Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy.

The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca2+-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products.

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives.

In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81-98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd(II)-catalyzed C-H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 µM, SI > 10.17) and 5bd (IC50 = 16.87 µM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC50 > 100 µg/mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.

Microwave-assisted palladium catalysed C-H acylation with aldehydes: synthesis and diversification of 3-acylthiophenes.

The use of MW allows the efficient palladium(II)-catalysed C-3 acylation of thiophenes with aldehydes via C(sp2)-H activation for the synthesis of (cyclo)alkyl/aryl thienyl ketones (43 examples). Compared to standard thermal conditions, the use of MW reduces the reaction time (15 to 30 min vs. 1 to 3 hours), leading to improved yields of the ketones (up to 92%). The control of positional selectivity is achieved by 2-pyridinyl and 2-pyrimidyl ortho-directing groups at C-2 of the thiophene scaffold. To show the synthetic applicability, selected ketones were subjected to further transformations, including intramolecular reactions to directly embed the directing group in the core structure of the new molecule.

Pd(II)-Catalyzed Fujiwara-Moritani Reactions for the Synthesis and Functionalization of Substituted Coumarins.

Highly substituted coumarins, privileged and versatile scaffolds for bioactive natural products and fluorescence imaging, are obtained via a Pd(II)-catalyzed direct C-H alkenylation reaction (Fujiwara-Moritani reaction), which has emerged as a powerful tool for the construction and functionalization of heterocyclic compounds because of its chemical versatility and its environmental advantages. Thus, a selective 6-endo cyclization led to 4-substituted coumarins in moderate yields. Selected examples have been further functionalized in C3 through a second intermolecular C-H alkenylation reaction to give coumarin-acrylate hybrids, whose fluorescence spectra have been measured.

Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents.

The development of new molecules for the treatment of leishmaniasis is, a neglected parasitic disease, is urgent as current anti-leishmanial therapeutics are hampered by drug toxicity and resistance. The pyrrolo[1,2-b]isoquinoline core was selected as starting point, and palladium-catalyzed Heck-initiated cascade reactions were developed for the synthesis of a series of C-10 substituted derivatives. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated. The best activity was found, in general, for the 10-arylmethyl substituted pyrroloisoquinolines. In particular, 2ad (IC50 = 3.30 µM, SI > 77.01) and 2bb (IC50 = 3.93 µM, SI > 58.77) were approximately 10-fold more potent and selective than the drug of reference (miltefosine), against L. amazonensis on in vitro promastigote assays, while 2ae was the more active compound in the in vitro amastigote assays (IC50 = 33.59 µM, SI > 8.93). Notably, almost all compounds showed low cytotoxicity, CC50 > 100 µg/mL in J774 cells, highest tested dose. In addition, we have developed the first Perturbation Theory Machine Learning (PTML) algorithm able to predict simultaneously multiple biological activity parameters (IC50, Ki, etc.) vs. any Leishmania species and target protein, with high values of specificity (>98%) and sensitivity (>90%) in both training and validation series. Therefore, this model may be useful to reduce time and assay costs (material and human resources) in the drug discovery process.

Molecular docking, SAR analysis and biophysical approaches in the study of the antibacterial activity of ceramides isolated from Cissus incisa.

The developing of antibacterial resistance is becoming in crisis. In this sense, natural products play a fundamental role in the discovery of antibacterial agents with diverse mechanisms of action. Phytochemical investigation of Cissus incisa leaves led to isolation and characterization of the ceramides mixture (1): (8E)-2-(tritriacont-9-enoyl amino)-1,3,4-octadecanetriol-8-ene (1-I); (8E)-2-(2',3'-dihydroxyoctacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-II); (8E)-2-(2'-hydroxyheptacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-III); and (8E)-2-(-2'-hydroxynonacosanoyl amino)-1,3,4-octadecanetriol-8-ene (1-IV). Until now, this is the first report of the ceramides (1-I), (1-II), and (1-IV). The structures were elucidated using NMR and mass spectrometry analyses. Antibacterial activity of ceramides (1) and acetylated derivates (2) was evaluated against nine multidrug-resistant bacteria by Microdilution method. (1) showed the best results against Gram-negatives, mainly against carbapenems-resistant Acinetobacter baumannii with MIC = 50 µg/mL. Structure-activity analysis and molecular docking revealed interactions between plant ceramides with membrane proteins, and enzymes associated with biological membranes of Gram-negative bacteria, through hydrogen bonding of functional groups. Vesicular contents release assay showed the capacity of (1) to disturb membrane permeability detected by an increase of fluorescence probe over time. The membrane disruption is not caused for ceramides lytic action on cell membranes, according in vitro hemolyticactivity results. Combining SAR analysis, bioinformatics and biophysical techniques, and also experimental tests, it was possible to explain the antibacterial action of these natural ceramides.

Cp*Co(III)-Catalyzed C-H Hydroarylation of Alkynes and Alkenes and Beyond: A Versatile Synthetic Tool.

The use of earth-abundant first-row transition metals, such as cobalt, in C-H activation reactions for the construction and functionalization of a wide variety of structures has become a central topic in synthetic chemistry over the last few years. In this context, the emergence of cobalt catalysts bearing pentamethylcyclopentadienyl ligands (Cp*) has had a major impact on the development of synthetic methodologies. Cp*Co(III) complexes have been proven to possess unique reactivity compared, for example, to their Rh(III) counterparts, obtaining improved chemo- or regioselectivities, as well as yielding new reactivities. This perspective is focused on recent advances on the alkylation and alkenylation reactions of (hetero)arenes with alkenes and alkynes under Cp*Co(III) catalysis.

