Synthesis and Critical View on the Structure-Activity Relationships of N-(Substituted phenyl)-/N-Diphenylmethyl-piperazine-Based Conjugates as Antimycobacterial Agents

This research focused on a three-step synthesis, analytical, physicochemical, and biological evaluation of hybrid molecules 6a–g, containing a lipophilic 3-trifluoromethylphenyl moiety, polar carbamoyloxy bridge, 2-hydroxypropan-1,3-diyl chain and 4-(substituted phenyl)-/4-diphenylmethylpiperazin-1-ium-1-yl fragment. The estimation of analytical and physicochemical descriptors (m/zmeasured via HPLC-UV/HR-MS, log ε2 (Ch–T) from UV/Vis spectrophotometry and log kw via RP-HPLC) as well as in vitro antimycobacterial and cytotoxic screening of given compounds were carried out (i.e., determination of MIC and IC50 values). These highly lipophilic molecules (log kw = 4.1170–5.2184) were tested against Mycobacterium tuberculosis H37Ra ATCC 25177 (Mtb H37Ra), M. kansasii DSM 44162 (MK), M. smegmatis ATCC 700084 (MS), and M. marinum CAMP 5644 (MM). The impact of the 6a–g set on the viability of human liver hepatocellular carcinoma (HepG2) cells was also investigated. 1-[2-Hydroxypropyl-{(3-trifluoromethyl)- phenyl}carbamoyloxy]-4-(3,4-dichlorophenyl)piperazin-1-ium chloride (6e) and 1-[2-hydroxy- propyl-{(3-trifluoromethyl)phenyl}carbamoyloxy]-4-(4-diphenylmethyl)piperazin-1-ium chloride (6g) most effectively inhibited the growth of Mtb H37Ra (MIC < 3.80 μM). The substance 6g also showed interesting activity against MM (MIC = 8.09 μM). All obtained data served as input values for structure-activity relationship evaluations using statistical principal component analysis. In fact, the toxicity of both 6e (IC50 = 29.39 μM) and 6g (IC50 = 22.18 μM) in HepG2 cells as well as selectivity index (SI) values (SI < 10.00) prevented to consider these promising antimycobacterials safe.

Quantitative and Qualitative Structure Activity Relationship Study for Development of the Pet- Inhibitory Activity of 3-Nitro-2,4,6- Trihydroxy Benzamides

The aim in this paper of these work present two components analyses quantitative and qualitative which consisted in the development and evaluation of the first component quantitative and structure activity relationships (QSAR) for the prediction In fact, various compounds inhibiting photosynthesis constitute the largest class of commercial herbicides. All of those inhibitors, including ureas, triazines, bis carbamates and phenols, interrupt photosynthetic electron transport (PET) by binding to quinine-binding protein (D1-protein). Various physicochemical descriptors were used in multiple linear regressions method (MLR) to develop the theoretical models, than using a cross-validation with leave-one-out method to optimize the model as well as possible to fit with the biological data, the most common approach used to study. The second quality of the compound compared with criteria for their power, these rules Lu Propose the four basic characteristics that Lipinski has identified.

Intrinsic physicochemical profile of marketed antibody-based biotherapeutics

Feeding biopharma pipelines with biotherapeutic candidates that possess desirable developability profiles can help improve the productivity of biologic drug discovery and development. Here, we have derived an in silico profile by analyzing computed physicochemical descriptors for the variable regions (Fv) found in 77 marketed antibody-based biotherapeutics. Fv regions of these biotherapeutics demonstrate significant diversities in their germlines, complementarity determining region loop lengths, hydrophobicity, and charge distributions. Furthermore, an analysis of 24 physicochemical descriptors, calculated using homology-based molecular models, has yielded five nonredundant descriptors whose distributions represent stability, isoelectric point, and molecular surface characteristics of their Fv regions. Fv regions of candidates from our internal discovery campaigns, human next-generation sequencing repertoires, and those in clinical-stages (CST) were assessed for similarity with the physicochemical profile derived here. The Fv regions in 33% of CST antibodies show physicochemical properties that are dissimilar to currently marketed biotherapeutics. In comparison, physicochemical characteristics of ∼29% of the Fv regions in human antibodies and ∼27% of our internal hits deviated significantly from those of marketed biotherapeutics. The early availability of this information can help guide hit selection, lead identification, and optimization of biotherapeutic candidates. Insights from this work can also help support portfolio risk assessment, in-licensing, and biopharma collaborations.

Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis

Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (Mpro) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (Mpro) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (Mpro), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.

NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II

Antigens presented on the cell surface have been subjected to multiple biological processes. Among them, C-terminal antigen processing constitutes one of the main bottlenecks of the peptide presentation pathways, as it delimits the peptidome that will be subjected downstream. Here, we present NetCleave, an open-source and retrainable algorithm for the prediction of the C-terminal antigen processing for both MHC-I and MHC-II pathways. NetCleave architecture consists of a neural network trained on 46 different physicochemical descriptors of the cleavage site amino acids. Our results demonstrate that prediction of C-terminal antigen processing achieves high accuracy on MHC-I (AUC of 0.91), while it remains challenging for MHC-II (AUC of 0.66). Moreover, we evaluated the performance of NetCleave and other prediction tools for the evaluation of four independent immunogenicity datasets (H2-Db, H2-Kb, HLA-A*02:01 and HLA-B:07:02). Overall, we demonstrate that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, and we provide one of the first evidence that C-terminal processing predictions may help in the discovery of immunogenic peptides.

