Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 12 de 12
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Nat Commun ; 11(1): 10, 2020 01 03.
Artigo em Inglês | MEDLINE | ID: mdl-31900408

RESUMO

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

2.
Mutagenesis ; 34(1): 67-82, 2019 03 06.
Artigo em Inglês | MEDLINE | ID: mdl-30189015

RESUMO

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.


Assuntos
Óxidos N-Cíclicos/química , Dano ao DNA/efeitos dos fármacos , Mutagênicos/química , Relação Quantitativa Estrutura-Atividade , Óxidos N-Cíclicos/toxicidade , Mutagênese/efeitos dos fármacos , Testes de Mutagenicidade , Mutagênicos/toxicidade
3.
Regul Toxicol Pharmacol ; 102: 53-64, 2019 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30562600

RESUMO

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.


Assuntos
Contaminação de Medicamentos , Guias como Assunto , Mutagênicos/classificação , Relação Quantitativa Estrutura-Atividade , Indústria Farmacêutica , Órgãos Governamentais , Mutagênicos/toxicidade , Medição de Risco
6.
Regul Toxicol Pharmacol ; 77: 13-24, 2016 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-26877192

RESUMO

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.


Assuntos
Testes de Carcinogenicidade/métodos , Dano ao DNA , Mineração de Dados/métodos , Mutagênese , Testes de Mutagenicidade/métodos , Mutagênicos/toxicidade , Toxicologia/métodos , Animais , Testes de Carcinogenicidade/normas , Simulação por Computador , Bases de Dados Factuais , Fidelidade a Diretrizes , Guias como Assunto , Humanos , Modelos Moleculares , Estrutura Molecular , Testes de Mutagenicidade/normas , Mutagênicos/química , Mutagênicos/classificação , Formulação de Políticas , Relação Quantitativa Estrutura-Atividade , Medição de Risco , Toxicologia/legislação & jurisprudência , Toxicologia/normas
7.
Regul Toxicol Pharmacol ; 77: 1-12, 2016 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-26879463

RESUMO

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.


Assuntos
Aminas/toxicidade , Mineração de Dados/métodos , Bases de Conhecimento , Mutagênese , Testes de Mutagenicidade/métodos , Mutagênicos/toxicidade , Aminas/química , Aminas/classificação , Animais , Simulação por Computador , Bases de Dados Factuais , Humanos , Modelos Moleculares , Estrutura Molecular , Mutagênicos/química , Mutagênicos/classificação , Reconhecimento Automatizado de Padrão , Relação Quantitativa Estrutura-Atividade , Medição de Risco
8.
Regul Toxicol Pharmacol ; 76: 79-86, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26785392

RESUMO

At the confluence of predictive and regulatory toxicologies, negative predictions may be the thin green line that prevents populations from being exposed to harm. Here, two novel approaches to making confident and robust negative in silico predictions for mutagenicity (as defined by the Ames test) have been evaluated. Analyses of 12 data sets containing >13,000 compounds, showed that negative predictivity is high (∼90%) for the best approach and features that either reduce the accuracy or certainty of negative predictions are identified as misclassified or unclassified respectively. However, negative predictivity remains high (and in excess of the prevalence of non-mutagens) even in the presence of these features, indicating that they are not flags for mutagenicity.


Assuntos
Simulação por Computador , DNA Bacteriano/efeitos dos fármacos , Modelos Moleculares , Mutagênese , Testes de Mutagenicidade/métodos , Mutação , Relação Quantitativa Estrutura-Atividade , Animais , DNA Bacteriano/genética , Reações Falso-Negativas , Humanos , Bases de Conhecimento , Reconhecimento Automatizado de Padrão , Medição de Risco
9.
Regul Toxicol Pharmacol ; 76: 7-20, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26708083

RESUMO

The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound.


Assuntos
Modelos Estatísticos , Mutagênese , Testes de Mutagenicidade/estatística & dados numéricos , Mutação , Relação Quantitativa Estrutura-Atividade , Algoritmos , Animais , DNA Bacteriano/efeitos dos fármacos , DNA Bacteriano/genética , Bases de Dados Factuais , Técnicas de Apoio para a Decisão , Humanos , Reprodutibilidade dos Testes , Medição de Risco , Software
10.
Regul Toxicol Pharmacol ; 73(1): 367-77, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26248005

RESUMO

The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission.


Assuntos
Testes de Mutagenicidade/métodos , Testes de Mutagenicidade/normas , Simulação por Computador/normas , DNA/química , Contaminação de Medicamentos/prevenção & controle , Mutagênicos , Relação Quantitativa Estrutura-Atividade
11.
Regul Toxicol Pharmacol ; 72(2): 335-49, 2015 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-25980641

RESUMO

The International Conference on Harmonization (ICH) M7 guidance for the assessment and control of DNA reactive impurities in pharmaceutical products includes the use of in silico prediction systems as part of the hazard identification and risk assessment strategy. This is the first internationally agreed guidance document to include the use of these types of approaches. The guideline requires the use of two complementary approaches, an expert rule-based method and a statistical algorithm. In addition, the guidance states that the output from these computer-based assessments can be reviewed using expert knowledge to provide additional support or resolve conflicting predictions. This approach is designed to maximize the sensitivity for correctly identifying DNA reactive compounds while providing a framework to reduce the number of compounds that need to be synthesized, purified and subsequently tested in an Ames assay. Using a data set of 801 chemicals and pharmaceutical intermediates, we have examined the relative predictive performances of some popular commercial in silico systems that are in common use across the pharmaceutical industry. The overall accuracy of each of these systems was fairly comparable ranging from 68% to 73%; however, the sensitivity of each system (i.e. how many Ames positive compounds are correctly identified) varied much more dramatically from 48% to 68%. We have explored how these systems can be combined under the ICH M7 guidance to enhance the detection of DNA reactive molecules. Finally, using four smaller sets of molecules, we have explored the value of expert knowledge in the review process, especially in cases where the two systems disagreed on their predictions, and the need for care when evaluating the predictions for large data sets.


Assuntos
Contaminação de Medicamentos , Mutagênicos/análise , Software , Algoritmos , Simulação por Computador , Medição de Risco
12.
J Biomed Biotechnol ; 2008: 218097, 2008.
Artigo em Inglês | MEDLINE | ID: mdl-18464915

RESUMO

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.


Assuntos
Interpretação Estatística de Dados , Sistemas de Apoio a Decisões Clínicas , Diagnóstico por Computador/métodos , Modelos Biológicos , Neoplasias da Próstata/classificação , Neoplasias da Próstata/diagnóstico , Humanos , Masculino , Modelos Estatísticos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA