Measurements of positive ions and air-earth current density at Maitri, Antarctica

Simultaneous measurements of the small-, intermediate- and large- positive ions and air earth current density made at a coastal station, Maitri at Antarctica during January to February 2005, are reported. Although, small and large positive ion concentrations do not show any systematic diurnal variations, variations in them are almost similar to each other. On the other hand, variations in intermediate positive ion concentrations are independent of variations in the small/large positive ions and exhibit a diurnal variation which is similar to that in atmospheric temperature on fair weather days with a maximum during the day and minimum during the night hours. No such diurnal variation in intermediate positive ion concentration is observed on cloudy days when variations in them are also similar to those insmall/large positive ion concentrations. Magnitude of diurnal variation in intermediate positive ion concentration on fair weather days increases with the lowering of atmospheric temperature in this season. Scavenging of ions by snowfall and trapping of Alha - rays from the ground radioactivity by a thin layer of snow on ground, is demonstrated from observations. Variations in intermediate positive ion concentration are explained on the basis of the formation of new particles by the photolytic nucleation process.Comment: 38 pages, 11 figure and 2 tabl

Computational approaches for modeling human intestinal absorption and permeability

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs): Influence of Protein Expression on Drug–Drug Interactions

ABSTRACT: The hepatic organic anion transporting poly-peptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug−drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans

A proposed framework for the systematic review and integrated assessment (SYRINA) of endocrine disrupting chemicals

Background - The issue of endocrine disrupting chemicals (EDCs) is receiving wide attention from both the scientific and regulatory communities. Recent analyses of the EDC literature have been criticized for failing to use transparent and objective approaches to draw conclusions about the strength of evidence linking EDC exposures to adverse health or environmental outcomes. Systematic review methodologies are ideal for addressing this issue as they provide transparent and consistent approaches to study selection and evaluation. Objective methods are needed for integrating the multiple streams of evidence (epidemiology, wildlife, laboratory animal, in vitro, and in silico data) that are relevant in assessing EDCs. Methods - We have developed a framework for the systematic review and integrated assessment (SYRINA) of EDC studies. The framework was designed for use with the International Program on Chemical Safety (IPCS) and World Health Organization (WHO) definition of an EDC, which requires appraisal of evidence regarding 1) association between exposure and an adverse effect, 2) association between exposure and endocrine disrupting activity, and 3) a plausible link between the adverse effect and the endocrine disrupting activity. Results - Building from existing methodologies for evaluating and synthesizing evidence, the SYRINA framework includes seven steps: 1) Formulate the problem; 2) Develop the review protocol; 3) Identify relevant evidence; 4) Evaluate evidence from individual studies; 5) Summarize and evaluate each stream of evidence; 6) Integrate evidence across all streams; 7) Draw conclusions, make recommendations, and evaluate uncertainties. The proposed method is tailored to the IPCS/WHO definition of an EDC but offers flexibility for use in the context of other definitions of EDCs. Conclusions - When using the SYRINA framework, the overall objective is to provide the evidence base needed to support decision making, including any action to avoid/minimise potential adverse effects of exposures. This framework allows for the evaluation and synthesis of evidence from multiple evidence streams. Finally, a decision regarding regulatory action is not only dependent on the strength of evidence, but also the consequences of action/inaction, e.g. limited or weak evidence may be sufficient to justify action if consequences are serious or irreversible.The workshops that supported the writing of this manuscript were funded by the Swedish Foundation for Strategic Environmental Research “Mistra”. LNV was funded by Award Number K22ES025811 from the National Institute of Environmental Health Sciences of the National Institutes of Health. TJW was funded by The Clarence Heller Foundation (A123547), the Passport Foundation, the Forsythia Foundation, the National Institute of Environmental Health Sciences (grants ES018135 and ESO22841), and U.S. EPA STAR grants (RD83467801 and RD83543301). JT was funded by the Academy of Finland and Sigrid Juselius. UH was funded by the Danish EPA. KAK was funded by the Canada Research Chairs program grant number 950–230607