Antimicrobial resistance (AMR) poses a serious global health concern, resulting in a significant number of deaths annually due to infections that are resistant to treatment. Amidst this crisis, antimicrobial peptides (AMPs) have emerged as promising alternatives to conventional antibiotics (ATBs). These cationic peptides, naturally produced by all kingdoms of life, play a crucial role in the innate immune system of multicellular organisms and in bacterial interspecies competition by exhibiting broad-spectrum activity against bacteria, fungi, viruses, and parasites. AMPs target bacterial pathogens through multiple mechanisms, most importantly by disrupting their membranes, leading to cell lysis. However, bacterial resistance to host AMPs has emerged due to a slow co-evolutionary process between microorganisms and their hosts. Alarmingly, the development of resistance to last-resort AMPs in the treatment of MDR infections, such as colistin, is attributed to the misuse of this peptide and the high rate of horizontal genetic transfer of the corresponding resistance genes. AMP-resistant bacteria employ diverse mechanisms, including but not limited to proteolytic degradation, extracellular trapping and inactivation, active efflux, as well as complex modifications in bacterial cell wall and membrane structures. This review comprehensively examines all constitutive and inducible molecular resistance mechanisms to AMPs supported by experimental evidence described to date in bacterial pathogens. We also explore the specificity of these mechanisms toward structurally diverse AMPs to broaden and enhance their potential in developing and applying them as therapeutics for MDR bacteria. Additionally, we provide insights into the significance of AMP resistance within the context of host–pathogen interactions.

This review aims to cover the topic of polycarbonate synthesis via ring-opening polymerization (ROP) of cyclic carbonates. We report a wide variety of ROP-initiating systems along with their detailed mechanisms. We focus on the challenges of preparing the polymers; the precise control of the properties of the materials, including molecular weight; the compositions of the copolymers and their structural characteristics. There is no one approach that works for all scales in cyclic carbonates ROP. A green process to produce polycarbonates is a luring challenge in terms of CO₂ utilization and the targeted domains for application. The main resolution seems to be the use of controlled incorporation of functional/reactive groups into polymer chains that can tailor the physicochemical and biological properties of the polymer matrices, producing what appears to be an unlimited field of applications. Glycerol carbonate (GC) is prepared from renewable glycerol and considered as a CO₂ fixation agent resulting in GC compound. This family of five-membered cyclic carbonates has attracted the attention of researchers as potential monomers for the synthesis of polycarbonates (PCs). This cyclic carbonate group presents a strong alternative to Bisphenol A (BPA), which is used mainly as a monomer for the production of polycarbonate and a precursor of epoxy resins. As of December 2016, BPA is listed as a substance of very high concern (SVHC) under the REACH regulation. In 2006, Mouloungui et al. reported the synthesis and oligomerization of GCs. The importance of GCs goes beyond their carbonate ring and their physical properties (high boiling point, high flash point, low volatility, high electrical conductivity) because they also contain a hydroxyl group. The latter offers the possibility of producing oligo and/or polycarbonate compounds that have hydroxyl groups that can potentially lead to different reaction mechanisms and the production of new classes of polycarbonates with a wide range of applications.

