A comparative molecular field analysis study of N-benzylpiperidines as acetylcholinesterase inhibitors

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC₅₀ value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC₅₀s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at megaache.collaborationspharma.com.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Chromatographic fingerprint analysis, antioxidant properties, and inhibition of cholinergic enzymes (acetylcholinesterase and butyrylcholinesterase) of phenolic extracts from Irvingia gabonensis (Aubry-Lecomte ex O'Rorke) Baill bark

BACKGROUND

Irvingia gabonensis stem bark is a medicinal plant used in most parts of Africa to manage a number of ailments including neurodegenerative diseases that occur without scientific basis. This work characterized the phenolic composition, evaluated the cholinergic enzymes (acetylcholinesterase, AChE and butyrylcholinesterase, BChE) inhibition, and assessed the antioxidant activity of phenolic extracts from I. gabonensis (Aubry-Lecomte ex O'Rorke) Baill bark.

METHODS

Total phenol and flavonoids content was evaluated in addition to antioxidant activity as shown by Fe2+ chelation, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability, and 2,2-azino-bis-(3-ethylbenthiazoline-6-sulfonic acid) (ABTS) radical scavenging ability. Inhibitory activities on AChE and BChE were evaluated.

RESULTS

The extract was found to be rich in phenolic acid (ellagic acid) and flavonoids (quercetrin, kaempferol, and apigenin). The phenolic extracts displayed DPPH radical scavenging ability (IC50=19.98 μg/mL), ABTS radical scavenging ability (IC50=18.25 μg/mL), iron chelation (IC50=113.10 μg/mL), and reducing power (Fe3+ to Fe2+) (5.94 mg ascorbic acid equivalent/100 g). Extracts of I. gabonensis inhibited AChE (IC50=32.90 μg/mL) and BChE (IC50=41.50 μg/mL) activities in concentration-dependent manner.

CONCLUSIONS

Hence, possible mechanism through which the stem bark executes their anti-Alzheimer's disease activity might be by inhibiting cholinesterase activities in addition to suppressing oxidative-stress-induced neurodegeneration.

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r² = 0.882 and r² = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.

3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM

A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q²_LOO-CV=1, R²_ext=0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares.

QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease

Alzheimer's Disease (AD) is a multifactorial neurological syndrome with the combination of aging, genetic, and environmental factors triggering the pathological decline. Interestingly, the importance of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the ß-amyloid peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method advanced the scope of rational drug design and the search for the mechanism of drug action. It is a well-established fact that the chemical and pharmaceutical effects of a compound are closely related to its physico-chemical properties, which can be calculated by various methods from the compound structure. This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried out for a variety of cholinesterase inhibitors for the treatment of AD. These predictive models will be potentially used for further designing better and safer drugs against AD.

Synthesis and pharmacological evaluation of donepezil-based agents as new cholinesterase/monoamine oxidase inhibitors for the potential application against Alzheimer's disease

In a continuing effort to develop multitargeted compounds as potential treatment agents against Alzheimer's disease (AD), a series of donepezil-like compounds were designed, synthesized and evaluated. In vitro studies showed that most of the designed compounds displayed potent inhibitory activities toward AChE, BuChE, MAO-B and MAO-A. Among them, w18 was a promising agent with balanced activities, which exhibited a moderate cholinesterase inhibition (IC₅₀, 0.220 μM for eeAChE; 1.23 μM for eqBuChE; 0.454 μM for hAChE) and an acceptable inhibitory activity against monoamine oxidases (IC₅₀, 3.14 μM for MAO-B; 13.4 μM for MAO-A). Moreover, w18 could also be a metal-chelator, and able to cross the blood-brain barrier with low cell toxicity on PC12 cells. Taken together, these results suggested that w18 might be a promising multitargeted compound for AD treatment.

Anticholinesterase and Antioxidative Properties of Aqueous Extract of Cola acuminata Seed In Vitro

Background. Cola acuminata seed, a commonly used stimulant in Nigeria, has been reportedly used for the management of neurodegenerative diseases in folklore without scientific basis. This study sought to investigate the anticholinesterase and antioxidant properties of aqueous extracts from C. acuminata seed in vitro. Methodology. The aqueous extract of C. acuminata seed was prepared (w/v) and its effect on acetylcholinesterase (AChE) and butyrylcholinesterase activities, as well as some prooxidant (FeSO₄, sodium nitroprusside (SNP), and quinolinic acid (QA)) induced lipid peroxidation in rat brain in vitro, was investigated. Results. The results revealed that C. acuminata seed extract inhibited AChE (IC₅₀ = 14.6 μg/mL) and BChE (IC₅₀ = 96.2 μg/mL) activities in a dose-dependent manner. Furthermore, incubation of rat's brain homogenates with some prooxidants caused a significant increase P < 0.05 in the brain malondialdehyde (MDA) content and inhibited MDA production dose-dependently and also exhibited further antioxidant properties as typified by their high radicals scavenging and Fe²⁺ chelating abilities. Conclusion. Inhibition of AChE and BChE activities has been the primary treatment method for mild Alzheimer's disease (AD). Therefore, one possible mechanism through which the seed exerts its neuroprotective properties is by inhibiting cholinesterase activities as well as preventing oxidative-stress-induced neurodegeneration. However, this is a preliminary study with possible physiological implications.

