Orazio Nicolotti

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. Citation: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267

Discovery of a novel class of potent coumarin monoamine oxidase B inhibitors: development and biopharmacological profiling of 7-[(3-chlorobenzyl)oxy]-4-[(methylamino)methyl]-2H-chromen-2-one methanesulfonate (NW-1772) as a highly potent, selective, reversible, and orally active monoamine oxidase B inhibitor.

In an effort to discover novel selective monoamine oxidase (MAO) B inhibitors with favorable physicochemical and pharmacokinetic profiles, 7-[(m-halogeno)benzyloxy]coumarins bearing properly selected polar substituents at position 4 were designed, synthesized, and evaluated as MAO inhibitors. Several compounds with MAO-B inhibitory activity in the nanomolar range and excellent MAO-B selectivity (selectivity index SI > 400) were identified. Structure-affinity relationships and docking simulations provided valuable insights into the enzyme-inhibitor binding interactions at position 4, which has been poorly explored. Furthermore, computational and experimental studies led to the identification and biopharmacological characterization of 7-[(3-chlorobenzyl)oxy]-4-[(methylamino)methyl]-2H-chromen-2-one methanesulfonate 22b (NW-1772) as an in vitro and in vivo potent and selective MAO-B inhibitor, with rapid blood-brain barrier penetration, short-acting and reversible inhibitory activity, slight inhibition of selected cytochrome P450s, and low in vitro toxicity. On the basis of this preliminary preclinical profile, inhibitor 22b might be viewed as a promising clinical candidate for the treatment of neurodegenerative diseases.

Applicability Domain for QSAR models: where theory meets reality

Quantitative Structure-Activity Relationships are widely acknowledged predictive methods employed, for years, in organic and medicinal chemistry. More recently, they have assumed a central role also in the context of the explorative toxicology for the protection of environment and human health. However, their real-life application has not been always enthusiastically welcomed, being often retrospectively used and, thus, of limited importance for prospective goals. The need of making more trustable predictions has thus addressed studies on the so-called Applicability Domain, which represents the chemical space from which a model is derived and where a prediction is considered to be reliable. In the present study, the authors survey a number of approaches used to build the Applicability Domain. In particular, they will focus on strategies based on: a) physico-chemical, b) structural and c) response domains. Moreover, some examples integrating different strategies will be also discussed to meet the needs of both model developers and downstream users. KeyWoRDS Applicability Domain, Interpolation Space, QSAR, Similarity, Structural Fragments

Design, Synthesis, and Biological Evaluation of Coumarin Derivatives Tethered to an Edrophonium‐like Fragment as Highly Potent and Selective Dual Binding Site Acetylcholinesterase Inhibitors

A large series of substituted coumarins linked through an appropriate spacer to 3‐hydroxy‐N,N‐dimethylanilino or 3‐hydroxy‐N,N,N‐trialkylbenzaminium moieties were synthesized and evaluated as acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors. The highest AChE inhibitory potency in the 3‐hydroxy‐N,N‐dimethylanilino series was observed with a 6,7‐dimethoxy‐3‐substituted coumarin derivative, which, along with an outstanding affinity (IC50=0.236 nM) exhibits excellent AChE/BChE selectivity (SI>300 000). Most of the synthesized 3‐hydroxy‐N,N,N‐trialkylbenzaminium salts display an AChE affinity in the sub‐nanomolar to picomolar range along with excellent AChE/BChE selectivities (SI values up to 138 333). The combined use of docking and molecular dynamics simulations permitted us to shed light on the observed structure–affinity and structure–selectivity relationships, to detect two possible alternative binding modes, and to assess the critical role of π–π stacking interactions in the AChE peripheral binding site.

Design, Synthesis, and Biological Evaluation of Imidazolyl Derivatives of 4,7-Disubstituted Coumarins as Aromatase Inhibitors Selective over 17-α-Hydroxylase/C17-20 Lyase

The design, synthesis, and biological evaluation of a series of new aromatase (AR, CYP19) inhibitors bearing an imidazole ring linked to a 7-substituted coumarin scaffold at position 4 (or 3) are reported. Many compounds exhibited an aromatase inhibitory potency in the nanomolar range along with a high selectivity over 17-α-hydroxylase/C17-20 lyase (CYP17). The most potent AR inhibitor was the 7-(3,4-difluorophenoxy)-4-imidazolylmethyl coumarin 24 endowed with an IC(50) = 47 nM. Docking simulations on a selected number of coumarin derivatives allowed the identification of the most important interactions driving the binding and clearly indicated the allowed and disallowed regions for appropriate structural modifications of coumarins and closely related heterocyclic molecular scaffolds.

Homo- and hetero-bivalent edrophonium-like ammonium salts as highly potent, dual binding site AChE inhibitors

A number of mono- and bis-quaternary ammonium salts, containing edrophonium-like and coumarin moieties tethered by an appropriate linker, proved to be highly potent and selective dual binding site acetylcholinesterase inhibitors with good selectivity over butyrylcholinesterase. Homobivalent bis-quaternary inhibitors 11 and 12, differing by only one methylene unit in the linker, were the most potent and selective inhibitors exhibiting a sub-nanomolar affinity (IC(50)=0.49 and 0.17 nM, respectively) and a high butyryl-/acetylcholinesterase affinity ratio (SI=1465 and 4165, respectively). The corresponding hetero-bivalent coumarinic inhibitors 13 and 14 were also endowed with excellent inhibitory potency but a lower AChE selectivity (IC(50)=2.1 and 1.0 nM, and SI=505 and 708, respectively). Docking simulations enabled clear interpretation of the structure-affinity relationships and detection of key binding interactions at the primary and peripheral AChE binding sites.

