E. Ziemons

The transfer of a method from a laboratory to a production site is an important step in the development cycle of new pharmaceutical products. Method transfers are increasingly implemented due to the economical pressure coming from the rationalization of production sites, analytical subcontracting and fusion of pharmaceutical groups. However, no official guidance regarding study design, data analysis, or decision procedures is present neither in FDA documents nor in ICH documents for method transfers. The experiments performed in such a transfer and the methodology used to accept or reject it should be fitted for purpose. In order to provide to analysts a global view of the problematic of analytical method transfer, this paper reviews the documentation available in the scientific literature about the design of transfer studies and the required sample size. Special focus is also made on the statistical methodologies available for decision making with particular emphasis on risk management. Examples of transfer of pharmaceutical, bio-pharmaceutical and biological methods published in the literature are reviewed in order to illustrate the various possibilities among the strategies for methods transfer.

Surface-enhanced Raman spectroscopy (SERS) is a sensitive analytical tool used in the pharmaceutical field in recent years. SERS keeps all the advantages of classical Raman spectroscopy while being is more sensitive allowing its use for the detection and the quantification of low-dose substances contained in pharmaceutical samples. However, the analytical performance of SERS is limited due to the difficulty to implement a quantitative methodology correctly validated. Nevertheless, some studies reported the development of SERS quantitative methods especially in pharmaceutical approaches. In this context, this review presents the main concepts of the SERS technique. The different steps that need to be applied to develop a SERS quantitative method are also deeply described. The last part of the present manuscript gives a critical overview of the different SERS pharmaceutical applications that were developed for a non-exhaustive list of pharmaceutical compounds with the aim to highlights the validation criteria for each application.

The development of a quantitative method determining the crystalline percentage in an amorphous solid dispersion is of great interest in the pharmaceutical field. Indeed, the crystalline Active Pharmaceutical Ingredient transformation into its amorphous state is increasingly used as it enhances the solubility and bioavailability of Biopharmaceutical Classification System class II drugs. One way to produce amorphous solid dispersions is the Hot-Melt Extrusion (HME) process. This study reported the development and the comparison of the analytical performances of two techniques, based on backscattering and transmission Raman spectroscopy, determining the crystalline remaining content in amorphous solid dispersions produced by HME. Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were performed on preprocessed data and tended towards the same conclusions: for the backscattering Raman results, the use of the DuoScan™ mode improved the PCA and PLS results, due to a larger analyzed sampling volume. For the transmission Raman results, the determination of low crystalline percentages was possible and the best regression model was obtained using this technique. Indeed, the latter acquired spectra through the whole sample volume, in contrast with the previous surface analyses performed using the backscattering mode. This study consequently highlighted the importance of the analyzed sampling volume.

A new application of surface-enhanced Raman scattering (SERS) in the field of plant material analysis is proposed in this study. The aim was to monitor the release of anatabine by methyl jasmonate (MeJa) elicited Bright Yellow-2 (BY-2) cells. Gold nanoparticles (AuNps) were used as SERS substrate. The first step was to study the SERS activity of anatabine in a complex matrix comprising the culture medium and BY-2 cells. The second step was the calibration. This one was successfully performed directly in the culture medium in order to take into account the matrix effect, by spiking the medium with different concentrations of anatabine, leading to solutions ranging from 250 to 5000µgL(-1). A univariate analysis was performed, the intensity of a band situated at 1028cm(-1), related to anatabine, was plotted against the anatabine concentration. A linear relationship was observed with a R(2) of 0.9951. During the monitoring study, after the MeJa elicitation, samples were collected from the culture medium containing BY-2 cells at 0, 24h, 48h, 72h and 96h and were analysed using SERS. Finally, the amount of anatabine released in the culture medium was determined using the response function, reaching a plateau after 72h of 82µg of anatabine released/g of fresh weight (FW) MeJa elicited BY-2 cells.

