DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

DDMut: predicting effects of mutations on protein stability using deep learning.

Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongoing limitations, including computational time, low predictive power, and biased predictions towards destabilising mutations. To fill this gap, we developed DDMut, a fast and accurate siamese network to predict changes in Gibbs Free Energy upon single and multiple point mutations, leveraging both forward and hypothetical reverse mutations to account for model anti-symmetry. Deep learning models were built by integrating graph-based representations of the localised 3D environment, with convolutional layers and transformer encoders. This combination better captured the distance patterns between atoms by extracting both short-range and long-range interactions. DDMut achieved Pearson's correlations of up to 0.70 (RMSE: 1.37 kcal/mol) on single point mutations, and 0.70 (RMSE: 1.84 kcal/mol) on double/triple mutants, outperforming most available methods across non-redundant blind test sets. Importantly, DDMut was highly scalable and demonstrated anti-symmetric performance on both destabilising and stabilising mutations. We believe DDMut will be a useful platform to better understand the functional consequences of mutations, and guide rational protein engineering. DDMut is freely available as a web server and API at https://biosig.lab.uq.edu.au/ddmut.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges.

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

Structural landscapes of PPI interfaces.

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.

DockNet: high-throughput protein-protein interface contact prediction.

MOTIVATION: Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results. RESULTS: Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained. AVAILABILITY AND IMPLEMENTATION: DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LEGO-CSM: a tool for functional characterization of proteins.

MOTIVATION: With the development of sequencing techniques, the discovery of new proteins significantly exceeds the human capacity and resources for experimentally characterizing protein functions. Localization, EC numbers, and GO terms with the structure-based Cutoff Scanning Matrix (LEGO-CSM) is a comprehensive web-based resource that fills this gap by leveraging the well-established and robust graph-based signatures to supervised learning models using both protein sequence and structure information to accurately model protein function in terms of Subcellular Localization, Enzyme Commission (EC) numbers, and Gene Ontology (GO) terms. RESULTS: We show our models perform as well as or better than alternative approaches, achieving area under the receiver operating characteristic curve of up to 0.93 for subcellular localization, up to 0.93 for EC, and up to 0.81 for GO terms on independent blind tests. AVAILABILITY AND IMPLEMENTATION: LEGO-CSM's web server is freely available at https://biosig.lab.uq.edu.au/lego_csm. In addition, all datasets used to train and test LEGO-CSM's models can be downloaded at https://biosig.lab.uq.edu.au/lego_csm/data.

Novel naphthoquinone-1H-1,2,3-triazole hybrids: Design, synthesis and evaluation as inductors of ROS-mediated apoptosis in the MCF-7 cells.

The search for novel anticancer drugs is essential to expand treatment options, overcome drug resistance, reduce toxicity, promote innovation, and tackle the economic impact. The importance of these studies lies in their contribution to advancing cancer research and enhancing patient outcomes in the battle against cancer. Here, we developed new asymmetric hybrids containing two different naphthoquinones linked by a 1,2,3-1H-triazole nucleus, which are potential new drugs for cancer treatment. The antitumor activity of the novel compounds was tested using the breast cancer cell lines MCF-7 and MDA-MB-231, using the non-cancer cell line MCF10A as control. Our results showed that two out of twenty-two substances tested presented potential antitumor activity against the breast cancer cell lines. These potential drugs, named here 12g and 12h were effective in reducing cell viability and promoting cell death of the tumor cell lines, exhibiting minimal effects on the control cell line. The mechanism of action of the novel drugs was assessed revealing that both drugs increased reactive oxygen species production with consequent activation of the AMPK pathway. Therefore, we concluded that 12g and 12h are novel AMPK activators presenting selective antitumor effects.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

Aryl Hydrocarbon Receptor as a Therapeutical Target of Environmentally Induced Skin Conditions.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor, expressed in several tissues and involved in the response to environmental stressors. Studies have already associated exposure to environmental factors, such as organic air pollutants, products of the skin microbiota, and solar radiation, with the development/worsening of skin conditions, mediated by AhR. On the other hand, recent studies have shown that synthetic and natural compounds are able to modulate the activation of some AhR signaling pathways, minimizing the harmful response of these environmental stressors in the skin. Thus, AhR constitutes a new therapeutic target for the prevention or treatment of skin conditions induced by the skin exposome. Herein, an overview of potential AhR ligands and their biologic effects in environmentally induced skin conditions are presented. The literature survey pointed out divergences in the mechanism of action from a therapeutic perspective. Although most studies point to the benefits of ligand downregulation of AhR signaling, counteracting the toxic effects of environmental factors on the skin, some studies suggest the AhR ligand activation as a therapeutical mechanism for some skin conditions. Furthermore, both agonist and antagonist profiles were identified in the AhR modulation by the synthetic and natural compounds raised. Despite that, this target is still little explored, and further studies are needed to elucidate the molecular mechanisms involved and identify new AhR ligands with therapeutic potential. SIGNIFICANCE STATEMENT: The aryl hydrocarbon receptor (AhR) is involved in different skin physiological and pathological processes, including toxic mechanisms of environmental factors. Synthetic and natural AhR ligands have demonstrated therapeutic potential for skin conditions induced by these agents. Thus, a comprehensive understanding of the skin toxicity mechanisms involving the AhR, as well as the use of AhR modulators from a therapeutic perspective, provides an alternative approach to the development of new treatments for skin disorders induced by the exposome.

