The benefits of validation of methods for toxicity testing outweigh its costs

The 4th Annual Forum on Endocrine Disrupters organized by the European Commission brought together authors of this article around the topic: "From bench to validated test guidelines: (pre)validation of test methods". Validation activities are meant to demonstrate the relevance and reliability of methods and approaches used in regulatory safety testing. These activities are essential to facilitate regulatory use, still they are largely underfunded and unattractive to the scientific community. In the last decade, there has been large amounts of funding invested in European research towards the development of approaches that can be used in regulatory decision-making, including for the identification of endocrine disrupters. There is a vast pool of candidate test methods for potential regulatory applications, but most of them will not be used due to the absence of consideration of their relevance and reliability outside the method developer's laboratory. The article explains the reasons why such a gap exists between the outputs of research projects and the uptake in a regulatory context. In parallel, there are also increasing expectations from the regulatory science community that validation becomes more efficient with respect to time and resources. This article shares some of the lessons learned and proposes paths forward for validation of new methods that are not intended as one-to-one replacements of animal studies. This includes submitting only mature methods for validation that were developed following good practices and good documentation, proposing a greater emphasis on well-documented transferability studies, and adopting a cost-sharing model between those who benefit from validated methods.

Validation activities for methods intended to be used to assess chemical safety have a cost but also bring substantial benefits when the validated methods are established as OECD Test Guidelines which results in mutual acceptance of data generated by the methods. The article discusses some of the challenges faced when method validation is underfunded and unattractive for researchers. Proposals are made to improve the current situation, gain efficiency and make validation a shared responsibility.

Comprehensive analysis of vitamin D3 impurities in oily drug products using supercritical fluid chromatography-mass spectrometry.

Vitamin D3 , an essential micronutrient, often requires supplementation via medicines or food supplements, which necessitate quality control (QC). This study presents the development of a method for detecting and quantifying seven impurities of vitamin D3 in oily drug products using supercritical fluid chromatography-mass spectrometry (SFC-MS). Targeted impurities include two esters of vitamin D3 and five non-esters including four that are isobaric to vitamin D3 . Firstly, a screening study highlighted the Torus 1-AA column and acetonitrile modifier as adequate for the separation, followed by optimization of the SFC conditions. Secondly, make-up solvent composition and MS settings were optimized to reach high sensitivity. For both the separation and MS response, the screening design of experiments proved useful. Lastly, a fast saponification and liquid-liquid extraction method was developed, enabling efficient sample cleanup and impurities recovery from the complex oily matrix. The SFC-MS method suitability was assessed in two validation studies. The first study employed the ICH Q2 guideline for impurity limit test to demonstrate method specificity and establish a limit of detection (LOD) and a limit of quantification (LOQ) at 0.2% and 0.5%, respectively, for ester impurities. The second study conducted a comprehensive quantitative assessment for three non-ester impurities using a total error approach, determining method validity through accuracy profiles. The validated method exhibited reliable performance across impurity concentrations from 0.1% to 2.0%, with estimated LODs ranging from 2 to 7 ng/mL. This study further promotes SFC-MS as a valuable, versatile, and green tool for routine pharmaceutical QC.

Protective Effect of Ticagrelor Against Infective Endocarditis Induced by Virulent Staphylococcus aureus in Mice.

In addition to its potent antiplatelet activity, ticagrelor possesses antibacterial properties against gram-positive bacteria. We wondered whether the typical clinical dosage of ticagrelor could prevent the development of infective endocarditis caused by highly virulent Staphylococcus aureus. Ticagrelor prevented vegetation formation in a mouse model of inflammation-induced endocarditis. The dosage achieved in patients under ticagrelor therapy altered bacterial toxin production and adherence on activated endothelial cells, thereby mitigating bacterial virulence. Besides the previously described bactericidal activity at high doses, ticagrelor at typical clinical doses possesses antivirulence activity against S aureus. Ticagrelor antiplatelet activity further interferes with the interplay between platelets and bacteria.

In-Field Implementation of Near-Infrared Quantitative Methods for Analysis of Medicines in Tropical Environments.

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method.

Usefulness of medicine screening tools in the frame of pharmaceutical post-marketing surveillance.

