Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model.

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

A forward selection algorithm to identify mutually exclusive alterations in cancer studies.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes.

MOTIVATION: With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide association studies (GWAS) have played an important role in identifying disease-related genetic variants and novel treatments. Complex longitudinal phenotypes are commonly collected in medical studies. However, since limited analytical approaches are available for longitudinal traits, these data are often underutilized. In this article, we develop a high-throughput machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a novel ℓ0-norm algorithm. RESULTS: Extensive simulations demonstrated that the proposed approach not only provided accurate selection of single nucleotide polymorphisms (SNPs) with comparable or higher power but also robust control of false positives. More importantly, this novel approach is highly scalable and could be approximately >1000 times faster than recently published approaches, making genome-wide multilocus analysis of longitudinal traits possible. In addition, our proposed approach can simultaneously analyze millions of SNPs if the computer memory allows, thereby potentially allowing a true multilocus analysis for high-dimensional genomic data. With application to the data from Alzheimer's Disease Neuroimaging Initiative, we confirmed that our approach can identify well-known SNPs associated with AD and were much faster than recently published approaches (≥6000 times). AVAILABILITY AND IMPLEMENTATION: The source code and the testing datasets are available at https://github.com/Myuan2019/EBE_APML0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-scale metabolic model of the rat liver predicts effects of diet restriction.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver's energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected "hub" genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver's response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs.

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Modeling Therapeutic Antibody-Small Molecule Drug-Drug Interactions Using a Three-Dimensional Perfusable Human Liver Coculture Platform.

Traditional in vitro human liver cell culture models lose key hepatic functions such as metabolic activity during short-term culture. Advanced three-dimensional (3D) liver coculture platforms offer the potential for extended hepatocyte functionality and allow for the study of more complex biologic interactions, which can improve and refine human drug safety evaluations. Here, we use a perfusion flow 3D microreactor platform for the coculture of cryopreserved primary human hepatocytes and Kupffer cells to study the regulation of cytochrome P450 3A4 isoform (CYP3A4) activity by chronic interleukin 6 (IL-6)-mediated inflammation over 2 weeks. Hepatocyte cultures remained stable over 2 weeks, with consistent albumin production and basal IL-6 levels. Direct IL-6 stimulation that mimics an inflammatory state induced a dose-dependent suppression of CYP3A4 activity, an increase in C-reactive protein (CRP) secretion, and a decrease in shed soluble interleukin-6 receptor (IL-6R) levels, indicating expected hepatic IL-6 bioactivity. Tocilizumab, an anti-IL-6R monoclonal antibody used to treat rheumatoid arthritis, has been demonstrated clinically to impact small molecule drug pharmacokinetics by modulating cytochrome P450 enzyme activities, an effect not observed in traditional hepatic cultures. We have now recapitulated the clinical observation in a 3D bioreactor system. Tocilizumab was shown to desuppress CYP3A4 activity while reducing the CRP concentration after 72 hours in the continued presence of IL-6. This change in CYP3A4 activity decreased the half-life and area under the curve up to the last measurable concentration (AUClast) of the small molecule CYP3A4 substrate simvastatin hydroxy acid, measured before and after tocilizumab treatment. We conclude that next-generation in vitro liver culture platforms are well suited for these types of long-term treatment studies and show promise for improved drug safety assessment.

Utilization of human nuclear receptors as an early counter screen for off-target activity: a case study with a compendium of 615 known drugs.

Off-target effects of drugs on nuclear hormone receptors (NHRs) may result in adverse effects in multiple organs/physiological processes. Reliable assessments of the NHR activities for drug candidates are therefore crucial for drug development. However, the highly permissive structures of NHRs for vastly different ligands make it challenging to predict interactions by examining the chemical structures of the ligands. Here, we report a detailed investigation on the agonistic and antagonistic activities of 615 known drugs or drug candidates against a panel of 6 NHRs: androgen, progesterone, estrogen α/ß, and thyroid hormone α/ß receptors. Our study revealed that 4.7 and 12.4% compounds have agonistic and antagonistic activities, respectively, against this panel of NHRs. Nonetheless, potent, unintended NHR hits are relatively rare among the known drugs, indicating that such interactions are perhaps not tolerated during drug development. However, we uncovered examples of compounds that unintentionally agonize or antagonize NHRs. In addition, a number of compounds showed multi-NHR activities, suggesting that the cross-talk between multiple NHRs co-operate to elicit in vivo effects. These data highlight the merits of counter screening drug candidate against NHRs during drug discovery/development.

