A human induced pluripotent stem cell-based in vitro assay predicts developmental toxicity through a retinoic acid receptor-mediated pathway for a series of related retinoid analogues.

The relative developmental toxicity potency of a series of retinoid analogues was evaluated using a human induced pluripotent stem (iPS) cell assay that measures changes in the biomarkers ornithine and cystine. Analogue potency was predicted, based on the assay endpoint of the ornithine/cystine (o/c) ratio, to be all-trans-retinoic acid>TTNPB>13-cis-retinoic acid≈9-cis-retinoic acid>acitretin>etretinate>retinol. These rankings correlate with in vivo data and demonstrate successful application of the assay to rank a series of related toxic and non-toxic compounds. The retinoic acid receptor α (RARα)-selective antagonist Ro 41-5253 inhibited the cystine perturbation caused by all-trans-retinoic acid, TTNPB, 13-cis-retinoic acid, 9-cis-retinoic acid, and acitretin. Ornithine was altered independent of RARα in all retinoids except acitretin. These results suggest a role for an RARα-mediated mechanism in retinoid-induced developmental toxicity through altered cystine metabolism.

Supporting read-across using biological data.

Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.

BACKGROUND: The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. OBJECTIVES: To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. METHODS: Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. RESULTS: A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. CONCLUSIONS: This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.

Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening.

A metabolic biomarker-based in vitro assay utilizing human embryonic stem (hES) cells was developed to identify the concentration of test compounds that perturbs cellular metabolism in a manner indicative of teratogenicity. This assay is designed to aid the early discovery-phase detection of potential human developmental toxicants. In this study, metabolomic data from hES cell culture media were used to assess potential biomarkers for development of a rapid in vitro teratogenicity assay. hES cells were treated with pharmaceuticals of known human teratogenicity at a concentration equivalent to their published human peak therapeutic plasma concentration. Two metabolite biomarkers (ornithine and cystine) were identified as indicators of developmental toxicity. A targeted exposure-based biomarker assay using these metabolites, along with a cytotoxicity endpoint, was then developed using a 9-point dose-response curve. The predictivity of the new assay was evaluated using a separate set of test compounds. To illustrate how the assay could be applied to compounds of unknown potential for developmental toxicity, an additional 10 compounds were evaluated that do not have data on human exposure during pregnancy, but have shown positive results in animal developmental toxicity studies. The new assay identified the potential developmental toxicants in the test set with 77% accuracy (57% sensitivity, 100% specificity). The assay had a high concordance (≥75%) with existing in vivo models, demonstrating that the new assay can predict the developmental toxicity potential of new compounds as part of discovery phase testing and provide a signal as to the likely outcome of required in vivo tests.

Metabonomic evaluation of metabolic dysregulation in rats induced by PF 376304, a novel inhibitor of phosphoinositide 3-kinase.

Phosphoinositide 3-kinase (PI3K) is an enzyme fundamental to the regulation of various metabolic processes. Metabonomic studies were undertaken in order to gain mechanistic insight into significant, yet unexplained, toxicity issues associated with PF 376304, a nonspecific PI3K inhibitor under development for anti-inflammatory indications. Two experiments were conducted in which rats were given daily doses of up to 1000 mg of PF 376304/kg for as long as 7 days. Mortality rapidly ensued (within 72 h) at doses of >or=300 mg/kg. Doses of >or=100 mg/kg were associated with a profound but transient glucosuria. Despite the magnitude of this effect, within 72 h urinary glucose excretion in surviving animals returned to control levels even with continued dosing. Other metabolic effects associated with drug treatment included increased urinary beta-hydroxybutyrate and creatine and decreased citrate. A time-course study revealed elevated serum glucose within 1 h, followed by increases in serum insulin and decreases in serum triglycerides. Serum corticosterone was also significantly elevated within 1 h of treatment. All metabolic effects were largely reversed within 24 h of administration of the third daily dose and remained that way through day 7. The likely explanation for the onset of effects involves the role of PI3K in regulation of glucose at multiple points, but the reversal of the effects in the presence of continued exposure to the drug has not been explained. Finally, the data demonstrate the power of metabonomics technology in mechanistic toxicology investigations.

Metabonomic identification of two distinct phenotypes in Sprague-Dawley (Crl:CD(SD)) rats.

Genetic drift in animal populations has been a recognized concern for many years. Less understood is the potential for phenotypic "drift" or variation that is not related to any genetic change. Recently, stock Sprague-Dawley (Crl:CD(SD)) rats obtained from the Charles River Raleigh facility demonstrated a distinct endogenous urinary metabonomic profile that differed from historical control SD urine spectral profiles obtained over the past several years in our laboratory. In follow-up studies, the origin of the variant phenotype was narrowed down to animals of both sexes that were housed in one specific room (Room 9) in the Raleigh facility. It is likely that the two phenotypes are related to distinct populations of gut flora that particularly impact the metabolism of aromatic molecules. The most pronounced difference between the two phenotypes is the relative amounts of hippuric acid versus other aromatic acid metabolites of chlorogenic acid. Though both molecular species are present in either phenotype, the marked variation in levels of these molecules between the two phenotypes has led to the designation of high hippuric acid (HIP) and high chlorogenic acid metabolites (CA) phenotypes. Specific urinary components that distinguish the phenotypes have been thoroughly characterized by NMR spectroscopy with additional, limited characterization by LC-MS (high performance liquid chromatography coupled with mass spectrometry). Co-habitation of rats from the two phenotypes rapidly facilitated a switch of the CA phenotype to the historical Sprague-Dawley phenotype (HIP). The impact of these variant phenotypes on drug metabolism and long-term safety assessment studies (e.g., carcinogenicity bioassays) is unknown.

Comparison of heterogeneous and homogeneous radioactivity flow detectors for simultaneous profiling and LC-MS/MS characterization of metabolites.

Methods for simultaneous liquid chromatography-radioactivity monitor (LC-RAM) metabolite profiling and LC-tandem mass spectrometry (MS/MS) characterization of metabolites are described. Profiling and characterization of metabolites from three drug candidates from different therapeutic areas were compared using in-line heterogeneous LC-RAM-MS/MS and homogeneous LC-RAM-MS/MS methods. Although comparison shows that simultaneous metabolite profiling and characterization can be achieved using either heterogeneous or homogeneous-LC-RAM-MS/MS systems, a homogeneous system has the advantage in the following aspects, (1) sensitivity; (2) ease of method transfer; (3) less peak broadening problems due to the drug or metabolites adhering to the RAM cell; (4) accuracy in quantitation of the metabolites; and (5) the ability to load larger volumes of unprocessed biological fluids. Furthermore, the study shows that some of the possible metabolites that do not ionize well with electrospray ionization (ESI) and eluded detection by heterogeneous-LC-RAM detection could be very easily detected and characterized using a homogeneous-LC-RAM-MS/MS system.