The negative consequences of Substandard and falsified (SF) medicines are widely documented nowadays and there is still an urgent need to find them in more efficient ways. Several screening tools have been developed for this purpose recently. In this study, three screening tools were used on 292 samples of ciprofloxacin and metronidazole collected in Cameroon. Each sample was then analyzed by HPLC and disintegration tests. Seven additional samples from the nitro-imidazole (secnidazole, ornidazole, tinidazole) and the fluoroquinolone (levofloxacin, ofloxacin, norfloxacin, moxifloxacin) families were analyzed to mimic falsified medicines. Placebo samples that contained only inert excipients were also tested to mimic falsified samples without active pharmaceutical ingredient (API). The three screening tools implemented were: a simplified visual inspection checklist, a low-cost handheld near infrared (NIR) spectrophotometer and paper analytical devices (PADs). Overall, 61.1% of the samples that failed disintegration and assay tests also failed the visual inspection checklist test. For the handheld NIR, one-class classifier models were built to detect the presence of ciprofloxacin and metronidazole, respectively. The APIs were correctly identified in all the samples with sensitivities and specificities of 100%. However, the importance of a representative and up-to-date spectral database was underlined by comparing models built with different calibration set spanning different variability spaces. The PADs were used only on ciprofloxacin samples and detected the API in all samples in which the presence of ciprofloxacin was confirmed by HPLC. However, these PADs were not specific to ciprofloxacin since they reacted like ciprofloxacin to other fluoroquinolone compounds. The advantages and drawbacks of each screening tool were highlighted. They are promising means in the frame of early detection of SF medicines and they can increase the speed of decision about SF medicines in the context of pharmaceutical post-marketing surveillance.

Optimization of silver nanoparticles synthesis by chemical reduction to enhance SERS quantitative performances: Early characterization using the quality by design approach.

Surface-enhanced Raman scattering (SERS) is a vibrational widely used technique thanks to its multiple advantages such as its high specificity and sensitivity. The Raman signal exaltation comes from the use of metallic nanoparticles (Nps) acting as antennas by amplifying the Raman scattering. Controlling the Nps synthesis is a major point for the implementation of SERS in routine analysis and especially in quantitative applications. Effectively, nature, size and shape of these Nps considerably influence the SERS response intensity and repeatability. The Lee-Meisel protocol is the most common synthesis route used by the SERS community due to the low cost, rapidity and ease of manufacturing. However, this process leads to a significant heterogeneity in terms of particle size and shape. In this context, this study aimed to synthesize repeatable and homogeneous silver nanoparticles (AgNps) by chemical reduction. The Quality by Design strategy from quality target product profile to early characterization design was considered to optimize this reaction. The first step of this strategy aimed to highlight critical parameters by the means of an early characterization design. Based on an Ishikawa diagram, five process parameters were studied: the reaction volume as categorical variable and the temperature, the time of reaction, the trisodium citrate concentration and pH as continuous variables. A D-Optimal design of 35 conditions was performed. Three critical quality attributes were selected to maximize the SERS intensity, minimize the variation coefficient on SERS intensities and the polydispersity index of the AgNps. Considering these factors, it appeared that concentration, pH and time of reaction were identified as having a critical impact on the Nps formation and can then be considered for the further optimization step.

ReadEDTest: A tool to assess the readiness of in vitro test methods under development for identifying endocrine disruptors.

Growing evidence shows that endocrine disruptors (EDs), known to affect the reproductive system, may also disturb other hormone-regulated functions leading to cancers, neurodevelopmental defects, metabolic and immune diseases. To reduce exposure to EDs and limit their health effects, development of screening and mechanism-based assays to identify EDs is encouraged. Nevertheless, the crucial validation step of test methods by regulatory bodies is a time- and resource-consuming process. One of the main raisons of this long duration process is that method developers, mainly researchers, are not fully aware of the regulatory needs to validate a test. We propose an online self-assessment questionnaire (SAQ) called ReadEDTest easy to be used by all researchers. The aim of ReadEDTest is to speed up the validation process by assessing readiness criteria of in vitro and fish embryo ED test methods under development. The SAQ is divided into 7 sections and 13 sub-sections containing essential information requested by the validating bodies. The readiness of the tests can be assessed by specific score limits for each sub-section. Results are displayed via a graphical representation to help identification of the sub-sections having sufficient or insufficient information. The relevance of the proposed innovative tool was supported using two test methods already validated by the OECD and four under development test methods.