Transcriptional Profiling of Laser Capture Microdissected Subpopulations of the Osteoblast Lineage Provides Insight Into the Early Response to Sclerostin Antibody in Rats.

Sclerostin antibody (Scl-Ab) increases bone formation through a process dependent on the activation of canonical Wnt signaling, although the specific signaling in the osteoblast lineage in vivo is largely unknown. To gain insight into the signaling pathways acutely modulated by Scl-Ab, the transcriptional response of subpopulations of the osteoblast lineage was assessed by TaqMan and microarray analyses of mRNA isolated from laser capture microdissection (LCM)-enriched samples from the vertebrae of ovariectomized rats during the first week after Scl-Ab administration. Briefly, 6-month-old Sprague-Dawley rats were ovariectomized and, after 2 months, received a single dose of vehicle (VEH) or 100 mg/kg Scl-Ab (n = 20/group). Lumbar vertebrae were collected at 6, 24, 72, and 168 hours postdose and cryosectioned for LCM. Osteocytes were captured from bone matrix, and osteoblasts and lining cells were captured from bone surfaces based on fluorochrome labeling. mRNA was isolated, amplified, and profiled by TaqMan and microarray. Expression analysis revealed that Scl-Ab caused strikingly similar transcriptional profiles across all three cell types. Only 13 known canonical Wnt target genes, the majority with known functions in bone, showed a significant change in expression by microarray in response to Scl-Ab, with Wisp1 and Twist1 being the most responsive. Coincident with increased expression of Wnt target genes was the upregulation of numerous extracellular matrix (ECM) genes. The acute and progressive upregulation of ECM genes in lining cells supports their activation into matrix-producing osteoblasts, consistent with modeling-based bone formation. A similar transcriptional profile in osteocytes may indicate that Scl-Ab stimulates perilacunar/pericanalicular matrix deposition. Pathway analyses indicated that Scl-Ab regulated a limited number of genes related to cell cycle arrest and B-cell development. These data describe the acute downstream signaling in response to Scl-Ab in vivo and demonstrate selected canonical Wnt target gene activation associated with increased bone formation in all mature osteoblast subpopulations.

A systematic assessment of mitochondrial function identified novel signatures for drug-induced mitochondrial disruption in cells.

Mitochondrial perturbation has been recognized as a contributing factor to various drug-induced organ toxicities. To address this issue, we developed a high-throughput flow cytometry-based mitochondrial signaling assay to systematically investigate mitochondrial/cellular parameters known to be directly impacted by mitochondrial dysfunction: mitochondrial membrane potential (MMP), mitochondrial reactive oxygen species (ROS), intracellular reduced glutathione (GSH) level, and cell viability. Modulation of these parameters by a training set of compounds, comprised of established mitochondrial poisons and 60 marketed drugs (30 nM to 1mM), was tested in HL-60 cells (a human pro-myelocytic leukemia cell line) cultured in either glucose-supplemented (GSM) or glucose-free (containing galactose/glutamine; GFM) RPMI-1640 media. Post-hoc bio-informatic analyses of IC50 or EC50 values for all parameters tested revealed that MMP depolarization in HL-60 cells cultured in GSM was the most reliable parameter for determining mitochondrial dysfunction in these cells. Disruptors of mitochondrial function depolarized MMP at concentrations lower than those that caused loss of cell viability, especially in cells cultured in GSM; cellular GSH levels correlated more closely to loss of viability in vitro. Some mitochondrial respiratory chain inhibitors increased mitochondrial ROS generation; however, measuring an increase in ROS alone was not sufficient to identify mitochondrial disruptors. Furthermore, hierarchical cluster analysis of all measured parameters provided confirmation that MMP depletion, without loss of cell viability, was the key signature for identifying mitochondrial disruptors. Subsequent classification of compounds based on ratios of IC50s of cell viability:MMP determined that this parameter is the most critical indicator of mitochondrial health in cells and provides a powerful tool to predict whether novel small molecule entities possess this liability.

Common handling procedures conducted in preclinical safety studies result in minimal hepatic gene expression changes in Sprague-Dawley rats.

