QSAR modeling studies of a library of Human Tyrosinase inhibitors

Melanogenesis is the chemical process responsible for synthesizing melanin, which occurs in melanocytes, in subcellular lysosome-like organelles called melanosomes. Melanin plays a vital role in protecting the skin from damage caused by ultraviolet rays. However, excess melanin production or abnormal distribution can cause various pigmentation disorders, such as over-tanning, age spots, and melasma. Skin disorders like these, have prompted the development of skin-whitening compounds to reduce melanin content. Furthermore, inhibition of melanin synthesis is considered a valid therapeutic strategy for treating advanced melanotic melanomas Human tyrosinase (hsTYR) is the most important enzyme involved in the melanogenesis process, as it catalyzes, at least, its first two steps. Tyrosinase from the white button mushroom Agaricus bisporus (abTYR) has been widely available at low cost from commercial sources for several decades, whereas hsTYR is still expensive and difficult to produce. The importance of discovering more and better hsTYR inhibitors has been widely discussed, as when tested against hsTYR, several abTYR inhibitors provide disappointing results, including some of the most extensively used depigmenting compounds now used in dermocosmetics. An in silico methodology that can be used to predict compound bioactivities is QSAR (quantitative structure-activity relationship) modelling. A QSAR model tries to find correlations between a biological activity of interest and molecular descriptors calculated from the compound structure. In this work, a QSAR model was developed to predict hsTYR inhibition activity using the PYTHON computer language and its PyQSAR package. To develop a QSAR model, a library of 196 known hsTYR inhibitors was gathered, and compounds were divided into 6 groups according to their scaffold structure. A total of 33 QSAR models were prepared using different combinations of the defined groups and different pools of molecular descriptors. QSAR model 32 was selected for further use as it presented good statistical robustness and had the highest number of compounds, 41 in total. Of the 28,933 molecular descriptors calculated by the OCHEM platform for the 41 compounds used, PyQSAR selected 4 to be used in the model: C-026; DISSM2C; MaxdssC; WHALES90_Rem. The statistical data obtained after the validation of the QSAR model by cross-validation was excellent, namely the determination coefficient (R2CV=0.9147), the value of the square root of the mean error (RMSE CV=0.1878) and the mean value of the score of the multiple linear regression method (Q2CV=0.8922). This QSAR model originates a mathematical equation that allows the prediction of hsTYR inhibition activity by new compounds with similar structures. A library of natural compounds, with a structure similar to those used to develop QSAR model 32, was created using the COCONUT database of natural compounds. A total of 1,628 natural compounds were gathered, their molecular descriptors were calculated, and the QSAR model 32 equation was applied. The results are displayed on a website and can be viewed by accessing the URL http://esa.ipb.pt/qsar/. The ZINC15 database was used to determine which of the compounds in the developed natural compound library would be available for purchase after predicting the hsTYR inhibitory activity of each compound in the library. A total of 18 different compounds were bought from different companies. To evaluate these compounds experimental ability to inhibit hsTYR and thus validate QSAR model 32, the compounds will be tested against this enzyme. If those compounds activity is confirmed, they may be used in cosmeceutical applications.A melanogénese é o processo químico responsável pela síntese da melanina, que ocorre nos melanócitos, em organelos subcelulares semelhantes aos lisossomas chamados melanossomas. A melanina desempenha um papel vital na proteção da pele dos danos causados pelos raios ultravioleta. No entanto, a produção excessiva de melanina ou distribuição anormal pode causar vários distúrbios de pigmentação, como bronzeamento excessivo, manchas senis e melasma. Distúrbios de pele como estes levaram ao desenvolvimento de compostos de clareamento da pele para reduzir o conteúdo de melanina. Além disso, a inibição da síntese de melanina é considerada uma estratégia terapêutica válida para o tratamento de melanomas melanóticos avançados A tirosinase humana (hsTYR) é a enzima mais importante envolvida no processo de melanogénese, pois catalisa, pelo menos, as suas duas primeiras etapas. A tirosinase do cogumelo branco Agaricus bisporus (abTYR) está amplamente disponível a baixo custo em fontes comerciais há várias décadas, enquanto a hsTYR ainda é cara e difícil de produzir. A importância de descobrir mais e melhores inibidores de hsTYR tem sido amplamente discutida, pois quando testados contra hsTYR, vários inibidores de abTYR fornecem resultados dececionantes, incluindo alguns dos compostos despigmentantes mais usados atualmente em dermocosméticos. Uma metodologia in silico que pode ser usada para prever bioatividades compostas é a modelação QSAR (quantitative structure-activity relationship). Um modelo QSAR tenta encontrar correlações entre uma atividade biológica de interesse e descritores moleculares calculados a partir da estrutura do composto. Neste trabalho, um modelo QSAR foi desenvolvido para prever a atividade de inibição de hsTYR usando a linguagem de computador PYTHON e seu pacote PyQSAR. Para desenvolver um modelo QSAR, uma biblioteca de 196 inibidores hsTYR conhecidos foi reunida e os compostos foram divididos em 6 grupos de acordo com sua estrutura de base. Um total de 33 modelos QSAR foram preparados usando diferentes combinações dos grupos definidos e diferentes pools de descritores moleculares. O modelo QSAR 32 foi selecionado para uso posterior por apresentar boa robustez estatística e possuir o maior número de compostos, 41 no total. Dos 28 933 descritores moleculares calculados pela plataforma OCHEM para os 41 compostos utilizados, o PyQSAR selecionou 4 para serem utilizados no modelo: C-026; DISSM2C; MaxdssC; WHALES90_Rem. Os dados estatísticos obtidos após a validação do modelo QSAR por validação cruzada foram excelentes, nomeadamente o coeficiente de correlação (R2CV=0,9147), o valor da raiz quadrada do erro médio (RMSE CV=0,1878) e o valor médio da pontuação do método de regressão linear múltipla (Q2CV=0,8922). Este modelo QSAR origina uma equação matemática que permite prever a atividade de inibição de hsTYR por novos compostos com estruturas semelhantes. Uma biblioteca de compostos naturais, com uma estrutura similar àquelas usadas para desenvolver o modelo QSAR 32, foi criada usando o banco de dados de compostos naturais COCONUT. Um total de 1 628 compostos naturais foram recolhidos, os seus descritores moleculares calculados e a equação do modelo QSAR 32 foi aplicada. Os resultados são apresentados num website criado por nós e podem ser visualizados acedendo ao URL http://esa.ipb.pt/qsar/. O banco de dados ZINC15 foi usado para determinar quais compostos na biblioteca de compostos naturais desenvolvidos estariam disponíveis para compra após prever a atividade inibitória de hsTYR de cada composto na biblioteca. Um total de 18 compostos diferentes foram comprados de diferentes empresas. Para avaliar a capacidade experimental destes compostos em inibir a hsTYR e assim validar o modelo QSAR 32, os compostos serão testados contra esta enzima. Caso a atividade desses compostos seja confirmada, eles poderão ser utilizados em aplicações cosmecêuticas