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy.

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

Prevalence of Poor Quality Ciprofloxacin and Metronidazole Tablets in Three Cities in Cameroon.

Quality is one of the essential components of medicines and needs to be ensured to preserve the population's health. This can be achieved through post-marketing quality control of medicines and is one of the most important duties of national regulatory authorities. In collaboration with the Cameroonian National Drug Quality Control and Valuation Laboratory, the decision was made to initiate a prevalence study to assess the quality of antiinfective medicines in Cameroon. A total of 150 samples of ciprofloxacin tablets and 142 samples of metronidazole tablets were collected from 76 licensed pharmacies and 75 informal vendors in three cities in Cameroon using a random strategy wherever possible and a mystery shopper approach. Three tests were carried out on each of the samples. Visual inspection allowed to find two falsified samples (0.7%) due to lack of information about the manufacturing company, and five more samples (1.7%) were deemed to be substandard due to flaws in the product. An additional 13 samples (4.5%) failed disintegration testing, and six (2.1%) others failed high-performance liquid chromatography assay testing due to insufficient active pharmaceutical ingredient (API) content. All samples were found to contain some API. A prevalence of 7.9% substandard or falsified (SF) medicines was found. Moreover, the prevalence of outlets selling SF medicines was greater in the informal sector (26.7%) than in the formal sector (2.6%). Although the prevalence of SF medicines found was low, efforts need to be made by national regulatory authorities to monitor the pharmaceutical market more closely.

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules.

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.

Investigation of potential substandard dry powder inhalers on EU and North African markets - evaluation of the delivered and fine particle doses.

Purpose: Discovery of falsified Symbicort 320/9 Turbohaler identified in the UK in 2013 demonstrated that falsified dry powder inhalers were also present in the European market. This work aimed to investigate the current situation of formoterol-containing dry powder inhalers in Europe and North Africa by assessing their aerodynamic performance profile. Methods: A total of eight registered formoterol-based dry powder inhalers over the European and North African markets were involved in this study, including the reference drug Foradil. Samples were prepared using a multistage liquid impinger (MsLI) and further analyzed by a validated HPLC-UV method to determine the delivered and the fine particle doses (FPDs). This study also examined the impact of freezing-thawing cycles on sample stability in terms of analytical purpose handling. Results: No substandard dry powder inhalers were identified among the medicinal products involved in this work. The delivered dose (DD) of assessed drugs varied from 8.33 to 9.69 µg, while the FPD was between 1.86 and 3.35 µg. As expected, this work confirmed that the capsule composition and the barrier properties of the primary packaging can affect the FPD of dry powder for inhalation use. Conclusions: The FPD of products C and B was, respectively, 17.4 and 14.2% superior to Foradil, products D and H had the closest values compared to the original drug, and product F was 34.5% inferior. Additionally, this work showed that a high FPD can be achieved using HPMC capsules and moisture-impermeable primary packaging.

Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions.

The ultimate goal of a one-class classifier like the "rigorous" soft independent modeling of class analogy (SIMCA) is to predict with a certain confidence probability, the conformity of future objects with a given reference class. However, the SIMCA model, as currently implemented often suffers from an undercoverage problem, meaning that its observed sensitivity often falls far below the desired theoretical confidence probability, hence undermining its intended use as a predictive tool. To overcome the issue, the most reported strategy in the literature, involves incrementing the nominal confidence probability until the desired sensitivity is obtained in cross-validation. This article proposes a statistical prediction interval-based strategy as an alternative strategy to properly overcome this undercoverage issue. The strategy uses the concept of predictive distributions sensu stricto to construct statistical prediction regions for the metrics. Firstly, a procedure based on goodness-of-fit criteria is used to select the best-fitting family of probability models for each metric or its monotonic transformation, among several plausible candidate families of right-skewed probability distributions for positive random variables, including the gamma and the lognormal families. Secondly, assuming the best-fitting distribution, a generalized linear model is fitted to each metric data using the Bayesian method. This method enables to conveniently estimate uncertainties about the parameters of the selected distribution. Propagating these uncertainties to the best-fitting probability model of the metric enables to derive its so-called posterior predictive distribution, which is then used to set its critical limit. Overall, the evaluation of the proposed approach on a diversity of real datasets shows that it yields unbiased and more accurate sensitivities than existing methods which are not based on predictive densities. It can even yield better specificities than the strategy that attempts to improve sensitivities of existing methods by "optimizing" the type 1 error, especially in low sample sizes' contexts.