Gene expression profiling is a tool to gain mechanistic understanding of adverse effects in response to compound exposure. However, little is known about how the common handling procedures of experimental animals during a preclinical study alter baseline gene expression. We report gene expression changes in the livers of female Sprague-Dawley rats following common handling procedures. Baseline gene expression changes identified in this study provide insight on how these changes may affect interpretation of gene expression profiles following compound exposure. Rats were divided into three groups. One group was not subjected to handling procedures and served as controls for both handled groups. Animals in the other two groups were weighed, subjected to restraint in Broome restrainers, and administered water via oral gavage daily for 1 or 4 days with tail vein blood collections at 1, 2, 4, and 8 hours postdose on days 1 and 4. Significantly altered genes were identified in livers of animals following 1 or 4 days of handling when compared to the unhandled animals. Gene changes in animals handled for 4 days were similar to those handled for 1 day, suggesting a lack of habituation. The altered genes were primarily immune function related genes. These findings, along with a correlating increase in corticosterone levels suggest that common handling procedures may cause a minor immune system perturbance.

Dose-related differences in the pharmacodynamic and toxicologic response to a novel hyperglycosylated analog of recombinant human erythropoietin in Sprague-Dawley rats with similarly high hematocrit.

We recently reported results that erythropoiesis-stimulating agent (ESA)-related thrombotic toxicities in preclinical species were not solely dependent on a high hematocrit (HCT) but also associated with increased ESA dose level, dose frequency, and dosing duration. In this article, we conclude that sequelae of an increased magnitude of ESA-stimulated erythropoiesis potentially contributed to thrombosis in the highest ESA dose groups. The results were obtained from two investigative studies we conducted in Sprague-Dawley rats administered a low (no thrombotic toxicities) or high (with thrombotic toxicities) dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114), 3 times weekly for up to 9 days or for 1 month. Despite similarly increased HCT at both dose levels, animals in the high-dose group had an increased magnitude of erythropoiesis measured by spleen weights, splenic erythropoiesis, and circulating reticulocytes. Resulting prothrombotic risk factors identified predominantly or uniquely in the high-dose group were higher numbers of immature reticulocytes and nucleated red blood cells in circulation, severe functional iron deficiency, and increased intravascular destruction of iron-deficient reticulocyte/red blood cells. No thrombotic events were detected in rats dosed up to 9 days suggesting a sustained high HCT is a requisite cofactor for development of ESA-related thrombotic toxicities.

Cytokines associated with increased erythropoiesis in Sprague-Dawley rats administered a novel hyperglycosylated analog of recombinant human erythropoietin.

We previously reported an increased incidence of thrombotic toxicities in Sprague-Dawley rats administered the highest dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114) for 1 month as not solely dependent on high hematocrit (HCT). Thereafter, we identified increased erythropoiesis as a prothrombotic risk factor increased in the AMG 114 high-dose group with thrombotic toxicities, compared to a low-dose group with no toxicities but similar HCT. Here, we identified pleiotropic cytokines as prothrombotic factors associated with AMG 114 dose level. Before a high HCT was achieved, rats in the AMG 114 high, but not the low-dose group, had imbalanced hemostasis (increased von Willebrand factor and prothrombin time, decreased antithrombin III) coexistent with cytokines implicated in thrombosis: monocyte chemotactic protein 1 (MCP-1), MCP-3, tissue inhibitor of metalloproteinases 1, macrophage inhibitory protein-2, oncostatin M, T-cell-specific protein, stem cell factor, vascular endothelial growth factor, and interleukin-11. While no unique pathway to erythropoiesis stimulating agent-related thrombosis was identified, cytokines associated with increased erythropoiesis contributed to a prothrombotic intravascular environment in the AMG 114 high-dose group, but not in lower dose groups with a similar high HCT.

A multifactorial approach to hepatobiliary transporter assessment enables improved therapeutic compound development.

