Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives

2013 ◽  
Author(s):  
Ronald Brown ◽  
Shannon White ◽  
Jennifer Goode ◽  
Prachi Pradeep ◽  
Stephen Merrill
Keyword(s):  
Medical Devices ◽  
Safety Evaluation ◽  
Toxicity Testing ◽  
Quantitative Structure Activity Relationship ◽  
Draft Guidance ◽  
Qsar Modeling ◽  
Wide Range ◽  
Qsar Models ◽  
Us Fda ◽  
Radiological Health

Patients may be exposed to potentially carcinogenic color additives released from polymers used to manufacture medical devices; therefore, the need exists to adequately assess the safety of these compounds. The US FDA Center for Devices and Radiological Health (CDRH) recently issued draft guidance that, when final, will include FDA’s recommendations for the safety evaluation of color additives and other potentially toxic chemical entities that may be released from device materials. Specifically, the draft guidance outlines an approach that calls for evaluating the potential for the color additive to be released from the device in concert with available toxicity information about the additive to determine what types of toxicity information, if any, are necessary. However, when toxicity data are not available from the literature for the compounds of interest, a scientific rationale can sometimes be provided for omission of these tests. Although the FDA has issued draft guidance on this topic, the Agency continues to explore alternative approaches to understand when additional toxicity testing is needed to assure the safety of medical devices that contain color additives. An emerging approach that may be useful for determining the need for further testing of compounds released from device materials is Quantitative Structure Activity Relationship (QSAR) modeling. In this paper, we have shown how three publically available QSAR models (OpenTox/Lazar, Toxtree, and the OECD Toolbox) are able to successfully predict the carcinogenic potential of a set of color additives with a wide range of structures. As a result, this computational modeling approach may serve as a useful tool for determining the need to conduct carcinogenicity testing of color additives intended for use in medical devices.

scholarly journals PreSS/MD: Predictor of Skin Sensitization Caused by Chemicals Leaching from Medical Devices

2022 ◽  
Author(s):  
Joyce Borba ◽  
Vinicius Alves ◽  
Rodolpho Braga ◽  
Daniel Korn ◽  
Nicole Kleinstreuer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Devices ◽  
Model Development ◽  
Safety Evaluation ◽  
Toxicity Testing ◽  
Toxicity Assessment ◽  
Machine Learning Algorithms ◽  
International Standards ◽  
Skin Sensitization ◽  
Qsar Models

Abstract Safety evaluation for medical devices includes the toxicity assessment of chemicals used in device manufacturing, cleansing and/or sterilization that may leach into a patient. According to international standards on biocompatibility assessments (ISO 10993), chemicals that could be released from medical devices should be evaluated for their potential to induce skin sensitization/allergenicity, and one of the commonly used approaches is the guinea pig maximization test (GPMT). However, there is growing trend in regulatory science to move away from costly animal assays to employing New Approach Methodologies including computational methods. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we named PreSS/MD (Predictor of Skin Sensitization for Medical Devices). To enable model development, we (i) collected, curated, and integrated the largest publicly available dataset for GPMT; (ii) 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 (iii) developed a publicly accessible web portal integrating PreSS/MD models that enables the prediction of GPMT outcomes for any molecules using. We expect that PreSS/MD will be used by both researchers and regulatory agencies to support safety assessment for medical devices and help replace, reduce or refine the use of animals in toxicity testing. PreSS/MD is freely available at https://pressmd.mml.unc.edu/. Keywords: sensitization, GPMT, QSAR, deep learning,

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals Rodent Carcinogenicity Dataset

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Natalja Fjodorova ◽  
Marjana Novič
Keyword(s):  
Molecular Structure ◽  
Quantitative Structure Activity Relationship ◽  
Future Research ◽  
Qsar Modeling ◽  
Carcinogenic Potency ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
Funded Project ◽  
Rodent Carcinogenicity

The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information: common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment.

scholarly journals A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multilinear Regression ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Antimalarial Agents ◽  
Inhibition Concentration ◽  
Qsar Models ◽  
Semi Empirical ◽  
Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

scholarly journals QSAR-modeling of desoxyuridine triphosphatase inhibitors in a series of some derivatives of uracil

