Acetaminophen determination in low-dose pharmaceutical syrup by NIR spectroscopy

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as "meets standard," "needs HPLC assay," or "fails standard." Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.

QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

Environmentally-friendly technology for rapid identification and quantification of emerging pollutants from wastewater using infrared spectroscopy

The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm^-1 to 14,000 cm^-1. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, β-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.

Simultaneous quantitative analysis of indomethacin and benzoic acid in gel using ultra-violet-visible spectrophotometry and chemometrics

BACKGROUND

In order to manufacture pharmaceutical products, real-time monitoring in the manufacturing process is necessary, but large equipment cost is required to achieve it.

OBJECTIVE

The aim of this research is to use ultra-violet-visible spectroscopy along with chemometrics procedure to simultaneously carry out quantitative analysis of indomethacin (IMC) and benzoic acid (BA) in the gel during pharmaceutical manufacturing process.

METHODS

The gel preparations contained 0.1-1.5% IMC, 0.015-0.225% BA, 2% carbopol® 941 and 95% ethanol solution. The calibration models were constructed using the partial least square regression (PLS).

RESULTS

The relationships of the measured and predicted concentrations for both IMC and BA had linear plots. The developed PLS calibration models were used to monitor the IMC and BA concentrations during mixing of the gels by the planetary centrifugal and conventional mixers, respectively. IMC and BA were gradually dispersed, dissolved and completely homogeneous within 30 min by the centrifugal mixer. In contrast, IMC and BA were slowly dispersed, dissolved and completely homogeneous at more than 60 min by the conventional mixer.

CONCLUSIONS

The ultra-violet-visible spectrophotometric method couples with multivariate chemometric techniques for quantitative data analysis were successfully applied for the simultaneous determination of major component IMC and trace component BA in the gel.

Application of Online Near Infrared for Process Understanding of Spray-Drying Solution Preparation

Solution preparation is the first unit operation of the manufacturing process for spray-dried solid dispersions. Visual inspection and offline high-performance liquid chromatography analysis are routinely used to assess the solution preparation end point as well as the final solution composition. However, the accuracy and appropriateness of these approaches are challenged by the scale of production and solvent evaporation during sample handling. Thus an appropriate online process analytical tool is needed to improve process and quality control for the solution preparation process. The objective of this report is to develop near infrared (NIR) models for real-time monitoring of the spray solution preparation process. These models were built and refined via 2 different experiments designs with different production scale. The potency of spray-dried intermediate was analyzed by high-performance liquid chromatography and used to verify the quantitative model. The results indicated that the quantitative NIR models can be used to predict the active pharmaceutical ingredient concentration of the final spray solution accurately with a standard error of prediction of 2.4 wt%. Based on this investigation, online NIR was deemed to be a suitable analytical tool on process and quality control for spray solution preparation.

Quantification of pharmaceutical compounds in wastewater samples by near infrared spectroscopy (NIR)

The quantification of pollutants, as pharmaceuticals, in wastewater is an issue of special concern. Usually, typical methods to quantify these products are time and reagent consuming. This paper describes the development and validation of a Fourier transform near-infrared (FT-NIR) spectroscopy methodology for the quantification of pharmaceuticals in wastewaters. For this purpose, 276 samples obtained from an activated sludge wastewater treatment process were analysed in the range of 200 cm^-1 to 14,000 cm^-1, and further treated by chemometric techniques to develop and validate the quantification models. The obtained results were found adequate for the prediction of ibuprofen, sulfamethoxazole, 17β-estradiol and carbamazepine with coefficients of determination (R²) around 0.95 and residual prediction deviation (RPD) values above four, for the overall (training and validation) data points. These results are very promising and confirm that this technology can be seen as an alternative for the quantification of pharmaceuticals in wastewater.

Dissolution assessment of allopurinol immediate release tablets by near infrared spectroscopy

The purpose of this study was to develop a NIR spectroscopic method for assessment of drug dissolution from allopurinol immediate release tablets. Thirty three different batches of allopurinol immediate release tablets containing constant amount of the active ingredient, but varying in excipients content and physical properties were introduced in a PLS calibration model. Correlating allopurinol dissolution reference values measured by the routinely used UV/Vis method, with the data extracted from the NIR spectra, values of correlation coefficient, bias, slope, residual prediction determination and root mean square error of prediction (0.9632, 0.328%, 1.001, 3.58, 3.75%) were evaluated. The obtained values implied that the NIR diffuse reflectance spectroscopy could serve as a faster and simpler alternative to the conventional dissolution procedure, even for the tablets with a very fast dissolution rate (>85% in 15minutes). Apart from the possibility of prediction of the allopurinol dissolution rate, the other multivariate technique, PCA, provided additional data on the non-chemical characteristics of the product, which could not be obtained from the reference dissolution values. Analysis on an independent set of samples confirmed that a difference between the UV/Vis reference method and the proposed NIR method was not significant. According to the presented results, the proposed NIR method may be suitable for practical application in routine analysis and for continuously monitoring the product's chemical and physical properties responsible for expected quality.

Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract

This study presented a new strategy of overall uncertainty measurement for near infrared (NIR) quantitative analysis of cryptotanshinone in tanshinone extract powders. The overall uncertainty of NIR analysis from validation data of precision, trueness and robustness study was fully investigated and discussed. Quality by design (QbD) elements, such as risk assessment and design of experiment (DOE) were utilized to organize the validation data. An "I×J×K" (series I, the number of repetitions J and level of concentrations K) full factorial design was used to calculate uncertainty from the precision and trueness data. And a 2(7-4) Plackett-Burmann matrix with four different influence factors resulted from the failure mode and effect analysis (FMEA) analysis was adapted for the robustness study. The overall uncertainty profile was introduced as a graphical decision making tool to evaluate the validity of NIR method over the predefined concentration range. In comparison with the T. Saffaj's method (Analyst, 2013, 138, 4677.) for overall uncertainty assessment, the proposed approach gave almost the same results, demonstrating that the proposed method was reasonable and valid. Moreover, the proposed method can help identify critical factors that influence the NIR prediction performance, which could be used for further optimization of the NIR analytical procedures in routine use.

Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods

The UV spectrophotometric methods for simultaneous quantitative determination of paracetamol and tramadol in paracetamol-tramadol tablets were developed. The spectrophotometric data obtained were processed by means of partial least squares (PLS) and genetic algorithm coupled with PLS (GA-PLS) methods in order to determine the content of active substances in the tablets. The results gained by chemometric processing of the spectroscopic data were statistically compared with those obtained by means of validated ultra-high performance liquid chromatographic (UHPLC) method. The accuracy and precision of data obtained by the developed chemometric models were verified by analysing the synthetic mixture of drugs, and by calculating recovery as well as relative standard error (RSE). A statistically good agreement was found between the amounts of paracetamol determined using PLS and GA-PLS algorithms, and that obtained by UHPLC analysis, whereas for tramadol GA-PLS results were proven to be more reliable compared to those of PLS. The simplest and the most accurate and precise models were constructed by using the PLS method for paracetamol (mean recovery 99.5%, RSE 0.89%) and the GA-PLS method for tramadol (mean recovery 99.4%, RSE 1.69%).

A new sensitive sensor for simultaneous differential pulse voltammetric determination of codeine and acetaminophen using a hydroquinone derivative and multiwall carbon nanotubes carbon paste electrode

A new sensitive sensor was fabricated for simultaneous determination of codeine and acetaminophen based on 4-hydroxy-2-(triphenylphosphonio)phenolate (HTP) and multiwall carbon nanotubes paste electrode at trace levels. The sensitivity of codeine determination was deeply affected by spiking multiwall carbon nanotubes and a modifier in carbon paste. Electron transfer coefficient, α, catalytic electron rate constant, k, and the exchange current density, j 0, for oxidation of codeine at the HTP-MWCNT-CPE were calculated using cyclic voltammetry. The calibration curve was linear over the range 0.2-844.7 μM with two linear segments, and the detection limit of 0.063 μM of codeine was obtained using differential pulse voltammetry. The modified electrode was separated codeine and acetaminophen signals by differential pulse voltammetry. The modified electrode was applied for the determination of codeine and acetaminophen in biological and pharmaceutical samples with satisfactory results.

Development, validation and comparison of NIR and Raman methods for the identification and assay of poor-quality oral quinine drops

Poor quality antimalarial drugs are one of the public's major health problems in Africa. The depth of this problem may be explained in part by the lack of effective enforcement and the lack of efficient local drug analysis laboratories. To tackle part of this issue, two spectroscopic methods with the ability to detect and to quantify quinine dihydrochloride in children's oral drops formulations were developed and validated. Raman and near infrared (NIR) spectroscopy were selected for the drug analysis due to their low cost, non-destructive and rapid characteristics. Both of the methods developed were successfully validated using the total error approach in the range of 50-150% of the target concentration (20%W/V) within the 10% acceptance limits. Samples collected on the Congolese pharmaceutical market were analyzed by both techniques to detect potentially substandard drugs. After a comparison of the analytical performance of both methods, it has been decided to implement the method based on NIR spectroscopy to perform the routine analysis of quinine oral drop samples in the Quality Control Laboratory of Drugs at the University of Kinshasa (DRC).

Dealing with heterogeneous classification problem in the framework of multi-instance learning

To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of multi-instance learning (MIL). Based on a variant of count-based MIL assumption, a maximum count least squares support vector machine (maxc-LS-SVM) learning algorithm was developed. The algorithm was tested on a set of toy datasets. It was found that maxc-LS-SVM inherits all the sound characters of both LS-SVM and MIL framework. A comparison study between the proposed approach and the other two MIL approaches (i.e., mi-SVM and MI-SVM) was performed on a real wolfberry fruit spectral dataset. The results demonstrate that by formulating the heterogeneous classification problem as a MIL one, the heterogeneous classification problem can be solved by the proposed maxc-LS-SVM algorithm effectively.

A Process Analytical Technology (PAT) approach to control a new API manufacturing process: development, validation and implementation

Pharmaceutical companies are progressively adopting and introducing Process Analytical Technology (PAT) and Quality-by-Design (QbD) concepts promoted by the regulatory agencies, aiming the building of the quality directly into the product by combining thorough scientific understanding and quality risk management. An analytical method based on near infrared (NIR) spectroscopy was developed as a PAT tool to control on-line an API (active pharmaceutical ingredient) manufacturing crystallization step during which the API and residual solvent contents need to be precisely determined to reach the predefined seeding point. An original methodology based on the QbD principles was designed to conduct the development and validation of the NIR method and to ensure that it is fitted for its intended use. On this basis, Partial least squares (PLS) models were developed and optimized using chemometrics methods. The method was fully validated according to the ICH Q2(R1) guideline and using the accuracy profile approach. The dosing ranges were evaluated to 9.0-12.0% w/w for the API and 0.18-1.50% w/w for the residual methanol. As by nature the variability of the sampling method and the reference method are included in the variability obtained for the NIR method during the validation phase, a real-time process monitoring exercise was performed to prove its fit for purpose. The implementation of this in-process control (IPC) method on the industrial plant from the launch of the new API synthesis process will enable automatic control of the final crystallization step in order to ensure a predefined quality level of the API. In addition, several valuable benefits are expected including reduction of the process time, suppression of a rather difficult sampling and tedious off-line analyses.

Non-invasive quantification of 5 fluorouracil and gemcitabine in aqueous matrix by direct measurement through glass vials using near-infrared spectroscopy

Fourier transform near infrared spectroscopy (NIRS) was used for quantitative analysis of two cytotoxic drugs used in pharmaceutical infusion, 5-fluorouracil (5FU) and gemcitabine (GEM), at therapeutic concentrations in aqueous matrix. Spectra were collected from 4000 cm(-1) to 13,000 cm(-1) by direct measurement through standard glass vials and calibration models were developed for 5FU and GEM using partial least-squares regression. NIR determination coefficient (R(2)) greater than 0.9992, root-mean-square-error of cross-validation (RMESCV) of 0.483 mg/ml for 5FU and 0.139 mg/ml for GEM and the root mean square error of prediction (RMSEP) of 0.519 for 5FU and 0.108 mg/ml for GEM show a good prediction ability of NIR spectroscopy to predict 5FU and GEM concentrations directly through a glass packaging. According to accuracy profile, the linearity was validated from 7 to 50mg/ml and 2 to 40 mg/ml for 5-fluorouracil and gemcitabine respectively. This new approach for cytotoxic drugs control at hospital has shown the feasibility of near infrared spectroscopy to quantify antineoplastic drugs in aqueous matrix by a direct measurement through glass vial in less than 1 min and by non-invasive measurement perfect to limit exposure of operator to cytotoxic drugs.

Quantification of ascorbic acid and sodium ascorbate in powder blends for tableting and in vitamin C chewable tablets by NIR-chemometry

The paper proposes a near infrared method able to directly and simultaneously quantify ascorbic acid and sodium ascorbate in powder blends for tableting and in vitamin C chewable tablets without any sample preparation. In the first step, calibration models for the quantification of ascorbic acid and sodium ascorbate in powder blends for tableting and subsequently in chewable vitamin C tablets (corresponding to 80-120 % active substance) were developed according to an experimental design with 2 variables and 5 levels. Then, using the best calibration models, the methods were fully validated in terms of recovery, precision and accuracy for both powder blends and vitamin C chewable tablets. The validated concentration range was 15.14-18.51 % for ascorbic acid and 12.06-14.49 % for sodium ascorbate in powder blends and 91.85-111.03 mg per tablet for ascorbic acid and 71.01-84.50 mg per tablet for sodium ascorbate in tablets. Validation results showed good precision and accuracy.

