Pharmacophore Modeling, Quantitative Structure−Activity Relationship Analysis, and Shape-Complemented in Silico Screening Allow Access to Novel Influenza Neuraminidase Inhibitors

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand–receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure–activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand–receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC₅₀ value of 57 nM.

Ligand-based pharmacophores, ligand–receptor contact fingerprints, physicochemical descriptors and machine learning were combined to probe binding of potent CLK4 antagonists. GFA-SVR gave the best model. Virtual screening identified 3 nanomolar hits.

Ligand-Based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

BACKGROUND

Chemokines are involved in several human diseases and in different stages of COVID-19 infection and play critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

METHODS

We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM.

RESULTS AND CONCLUSION

These results prove the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases towards new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

QSAR-guided pharmacophoric modeling reveals important structural requirements for Polo kinase 1 (Plk1) inhibitors

Targeting Polo-like kinase 1 (Plk1) by molecular inhibitors is being a promising approach for tumor therapy. Nevertheless, insufficient methodical analyses have been done to characterize the interactions inside the Plk1 binding pocket. In this study, an extensive combined ligand and structure-based drug design workflow was conducted to data-mine the structural requirements for Plk1 inhibition. Consequently, the binding modes of 368 previously known Plk1 inhibitors were investigated by pharmacophore generation technique. The resulted pharmacophores were engaged in the context of Genetic function algorithm (GFA) and Multiple linear regression (MLR) analyses to search for a prognostic QSAR model. The most successful QSAR model was with statistical criteria of (r2277 = 0.76, r2adj = 0.76, r2pred = 0.75, Q2 = 0.73). Our QSAR-selected pharmacophores were validated by Receiver Operating Characteristic (ROC) curve analysis. Later on, the best QSAR model and its associated pharmacophoric hypotheses (HypoB-T4-5, HypoI-T2-7, HypoD-T4-3, and HypoC-T3-3) were used to identify new Plk1 inhibitory hits retrieved from the National Cancer Institute (NCI) database. The most potent hits exhibited experimental anti-Plk1 IC50 of 1.49, 3.79. 5.26 and 6.35 μM. Noticeably, our hits, were found to interact with the Plk1 kinase domain through some important amino acid residues namely, Cys67, Lys82, Cys133, Phe183, and Asp194.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC₅₀ of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Ligand Based Pharmacophore Modeling Followed by Biological Screening Lead to Discovery of Novel PDK1 Inhibitors as Anticancer Agents

BACKGROUND

Phosphoinositide-Dependent Kinase-1 (PDK1) is a serine/threonine kinase, which belongs to AGC kinase family required by cancer cells.

METHODS

Pharmacophoric space of 86 PDK1 inhibitors using six diverse sets of inhibitors was explored to identify high-quality pharmacophores. The best combination of pharmacophoric models and physicochemical descriptors was selected by genetic algorithm-based QSAR analysis that can elucidate the variation of bioactivity within the training inhibitors. Two successful orthogonal pharmacophores emerged in the optimum QSAR equation (r2 69 = 0.90, r2 LOO= 0.86, F= 51.92, r2 PRESS against 17 test inhibitors = 0.79). Receiver Operating Characteristic (ROC) curve analyses were used to estimate the QSAR-selected pharmacophores.

RESULTS

5 out of 11 compounds tested had shown potential intracellular PDK1 inhibition with the highest inhibition percent for compounds 92 and 93 as follows; 90 and 92% PDK1 inhibition, respectively.

CONCLUSION

PDK1 inhibitors are potential anticancer agents that can be discovered by combination method of ligand based design with QSAR and ROC analysis.

Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors

Targeting tropomycin kinase A (TrkA) by small molecule inhibitors is considered as a promising strategy for treating several human cancers. To achieve this goal, a ligand based QSAR model was applied using the Discovery studio 4.5 (DS 4.5). Hence, a total list of 161 TrkA inhibitors was investigated. The TrkA inhibitors were extensively explored to detect their optimal physicochemical properties and pharmacophoric binding modes, which were converted into numeric descriptors and allowed to compete within the context of the Genetic Function Algorithm (GFA) approximations to find the subset of terms that correlates best with the activity. The resulted successful QSAR equation had statistical criteria of (r²₁₂₉=0.67, r²_LOO=0.61 r²_PRESS against 32 external test inhibitors=0.50). Afterwards, the most successful pharmacophore: HypoB-T5-3, was used to screen compounds within the National Cancer institute (NCI) database. Only 41 compounds were retrieved and 21 of them exhibited anti-TrkA activity. The most potent hit had an IC₅₀ value of 2.4μM. Later, upon docking the active hits into the TrkA binding pocket, important interactions were revealed including hydrogen bonding with the amino acids Asp668 and Lys544 in addition to the cation-π interactions with the sidechain of Arg559.

Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Akt1 and Akt3 are important serine/threonine-specific protein kinases involved in G2 phase required by cancer cells to maintain cell cycle and to prevent cell death. Accordingly, inhibitors of these kinases should have potent anti-cancer properties. This prompted us to use pharmacophore/QSAR modeling to identify optimal binding models and physicochemical descriptors that explain bioactivity variation within a set of 74 diverse Akt3 inhibitors. Two successful orthogonal pharmacophores were identified and further validated using receiver operating characteristic (ROC) curve analyses. The pharmacophoric models and associated QSAR equation were applied to screen the national cancer institute (NCI) list of compounds for new Akt3 inhibitors. Six hits showed significant experimental anti-Akt3 IC₅₀ values, out of which one compound exhibited dual low micromolar anti-Akt1 and anti-Akt3 inhibitory profiles.

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

The Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging virus that poses a major challenge to clinical management.

The 3C‐like protease (3CL^pro) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS‐CoV 3CL^pro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4‐CoV 3CL^pro. HKU4‐CoV 3CL^pro shares high sequence identity (81%) with the MERS‐CoV enzyme and thus represents a potential surrogate model for anti‐MERS drug discovery. We used 2 well‐established methods: Quantitative structure‐activity relationship (QSAR)‐guided modeling and docking‐based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding‐pocket regions involved in 3CL^pro‐ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CL^pro inhibitors. The identified hits were tested for HKU4‐CoV and MERS‐CoV 3CL^pro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS‐CoV 3CL^pro and represent a potential starting point for the development of novel anti‐MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS‐CoV 3CL^pro.

Highlights

MERS‐CoV is an emerging virus that is closely related to the bat HKU4‐CoV.

3CL^pro is a potential drug target for coronavirus infection.

HKU4‐CoV 3CL^pro is a useful surrogate model for the identification of MERS‐CoV 3CL^pro enzyme inhibitors.

dbCICA is a very robust modeling method for hit identification.

The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CL^pro inhibitors development.

Discovery of novel potent nuclear factor kappa-B inhibitors (IKK-β) via extensive ligand-based modeling and virtual screening

Inhibitor kappa-B kinase-beta (IKK-β) controls the activation of nuclear transcription factor kappa-B and has been linked to inflammation and cancer. Therefore, inhibitors of this kinase should have potent anti-inflammatory and anticancer properties. Accordingly, we explored the pharmacophoric space of 218 IKK-β inhibitors to identify high-quality binding models. Subsequently, genetic algorithm-based quantitative structure activity relationship (QSAR) analysis was employed to select the best possible combination of pharmacophoric models and physicochemical descriptors that explain bioactivity variation among training compounds. Three successful pharmacophores emerged in 2 optimal QSAR equations (r₁²₁₇₅  = 0.733, r₁²_LOO  = 0.52, F1 = 65.62, r₁²_PRESS against 43 test inhibitors = 0.63 and r₂²₁₇₅  = 0.683, r₂²_LOO  = 0.52, F2 = 72.66, r₂²_PRESS against 43 test inhibitors = 0.65). Two pharmacophores were merged in a single binding model. Receiver operating characteristic curve validation proved the excellent qualities of this model. The merged pharmacophore and the associated QSAR equations were applied to screen the National Cancer Institute list of compounds. Ten hits were found to exhibit potent anti-IKK-β bioactivity, out of which, one illustrates IC50 of 11.0nM.

Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation

AIM

FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC₅₀ of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy.

CONCLUSION

Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.

Innovative computer-aided methods for the discovery of new kinase ligands

Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.

Ligand-based modeling of diverse aryalkylamines yields new potent P-glycoprotein inhibitors

The P-glycoprotein (P-gp) efflux pump has an important role as a natural detoxification system in many types of normal and cancer cells. P-gp is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents, and therefore, it is clinically validated target for cancer therapy. Accordingly, in this study we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent P-gp inhibitors employing 130 known P-gp ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) or multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new promising P-gp inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Several potent hits were captured. The most potent hit decreased the IC50 of doxorubicin from 0.906 to 0.190 μM on doxorubicin resistant MCF7 cell-line.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.

Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis

Phosphoinositide 3-kinase gamma (PI3Kγ) is member of a family of enzymes involved in cancer pathogenesis. Accordingly, considerable efforts have been carried out to develop new PI3Kγ inhibitors. Towards this end we explored the pharmacophoric space of PI3Kγ using three diverse sets of inhibitors. Subsequently, we employed genetic algorithm-based QSAR analysis to select optimal combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within training inhibitors. Interestingly, two successful pharmacophores were selected within two statistically consistent QSAR models. The close similarity among the two binding models prompted us to merge them in a hybrid pharmacophore. The resulting model showed superior receiver operator characteristic curve (ROC) and closely resembled binding interactions seen in crystallographic ligand-PI3Kγ complexes. The resulting model was employed to screen the national cancer institute (NCI) list of compounds to search for new PI3Kγ ligands. After testing captured hits in vitro, 19 compounds showed nanomolar IC50 values against PI3Kγ. The chemical structures and purities of most potent hits were validated using NMR and MS experiments.

Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

Elaborate ligand-based modeling reveal new migration inhibitory factor inhibitors

Recent research suggested the involvement of migration inhibitor factor (MIF) in cancer and inflammatory diseases, which prompted several attempts to develop new MIF inhibitors. Accordingly, we investigated the pharmacophoric space of 79 MIF inhibitors using seven diverse subsets of inhibitors to identify plausible binding hypotheses (pharmacophores). Subsequently, we implemented genetic algorithm and multiple linear regression analysis to select optimal combination of pharmacophores and physicochemical descriptors capable of explaining bioactivity variation within the training compounds (QSAR model, r63=0.62, F=42.8, rLOO(2)=0.721,rPRESS(2) against 16 external test inhibitors=0.58). Two orthogonal pharmacophores appeared in the optimal QSAR model suggestive of at least two binding modes available to ligands inside MIF binding pocket. Subsequent validation using receiver operating characteristic (ROC) curves analysis established the validity of these two pharmacophores. We employed these pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Eight compounds gave >50% inhibition at 100μM. Two molecules illustrated >75% inhibition at 10μM.

