Effect of Cisplatin and Its Cationic Analogues in the Phase Behavior and Permeability of Model Lipid Bilayers.

Increasing evidence suggests a critical role of lipids in both the mechanisms of toxicity and resistance of cells to platinum(II) complexes. In particular, cisplatin and other analogues were reported to interact with lipids and transiently promote lipid phase changes both in the bulk membranes and in specific membrane domains. However, these processes are complex and not fully understood. In this work, cisplatin and its cationic species formed at pH 7.4 in low chloride concentrations were tested for their ability to induce phase changes in model membranes with different lipid compositions. Fluorescent probes that partition to different lipid phases were used to report on the fluidity of the membrane, and a leakage assay was performed to evaluate the effect of cisplatin in the permeability of these vesicles. The results showed that platinum(II) complex effects on membrane fluidity depend on membrane lipid composition and properties, promoting a stronger decrease in the fluidity of membranes containing gel phase. Moreover, at high concentration, these complexes were prone to alter the permeability of lipid membranes without inducing their collapse or aggregation.

Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening.

Urease is a metalloenzyme that catalyzes the hydrolysis of urea, and its modulation has an important role in both the agricultural and medical industry. Even though numerous molecules have been tested against ureases of different species, their clinical translation has been limited due to chemical and metabolic stability as well as side effects. Therefore, screening new compounds against urease would be of interest in part due to rising concerns regarding antibiotic resistance. In this work, we collected and curated a diverse set of 2640 publicly available small-molecule inhibitors of jack bean urease and developed a classifier using a random forest machine learning method with high predictive performance. In addition, the physicochemical features of compounds were paired with molecular docking and protein-ligand fingerprint analysis to gather insight into the current activity landscape. We observed that the docking score could not differentiate active from inactive compounds within each chemical family, but scores were correlated with compound activity when all compounds were considered. Additionally, a decision tree model was built based on 2D and 3D Morgan fingerprints to mine patterns of the known active-class compounds. The final machine learning model showed good prediction performance against the test set (81% and 77% precision for active and inactive compounds, respectively). Finally, this model was employed, as a proof-of-concept, on an in-house library to predict new hits that were then tested against urease and found to be active. This is, to date, the largest, most diverse dataset of compounds used to develop predictive in silico models. Overall, the results highlight the usefulness of using machine learning classifiers and molecular docking to predict novel urease inhibitors.

Evaluation of Encapsulation Potential of Selected Star-Hyperbranched Polyglycidol Architectures: Predictive Molecular Dynamics Simulations and Experimental Validation.

Polymers, including non-linear copolymers, have great potential in the development of drug delivery systems with many advantages, but the design requires optimizing polymer-drug interactions. Molecular dynamics (MD) simulations can provide insights into polymer-drug interactions for designing delivery systems, but mimicking formulation processes such as drying is often not included in in silico studies. This study demonstrates an MD approach to model drying of systems comprising either hydrophilic tinidazole or hydrophobic clotrimazole drugs with amphiphilic hyperbranched copolyethers. The simulated drying protocol was critical for elucidating drug encapsulation and binding mechanisms. Experimentally, two polymers were synthesized and shown to encapsulate clotrimazole with up to 83% efficiency, guided by interactions with the hydrophobic core observed in simulations. In contrast, tinidazole is associated with surface regions, indicating capacity differences between drug types. Overall, this work highlights MD simulation of the drying process as an important tool for predicting drug-polymer complex behaviour. The modelled formulation protocol enabled high encapsulation efficiency and opened possibilities for the design of delivery systems based on computationally derived binding mechanisms. This demonstrates a computational-experimental approach where simulated drying was integral to elucidating interactions and developing optimized complexes, emphasizing the value of molecular modelling for the development of drug delivery formulations.

Exploring the Chemical Space of Urease Inhibitors to Extract Meaningful Trends and Drivers of Activity.