High-throughput NIR-chemometric methods for determination of drug content and pharmaceutical properties of indapamide tablets

This paper describes the development, validation and application of NIR-chemometric methods for API content and pharmaceutical characterization (disintegration time and crushing strength) of indapamide intact tablets. Development of the method for chemical characterization was performed on samples corresponding to 80, 90, 100, 110 and 120% of indapamide content and for pharmaceutical characterization on samples prepared at nine different compression forces (covering the interval 7-45 kN). NIR spectra of prepared tablets were recorded in transmission mode, and partial least-squares followed by leave-one-out cross-validation were used to develop models for the prediction of the drug content and the pharmaceutical properties of tablets. All developed models were validated in terms of trueness, precision and accuracy. No statistical differences were found between results predicted by NIR-chemometric methods and the ones determined by reference methods. Therefore, the developed NIR-chemometric methods meet the requirements of a high-throughput method for the determination of drug content, pharmaceutical properties of indapamide tablets.

Flexibility and applicability of β-expectation tolerance interval approach to assess the fitness of purpose of pharmaceutical analytical methods

An innovative versatile strategy using Total Error has been proposed to decide about the method's validity that controls the risk of accepting an unsuitable assay together with the ability to predict the reliability of future results. This strategy is based on the simultaneous combination of systematic (bias) and random (imprecision) error of analytical methods. Using validation standards, both types of error are combined through the use of a prediction interval or β-expectation tolerance interval. Finally, an accuracy profile is built by connecting, on one hand all the upper tolerance limits, and on the other hand all the lower tolerance limits. This profile combined with pre-specified acceptance limits allows the evaluation of the validity of any quantitative analytical method and thus their fitness for their intended purpose. In this work, the approach of accuracy profile was evaluated on several types of analytical methods encountered in the pharmaceutical industrial field and also covering different pharmaceutical matrices. The four studied examples depicted the flexibility and applicability of this approach for different matrices ranging from tablets to syrups, different techniques such as liquid chromatography, or UV spectrophotometry, and for different categories of assays commonly encountered in the pharmaceutical industry i.e. content assays, dissolution assays, and quantitative impurity assays. The accuracy profile approach assesses the fitness of purpose of these methods for their future routine application. It also allows the selection of the most suitable calibration curve, the adequate evaluation of a potential matrix effect and propose efficient solution and the correct definition of the limits of quantification of the studied analytical procedures.

Development of NIRS method for quality control of drug combination artesunate-azithromycin for the treatment of severe malaria

Near infrared spectroscopy (NIRS) methods were developed for the determination of analytical content of an antimalarial-antibiotic (artesunate and azithromycin) co-formulation in hard gelatin capsule (HGC). The NIRS consists of pre-processing treatment of spectra (raw spectra and first-derivation of two spectral zones), a unique principal component analysis model to ensure the specificity and then two partial least-squares regression models for the determination content of each active pharmaceutical ingredient. The NIRS methods were developed and validated with no reference method, since the manufacturing process of HGC is basically mixed excipients with active pharmaceutical ingredients. The accuracy profiles showed β-expectation tolerance limits within the acceptance limits (±5%). The analytical control approach performed by reversed phase (HPLC) required two different methods involving two different preparation and chromatographic methods. NIRS offers advantages in terms of lower costs of equipment and procedures, time saving, environmentally friendly.

Critical review of near-infrared spectroscopic methods validations in pharmaceutical applications

Based on the large number of publications reported over the past five years, near-infrared spectroscopy (NIRS) is more and more considered an attractive and promising analytical tool regarding Process Analytical Technology and Green Chemistry. From the reviewed literature, few of these publications present a thoroughly validated NIRS method even if some guidelines have been published by different groups and regulatory authorities. However, as any analytical method, the validation of NIRS method is a mandatory step at the end of the development in order to give enough guarantees that each of the future results during routine use will be close enough to the true value. Besides the introduction of PAT concepts in the revised document of the European Pharmacopoeia (2.2.40) dealing with near-infrared spectroscopy recently published in Pharmeuropa, it agrees very well with this mandatory step. Indeed, the latter suggests to use similar analytical performance characteristics than those required for any analytical procedure based on acceptance criteria consistent with the intended use of the method. In this context, this review gives a comprehensive and critical overview of the methodologies applied to assess the validity of quantitative NIRS methods used in pharmaceutical applications.

High-Throughput NIR-Chemometric Method for Meloxicam Assay from Powder Blends for Tableting

A near infrared (NIR) method able to directly quantify the active content in pharmaceutical powder blends used for manufacturing meloxicam tablets, without any sample preparation, was developed and fully validated. To develop calibration models for the assay of meloxicam in powder blends for tableting, the NIR reflectance spectra of different meloxicam powder blends prepared according to a calibration protocol was analysed using different preprocessing methods by partial last-square regression (PLS) and principal component regression (PCR).The best calibration model was found when partial last-square regression (PLS) was used as regression algorithm in association with Smoothing-Savitsky as pre-processing spectrum method. The trueness, precision (repeatability and intermediate precision), accuracy, linearity and range of application of the developed NIR method were validated according to the International Conference of Harmonization (ICH) and Medicine European Agency (EMA) guidelines and found to be fit for its intended purpose.