Building robust calibration models for the analysis of estrogens by gas chromatography with mass spectrometry detection

Five hormonal growth promotants (diethylstilbestrol, hexestrol, dienestrol, 17-β-estradiol and 17-α-ethynylestradiol) have been analysed by gas chromatography with mass spectrometry detection (GC/MS, SIM mode) for four non-consecutive days. The aim is to build models with stable predictions. The strategies applied are internal standardization and global models carried out by gathering signals recorded on several days. Two models were examined: univariate models (with standardized peak area) and PARAFAC2 (the analyte scores were standardized by the scores of the internal standard). Internal standardization has been proved to be efficient for both models of dienestrol and ethynylestradiol. The mean relative error in absolute value when samples recorded on a different day to the calibration set are quantified by PARAFAC2 is 7.00% and 7.11% for dienestrol and ethynylestradiol, respectively. For diethylstilbestrol and estradiol, internal standardization was combined with global calibration models built with signals recorded under several sources of variability (different days). Thus predictions become steadier over time and in the estradiol example, errors decrease from 33.10% to 9.76%. The mean relative error in absolute value with PARAFAC2 updated models oscillates between 6.34% for ethynylestradiol and 10.74% for diethylstilbestrol. For univariate updated models errors range from 6.42% to 14.19% for ethynylestradiol and estradiol respectively. The combination of both strategies has been proved to be efficient independently of the analyte and of the signal order.     

Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.     

NIR analysis for batch process of ethanol precipitation coupled with a new calibration model updating strategy

Ethanol precipitation plays a major role in the pretreatment of Flos Lonicerae Japonicae of Qingkailing injection, and is also one of the most popular purification techniques in Chinese herbal medicines. In order to monitor and have a better understanding of the ethanol precipitation process, a PLS model was built based on NIR spectroscopy and HPLC analysis of chlorogenic acid content within the framework of FDA's PAT initiative. Nevertheless, due to the complex mechanism of and the raw materials’ natural variability introduced into the ethanol precipitation process, it was unable to foresee the variations in new batches which may jeopardize the robustness of the established model. Therefore, based on the simple interval calculation (SIC) theory, a new model expansion updating strategy which could continuously expand the variation coverage of the calibration model along with the batch proceeding of ethanol precipitation process was proposed. Effects of model updating were validated by an individual batch with 60 samples. After two times of updating, the root mean squared error of prediction (RMSEP) decreased from 0.268 mg mL⁻¹ to 0.199 mg mL⁻¹, while the insiders in the object status plot (OSP) increased from 44 to 58, demonstrating the good performance of the proposed approach.     

Application of orthogonal space regression to calibration transfer without standards

To transfer a calibration model in cases where the standardization samples are rare or unstable, a method based on orthogonal space regression (OSR) is proposed. It uses virtual standardization spectra to account for response changes between instruments or batches. A comparative study of the proposed OSR, piecewise direct standardization, finite impulse response, orthogonal signal correction, and model updating (MU) was conducted on both pharmaceutical tablet data and chlorogenic acid data. The results of these studies suggest that both the OSR and the MU are superior to the other transfer techniques in terms of root‐mean‐squared error of prediction and ratio of performance to interquartile distance. Moreover, OSR requires no identical standard samples, and it avoids re‐optimizing the transfer models. In conclusion, both the differences among spectra measured on different spectrometers and the differences between different batches can be corrected successfully using the OSR method. Copyright © 2013 John Wiley & Sons, Ltd.     