The bile salt export pump (BSEP) is expressed at the canalicular domain of hepatocytes, where it serves as the primary route of elimination for monovalent bile acids (BAs) into the bile canaliculi. The most compelling evidence linking dysfunction in BA transport with liver injury in humans is found with carriers of mutations that render BSEP nonfunctional. Based on mounting evidence, there appears to be a strong association between drug-induced BSEP interference and liver injury in humans; however, causality has not been established. For this reason, drug-induced BSEP interference is best considered a susceptibility factor for liver injury as other host- or drug-related properties may contribute to the development of hepatotoxicity. To better understand the association between BSEP interference and liver injury in humans, over 600 marketed or withdrawn drugs were evaluated in BSEP expressing membrane vesicles. The example of a compound that failed during phase 1 human trials is also described, AMG 009. AMG 009 showed evidence of liver injury in humans that was not predicted by preclinical safety studies, and BSEP inhibition was implicated. For 109 of the drugs with some effect on in vitro BSEP function, clinical use, associations with hepatotoxicity, pharmacokinetic data, and other information were annotated. A steady state concentration (C(ss)) for each of these annotated drugs was estimated, and a ratio between this value and measured IC50 potency values were calculated in an attempt to relate exposure to in vitro potencies. When factoring for exposure, 95% of the annotated compounds with a C(ss)/BSEP IC50 ratio ≥ 0.1 were associated with some form of liver injury. We then investigated the relationship between clinical evidence of liver injury and effects to multidrug resistance-associated proteins (MRPs) believed to play a role in BA homeostasis. The effect of 600+ drugs on MRP2, MRP3, and MRP4 function was also evaluated in membrane vesicle assays. Drugs with a C(ss)/BSEP IC50 ratio ≥ 0.1 and a C(ss)/MRP IC50 ratio ≥ 0.1 had almost a 100% correlation with some evidence of liver injury in humans. These data suggest that integration of exposure data, and knowledge of an effect to not only BSEP but also one or more of the MRPs, is a useful tool for informing the potential for liver injury due to altered BA transport.

Membrane vesicle ABC transporter assays for drug safety assessment.

The use of plasma membrane vesicles that overexpress the bile salt export pump (BSEP) or multidrug resistance-associated protein 2, 3, or 4 (MRP2-4) with an in vitro vacuum filtration system offers a rapid and reliable means for screening drug candidates for their effects on transporter function in hepatocytes and thus their potential for causing drug-induced liver injury (DILI). Comparison of transporter activity in the presence and absence of ATP allows for determination of a specific assay window for each transporter. This window is used to determine the degree to which each test compound inhibits transporter activity. This assay battery is helpful for prioritizing and rank-ordering compounds within a chemical series with respect to each other and in the context of known inhibitors of transporter activity and/or liver injury. This model can be used to influence the drug development process at an early stage and provide rapid feedback regarding the selection of compounds for advancement to in vivo safety evaluations. A detailed protocol for the high-throughput assessment of ABC transporter function is provided, including specific recommendations for curve-fitting to help ensure consistent results.

Perturbation of microRNAs in rat heart during chronic doxorubicin treatment.

Anti-cancer therapy based on anthracyclines (DNA intercalating Topoisomerase II inhibitors) is limited by adverse effects of these compounds on the cardiovascular system, ultimately causing heart failure. Despite extensive investigations into the effects of doxorubicin on the cardiovascular system, the molecular mechanisms of toxicity remain largely unknown. MicroRNAs are endogenously transcribed non-coding 22 nucleotide long RNAs that regulate gene expression by decreasing mRNA stability and translation and play key roles in cardiac physiology and pathologies. Increasing doses of doxorubicin, but not etoposide (a Topoisomerase II inhibitor devoid of cardiovascular toxicity), specifically induced the up-regulation of miR-208b, miR-216b, miR-215, miR-34c and miR-367 in rat hearts. Furthermore, the lowest dosing regime (1 mg/kg/week for 2 weeks) led to a detectable increase of miR-216b in the absence of histopathological findings or alteration of classical cardiac stress biomarkers. In silico microRNA target predictions suggested that a number of doxorubicin-responsive microRNAs may regulate mRNAs involved in cardiac tissue remodeling. In particular miR-34c was able to mediate the DOX-induced changes of Sipa1 mRNA (a mitogen-induced Rap/Ran GTPase activating protein) at the post-transcriptional level and in a seed sequence dependent manner. Our results show that integrated heart tissue microRNA and mRNA profiling can provide valuable early genomic biomarkers of drug-induced cardiac injury as well as novel mechanistic insight into the underlying molecular pathways.

The evolution of bioinformatics in toxicology: advancing toxicogenomics.