Emerging analytical techniques for pharmaceutical quality control: Where are we in 2022?

Quality control is a fundamental and critical activity in the pharmaceutical industry that guarantees the quality of medicines. QC analyses are currently performed using several well-known techniques, mainly liquid and gas chromatography. However, current trends are focused on the development of new techniques to reduce analysis time and cost, to improve the performances and decrease ecological footprint. In this context, analytical scientists developed and studied emerging technologies based on spectroscopy and chromatography. The present review aims to give an overview of the recent development of vibrational spectroscopy, supercritical fluid chromatography and multi-dimensional chromatography. Selected emerging techniques are discussed using SWOT analysis and published pharmaceutical QC applications are discussed.

Comparison of Vibrational Spectroscopic Techniques for Quantification of Water in Natural Deep Eutectic Solvents.

Vibrational spectroscopic techniques, i.e., attenuated total reflectance infrared (ATR-IR), near infrared spectroscopy (NIRS) and Raman spectroscopy (RS), coupled with Partial Least Squares Regression (PLSR), were evaluated as cost-effective label-free and reagent-free tools to monitor water content in Levulinic Acid/L-Proline (LALP) (2:1, mol/mol) Natural Deep Eutectic Solvent (NADES). ATR-IR delivered the best outcome of Root Mean Squared Error (RMSE) of Cross-Validation (CV) = 0.27% added water concentration, RMSE of Prediction (P) = 0.27% added water concentration and mean % relative error = 2.59%. Two NIRS instruments (benchtop and handheld) were also compared during the study, respectively yielding RMSECV = 0.35% added water concentration, RMSEP = 0.56% added water concentration and mean % relative error = 5.13% added water concentration, and RMECV = 0.36% added water concentration, RMSEP = 0.68% added water concentration and mean % relative error = 6.23%. RS analysis performed in quartz cuvettes enabled accurate water quantification with RMECV = 0.43% added water concentration, RMSEP = 0.67% added water concentration and mean % relative error = 6.75%. While the vibrational spectroscopic techniques studied have shown high performance in relation to reliable determination of water concentration, their accuracy is most likely related to their sensitivity to detect the LALP compounds in the NADES. For instance, whereas ATR-IR spectra display strong features from water, Levulinic Acid and L-Proline that contribute to the PLSR predictive models constructed, NIRS and RS spectra are respectively dominated by either water or LALP compounds, representing partial molecular information and moderate accuracy compared to ATR-IR. However, while ATR-IR instruments are common in chemistry and physics laboratories, making the technique readily transferable to water quantification in NADES, Raman spectroscopy offers promising potential for future development for in situ, sample withdrawal-free analysis for high throughput and online monitoring.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept.

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R2CV.

Development and validation of an integrated microfluidic device with an in-line Surface Enhanced Raman Spectroscopy (SERS) detection of glyphosate in drinking water.

Glyphosate, also known as N-(phosphonomethyl)glycine, is one of the most widely used herbicides in the world. However, the controversy surrounding the toxicity of glyphosate and its main breakdown product, aminomethylphosphonic acid (AMPA), remains a serious public concern. Therefore, there is a clear need to develop a rapid, sensitive and automated alternative method for the quantification of glyphosate and AMPA. In this context, surface enhanced Raman spectroscopy (SERS) coupled with a microfluidic system for the determination of glyphosate in tap water was developed, optimized and validated. The design of the microfluidic configuration for this application was built constructed to integrate the synthesis of the SERS substrate through to the detection of the analyte. To optimize the microfluidic setup, a design of experiments approach was used to maximize the SERS signal of glyphosate. Subsequently, an approach based on the European guideline document SANTE/11312/2021 was used to validate the method in the range of 78-480 µg/L using the normalized band intensities. The limit of detection and quantification obtained for glyphosate were 40 and 78 µg/L, respectively. Recoveries were in the range 76-117%, while repeatability and intra-day reproducibility were ≤17%. Finally, the method was also tested for the determination of AMPA in tap water matrix and for the simultaneous detection of AMPA and glyphosate.

