Photodynamic therapy (PDT) is a promising cancer treatment which involves a photosensitizer (PS), light at a specific wavelength for PS activation and oxygen, which combine to elicit cell death. While the illumination required to activate a PS imparts a certain amount of selectivity to PDT treatments, poor tumor accumulation and cell internalization are still inherent properties of most intravenously administered PSs. As a result, common consequences of PDT include skin photosensitivity. To overcome the mentioned issues, PSs may be tailored to specifically target overexpressed biomarkers of tumors. This active targeting can be achieved by direct conjugation of the PS to a ligand with enhanced affinity for a target overexpressed on cancer cells and/or other cells of the tumor microenvironment. Alternatively, PSs may be incorporated into ligand-targeted nanocarriers, which may also encompass multi-functionalities, including diagnosis and therapy. In this review, we highlight the major advances in active targeting of PSs, either by means of ligand-derived bioconjugates or by exploiting ligand-targeting nanocarriers.

Various cationic photosensitizers employed in antimicrobial photodynamic therapy (aPDT) have the ability to photoinactivate planktonic bacteria under conditions of low phototoxicity to mammalian cells and without generating antimicrobial resistance (AMR). However, the photoinactivation of biofilms requires orders-of-magnitude higher photosensitizer concentrations, which become toxic to host cells. Remarkably, the bactericidal effect of a dicationic di-imidazolyl chlorin toward planktonic S. aureus and E. coli was observed in this work for concentrations below 400 nM under illumination at 660 nm and below 50 mu M for the corresponding biofilms. At the latter concentrations, the chlorin is phototoxic toward human keratinocyte cells. However, in the presence of 50 mM KI, bactericidal concentrations are reduced to less than 50 nM for planktonic bacteria and to less than 1 mu M for biofilms. It is shown that the potentiation with KI involves the triiodide anion. This potentiation elicits a bactericidal effect without appreciable cytotoxicity to keratinocytes. It becomes possible to selectively inactivate biofilms with aPDT. An exploratory study treating mice with wounds infected with E. coli expressing GFP with 20 mu M chlorin and 120 J cm(-2) at 652 nm confirmed the potential of this chlorin to control localized infections.

Interleukin-1 receptor type 1 (IL-1R1) is a key player in inflammation and immune responses. This receptor regulates IL-1 activity in two forms: as a membrane-bound form and as a soluble ectodomain. The details and differences between the conformational dynamics of the membrane-bound and the soluble IL-1R1 ectodomains (ECDs) remain largely elusive. Here, we study and compare the structural dynamics of the soluble and membrane-bound IL-1R1-ECDs using molecular dynamics (MD) simulations, focusing on the flexible interdomain linker of the ECD, as well as the spatial rearrangements between the Ig-like domains of the ECD. To explore the membrane-bound conformations, a full-length IL-1R1 structural model was developed and subjected to classical equilibrium MD. Comparative analysis of multiple MD trajectories of the soluble and the membrane-bound IL-1R1-ECDs reveals that (i) as somewhat expected, the extent of the visited “open-to-closed” transitional states differs significantly between the soluble and membrane-bound forms; (ii) the soluble form presents open-closed transitions, sampling a wider rotational motion between the Ig-like domains of the ECD, visiting closed and “twisted” conformations in higher extent, whereas the membrane-bound form is characterized by more conformationally restricted states; (iii) interestingly, the backbone dihedral angles of residues Glu202, Glu203 and Asn204, located in the flexible linker, display the highest variations during the transition between discrete conformational states detected in IL-1R1, thus appearing to work as the “central wheel of a clock’s movement”. The simulations and analyses presented in this contribution offer a deeper insight into the structure and dynamics of IL-1R1, which may be explored in a drug discovery setting.

Antibiotic resistance in Helicobacter pylori is increasing rapidly and emerging as a major factor in treatment failure. We aimed to identify genetic mutations associated with resistance to clarithromycin (23S rRNA peptidyl transferase), fluoroquinolones (gyrA), and metronidazole (rdxA), and to explore their mechanisms of action through molecular modeling. H. pylori detection and the molecular characterization of genes were conducted directly on gastric biopsies by real-time PCR followed by nucleotide sequencing. A 3D model was used to evaluate molecular interactions between the antibiotics and respective target proteins. H. pylori was identified in 66.7% of 33 patients. An analysis of 23SrRNA revealed novel mutations that, by in silico analysis, do not appear to contribute to clarithromycin resistance. In gyrA, mutations in amino acid residues 87 and 91 had an incidence of 27%, and the in silico analysis revealed that these positions are relevant in the binding and resistance to fluoroquinolones. It is also reported for other mutations, some of which are never described. All rdxA mutations were missense, with R16H, M56V, H97T, G98S, A118T, V123T, and R131K predicted by in silico analysis to impact metronidazole resistance. Monitoring H. pylori gene mutations is crucial for tailoring effective antibiotic therapies. Our study advances personalized medicine by introducing novel methods to detect resistance-related mutations and uncovering the molecular mechanisms driving this resistance.

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.