Pd(II)-Catalyzed C-H Acylation of (Hetero)arenes-Recent Advances.

Di(hetero)aryl ketones are important motifs present in natural products, pharmaceuticals or agrochemicals. In recent years, Pd(II)-catalyzed acylation of (hetero)arenes in the presence of an oxidant has emerged as a catalytic alternative to classical acylation methods, reducing the production of toxic metal waste. Different directing groups and acyl sources are being studied for this purpose, although further development is required to face mainly selectivity problems in order to be applied in the synthesis of more complex molecules. Selected recent developments and applications are covered in this review.

Amide-Directed Intramolecular Co(III)-Catalyzed C-H Hydroarylation of Alkenes for the Synthesis of Dihydrobenzofurans with a Quaternary Center.

The first example of Cp*Co(III)-catalyzed intramolecular hydroarylation of allyl aryl ethers using an amide directing group for the preparation of 3,3-disubstituted dihydrobenzofurans in high yields is described. The reaction of the unactivated alkene is completely selective for the formation of the quaternary center, allowing different substitution patterns on the aromatic ring and the alkene. The cyclization can also be extended to the formation of six-membered rings and to N-homoallylindoles.

Intramolecular Palladium(II)-Catalyzed 6-endo C-H Alkenylation Directed by the Remote N-Protecting Group: Mechanistic Insight and Application to the Synthesis of Dihydroquinolines.

A protocol for the Pd(II)-catalyzed C-H alkenylation reaction of substituted N-allylanilines via an unusual 6-endo process has been developed. A density functional theory (DFT) study of the mechanistic pathway has shown that the coordination of the remote protecting group to the palladium center is determinant for the control of the regioselectivity in favor of the 6-endo process. The reaction would proceed via prior activation of the alkene. This procedure constitutes a mild and efficient method for the synthesis of 1,4-dihydroquinoline derivatives from simple and readily accessible substrates.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry.

AIMS: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays.

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.

Carbopalladation/Suzuki Coupling Cascade for the Generation of Quaternary Centers: Access to Pyrrolo[1,2-b]isoquinolines.

A convergent route to pyrrolo[1,2-b]isoquinolines with a quaternary center at C-10 has been developed, which implies a sequential Pd(0)-catalyzed carbopalladation followed by cross-coupling reaction with boronic acids. The adequate catalytic system and experimental conditions, with and without the use of phosphane ligands, have been selected to control the chemoselectivity of the process, allowing a 6-exo-carbopalladation to generate a quaternary center and avoiding a direct Suzuki coupling. A variety of electron-rich and electron-deficient arylboronic acids can be used providing an efficient route to substituted pyrrolo[1,2-b]isoquinolines in moderate to good yields (up to 94%, 22 examples).

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks.

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN s) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 µg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.

Palladium(II)-Catalyzed Intramolecular C-H Alkenylation for the Synthesis of Chromanes.

The intramolecular Pd(II)-catalyzed alkenylation of aryl homoallyl ethers constitutes a mild, versatile, and efficient procedure for the synthesis of highly and diversely substituted chromanes and 2 H-chromenes. The use of p-TsOH as an additive allows more efficient reactions that could be carried out a room temperature in most cases. The procedure has a wide scope, allowing the synthesis of alkylidenechromanes and 2 H-chromenes substituted at C-2 or C-3 of the chromene moiety, thus accessing relevant flavenes and isoflavenes, and even coumarins, in high yields (59 to 91%, 32 examples).

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported.

Palladium-Catalyzed Dehydrogenative Coupling: An Efficient Synthetic Strategy for the Construction of the Quinoline Core.

Palladium-catalyzed dehydrogenative coupling is an efficient synthetic strategy for the construction of quinoline scaffolds, a privileged structure and prevalent motif in many natural and biologically active products, in particular in marine alkaloids. Thus, quinolines and 1,2-dihydroquinolines can be selectively obtained in moderate-to-good yields via intramolecular C-H alkenylation reactions, by choosing the reaction conditions. This methodology provides a direct method for the construction of this type of quinoline through an efficient and atom economical procedure, and constitutes significant advance over the existing procedures that require preactivated reaction partners.

Phenolic Activation in Chiral Brønsted Acid-Catalyzed Intramolecular α-Amidoalkylation Reactions for the Synthesis of Fused Isoquinolines.

An organolithium addition-intramolecular α-amidoalkylation sequence on N-phenethylimides has been developed for the synthesis of fused tetrahydroisoquinoline systems using 1,1'-bi-2-naphthol (binol)-derived Brønsted acids. This transformation is the first in which activated benzene derivatives are used as internal nucleophiles, instead of electron-rich heteroaromatics, generating a quaternary stereocenter. Phenolic substitution on the aromatic ring of the phenethylamino moiety and the use of binol-derived N-triflylphosphoramides as catalysts are determinants to achieve reasonable levels of enantioselection, that is, up to 75% enantiomeric excess, in the α-amidoalkylation step. The procedure is complementary to the intermolecular α-amidoalkylation process, as opposite enantiomers are formed, and to the Pictet-Spengler cyclization, which allows the formation of tertiary stereocenters.

Chiral Brønsted Acid-Catalyzed Enantioselective α-Amidoalkylation Reactions: A Joint Experimental and Predictive Study.

Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid-catalyzed enantioselective α-amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure-reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)-QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long-term empirical investigation.