Indolinone-based acetylcholinesterase inhibitors: synthesis, biological activity and molecular modeling

A series of indolinone-based compounds bearing benzylpyridinium moiety was designed as dual-binding inhibitors of acetylcholinesterase (AChE). The target compounds 3a-u were synthesized by condensation of oxindole and pyridin-4-carbalehyde, and subsequent N-benzylation. The anti-cholinesterase activity evaluation of synthesized compounds revealed that most of them had very potent inhibitory activity against AChE, superior to standard drug donepezil. Particularly, 2-chlorobenzyl derivative 3c was the most potent compound against AChE with IC50 value of 0.44 nM, being 32-fold more potent than donepezil. Also, most of compounds were more potent than standard drug donepezil against butyrylcholinesterase (BuChE). Docking study revealed that the hydrophobic aromatic part (indoline) of representative compound 3c binds to the PAS and the N-benzylpyridinium residue binds to the CAS of AChE.

Synthesis and biological evaluation of a new series of ebselen derivatives as glutathione peroxidase (GPx) mimics and cholinesterase inhibitors against Alzheimer's disease

A series of ebselen derivatives were designed, synthesised and evaluated as inhibitors of cholinesterases (ChEs) and glutathione peroxidase (GPx) mimics. Most of the compounds were found to be potent against AChEs and BuChE, compounds 5e and 5i, proved to be the most potent against AChE with IC₅₀ values of 0.76 and 0.46 μM, respectively. Among these hybrids, most of the compounds were found to be good GPx mimics compare with ebselen. The selected compounds 5e and 5i were also used to determine the catalytic parameters and in vitro hydrogen peroxide scavenging activity. The results indicate that compounds 5e and 5i may be excellent multifunctional agents for the treatment of AD.

The 3D-QSAR study of 110 diverse, dual binding, acetylcholinesterase inhibitors based on alignment independent descriptors (GRIND-2). The effects of conformation on predictive power and interpretability of the models

The 3D-QSAR analysis based on alignment independent descriptors (GRIND-2) was performed on the set of 110 structurally diverse, dual binding AChE reversible inhibitors. Three separate models were built, based on different conformations, generated following next criteria: (i) minimum energy conformations, (ii) conformation most similar to the co-crystalized ligand conformation, and (iii) docked conformation. We found that regardless on conformation used, all the three models had good statistic and predictivity. The models revealed the importance of protonated pyridine nitrogen of tacrine moiety for anti AChE activity, and recognized HBA and HBD interactions as highly important for the potency. This was revealed by the variables associated with protonated pyridinium nitrogen, and the two amino groups of the linker. MIFs calculated with the N1 (pyridinium nitrogen) and the DRY GRID probes in the AChE active site enabled us to establish the relationship between amino acid residues within AChE active site and the variables having high impact on models. External predictive power of the models was tested on the set of 40 AChE reversible inhibitors, most of them structurally different from the training set. Some of those compounds were tested on the different enzyme source. We found that external predictivity was highly sensitive on conformations used. Model based on docked conformations had superior predictive ability, emphasizing the need for the employment of conformations built by taking into account geometrical restrictions of AChE active site gorge.

Synthesis and biological evaluation of thiophene derivatives as acetylcholinesterase inhibitors

A series of new thiophene derivatives has been synthesized using the Gewald protocol. The acetylcholinesterase inhibition activity was assayed according to Ellman’s method using donepezil as reference. Some of the compounds were found to be more potent inhibitors than the reference. 2-(2-(4-(4-Methoxyphenyl)piperazin-1-yl)acetamido)-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carboxamide (IIId) showed 60% inhibition, compared to only 40% inhibition by donepezil.

Synthesis of new indole derivatives structurally related to donepezil and their biological evaluation as acetylcholinesterase inhibitors

New series of indole derivatives analogous to donepezil, a well known anti-Alzheimer and acetylcholinesterase inhibitor drug, was synthesized. A full chemical characterization of the new compounds is provided. Biological evaluation of the new compounds as acetylcholinesterase inhibitors was performed. Most of the compounds were found to have potent acetylcholinesterase inhibitor activity compared to donepezil as standard. The compound 1-(2-(4-(2-fluorobenzyl) piperazin-1-yl)acetyl)indoline-2,3-dione (IIId) was found to be the most potent.

Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors

PURPOSE

Acetylcholinesterase (AChE) is both a therapeutic target for Alzheimer's disease and a target for organophosphorus, carbamates and chemical warfare agents. Prediction of the likelihood of compounds interacting with this enzyme is therefore important from both therapeutic and toxicological perspectives.

MATERIALS AND METHODS

Support vector machine classification and regression models with molecular descriptors derived from Shape Signatures and the Molecular Operating Environment (MOE) application software were built and tested using a set of piperidine AChE inhibitors (N = 110).

RESULTS

The combination of the alignment free Shape Signatures and 2D MOE descriptors with the Support Vector Regression method outperforms the models based solely on 2D and internal 3D (i3D) MOE descriptors, and is comparable with the best previously reported PLS model based on CoMFA molecular descriptors (r(2)(test,SVR) = 0.48 vs. r(2)(test,PLS) = 0.47 from Sutherland et al. J Med Chem 47:5541-5554, 2004). Support Vector Classification algorithms proved superior to a classifier based on scores from the molecular docking program GOLD, with the overall prediction accuracies being Q(SVC(10CV)) = 74% and Q(SVC(LNO)) = 67% vs. Q(GOLD) = 56%.

CONCLUSIONS

These new machine learning models with combined descriptor schemes may find utility for predicting novel AChE inhibitors.

3D-QSAR Studies on natural acetylcholinesterase inhibitors of Sarcococca saligna by comparative molecular field analysis (CoMFA)

We have derived a comprehensive structure-activity relationship (SAR) picture for a new series of natural acetylcholinesterase inhibitors isolated from Sarcococca saligna. A set of 32 previously isolated and tested pregnane-type steroidal alkaloids inhibitors were investigated with respect to their IC(50) values (pIC(50)) against the AChE enzyme in order to derive CoMFA models using atom-based alignment. A highly significant CoMFA model was obtained with r(2) value of 0.974. The q(2) (cross validation r(2)) value also confirms the statistical significance of our model.

Acetyl and butyrylcholinesterase-inhibiting triterpenoid alkaloids from Buxus papillosa

Three triterpenoid alkaloids, buxakashmiramine [(20S)-20-dimethylamino-4',6'-dimethoxy-5'-hydroxybenzoylamino-3beta-methyl-buxan-31-ol] (1), buxakarachiamine [(20S)-20-dimethylamino-2'-hydroxy-3beta-methyl-3'-methyl-butanoylamino-9,10-seco-buxa-9(11), 10(19)-dien-31-ol] (2) and buxahejramine [(20S)-20-dimethylamino-2'-hydroxy-3beta-methyl-3'-methyl-pentanoylamino-9,10-seco-buxa-9(11), 10(19)-dien-31-ol] (3) were isolated from the leaves of Buxus papillosa. Four known bases, cycloprotobuxine-C (4), cyclovirobuxeine-A (5), cyclomicrophylline-A (6) and semperviraminol (7) were isolated for the first time from this species. Their structures were established through extensive spectroscopic studies. Most of these compounds exhibited anticholinesterase activity.

QSAR models using a large diverse set of estrogens

Endocrine disruptors (EDs) have a variety of adverse effects in humans and animals. About 58,000 chemicals, most having little safety data, must be tested in a group of tiered assays. As assays will take years, it is important to develop rapid methods to help in priority setting. For application to large data sets, we have developed an integrated system that contains sequential four phases to predict the ability of chemicals to bind to the estrogen receptor (ER), a prevalent mechanism for estrogenic EDs. Here we report the results of evaluating two types of QSAR models for inclusion in phase III to quantitatively predict chemical binding to the ER. Our data set for the relative binding affinities (RBAs) to the ER consists of 130 chemicals covering a wide range of structural diversity and a 6 orders of magnitude spread of RBAs. CoMFA and HQSAR models were constructed and compared for performance. The CoMFA model had a r2 = 0.91 and a q2LOO = 0.66. HQSAR showed reduced performance compared to CoMFA with r2 = 0.76 and q2LOO = 0.59. A number of parameters were examined to improve the CoMFA model. Of these, a phenol indicator increased the q2LOO to 0.71. When up to 50% of the chemicals were left out in the leave-N-out cross-validation, the q2 remained significant. Finally, the models were tested by using two test sets; the q2pred for these were 0.71 and 0.62, a significant result which demonstrates the utility of the CoMFA model for predicting the RBAs of chemicals not included in the training set. If used in conjunction with phases I and II, which reduced the size of the data set dramatically by eliminating most inactive chemicals, the current CoMFA model (phase III) can be used to predict the RBA of chemicals with sufficient accuracy and to provide quantitative information for priority setting.

