Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles

Using the Coefficient of Conformism of a Correlative Prediction in Simulation of Cardiotoxicity

The optimal descriptors generated by the CORAL software are studied as potential models of cardiotoxicity. Two significantly different cardiotoxicity databases are studied here. Database 1 contains 394 hERG inhibitors (pIC50) and external 200 substances that are potential drugs, which were used to confirm the predictive potential of the approach for Database 1. Database 2 contains cardiotoxicity data for 13864 different compounds in a format where active is denoted as 1 and inactive is denoted as 0. The same model-building algorithms were applied to all three databases using the Monte Carlo method and Las Vegas algorithm. The latter was used to rationally distribute the available data into training and validation sets. The Monte Carlo optimization for the correlation weights of different molecular features extracted from SMILES was improved by including the conformity coefficient of the correlation prediction (CCCP). This improvement provided greater predictive potential in the considered models.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity

The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).

Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

PreS/MD: Predictor of Sensitization Hazard for Chemical Substances Released From Medical Devices

In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ new approach methodologies, including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (predictor of sensitization for medical devices). To enable model development, we (1) collected, curated, and integrated the largest publicly available dataset for GPMT results; (2) succeeded in developing externally predictive (balanced accuracy of 70%-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) quantitative structure-activity relationships (QSAR) models for GPMT using machine learning algorithms, including deep learning; and (3) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida

Soil pollution is a critical environmental challenge: the substances released in the soil can adversely affect humans and the ecosystem. Several bioassays were developed to investigate the soil ecotoxicity of chemicals with soil microbes, plants, invertebrates and vertebrates. The 28-day collembolan reproduction test with the springtail Folsomia candida is a recently introduced bioassay described by OECD guideline 232. Although the importance of springtails for maintaining soil quality, toxicity data for Collembola are still limited. We have developed two QSAR models for the prediction of reproductive toxicity induced by organic compounds in Folsomia candida using 28 days NOEC data. We assembled a dataset with the highest number of compounds available so far: 54 compounds were collected from publicly available sources, including plant protection products, reactive intermediates and industrial chemicals, household and cosmetic ingredients, drugs, environmental transformation products and polycyclic aromatic hydrocarbons. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with respectively the open source tools Small Dataset Modeler and CORAL software. Both QSAR models gave good predictive performance even though based on a small dataset, so they could serve for the ecological risk assessment of chemicals for terrestrial organisms.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity

BACKGROUND

Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points.

METHODS

We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets.

RESULTS

In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays.

CONCLUSIONS

We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

CO2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization

The first report of quasi-SMILES-based QSPR models for CO2 capture of MOFs based on experimental data.

Semi-correlations as a tool to model for skin sensitization

Semi-correlation specifically assesses the correlation between a binary variable and a continuous variable. Semi-correlations were applied to develop binary models for various endpoints. We applied the semi-correlation to develop models of two kinds of skin sensitization one related to animals (local lymph node assay LLNA) and one to human beings (direct peptide reactivity assay DPRA and/or human cell line activation test h-CLAT). The models refer to binary classification for a two-level strategy: the first level (analysis of all compounds) is used in the format "sensitizer or non-sensitizer", and the second level (only sensitizers) is a further classification in the format "strong or weak sensitizer". The ranges of statistical characteristics of the models depend on the endpoint, LLNA or DPRA/h-CLAT: for the first level, sensitivity: 0.69-0.88, specificity: 0.75-0.89, accuracy: 0.77-0.87, Matthew's correlation coefficient (MCC): 0.54-0.57 and for the second level, sensitivity: 0.70-1.0, specificity: 0.78-0.83, accuracy: 0.77-0.87, MCC: 0.54-0.76. Thus, the described approach can be applied to building up models of the skin sensitization potency.

The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method

Perhaps there is some similarity between the coronavirus of 2017 and the COVID-19. Consequently, a predictive model for the antiviral activity for the Middle East respiratory syndrome coronavirus (MERS-CoV, 2017) could be useful for designing the strategy and tactics in the struggle with coronaviruses in general and with COVID 19 in particular. Quantitative structure-activity relationships (QSARs) of inhibitory activity to MERS-CoV were developed. The index of ideality of correlation was applied to build up these models for the antiviral activity. The statistical quality of the best model is quite good (r² = 0.84). A mechanistic interpretation of these models based on the molecular features with strong positive (i.e. promoters for endpoint increase) and strong negative (i.e. promoters for endpoint decrease) influence on the inhibitory activity is suggested. A collection of possible biologically active compounds, constructed using data on the above molecular features which are statistically reliable promoters of increase or decrease of the activity, is presented.

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R² = 0.60; RMSE = 0.63).

SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model

The main protease (M^pro) of SARS-associated coronavirus (SARS-CoV) had caused a high rate of mortality in 2003. Current events (2019-2020) substantiate important challenges for society due to coronaviruses. Consequently, advancing models for the antiviral activity of therapeutic agents is a necessary component of the fast development of treatment for the virus. An analogy between anti-SARS agents suggested in 2017 and anti-coronavirus COVID-19 agents are quite probable. Quantitative structure-activity relationships for SARS-CoV are developed and proposed in this study. The statistical quality of these models is quite good. Mechanistic interpretation of developed models is based on the statistical and probability quality of molecular alerts extracted from SMILES. The novel, designed structures of molecules able to possess anti-SARS activities are suggested. For the final assessment of the designed molecules inhibitory potential, developed from the obtained QSAR model, molecular docking studies were applied. Results obtained from molecular docking studies were in a good correlation with the results obtained from QSAR modeling.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Background:

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods:

Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results:

The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion:

The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria

Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF₁) and index of ideality correlation target function (TF₂) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF₂ improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF₂ reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure-property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The 'ideal correlation' is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.

CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats

Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD₅₀) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were discussed.