Feasibility and extension of universal quantitative models for moisture content determination in beta-lactam powder injections by near-infrared spectroscopy

In present work, we investigated the feasibility of universal calibration models for moisture content determination of a much complicated products system of powder injections to simulate the process of building universal models for drug preparations with same INN (International Nonproprietary Name) from diverse formulations and sources. We also extended the applicability of universal model by model updating and calibration transfer. Firstly, a moisture content quantitative model for ceftriaxone sodium for injection was developed, the results show that calibration model established for products of some manufacturers is also available for the products of others. Then, we further constructed a multiplex calibration model for seven cephalosporins for injection ranging from 0.40 to 9.90%, yielding RMSECV and RMSEP of 0.283 and 0.261, respectively. However, this multiplex model could not predict samples of another cephalosporin (ceftezole sodium) and one penicillins (penicillin G procaine) for injection accurately. With regard to such limits and the extension of universal models, two solutions are proposed: model updating (MU) and calibration transfer. Overall, model updating is a robust method for the analytical problem under consideration. When timely model updating is impractical, piecewise direct standardization (PDS) algorithm is more desirable and applied to transfer calibration model between different powder injections. Both two solutions have proven to be effective to extend the applicability of original universal models for the new products emerging.     

Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study

Alongside the validation, the concept of applicability domain (AD) is probably one of the most important aspects which determine the quality as well as reliability of the established quantitative structure–activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the Euclidean distance (ED) metric in the structure-representation vector space. The performance of the method was evaluated and explained in a case study by using a pre-built and validated CP ANN model for prediction of the transport activity of the transmembrane protein bilitranslocase for a diverse set of compounds. The method was tested on two more datasets in order to confirm its performance for evaluation of the applicability domain in CP ANN models. The chemical compounds determined as potential outliers, i.e., outside of the CP ANN model AD, were confirmed in a comparative AD assessment by using the leverage approach. Moreover, the method offers a graphical depiction of the AD for fast and simple determination of the extreme points.     

A new non-invasive, quantitative Raman technique for the determination of an active ingredient in pharmaceutical liquids by direct measurement through a plastic bottle

The concentration of an active pharmaceutical ingredient (povidone) in a commercial eyewash solution has been measured directly through a plastic (low-density polyethylene: LDPE) container using a wide area illumination (WAI) Raman scheme. The WAI scheme allows excitation using a 6 mm laser spot (focal length: 248 mm) that is designed to cover a wide sample area. As a result, it has the potential to improve the reliability Raman measurements by significantly enhancing representative sample interrogation, thus improving the reproducibility of sampling. It also decreases the sensitivity of sample placement with regard to the excitation focal plane. Simultaneously, isobutyric anhydride was placed in front of the bottles to use for a synchronous external standard configuration. This helps to correct the problematic variation of Raman intensity from the inherent fluctuation in laser power. Using the WAI Raman scheme combined with the synchronous standard method, the povidone concentration was successfully measured with spectral collection that was performed through a plastic barrier. The conventional Raman scheme was difficult to employ for the same purpose because of the degraded spectral reproducibility resulting from the smaller laser illumination area and the sensitivity of such an approach to the position of the sample bottle. The result from this study suggests that the WAI scheme exhibits a strong potential for the non-destructive quantitative analysis of pharmaceuticals measured directly in plastic containers. Preliminary work also shows that similar measurements can also be made in glass bottles. If implemented, this technique could be utilized as a simple and rugged method for quality assurance of final products in a manner consistent with Process analytical technology (PAT) requirements.     

基于过程分析技术和设计空间的金银花醇沉加醇过程终点检测

建立了一种新的基于过程分析技术(PAT)和质量源于设计(QbD)设计空间的中药制药过程终点分析与控制方法.以近红外(NIR)光谱技术为PAT工具, 采集正常操作条件下制药过程的多批次NIR光谱; 采用主成分分析结合移动块相对标准偏差(PCA-MBRSD)法, 确定每一批次过程的理想终点样本(DEPs), 由多批DEPs的光谱信息构成过程终点设计空间; 在过程终点设计空间确定的范围内, 建立多变量统计过程控制(MSPC)模型, 利用多变量Hotelling T²和SPE控制图对过程终点进行判断.应用上述方法, 进行了金银花醇沉加醇过程终点检测研究, 结果表明该方法灵敏、准确, 适宜于中药制药过程终点检测.