Solving Differential Equations in R: Package deSolve

In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the ODEPACK package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to RâÂÂs high-level procedures. deSolve is the successor of package odesolve which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening

Hubungan Penggunaan Dan Penanganan Pestisida Pada Petani Bawang Merah Terhadap Residu Pestisida Dalam Tanah Di Lahan Pertanian Desa Wanasari Kecamatan Wanasari Kabupaten Brebes

Excessive use of pesticides causing pollution and environmental damage agriculture. Examination in Brebes on 31 samples of fruits and vegetables, found 22% of samples contain detectable residues of organophosphate and found two soil samples (10%) contained residues organochlorin. The purpose of this study was to determine the relationship of the use and handling of pesticides on their onion farmers against pesticide residues in the soil on agricultural land Wanasari Village, District Wanasari, Brebes. This study is observational method with cross sectional approach. The population in this study were all farmers in the Wanasari conducting spraying. Collecting data using the tool Banu questionnaire and examination of pesticide residues in soil using GC-MS Gas Chromatography - Mass Spectrometry. The results of this study are of 55 69.1 onion farmers use pesticides are not good. The use of pesticides covering 80% is not good in mixing pesticides, 87.3% use a smaller dose, 49.1% use pesticides that are not registered with the Ministry of Agriculture, 87.3% is not good in the way of spraying and 87.3 does well in frequency spraying. Handling pesticides in agricultural land is not good 59.1%, ie 74.5% is not good in handling pesticide containers, 90.9% is not good in storage of pesticides, 89.1% is not good in handling a spill and 87.3% did not either in place to clean pesticide containers. The research result is negative soil samples pesticide residues. The conclusion was that no pesticide residue class organochlorin

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available

Toxicokinetic Triage for Environmental Chemicals

Toxicokinetic (TK) models link administered doses to plasma, blood, and tissue concentrations. High-throughput TK (HTTK) performs in vitro to in vivo extrapolation to predict TK from rapid in vitro measurements and chemical structure-based properties. A significant toxicological application of HTTK has been “reverse dosimetry,” in which bioactive concentrations from in vitro screening studies are converted into in vivo doses (mg/kg BW/day). These doses are predicted to produce steady-state plasma concentrations that are equivalent to in vitro bioactive concentrations. In this study, we evaluate the impact of the approximations and assumptions necessary for reverse dosimetry and develop methods to determine whether HTTK tools are appropriate or may lead to false conclusions for a particular chemical. Based on literature in vivo data for 87 chemicals, we identified specific properties (eg, in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. For 271 chemicals we developed a generic HT physiologically based TK (HTPBTK) model that predicts non-steady-state chemical concentration time-courses for a variety of exposure scenarios. We used this HTPBTK model to find that assumptions previously used for reverse dosimetry are usually appropriate, except most notably for highly bioaccumulative compounds. For the thousands of man-made chemicals in the environment that currently have no TK data, we propose a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions. For 349 chemicals with literature HTTK data, we differentiated those chemicals for which HTTK approaches are likely to be sufficient, from those that may require additional data

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Assessing Susceptibility from Early-Life Exposure to Carcinogens

Cancer risk assessment methods currently assume that children and adults are equally susceptible to exposure to chemicals. We reviewed available scientific literature to determine whether this was scientifically supported. We identified more than 50 chemicals causing cancer after perinatal exposure. Human data are extremely limited, with radiation exposures showing increased early susceptibility at some tumor sites. Twenty-seven rodent studies for 18 chemicals had sufficient data after postnatal and adult exposures to quantitatively estimate potential increased susceptibility from early-life exposure, calculated as the ratio of juvenile to adult cancer potencies for three study types: acute dosing, repeated dosing, and lifetime dosing. Twelve of the chemicals act through a mutagenic mode of action. For these, the geometric mean ratio was 11 for lifetime exposures and 8.7 for repeat exposures, with a ratio of 10 for these studies combined. The geometric mean ratio for acute studies is 1.5, which was influenced by tissue-specific results [geometric mean ratios for kidney, leukemia, liver, lymph, mammary, nerve, reticular tissue, thymic lymphoma, and uterus/vagina > 1 (range, 1.6–8.1); forestomach, harderian gland, ovaries, and thyroid < 1 (range, 0.033–0.45)]. Chemicals causing cancer through other modes of action indicate some increased susceptibility from postnatal exposure (geometric mean ratio is 3.4 for lifetime exposure, 2.2 for repeat exposure). Early exposures to compounds with endocrine activity sometimes produce different tumors after exposures at different ages. These analyses suggest increased susceptibility to cancer from early-life exposure, particularly for chemicals acting through a mutagenic mode of action

Quantitation of Aberrant Interlocus T-Cell Receptor Rearrangements in Mouse Thymocytes and the Effect of the Herbicide 2,4-Dichlorophenoxyacetic Acid

Small studies in human populations have suggested a correlation between the frequency of errors in antigen receptor gene assembly and lymphoid malignancy risk. In particular, agricultural workers exposed to pesticides have both an increased risk for lymphoma and an increased frequency of errors in antigen receptor gene assembly. In order to further investigate the potential of such errors to serve as a mechanistically based biomarker of lymphoid cancer risk, we have developed a sensitive PCR assay for quantifying errors of V(D)J recombination in the thymocytes of mice. This assay measures interlocus rearrangements between two T-cell receptor loci, V-gamma and Jbeta, located on chromosomes 13 and 6, respectively. The baseline frequency in four strains of mice was determined at several ages (2–8 weeks of age) and was found to be stable at ~1.5 x 10-5 per thymocyte. Strain AKR, which has a high susceptibility to T-cell lymphomas, did not show an elevated frequency of aberrant V(D)J events. We used this assay to examine the effects of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) on the frequency of these events. Female B6C3F1 mice, 27 days of age, were exposed to 2,4-D by gavage at doses of 0, 3, 10, 30, and 100 mg/ kg/day for 4 successive days and sacrificed on day 5. Thymus DNA was isolated and examined for illegitimate V(D)J recombination-mediated gene rearrangements. In addition, pregnant mice were exposed to 2,4-D and thymocytes from the offspring examined at 2 weeks of age. No significant increase in aberrant V(D)J rearrangements was found, indicating that under these conditions 2,4-D does not appear to effect this important mechanism of carcinogenesis