Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products.

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

Tackling quantitative polymorphic analysis through fixed-dose combination tablets production. Pyrazinamide polymorphic assessment.

Pyrazinamide (PZA), Rifampicin (RIF), Isoniazid (ISH) and Ethambutol (ETB) form the core for the treatment of Tuberculosis, today a devastating disease in low-income populations around the world. These drugs are usually administrated by fixed-dose combination (FDC) products, to favour the patient compliance and prevent bacterial resistance. PZA exists in four enantiotropically-related polymorphs (Forms α, δ, ß and γ), but only Form α is considered suitable for pharmaceutical products due to its stability and bioavailability properties. The classical approaches to address solid-state (microscopy, X-ray diffraction and calorimetry) shows limitations for quantification of polymorphs in the presence of excipients and other active components, as in the case of FDC tablets. In this work, an overall strategy was developed using near infrared spectroscopy (NIR) coupled to partial least squares regression (PLS) to quantify Form α of PZA in drug substance (raw material) and PZA/RIF/ISH-FDC tablets. For this purpose, two PLS models were constructed, one for drug substance preparing training (n = 30) and validation (n = 18) samples with a ternary composition (Form α/Form δ/Form γ), and other for FDC drug products, also including the appropriate amount of RIF, ISH and the matrix of excipients in order to simulate the environment of PZA/RIF/ISH association. The NIR-PLS models were optimized using a novel smart approach based on radial optimization (full range, 3 L V and MSC-D' and SNV-D' as pre-treatment, for raw material and FDC tablets, respectively). During the validation step, both methods showed no bias or systematic errors and yielded satisfactory recoveries (102.5 ± 3.1 % for drug substance and 98.7 ± 1.5 % for FDC tablets). When commercial drug substance was tested, NIR-PLS was able to predict the content of Form α (0.98 ± 0.01 w/w). The model for FDC tablets allowed estimating polymorphic purity in intact (0.984 ± 0.003 w/w), sectioned (0.986 ± 0.002 w/w), and powered (0.985 ± 0.004 w/w) tablets, showing the methodology could be applied to a different stage of the process (i.e premixed-powders or granulates). The suitability of the method was also verified when Form α was satisfactorily analysed in FDC fortified with Form δ and Form γ to reach 0.78, 0.88 and 0.98 w/w, Form α. This strategy results in an excellent alternative to ensure the polymorphic purity of PZA throughout the overall pharmaceutical manufacturing process.

New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence.

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.

Fast spectroscopic monitoring of inhibitors in the 2G ethanol process.

One of the main challenges of second generation (2G) ethanol production is the high quantities of phenolic compounds and furan derivatives generated in the pretreatment of the lignocellulosic biomass, which inhibit the enzymatic hydrolysis and fermentation steps. Fast monitoring of these inhibitory compounds could provide better control of the pretreatment, hydrolysis, and fermentation processes by enabling the implementation of strategic process control actions. We investigated the feasibility of monitoring these inhibitory compounds by ultraviolet-visible (UV-Vis) spectroscopy associated with partial least squares (PLS) regression. Hydroxymethylfurfural, furfural, vanillin, and ferulic and p-coumaric acids generated during different severities of liquid hot water pretreatment of sugarcane bagasse were quantified with highly accuracy. In cross-validation (leave-one-out), the PLS-UV-Vis method presented root mean square error of prediction (RMSECV) of around only 5.0%. The results demonstrated that the monitoring performance achieved with PLS-UV-Vis could support future studies of optimization and control protocols for application in industrial processes.

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination.

Phenolic compounds of Brazilian beers from different types and styles and application of chemometrics for modeling antioxidant capacity.

In the present study we aimed at investigating, for the first time, phenolic compounds in Brazilian beers of different types and styles. We also aimed at applying chemometrics for modeling beer's antioxidant capacity as a function of their physicochemical attributes (density, refractive index, bitterness and ethanol content). Samples (n=29) were analyzed by PCA originating five groups, especially according to ethanol contents and bitterness. In general, Group V (alcoholic beers with very high bitterness) presented higher refractive index, bitterness, ethanol and phenolics contents than Groups I (non-alcoholic beers) and II (alcoholic beers with low bitterness). Brazilian beers phenolics profile was distinct from that of European beers, with high contents of gallic acid (0.5-14.7 mg/L) and low contents of ferulic acid (0.2-1.8 mg/L). Using PLS, beer's antioxidant capacity measured by FRAP assay could be predicted with acceptable precision by data of ethanol content and density, bitterness and refractive index values.

Quantitative analysis of mebendazole polymorphs in pharmaceutical raw materials using near-infrared spectroscopy.

This work evaluates the feasibility of using NIR spectroscopy for quantification of three polymorphs of mebendazole (MBZ) in pharmaceutical raw materials. Thirty ternary mixtures of polymorphic forms of MBZ were prepared, varying the content of forms A and C from 0 to 100% (w/w), and for form B from 0 to 30% (w/w). Reflectance NIR spectra were used to develop partial least square (PLS) regression models using all spectral variables and the variables with significant regression coefficients selected by the Jack-Knife algorithm (PLS/JK). MBZ polymorphs were quantified with RMSEP values of 2.37% w/w, 1.23% w/w and 1.48% w/w for polymorphs A, B and C, respectively. This is an easy, fast and feasible method for monitoring the quality of raw pharmaceutical materials of MBZ according to polymorph purity.

Evaluation of analytical tools and multivariate methods for quantification of co-former crystals in ibuprofen-nicotinamide co-crystals.

Co-crystals are multicomponent substances designed by the addition of two or more different molecules in a same crystallographic pattern, in which it differs from the crystallographic motif of its co-formers. The addition of highly soluble molecules, like nicotinamide, in the crystallographic pattern of ibuprofen enhances its solubility more than 7.5 times, improving the properties of this widely used drug. Several analytical solid state techniques are used to characterize the ibuprofen-nicotinamide co-crystal, being the most used: mid-infrared (ATR-FTIR), differential scanning calorimetry (DSC), X-ray diffraction (XRPD) and Raman spectroscopy. These analytical solid state techniques were evaluated to quantify a mixture of ibuprofen-nicotinamide co-crystal and its co-formers in order to develop a calibration model to evaluate the co-crystal purity after its synthesis. Raman spectroscopy showed better result than all other techniques with a combination of multivariate calibration tools, presenting lower values of calibration and prediction errors. The partial least squares regression model gave a mean error lower than 5% for all components presented in the mixture. DSC and mid-infrared spectroscopy proved to be insufficient for quantification of the ternary mixture. XRPD presented good results for quantification of the co-formers, ibuprofen and nicotinamide, but fair results for the co-crystal. This is the first report of quantification of ibuprofen-nicotinamide co-crystal, among its co-formers. The quantification is of great importance to determine the yield of the co-crystallization reactions and the purity of the product obtained.