(13)C NMR and electron ionization mass spectrometric data-activity relationship model of estrogen receptor binding

Two Spectroscopic Data-Activity Relationship (SDAR) models based on (13)C nuclear magnetic resonance (NMR) and electron ionization mass spectra (EI MS) data were developed for 108 compounds whose relative binding affinities (RBA) to the estrogen receptor are known. The (13)C NMR and EI MS data were used as spectrometric digital fingerprints to reflect the electronic and structural characteristics of the compounds. Both SDAR models segregated the 108 compounds into 20 strong, 15 medium, and 73 weak relative binding classifications. The first SDAR model, based on (13)C NMR data alone, gave a leave-one-out (LOO) cross-validation of 75.0%. The second SDAR model, based on a composite of (13)C NMR and EI MS data, gave a LOO cross-validation of 82.4%. Many of the misidentifications from the cross-validations were between medium and weak classifications, where there were fewer specific spectrometric characteristics to identify the relationship of spectra to estrogen receptor binding. Real and predicted (13)C NMR chemical shifts were used to test the predictive behavior of both SDAR models. The ease of use and speed of SDAR modeling may facilitate their use with other toxicological endpoints.

Clinical profile of donepezil in the treatment of Alzheimer's disease

Although the underlying pathogenesis of Alzheimer's disease (AD) is not fully understood, one of its key features is the widespread loss of central cholinergic innervation, known to be fundamental for cognitive processes. This finding led to the hypothesis that pharmacological enhancement of acetylcholine (ACh) neurotransmission may alleviate the symptoms of AD. Currently, cholinergic therapy, particularly cholinesterase (ChE) inhibition, represents the most realistic approach to the symptomatic treatment of AD. Donepezil HCl, for example, is a piperidine-based, reversible acetylcholinesterase (AChE) inhibitor, chemically distinct from other ChE inhibitors and rationally designed for the symptomatic treatment of AD. It is highly selective for centrally acting AChE, with little or no affinity for butyrylcholinesterase, present predominantly in the periphery. Phase I and II clinical trials demonstrated donepezil's favourable pharmacokinetic, pharmacodynamic and safety profile with no requirement for dose modification in the elderly or in patients with renal or hepatic impairment. Furthermore, its long half-life supports a simple and convenient once-daily dosing regimen. Subsequent to encouraging phase II clinical trial results, two pivotal, randomized, double-blind phase III trials (of 15 and 30 weeks' duration) demonstrated highly significant improvements in cognition and global function in mild to moderately severe AD patients treated with either 5 or 10 mg/day donepezil compared with placebo. Adverse events in the phase II and III trials, primarily cholinergic in nature, were transient and generally mild in severity and resolved during continued donepezil administration. Thus, the donepezil clinical trials programme has shown that this drug is a clinically effective and well-tolerated, once-daily treatment for the symptoms of mild to moderately severe AD.

SAR of 9-amino-1,2,3,4-tetrahydroacridine-based acetylcholinesterase inhibitors: synthesis, enzyme inhibitory activity, QSAR, and structure-based CoMFA of tacrine analogues

In this study, we attempted to derive a comprehensive SAR picture for the class of acetylcholinesterase (AChE) inhibitors related to tacrine, a drug currently in use for the treatment of the Alzheimer's disease. To this aim, we synthesized and tested a series of 9-amino-1,2,3,4-tetrahydroacridine derivatives substituted in the positions 6 and 7 of the acridine nucleus and bearing selected groups on the 9-amino function. By means of the Hansch approach, QSAR equations were obtained, quantitatively accounting for both the detrimental steric effect of substituents in position 7 and the favorable electron-attracting effect exerted by substituents in positions 6 and 7 of the 9-amino-1,2,3,4-tetrahydroacridine derivatives. The three-dimensional (3D) properties of the inhibitors were taken into consideration by performing a CoMFA analysis on the series of AChE inhibitors made by 12 9-amino-1,2,3, 4-tetrahydroacridines and 13 11H-indeno[1,2-b]quinolin-10-ylamines previously developed in our laboratory. The alignment of the molecules to be submitted to the CoMFA procedure was carried out by taking advantage of docking models calculated for the interactions of both the unsubstituted 9-amino-1,2,3,4-tetrahydroacridine and 11H-indeno[1,2-b]quinolin-10-ylamine with the target enzyme. A highly significant CoMFA model was obtained using the steric field alone, and the features of such a 3D QSAR model were compared with the classical QSAR equations previously calculated. The two models appeared consistent, the main aspects they had in common being (a) the individuation of the strongly negative contribution of the substituents in position 7 of tacrine and (b) a tentative assignment of the hydrophobic character to the favorable effect exerted by the substituents in position 6. Finally, a new previously unreported tacrine derivative designed on the basis of both the classical and the 3D QSAR equations was synthesized and kinetically evaluated, to test the predictive ability of the QSAR models. The 6-bromo-9-amino-1,2,3,4-tetrahydroacridine was predicted to have a pIC(50) value of 7.31 by the classical QSAR model and 7.40 by the CoMFA model, while its experimental IC(50) value was equal to 0.066 (+/-0.009) microM, corresponding to a pIC(50) of 7.18, showing a reasonable agreement between predicted and observed AChE inhibition data.

