Evidence on the inhibitory effect of Brassica plants against Acinetobacter baumannii lipases: phytochemical analysis, in vitro, and molecular docking studies.

BACKGROUND: Infections caused by Acinetobacter baumannii are becoming a rising public health problem due to its high degree of acquired and intrinsic resistance mechanisms. Bacterial lipases penetrate and damage host tissues, resulting in multiple infections. Because there are very few effective inhibitors of bacterial lipases, new alternatives for treating A. baumannii infections are urgently needed. In recent years, Brassica vegetables have received a lot of attention since their phytochemical compounds have been directly linked to diverse antimicrobial actions by inhibiting the growth of various Gram-positive and Gram-negative bacteria, yeast, and fungi. Despite their longstanding antibacterial history, there is currently a lack of scientific evidence to support their role in the management of infections caused by the nosocomial bacterium, A. baumannii. This study aimed to address this gap in knowledge by examining the antibacterial and lipase inhibitory effects of six commonly consumed Brassica greens, Chinese cabbage (CC), curly and Tuscan kale (CK and TK), red and green Pak choi (RP and GP), and Brussels sprouts (BR), against A. baumannii in relation to their chemical profiles. METHODS: The secondary metabolites of the six extracts were identified using LC-QTOF-MS/MS analysis, and they were subsequently correlated with the lipase inhibitory activity using multivariate data analysis and molecular docking. RESULTS: In total, 99 metabolites from various chemical classes were identified in the extracts. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed the chemical similarities and variabilities among the specimens, with glucosinolates and phenolic compounds being the major metabolites. RP and GP showed the highest antibacterial activity against A. baumannii, followed by CK. Additionally, four species showed a significant effect on the bacterial growth curves and demonstrated relevant inhibition of A. baumannii lipolytic activity. CK showed the greatest inhibition (26%), followed by RP (21%), GP (21%), and TK (15%). Orthogonal partial least squares-discriminant analysis (OPLS-DA) pinpointed 9 metabolites positively correlated with the observed bioactivities. Further, the biomarkers displayed good binding affinities towards lipase active sites ranging from -70.61 to -30.91 kcal/mol, compared to orlistat. CONCLUSION: This study emphasizes the significance of Brassica vegetables as a novel natural source of potential inhibitors of lipase from A. baumannii.

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment.

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

Assessment of the effect of drying on Brassica greens via a multiplex approach based on LC-QTOF-MS/MS, molecular networking, and chemometrics along with their antioxidant and anticancer activities.

Turnip (Brassica rapa var rapa L.) leaves are a rich source of versatile bioactive phytochemicals with great potential in the food and herbal industries. However, the effect of drying on its constituents has never been studied before. Hereto, three drying techniques were compared, namely, lyophilization (LY), vacuum oven (VO), and shade drying (SD). Chemical profiling utilizing liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS) combined with chemometrics showed the different impacts of the drying methods on the phytochemical composition of the alcoholic leaf extracts. Unsupervised principal component analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) of the LC-QTOF-MS/MS data showed distinct distant clustering across the three drying techniques. Loading plots and VIP scores demonstrated that sinapic acid, isorhamnetin glycosides, and sinapoyl malate were key markers for LY samples. Meanwhile, oxygenated and polyunsaturated fatty acids were characteristic for SD samples and oxygenated polyunsaturated fatty acids and verbascoside were characteristic for VO samples. LY resulted in the highest total phenolics (TP) and total flavonoid (TF) contents followed by SD and VO. LY and SD samples had much higher antioxidant activity than VO measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), oxygen radical absorbance capacity (ORAC), and iron metal chelation assays. According to the anticancer activity, the drying methods were ranked in descending order as SD > LY â‰« VO when tested against colon, breast, liver, and lung cancer cell lines. Among the identified compounds, flavonoids and omega-3 fatty acids were key metabolites responsible for the anticancer activity as revealed by partial least squares (PLS) regression and correlation analyses. In conclusion, compared to LY, SD projected out as a cost-effective drying method without compromising the phytochemical and biological activities of Brassica greens. The current findings lay the foundation for further studies concerned with the valorization of Brassica greens.

Identification of cyclooxygenase-II inhibitory saponins from fenugreek wastes: Insights from liquid chromatography-tandem mass spectrometry metabolomics, molecular networking, and molecular docking.