Molecular modeling, docking and ADMET studies towards development of novel Disopyramide analogs for potential inhibition of human voltage gated sodium channel proteins

The sodium "channelopathies" are the first among the ion channel diseases identified and have attracted widespread clinical and scientific interests. Human voltage gated sodium channels are sites of action of several antiarrhythmic drugs, local anesthetics and related antiepileptic drugs. The present study aims to optimize the activity of Disopyramide, by modification in its structures which may improve the drug action by reducing its side effects. Herein, we have selected Human voltage-gated sodium channel protein type 5 as a potent molecular target. Nearly eighty analogs of Disopyramide are designed and optimized. Thirty are selected for energy minimization using Discovery studio and the LigPrep 2.5. Prior to docking, the active sites of all the proteins are identified. The processing, optimization and minimization of all the proteins is done in Protein preparation wizard. The docking study is performed using the GLIDE. Finally top five ranked lead molecules with better dock scores are identified as having strong binding affinity to 2KAV protein than Disopyramide based on XP G scores. These five leads are further docked with other similar voltage gated sodium channel proteins (PDB IDs: 2KBI, 4DCK, 2L53 and 4DJC) and the best scoring analog with each protein is identified. Drug likeliness and comparative bioactivity analysis for all the analogs is done using QikProp 3.4. Results have shown that the top five lead molecules would have the potential to act as better drugs as compared to Disopyramide and would be of interest as promising starting point for designing compounds against various Sodium channelopathies.

A simple synthesis of N-perfluoroacylated and N-acylated glycals of neuraminic acid with a cyclic aminic substituent at the 4α position as possible inhibitors of sialidases

A simple protocol for the synthesis of N-perfluoroacylated and N-acylated glycals of neuraminic acid, with a secondary cyclic amine (morpholine or piperidine) at the 4α position, has been set-up, starting from peracetylated N-acetylneuraminic acid methyl ester that undergoes, sequentially to its direct N-transacylation followed by a C-4 amination, a β-elimination, and a selective hydrolysis of the ester functions, without affecting the sensitive perfluorinated amide.

Elaborate ligand-based modeling reveals new nanomolar heat shock protein 90α inhibitors

Heat shock protein (Hsp90α) has been recently implicated in cancer prompting several attempts to discover and optimize new Hsp90α inhibitors. Toward this end, we explored the pharmacophoric space of 83 Hsp90α inhibitors using six diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure activity relationship (QSAR) of optimal predictive potential (r(67)(2)=0.811, F 42.8, r(LOO)(2)=0.748, r(PRESS)(2) (against 16 external test inhibitors) = 0.619). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the Hsp90α binding pocket. Receiver operating characteristic (ROC) curves analysis established the validity of QSAR-selected pharmacophores. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds and our in-house-built drugs and agrochemicals database (DAC). Twenty-five nanomolar and low micromolar Hsp90α inhibitors were identified. The most potent were formoterol, amodaquine, primaquine, and midodrine with IC(50) values of 3, 5, 6, and 20 nM, respectively.

Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH₂- bonds.

Pharmacophore-based virtual screening

This chapter is a review of the most recent developments in the field of pharmacophore modeling, covering both methodology and application. Pharmacophore-based virtual screening is nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Nevertheless, like any empirical approach, it has specific limitations and efforts to improve the methodology are still ongoing. Fundamentally, the core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, according to their dominant physico-chemical features, is both the main advantage and the main drawback of pharmacophore modeling. The advantage is the one of simplicity - the complex nature of noncovalent ligand binding interactions is rendered intuitive and comprehensible by the human mind. Although computers are much better suited for comparisons of pharmacophore patterns, a chemist's intuition is primarily scaffold-oriented. Its underlying simplifications render pharmacophore modeling unable to provide perfect predictions of ligand binding propensities - not even if all its subsisting technical problems would be solved. Each step in pharmacophore modeling and exploitation has specific drawbacks: from insufficient or inaccurate conformational sampling to ambiguities in pharmacophore typing (mainly due to uncertainty regarding the tautomeric/protonation status of compounds), to computer time limitations in complex molecular overlay calculations, and to the choice of inappropriate anchoring points in active sites when ligand cocrystals structures are not available. Yet, imperfections notwithstanding, the approach is accurate enough in order to be practically useful and actually is the most used virtual screening technique in medicinal chemistry - notably for "scaffold hopping" approaches, allowing the discovery of new chemical classes carriers of a desired biological activity.