Comparative molecular field analysis of fungal squalene epoxidase inhibitors

Comparative molecular field analysis (CoMFA) of fungal squalene epoxidase inhibitors exhibiting antifungal activity reported in terms of minimum inhibitory concentration (MIC) was performed. Ninety-two molecules belonging to different chemical classes, namely terbinafine analogues, benzylamines, homopropargylamines, and carbon analogues were divided into training set and test set. The initial conformations of the inhibitors obtained from molecular dynamics simulations for 50 ps in aqueous solution were used in the study. Out of three charges used in the study, Gasteiger-H]uckel charges result in models with good internal predictivity. Initial analysis of 92 molecules (analysis A) resulted in models with low predictive r(2) values for activity against three organisms. This data set was modified by exclusion of 13 molecules, and analysis was performed again. This analysis of 79 molecules (analysis B) resulted in improvement in predictivity of the CoMFA models and cross-validated r(2) values of 0.583, 0.509, and 0.502 for Candida albicans, Aspergillus fumigatus, and Trichophyton mentagrophytes, respectively. These models were used to predict the activities of the molecules belonging to the test set. The models from analysis B show better correlative and predictive properties than analysis A. Comparison of CoMFA contour maps for activity against three different fungi revealed differentiating structural requirements.

Conformational Switching and the Synthesis of Spiro[2H-indol]-3(1H)-ones by Radical Cyclization

Radical cyclization of 1-(2-bromophenylamino)cyclohexanecarbonitriles (3, X = CH) and 4-(2-bromophenylamino)-4-piperidinecarbonitriles (3, X = N) provide spiro[2H-indole-2-cyclohexan]-3(1H)-imines (5, X = CH) and spiro[2H-indole-2,4'-piperidin]-3(1H)-imines (5, X = N), respectively, in 33-57% yields. This contradicts a recent report that 1-(2-bromophenylamino)cyclohexanecarbonitrile (3, X-R(2) = CH(2)), treated under apparently identical conditions, led only to nitrile transfer product 6 (X-R(2) = CH(2)). Acidic hydrolyses of the imines provide the corresponding ketones 2 in quantitative yields. Single-crystal X-ray analyses of ketone 2e and nitrile 3e indicate that the relative configuration of the aromatic nitrogen has been inverted during the cyclization. In addition, NOE NMR analyses of spiroindolepiperidine 2c and its aniline-nitrogen-methylated analogue 10a show that the relative conformation of the piperidine ring has inverted. Thus, methylation of 2c acts as a conformational "switch" for the spiroindolepiperidine ring system.

Automated docking of 82 N-benzylpiperidine derivatives to mouse acetylcholinesterase and comparative molecular field analysis with 'natural' alignment

Automated docking and three-dimensional Quantitative Structure-Activity Relationship studies (3D QSAR) were performed for a series of 82 reversible, competitive and selective acetylcholinesterase (AChE) inhibitors. The suggested automated docking technique, making use of constraints taken from experimental crystallographic data, allowed to dock all the 82 substituted N-benzylpiperidines to the crystal structure of mouse AChE, because of short computational times. A 3D QSAR model was then established using the CoMFA method. In contrast to conventional CoMFA studies, the compounds were not fitted to a reference molecule but taken in their 'natural' alignment obtained by the docking study. The established and validated CoMFA model was then applied to another series of 29 N-benzylpiperidine derivatives whose AChE inhibitory activity data were measured under different experimental conditions. A good correlation between predicted and experimental activity data shows that the model can be extended to AChE inhibitory activity data measured on another acetylcholinesterase and/or at different incubation times and pH level.

Rational design of anti-dementia therapy

Understanding the pharmacokinetic (PK) and pharmacodynamic (PD) characteristics of new drugs is essential. The application of this information facilitates the design of a rational clinical trial programme and optimizes the chances for rapid and successful clinical development. Upon completion of each development phase, the information obtained must be evaluated critically to maximize the design of subsequent studies. Phase I trials represent the first time the drug is administered in humans and their primary objective is to evaluate the drug's safety and tolerability in man. Phase II trials represent the first time that the drug is administered to the target patient population. The objectives of Phase II trials are to verify the safety and tolerability of the drug in patients, and also to evaluate for the first time the clinical efficacy of the drug. During these phases of development, the use of PK and PD measures is helpful for establishing the therapeutic dose range as well as the suitability of the chosen efficacy measures for use in the pivotal Phase III trials. Here, using several examples of PK and PD data obtained from the clinical development studies of donepezil HCl, the application of these measures on the development of a drug for the treatment of Alzheimer's disease is discussed.

