The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure-Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic CD8+) population.

The study of the metabolism of xenobiotics by the human body is an essential stage in the complex and expensive process of drug discovery, being one of the main causes of disapproval and/or withdrawal of drugs. Regarding this, enzymes known as cytochromes P450 (CYPs) play a very decisive role in the biotransformation of many chemicals. For this reason, the use of chemoinformatics to predict and /or analyze from different points of view CYPs-mediated drug metabolism, can help to reduce time and financial resources. This work is focused on the most remarkable advances in the last 5 years of the chemoinformatics tools towards the virtual analysis of CYPs-mediated drug metabolism. First, a brief section is dedicated to the applicability of chemoinformatics in different areas associated with drug metabolism. Then, both the models for prediction of CYPs substrates and those allowing the assessment of sites of metabolism (SOM) are discussed. At the same time, the principal limitations of the current chemoinformatic tools are pointed out. Finally, and taking into account that metabolism is an essential step in the whole process of designing any drug, we introduce here as a case of study, the first multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model is to integrate different types of biological profiles such as ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles and anti-staphylococci activities. The mtk-QSBER model was created by employing a heterogeneous dataset of more than 66000 cases tested in 6510 different experimental conditions. The model displayed a total accuracy higher than 94%. To the best of our knowledge, this is the first attempt to complement metabolism assays with other relevant biological data in order to speed up the discovery of efficacious anti-staphylococci agents.

<p>Ring-annelated corannulenes behave as better C₆₀/C₇₀ receptor than corannulene C₂₀H₁₀. The interaction is dominated by dispersion, with CH⋯π interactions playing a very important role.</p>

PRRXL1 [paired related homeobox-like 1; also known as DRG11 (dorsal root ganglia 11)] is a paired-like homeodomain transcription factor expressed in DRG and dSC (dorsal spinal cord) nociceptive neurons. PRRXL1 is crucial for the establishment and maintenance of nociceptive circuitry, as Prrxl1(-/-) mice present neuronal loss, reduced pain sensitivity and failure to thrive. In the present study, we show that PRRXL1 is highly phosphorylated in vivo, and that its multiple band pattern on electrophoretic analysis is the result of different phosphorylation states. PRRXL1 phosphorylation appears to be differentially regulated along the dSC and DRG development and it is mapped to two functional domains. One region comprises amino acids 107-143, whereas the other one encompasses amino acids 227-263 and displays repressor activity. Using an immunoprecipitation MS approach, two phosphorylation sites were identified, Ser(119) and Ser(238). Phosphorylation at Ser(119) is shown to be determinant for PRRXL1 conformation and transcriptional activity. Ser(119) phosphorylation is thus proposed as a mechanism for regulating PRRXL1 function and conformation during nociceptive system development.

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Schizophrenia is a complex psychiatric disorder associated with the distortion of striatopallidal neurotransmission of central nervous system. Phosphodiesterase10A (PDE10A) enzyme plays crucial role in cellular signaling pathways in schizophrenia. Inhibition of this enzyme may facilitate better treatment of this disease. 2D-QSAR, HQSAR, pharmacophore mapping, molecular docking, and 3D-QSAR analyses were performed on 81 cinnoline derivatives having PDE10A inhibitory activity. 2D-QSAR models were developed by multiple linear regression and partial least square analyses using both atom based and whole molecular descriptors. The best model, having considerable internal (q 2 = 0.812) and external (R2pred = 0.691) predictabilities, demonstrated importance of atom-based topological and whole molecular E-state as well as 3D topological indices. The best HQSAR model was also found to be statistically significant (q2 = 0.664, R 2pred = 0.513) and it highlighted some important structural features. PHASE-based pharmacophore hypothesis showed the importance of three hydrogen bond acceptor and one each of ring aromatic and hydrophobic features for higher activity. 3D-QSAR CoMFA and CoMSIA models were generated on two different types of alignment procedures-(1) pharmacophore (PHASE) based and (2) docking (GLIDE) based. GLIDE-based alignment produced better results for both CoMFA (q2 = 0.578; R2pred =0.841) and CoMSIA (Q2 = 0.610; R2pred = 0.824 ) methods. Molecular dynamics (MDs) simulations were performed for two ligand-receptor complexes and these simulations explored some crucial factors for higher activity. These findings of MD simulations were consistent with the interpretations obtained from other methods of analyses. The current study may help in designing new PDE10A inhibitors of this class. © 2014 Springer International Publishing.

Validated comparative 2D- and 3D-QSAR modeling and docking studies were performed for forty-five quinolone carboxylic acids having cytotoxic, antiviral, and anti-HIV-1 IN activity. Statistically significant 2D-QSAR model was developed through MLR and PLS analyses on unsplitted as well as splitted dataset and validated. The models were validated on external set compounds. Chemical potential, Mulliken charge at C8, and ETSA index at C3 are important for cytotoxicity. Global hardness, electrophilic frontier electron density at C10, ETSA index at O21, and C13 play pivotal role for antiviral activity. Mulliken charge at C5, ETSA index at C14, RTSA index at C8, and C13 and LUMO density on C7 are important for anti-HIV-1 IN activity. HQSAR study suggested that maximum contributing fragments include C2 and C14 and substitutions at C13 and C14 for anti-HIV-1 IN activity and antiviral activity, respectively. The positively contributing fragments include C8, C9, C10, C11, and C16 are beneficial for cytotoxicity. CoMFA study suggested that favorable steric region located near C14 is important for anti-HIV-1 IN activity, steric factor at C8 substitution is important for antiviral and cytotoxicity activities. CoMSIA study correlates the steric region found in CoMFA study; hydrophobic favorable regions are located around C8 and near C13. For antiviral activity, unfavorable hydrogen bond acceptor region is observed near C8 substitution, favorable hydrogen bond acceptor region is observed at N 1 substitution. For cytotoxic activity, favorable electrostatic region is located around quinolone and benzene ring. Docking study suggested that Glu152, Gln148, and Asn155 residues of the HIV-1 integrase enzyme bind with the molecule. © 2013 Springer Science+Business Media New York.