INTRODUCTION: This research explores sustainable applications for waste generated from fenugreek (Trigonella foenum-graecum), a plant with both nutritional and medicinal uses. The study specifically targets waste components as potential sources of nutrients and bioactive compounds. OBJECTIVES: The focus is to conduct detailed metabolic profiling of fenugreek waste, assess its anti-inflammatory properties by studying its cyclooxygenase (COX) inhibitory effect, and correlate this effect to the metabolite fingerprint. MATERIALS AND METHODS: Ethanolic extracts of fenugreek fruit pericarp and a combination of leaves and stems were subjected to untargeted metabolic profiling using liquid chromatography-mass spectrometry integrated with online database searches and molecular networking as an effective dereplication strategy. The study also scrutinized the COX inhibitory capabilities of these extracts and saponin-rich fractions prepared therefrom. Molecular docking was employed to investigate the specific interactions between the identified saponins and COX enzymes. RESULTS: The analysis led to the annotation of 81 metabolites, among which saponins were predominant. The saponin-rich fraction of the fruit pericarp extract displayed the strongest COX-II inhibitory activity in the in vitro inhibition assay (IC50 value of 81.64 ± 3.98 µg/mL). The molecular docking study supported the selectivity of the identified saponins towards COX-II. The two major identified saponins, namely, proto-yamogenin 3-O-[deoxyhexosyl (1 → 2)] [hexosyl (1 → 4)] hexoside 26-O-hexoside and trigofenoside A, were predicted to have the highest affinity to the COX-II receptor site. CONCLUSION: In the present study, we focused on the identification of COX-II inhibitory saponins in fenugreek waste through an integrated approach. The findings offer valuable insights into potential anti-inflammatory and cancer chemoprotective applications of fenugreek waste.

Stability and Biosimilarity Assessment of Bevacizumab Monoclonal Antibody; Orthogonal Testing Protocol Coupled With Peptide Mapping-Principal Component Analysis.

BACKGROUND: Biologics are essential in cancer treatment because they stimulate the body's natural response to fight cancer, but they are expensive. Biosimilars are more affordable compared to patent biologicals, but it must be verified that they are as effective as their innovators. Characterization of biosimilars and assessment of interchangeability requires many data points for verification. OBJECTIVE: The proposed study provides a quality assessment of two new bevacizumab (BVZ) biosimilars, produced by Amgen and Biocad, Inc., through the development and greenness assessment of an orthogonal testing protocol and purity indicating assay, including size-exclusion (SE-HPLC), reversed-phase (RP-HPLC), and cation exchange chromatography (CEX-HPLC) in addition to dynamic light scattering (DLS) and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). METHODS: SE-HPLC method was performed and validated to screen the BVZ monomer and its aggregates and/or fragments. Peak purity and system suitability parameters were calculated. Results indicate that the orthogonal protocol is a useful tool for assessing monoclonal antibody stability. It is a key criterion for biosimilarity assessment. DLS and SDS-PAGE results were compared to each other to reveal close retention times and banding patterns between BVZ innovator and its biosimilars. These results showed that Avastin® and the investigated biosimilars have the same profile in terms of peak area of related compounds within the acceptance limit and apparent molecular weight, and the SDS-PAGE technique was found to be the most eco-friendly technique among others. CONCLUSIONS: The results obtained highlighted the importance of assessing similarities and differences in ensuring the biosimilarity and interchangeability of the studied products. HIGHLIGHTS: BVZ is one of the essential monoclonal antibodies in the treatment of colorectal cancer (CRC). BVZ biosimilars were evaluated by developing an orthogonal testing protocol and a purity-indicating assay. The size-exclusion (SE)-HPLC method was applied and validated to monitor the BVZ monomer and its aggregates. The results demonstrated the importance of assessing the stability and biosimilarity of BVZ.

Development and validation of chemometric-assisted spectrophotometric models for efficient quantitation of a binary mixture of supportive treatments in COVID-19 in the presence of its toxic impurities: a comparative study for eco-friendly assessment.