Critical review on recent trends in cannabinoid determination on cannabis herbal samples: From chromatographic to vibrational spectroscopic techniques.

Cannabis has been at the center of scientific attention for some years now. Since its pharmacological potential has been highlighted, cannabis has become a hot topic in research laboratories, leading to the publication of many scientific studies. Focusing on analytical chemistry, an enormous number of analytical methods for cannabinoid (CNB) determination have been published, involving various techniques. However, no globally accepted reference method for CNB determination has yet been chosen. This review aims to identify very recent analytical methods developed to analyze phytocannabinoids in cannabis herbal samples. For certain techniques, stagnation in terms of employed operational conditions can be observed. In this context, a reference method of analysis should be proposed and accepted worldwide to standardize CNB determination. In contrast, for other techniques, we are witnessing a scientific ferment, which is resulting in the development of new interesting analytical options. In this regard, particular focus has been given to these niche techniques, which are now emerging in the analytical panorama of cannabis analysis, offering new important perspectives for the future of cannabis testing. Supercritical fluid chromatography and infrared spectroscopy showed tangible advantages when applied to CNB determination in herbal samples.

A pharmaceutical-related molecules dataset for reversed-phase chromatography retention time prediction built on combining pH and gradient time conditions.

There is a rising interest in the modeling and predicting of chromatographic retention. The progress towards more complex and comprehensive models emphasized the need for broad reliable datasets. The present dataset comprises small pharmaceutical compounds selected to cover a wide range in terms of physicochemical properties that are known to impact the retention in reversed-phase liquid chromatography. Moreover, this dataset was analyzed at five pH with two gradient slopes. It provides a reliable dataset with a diversity of conditions and compounds to support the building of new models. To enhance the robustness of the dataset, the compounds were injected individually, and each sequence of injections included a quality control sample. This unambiguous detection of each compound as well as a systematic analysis of a quality control sample ensured the quality of the reported retention times. Moreover, three different liquid chromatographic systems were used to increase the robustness of the dataset.

Interpretable One-Class Classification of Raman Spectra Using Prediction Bands Estimated by Wavelet Regression.

Previously, we introduced a novel one-class classification (OCC) concept for spectra. It uses as acceptance space for genuine spectra of the target chemical, a prediction band in the wavelengths' space. As a decision rule, test spectra falling substantially outside this band are rejected as noncomplying with the target, and their deviations are documented in the wavelengths' space. This band-based OCC concept was applied to smooth signals like near-infrared (NIR) spectra. A regression model based on a smoothed principal component (PC) representation of the training spectra was used to predict unseen trajectories of future spectra. The boundaries of the most central predicted trajectories were chosen as critical trajectories. We now propose a methodology to construct a similar band-based one-class classifier for Raman spectra, which are sharper and noisier than NIR spectra. The spectra are transformed by a composition of wavelet and principal component (wPC) expansions instead of just a PC expansion in the previous methodology for NIR spectra. Wavelets can capture sharp features of Raman signals and provide a framework to efficiently denoise them. A multinormal prediction model is then used to derive predictions of future wPC scores of unseen spectra. These predicted wPC scores are then backtransformed to obtain predictions of future trajectories of unseen spectra in the wavelengths' space, whose most central region defines the acceptance band or space. This band-based one-class classifier successfully classified the first derivatives of real pharmaceutical Raman spectra, while enjoying the advantage of documenting deviations from the critical trajectories in the wavelengths' space and hence is more interpretable.

Selection of essential spectra to improve the multivariate curve resolution of minor compounds in complex pharmaceutical formulations.

Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.