Blocking the catalytic activity of urease has been shown to have a key role in different diseases as well as in different agricultural applications. A vast array of molecules have been tested against ureases of different species, but the clinical translation of these compounds has been limited due to challenges of potency, chemical and metabolic stability as well as promiscuity against other proteins. The design and development of new compounds greatly benefit from insights from previously tested compounds; however, no large-scale studies surveying the urease inhibitors' chemical space exist that can provide an overview of developed compounds to data. Therefore, given the increasing interest in developing new compounds for this target, we carried out a comprehensive analysis of the activity landscape published so far. To do so, we assembled and curated a data set of compounds tested against urease. To the best of our knowledge, this is the largest data set of urease inhibitors to date, composed of 3200 compounds of diverse structures. We characterized the data set in terms of chemical space coverage, molecular scaffolds, distribution with respect to physicochemical properties, as well as temporal trends of drug development. Through these analyses, we highlighted different substructures and functional groups responsible for distinct activity and inactivity against ureases. Furthermore, activity cliffs were assessed, and the chemical space of urease inhibitors was compared to DrugBank. Finally, we extracted meaningful patterns associated with activity using a decision tree algorithm. Overall, this study provides a critical overview of urease inhibitor research carried out in the last few decades and enabled finding underlying SAR patterns such as under-reported chemical functional groups that contribute to the overall activity. With this work, we propose different rules and practical implications that can guide the design or selection of novel compounds to be screened as well as lead optimization.

Experimental and numerical evaluation of a new visor concept with aerodynamic sealing to protect medical professionals from contaminated droplets and aerosols.

The fast spreading of the SARS-CoV-2 virus led to a significant increase in the demand for personal protective equipment (PPE). Healthcare professionals, mainly dentists, work near the patients, increasing their risk of infection. This paper investigates the effectiveness of an air-curtain sealing effect in a newly designed visor developed to reduce the risk of contracting a respiratory infection. This PPE was developed by computational fluid dynamics (CFD) modeling. CFD results show that the aerodynamic sealing in this PPE device effectively protects the user's face by 43% from a contaminated environment. The experiments considered two different tests: one using a tracer gas (CO2 ) to simulate a gaseous contaminant inside and outside the PPE face shield and a second test using smoke to simulate aerosol transport and evaluate the PPE efficiency. The particle concentration within the aerodynamically sealed PPE was evaluated and compared with the protection efficiency of other PPE. Results show similar protection levels for particles in the 1-5 µm range between the prototype and a KN95 respirator. The combined use of this novel PPE with aerodynamic sealing and a physical mask (KN95 or surgical) produced protection efficiency values within the range of 57%-70% for particles greater than 0.5 µm. This study reveals the potential of using an air curtain combined with a face shield to reduce the risks from contaminated environments.

Practical computational toolkits for dendrimers and dendrons structure design.

Dendrimers and dendrons offer an excellent platform for developing novel drug delivery systems and medicines. The rational design and further development of these repetitively branched systems are restricted by difficulties in scalable synthesis and structural determination, which can be overcome by judicious use of molecular modelling and molecular simulations. A major difficulty to utilise in silico studies to design dendrimers lies in the laborious generation of their structures. Current modelling tools utilise automated assembly of simpler dendrimers or the inefficient manual assembly of monomer precursors to generate more complicated dendrimer structures. Herein we describe two novel graphical user interface toolkits written in Python that provide an improved degree of automation for rapid assembly of dendrimers and generation of their 2D and 3D structures. Our first toolkit uses the RDkit library, SMILES nomenclature of monomers and SMARTS reaction nomenclature to generate SMILES and mol files of dendrimers without 3D coordinates. These files are used for simple graphical representations and storing their structures in databases. The second toolkit assembles complex topology dendrimers from monomers to construct 3D dendrimer structures to be used as starting points for simulation using existing and widely available software and force fields. Both tools were validated for ease-of-use to prototype dendrimer structure and the second toolkit was especially relevant for dendrimers of high complexity and size.

Design of polymeric nanoparticles and its applications as drug delivery systems for acne treatment.