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.

Physiologically based pharmacokinetic modelling of semaglutide in children and adolescents with healthy and obese body weights.

AIMS: To develop paediatric physiologically based pharmacokinetic modelling (PBPK) models of semaglutide to estimate the pharmacokinetic profile for subcutaneous injections in children and adolescents with healthy and obese body weights. METHODS: Pharmacokinetic modelling and simulations of semaglutide subcutaneous injections were performed using the Transdermal Compartmental Absorption & Transit model implemented in GastroPlus v.9.5 modules. A PBPK model of semaglutide was developed and verified in the adult population, by comparing the simulated plasma exposure with the observed data, and further scaled to the paediatric populations with normal and obese body weight. RESULTS: The semaglutide PBPK model was successfully developed in adults and scaled to the paediatric population. Our paediatric PBPK simulations indicated a significant increase in maximum plasma concentrations for the 10-14 years' paediatric population with healthy body weights, which was higher than the observed values in adults at the reference dose. Since gastrointestinal adverse events are related to increased semaglutide concentrations, peak concentrations outside the target range may represent a safety risk for this paediatric age group. Besides, paediatric PBPK models indicated that body weight was inversely related to semaglutide maximum plasma concentration, corroborating the consensus on the influence of body weight on semaglutide PK in adults. CONCLUSION: Paediatric PBPK was successfully achieved using a top-down approach and drug-related parameters. The development of unprecedented PBPK models will support paediatric clinical therapy for applying aid-safe dosing regimens for the paediatric population in diabetes treatment.

Triazoles with inhibitory action on P2X7R impaired the acute inflammatory response in vivo and modulated the hemostatic balance in vitro and ex vivo.

OBJECTIVE: The present study aimed to investigate five triazole compounds as P2X7R inhibitors and evaluate their ability to reduce acute inflammation in vivo. MATERIAL: The synthetic compounds were labeled 5e, 8h, 9i, 11, and 12. TREATMENT: We administered 500 ng/kg triazole analogs in vivo, (1-10 µM) in vitro, and 1000 mg/kg for toxicological assays. METHODS: For this, we used in vitro experiments, such as platelet aggregation, in vivo experiments of paw edema and peritonitis in mice, and in silico experiments. RESULTS: The tested substances 5e, 8h, 9i, 11, and 12 produced a significant reduction in paw edema. Molecules 5e, 8h, 9i, 11, and 12 inhibited carrageenan-induced peritonitis. Substances 5e, 8h, 9i, 11, and 12 showed an anticoagulant effect, and 5e at a concentration of 10 µM acted as a procoagulant. All derivatives, except for 11, had pharmacokinetic, physicochemical, and toxicological properties suitable for substances that are candidates for new drugs. In addition, the ADMET risk assessment shows that derivatives 8h, 11, 5e, and 9i have high pharmacological potential. Finally, docking tests indicated that the derivatives have binding energies comparable to the reference antagonist with a competitive inhibition profile. CONCLUSIONS: Together, the results indicate that the molecules tested as antagonist drugs of P2X7R had anti-inflammatory action against the acute inflammatory response.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

MTR3D: identifying regions within protein tertiary structures under purifying selection.

The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21-41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5-8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann-Whitney U test P < 2.2 × 10-16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.

_Combining return to sport, psychological readiness, body mass, hamstring strength symmetry, and hamstring/quadriceps ratio increases the risk of a second anterior cruciate ligament injury.