Three-dimensional quantitative structure-activity relationship of interleukin 1-beta converting enzyme inhibitors: A comparative molecular field analysis study

A three-dimensional quantitative structure-activity relationship (QSAR) study using the comparative molecular field analysis (CoMFA) method was performed on a series of interleukin 1-beta converting enzyme (ICE) inhibitors. The compounds studied have been reported to be time-dependent inhibitors of ICE. This study was performed using 49 compounds, in which the CoMFA models were developed using a training set of 39 compounds. All the compounds were modeled using the X-ray crystal structure of tetrapeptide aldehyde inhibitor/ICE complex. The inhibitor compounds were considered both as neutral species and as P1 carboxylate ionized species. Superimpositions were performed using two alignment rules, namely, an alignment of the structures based on RMS fitting of the backbone heavy atoms of each structure to compound 2 and an alignment based on SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules to the fields of compound 2. Use of LUMO energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the CoMFA models. Steric and electrostatic fields of the inhibitors were found to be the relevant descriptors for structure-activity relationships. The predictive ability of the CoMFA model was evaluated by using a test set of 10 compounds (r2pred as high as 0.859). Further comparison of the coefficient contour maps with the steric and electrostatic properties of the receptor show a high level of compatibility.

Comparison of estrogen receptor alpha and beta subtypes based on comparative molecular field analysis (CoMFA)

A substantial body of evidence indicates that both humans and wildlife suffer adverse health effects from exposure to environmental chemicals that are capable of interacting with the endocrine system. The recent cloning of the estrogen receptor beta subtype (ER-beta) suggests that the selective effects of estrogenic compounds may arise in part by the control of different subsets of estrogen-responsive promoters by the two ER subtypes, ER-alpha and ER-beta. In order to identify the structural prerequisites for ligand-ER binding and to discriminate ER-alpha and ER-beta in terms of their ligand-binding specificities, Comparative Molecular Field Analysis (CoMFA) was employed to construct a three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model on a data set of 31 structurally-diverse compounds for which competitive binding affinities have been measured against both ER-alpha and ER-beta. Structural alignment of the molecules in CoMFA was achieved by maximizing overlap of their steric and electrostatic fields using the Steric and Electrostatic ALignment (SEAL) algorithm. The final CoMFA models, generated by correlating the calculated 3D steric and electrostatic fields with the experimentally observed binding affinities using partial least-squares (PLS) regression, exhibited excellent self-consistency (r2 > 0.99) as well as high internal predictive ability (q2 > 0.65) based on cross-validation. CoMFA-predicted values of RBA for a test set of compounds outside of the training set were consistent with experimental observations. These CoMFA models can serve as guides for the rational design of ER ligands that possess preferential binding affinities for either ER-alpha or ER-beta. These models can also prove useful in risk assessment programs to identify real or suspected EDCs.

Derivation of a pharmacophore model for anandamide using constrained conformational searching and comparative molecular field analysis

Constrained molecular dynamics simulations on anandamide, together with a systematic distance comparison search, have revealed a specific low-energy conformer whose spatial disposition of the pharmacophoric elements closely matches that of HHC. This conformer enables near superposition of the following: (1) the oxygen of the carboxyamide and the phenolic hydroxyl group of HHC, (2) the hydroxyl group of the ethanol and the cyclohexyl hydroxyl group of HHC, (3) the alkyl tail and the lipophilic side chain of HHC, and (4) the polyolefin loop and the tricyclic ring structure of HHC. The close matching of common pharmacophoric elements of anandamide with HHC offers persuasive evidence of the biological relevance of this conformer. The proposed pharmacophore model was capable of discriminating between structurally related compounds exhibiting different pharmacological potency for the CB1 cannabinoid receptor, i.e., anandamide and N-(2-hydroxyethyl)prostaglandinamide. Furthermore, a 3D-QSAR model was derived using CoMFA for a training set of 29 classical and nonclassical analogues which rationalized the binding affinity in terms of steric and electrostatic properties and, more importantly, which predicted the potency of anandamide in excellent agreement with experimental data. The ABC tricyclic HU-210/HU-211 and ACD tricyclic CP55,243/CP55,244 enantiomeric pairs were employed as test compounds to validate the present CoMFA model. For each enantiomeric pair, the CoMFA-predicted log Ki values correctly identified that enantiomer exhibiting the higher affinity for the receptor.

Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor

Three different QSAR methods, Comparative Molecular Field Analysis (CoMFA), classical QSAR (utilizing the CODESSA program), and Hologram QSAR (HQSAR), are compared in terms of their potential for screening large data sets of chemicals as endocrine disrupting compounds (EDCs). While CoMFA and CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) have been commercially available for some time, HQSAR is a novel QSAR technique. HQSAR attempts to correlate molecular structure with biological activity for a series of compounds using molecular holograms constructed from counts of sub-structural molecular fragments. In addition to using r2 and q2 (cross-validated r2) in assessing the statistical quality of QSAR models, another statistical parameter was defined to be the ratio of the standard error to the activity range. The statistical quality of the QSAR models constructed using CoMFA and HQSAR techniques were comparable and were generally better than those produced with CODESSA. It is notable that only 2D-connectivity, bond and elemental atom-type information were considered in building HQSAR models. Since HQSAR requires no conformational analysis or structural alignment, it is straightforward to use and lends itself readily to the rapid screening of large numbers of compounds. Among the QSAR methods considered, HQSAR appears to offer many attractive features, such as speed, reproducibility and ease of use, which portend its utility for prioritizing large numbers of potential EDCs for subsequent toxicological testing and risk assessment.

Quantitative structure-activity relationship studies of a series of sulfa drugs as inhibitors of Pneumocystis carinii dihydropteroate synthetase

Sulfone and sulfanilamide sulfa drugs have been shown to inhibit dihydropteroate synthetase (DHPS) isolated from Pneumocystis carinii. In order to develop a pharmacophoric model for this inhibition, quantitative structure-activity relationships (QSAR) for sulfa drugs active against DHPS have been studied. Accurate 50% inhibitory concentrations were collected for 44 analogs, and other parameters, such as partition coefficients and molar refractivity, were calculated. Conventional multiple regression analysis of these data did not provide acceptable QSAR. However, three-dimensional QSAR provided by comparative molecular field analysis did give excellent results. Upon removal of poorly correlated analogs, a data set of 36 analogs, all having a common NHSO2 group, provided a cross-validated r2 value of 0.699 and conventional r2 value of 0.964. The resulting pharmacophore model should be useful for understanding and predicting the binding of DHPS by new sulfa drugs.

Molecular modeling of the intestinal bile acid carrier: a comparative molecular field analysis study

A structure-binding activity relationship for the intestinal bile acid transporter has been developed using data from a series of bile acid analogs in a comparative molecular field analysis (CoMFA). The studied compounds consisted of a series of bile acid-peptide conjugates, with modifications at the 24 position of the cholic acid sterol nucleus, and compounds with slight modifications at the 3, 7, and 12 positions. For the CoMFA study, these compounds were divided into a training set and a test set, comprising 25 and 5 molecules, respectively. The best three-dimensional quantitative structure-activity relationship model found rationalizes the steric and electrostatic factors which modulate affinity to the bile acid carrier with a cross-validated, conventional and predictive r2 of 0.63, 0.96, and 0.69, respectively, indicating a good predictive model for carrier affinity. Binding is facilitated by positioning an electronegative moiety at the 24-27 position, and also by steric bulk at the end of the side chain. The model suggests substitutions at positions 3, 7, 12, and 24 that could lead to new substrates with reasonable affinity for the carrier.

A comparative QSAR analysis of acetylcholinesterase inhibitors currently studied for the treatment of Alzheimer's disease

Considering the relevance of acetylcholinesterase inhibitors as potential agents for the treatment of the Alzheimer's disease, we have undertaken a comparative QSAR analysis aimed at individuating the physico-chemical properties governing the inhibitory activity of such compounds. The QSAR equations for 13 series of derivatives have been calculated and discussed. The series studied are all those we found in the literature suitable for a QSAR analysis and represent the three main classes of acetylcholinesterase inhibitors currently investigated, namely, physostigmine analogues, 1,2,3,4-tetrahydroacridines and benzylamines. The equations we obtained show that, within each class, the main physico-chemical properties affecting the inhibitory activity are almost the same for all the series and can be individuated by the use of proper parameters. The conclusions of this study can be summarized as follows: (a) hydrophobicity plays a critical role in both the physostigmine- and the benzylamine-derived classes; (b) electronic effects are important for the interactions carried out by the variable portion of benzylamine derivatives; and (c) steric factors are also significant, but, as in other cases, the collinearity between steric and hydrophobic parameters does not allow one to draw any final conclusion.

Role of ionic interactions in cholinesterase catalysis

Acetylcholinesterase (AChE, EC 3.1.1.7) is an enzyme terminating the transmission of nerve impulse in synapses by rapid and selective hydrolysis of the neurotransmitter acetylcholine. Recent years have added a considerable amount of structural knowledge about this protein as well as opened new perspectives to the study of the molecular mechanism of cholinesterase catalysis. In this paper the current state of understanding the molecular recognition by cholinesterases is critically surveyed with particular emphasis on the role of electrostatic interactions.

