Nancy C. Baker

A Computational Model Predicting Disruption of Blood Vessel Development

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPAs ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology.

Mining connections between chemicals, proteins, and diseases extracted from Medline annotations

The biomedical literature is an important source of information about the biological activity and effects of chemicals. We present an application that extracts terms indicating biological activity of chemicals from Medline records, associates them with chemical name and stores the terms in a repository called ChemoText. We describe the construction of ChemoText and then demonstrate its utility in drug research by employing Swansons ABC discovery paradigm. We reproduce Swansons discovery of a connection between magnesium and migraine in a novel approach that uses only proteins as the intermediate B terms. We validate our methods by using a cutoff date and evaluate them by calculating precision and recall. In addition to magnesium, we have identified valproic acid and nitric oxide as chemicals which developed links to migraine. We hypothesize, based on protein annotations, that zinc and retinoic acid may play a role in migraine. The ChemoText repository has promise as a data source for drug discovery.

Systems Toxicology of Male Reproductive Development: Profiling 774 Chemicals for Molecular Targets and Adverse Outcomes.

Background: Trends in male reproductive health have been reported for increased rates of testicular germ cell tumors, low semen quality, cryptorchidism, and hypospadias, which have been associated with prenatal environmental chemical exposure based on human and animal studies. Objective: In the present study we aimed to identify significant correlations between environmental chemicals, molecular targets, and adverse outcomes across a broad chemical landscape with emphasis on developmental toxicity of the male reproductive system. Methods: We used U.S. EPA’s animal study database (ToxRefDB) and a comprehensive literature analysis to identify 774 chemicals that have been evaluated for adverse effects on male reproductive parameters, and then used U.S. EPA’s in vitro high-throughput screening (HTS) database (ToxCastDB) to profile their bioactivity across approximately 800 molecular and cellular features. Results: A phenotypic hierarchy of testicular atrophy, sperm effects, tumors, and malformations, a composite resembling the human testicular dysgenesis syndrome (TDS) hypothesis, was observed in 281 chemicals. A subset of 54 chemicals with male developmental consequences had in vitro bioactivity on molecular targets that could be condensed into 156 gene annotations in a bipartite network. Conclusion: Computational modeling of available in vivo and in vitro data for chemicals that produce adverse effects on male reproductive end points revealed a phenotypic hierarchy across animal studies consistent with the human TDS hypothesis. We confirmed the known role of estrogen and androgen signaling pathways in rodent TDS, and importantly, broadened the list of molecular targets to include retinoic acid signaling, vascular remodeling proteins, G-protein coupled receptors (GPCRs), and cytochrome P450s. Citation: Leung MC, Phuong J, Baker NC, Sipes NS, Klinefelter GR, Martin MT, McLaurin KW, Setzer RW, Darney SP, Judson RS, Knudsen TB. 2016. Systems toxicology of male reproductive development: profiling 774 chemicals for molecular targets and adverse outcomes. Environ Health Perspect 124:1050–1061; http://dx.doi.org/10.1289/ehp.1510385

A bibliometric review of drug repurposing

We have conducted a bibliometric review of drug repurposing by scanning >25 million papers in PubMed and using text-mining methods to gather, count and analyze chemical-disease therapeutic relationships. We find that >60% of the ∼35,000 drugs or drug candidates identified in our study have been tried in more than one disease, including 189 drugs that have been tried in >300 diseases each. Whereas in the majority of cases these drugs were applied in therapeutic areas close to their original use, there have been striking, and perhaps instructive, successful attempts of drug repurposing for unexpected, novel therapeutic areas.

Blood-brain barrier development: Systems modeling and predictive toxicology

The blood‐brain barrier (BBB) serves as a gateway for passage of drugs, chemicals, nutrients, metabolites, and hormones between vascular and neural compartments in the brain. Here, we review BBB development with regard to the microphysiology of the neurovascular unit (NVU) and the impact of BBB disruption on brain development. Our focus is on modeling these complex systems. Extant in silico models are available as tools to predict the probability of drug/chemical passage across the BBB; in vitro platforms for high‐throughput screening and high‐content imaging provide novel data streams for profiling chemical‐biological interactions; and engineered human cell‐based microphysiological systems provide empirical models with which to investigate the dynamics of NVU function. Computational models are needed that bring together kinetic and dynamic aspects of NVU function across gestation and under various physiological and toxicological scenarios. This integration will inform adverse outcome pathways to reduce uncertainty in translating in vitro data and in silico models for use in risk assessments that aim to protect neurodevelopmental health.

Building a developmental toxicity ontology.

BACKGROUND As more information is generated about modes of action for developmental toxicity and more data are generated using high-throughput and high-content technologies, it is becoming necessary to organize that information. This report discussed the need for a systematic representation of knowledge about developmental toxicity (i.e., an ontology) and proposes a method to build one based on knowledge of developmental biology and mode of action/ adverse outcome pathways in developmental toxicity. METHODS This report is the result of a consensus working group developing a plan to create an ontology for developmental toxicity that spans multiple levels of biological organization. RESULTS This report provide a description of some of the challenges in building a developmental toxicity ontology and outlines a proposed methodology to meet those challenges. As the ontology is built on currently available web-based resources, a review of these resources is provided. Case studies on one of the most well-understood morphogens and developmental toxicants, retinoic acid, are presented as examples of how such an ontology might be developed. DISCUSSION This report outlines an approach to construct a developmental toxicity ontology. Such an ontology will facilitate computer-based prediction of substances likely to induce human developmental toxicity.

Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity

We have explored the potential of using side effect profiles of drugs to predict their bioactivities at the receptor level. Serotonin 5‐HT6 binding and dopamine antagonism were investigated in separate studies. A set of 5‐HT6 binders and non‐binders was retrieved from the PDSP Ki database, whereas dopamine antagonists were retrieved from the MeSH Pharmaceutical Action file. The side effect data was extracted from ChemoText, a data repository containing MeSH annotations pulled from MEDLINE records. These side effects profiles were treated as molecular descriptors enabling a QSAR‐like approach to build models that could reliably discriminate different classes of molecules, e.g., binders versus non‐binders, and dopamine antagonists versus non‐antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5‐HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature‐based validation were found to be likely or known 5‐HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug’s unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.

Chemotext: A Publicly Available Web Server for Mining Drug–Target–Disease Relationships in PubMed

Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known DTD relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu .

Abstract Sifter: a comprehensive front-end system to PubMed

The Abstract Sifter is a Microsoft Excel based application that enhances existing search capabilities of PubMed. The Abstract Sifter assists researchers to search effectively, triage results, and keep track of articles of interest. The tool implements an innovative “sifter” functionality for relevance ranking, giving the researcher a way to find articles of interest quickly. The tool also gives researchers a view of the literature landscape for a set of entities such as chemicals or genes. The Abstract Sifter is available as a Microsoft Excel macro-enabled workbook application.