A Novel Framework for Predicting In Vivo Toxicities from In Vitro Data Using Optimal Methods for Dense and Sparse Matrix Reordering and Logistic Regression

Abstract

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2008). Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9, 458-474.; DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2010b). A network flow model for biclustering via optimal re-ordering of data matrices. J. Global. Optim. 47, 343-354.) to identify biclusters corresponding to subsets of chemicals that have similar responses over distinct subsets of the in vitro assays. The biclustering of the in vitro assays is shown to result in significant clustering based on assay target (e.g., cytochrome P450 [CYP] and nuclear receptors) and type (e.g., downregulated BioMAP and biochemical high-throughput screening protein kinase activity assays). An optimal method based on mixed-integer linear optimization for reordering sparse data matrices (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Li, G. Y., Rabinowitz, J. D., and Rabitz, H. A. (2010a). Enhancing molecular discovery using descriptor-free rearrangement clustering techniques for sparse data sets. AIChE J. 56, 405-418.; McAllister, S. R., DiMaggio, P. A., and Floudas, C. A. (2009). Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem. J. Global. Optim. 45, 111-129) is then applied to the in vivo data set (21.7% sparse) in order to cluster end points that have similar lowest effect level (LEL) values, where it is observed that the end points are effectively clustered according to (1) animal species (i.e., the chronic mouse and chronic rat end points were clearly separated) and (2) similar physiological attributes (i.e., liver- and reproductive-related end points were found to separately cluster together). As the liver and reproductive end points exhibited the largest degree of correlation, we further analyzed them using regularized logistic regression in a rank-and-drop framework to identify which subset of in vitro features could be utilized for in vivo toxicity prediction. It was observed that the in vivo end points that had similar LEL responses over the 309 chemicals (as determined by the sparse clustering results) also shared a significant subset of selected in vitro descriptors. Comparing the significant descriptors between the two different categories of end points revealed a specificity of the CYP assays for the liver end points and preferential selection of the estrogen/androgen nuclear receptors by the reproductive end points.

Structure Prediction of Loops with Fixed and Flexible Stems

“…An overview of each of the steps of ICON is presented below, and the details can be found in literature. 47,48 …”

“…Once the optimal path through all conformers is determined, we propose an integer linear programming (ILP) model to determine the cluster boundaries for a given optimal ordering. 48 Since for any node on the TSP path, we know the immediate neighbors on the path, the aim is to simply determine the points on the TSP path where immediate neighbors on the path fall into separate clusters. This would be sufficient to identify the boundaries of clusters.…”

“…Details of the mathematical implementation of the model can be found elsewhere. 48 Subsequently, the cluster centroids for each cluster are identified by determining the cluster element with the minimum distance to all other elements of the cluster, with the distance being defined again as in Equation 7. Following this, we eliminate loosely bound clusters by analyzing cluster densities.…”

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

“…The original work of Biospectra by Fliri et al in 2005 is the first large-scale screening between 1045 drugs and 92 proteins. This works identified important molecular mechanisms of drug clinical effects and has been the foundation of many following research on drug side-effects 21222324 . The broad-scale in vitro pharmacology profiling by Novartis research analyzed drug promiscuity against 220 targets (including 73 unintended targets) 20 .…”

Triadic Formal Concept Analysis and triclustering: searching for optimal patterns

“…Yet these different frameworks considerably overlap in applications. Among those: finding co-regulated genes over gene expression data (Madeira and Oliveira 2004;Besson et al 2005;Barkow et al 2006;Tarca et al 2007;Hanczar and Nadif 2010;Kaytoue et al 2011;Eren et al 2013), prediction of biological activity of chemical compounds (Blinova et al 2003;Kuznetsov and Samokhin 2005;DiMaggio et al 2010;Asses et al 2012), summarization and classification of texts (Dhillon 2001;Cimiano et al 2005;Banerjee et al 2007;Ignatov and Kuznetsov 2009;Carpineto et al 2009), structuring websearch results and browsing navigation in Information Retrieval (Carpineto and Romano 2005;Koester 2006;Eklund et al 2012;Poelmans et al 2012), finding communities in two-mode networks in Social Network Analysis (Duquenne 1996;Freeman 1996;Latapy et al 2008;Roth et al 2008;Gnatyshak et al 2012) and Recommender Systems (Boucher-Ryan and Bridge 2006;Symeonidis et al 2008;Ignatov and Kuznetsov 2008;Nanopoulos et al 2010;Ignatov et al 2014).…”