Development and validation of NIR-chemometric methods for chemical and pharmaceutical characterization of meloxicam tablets

UV/VIS-imaging of white caffeine tablets for prediction of CQAs: API content, crushing strength, friability, disintegration time and dissolution profile

The paper provides a demonstration of how UV/VIS imaging can be employed to evaluate the crushing strength, friability, disintegration time and dissolution profile of tablets comprised of solely white components. The samples were produced using different levels of compression force and API content of anhydrous caffeine. Images were acquired from both sides of the samples using UV illumination for the API content prediction, while the other parameters were assessed using VIS illumination. Based on the color histograms of the UV images, API content was predicted with 5.6 % relative error. Textural analysis of the VIS images yielded crushing strength predictions under 10 % relative error. Regarding friability, three groups were established according to the weight loss of the samples. Likewise, the evaluation of disintegration time led to the identification of three groups: <10 s, 11-35 s, and over 36 s. Successful classification of the samples was achieved with machine learning algorithms. Finally, immediate release dissolution profiles were accurately predicted under 5 % of RMSE with an artificial neural network. The 50 ms exposition time during image acquisition and the resulting outcomes underscore the practicality of machine vision for real-time quality control in solid dosage forms, regardless of the color of the API.

Spectrophotometry and pharmaceutical PAT/RTRT: Practical challenges and regulatory landscape from development to product lifecycle

European Pharmacopoeia includes dedicated chapters for Raman, NIR and Chemometrics, as well as there is a lot of Academia research on the matter. Despite that, the word innovation is often associated to such tools and there is a still slow implementation at industry. The paper is the outcome of the Associazione Farmaceutici dell'Industria (AFI) Study Group on Process Innovation and Product Lifecycle; the aim is to describe some case studies referring to practical approaches in pharmaceutical industry, in order to depict challenges and opportunities for the implementation of spectroscopic techniques. Case studies include: feasibility and pre-screening evaluations, chemometric model development approaches, way for the method maintenance during commercial manufacturing, challenges for implementation on existing equipment and on sterile processes. Case studies refer to oral solid products, liquid products and sterile Active Pharmaceutical Ingradient (API) manufacturing. There are already successful and robust spectroscopic applications in pharmaceutical industry and the technology is mature: this is the outcome of a strong applied research performed at pharmaceutical production departments. It is necessary to acknowledge efforts done by industry as Research for strengthening the cooperation with Academia, so that advantage of process innovation might reach the patients in a fastest way.

Optimization of PLS modeling parameters via quality by design concept for Gardenia jasminoides Ellis using online NIR sensor

This paper discussed the process parameters optimization of partial least-square (PLS) modeling according to quality by design (QbD) concept. D-optimal design and online near-infrared (NIR) sensor were proposed to analysis the Geniposide in Gardenia jasminoides Ellis using above process parameters to achieve robustness PLS model. Four critical model parameters (CMPs) were identified to construct a D-optimal design, which included the selection of sample set, spectra pre-processing, latent variables and variable selection methods. NIR sensor dataset was obtained under a pilot scale system. The D-optimal design optimization strategy resulted in a robust PLS model with the optimal parameters, 1/2 samples for calibration sets through Baseline spectra pre-processing with SiPLS-selecting variables under 8 factors. The critical evaluation attributes (CEAs) of PLS model were recommended as follows: the RMSEC and R_cal² of the calibration set were 0.005901 and 0.9983. The RMSEP and R_pre² of the validation set were 0.02002 and 0.9845. The multivariate detection limit (MDL) was 1.143 × 10^-3. Therefore, design space of CMPs which affected CEAs of PLS model was established. The result demonstrated that the proposed method was beneficial for the robustness of PLS model, which also showed a significant guideline for the design and development of PLS model.

Near Infra-Red spectroscopy for content uniformity of powder blends - Focus on calibration set development, orthogonality transfer and robustness testing

The aim of this work was to develop and validate a NIR method for the quantification of three active ingredients from powder blends. Calibration set formulations were selected based on a D-optimal experimental design with three factors (ibuprofen, paracetamol, caffeine) and five variation levels (80-90-100-110-120%). NIR spectra were recorded in transmittance mode using a rotating sample configuration. Prior to model development the effect of spectral pre-processing was assessed by evaluating its impact over the transfer of orthogonality from concentration space to spectral space. NIR method was validated on the full calibration range with external prediction sets, using the accuracy profile approach. Robustness testing results showed that the accuracy of predictions for the analyte found in lower concentrations (caffeine) was influenced by relative humidity, while paracetamol/ibuprofen predictions were robust to all factors. Redefinition of interfering factor variation level was beneficial to reduce the bias in caffeine content predictions. Also, alternative solutions are provided for ensuring robustness and successful routine use.

Development, validation and comparison of near infrared and Raman spectroscopic methods for fast characterization of tablets with amlodipine and valsartan

The objective of this study was to develop, validate and compare NIR and Raman spectroscopic methods for fast characterization in terms of API content and tensile strength of fixed-dose combination tablets containing amlodipine and valsartan. For the APIs assay NIR-transmittance and Raman-reflectance methods were considered, whereas for the tensile strength assay Raman spectra were recorded in reflectance configuration and NIR spectra were recorded in both reflectance and transmittance. Multivariate calibration models (PLS) were built by applying different pre-processing methods (SNV, MSC, SD+SNV) on certain spectral regions. Correlating pre-processed spectral data with tablet properties resulted in highly predictive models except in the case of NIR-transmittance spectra for tensile strength estimation. The best models selected by cross-validation were further validated on independent samples in terms of linearity, trueness, accuracy and precision. Using Bland and Altman analysis the analytical performance of the NIR and Raman methods were compared, demonstrating their similarity considering the investigated applications. The two spectroscopic methods can be used in association to confirm each others results for at-line characterization of the pharmaceutical product.

Optimization of Parameter Selection for Partial Least Squares Model Development

In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal(2)) and validation (Rpre(2)) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

Simultaneous quantification of simvastatin and excipients in liposomes using near infrared spectroscopy and chemometry

This work describes the development and validation of a near infrared (NIR) spectroscopy method coupled with an appropriate multivariate calibration algorithm for the simultaneous quantification of encapsulated drug, simvastatin (SIM) and excipients, L-α-phosphatidylcholine (LPC) and cholesterol (CHO) in liposomes. The development of calibration models for each compound was based on a D-optimal experimental design consisting of 63 standard mixtures containing LPC, CHO and SIM in chloroform. For each compound, different spectral pretreatment methods were applied in association with selected spectral regions. Partial least-square regression (PLS) was performed using OPUS 6.5 software. Calibration set and cross-validation was carried out in order to select the best model to be used further. Straight line subtraction (SLS) was the best pre-treatment method for each compound, although the selected spectral regions were different. The method developed for each compound was validated in terms of linearity, trueness, precision and accuracy. Finally, the method has been successfully used for simultaneous quantification of SIM and excipients in liposomes. The encapsulation efficiency of SIM determined by this method was similar with that obtained by the use of reference HPLC method.