Variographic analysis: A new methodology for quality assurance of pharmaceutical blending processes

Sánchez-Paternina, Adriluz; Sierra-Vega, Nobel O.; Cárdenas, Vanessa; Méndez, Rafael; Esbensen, Kim H.; Romañach, Rodolfo J.

doi:10.1016/j.compchemeng.2019.02.010

Cited by 19 publications

(5 citation statements)

References 41 publications

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“…Activities such as thief sampling, an unreliable method that requires the interruption of a manufacturing process and produces little information, would not be part of APM. [60,61] Downtime, such as equipment cleaning, which is necessary when facilities are shared by more than one product, should be minimized. Cleaning processes often require a significant number of solvents and detergents.…”

Section: Optimizedmentioning

confidence: 99%

Advanced pharmaceutical manufacturing: A functional definition

Romañach

Stelzer

Sánchez

et al. 2023

J Adv Manuf & Process

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The term "Advanced Pharmaceutical Manufacturing" (APM) has become an ubiquitous buzzword with deep potential policy implications. There is a real danger that APM will be seen as a general panacea for solving economic woes and drug shortages, devoiding it from specific meaning, and depriving the technical community of focus much needed for its development and implementation. This paper first discusses several semantic definitions of APM that have been proposed in the field. A functional definition of APM is then presented as: A system that is designed using predictive models, where automation minimizes human intervention while enabling closed loop process control and real time quality assurance, where performance has been optimized to maximize desired process goals, where flexible amounts of product with equivalent attributes can be manufactured, and where equivalent processes can be implemented at multiple locations to manufacture products with equivalent critical quality attributes. The proposed definition is presented to motivate discussion and elicit contributions from other stakeholders, and to contribute to the development of a consensus framework for design, development, implementation, and evaluation of APM.

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Section: Optimizedmentioning

confidence: 99%

Advanced pharmaceutical manufacturing: A functional definition

Romañach

Stelzer

Sánchez

et al. 2023

J Adv Manuf & Process

Self Cite

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“…As a major example, referral is made to the industrial pharmaceutical sector in which recently has been published a series of pioneering studies in which RSV and variographics play a key role in evaluating the performance of new online, in-line and at-line pharmaceutical PAT approaches, of particular interest for the NIR community. 5,[13][14][15][16][17][18] For the sake of space, however, this review must restrict itself just referring to these initiating references, in which variographic characterization of heterogeneity versus sampling approach is shown in detail.…”

Section: The Replication Experiments (Re)mentioning

confidence: 99%

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Esbensen

Abu-Khalaf

2022

Journal of Near Infrared Spectroscopy

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Non-representative sampling of materials, lots and processes intended for NIR analysis is often fraught with hidden contributions to the full Measurement Uncertainty MUtotal = TSE + TAENIR. The Total Sampling Error (TSE) can dominate over the Total Analytical Error TAENIR by factors 5-10-25, depending on the degree of material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material actually analysed. Part 1 presented a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or reduced maximally), especially the sampling bias, as a prerequisite to achieve fully representative sampling. The key for this is the Theory of Sampling (TOS), which is presented in two parts in a novel compact fashion. Part 2 introduces (i) application of TOS to process sampling, specifically addressing and illustrating how this manifests itself in the realm of PAT, Process Analytical Technology, and (ii) an empirical safeguard facility, termed the Replication Experiment (RE), with which to estimate the effective sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis. The RE is a defence against compromising the analytical responsibilities. Ignorance, either caused by lack of awareness or training, or by wilful neglect, of the demand for TSE minimisation, is a breach of due diligence concerning analysis QC/QA. Part 2 ends with a special focus on: “What does all this TOS mean specifically for NIR analysis?”. The answer to this question will perhaps surprise many. There is nothing special that need worrying NIR analysts relative to professionals from all other analytical modalities; all that is needed is embedded in the general TOS framework. Still, this review concludes by answering a set of typical concerns from NIR practitioners.

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“…The models, methods and tools used for the design and optimization of blending processes includes the following main directions: recursive least square algorithm supported by neural networks [6], sieve analyses to show the effect of different mixing parameters and equipment on the loading capacity [30], integrated process monitoring approach for evaluating powder blending process kinetics and determining blending process end-point [31], logarithm-transform piecewise linearization method for the optimization of gasoline-blending processes [32], adaptive algorithms for near-infrared spectroscopy [33], model-based expert control strategy using neural networks for the coal blending process [34], imaging techniques to determine the real-time distribution of mixture components [35], weighted incremental minimax probability machine-based method for quality prediction [36], variographic analysis [37], quadratic polynomial equations and multiple regression analysis using response surface methodology [38], the isoconversional method using Friedman's approximation [39], artificial neural networks [40], stochastic optimization for real-time operation of alumina-blending process [41]. The process mapping is an important tool to identify the potential correlations between the critical process parameters of blending technology and predefined quality attributes.…”

Section: Content Analysismentioning

confidence: 99%

Supply Chain Design for Blending Technologies

Bányai

Veres

2022

Sustainability

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When optimizing blending technologies, the main objective is to determine the right mixing ratio of the raw materials, depending on the different qualities and costs of the raw materials available. It can be concluded that research is mainly focused on answering technological questions, and only very few studies take into account the logistics processes related to blending technologies, their design, cost-efficiency, utilization and sustainability including energy efficiency and environmental impact. Based on this fact, within the frame of this research the authors describe a new approach, extending the basic model of blending problems by adding new supply chain efficiency-related components that makes it possible to take logistics parameters related to the raw materials supply (available stocks, batch sizes, transport and storage costs, supply chain structure) into consideration. A mathematical model of this supply chain optimization problem for blending technologies is described including routing and assignment problems in the supply chain, while technological objectives are also taken into consideration as technological objective functions and constraints. The optimization problem described in the model is a problem with non-deterministic polynomial-time hardness (NP-hard), which means that there are no known efficient analytical methods to solve the logistics-related supply chain optimization of blending technologies. As a solution algorithm, the authors have used an evolutive solver and a new metrics, which improved the efficiency of the comparison of distances between solutions of routing problems represented by permutation arrays. The scenario analysis, which focuses on the integrated optimization of technological and logistics problems validates the model and evaluates the solution algorithm and the new metrics. Using the mentioned algorithm, the supply chain processes of the blending technologies can be improved from availability, efficiency, sustainability point of view.

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Variographic analysis: A new methodology for quality assurance of pharmaceutical blending processes

Cited by 19 publications

References 41 publications

Advanced pharmaceutical manufacturing: A functional definition

Advanced pharmaceutical manufacturing: A functional definition

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Supply Chain Design for Blending Technologies

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