QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity.

The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Development of human biotransformation QSARs and application for PBT assessment refinement.

Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives.

QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists.

The antiandrogens, such as bicalutamide, targeting the androgen receptor (AR), are the main endocrine therapies for prostate cancer (PCa). But as drug resistance to antiandrogens emerges in advanced PCa, there presents a high medical need for exploitation of novel AR antagonists. In this work, the relationships between the molecular structures and antiandrogenic activities of a series of 7α-substituted dihydrotestosterone derivatives were investigated. The proposed MLR model obtained high predictive ability. The thoroughly validated QSAR model was used to virtually screen new dihydrotestosterones derivatives taken from PubChem, resulting in the finding of novel compounds CID_70128824, CID_70127147, and CID_70126881, whose in silico bioactivities are much higher than the published best one, even higher than bicalutamide. In addition, molecular docking, molecular dynamics (MD) simulations, and MM/GBSA have been employed to analyze and compare the binding modes between the novel compounds and AR. Through the analysis of the binding free energy and residue energy decomposition, we concluded that the newly discovered chemicals can in silico bind to AR with similar position and mechanism to the reported active compound and the van der Waals interaction is the main driving force during the binding process.

Synthesis, photodynamic activity, and quantitative structure-activity relationship modelling of a series of BODIPYs.

Here we report the synthesis of eleven new BODIPYs (14-24) characterized by the presence of an aromatic ring on the 8 (meso) position and of iodine atoms on the pyrrolic 2,6 positions. These molecules, together with twelve BODIPYs already reported by us (1-12), represent a large panel of BODIPYs showing different atoms or groups as substituent of the aromatic moiety. Two physico-chemical features (1O2 generation rate and lipophilicity), which can play a fundamental role in the outcome as photosensitizers, have been studied. The in vitro photo-induced cell-killing efficacy of 23 PSs was studied on the SKOV3 cell line treating the cells for 24h in the dark then irradiating for 2h with a green LED device (fluence 25.2J/cm2). The cell-killing efficacy was assessed with the MTT test and compared with that one of meso un-substituted compound (13). In order to understand the possible effect of the substituents, a predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, was developed. The results clearly indicate that the presence of an aromatic ring is fundamental for an excellent photodynamic response, whereas the electronic effects and the position of the substituents on the aromatic ring do not influence the photodynamic efficacy.

Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity.

Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs.

A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.

PBT assessment and prioritization of contaminants of emerging concern: Pharmaceuticals.

The strong and widespread use of pharmaceuticals, together with incorrect disposal procedures, has recently made these products contaminants of emerging concern (CEC). Unfortunately, little is known about pharmaceuticals' environmental behaviour and ecotoxicity, so that EMEA (European Medicines Agency) released guidelines for the pharmaceuticals' environmental risk assessment. In particular, there is a severe lack of information about persistence, bioaccumulation and toxicity (PBT) of the majority of the thousands of substances on the market. Computational tools, like QSAR (Quantitative Structure Activity Relationship) models, are the only way to screen large sets of chemicals in short time, with the aim of ranking, highlighting and prioritizing the most environmentally hazardous for focusing further experimental studies. In this work we propose a screening method to assess the potential persistence, bioaccumulation and toxicity of more than 1200 pharmaceutical ingredients, based on the application of two different QSAR models. We applied the Insubria-PBT Index, a MLR (Multiple Linear Regression) QSAR model based on four simple molecular descriptors, implemented in QSARINS software, and able to synthesize the PBT potential in a unique cumulative value and the US-EPA PBT Profiler that assesses the PBT behaviour evaluating separately P, B and T. Particular attention was given to the study of Applicability Domain in order to provide reliable predictions. An agreement of 86% was found between the two models and a priority list of 35 pharmaceuticals, highlighted as potential PBTs by consensus, was proposed for further experimental validation. Moreover, the results of this computational screening are in agreement with preliminary experimental data in the literature. This study shows how in silico models can be applied in the hazard assessment to perform preliminary screening and prioritization of chemicals, and how the identification of the structural features, mainly associated with the potential PBT behaviour of the prioritized pharmaceuticals, is particularly relevant to perform the rational a priori design of new, environmentally safer, pharmaceuticals.

Are some "safer alternatives" hazardous as PBTs? The case study of new flame retardants.

Some brominated flame retardants (BFRs), as PBDEs, are persistent, bioaccumulative, toxic (PBT) and are restricted/prohibited under various legislations. They are replaced by "safer" flame retardants (FRs), such as new BFRs or organophosphorous compounds. However, informations on the PBT behaviour of these substitutes are often lacking. The PBT assessment is required by the REACH regulation and the PBT chemicals should be subjected to authorization. Several new FRs, proposed and already used as safer alternatives to PBDEs, are here screened by the cumulative PBT Index model, implemented in QSARINS (QSAR-Insubria), new software for the development/validation of QSAR models. The results, obtained directly from the chemical structure for the three studied characteristics altogether, were compared with those from the US-EPA PBT Profiler: the two different approaches are in good agreement, supporting the utility of a consensus approach in these screenings. A priority list of the most harmful FRs, predicted in agreement by the two modelling tools, has been proposed, highlighting that some supposed "safer alternatives" are detected as intrinsically hazardous for their PBT properties. This study also shows that the PBT Index could be a valid tool to evaluate appropriate and safer substitutes, a priori from the chemical design, in a benign by design approach, avoiding unnecessary synthesis and tests.

