Search

Non-small cell lung cancer (NSCLC) is among the main causes of mortality from cancer around the globe, affecting all genders. Current treatments mainly focus on tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, resistance mechanisms, such as the emergence of T790M and C797S EGFR mutations and upregulation of VEGFR-2, often hinder the effectiveness of TKIs. Thereby, EGFR and VEGFR-2 present an intriguing opportunity for the treatment of NSCLC by developing dual-acting drugs. This research aims to evaluate prospective Moringa oleifera L. (MO)-originated compounds to efficiently block both of these receptors. In our research, we screened a library of 200 compounds sourced from MO, a plant known for its remarkable therapeutic potential. We identified five intriguing phytocompounds: hesperetin, gossypetin, quercetin, gallocatechin, and epigallocatechin, as potential anti-cancer agents. The compounds have demonstrated notable binding affinity in virtual screening and multi-stage molecular docking analysis, surpassing the controls, Erlotinib and Bevacizumab + Rituximab. In addition, these compounds demonstrate top-notch drug-likeness and ADMET properties. The five promising drug candidates also had a strong ability to bind to receptors and stayed stable with them during the 200 ns molecular dynamics (MD) simulation and MM-GBSA calculation. Furthermore, DFT analysis indicates that hesperetin, gossypetin, and quercetagetin stand out as the most promising drug candidates among all others. The findings of our study suggest that these three therapeutic candidates can precisely target both EGFR and VEGFR-2 and can potentially act on both of these pathways as a single agent., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Among the different chemotherapeutic classes available today, the 6-fluoroquinolone (6-FQ) antibacterials are still one of the most effective cures in fighting tuberculosis (TB). Nowadays, the development of novel 6-FQs for treatment of TB mainly depends on understanding how the structural modifications of the main quinolone scaffold at specific positions affect the anti-mycobacterial activity. Alongside the structure-activity relationship (SAR) studies of the 6-FQ antibacterials, which can be considered as a golden rule in the development of novel active antitubercular 6-FQs, the structure side effects relationship (SSER) of these drugs must be also taken into account. In the present study we focus on a proficient implementation of the existing knowledge-based expert systems for design of novel 6-FQ antibacterials with possible enhanced biological activity against Mycobaterium tuberculosis as well as lower toxicity. Following the SAR in silico studies of the quinolone antibacterials against M. tuberculosis performed in our laboratory, a large set of 6-FQs was selected. Several new 6-FQ derivatives were proposed as drug candidates for further research and development. The 6- FQs identified as potentially effective against M. tuberculosis were subjected to an additional SSER study for prediction of their toxicological profile. The assessment of structurally-driven adverse effects which might hamper the potential of new drug candidates is mandatory for an effective drug design. We applied publicly available knowledge-based (expert) systems and Quantitative Structure-Activity Relationship (QSAR) models in order to prepare a priority list of active compounds. A preferred order of drug candidates was obtained, so that the less harmful candidates were identified for further testing. TOXTREE expert system as well as some QSAR models developed in the framework of EC funded project CAESAR were used to assess toxicity. CAESAR models were developed according to the OECD principles for the validation of QSAR and they turn to be appropriate tools for in silico tests regarding five different toxicity endpoints. Those endpoints with high relevance for REACH are: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. We used the above-mentioned freely available models to select a set of less harmful active 6-FQs as candidates for clinical studies.

Previously published data (Gasperlin et al., 1998) on viscoelastic behaviour of lipophilic semisolid emulsion systems and the prediction of their physical stability by neural network modelling are analysed in further detail. Most attention has been paid to viscosity, which with storage (G') and loss modulus (G"), is one of the most important rheological parameters influenced by structure. Complex dynamic viscosity (eta*) was measured by oscillatory rheometry. The viscosity dependence of the lipophilic semisolid emulsions on the ratio of the particular components was defined by the neural network (error back-propagation algorithm), linear and incomplete polynomial models of higher orders. Polynomial models were used to complement the neural network model and to determine the relationship between variables. Since the viscosity was expressed in the whole measured frequency range, modelling was more complex and indirect modelling was introduced. The determined models were tested and the results confirm their usefulness for the explanation and prediction of the rheological characteristics of emulsion systems. The trained and tested neural network model proved to be a highly effective and applicable tool for predicting the viscosity of a lipophilic semisolid emulsion system of given composition.