Array-based optical sensing is an efficient technique for the determination and discrimination of small organic molecules. This study is aimed at the development of a simple and rapid strategy for obtaining an optical response from a wide range of low-molecular-weight organic compounds. We have suggested a colorimetric and fluorimetric sensing platform based on the combination of two response mechanisms using carbocyanine dyes: aggregation and oxidation. In the first one, the analyte forms ternary aggregates with an oppositely charged surfactant wherein the dye is solubilized in the hydrophobic domains of the surfactant accompanied with fluorescent enhancement. The second mechanism is based on the effect of the analyte on the catalytic reaction rate of dye oxidation with H₂O₂ in the presence of a metal ion (Cu²⁺, Pd²⁺), which entails fluorescence waning and color change. The reaction mixture in a 96-well plate is photographed in visible light (colorimetry) and the near-IR region under red light excitation (fluorimetry). In this proof-of-concept study, we demonstrated the feasibility of discrimination of nine medicinal compounds using principal component analysis: four cephalosporins (ceftriaxone, cefazolin, ceftazidime, cefotaxime), three phenothiazines (promethazine, promazine, chlorpromazine), and two penicillins (benzylpenicillin, ampicillin) in an aqueous solution and in the presence of turkey meat extract. The suggested platform allows simple and rapid recognition of analytes of various nature without using spectral equipment, except for a photo camera.

Currently, molecular dynamics simulation is being widely applied to predict drug–polymer interaction, and to optimize drug delivery systems. Our study describes a combination of in silico and in vitro approaches aimed at improvement in polymer-based nanoparticle design for cancer treatment. We applied the PASS service to predict the biological activity of novel carboplatin derivatives. Subsequent molecular dynamics simulations revealed the dependence between the drug–polymer binding energy along with encapsulation efficacy, drug release profile, and the derivatives’ chemical structure. We applied ICP-MS analysis, the MTT test, and hemolytic activity assay to evaluate drug loading, antitumor activity, and hemocompatibility of the formulated nanoparticles. The drug encapsulation efficacy varied from 0.2% to 1% and correlated with in silico modelling results. The PLGA nanoparticles revealed higher antitumor activity against A549 human non-small-cell lung carcinoma cells compared to non-encapsulated carboplatin derivatives with IC₅₀ values of 1.40–23.20 µM and 7.32–79.30 µM, respectively; the similar cytotoxicity profiles were observed against H69 and MCF-7 cells. The nanoparticles efficiently induced apoptosis in A549 cells. Thus, nanoparticles loaded with novel carboplatin derivatives demonstrated high application potential for anticancer therapy due to their efficacy and high hemocompatibility. Our results demonstrated the combination of in silico and in vitro methods applicability for the optimization of encapsulation and antitumor efficacy in novel drug delivery systems design.

Optical sensor arrays are widely used in obtaining fingerprints of samples, allowing for solutions of recognition and identification problems. An approach to extending the functionality of the sensor arrays is using a kinetic factor by conducting indicator reactions that proceed at measurable rates. In this study, we propose a method for the discrimination of proteins based on their oxidation by sodium hypochlorite with the formation of the products, which, in turn, feature oxidation properties. As reducing agents to visualize these products, carbocyanine dyes IR-783 and Cy5.5-COOH are added to the reaction mixture at pH 5.3, and different spectral characteristics are registered every several minutes (absorbance in the visible region and fluorescence under excitation by UV (254 and 365 nm) and red light). The intensities of the photographic images of the 96-well plate are processed by principal component analysis (PCA) and linear discriminant analysis (LDA). Six model proteins (bovine and human serum albumins, γ-globulin, lysozyme, pepsin, and proteinase K) and 10 rennet samples (mixtures of chymosin and pepsin from different manufacturers) are recognized by the proposed method. The method is rapid and simple and uses only commercially available reagents.