Design of experiments applications in bioprocessing: Concepts and approach

Kumar, Vijesh; Bhalla, Akriti; Rathore, Anurag S.

doi:10.1002/btpr.1821

Cited by 110 publications

(69 citation statements)

References 38 publications

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“…Process models have often been based on mechanistic knowledge about the underlying processes on a physical, chemical, or biological level. These models, also known as phenomenological models, not only have the advantage of encompassing a summary of available knowledge, but also provide value in planning experiments, or in determining which CPPs need to be monitored and controlled tightly (81,82). The required mechanistic knowledge for setting up models has been traditionally based on experimental observations leading to formulation of hypotheses on a case-by-case basis.…”

Section: Promising Km Tools For Pharmaceutical Production Systemsmentioning

confidence: 99%

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

et al. 2016

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Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks were screened and a query to gather industrial inputs from 17 major biopharmaceutical organizations was performed. Three KM tool classes were identified as having high relevance for biopharmaceutical production systems and have been further explored: knowledge indicators, ontologies, and process modeling. A proposed categorization of 16 distinct KM tool classes allowed for the identification of holistic technologies supporting QbD. In addition, the classification allowed for addressing the disparity between industrial and academic expectations regarding the application of KM methodologies. This is a first of a kind attempt and thus we think that this paper would be of considerable interest to those in academia and industry that are engaged in accelerating development and commercialization of biopharmaceuticals.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2043-9) contains supplementary material, which is available to authorized users.

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Section: Promising Km Tools For Pharmaceutical Production Systemsmentioning

confidence: 99%

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

et al. 2016

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“…1, Y is the predicted yield, μ0 is the linear interaction coefficient, μi is the quadratic interaction coefficient, μij is the interaction coefficient, and Zn terms are variables. The design and analysis of experiments were carried out using "STATISTICA 8.0" software, evaluation version (Mao et al 2007;Kammoun et al 2008;Yong et al 2011;Kumar et al 2014).…”

Section: Optimization Of Selected Medium Components By Central Composmentioning

confidence: 99%

“…The four most significant variables were chosen for further optimization by CCD for enhanced production of OTC from S. rimosus using dry pods. The selected significant variables, maltose concentration, CaCO3 concentration, inoculum size, and moisture (%) were optimized for maximum production of oxytetracycline by RSM (Kumar et al 2014). The CCD experimental model was applied, leading to a second order polynomial equation for determining the optimized value (Zeng et al 2006).…”

Section: Statistical Optimizationmentioning

confidence: 99%

“…Statistical optimization has been used to reduce the number of trials and cost of experiments by proper design and analysis to find out the optimum concentrations of a series of medium ingredients that are contribute for the maximum product formation (Zeng et al 2006;Mao et al 2007;Kumar et al 2014). Response surface methodology is used for the study of linear, square, and interaction effect of the variables on the production of bio-chemicals (Lotfy 2007;Wang et al 2008Wang et al , 2013Yu et al 2008;Singh et al 2012;Uppuluri et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

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Production and Statistical Optimization of Oxytetracycline from Streptomyces rimosus NCIM 2213 using a New Cellulosic Substrate, Prosopis juliflora

Ramasamy¹,

Balakrishna²,

Selvaraj³

et al. 2014

BioResources

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Prosopis juliflora is a drought-resistant evergreen spiny tree that grows in semi-arid and arid tracts of tropical and sub-tropical regions of the world. Dry pods of P. juliflora are a rich source of carbon (40% total sugar) and nitrogen (15% of total nitrogen) and so can be considered as a good substrate for the microbial growth. The present study was mainly focused on the utilization of these pods for the production and statistical optimization of oxytetracycline (OTC) from Streptomyces rimosus NCIM 2213 under SSF. The spectral characterization and chemical color reactions of purified OTC by UV, FTIR, 1H NMR, 13C NMR, and HPLC revealed that the structure was homologous to a standard sample. A central composite design with 26 trails yielded the following critical values of supplements to be added to the dry pods: maltose (0.125 g/gds), Inoculum size (0.617 mL/gds), CaCO3 (0.0026 g/gds), and moisture content (74.87%) with the maximum OTC yield 5.02 mg/gds.

