Dengue fever, caused by the dengue virus (DENV), poses a significant global health challenge, particularly in tropical and subtropical regions. Recent increases in indigenous DENV cases in Europe are concerning, reflecting rising incidence linked to climate change and the spread of Aedes albopictus mosquitoes. These vectors thrive under environmental conditions like temperature and humidity, which are increasingly influenced by climate change. Additionally, global travel accelerates the cross-border spread of mosquito-borne diseases. DENV manifests clinically in a spectrum from asymptomatic cases to severe conditions like dengue hemorrhagic fever and dengue shock syndrome, influenced by viral serotype and host factors. In 2024, Brazil experienced a fourfold increase in dengue cases compared to 2023, accompanied by higher mortality. Conventional control measures, such as vector control, community engagement, and vaccination, proved insufficient as climate change exacerbated mosquito proliferation, challenging containment efforts. In this regard, our review analyzes prevention measures and therapeutic protocols during the outbreak while addressing DENV transmission dynamics, clinical presentations, and epidemiological shifts. It also evaluates diagnostic strategies combining clinical assessment with serological and molecular testing, providing information to improve diagnostic and preventive measures. The global expansion of dengue-endemic regions, including outbreaks in Europe, highlights the urgent need for enhanced surveillance, proactive interventions, and international collaboration to mitigate the growing threat of Dengue and other arboviruses like West Nile, Zika, Chikungunya, Oropouche, and Yellow Fever viruses.

Proteases represent common targets in combating infectious diseases, including COVID-19. The 3-chymotrypsin-like protease (3CLpro) is a validated molecular target for COVID-19, and it is key for developing potent and selective inhibitors for inhibiting viral replication of SARS-CoV-2. In this review, we discuss structural relationships and diverse subsites of 3CLpro, shedding light on the pivotal role of dimerization and active site architecture in substrate recognition and catalysis. Our analysis of bioinformatics and other published studies motivated us to investigate a novel catalytic mechanism for the SARS-CoV-2 polyprotein cleavage by 3CLpro, centering on the triad mechanism involving His41-Cys145-Asp187 and its indispensable role in viral replication. Our hypothesis is that Asp187 may participate in modulating the pK_a of the His41, in which catalytic histidine may act as an acid and/or a base in the catalytic mechanism. Recognizing Asp187 as a crucial component in the catalytic process underscores its significance as a fundamental pharmacophoric element in drug design. Next, we provide an overview of both covalent and non-covalent inhibitors, elucidating advancements in drug development observed in preclinical and clinical trials. By highlighting various chemical classes and their pharmacokinetic profiles, our review aims to guide future research directions toward the development of highly selective inhibitors, underscore the significance of 3CLpro as a validated therapeutic target, and propel the progression of drug candidates through preclinical and clinical phases.

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) are constantly threatening global public health. With no end date, the pandemic persists with the emergence of novel variants that threaten the effectiveness of diagnostic tests and vaccines. Mutations in the Spike surface protein of the virus are regularly observed in the new variants, potentializing the emergence of novel viruses with different tropism from the current ones, which may change the severity and symptoms of the disease. Growing evidence has shown that mutations are being selected in favor of variants that are more capable of evading the action of neutralizing antibodies. In this context, the most important factor guiding the evolution of SARS-CoV-2 is its interaction with the host’s immune system. Thus, as current vaccines cannot block the transmission of the virus, measures complementary to vaccination, such as the use of masks, hand hygiene, and keeping environments ventilated remain essential to delay the emergence of new variants. Importantly, in addition to the involvement of the immune system in the evolution of the virus, we highlight several chemical parameters that influence the molecular interactions between viruses and host cells during invasion and are also critical tools making novel variants more transmissible. In this review, we dissect the impacts of the Spike mutations on biological parameters such as (1) the increase in Spike binding affinity to hACE2; (2) bound time for the receptor to be cleaved by the proteases; (3) how mutations associate with the increase in RBD up-conformation state in the Spike ectodomain; (4) expansion of uncleaved Spike protein in the virion particles; (5) increment in Spike concentration per virion particles; and (6) evasion of the immune system. These factors play key roles in the fast spreading of SARS-CoV-2 variants of concern, including the Omicron.

The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q²_LOO = 0.60, r² = 0.80 and r²_pred = 0.91), partial-least-square (PLS) regression (q²_LOO = 0.83, r² = 0.62 and r²_pred = 0.70) and sequential minimal optimization (SMO) regression (q²_LOO = 0.70, r² = 0.80 and r²_pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CL_pro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CL_pro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CL_pro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties. Communicated by Ramaswamy H. Sarma

Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neural networks (ANNs) and kernel-based partial least squares regression (KPLS) were developed using anti-T. cruzi activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding q² values of 0.81 and 0.84 for the ANN and KPLS models, respectively. The results of this investigation highlighted privileged molecular scaffolds and the optimum physicochemical space associated with high anti-T. cruzi activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties.

The SARS-CoV-2 main protease (Mpro) is a validated therapeutic target for inhibiting viral replication. Few compounds have advanced clinically, underscoring the difficulty in optimizing both target affinity and drug-like properties. To address this challenge, we integrated machine learning (ML), molecular docking, and molecular dynamics (MD) simulations to investigate the balance between pharmacodynamic (PD) and pharmacokinetic (PK) properties in Mpro inhibitor design. We developed ML models to classify Mpro inhibitors based on experimental IC₅₀ data, combining molecular descriptors with structural insights from MD simulations. Our Support Vector Machine (SVM) model achieved strong performance (training accuracy = 0.84, ROC AUC = 0.91; test accuracy = 0.79, ROC AUC = 0.86), while our Logistic Regression model (training accuracy = 0.78, ROC AUC = 0.85; test accuracy = 0.76, ROC AUC = 0.83). Notably, PK descriptors often exhibited opposing trends to binding affinity: hydrophilic features enhanced binding affinity but compromised PK properties, whereas hydrogen bonding, hydrophobic, and π–π interactions in Mpro subsites S2 and S3/S4 are fundamental for binding affinity. Our findings highlight the need for a balanced approach in Mpro inhibitor design, strategically targeting these subsites may balance PD and PK properties. For the first time, we demonstrate antagonistic trends between pharmacokinetic (PK) and pharmacodynamic (PD) features through the integrated application of ML/MD. This study provides a computational framework for rational Mpro inhibitors, combining ML and MD to investigate the complex interplay between enzyme inhibition and drug likeness. These insights may guide the hit-to-lead optimization of the novel next-generation Mpro inhibitors of SARS-CoV-2 with preclinical and clinical potential.