The use of sustainable solvents has increased significantly in recent years due to advancements in green analytical methods. The number of impurities in the drug substance determines how safe the finished product is. Therefore, during the whole medication planning process, contaminants need to be closely watched. Using chemometric models, the concentrations of hyoscine N-butyl bromide (HYO) and paracetamol (PAR) were determined in the presence of three PAR impurities [P-nitrophenol (PNP), P-aminophenol (PAP), and P-chloroacetanilide (PCA), as well as DL-tropic acid (TRO) as a HYO impurity]. It was possible to isolate and measure these dangerous impurities. Fever and spasms associated with COVID-19 are reported to be considerably reduced when PAR and HYO are taken together. Artificial neural networks, principal component regression, multivariate curve resolution-alternating least squares, and partial least squares are the four chemometric-assisted spectrophotometric models that were created and verified. All of the proposed methods' quantitative analytical potency was assessed using recoveries%, root mean square error of prediction, and standard error of prediction. For PAR, HYO, PNP, PCA, TRO, and PAP, respectively, the indicated approaches were used in the ranges of 4.00-8.00, 16.00-24.00, 1.00-5.00, 0.40-0.80, 4.00-12.00, and 2.00-6.00 µg/mL. They are able to get around difficulties like collinearity and spectral overlaps. After statistical testing, there was no discernible difference between the recommended methods and the published one. The degree of greenness of the established models was evaluated using three different green assessment methods. In the presence of their harmful impurities, PAR and HYO could be identified using the recommended methods.

A comprehensive analytical framework integrating liquid chromatography-tandem mass spectrometry metabolomics with chemometrics for metabolite profiling of lettuce varieties and discovery of antibacterial agents.

This study comprehensively characterized the metabolite profiles of six lettuce varieties and established the correlation between the elucidated profiles and their antivirulence effects. A total of 195 metabolites were annotated using LC-QTOF-MS/MS metabolomics assisted by molecular networking and integrated with chemometrics. Red varieties (red longifolia and lolla rosa) demonstrated higher chlorogenic and chicoric acids suggesting their antioxidant properties. In parallel, amino acids and disaccharides were enriched in romaine longifolia rationalizing its palatable taste and nutritional potential, while crispa, capitata, and lolla bionda presented a high ß-carboline alkaloid content. The antibacterial and antihemolytic potential of all varieties against methicillin-sensitive and methicillin-resistant Staphylococcus aureus was assessed and validated by prominent downregulation of α-hemolysin transcriptional levels in both strains. Moreover, correlation analysis revealed sesquiterpenes, ß-carboline alkaloids, amino acids, and oxy-fatty acids as the main bioactives. Results emphasize lettuce significance as a functional food and nutraceutical source, and highlight varieties naturally rich in antibacterial agents to adapt breeding programs.

Design of screen-printed potentiometric platform for sensitive determination of mirabegron in spiked human plasma; molecular docking and transducer optimization.

The integration of molecular modelling simulation and electrochemical sensors is of high interest. Herein, for the first time, a portable solid-contact potentiometric electrode was designed for the sensitive determination of mirabegron (MIR) in human plasma and pharmaceutical formulation. A two-step optimization protocol was investigated for the fabrication of an ion on sensing polymeric membrane. First, molecular docking was used for optimum ionophore selection. Calix[6]arene showed the highest affinity towards MIR with a better docking score (-4.35) and potential energy (-65.23) compared to other calixarene derivatives. Second, carbon nanotubes and gold nanoparticles were investigated as ion-electron transducers using a drop-casting procedure. Gold nanoparticle-based sensors showed better slope, potential stability, and rapid response compared to carbon nanotubes. The proposed solid contact sensors (V-VII) showed comparable sensitivity and ease of handling compared to liquid contact sensors (I-IV). The optimized gold nanoparticles sensor VII produced a Nernstian response over the range of 9.77 × 10-7 to 1 × 10-3 M with LOD of 2.4 × 10-7 M. It has also been used to determine MIR in its pharmaceutical formulation in the presence of a co-formulated antioxidant butylated hydroxytoluene and spiked human plasma. This would offer a feasible and economic platform for monitoring MIR in pharmaceutical preparation and biological fluids.

Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study.

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D0) and Fourier self-deconvolution (FSD), as TRI was directly determined by D0 at 367.0 nm in the range (2.00-10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00-8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00-7.00 µg/mL) for TRI and (2.00-6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.

Chemometric Quality Assessment of Doxylamine Succinate With Its Degradation Product: Implementation of Two Predictive Models on UV-Spectrophotometric Data of Anti-Emetic Binary Mixture.

