Predicting respiratory failure in patients infected by SARS-CoV-2 by admission sex-specific biomarkers

Background: Several biomarkers have been identified to predict the outcome of COVID-19 severity, but few data are available regarding sex differences in their predictive role. Aim of this study was to identify sex-specific biomarkers of severity and progression of acute respiratory distress syndrome (ARDS) in COVID-19. Methods: Plasma levels of sex hormones (testosterone and 17β-estradiol), sex-hormone dependent circulating molecules (ACE2 and Angiotensin1-7) and other known biomarkers for COVID-19 severity were measured in male and female COVID-19 patients at admission to hospital. The association of plasma biomarker levels with ARDS severity at admission and with the occurrence of respiratory deterioration during hospitalization was analysed in aggregated and sex disaggregated form. Results: Our data show that some biomarkers could be predictive both for males and female patients and others only for one sex. Angiotensin1-7 plasma levels and neutrophil count predicted the outcome of ARDS only in females, whereas testosterone plasma levels and lymphocytes counts only in males. Conclusions: Sex is a biological variable affecting the choice of the correct biomarker that might predict worsening of COVID-19 to severe respiratory failure. The definition of sex specific biomarkers can be useful to alert patients to be safely discharged versus those who need respiratory monitoring

CoMFA and CoMSIA analyses on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole derivatives as selective CB2 receptor agonists.

Novel classes of cannabinoid 2 receptor (CB2) agonists based on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over cannabinoid 1 receptor (CB1). A computational study of comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed, initially on each series of agonists, and subsequently on all compounds together, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing cross-validated r2 (rcv 2) = 0.680, non crossvalidated r2 (rncv 2) = 0.97 and test set r2 r2 pred \ubc 0:93. The study provides useful suggestions for the design of new analogues with improved affinity

In silico rationalization of the structural and physicochemical requirements for photobiological activity in angelicine derivatives and their heteroanalogues

In PUVA (Psoralen plus UVA) chemotherapy 8-methoxypsoralen is the most widely used compound, although its efficacy is endowedwith undesired side effects. In order to have an evident anti-proliferative activity with a reduced phototoxicity, many linear and angular derivatives have been synthesised. In this paper we describe a QSAR study in which, by means of the neural networks methodology, a useful model for predicting biological activity, expressed as ID50 (the UVA dose that reduces to 50% the DNA synthesis in Ehrlich cells), has been derived. A decision tree that is able to discriminate between active and inactive compounds has been built based on recursive partitioning. The study shows the key structural features responsible for the activity and could be a helpful tool in the rational design of new, less toxic, photochemotherapeuthic agents

CoMFA and CoMSIA analyses on 4-oxo-1,4-dihydroquinoline and 4-oxo-1,4-dihydro-1,5-, -1,6- and -1,8-naphthyridine derivatives as selective CB2 receptor agonists

Novel classes of CB2 agonists based on 4-oxo- 1,4-dihydroquinoline and 4-oxo-1,4-dihydro-1,5-, -1,6- and -1,8-naphthyridine scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over CB1. A computational study of comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing r2ncv= 0,84, r2cv=0,619,SEE=0,369, and r2pred= 0,75. The study provides useful suggestions for the synthesis of new selective analogues with improved affinity

Computational studies of the binding mode and 3D-QSAR analyses of symmetric formimidoester disulfides: a new class of non-nucleoside HIV-1 reverse transcriptase inhibitors

Symmetric formimidoester disulfides (DSs) have recently been identified as a new class of potent nonnucleoside HIV-1 reverse transcriptase (RT) inhibitors. Given that three geometric isomers for DSs are possible, a computational strategy based on molecular docking studies, followed by comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was used in order to identify the most probable DS isomer interacting with RT, to elucidate the atomic details of the RT/DS interaction, and to identify key features impacting DS antiretroviral activity. The CoMFA model was found to be the more predictive, with values of r2 ncv \ubc 0:95, r2 cv \ubc 0:482, SEE=0.264, F=80, and r2 pred \ubc 0:73

Acylthiocarbamates as non-nucleoside HIV-1 reverse transcriptase inhibitors: docking studies and ligand-based CoMFA and CoMSIA analyses

Acylthiocarbamates (ATCs) have been identified as a class of potent non-nucleoside HIV-1 reverse transcriptase (RT) inhibitors. A computational strategy based on molecular docking studies followed by comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was used to identify the most important features impacting ATC antiretroviral activity. The CoMSIA model proved to be the more predictive, with r2 ncv=0.89, rcv 2=0.38, standard error of estimate (SEE)= 0.494, F=84, and r2 pred=0.81. The results of these studies will be useful in designing new ATCs with improved potency, also against clinically relevant resistant mutants