The simulated binding of (+/-)-2,3-dihydro-5,6-dimethoxy-2-[[1-(phenylmethyl)-4-piperidinyl]meth yl] -1H-inden-1-one hydrochloride (E2020) and related inhibitors to free and acylated acetylcholinesterases and corresponding structure-activity analyses

The simulated binding profiles of acetylcholine, ACh, and the inhibitor (+/-)-2,3-dihydro-5,6- dimethoxy-2-[[1-(phenylmethyl)-4-piperidinyl]methyl]-1H-inden-1-on e hydrochloride (E2020), 1, and some of its analogs to acetylcholinesterase, AChE, were determined using full force field energetics and allowing complete conformational flexibility in both the ligand and receptor. A new mode of binding of ACh to AChE was found which involves the carboxyl oxygen of ACh interacting with Gly 118 and 119. Multiple modes of binding of 1 and some of its analogs were found which include alignment models observed in previous more restricted modeling studies. The key ligand-receptor interactions identified, and the corresponding energetics, are consistent on a relative basis, with observed binding constants for both the individual isomers of each of the inhibitors, as well as among the inhibitors themselves. The multiple modes of binding of 1 to AChE arises from small changes in binding at a single subsite and also from multiple subsite changes. Thus, an independent subsite model for ligand-receptor binding holds for some modes of binding, but not for others. A comparison of the simulated AChE-1 (and analog inhibitors) binding models to the receptor-independent 3D-QSARs previously developed for this class of inhibitors reveals extensive mutual consistency. The findings from these two modeling studies provides greater guidelines for inhibitor design than can be realized from either one. The combined docking and 3D-QSAR studies permit a detailed understanding of the SAR of more than 100 compound 1 analog inhibitors. A simple molecular recognition model can also be gleaned from the docking studies. A cylindrical "plug" (the inhibitor) having a large dipole moment must sterically fit into a cylindrical hole (the active site gorge of AChE), the lining of which also has a large dipole moment. Our simulations suggest that the dynamic "back door" to the active site of AChE does not form a large enough opening for sufficiently long time periods so as to be an effective entrance/exit pathway.

Comparative molecular field analysis of haptens docked to the multispecific antibody IgE(Lb4)

Using comparative molecular field analysis (CoMFA), three-dimensional quantitative structure-activity relationships were developed for 27 haptens which bind to the monoclonal antibody IgE(Lb4). In order to obtain an alignment for these structurally very diverse antigens, the compounds were docked to a previously modeled receptor structure using the automated docking program AUTODOCK (Goodsell, D.S.; Olson, A.J. Proteins: Struct., Funct., Genet. 1990, 8, 195-202). Remarkably, this alignment method yielded highly consistent QSAR models, as indicated by the corresponding cross-validated r2 values (0.809 for a model with carbon as probe atom, 0.773 for a model with hydrogen as probe atom). Conventional alignment failed in providing a basis for self-consistent CoMFAs. Amino acids Tyr H 50, Tyr H 52, and Trp H 95 of the receptor appeared to be of crucial importance for binding of various antigens. These findings are consistent with earlier considerations of aromatic residues being responsible for the multispecificity of certain immunoglobulins.

Use of moment of inertia in comparative molecular field analysis to model chromatographic retention of nonpolar solutes

A quantitative structure-retention relationship (QSRR) was developed from chromatographic data on 31 unsubstituted 3-6 ring polycyclic aromatic hydrocarbons (PAHs) using the 3D-QSAR method known as comparative molecular field analysis (CoMFA). The resulting CoMFA model gave an excellent correlation to high-performance liquid chromatography retention data for these PAHs yielding r2 values of 0.947 (conventional) and 0.865 (cross-validated). The steric and electrostatic contributions to the CoMFA model were 100% and 0%, respectively. A unique feature of this study was the use of moment of inertia, I, as a basis for CoMFA alignment of the PAH molecules. The moment of inertia also provided an alternative method for calculating the solute length-to-breadth ratio (L/B), which has been applied in previous QSRR studies as a molecular descriptor for PAH retention. By virtue of its mathematical simplicity and lack of ambiguity, the present derivation of L/B from I offers several advantages over other geometry-based schemes. Finally, Ix was evaluated for use as a molecular descriptor in QSRR regression analysis to predict the log of the retention index (log I) for these PAHs. The correlation with PAH retention was weak when the moment of inertia was considered alone but improved dramatically (r2 = 0.928) when the moment of inertia and connectivity index chi were used in combination as descriptors.