BACKGROUND: The combination of pyridoxine hydrochloride (PYR) and doxylamine succinate (DOX) as an antiemetic binary mixture is used to treat nausea and vomiting during pregnancy. OBJECTIVE: Two validated, accurate, and selective chemometric models were developed to assay binary mixture in the presence of DOX oxidative degradation product (DOX DEG) that could be characterized using LC-MS. METHODS: Partial least squares (PLS) regression and principal component regression (PCR) were selected for the determination of our binary mixture in presence of degradation. To exhibit a training set of 25 mixtures that had various percentages of tested substances in five level 3 variables, an experimental design was chosen. A set of 18 synthetic mixtures in the concentration range 10.0-50.0 µg/mL, 12.00-20.0 µg/mL, and 6.0-30.0 µg/mL for PYR, DOX, and DOX DEG, respectively, were used in the construction of the calibration models. Then set of seven synthetic mixtures with different concentrations were used in the construction of the validation models. RESULTS: In validation samples with low root mean square error of prediction (RMSEP), the suggested models successfully predicted the concentrations of our drugs. The models developed were evaluated by RMSEP calculation, and the values obtained were 0.341, 0.196, and 0.388 for PYR, DOX, and DOX DEG, respectively, using PLS. While using PCR, RMSEP calculation and the values obtained were 0.400, 0.256, and 0.375 for PYR, DOX, and DOX DEG, respectively. The developed models were validated according to ICH strategies. CONCLUSIONS: The corresponding methods are suitable to determine PYR and DOX in pure form, pharmaceutical dosage form, and in the presence of DOX DEG product. HIGHLIGHTS: The study of drug breakdown pathways is very important nowadays, so even in the presence of degradation and extreme spectral overlapping, the suggested PLS and PCR spectrophotometric approaches were able to identify PYR and DOX.

Chemometric quality assessment of Paracetamol and Phenylephrine Hydrochloride with Paracetamol impurities; comparative UV-spectrophotometric implementation of four predictive models.

Spectrophotometric data analysis using multivariate approaches has many useful applications. One of these applications is the analysis of active ingredients in presence of impurities. Four chemometric-assisted spectrophotometric methods, namely, principal component regression (PCR), partial least-squares (PLS), artificial neural networks (ANN) and multivariate curve resolution-alternating least squares (MCR-ALS) were proposed and validated. The developed chemometric methods were compared to resolve the severely overlapped spectrum of Paracetamol (PAR) and Phenylephrine HCl (PHE) along with PAR impurities namely, P-Aminophenol (PAP), P-Nitrophenol (PNP), Acetanilide (ACT) and P-Chloroacetanilide (CAC). The four multivariate calibration methods succeeded in simultaneous determination of PAR and PHE with further quantification of PAR impurities. So, the proposed methods could be used with no need of any separation step and successfully applied for pharmaceutical formulation analysis. Furthermore, statistical comparison between the results obtained by the proposed chemometric methods and the official ones showed no significant differences.

Coupling of Trastuzumab chromatographic profiling with machine learning tools: A complementary approach for biosimilarity and stability assessment.

Biosimilar products present a growing opportunity to improve the global healthcare systems. The amount of accepted variability during the comparative assessments of biosimilar products introduces a significant challenge for both the biosimilar developers and the regulatory authorities. The aim of this study was to explore unsupervised machine learning tools as a mathematical aid for the interpretation and visualization of such comparability under control and stress conditions using data extracted from high throughput analytical techniques. For this purpose, a head-to-head analysis of the physicochemical characteristics of three Trastuzumab (TTZ) approved biosimilars and the originator product (Herceptin®) was performed. The studied quality attributes included the primary structure and identity by peptide mapping (PM) with reversed-phase chromatography-UV detection, size and charge profiles by stability-indicating size exclusion and cation exchange chromatography. Stress conditions involved pH and thermal stress. Principal component analysis (PCA) and two of the widely used cluster analysis tools, namely, K-means and Density-based Spatial Clustering of Applications with Noise (DBSCAN), were explored for clustering and feature representation of varied analytical datasets. It has been shown that the clustering patterns delineated by the used algorithms changed based on the included chromatographic profiles. The applied data analysis tools were found effective in revealing patterns of similarity and variability between i) intact and stressed as well as ii) originator and biosimilar samples.

Chemometric-enhanced metabolic profiling of five Pinus species using HPLC-MS/MS spectrometry: Correlation to in vitro anti-aging, anti-Alzheimer and antidiabetic activities.

Detailed metabolic profiling of needles of five Pinus species was investigated using complementary HPLC-MS/MS techniques together with supervised and unsupervised chemometric tools. This resulted in putative identification of 44 compounds belonging to flavonoids, phenolics, lignans, diterpenes and fatty acids. Unsupervised principal component analysis showed that differences were maintained across the metabolites characteristic of each Pinus species, are mainly related to di-O-p-coumaroyltrifolin, p-coumaroyl quinic acid derivative, arachidonic acid, hydroxypalmitic acid, isopimaric acid and its derivative. A supervised Partial Least Squares regression analysis was performed to correlate HPLC-MS/MS profiles with the variation observed in the in vitro anticholinesterase, antiaging and anti-diabetic potential. All investigated Pinus extracts exerted their antiaging activity via increasing telomerase and TERT levels in normal human melanocytes cells compared to the control (untreated cells). Profound inhibition activities of acetylcholinesterase and dipeptidyl peptidase-4 were also observed with P. pinea and P. canariensis extracts having comparable antidiabetic activities to sitagliptin as a standard antidiabetic drug. Our findings suggested that pine needles are a good source of phenolics and diterpenoids that have possible health promoting activities in management and alleviation of diabetic conditions and Alzheimer disease.

