 publications

In Vivo Predictive Dissolution Testing of Montelukast Sodium Formulations Administered with Drinks and Soft Foods to Infants

In Vivo Predictive Dissolution Testing of Montelukast Sodium Formulations Administered with Drinks and Soft Foods to Infants

A Rational Approach to Predicting Immediate Release Formulation Behavior in Multiple Gastric Motility Patterns: A Combination of a Biorelevant Apparatus, Design of Experiments, and Machine Learning

Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to formulation performance

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