PURPOSE: To investigate the combinations of variables that comprise the biopsychosocial model domains to identify clinical profiles of risk and protection of second anterior cruciate ligament injury. METHODS: One hundred and forty-five patients for return-to-sport testing after anterior cruciate ligament (ACL) reconstruction (ACLR) were contacted, and 97 were deemed eligible. All were evaluated between 6 and 24 months and followed up for 2 years. Participants answered the International Knee Documentation Committee (IKDC) and Anterior Cruciate Ligament-Return to Sport after Injury Scale (ACL-RSI), performed the postural stability assessment using the Biodex Balance System, and assessed muscle strength at 60° and 300°/s on the isokinetic dynamometer. Personal factors (age, gender, body mass index), body structures (graft type and concomitant injuries), and environmental factors (time between surgery and evaluation) were also collected. The participants were asked about the occurrence of a second ACL injury and return to sport after 2 years of follow-up. Classification and regression tree (CART) analysis was used to determine predictors of a second ACL injury. The receiver operating characteristic (ROC) curve was performed to verify the accuracy of the CART analysis, in addition to the sensitivity, specificity, and relative risk (RR) of the model. RESULTS: Of the initial 97 participants, 88 (89.8%) responded to follow-up and 14 (15.9%) had a second ACL injury (11 graft ruptures and three contralateral ACL). CART analysis identified the following variables as predictors of second ACL injury: return to sport, hamstring strength symmetry at 300°/s, ACL-RSI score, hamstrings/quadriceps ratio at 60°/s, and body mass index (BMI). CART correctly identified 9 (64.3%) of the 14 participants who were reinjured and 71 (95.9%) of the 74 participants who were not. The total correct classification was 90.9%. The area under the ROC curve was 0.88 (95% CI 0.72-0.99; p < 0.001), and the model showed a sensitivity of 75% (95% CI 42.8-94.5), specificity of 93.4% (95% CI 85.3-97.8), and RR of 15.9 (95% CI 4.9-51.4; p < 0.0001). CONCLUSION: The combination of hamstring strength symmetry, hamstring/quadriceps ratio (body functions); return to sport (activity and participation); psychological readiness; and BMI (personal factors) could identify three clinical risk profiles for a second ACL injury with good accuracy. LEVEL OF EVIDENCE: IV.

Synthesis, Biological Evaluation and Molecular Modeling Studies of Naphthoquinone Sulfonamides and Sulfonate Ester Derivatives as P2X7 Inhibitors.

ATP acts in the extracellular environment as an important signal, activating a family of receptors called purinergic receptors. In recent years, interest in the potential therapeutics of purinergic components, including agonists and antagonists of receptors, has increased. Currently, many observations have indicated that ATP acts as an important mediator of inflammatory responses and, when found in high concentrations in the extracellular space, is related to the activation of the P2X7 purinergic receptor. In this sense, the search for new inhibitors for this receptor has attracted a great deal of attention in recent years. Sulfonamide derivatives have been reported to be potent inhibitors of P2X receptors. In this study, ten naphthoquinone sulfonamide derivatives and five naphthoquinone sulfonate ester derivatives were tested for their inhibitory activity on the P2X7 receptor expressed in peritoneal macrophages. Some compounds showed promising results, displaying IC50 values lower than that of A740003. Molecular docking and dynamic studies also indicated that the active compounds bind to an allosteric site on P2X7R. The binding free energy indicates that sulfonamides have an affinity for the P2X7 receptor similar to A740003. Therefore, the compounds studied herein present potential P2X7R inhibition.

Molluscicidal and Cercaricidal Effects of Myrciaria floribunda Essential Oil Nanoemulsion.

Schistosomiasis is a tropical disease transmitted in an aqueous environment by cercariae from the Schistosoma genus. This disease affects 200 million people living in risk areas around the world. The control of schistosomiasis is realized by chemotherapy, wastewater sanitation, health education, and mollusk control using molluscicidal agents. This work evaluates the effects of a nanoemulsion containing essential oil from Myrciaria floribunda leaves as a molluscicidal and cercaricidal agent against Biomphalaria glabrata mollusks and Schistosoma mansoni cercariae. The Myrciaria floribunda essential oil from leaves showed nerolidol, ß-selinene, 1,8 cineol, and zonarene as major constituents. The formulation study suggested the F3 formulation as the most promising nanoemulsion with polysorbate 20 and sorbitan monooleate 80 (4:1) with 5% (w/w) essential oil as it showed a smaller droplet size of approximately 100 nm with a PDI lower than 0.3 and prominent bluish reflection. Furthermore, this nanoemulsion showed stability after 200 days under refrigeration. The Myrciaria floribunda nanoemulsion showed LC50 values of 48.11 µg/mL, 29.66 µg/mL, and 47.02 µg/mL in Biomphalaria glabrata embryos, juveniles, and adult mollusks, respectively, after 48 h and 83.88 µg/mL for Schistosoma mansoni cercariae after 2 h. In addition, a survival of 80% was observed in Danio rerio, and the in silico toxicity assay showed lower overall human toxicity potential to the major compounds in the essential oil compared to the reference molluscicide niclosamide. These results suggest that the nanoemulsion of Myrciaria floribunda leaves may be a promising alternative for schistosomiasis control.

Development of SEDDS formulation containing caffeine for dermal delivery.