A Combined Quantitative Structure-Activity Relationship Research of Quinolinone Derivatives as Androgen Receptor Antagonists.

Antiandrogens bicalutamide, flutamide and enzalutamide etc. have been used in clinical trials to treat prostate cancer by binding to and antagonizing androgen receptor (AR). Although initially effective, the drug resistance problem will emerge eventually, which results in a high medical need for novel AR antagonist exploitation. Here in this work, to facilitate the rational design of novel AR antagonists, we studied the structure-activity relationships of a series of 2-quinolinone derivatives and investigated the structural requirements for their antiandrogenic activities. Different modeling methods, including 2D MLR, 3D CoMFA and CoMSIA, were implemented to evolve QSAR models. All these models, thoroughly validated, demonstrated satisfactory results especially for the good predictive abilities. The contour maps from 3D CoMFA and CoMSIA models provide visualized explanation of key structural characteristics relevant to the antiandrogenic activities, which is summarized to a position-specific conclusion at the end. The obtained results from this research are practically useful for rational design and screening of promising chemicals with high antiandrogenic activities.

Ligand Efficiency Outperforms pIC50 on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists.

The concept of ligand efficiency is defined as biological activity in each molecular size and is widely accepted throughout the drug design community. Among different LE indices, surface efficiency index (SEI) was reported to be the best one in support vector machine modeling, much better than the generally and traditionally used end-point pIC50. In this study, 2D multiple linear regression and 3D comparative molecular field analysis methods are employed to investigate the structure-activity relationships of a series of androgen receptor antagonists, using pIC50 and SEI as dependent variables to verify the influence of using different kinds of end-points. The obtained results suggest that SEI outperforms pIC50 on both MLR and CoMFA models with higher stability and predictive ability. After analyzing the characteristics of the two dependent variables SEI and pIC50, we deduce that the superiority of SEI maybe lie in that SEI could reflect the relationship between molecular structures and corresponding bioactivities, in nature, better than pIC50. This study indicates that SEI could be a more rational parameter to be optimized in the drug discovery process than pIC50.

Modeling ready biodegradability of fragrance materials.

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials.

PBT assessment and prioritization by PBT Index and consensus modeling: comparison of screening results from structural models.

The limited availability of comprehensive data for Persistence, Bioaccumulation and Toxicity (PBT) of chemicals is a serious hindrance to the assignment of compounds to the categories of PBT and vPvB; REACH regulation requires authorization for the use of such chemicals, and additionally plans for safer alternatives. In the context of screening and priority-setting tools for PBT-assessment, the cumulative PBT Index model, implemented in QSARINS (QSAR-INSUBRIA), new software tool for the development and validation of multiple linear regression QSAR models, offers a new holistic approach for the identification of chemicals with cumulative PBT properties directly from their molecular structure. In this study the Insubria PBT Index in QSARINS is applied to the screening and prioritization of various data sets, containing a large variety of chemicals of heterogeneous molecular structure, previously screened by various authors by different methods, for their potential PBT behavior. Particular attention is devoted to the model Applicability Domain, using different approaches such as Descriptor Range, Leverage, and Principal Component Analysis (PCA) of the modeling molecular descriptors, in order to discriminate between interpolated and extrapolated predictions. The results of this screening, which is based only on the molecular structure features and is not dependent on single threshold values for P, B and T, are compared with those obtained by the on-line US-EPA PBT Profiler. Good agreement between the various approaches is found, supporting the utility of a consensus approach in priority-setting studies. The main discrepancies are highlighted and commented on. Moreover, a priority list containing the most hazardous compounds identified in agreement between the two tools is drafted. The PBT Index, implemented in QSARINS, which was demonstrated to be a practical, precautionary and reliable screening tool for PBT-behavior directly from the molecular structure, can be usefully applied for focusing experimental studies, and even before chemical synthesis, in a "benign by design" approach of safer alternatives.

The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS.

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Structural requirements of 3-carboxyl-4(1H)-quinolones as potential antimalarials from 2D and 3D QSAR analysis.

Malaria is a fatal tropical and subtropical disease caused by the protozoal species Plasmodium. Many commonly available antimalarial drugs and therapies are becoming ineffective because of the emergence of multidrug resistant Plasmodium falciparum, which drives the need for the development of new antimalarial drugs. Recently, a series of 3-carboxyl-4(1H)-quinolone analogs, derived from the famous compound endochin, were reported as promising candidates for orally efficacious antimalarials. In this study, to analyze the structure-activity relationships (SAR) of these quinolones and investigate the structural requirements for antimalarial activity, the 2D multiple linear regressions (MLR) method and 3D comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods are employed to evolve different QSAR models. All these models give satisfactory results with highly accurate fitting and strong external predictive abilities for chemicals not used in model development. Furthermore, the contour maps from 3D models can provide an intuitive understanding of the key structure features responsible for the antimalarial activities. In conclusion, we summarize the detailed position-specific structural requirements of these derivatives accordingly. All these results are helpful for the rational design of new compounds with higher antimalarial bioactivities.