Introducing Uncertainty in Predictive ModelingFriend or Foe?

Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various degrees and in various ways in order to investigate the effect on the predictive performance of the state-of-the-art method decision tree ensembles. A number of strategies to handle uncertainty in decision tree ensembles were evaluated. The main conclusion of the study is that uncertainty to a large extent may be introduced in chemical descriptors without impairing the predictive performance of ensembles and without the predictive performance being significantly reduced from a practical point of view. The investigation further showed that even when distributions of uncertain values were provided, the ensembles method could generate equally effective models from single-point samples from these distributions. Hence, there seems to be no advantage in using more elaborate methods for handling uncertainty in chemical descriptors when using decision tree ensembles as a modeling method for the considered types of introduced uncertainty

Introducing Uncertainty in Predictive ModelingFriend or Foe?

Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various degrees and in various ways in order to investigate the effect on the predictive performance of the state-of-the-art method decision tree ensembles. A number of strategies to handle uncertainty in decision tree ensembles were evaluated. The main conclusion of the study is that uncertainty to a large extent may be introduced in chemical descriptors without impairing the predictive performance of ensembles and without the predictive performance being significantly reduced from a practical point of view. The investigation further showed that even when distributions of uncertain values were provided, the ensembles method could generate equally effective models from single-point samples from these distributions. Hence, there seems to be no advantage in using more elaborate methods for handling uncertainty in chemical descriptors when using decision tree ensembles as a modeling method for the considered types of introduced uncertainty

<i>In Silico</i> Categorization of <i>in Vivo</i> Intrinsic Clearance Using Machine Learning

Machine learning has recently become popular and much used within the life science research domain, e.g., for finding quantitative structure–activity relationships (QSARs) between molecular structures and different biological end points. In the work presented here, we have applied orthogonal partial least-squares (OPLS), principal component analysis (PCA), and random forests (RF) methods for classification as well as regression analysis to a publicly available <i>in vivo</i> data set in order to assess the intrinsic metabolic clearance (CL_int) in humans. The derived classification models are able to identify compounds with CL_int lower and higher than 1500 mL/min, respectively, with nearly 80% accuracy. The most relevant descriptors are of lipophilicity and charge/polarizability types. Furthermore, the accuracy from a classification model based on regression analysis, using the 1500 mL/min cutoff, is also around 80%. These results suggest the usefulness of machine learning techniques to derive robust and predictive models in the area of <i>in vivo</i> ADMET (absorption, distribution, metabolism, elimination, and toxicity) modeling

MOESM2 of Maximizing gain in high-throughput screening using conformal prediction

Additional file 2. Information about the applied datasets, performance of the predictive models, and evaluation of the gain- cost function for the different datasets and settings

A Pragmatic Approach Using First-Principle Methods to Address Site of Metabolism with Implications for Reactive Metabolite Formation

A majority of xenobiotics are metabolized by cytochrome P450 (CYP) enzymes. The discovery of drug candidates with low propensity to form reactive metabolites and low clearance can be facilitated by understanding CYP-mediated xenobiotic metabolism. Being able to predict the sites where reactive metabolites form is beneficial in drug design to produce drug candidates free of reactive metabolite issues. Herein, we report a pragmatic protocol using first-principle density functional theory (DFT) calculations for predicting sites of epoxidation and hydroxylation of aromatic substrates mediated by CYP. The method is based on the relative stabilities of the CYP-substrate intermediates or the substrate epoxides. Consequently, it concerns mainly the electronic reactivity of the substrates. Comparing to the experimental findings, the presented protocol gave excellent first-ranked epoxidation site predictions of 83%, and when the test was extended to CYP-mediated sites of aromatic hydroxylation, satisfactory results were also obtained (73%). This indicates that our assumptions are valid and also implies that the intrinsic reactivities of the substrates are in general more important than their binding poses in proteins, although the protocol may benefit from the addition of docking information

Cell viability of the C17.2 cells during exposure of a wide range of concentrations for four different compounds.

<p>The IC10 concentration was calculated and was further used to validate proof of concept of the 30 selected genes. Cells exposed to a) D-mannitol (negative control) b) acrylamide c) methylmercury chloride d) valproic acid sodium salt. The data are presented as the mean of 3 independent experiments preformed in hexaplicates. Results were analyzed using two-way ANOVA followed by Dunnett’s multiple comparisons test. The bars represent the mean ± SEM. *<i>p</i> ≤ 0.05, **<i>p</i> ≤ 0.01, ***<i>p</i> ≤ 0.001 compared to control (cells exposed to only cell medium). The inhibitory concentration 10% (IC10) was determined from nonlinear regression to fit the data to the log(inhibitor) vs response(variable slope) curve using the Hill slope (slope factor), equation Y = Bottom + (Top-Bottom)/(1+10^((LogIC10-X)*HillSlope)) (GraphPad Prism 7.02).</p

Whole genome microarray analysis of neural progenitor C17.2 cells during differentiation and validation of 30 neural mRNA biomarkers for estimation of developmental neurotoxicity

<div><p>Despite its high relevance, developmental neurotoxicity (DNT) is one of the least studied forms of toxicity. Current guidelines for DNT testing are based on <i>in vivo</i> testing and they require extensive resources. Transcriptomic approaches using relevant <i>in vitro</i> models have been suggested as a useful tool for identifying possible DNT-generating compounds. In this study, we performed whole genome microarray analysis on the murine progenitor cell line C17.2 following 5 and 10 days of differentiation. We identified 30 genes that are strongly associated with neural differentiation. The C17.2 cell line can be differentiated into a co-culture of both neurons and neuroglial cells, giving a more relevant picture of the brain than using neuronal cells alone. Among the most highly upregulated genes were genes involved in neurogenesis (CHRDL1), axonal guidance (BMP4), neuronal connectivity (PLXDC2), axonogenesis (RTN4R) and astrocyte differentiation (S100B). The 30 biomarkers were further validated by exposure to non-cytotoxic concentrations of two DNT-inducing compounds (valproic acid and methylmercury) and one neurotoxic chemical possessing a possible DNT activity (acrylamide). Twenty-eight of the 30 biomarkers were altered by at least one of the neurotoxic substances, proving the importance of these biomarkers during differentiation. These results suggest that gene expression profiling using a predefined set of biomarkers could be used as a sensitive tool for initial DNT screening of chemicals. Using a predefined set of mRNA biomarkers, instead of the whole genome, makes this model affordable and high-throughput. The use of such models could help speed up the initial screening of substances, possibly indicating alerts that need to be further studied in more sophisticated models.</p></div

Mapping of the 30 genes selected as important for neural differentiation of the C17.2 cell line.

<p>a) Heatmap of the 30 selected genes for the contrasts 10 days of differentiation (Day 10) vs undifferentiated cells (Day 0), 5 days of differentiation (Day 5) vs undifferentiated and 10 days of differentiation vs 5 days of differentiation are illustrated. Genes are ordered according to average log2(fold change) in the contrast Day 10 vs Day 0. b) Map displaying the biological pathways/networks that the selected genes are involved in according to the IPA database as well as after manual review of published literature.</p

PCA plot of independent experimental seed-outs.

<p>The data clusters according to the different contrasts, i.e. 10 days vs 5 days of differentiation, 10 days vs undifferentiated, 5 days vs undifferentiated, showing robustness of the cell model as well as technical reproducibility. The first two principal components explained 72.5% of the information (variation) of the dataset (for PC1: 55.7%, for PC2: 16.8%).</p