2019 ◽  
Vol 65 (2) ◽  
pp. 103-113
Author(s):  
Yu.Z. Martynova ◽  
V.R. Khairullina ◽  
A.R. Gimadieva ◽  
A.G. Mustafin
Keyword(s):  
Practical Application ◽  
Qsar Modeling ◽  
Dna Biosynthesis ◽  
Promising Target ◽  
Wide Range ◽  
Consensus Models ◽  
Qsar Models ◽  
Selection Stage ◽  
The Relationship ◽  
Derivatives Of

Due to the widespread prevalence, deoxyuridine triphosphatase (UTPase) is considered by modern biochemists and physicians as a promising target for the development of drugs with a wide range of activities. The therapeutic effect of these drugs will be due to suppression of DNA biosynthesis in various viruses, bacteria and protozoa. In order to rationalize the search for new dUTPase inhibitors, domestic and foreign researchers are actively using the QSAR methodology at the selection stage of hit compounds. However, the practical application of this methodology is impossible without existence of valid QSAR models. With the use of the GUSAR 2013 program, a quantitative analysis of the relationship between the structure and efficacy of 135 dUTPase inhibitors based on uracil derivatives was performed in the IC50 range of 30¸185000 nmol/L. Six statistically significant valid consensus models, characterized by high descriptive ability and moderate prognostic ability on the structures of training and test samples, are constructed. To build valid QSAR models for dUTPase inhibitors can use QNA or MNA descriptors and their combinations in a consensus approach.

scholarly journals Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules

2019 ◽  
Vol 20 (9) ◽  
pp. 2311 ◽  
Author(s):  
Giuseppe Floresta ◽  
Antonio Rescifina ◽  
Vincenzo Abbate
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Mu Opioid Receptor ◽  
Scaffold Hopping ◽  
Consensus Model ◽  
Qsar Modeling ◽  
Mu Opioid ◽  
Structure Activity ◽  
Opioid Binding ◽  
Qsar Models

Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new OR ligands with fentanyl-like structures.

Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling

2021 ◽  
Author(s):  
Ya Wang ◽  
Weihao Tang ◽  
Yue Peng ◽  
Zhongfang Chen ◽  
Jingwen Chen ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Organic Pollutants ◽  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Boron Nitride Nanosheets ◽  
Structure Activity ◽  
Aqueous Environments ◽  
Qsar Models

Four quantitative structure–activity relationship (QSAR) models were developed for predicting the log K values of organic pollutants adsorbed onto boron nitride nanosheets in gaseous and aqueous environments.

A Roundtable Discussion: Home Healthcare—Not A Hospital in the Home

2013 ◽  
Vol 47 (s1) ◽  
pp. 10-15 ◽  
Author(s):  
Mary K. Logan
Keyword(s):  
Medical Devices ◽  
The United States ◽  
Home Healthcare ◽  
Continuing Care ◽  
Draft Guidance ◽  
Clinical Environment ◽  
Roundtable Discussion ◽  
Design Considerations ◽  
Radiological Health ◽  
Effective Use

Home healthcare is vital for a large percentage of the population. According to data from the U.S. Food and Drug Administration (FDA) and the Centers for Disease Control (CDC), 7 million people in the United States receive home healthcare annually. The use of medical devices in the home and other nonclinical environments is increasing dramatically. By the year 2050, an estimated 27 million people will need continuing care in the home or in the community and not in a controlled clinical environment.1 The FDA recently announced its Home Use Devices Initiative and issued the document, “Draft Guidance for Industry and FDA Staff—Design Considerations for Devices Intended for Home Use” on Dec. 12, 2012.2 The Center for Devices and Radiological Health (CDRH) regulates medical devices, but that regulatory authority alone is not enough to ensure safe and effective use of devices in the home. To address these and other issues, AAMI and FDA will co-host a summit on healthcare technology in nonclinical settings Oct. 9–10, 2013.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

Sign in / Sign up

Export Citation Format

Share Document