Naltrexone (NTX) and bupropion (BUP) are used in combination in clinical practice for obesity; however, the existing analytical methods for this drug combination are not sustainable. This work aims to develop an analytical method that is faster, easier and less expensive compared with high performance liquid chromatography (HPLC) and involves green technology without solvents to quantify BUP and NTX in combination. The objective was to validate a method using mid-infrared (MIR) spectroscopy associated with multivariate calibration and chemometrics to simultaneously measure NTX and BUP in a pharmaceutical capsule form. The models were developed using MIR spectroscopy with diffuse reflectance, with interval partial least squares (iPLS) variable selection. The working range selected to optimize the model was from 1885.8 to 1585.4 cm-1. The chosen model was obtained with partial least squares (PLS2) and with data pre-processed by first derivative Savitzky-Golay smoothing followed by mean centering, using four latent variables, providing a root mean square error of prediction of 1.8 mg g-1 for NTX and 6.42 mg g-1 for BUP. The method was validated according to current international standards. In conclusion, the methods developed in the MIR region provided statistically similar results to the validated chromatographic method for commercial pharmaceutical formulations.

To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

Analytical validation has fundamental importance in the scope of Good Manufacturing Practice (GMP) for pharmaceutical products since it establishes scientific evidence that an analytical procedure provides reliable results. However, even with validation guidelines available it is very common to observe misunderstandings in the execution of validation and data interpretation. The misguided approaches of validation guidelines, allied with a disregard for the peculiarities of the analytical techniques, the nature of the sample, and the analytical purpose, have significantly contributed to oversights in analytical validation. This work aims to present a critical overview of the validation process in pharmaceutical analysis, addressing relevant aspects of various analytical performance parameters, their different means of accomplishment and limitations in face of the analytical techniques, the nature of the sample, and the analytical purpose. To help in the planning and execution of the validation process, some case studies are discussed, mainly in the area of high-performance liquid chromatography (HPLC).

Currently, treatment of Chagas disease is restricted to the use of benznidazole and nifurtimox; however, nifurtimox was temporarily suspended in some countries. Based on the limitations in pharmacological treatments, it is essential to guarantee the quality of benznidazole. Therefore, this work aimed to identify the degradation products of benznidazole and to validate a stability indicating method. The stability test was performed through a forced degradation study, exposing benznidazole under a range of conditions. Benznidazole proved to be unstable under acidic and alkaline conditions, and three products were identified by liquid chromatography coupled to a hybrid quadrupole time-of-flight mass spectrometer (UPLC-QTOF-MS). Furthermore, a stability indicating method by high performance liquid chromatography with a diode array detector (HPLC-DAD) for benznidazole was developed and validated according to ICH (International Conference on Harmonisation) and ANVISA (Agência Nacional de Vigilância Sanitária) guidelines. The method was linear (180-420 µg mL-1 range), selective in the presence of degradation products, precise (relative standard deviation (RSD) < 0.6%), accurate (recovery > 99.2%), robust, and free of matrix effects. The method was successfully able to quantify the benznidazole in pharmaceutical formulations.