A Bayesian Approach to the ICH Q8 Definition of Design Space

Peterson, John J.

doi:10.1080/10543400802278197

Cited by 121 publications

(74 citation statements)

References 9 publications

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“…18 QbD mines and understands the effects of input (formulation and process) parameters on the critical quality attributes (CQAs) to constantly manufacture a medicament with the desired quality. 19 A QbD-based drug product development would comprise a screening design of experiment to define the design space and classify the formulation and process parameters according to their influences on the critical quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Nicotinamide polymeric nanoemulsified systems: a quality-by-design case study for a sustained antimicrobial activity

Zidan

Ahmed

Aljaeid

2016

IJN

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Nicotinamide, the amide form of vitamin B3, was demonstrated to combat some of the antibiotic-resistant infections that are increasingly common around the world. The objective of this study was to thoroughly understand the formulation and process variabilities affecting the preparation of nicotinamide-loaded polymeric nanoemulsified particles. The quality target product profile and critical quality attributes of the proposed product were presented. Plackett–Burman screening design was employed to screen eight variables for their influences on the formulation’s critical characteristics. The formulations were prepared by an oil-in-water emulsification followed by solvent replacement. The prepared systems were characterized by entrapment capacity (EC), entrapment efficiency (EE), particle size, polydispersity index, zeta potential, transmission electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, powder X-ray diffraction, in vitro drug release, and their antibacterial activity against bacterial scrums. EC, EE, particle size, polydispersity index, zeta potential, and percentage release in 24 hours were found to be in the range of 33.5%–68.8%, 53.1%–67.1%, 43.3–243.3 nm, 0.08–0.28, 9.5–53.3 mV, and 5.8%–22.4%, respectively. One-way analysis of variance and Pareto charts revealed that the experimental loadings of 2-hydroxypropyl-β-cyclodextrin and Eudragit ® S100 were the most significant for their effects on nicotinamide EC and EE. Moreover, the polymeric nanoemulsified particles demonstrated a sustained release profile for nicotinamide. The Fourier transform infrared spectroscopy, differential scanning calorimetry, and X-ray diffraction demonstrated a significant interaction between the drug and 2-hydroxypropyl-β-cyclodextrin that might modulate the sustained release behavior. Furthermore, the formulations provided a sustained antibacterial activity that depended on nicotinamide-loading concentration, release rate, and incubation time. In conclusion, the study demonstrated the potential of polymeric nanoemulsified system to sustain the release and antibacterial activity of nicotinamide.

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Section: Introductionmentioning

confidence: 99%

Nicotinamide polymeric nanoemulsified systems: a quality-by-design case study for a sustained antimicrobial activity

Zidan

Ahmed

Aljaeid

2016

IJN

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“…In a multivariate design problem, correlations among the CQAs might be expected to occur as the design space is more finely defined. In such cases, one quantitative solution for the system of linear regressions in the presence of cross-equation correlations is Bseemingly unrelated regression^; this method is described in (9,20,21). We used either SAS (Proc Syslin) or R package Bsystemfit^for finding estimates of the regression parameters and the cross-equation covariances and correlations.…”

Section: Quantitative Methodsmentioning

confidence: 99%

“…The ICH quality guidelines call for defining the design space under quality risk management (QRM) principles. QRM is growing rapidly in both theory and application to pharmaceutical product life cycle management (1,2,5,6), and an increasing number of pharmaceutical development teams are applying quantitative risk management approaches to pharmaceutical QbD (7)(8)(9). Qualitative risk management tools excel for building structural and quantitative models as support for a risk-based selection of critical quality attributes necessary for creating a design space.…”

Section: Introductionmentioning

confidence: 99%

“…At the time of that seminal work and for the decades following, probabilistic risk simulations of design space problems, i.e., using Monte Carlo sampling, typically required access to mainframe computers and significant computation times. More recently, the rapid evolution of desktop computing power has brought Monte Carlo simulation into the realm of routine risk analysis and as such, probabilistic risk analysis using Monte Carlo methods support risk modeling for a wide range of disciplines (16,17) including design of experiment (DOE)-based process design (9,18,19). Numerous desktop software applications are now available as add-ons to spreadsheet software, components of sophisticated statistical packages, or as stand-alone software applications.…”

Section: Introductionmentioning

confidence: 99%

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Quality-by-Design II: Application of Quantitative Risk Analysis to the Formulation of Ciprofloxacin Tablets

et al. 2015

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Abstract. Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries. The goal of this paper is to demonstrate a relatively simple strategy for design space definition using a simplified Bayesian Monte Carlo simulation. This paper builds on a previous paper that used failure mode and effects analysis (FMEA) qualitative risk assessment and Plackett-Burman design of experiments to identity the critical quality attributes. The results show that the sequential use of qualitative and quantitative risk assessments can focus the design of experiments on a reduced set of critical material and process parameters that determine a robust design space under conditions of limited laboratory experimentation. This approach provides a strategy by which the degree of risk associated with each known parameter can be calculated and allocates resources in a manner that manages risk to an acceptable level.

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“…The key statistical challenges to overcome to make QbD a reality for such longitudinal data are critical: It can be postulated that a Bayesian approach is the only option to practically achieve such an objective as required by ICH-Q8. The justification and added value of the use of Bayesian statistics for proper QbD implementation has been discussed extensively by several authors such as Peterson et al [13][14][15][16][17], Miró-Quesada [12] and Lebrun et al [11].…”

Section: Introductionmentioning

confidence: 99%

A Quality By Design Approach For Longitudinal Quality Attributes

Lebrun

Giacoletti²,

Scherder³

et al. 2014

Journal of Biopharmaceutical Statistics

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The concept of Quality by Design (QbD) as published in ICH-Q8 is currently one of the most recurrent topics in the pharmaceutical literature. This guideline recommends the use of information and prior knowledge gathered during pharmaceutical development studies to provide a scientific rationale for the manufacturing process of a product and provide guarantee of future quality. This poses several challenges from a statistical standpoint and requires a shift in paradigm from traditional statistical practices. First, to provide "assurance of quality" of future lots implies the need to make predictions regarding quality given past evidence and data. Second, the Quality Attributes described in the Q8 guidelines are not always a set of unique, independent measurements. In many cases, these criteria are complicated longitudinal data with successive acceptance criteria over a defined period of time. A common example is a dissolution profile for a modified or extended release solid dosage form that must fall within acceptance limits at several time points. A Bayesian approach for longitudinal data obtained in various conditions of a Design of Experiment is provided to elegantly address the ICH-Q8 recommendation to provide assurance of quality and derive a scientifically sound Design Space.

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A Bayesian Approach to the ICH Q8 Definition of Design Space

Cited by 121 publications

References 9 publications

Nicotinamide polymeric nanoemulsified systems: a quality-by-design case study for a sustained antimicrobial activity

Nicotinamide polymeric nanoemulsified systems: a quality-by-design case study for a sustained antimicrobial activity

Quality-by-Design II: Application of Quantitative Risk Analysis to the Formulation of Ciprofloxacin Tablets

A Quality By Design Approach For Longitudinal Quality Attributes

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