Process analytical technology case study: Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.     

近红外光谱技术快速检测原料乳中掺杂物

采用近红外光谱技术结合化学计量学方法,对原料乳中常见的2种掺杂物——大豆分离蛋白与植脂末进行定量分析研究。先通过不同光谱预处理方法结合偏最小二乘法（PLS）建模评价不同预处理方法的效果,结果表明通过平滑处理结合多元散射校正（MSC）进行光谱预处理效果最佳,大豆分离蛋白PLS定量模型相关系数（R2）与交叉验证均方差（RMSECV）分别为0.980 9、0.127 5,植脂末PLS模型分别为0.972 2、0.130 8。随后比较了不同建模方法的效果,结果发现：采用径向基神经网络（RBF）对大豆分离蛋白的建模效果最佳,R2为0.999 4,测试集均方根误差为0.003 1;采用广义回归神经网络（GRNN）方法对植脂末建模效果最佳,R2为0.998 9,测试集均方根误差为0.004 5。因此,合理结合近红外光谱技术与化学计量学方法可快速、准确检测原料乳中大豆分离蛋白和植脂末这2种掺杂物含量。     

Raw material criticality assessment as a complement to environmental life cycle assessment: Examining methods for product‐level supply risk assessment

The diversity of raw materials used in modern products, compounded by the risk of supply disruptions—due to uneven geological distribution of resources, along with socioeconomic factors like production concentration and political (in)stability of raw material producing countries—has drawn attention to the subject of raw material “criticality.” In this article, we review the state of the art regarding the integration of criticality assessment, herein termed “product‐level supply risk assessment,” as a complement to environmental life cycle assessment. We describe and compare three methods explicitly developed for this purpose—Geopolitical Supply Risk (GeoPolRisk), Economic Scarcity Potential (ESP), and the Integrated Method to Assess Resource Efficiency (ESSENZ)—based on a set of criteria including considerations of data sources, uncertainties, and other contentious methodological aspects. We test the methods on a case study of a European‐manufactured electric vehicle, and conclude with guidance for appropriate application and interpretation, along with opportunities for further methodological development. Although the GeoPolRisk, ESP, and ESSENZ methods have several limitations, they can be useful for preliminary assessments of the potential impacts of raw material supply risks on a product system (i.e., “outside‐in” impacts) alongside the impacts of a product system on the environment (i.e., “inside‐out” impacts). Care is needed to not overlook critical raw materials used in small amounts but nonetheless important to product functionality. Further methodological development could address regional and firm‐level supply risks, multiple supply‐chain stages, and material recycling, while improving coverage of supply risk characterization factors.     

An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.     

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308–316, 2017     

Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches

Understanding variability in raw materials and their impacts on product quality is of critical importance in the biopharmaceutical manufacturing processes. For this purpose, several spectroscopic techniques have been studied for raw material characterization, providing fast and nondestructive ways to measure quality of raw materials. However, investigations of correlation between spectra of raw materials and cell culture performance have been scarce due to their complexity and uncertainty. In this study, near-infrared spectra and bioassays of multiple soy hydrolysate lots manufactured by different vendors were analyzed using chemometrics approaches in order to address variability of raw materials as well as correlation between raw material properties and corresponding cell culture performance. Principal component analysis revealed that near-infrared spectra of different soy lots contain enough physicochemical information about soy hydrolysates to allow identification of lot-to-lot variability as well as vendor-to-vendor differences. The identified compositional variability was further analyzed in order to estimate cell growth and protein production of two mammalian cell lines under the condition of varying soy dosages using partial least square regression combined with optimal variable selection. The performance of the resulting models demonstrates the potential of near-infrared spectroscopy as a robust lot selection tool for raw materials while providing a biological link between chemical composition of raw materials and cell culture performance.     

Raw material selectivity in Late Pliocene Oldowan sites in the Makaamitalu Basin, Hadar, Ethiopia

We report the results of an analysis of raw material selection patterns in the assemblages from two Late Pliocene in situ archaeological localities in the Makaamitalu Basin (Hadar, Ethiopia). While the same local conglomerate was used as a raw material source for both archaeological occurrences, different selection criteria are identified. At A.L. 894, selection for quality is subtle and the clearest selection is against non-homogeneous raw materials. In the A.L. 666 assemblage, higher-quality raw materials were selected and some rare raw materials reached the locality from unknown sources. A comparison between the Makaamitalu and other Oldowan assemblages reveals an overall shift toward higher complexity of both selectivity and transport behaviors from ca. 2.0 Ma onward, contrasting a typo-technological conservatism that pertains until ∼1.6 Ma. It is hypothesized that an increase in complexity of behaviors related to raw material selection and acquisition involved changes in the intensity and fidelity of technological knowledge transmission.     

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N = 111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.     

我国生物柴油原料来源的多样性探讨

生物柴油作为一种重要的生物质能源已引起世人关注,其生产和应用已成为缓解能源危机的重要组成部分。本文分析了我国生物柴油生产原料多样性的必要性,阐述了可用于生物柴油生产的原料类型和应用开发现状,以及用于生产时所存在的问题,提出了相应的解决对策,最后对生物柴油的应用前景给予了展望。     

Process analytical technology case study,part III: Calibration monitoring and transfer

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T² and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.     