In this study, we evaluated the physicochemical properties related to the previously reported anticancer activity of a dataset comprising thirty 1,2-dihydropyridine derivatives. We utilized Principal Component Analysis (PCA) to identify the most significant influencing factors. The PCA analysis showed that the first two principal components accounted for 59.91% of the total variance, indicating a strong correlation between the molecules and specific descriptors. Among the 239 descriptors analyzed, 18 were positively correlated with anticancer activity, clustering with the 12 most active compounds based on their IC₅₀ values. Six of these variables—LogP, Csp3, b_1rotN, LogS, TPSA, and lip_don—are related to drug-likeness potential. Thus, we then ranked the 12 compounds according to these six variables and excluded those violating the drug-likeness criteria, resulting in a shortlist of nine compounds. Next, we investigated the binding affinity of these nine shortlisted compounds with the use of molecular docking towards the PIM-1 Kinase enzyme (PDB: 2OBJ), which is overexpressed in various cancer cells. Compound 6 exhibited the best docking score among the docked compounds, with a docking score of −11.77 kcal/mol, compared to −12.08 kcal/mol for the reference PIM-1 kinase inhibitor, 6-(5-bromo-2-hydroxyphenyl)-2-oxo-4-phenyl-1,2-dihydropyridine-3-carbonitrile. To discover new PIM-1 kinase inhibitors, we designed nine novel compounds featuring hybrid structures of compound 6 and the reference inhibitor. Among these, compound 31 displayed the best binding affinity, with a docking score of −13.11 kcal/mol. Additionally, we performed PubChem database mining using the structure of compound 6 and the similarity search tool, identifying 16 structurally related compounds with various reported biological properties. Among these, compound 52 exhibited the best binding affinity, with a docking score of −13.03 kcal/mol. Finally, molecular dynamics (MD) studies were conducted to confirm the stability of the protein–ligand complexes obtained from docking the studied compounds to PIM-1 kinase, validating the potential of these compounds as PIM-1 kinase inhibitors.

To find potential inhibitors of Naegleria fowleri S-adenosyl-L-homocysteine hydrolase (NfSAHH), a brain-eating parasite, structure-based drug design was adopted. N. fowlerica causes primary amebic meningoencephalitis (PAM), a fatal central nervous system (CNS) disorder if untreated. NfSAHH protein (PDB ID: 5v96), involved in parasite growth and gene regulation, was targeted and screened against 163 metabolites from Gossypium hirsutum (cotton plant). With the aid of different software and web tools, the metabolites were subjected to several analyses. The RMSD was evaluated to validate our molecular docking strategy. Neplanocin A, a common anti-parasitic medication, was used as a reference to select top ligands for post-docking studies. Significant interactions were observed with residues THR-198, HIS-395, and MET-400. The drug-likeness of the top fifty hits was analyzed using Lipinski, Ghose, Veber, Egan, and Muegge rules. The top ten compounds following Lipinski’s RO5 were studied regarding medicinal chemistry, pharmacokinetic simulation, and Swiss target prediction. Advanced strategies, including molecular dynamic simulations, binding energy calculations, and principal component analysis, were employed for the top three hits, namely curcumin, heliocide H2, and piceid, which indicated that heliocide H2 is the most promising candidate, while curcumin and piceid may need further optimization to improve their stability. Overall, the top ten phytochemicals, dotriacontanol, melissic acid, curcumin, 6,6′-dimethoxygossypol, phytosphingosine 2, methyl stearate, stearic acid, piceid, heliocide H2, and 6-methoxygossypol, reported in our study, are worthy enough to be subjected to in vivo and in vitro experimentation to find a novel drug to treat PAM.

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related to manufacturing processes. Initially, a comprehensive PCA approach was employed for the entire dataset, revealing a moderate influence of the first two principal components (PCs), which collectively accounted for around 38% of the variance. Membrane samples exhibited close proximity, which prevented the formation of any clusters. To address this limitation, a subset acquisition strategy was followed, based on the findings of the PCA for the entire dataset. This resulted in an enhanced overall contribution and the revelation of diverse patterns among the membranes and the considered manufacturing factors (total variance between 55% and 77%). The segmentation of the data unveiled a robust correlation between silica (SiO₂) concentration and pervaporation conditions. Additionally, a notable clustering of the chemical compositions of the preparation solutions underscored their significant influence on the operational efficacy of MFI zeolite membranes. On the other hand, an exclusive chemical composition of the preparation solution was noticed. This highlighted the high influence of the chemical composition on the operational efficiency of MFI zeolite membranes. The coupling of PCA with experimental results can provide a data-driven enhancement strategy for the manufacturing of MFI-type zeolite membranes used for ethanol/water separation.