The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS

BACKGROUND: Melting point (MP) is an important property in regards to the solubility of chemical compounds. Its prediction from chemical structure remains a highly challenging task for quantitative structure-activity relationship studies. Success in this area of research critically depends on the availability of high quality MP data as well as accurate chemical structure representations in order to develop models. Currently, available datasets for MP predictions have been limited to around 50k molecules while lots more data are routinely generated following the synthesis of novel materials. Significant amounts of MP data are freely available within the patent literature and, if it were available in the appropriate form, could potentially be used to develop predictive models. RESULTS: We have developed a pipeline for the automated extraction and annotation of chemical data from published PATENTS. Almost 300,000 data points have been collected and used to develop models to predict melting and pyrolysis (decomposition) points using tools available on the OCHEM modeling platform (http://ochem.eu). A number of technical challenges were simultaneously solved to develop models based on these data. These included the handing of sparse data matrices with >200,000,000,000 entries and parallel calculations using 32 × 6 cores per task using 13 descriptor sets totaling more than 700,000 descriptors. We showed that models developed using data collected from PATENTS had similar or better prediction accuracy compared to the highly curated data used in previous publications. The separation of data for chemicals that decomposed rather than melting, from compounds that did undergo a normal melting transition, was performed and models for both pyrolysis and MPs were developed. The accuracy of the consensus MP models for molecules from the drug-like region of chemical space was similar to their estimated experimental accuracy, 32 °C. Last but not least, important structural features related to the pyrolysis of chemicals were identified, and a model to predict whether a compound will decompose instead of melting was developed. CONCLUSIONS: We have shown that automated tools for the analysis of chemical information have reached a mature stage allowing for the extraction and collection of high quality data to enable the development of structure-activity relationship models. The developed models and data are publicly available at http://ochem.eu/article/99826

Tox-Database.net : a curated resource for data describing chemical triggered in vitro cardiac ion channels inhibition

BACKGROUND: Drugs safety issues are now recognized as being factors generating the most reasons for drug withdrawals at various levels of development and at the post-approval stage. Among them cardiotoxicity remains the main reason, despite the substantial effort put into in vitro and in vivo testing, with the main focus put on hERG channel inhibition as the hypothesized surrogate of drug proarrhythmic potency. The large interest in the IKr current has resulted in the development of predictive tools and informative databases describing a drug's susceptibility to interactions with the hERG channel, although there are no similar, publicly available sets of data describing other ionic currents driven by the human cardiomyocyte ionic channels, which are recognized as an overlooked drug safety target. DISCUSSION: The aim of this database development and publication was to provide a scientifically useful, easily usable and clearly verifiable set of information describing not only IKr (hERG), but also other human cardiomyocyte specific ionic channels inhibition data (IKs, INa, ICa). SUMMARY: The broad range of data (chemical space and in vitro settings) and the easy to use user interface makes tox-database.net a useful tool for interested scientists. DATABASE URL: http://tox-database.net

In Silico Resources to Assist in the Development and Evaluation of Physiologically-Based Kinetic Models

Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed

Prediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learning

The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (gCO2/gsorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion