Luminescent properties of Bi-doped polycrystalline KAlCl4

We observed an intensive near-infrared luminescence in Bi-doped KAlCl4 polycrystalline material. Luminescence dependence on the excitation wavelength and temperature of the sample was studied. Our experimental results allow asserting that the luminescence peaked near 1 um belongs solely to Bi+ ion which isomorphically substitutes potassium in the crystal. It was also demonstrated that Bi+ luminescence features strongly depend on the local ion surroundings

Utilização da Tecnologia Sulco-camalhão na Produção de Soja e Milho em Terras Baixas do Rio Grande do Sul.

As pesquisas da Embrapa Clima Temperado direcionadas ao cultivo de soja e milho utilizando a tecnologia sulco-camalhão em áreas de terras baixas do Rio Grande do Sul iniciaram há mais de duas décadas. Historicamente, os resultados sempre indicaram benefícios ao desenvolvimento das culturas e aumento de produtividade, independentemente das condições climáticas da safra. Entretanto, a expansão e adoção plena da tecnologia nesse agroecossistema é recente, tendo sido impulsionada pela introdução no mercado nacional de outras duas tecnologias associadas, a sistematização do solo com declividade variada e o uso de politubos para a irrigação, os quais possibilitaram adequar a superfície do terreno com menor movimentação de solo, permitindo uma irrigação precisa e eficiente. Nesse cenário tecnológico, concebeu-se o Projeto Sulco, uma parceria entre Embrapa Clima Temperado, que atua como responsável técnica, e as empresas privadas Centeno & Bergamasco LTDA, Trimble Brasil Soluções, AGCO América do Sul, PIPE Brasil, KLR Kohler Implementos Agrícolas e Pioneer Sementes Brasil. Essa parceria visa o refinamento e a difusão da tecnologia sulco-camalhão para cultivos de sequeiro em terras baixas do Sul do Brasil. Para tanto, tem implantado lavouras-piloto de soja e de milho em várias regiões arrozeiras do Rio Grande do Sul. Todas as iniciativas realizadas têm mostrado que a tecnologia representa uma alternativa acessível e de baixo custo para a produção de soja e milho com elevadas produtividades em terras baixas, garantindo rentabilidade e estabilidade de produção e contribuindo fortemente para a sustentabilidade do sistema produtivo. Esta publicação tem o propósito d

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug’s particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation development

Automated Potentiometric Titrations in KCl/Water-Saturated Octanol: Method for Quantifying Factors Influencing Ion-Pair Partitioning

The knowledge base of factors influencing ion pair partitioning is very sparse, primarily because of the difficulty in determining accurate log PI values of desirable low molecular weight (MW) reference compounds. We have developed a potentiometric titration procedure in KCl/water-saturated octanol that provides a link to log PI through the thermodynamic cycle of ionization and partitioning. These titrations have the advantage of being independent of the magnitude of log P, while maintaining a reproducibility of a few hundredths of a log P in the calculated difference between log P neutral and log P ion pair (diff (log PN − I)). Simple model compounds can be used. The titration procedure is described in detail, along with a program for calculating pKa′′ values incorporating the ionization of water in octanol. Hydrogen bonding and steric factors have a greater influence on ion pairs than they do on neutral species, yet these factors are missing from current programs used to calculate log PI and log D. In contrast to the common assumption that diff (log PN − I) is the same for all amines, they can actually vary more than 3 log units, as in our examples. A major factor affecting log PI is the ability of water and the counterion to approach the charge center. Bulky substituents near the charge center have a negative influence on log PI. On the other hand, hydrogen bonding groups near the charge center have the opposite effect by lowering the free energy of the ion pair. The use of this titration method to determine substituent ion pair stabilization values (IPS) should bring about more accurate log D calculations and encourage species-specific QSAR involving log DN and log DI. This work also brings attention to the fascinating world of nature’s highly stabilized ion pairs

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

<p>Abstract</p> <p>Background</p> <p>The current chemical space of known small molecules is estimated to exceed 10⁶⁰structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.</p> <p>Results</p> <p>The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.</p> <p>Conclusions</p> <p>Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.</p

The Cumulative Effects of Polymorphisms in the DNA Mismatch Repair Genes and Tobacco Smoking in Oesophageal Cancer Risk

The DNA mismatch repair (MMR) enzymes repair errors in DNA that occur during normal DNA metabolism or are induced by certain cancer-contributing exposures. We assessed the association between 10 single-nucleotide polymorphisms (SNPs) in 5 MMR genes and oesophageal cancer risk in South Africans. Prior to genotyping, SNPs were selected from the HapMap database, based on their significantly different genotypic distributions between European ancestry populations and four HapMap populations of African origin. In the Mixed Ancestry group, the MSH3 rs26279 G/G versus A/A or A/G genotype was positively associated with cancer (OR = 2.71; 95% CI: 1.34–5.50). Similar associations were observed for PMS1 rs5742938 (GG versus AA or AG: OR = 1.73; 95% CI: 1.07–2.79) and MLH3 rs28756991 (AA or GA versus GG: OR = 2.07; 95% IC: 1.04–4.12). In Black individuals, however, no association between MMR polymorhisms and cancer risk was observed in individual SNP analysis. The interactions between MMR genes were evaluated using the model-based multifactor-dimensionality reduction approach, which showed a significant genetic interaction between SNPs in MSH2, MSH3 and PMS1 genes in Black and Mixed Ancestry subjects, respectively. The data also implies that pathogenesis of common polymorphisms in MMR genes is influenced by exposure to tobacco smoke. In conclusion, our findings suggest that common polymorphisms in MMR genes and/or their combined effects might be involved in the aetiology of oesophageal cancer

Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types