Bisphenol A (BPA) is well known for its use in plastic manufacture and thermal paper production despite its risk of health toxicity as an endocrine disruptor in humans. Since the publication of new legislation regarding the use of BPA, manufacturers have begun to replace BPA with other phenolic molecules such as bisphenol F (BPF) and bisphenol B (BPB), but there are no guarantees regarding the health safety of these compounds at this time. In this context, a very simple, cheap and fast surface-enhanced Raman scattering (SERS) method was developed for the sensitive detection of these molecules in spiked tap water solutions. Silver nanoparticles were used as SERS substrates. An original strategy was employed to circumvent the issue of the affinity of bisphenols for metallic surfaces and the silver nanoparticles surface was functionalized using pyridine in order to improve again the sensitivity of the detection. Semi-quantitative detections were performed in tap water solutions at a concentrations range from 0.25 to 20 μg L(-1) for BPA and BPB and from 5 to 100 μg L(-1) for BPF. Moreover, a feasibility study for performing a multiplex-SERS detection of these molecules was also performed before successfully implementing the developed SERS method on real samples.

Saffaj and Ihssane, recently proposed an uncertainty profile for evaluating the validity of analytical methods using the statistical methodology of γ-confidence β-content tolerance intervals. This profile assesses the validity of the method by comparing the method measurement uncertainty to a predefined acceptance limit stating the maximum uncertainty suitable for the method under study. In this letter we comment on the response (T. Saffaj, B. Ihssane, Talanta 94 (2012) 361-362) these authors have made to our previous letter (E. Rozet, E. Ziemons, R.D. Marini, B. Boulanger, Ph. Hubert, Talanta 88 (2012) 769-771). In particular, we demonstrate that β-expectation tolerance intervals are prediction intervals, we show that β-expectation tolerance intervals are highly useful for assessing analytical methods validation and for estimating measurement uncertainty and finally we show what are the differences and implications for these two topics (validation and uncertainty) when using either the methodology of β-expectation tolerance intervals or the γ-confidence β-content tolerance tolerance interval one.

The understanding of the method is a major concern when developing a stability-indicating method and even more so when dealing with impurity assays from complex matrices. In the presented case study, a Quality-by-Design approach was applied in order to optimize a routinely used method. An analytical issue occurring at the last stage of a long-term stability study involving unexpected impurities perturbing the monitoring of characterized impurities needed to be resolved. A compliant Quality-by-Design (QbD) methodology based on a Design of Experiments (DoE) approach was evaluated within the framework of a Liquid Chromatography (LC) method. This approach allows the investigation of Critical Process Parameters (CPPs), which have an impact on Critical Quality Attributes (CQAs) and, consequently, on LC selectivity. Using polynomial regression response modeling as well as Monte Carlo simulations for error propagation, Design Space (DS) was computed in order to determine robust working conditions for the developed stability-indicating method. This QbD compliant development was conducted in two phases allowing the use of the Design Space knowledge acquired during the first phase to define the experimental domain of the second phase, which constitutes a learning process. The selected working condition was then fully validated using accuracy profiles based on statistical tolerance intervals in order to evaluate the reliability of the results generated by this LC/ESI-MS stability-indicating method. A comparison was made between the traditional Quality-by-Testing (QbT) approach and the QbD strategy, highlighting the benefit of this QbD strategy in the case of an unexpected impurities issue. On this basis, the advantages of a systematic use of the QbD methodology were discussed.

... Frédérich, Michel, Membre du jury/Committee Member. Lejeune, Robert, Membre du jury/Committee Member. Thiebaut, Didier, Membre du jury/Committee Member. Thunus, Léopold, Membre du jury/Committee Member. Tilquin, Bernard, Membre du jury/Committee Member. ...

The reliability of analytical results obtained with quantitative analytical methods is highly dependent on the selection of the adequate model used as the calibration curve. To select the adequate response function or model the most used and known parameter is to determine the coefficient R(2). However, it is well-known that it suffers many inconveniences, such as leading to overfitting the data. A proposed solution is to use the adjusted determination coefficient R(adj)(2) that aims at reducing this problem. However, there is another family of criteria that exists to allow the selection of an adequate model: the information criteria AIC, AICc, and BIC. These criteria have rarely been used in analytical chemistry to select the adequate calibration curve. This works aims at assessing the performance of the statistical information criteria as well as R(2) and R(adj)(2) for the selection of an adequate calibration curve. They are applied to several analytical methods covering liquid chromatographic methods, as well as electrophoretic ones involved in the analysis of active substances in biological fluids or aimed at quantifying impurities in drug substances. In addition, Monte Carlo simulations are performed to assess the efficacy of these statistical criteria to select the adequate calibration curve.