In vitro analytical dissolution profiling of antiemetic delayed release tablets in two different dissolution media: Validated spectrophotometric methods versus reported HPLC.

The combination of pyridoxine HCl (PYR) and doxylamine succinate (DOX) was proved to be effective and safe acting as the first line of pregnancy medication for vomiting and nausea under a trade name; Vomibreak® delayed release tablets. This combination has been available in the Egyptian market since 2016. Dissolution study is a meaningful tool that represents a predictor of output because the rate controlling steps in any drug's absorption is the rate of discharging from its medicinal formulation. Generally, the dissolution test of all delayed release tablets is operated at two stages: first the acid stage then the buffer stage. In our work, the acid stage was performed in 0.1 N hydrochloric acid (0.1 M HCl) and the buffer one was in 0.2 M sodium phosphate buffer (0.2 M Na-PB), pH = 6.8, according to FDA guidelines. In present work, for the first time, this binary mixture was quantitatively determined by applying four spectrophotometric methods. PYR was directly determined by zero order spectra method (D0) at 291.0 nm in the range 2.0-26.0 µg/mL in the acid stage and at 325.0 nm in the range 5.0-35.0 µg/mL in the buffer stage, where DOX show no interference in both cases. However, DOX was determined by three methods, namely, Dual wavelength (DW), Ratio difference (RD) and Derivative ratio (DD1). DD1 was the chosen method for determination of DOX in the two-phase dissolution study of Vomibreak® tablets at 249.0 nm in the range 2.0-44.0 µg/mL and 273.0 nm in the range 5.0-100.0 µg/mL in acid and buffer phases, respectively. All of the suggested methods were tested in compliance with ICH guidelines, where all methods were found to be reliable, reproducible, and selective. A statistical comparison was computed between two analytical techniques of critical importance in the development of two media dissolution profile: proposed UV- spectrophotometric and reported HPLC methods where no significant difference was found. Difference (ƒ1) and similarity (ƒ2) factors were calculated for PYR and DOX and shown that ƒ1 was 1.490 and 1.654 and ƒ2 was 94.431 and 92.396 for PYR and DOX, respectively.

Coupling of on-column trypsin digestion-peptide mapping and principal component analysis for stability and biosimilarity assessment of recombinant human growth hormone.

Peptide mapping (PM) is a vital technique in biopharmaceutical industry. The fingerprint obtained helps to qualitatively confirm host stability as well as verify primary structure, purity and integrity of the target protein. Yet, in-solution digestion followed by tandem mass spectrometry is not suitable as a routine quality control test. It is time consuming and requires sophisticated, expensive instruments and highly skilled operators. In an attempt to enhance the fuctionality of PM and extract multi-dimentional data about various critical quality attributes and comparability of biosimilars, coupling of PM generated using immobilized trypsin followed by HPLC-UV to principal component analysis (PCA) is proposed. Recombinant human growth hormone (rhGH); was selected as a model biopharmaceutical since it is available in the market from different manufacturers and its PM is a well-established pharmacopoeial test. Samples of different rhGH biosimilars as well as degraded samples: deamidated and oxidized were subjected to trypsin digestion followed by RP-HPLC-UV analysis. PCA of the entire chromatograms of test and reference samples was then conducted. Comparison of the scores of samples and investigation of the loadings plots clearly indicated the applicability of PM-PCA for: i) identity testing, ii) biosimilarity assessment and iii) stability evaluation. Hotelling's T2 and Q statistics were employed at 95% confidence level to measure the variation and to test the conformance of each sample to the PCA model, respectively. Coupling of PM to PCA provided a novel tool to identify peptide fragments responsible for variation between the test and reference samples as well as evaluation of the extent and relative significance of this variability. Transformation of conventional PM that is largely based on subjective visual comparison into an objective statiscally-guided analysis framework should provide a simple and economic tool to help both manufacturers and regulatory authorities in quality and biosimilarity assessment of biopharmaceuticals.