OBJECTIVE: The objective of this work was to develop a self-emulsifying drug delivery system (SEDDS) containing caffeine for the treatment of cellulite. METHODS: SEDDS were prepared using the solution method. 0.5% (w/v) caffeine was added to the previously selected excipients. The system was characterized by droplet size, zeta potential, emulsification time and long-term stability. In vitro release and skin permeation were investigated using Franz-type diffusion cells. The cytotoxicity was evaluated on normal human keratinocytes. RESULTS: Caffeine SEDDS were thermodynamically stable, with a zeta potential less than - 22 mV and droplet size around 30 nm, and were long-term stable. The permeation study showed that the formulation promoted caffeine accumulation in the skin layers, suggesting an increase in local circulation. Cytotoxicity studies on HaCaT cells were not conclusive as the surfactant used indicated false-positive results due to its high molar mass. CONCLUSION: It was possible to obtain a stable SEDDS that could cause an increase in blood flow in the applied area, resulting in cellulite reduction.

OBJECTIF: L'objectif de ce travail était de développer un système d'administration de médicaments auto-émulsifiants (SEDDS) contenant de la caféine pour le traitement de la cellulite. MÉTHODES: Les SEDDS ont été préparés par la méthode en solution. 0,5 % (p/v) de caféine a été ajouté aux excipients préalablement sélectionnés. Le système a été caractérisé par la taille des gouttelettes, le potentiel zêta, le temps d'émulsification et la stabilité à long terme. La libération in vitro et la perméation cutanée ont été étudiées dans des cellules de diffusion de type Franz. La cytotoxicité était évaluée sur des kératinocytes humains normaux. RÉSULTATS: Les SEDDS de caféine étaient thermodynamiquement stables, avec un potentiel Zeta inférieur à -22 mV et une taille de gouttelettes d'environ 30 nm, et stables à long terme. L'étude de perméation a montré que les formulations favorisent l'accumulation de caféine dans les couches de la peau, suggérant une augmentation de la circulation locale. Les études de cytotoxicité sur les cellules HaCaT n'ont pas été concluantes car le surfactant utilisé indique des résultats faussement positifs dus à une masse molaire élevée. CONCLUSION: Il a été possible d'obtenir un SEDDS stable qui peut provoquer une augmentation du flux sanguin dans la zone appliquée, entraînant une réduction de la cellulite.

Immune hallmarks of rheumatoid arthritis management: A brief review.

The purpose of this review was to examine current evidence on immunomodulation mediated by conventional drugs and the use of novel biological agents for the treatment of rheumatoid arthritis (RA). Currently, treatment is focused on maximizing quality of life through sustained clinical remission and/or attenuating disease activity. To do so, disease-modifying antirheumatic drugs, especially methotrexate, are used alone or in combination with other drugs, including leflunomide, biological disease-modifying antirheumatic drugs (bDMARDs) and targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs). The most recent strategies modulate the immune response of the individual RA patient using tsDMARDs such as JAK inhibitors and bDMARDs such as ig-CTLA-4, anti- IL6R, anti-TNF-α and anti-CD20. To better understand current immunopharmacological interventions, we also looked at documented mechanisms of RA-mediated immunomodulation, highlighting perspectives potentially boosting RA treatment.

Antimycobacterial and anti-inflammatory activities of thiourea derivatives focusing on treatment approaches for severe pulmonary tuberculosis.

Tuberculosis (TB) remains a serious public health problem and one of the main concern is the emergence of multidrug-resistant and extensively resistant TB. Hyper-reactive patients develop inflammatory necrotic lung lesions that aggravate the pathology and facilitate transmission of mycobacteria. Treatment of severe TB is a major clinical challenge that has few effective solutions and patients face a poor prognosis, years of treatment and different adverse drug reactions. In this work, fifteen novel and thirty-one unusual thiourea derivatives were synthesized and evaluated in vitro for their antimycobacterial and anti-inflammatory potential and, in silico for ADMET parameters and for structure-activity relationship (SAR). Thioureas derivatives 10, 15, 16, 28 and 29 that had shown low cytotoxicity and high activities were selected for further investigation, after SAR study. These five thioureas derivatives inhibited Mtb H37Rv growth in bacterial culture and in infected macrophages, highlighting thiourea derivative 28 (MIC50 2.0 ± 1.1 and 2.3 ± 1.1 µM, respectively). Moreover, these compounds were active against the hypervirulent clinical Mtb strain M299, in bacterial culture, especially 16, 28 and 29, and in extracellular clumps, highlighting 29, with MIC50 5.6 ± 1.2 µM. Regarding inflammation, they inhibited NO through the suppression of iNOS expression, and also inhibited the production of TNF-α and IL-1ß. In silico studies were carried out suggesting that these five compounds could be administered by oral route and have low toxicological effects when compared to rifampicin. In conclusion, our data show that, at least, thiourea derivatives 16, 28 and 29 are promising antimycobacterial and anti-inflammatory agents, and candidates for further prospective studies aiming new anti-TB drugs, that can be used on a dual approach for the treatment of severe TB cases associated with exacerbated inflammation.