Biotransformation of Silver Nanoparticles into Oro-Gastrointestinal Tract by Integrated In Vitro Testing Assay: Generation of Exposure-Dependent Physical Descriptors for Nanomaterial Grouping

Grouping approaches of nanomaterials have the potential to facilitate high throughput and cost effective nanomaterial screening. However, an effective grouping of nanomaterials hinges on the application of suitable physicochemical descriptors to identify similarities. To address the problem, we developed an integrated testing approach coupling acellular and cellular phases, to study the full life cycle of ingested silver nanoparticles (NPs) and silver salts in the oro-gastrointestinal (OGI) tract including their impact on cellular uptake and integrity. This approach enables the derivation of exposure-dependent physical descriptors (EDPDs) upon biotransformation of undigested nanoparticles, digested nanoparticles and digested silver salts. These descriptors are identified in: size, crystallinity, chemistry of the core material, dissolution, high and low molecular weight Ag-biomolecule soluble complexes, and are compared in terms of similarities in a grouping hypothesis. Experimental results indicate that digested silver nanoparticles are neither similar to pristine nanoparticles nor completely similar to digested silver salts, due to the presence of different chemical nanoforms (silver and silver chloride nanocrystals), which were characterized in terms of their interactions with the digestive matrices. Interestingly, the cellular responses observed in the cellular phase of the integrated assay (uptake and inflammation) are also similar for the digested samples, clearly indicating a possible role of the soluble fraction of silver complexes. This study highlights the importance of quantifying exposure-related physical descriptors to advance grouping of NPs based on structural similarities.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

Concerted measurements of lipids in seawater and on submicrometer aerosol particles at the Cabo Verde islands: biogenic sources, selective transfer and high enrichments

In the marine environment, measurements of lipids as representative species within different lipid classes have been performed to characterize their oceanic sources and their transfer from the ocean into the atmosphere to marine aerosol particles. The set of lipid classes includes hydrocarbons (HC); fatty acid methyl esters (ME); free fatty acids (FFA); alcohols (ALC); 1,3-diacylglycerols (1,3 DG); 1,2-diacylglycerols (1,2 DG); monoacylglycerols (MG); wax esters (WE); triacylglycerols (TG); and phospholipids (PP) including phosphatidylglycerols (PG), phosphatidylethanolamine (PE), phosphatidylcholines (PC), as well as glycolipids (GL) which cover sulfoquinovosyldiacylglycerols (SQDG), monogalactosyl-diacylglycerols (MGDG), digalactosyldiacylglycerols (DGDG) and sterols (ST). These introduced lipid classes have been analyzed in the dissolved and particulate fraction of seawater, differentiating between underlying water (ULW) and the sea surface microlayer (SML) on the one hand. On the other hand, they have been examined on ambient submicrometer aerosol particle samples (PM1) which were collected at the Cape Verde Atmospheric Observatory (CVAO) by applying concerted measurements. These different lipids are found in all marine compartments but in different compositions. Along the campaign, certain variabilities are observed for the concentration of dissolved (∑DLULW: 39.8–128.5 µg L−1, ∑DLSML: 55.7–121.5 µg L−1) and particulate (∑PLULW: 36.4–93.5 µg L−1, ∑PLSML: 61.0–118.1 µg L−1) lipids in the seawater of the tropical North Atlantic Ocean. Only slight SML enrichments are observed for the lipids with an enrichment factor EFSML of 1.1–1.4 (DL) and 1.0–1.7 (PL). On PM1 aerosol particles, a total lipid concentration between 75.2–219.5 ng m−3 (averaged: 119.9 ng m−3) is measured. As also bacteria – besides phytoplankton sources – influence the lipid concentrations in seawater and on the aerosol particles, the lipid abundance cannot be exclusively explained by the phytoplankton tracer (chlorophyll a). The concentration and enrichment of lipids in the SML are not related to physicochemical properties which describe the surface activity. On the aerosol particles, an EFaer (the enrichment factor on the submicrometer aerosol particles compared to the SML) between 9×104–7×105 is observed. Regarding the individual lipid groups on the aerosol particles, a statistically significant correlation (R2=0.45, p=0.028) was found between EFaer and lipophilicity (expressed by the KOW value), which was not present for the SML. But simple physicochemical descriptors are overall not sufficient to fully explain the transfer of lipids. As our findings show that additional processes such as formation and degradation influence the ocean–atmosphere transfer of both OM in general and of lipids in particular, they have to be considered in OM transfer models. Moreover, our data suggest that the extent of the enrichment of the lipid class constituents on the aerosol particles might be related to the distribution of the lipid within the bubble–air–water interface. The lipids TG and ALC which are preferably arranged within the bubble interface are transferred to the aerosol particles to the highest extent. Finally, the connection between ice nucleation particles (INPs) in seawater, which are already active at higher temperatures (−10 to −15 ∘C), and the lipid classes PE and FFA suggests that lipids formed in the ocean have the potential to contribute to (biogenic) INP activity when transferred into the atmosphere.