OBJECTIVE: The aim of this study was to evaluate a formulation made of poly(lactide-co-glycolide) (PLGA) nanoparticles containing azelaic acid for potential acne treatment. METHODS: Azelaic acid-loaded PLGA nanoparticles were prepared by spontaneous emulsification processes using poloxamer 188 as stabilizer. Several manufacturing parameters such as stirring rate, concentration of stabilizer and different recovery methods were investigated. Nanoparticles were evaluated in terms of size, zeta potential, encapsulation efficiency, release kinetics and permeation kinetics in vitro. Furthermore, in vitro toxicological studies were performed in Saccharomyces cerevisiae model. RESULTS: The results showed that by adjusting some formulation conditions it was possible to obtain nanoparticles with high loading and a controlled drug release. Freeze-dried recovery altered the nanoparticles structure by enhancing porous structures and mannitol was required to control the mean particle size. The centrifugation recovery was found to be the best approach to nanoparticles recovery. Similar toxicity profiles were observed for both drug-free and azelaic acid-loaded nanoparticles, with concentration-dependent decreases in cell viability. CONCLUSION: These results indicate a potential formulation for controlled release delivery of azelaic acid to the follicular unit.

Molecular Modeling to Study Dendrimers for Biomedical Applications.

Molecular modeling techniques provide a powerful tool to study the properties of molecules and their interactions at the molecular level. The use of computational techniques to predict interaction patterns and molecular properties can inform the design of drug delivery systems and therapeutic agents. Dendrimers are hyperbranched macromolecular structures that comprise repetitive building blocks and have defined architecture and functionality. Their unique structural features can be exploited to design novel carriers for both therapeutic and diagnostic agents. Many studies have been performed to iteratively optimise the properties of dendrimers in solution as well as their interaction with drugs, nucleic acids, proteins and lipid membranes. Key features including dendrimer size and surface have been revealed that can be modified to increase their performance as drug carriers. Computational studies have supported experimental work by providing valuable insights about dendrimer structure and possible molecular interactions at the molecular level. The progress in computational simulation techniques and models provides a basis to improve our ability to better predict and understand the biological activities and interactions of dendrimers. This review will focus on the use of molecular modeling tools for the study and design of dendrimers, with particular emphasis on the efforts that have been made to improve the efficacy of this class of molecules in biomedical applications.

Development and evaluation of a novel topical treatment for acne with azelaic acid-loaded nanoparticles.

Azelaic acid (AzA) is used in the treatment of acne. However, side effects and low compliance have been associated with several topical treatments with AzA. Nanotechnology presents a strategy that can overcome these problems. Polymeric nanoparticles can control drug release and targeting and reduce local drug toxicity. The aim of this study was to produce and evaluate an innovative topical treatment for acne with AzA-loaded poly-DL-lactide/glycolide copolymer nanoparticles. A soft white powder of nanoparticles was prepared. The mean size of loaded nanoparticles was < 400 nm and zeta potential was negative. Spherical nanoparticles were observed by scanning electron microscopy. Encapsulation efficiency was around 80% and a strong interaction between the polymer and the drug was confirmed by differential scanning calorimetric analysis. In vitro drug release studies suggested a controlled and pulsatile release profile. System efficacy tests suggested similar results between the loaded nanoparticles and the nonencapsulated drug against the most common bacteria associated with acne. Cytotoxicity of AzA-loaded nanoparticles was concentration dependent, although not pronounced. The occluded patch test seemed to indicate that the formulation excipients were safe and thus AzA-loaded nanoparticles appear to be an efficient and safe treatment for acne.

Core-shell polycationic polyurea pharmadendrimers: new-generation of sustainable broad-spectrum antibiotics and antifungals.

The efficacy of conventional antimicrobials is falling to critical levels and raising alarming concerns around the globe. In this scenery, engineered nanoparticles emerged as a solid strategy to fight growing deadly infections. Here, we show the in vitro and in vivo performance of pharmadendrimers, a novel class of engineered polyurea dendrimers that are synthetic mimics of antibacterial peptides, against a collection of both Gram-positive and Gram-negative bacteria and fungi. These nanobiomaterials are stable solids prepared by low-cost and green processes, display a dense positively charged core-shell, and are biocompatible and hemocompatible drugs. Mechanistic data, corroborated by coarse-grained molecular dynamics simulations, points towards a fast-killing mechanism via membrane disruption, triggered by electrostatic interactions. Altogether this study provides strong evidence and support for the future use of polyurea pharmadendrimers in antibacterial and antifungal nanotherapeutics.