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“…33 At any rate, the single agent method is not only a well-researched method, it also has some disadvantages such as great loss of time, more trial work, and lack of information about the interaction between variables involved in the process. 33 At any rate, the single agent method is not only a well-researched method, it also has some disadvantages such as great loss of time, more trial work, and lack of information about the interaction between variables involved in the process.…”

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confidence: 99%

Inulinase production and mathematical modeling from carob extract by using Aspergillus niger

Ilgın

Germeç

Turhan

2019

Biotechnology Progress

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The main objectives of the study were to produce inulinase from carob extract by Aspergillus niger A42 (ATCC 204447) and to model the inulinase fermentation in the optimum carob extract-based medium. In the study, carob extract was used as a novel and renewable carbon source in the production of A. niger inulinase. For medium optimization, eight different variables including initial sugar concentration ( Bx), (NH 4 ) 2 HPO 4 , MgSO 4 .7H 2 O, KH 2 PO 4 , NH 4 NO 3 , yeast extract, peptone, and ZnSO 4 .7H 2 O were employed. After fermentations, optimum medium composition contained 1% yeast extract in 5 Bx carob extract. As a result of the fermentation, the maximum inulinase activity, maximum invertase-type activity, I/S ratio, maximum inulinase-and invertasetype activity rates, maximum sugar consumption rate, and sugar utilization yield were 1507.03 U/ml, 1552.86 U/ml, 0.97, 175.82 and 323.76 U/ml/day, 13.26 g/L/day, and 98.52%, respectively. Regarding mathematical modeling, the actual inulinase production and sugar consumption data were successfully predicted by Baranyi and Cone models based on the model evaluation and validation results and the predicted kinetic values, respectively. Consequently, this was the first report in which carob extract was used in the production of inulinase as a carbon source. Additionally, the best-selected models can serve as universal equations in modeling the inulinase production and sugar consumption in shake flask fermentation with carob extract medium. K E Y W O R D S Aspergillus, carob extract, flexible models, fungal inulinase, optimization 1 | INTRODUCTION Developments in the field of biotechnology play an important role in the effective use of all kinds of agricultural products and waste in the world. The agricultural products which are rich in food additive and sugar but limited in evaluation potential are used in the production of high added-value products by biotechnological processes. 1,2 The enzyme industry is the result of a rapid development seen primarily over the past four decades thanks to the evolution of modern biotechnology. This development allowed the introduction of enzymes into true industrial products and processes, for example, within the detergent, textile, and starch industries. 3 Inulinase (2,1-β-D-fructanohydrolase EC 3.2.1.7) targets on the β-2,1 linkage of inulin and hydrolyzes it into fructose. Inulin and inulinase can be used for production of either ultra-high fructose syrups, with D-fructose content over 95% by exo-enzymatic hydrolysis, or for production of oligofructose syrups by endo-enzymatic hydrolysis. 4 Inulinases are classified into endo-and exoinulinases, depending on their mode of action. Endo-inulinases (2,1-β-D-fructan fructanohydrolase; EC 3.2.1.7) are specific for inulin and hydrolyze it by breaking bonds between fructose units that are located away from the ends of the polymer network, to produce oligosaccharides. Exoinulinases (β-D-fructohydrolase; EC 3.2.1.8) split terminal fructose units from the nonreducing end of the inulin mo...

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Design of experiments applications in bioprocessing: Concepts and approach

Cited by 110 publications

References 38 publications

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

Production and Statistical Optimization of Oxytetracycline from Streptomyces rimosus NCIM 2213 using a New Cellulosic Substrate, Prosopis juliflora

Inulinase production and mathematical modeling from carob extract by using Aspergillus niger

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