Comparative determination of polymorphs of indomethacin in powders and tablets by chemometrical near-infrared spectroscopy and X-ray powder diffractometry

The purpose of this research was to develop a rapid chemometrical method based on near-infrared (NIR) spectroscopy to determine indomethacin (IMC) polymorphic content in mixed pharmaceutical powder and tablets. Mixed powder samples with known polymorphic contents of forms α and γ were obtained from physical mixing of 50% of IMC standard polymorphic sample and 50% of excipient mixed powder sample consisting of lactose, corn starch, and hydroxypropyl-cellulose. The tablets were obtained by compressing the mixed powder at 245 MPa. X-ray powder diffraction profiles and NIR spectra were recorded for 6 kinds of standard materials with various polymorphic contents. The principal component regression analysis was performed based on normalized NIR spectra sets of mixed powder standard samples and tablets. The relationships between the actual and predicted polymorphic contents of form g in the mixed powder measured using x-ray powder diffraction and NIR spectroscopy show a straight line with a slope of 0.960 and 0.995, and correlation coefficient constants of 0.970 and 0.993, respectively. The predicted content values of unknown samples by x-ray powder diffraction and NIR spectroscopy were reproducible and in close agreement, but those by NIR spectroscopy had smaller SDs than those by x-ray powder diffraction. The results suggest that NIR spectroscopy provides a more accurate quantitative analysis of polymorphic content in pharmaceutical mixed powder and tablets than does conventional x-ray powder diffractometry.     

Comparison of the Vibration Mode of Metals in HNO3 by a Partial Least-Squares Regression Analysis of Near-Infrared Spectra

The near-infrared (NIR) spectra of such metals as Cu(II), Mn(II), Zn(II) and Fe(III) in HNO₃ in the 700–1860 nm region were subjected to a partial least-squares regression analysis and leave-out cross-validation to develop chemometric models. The models yielded a coefficient of determination in cross validation of 0.9744 [Cu(II)], 0.9631 [Mn(II)], 0.9154 [Zn(II)] and 0.741 [Fe(III)]. The regression coefficients for Cu(II), Mn(II) and Zn(II), but not for Fe(III), showed strong negative peaks at around 1050–1200 nm, a zone where spectral bands have been reported to decrease with increasing pH value. A positive peak at around 710–750 nm, which may have been due to water absorption, was observed in regression coefficients of Cu(II), Mn(II) and Zn(II) but not in Fe(III), while a negative peak was observed in that for Fe(III) at around 710–750 nm. These results indicate that the divalent cations [Cu(II), Mn(II) and Zn(II)] showed different absorption in the NIR region from the trivalent cation [Fe(III)], suggesting that the vibration mode of water, which mirrors the interaction between cations and water, may be influenced by valency.     

Quantification of protein mixture in chromatographic separation using multi‐wavelength UV spectra

In therapeutic protein production, the protein purification with chromatographic processes is of high importance in separating the qualified proteins from the impurities for consistent product quality. Therefore, to aid real‐time monitoring of the protein purification processes, various kinds of methodologies have been proposed until now. However, the majority of them still rely on the use of a single ultraviolet (UV) absorbance or the utilization of expensive and time‐consuming instruments, thus requiring a simple, fast, and cost‐effective methodology for protein quantification. In this study, the feasibility of using multiwavelength UV spectroscopy was investigated as an alternative tool for the real‐time monitoring of the protein mixtures in protein purification. To this end, three different proteins were selected as a model system for the protein mixture, and the multivariate UV spectra were analyzed to construct the reliable quantification models for different proteins of interest. By using various chemometrics tools, such as partial least squares (PLS), the validity of estimating the protein concentration from the UV spectra of the mixture samples was rigorously analyzed with their prediction performance, and the results indicated that the multiwavelength UV spectra had sufficient sensitivity and accuracy to estimate the protein concentrations in mixture, demonstrating its usefulness for the rapid quantification of the protein mixtures in protein purification. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:664–671, 2013     

黄酒酿造原料及生产工艺研究现状与展望

黄酒的质量取决于酿造原料、酒曲、加工工艺以及酿造环境,不同的酿造原料、酒曲、加工工艺和环境能赋予黄酒不同的品质与风味。本文在检索文献、比较分析新旧版黄酒国标的基础上,介绍了南北地区黄酒酿造原料的差异以及新型功能性黄酒原辅料研发现状,综述了浸米、蒸饭与发酵三个工艺关键环节的最新研究进展,旨在为黄酒技术的创新,功能性、饮料性、国际性黄酒新产品开发提供借鉴及参考。     

Chelator used in pectin extraction triggers ethylene production by tomato fruit

In our search for an endogenous ethylene trigger from tomato ( Lycopersicon esculentum Mill. ev. Rutgers) fruit cell wall alkaline soluble pectin (ASP), we purified an active component using DEAE-Sepharose chromatography followed by elution on Bio-Gel P-100 or Superose 12. The purified active fraction produced a single band on silver-stained SDS-PAGE of approximately M_r 20000. Using two-dimensional proton-proton and proton-carbon correlation spectroscopy, we identified the repeating sub-unit as trans-1,2-diamino-cyclohexane- N,N,N',N'-tetraacetic acid (CDTA), a chelator used to extract ASP. Although the ASP undergoes extensive dialysis during its extraction which should remove CDTA, the CDTA apparently forms a large molecular weight polymer which does not diffuse out of the dialysis tubing. Infiltration of commercially prepared CDTA into mature green tomato fruit stimulated ethylene production. The ethylene stimulatory effect of CDTA was not affected by the presence of equimolar amounts of CaCl₂, or nmol g^-1 amounts of the calcium channel blockers, nifedipine or verapamil. EDTA, EGTA, and diethylenetriaminepentaacetic acid, other divalent cation chelators, also stimulated ethylene production when they were infiltrated into tomato fruit. Neither the purified material nor commercial CDTA stimulated ethylene production when they were infiltrated into leaf tissue.