Daniela Trisciuzzi

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

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

BACKGROUND The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals. RESULTS The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals. CONCLUSION The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.

Novel chemotypes targeting tubulin at the colchicine binding site and unbiasing P-glycoprotein

Retrospective validation studies carried out on three benchmark databases containing a small fraction (that is 2.80%) of known tubulin binders permitted us to develop a computational platform very effective in selecting easier manageable subsets showing by far higher percentages of actives (about 25%). These studies relied on the hierarchical application of multilayer in silico screenings employing filters implying molecular shape similarity; a structure-based pharmacophore model and molecular docking campaigns. Building on this validated approach, we performed intensive prospective studies to screen a large chemical collection, including up to 3.7 millions of commercial compounds, to across an unexplored and patent space in the search of novel colchicine binding site inhibitors. Our investigation was successful in identifying a pool of 31 initial hits showing new molecular scaffolds (such as 4,5-dihydro-1H-pyrrolo[3,4-c]pyrazol-6-one and pyrazolo[1,5-a]pyrimidine). This panel of new hits resulted antiproliferative activity in the low μM range towards MCF-7 human breast cancer, HepG2 human liver cancer, HeLa human ovarian cancer and SHSY5Y human glioblastoma cell lines as well as interesting concentration-dependent inhibition of tubulin polymerization assessed through fluorescence polymerization assays. Unlike typical tubulin inhibitors, a satisfactorily low sensitivity towards P-gp was also measured in bi-directional transport studies across MDCKII-MDR1 cells for a selected subset of seven compounds.

Strategies of Virtual Screening in Medicinal Chemistry

Virtualscreeningrepresentsaneffectivecomputationalstrategytorise-upthechancesoffindingnew bioactivecompoundsbyacceleratingthetimeneededtomovefromaninitialintuitiontomarket. Classically, themostpursuedapproaches relyon ligand-andstructure-based studies, the former employedwhenstructuraldatainformationaboutthetargetismissingwhilethelatteremployedwhen X-ray/NMRsolvedorhomologymodelsareinsteadavailableforthetarget.Theauthorswillfocus onthemostadvancedtechniquesappliedinthisarea.Inparticular,theywillsurveythekeyconcepts ofvirtualscreeningbydiscussinghowtoproperlyselectchemicallibraries,howtomakedatabase curation,howtoapplyingand-andstructure-basedtechniques,howtowiselyusepost-processing methods.EmphasiswillbealsogiventothemostmeaningfuldatabasesusedinVSprotocols.For theeaseofdiscussionseveralexampleswillbepresented. KeywoRdS Drug Discovery, Ligandand Structure-based Approaches, Molecular Database, Virtual Screening

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target‐specific models based on a clear cause‐effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end‐users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

Human Aquaporin-4 and Molecular Modeling: Historical Perspective and View to the Future

Among the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges.

A rational approach to elucidate human monoamine oxidase molecular selectivity

ABSTRACT Designing highly selective human monoamine oxidase (hMAO) inhibitors is a challenging goal on the road to a more effective treatment of depression and anxiety (inhibition of hMAO‐A isoform) as well as neurodegenerative diseases (inhibition of hMAO‐B isoform). To uncover the molecular rationale of hMAOs selectivity, two recently prepared 2H‐chromene‐2‐ones, namely compounds 1 and 2, were herein chosen as molecular probes being highly selective toward hMAO‐A and hMAO‐B, respectively. We performed molecular dynamics (MD) studies on four different complexes, cross‐simulating one at a time the two hMAO‐isoforms (dimer embedded in a lipid bilayer) with the two considered probes. Our comparative analysis on the obtained 100 ns trajectories discloses a stable H‐bond interaction between 1 and Gln215 as crucial for ligand selectivity toward hMAO‐A whereas a water‐mediated interaction might explain the observed hMAO‐B selectivity of compound 2. Such hypotheses are further supported by binding free energy calculations carried out applying the molecular mechanics generalized Born surface area (MM‐GBSA) method and allowing us to evaluate the contribution of each residue to the observed isoform selectivity. Taken as whole, this study represents the first attempt to explain at molecular level hMAO isoform selectivity and a valuable yardstick for better addressing the design of new and highly selective MAO inhibitors. Graphical abstract Figure. No Caption available.

Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

Comparative Molecular Dynamics Study of Neuromyelitis Optica-Immunoglobulin G Binding to Aquaporin-4 Extracellular Domains

Neuromyelitis optica (NMO) is an inflammatory demyelinating disease of the central nervous system in which most patients have serum autoantibodies (called NMO-IgG) that bind to astrocyte water channel aquaporin-4 (AQP4). A potential therapeutic strategy in NMO is to block the interaction of NMO-IgG with AQP4. Building on recent observation that some single-point and compound mutations of the AQP4 extracellular loop C prevent NMO-IgG binding, we carried out comparative Molecular Dynamics (MD) investigations on three AQP4 mutants, TP137-138AA, N153Q and V150G, whose 295-ns long trajectories were compared to that of wild type human AQP4. A robust conclusion of our modeling is that loop C mutations affect the conformation of neighboring extracellular loop A, thereby interfering with NMO-IgG binding. Analysis of individual mutations suggested specific hydrogen bonding and other molecular interactions involved in AQP4-IgG binding to AQP4.

Molecular Docking for Predictive Toxicology

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.