Design, synthesis and biological evaluation of coumarin alkylamines as potent and selective dual binding site inhibitors of acetylcholinesterase.

Acetylcholinesterase inhibitors (AChEIs) are currently the drugs of choice, although only symptomatic and palliative, for the treatment of Alzheimers disease (AD). Donepezil is one of most used AChEIs in AD therapy, acting as a dual binding site, reversible inhibitor of AChE with high selectivity over butyrylcholinesterase (BChE). Through a combined target- and ligand-based approach, a series of coumarin alkylamines matching the structural determinants of donepezil were designed and prepared. 6,7-Dimethoxycoumarin derivatives carrying a protonatable benzylamino group, linked to position 3 by suitable linkers, exhibited fairly good AChE inhibitory activity and a high selectivity over BChE. The inhibitory potency was strongly influenced by the length and shape of the spacer and by the methoxy substituents on the coumarin scaffold. The inhibition mechanism, assessed for the most active compound 13 (IC(50) 7.6 nM) resulted in a mixed-type, thus confirming its binding at both the catalytic and peripheral binding sites of AChE.

Targeting monoamine oxidases with multipotent ligands: an emerging strategy in the search of new drugs against neurodegenerative diseases.

The socioeconomic burden of multi-factorial pathologies, such as neurodegenerative diseases (NDs), is enormous worldwide. Unfortunately, no proven disease-modifying therapy is available yet and in most cases (e.g., Alzheimers and Parkinsons disease) the approved drugs exert only palliative and symptomatic effects. Nowadays, an emerging strategy for the discovery of disease-modifying drugs is based on the multi-target directed ligand (MTDL) design, an innovative shift from the traditional approach one-drug-one-target to the more ambitious one-drug-more-targets goal. Herein, we review the discovery strategy, the mechanism of action and the biopharmacological evaluation of multipotent ligands exhibiting monoamine oxidase (MAO) inhibition as the core activity with a potential for the treatment of NDs. In particular, MAO inhibitors exhibiting additional acetylcholinesterase (AChE) or nitric oxide synthase (NOS) inhibition, or ion chelation/antioxidant-radical scavenging/anti-inflammatory/A2A receptor antagonist/APP processing modulating activities have been thoroughly examined.

QSAR and QSPR studies of a highly structured physicochemical domain

The relevance of terms other than linear when deriving quantitative structure-activity relationship/quantitative structure-property relationship (QSAR/QSPR) models has been rarely considered so far. In this study, the impact of quadratic and interacting terms has been taken into account. The first effect of including such highly structured terms is a significant extension of the parametric domain that moves from the initial N to N(N + 3)/2 parameters. This substantial enlargement over the conventional linear boundaries involves a higher computational cost due to the increased combinatorial number of resulting theoretical QSAR/QSPR models. To face this issue, novel genetic-algorithm-based software, MGZ (multigenetic zooming), was developed and used for both variable selection and model building. To speed up the entire process of domain searching, MGZ was supported with multiple independent evolving populations and genetic storms to further QSAR/QSPR analyses. In addition, a novel fitness function was developed to score models on the basis of their inner predictive capability, assessed on the training set, structure complexity, and presence of nonlinear terms. The models were further validated by monitoring model redundancy and performing intensive randomization runs. The Selwood data set was used as a reference set to derive QSAR models. Furthermore, a QSPR study was conducted on the solubility data set of a large array of organic compounds. The results reported in the present paper demonstrate that our approach is successful in finding linear models, which are at least as good as the models previously derived using standard statistical approaches, and in deriving new nonlinear models with good statistical figures.

An integrated approach to ligand- and structure-based drug design: development and application to a series of serine protease inhibitors.

A novel approach was developed to rationally interface structure- and ligand-based drug design through the rescoring of docking poses and automated generation of molecular alignments for 3D quantitative structure-activity relationship investigations. The procedure was driven by a genetic algorithm optimizing the value of a novel fitness function, accounting simultaneously for best regressions among binding-energy docking scores and affinities and for minimal geometric deviations from properly established crystal-based binding geometry. The GRID/CPCA method, as implemented in GOLPE, was used to feature molecular determinants of ligand binding affinity for each molecular alignment. In addition, unlike standard procedures, a novel multipoint equation was adopted to predict the binding affinity of ligands in the prediction set. Selectivity was investigated through square plots reporting experimental versus recalculated binding affinities on the targets under examination. The application of our approach to the modeling of affinity data of a large series of 3-amidinophenylalanine inhibitors of thrombin, trypsin, and factor Xa generated easily interpretable and independent models with robust statistics. As a further validation study, our approach was successfully applied to a series of 3,4,7-substituted coumarins, acting as selective MAO-B inhibitors.