A single MCR-ALS model for drug analysis in different formulations: Application on diazepam commercial preparations

Dual-responsive chondroitin sulfate self-assembling nanoparticles for combination therapy in metastatic cancer cells

In this study, we developed self-assembling nanoparticles (LCPs) able to trigger the release of Chlorambucil (Chl) and Doxorubicin (DOX) to MDA-MB-231 cells by exploiting the enzyme and redox signals. The DOX loaded LCPs was prepared by the self-assembly of two chondroitin sulphate (CS) derivatives, obtained by the covalent conjugation of Lipoic Acid (LA) and Chlorambucil (Chl) to the CS backbone. After the physic-chemical characterization of the conjugates by FT-IR, 1H NMR, and determination of the critical aggregation concentration, spherical nanoparticles with mean hydrodynamic diameter of 45 nm (P.D.I. 0.24) and Z-potential of - 44 mV were obtained by water addition/solvent evaporation method. In vitro experiments for the release of Chl and DOX were performed in healthy and cancer cells, using a cell culture media to maintain the physiological intracellular conditions (pH 7.4) (and concentration of esterase and GSH. The results allowed the selective release of the payloads to be detected: Chl release of 0 and 41% were obtained after 2 h incubation in normal and in cancer cells respectively, while values of 35 (in healthy cells) and 60% (in cancer cells) were recorded for DOX release after 96 h. Finally, viability studies proved the ability of the newly proposed nanosystem to enhance the cytotoxic activity of the two drugs against cancer cells.

Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review

The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification. We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set. To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w. We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.

Interaction of letrozole and its degradation products with aromatase: chemometric assessment of kinetics and structure-based binding validation

Letrozole is one of the most prescribed drugs for the treatment of breast cancer in post-menopausal women, and it is endowed with selective peripheral aromatase inhibitory activity. The efficacy of this drug is also a consequence of its long-lasting activity, likely due to its metabolic stability. The reactivity of cyano groups in the letrozole structure could, however, lead to chemical derivatives still endowed with residual biological activity. Herein, the chemical degradation process of the drug was studied by coupling multivariate curve resolution and spectrophotometric methodologies in order to assess a detailed kinetic profile. Three main derivatives were identified after drug exposure to different degradation conditions, consisting of acid-base and oxidative environments and stressing light. Molecular docking confirmed the capability of these compounds to accommodate into the active site of the enzyme, suggesting that the sustained inhibitory activity of letrozole may be at least in part attributed to the degradation compounds.

Monitoring the Isothermal Dehydration of Crystalline Hydrates Using Low-Frequency Raman Spectroscopy

Detection of the solid-state forms of pharmaceutical compounds is important from the drug performance point of view. Low-frequency Raman (LFR) spectroscopy has been demonstrated to be very sensitive in detecting the different solid-state forms of pharmaceutically relevant compounds. The potential of LFR spectroscopy to probe the in situ isothermal dehydration was studied using piroxicam monohydrate (PXM) and theophylline monohydrate (TPMH) as the model drugs. The dehydration of PXM and TPMH at four different temperatures (95, 100, 105, and 110 °C and 50, 60, 70, and 80 °C, respectively) was monitored in both the low- (20-300 cm^-1) and mid-frequency (335-1800 cm^-1) regions of the Raman spectra. Principal component analysis and multivariate curve resolution were applied for the analysis of the Raman data. Spectral differences observed in both regions highlighted the formation of specific anhydrous forms of piroxicam and theophylline from their respective monohydrates. The formation of the anhydrous forms was detected on different timescales (approx. 2 min) between the low and mid-frequency Raman regions. This finding highlights the differing nature of the vibrations being detected between these two spectral regions. Computational simulations performed were also in agreement with the experimental results, and allowed elucidating the origin of different spectral features.

Interpol review of controlled substances 2016-2019

This review paper covers the forensic-relevant literature in controlled substances from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

Simultaneous Determination of Drugs Affecting Central Nervous System (CNS) in Bulk and Pharmaceutical Formulations Using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)

The quality of medications is important to maintain the overall health care of patients. This study aims to develop and validate a spectrophotometric method using multivariate curve resolution-alternating least squares (MCR-ALS) with correlation constraint for simultaneous resolution and quantification of selected drugs affecting the central nervous system (imipramine, carbamazepine, chlorpromazine, haloperidol, and phenytoin) in different pharmaceutical dosage forms. Figures of merit such as root-mean-square error of prediction, bias, standard error of prediction, and relative error of prediction for the developed method were calculated. High values of correlation coefficients ranged between 0.9993 and 0.9998 reflected high predictive ability of the developed method. The results are linear in the concentration range of 0.3-5 μg/mL for carbamazepine, 0.3-15 μg/mL for chlorpromazine, 0.5-10 μg/mL for haloperidol, 0.5-10 μg/mL for imipramine, and 3-20 μg/mL for phenytoin. The optimized method was successfully applied for the analysis of the studied drugs in their pharmaceutical products without any separation step. The optimized method was also compared with a reported HPLC method using Student's t test and F ratio at 95% confidence level, and the results showed no significant difference regarding accuracy and precision. The proposed chemometric method is fast, reliable, and cost-effective and can be used as an eco-friendly alternative to chromatographic techniques for the analysis of the studied drugs in commercial pharmaceutical products.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

A two-step chemometric procedure was developed on the attenuated total reflection-Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi-skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square-discriminant analysis (PLS-DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.