Defining the design space for freeze-dried orodispersible tablets with meloxicam

Iurian, Sonia; Tomuță, Ioan; Bogdan, Cătălina; Rus, Lucia; Tőkés, Tímea; Barbu–Tudoran, Lucian; Achim, Marcela; Moldovan, Mirela; Leucuţa, Sorin E.

doi:10.1080/03639045.2016.1188108

Cited by 29 publications

(30 citation statements)

References 32 publications

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“…Given that one input was qualitative (the type of extragranular diluent), two design spaces were generated, one for each variable. The green area represents the design space and each point from this area predicts, with a low probability of failure under 1 %, a possible hydrophilic matrix formulation that would possess the described CQAs (20). The dotted frame inside the design space is the design space hypercube, which defines the proven acceptable range (PAR).…”

Section: Design Space and Formulation Optimizationmentioning

confidence: 99%

“…This acceptable range is defined by a design space hypercube, which represents the largest possible regular hypercube that can be inserted into the irregular design space and shows the volume in which all factor combinations can be used without compromising the product's CQAs (19). After the determination of the design space and PAR, the optimal formulations were determined by defining combinations of factor values that predict a result as close as possible to the target values of all responses (20).…”

Section: Experimental Designmentioning

confidence: 99%

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Formulation and pharmaceutical development of quetiapine fumarate sustained release matrix tablets using a QbD approach

et al. 2017

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The main objective of the present study was to apply QbD methodology in the development of once-a-day sustained release quetiapine tablets. The quality target product profile (QTPP) was defined after the pharmaceutical properties and kinetic release of the innovator product, Seroquel XR 200 mg. For the D-optimal experimental design, the level and ratio of matrix forming agents and the type of extragranular diluent were chosen as independent inputs, which represented critical formulation factors. The critical quality attributes (CQAs) studied were the cumulative percentages of quetiapine released after certain time intervals. After the analysis of the experimental design, optimal formulas and the design space were defined. Optimal formulas demonstrated zero-order release kinetics and a dissolution profile similar to the innovator product, with f 2 values of 74.53 and 83.74. It was concluded that the QbD approach allowed fast development of sustained release tablets with similar dissolution behavior as the innovator product.Keywords: quality by design, design of experiments, sustained release, hydrophilic matrix, quetiapineIn the past years, the main goal of antipsychotic drug development has been to develop molecules with higher efficacy and fewer side effects usually associated with the classic antipsychotic medications. The newly developed agents, called atypical antipsychotics, are successfully used in the treatment of both positive and negative symptoms of schizophrenia and are associated with fewer neurological and endocrine side effects compared to older medications (1). Quetiapine is a recently introduced atypical antipsychotic, which has an excellent risk/benefit ratio and is indicated as the first-line option for the treatment of psychotic disorder manifestations and schizophrenia (2). Quetiapine is available as fumarate salt in immediate as well as in sustained released formulations, the innovative product being Seroquel. The sustained release formulation was introduced several years ago and its primary objective is to release the drug slowly over an extended ALEXANDRU GAVAN

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Section: Design Space and Formulation Optimizationmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Formulation and pharmaceutical development of quetiapine fumarate sustained release matrix tablets using a QbD approach

et al. 2017

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“…The goodness of fit of a model is given by the value of R 2 and represents the variation of the response explained by the model. Q 2 represents the goodness of prediction and reveals how well the model can predict new experiments [30]. Model validity provides insights regarding the model error, while reproducibility (pure error) evaluates the variation of response under identical conditions compared to its total variation.…”

Section: Experimental Designmentioning

confidence: 99%

“…DoE is also useful to identify the individual and interacting factors. Using response surface, methodology may lead to the development of Design Space (DS), which assure the quality of the desired product and can be defined for both the formulation and process parameters [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Definition and validation of the Design Space for co-milled nasal powder containing nanosized lamotrigine

Gieszinger

Tomuță

Casian

et al. 2018

Drug Development and Industrial Pharmacy

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“…Usually, the variables that have an impact on CQAs are interconnected; therefore, their study using design of experiments (DoEs) was useful to assess and predict both their individual and interacting effects. Statistical calculations of the multifactorial relationships with the use of response surface methodology lead to the development of a design space, meaning a domain of input variables, which guarantee the delivery of a product with the required characteristics 5,7…”

Section: Introductionmentioning

confidence: 99%

Risk assessment and experimental design in the development of a prolonged release drug delivery system with paliperidone

Iurian

Turdean

Tomuță

2017

DDDT

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This study focuses on the development of a drug product based on a risk assessment-based approach, within the quality by design paradigm. A prolonged release system was proposed for paliperidone (Pal) delivery, containing Kollidon® SR as an insoluble matrix agent and hydroxypropyl cellulose, hydroxypropyl methylcellulose (HPMC), or sodium carboxymethyl cellulose as a hydrophilic polymer. The experimental part was preceded by the identification of potential sources of variability through Ishikawa diagrams, and failure mode and effects analysis was used to deliver the critical process parameters that were further optimized by design of experiments. A D-optimal design was used to investigate the effects of Kollidon SR ratio (X1), the type of hydrophilic polymer (X2), and the percentage of hydrophilic polymer (X3) on the percentages of dissolved Pal over 24 h (Y1–Y9). Effects expressed as regression coefficients and response surfaces were generated, along with a design space for the preparation of a target formulation in an experimental area with low error risk. The optimal formulation contained 27.62% Kollidon SR and 8.73% HPMC and achieved the prolonged release of Pal, with low burst effect, at ratios that were very close to the ones predicted by the model. Thus, the parameters with the highest impact on the final product quality were studied, and safe ranges were established for their variations. Finally, a risk mitigation and control strategy was proposed to assure the quality of the system, by constant process monitoring.

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Defining the design space for freeze-dried orodispersible tablets with meloxicam

Abstract: From the generated design space, an optimal formulation was obtained and the results validated the experimental design. The QbD approach was an efficient manner of understanding formulation and process parameters at the freeze-dried orodispersible tablets preparation.

Cited by 29 publications

References 32 publications

Formulation and pharmaceutical development of quetiapine fumarate sustained release matrix tablets using a QbD approach

Formulation and pharmaceutical development of quetiapine fumarate sustained release matrix tablets using a QbD approach

Definition and validation of the Design Space for co-milled nasal powder containing nanosized lamotrigine

Risk assessment and experimental design in the development of a prolonged release drug delivery system with paliperidone

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