As one reflects back through the past 50 years of scientific research, a significant accomplishment was the advance into the genomic era. Basic research scientists have uncovered the genetic code and the foundation of the most fundamental building blocks for the molecular activity that supports biological structure and function. Accompanying these structural and functional discoveries is the advance of techniques and technologies to probe molecular events, in time, across environmental and chemical exposures, within individuals, and across species. The field of toxicology has kept pace with advances in molecular study, and the past 50 years recognizes significant growth and explosive understanding of the impact of the compounds and environment to basic cellular and molecular machinery. The advancement of molecular techniques applied in a whole-genomic capacity to the study of toxicant effects, toxicogenomics, is no doubt a significant milestone for toxicological research. Toxicogenomics has also provided an avenue for advancing a joining of multidisciplinary sciences including engineering and informatics in traditional toxicological research. This review will cover the evolution of the field of toxicogenomics in the context of informatics integration its current promise, and limitations.

Interference with bile salt export pump function is a susceptibility factor for human liver injury in drug development.

The bile salt export pump (BSEP) is an efflux transporter, driving the elimination of endobiotic and xenobiotic substrates from hepatocytes into the bile. More specifically, it is responsible for the elimination of monovalent, conjugated bile salts, with little or no assistance from other apical transporters. Disruption of BSEP activity through genetic disorders is known to manifest in clinical liver injury such as progressive familial intrahepatic cholestasis type 2. Drug-induced disruption of BSEP is hypothesized to play a role in the development of liver injury for several marketed or withdrawn therapeutics. Unfortunately, preclinical animal models have been poor predictors of the liver injury associated with BSEP interference observed for humans, possibly because of interspecies differences in bile acid composition, differences in hepatobiliary transporter modulation or constitutive expression, as well as other mechanisms. Thus, a BSEP-mediated liver liability may go undetected until the later stages of drug development, such as during clinical trials or even postlicensing. In the absence of a relevant preclinical test system for BSEP-mediated liver injury, the toxicological relevance of available in vitro models to human health rely on the use of benchmark compounds with known clinical outcomes, such as marketed or withdrawn drugs. In this study, membrane vesicles harvested from BSEP-transfected insect cells were used to assess the activity of more than 200 benchmark compounds to thoroughly investigate the relationship between interference with BSEP function and liver injury. The data suggest a relatively strong association between the pharmacological interference with BSEP function and human hepatotoxicity. Although the most accurate translation of risk would incorporate pharmacological potency, pharmacokinetics, clearance mechanisms, tissue distribution, physicochemical properties, indication, and other drug attributes, the additional understanding of a compound's potency for BSEP interference should help to limit or avoid BSEP-related liver liabilities in humans that are not often detected by standard preclinical animal models.

Application of genomics for identification of systemic toxicity triggers associated with VEGF-R inhibitors.

The key to the discovery of new pharmaceuticals is to develop molecules that interact with the intended target and minimize interaction with unintended molecular targets, therefore minimizing toxicity. This is aided by the use of various in vitro selectivity assays that are used to select agents most potent for the desired target. Typically, molecules from similar chemical series, with similar in vitro potencies, are expected to yield comparable in vivo pharmacological and toxicological profiles, predictive of target effects. However, in this study, we investigated the in vivo effects of two analogue compounds that similarly inhibit several receptor tyrosine kinases such as vascular endothelial growth factor receptor 1 (VEGFR/Flt1), vascular endothelial growth factor 2 (VEGFR2/kinase domain receptor/Flk-1), vascular endothelial growth factor receptor 3 (VEGFR3/Flt4), platelet-derived growth factor receptor (PDGFR), and Kit receptors, which bear similar chemical structures, have comparable potencies, but differ markedly in their rodent toxicity profiles. Global gene expression data were used to generate hypotheses regarding the existence of toxicity triggers that would reflect the perturbation of signaling in multiple organs such as the liver, adrenal glands, and the pancreas in response to compound treatment. We concluded that differences in pharmacokinetic properties of the two analogues, such as volume of distribution, half-life, and organ concentrations, resulted in marked differences in the chemical burden on target organs and may have contributed to the vast differences in toxicity profiles observed with the two otherwise similar molecules. We propose including select toxicokinetic parameters such as V(ss), T(1/2), and T(max) as additional criteria that could be used to rank order compounds from the same pharmacological series to possibly minimize organ toxicity. Assessment of toxicokinetics is not an atypical activity on toxicology studies, even in early screening studies; however, these data may not always be used in decision making for selecting or eliminating one compound over another. Finally, we illustrate that in vivo gene expression profiles can serve as a complementary assessor of this activity and simultaneously help provide an assessment of on or off-target biological activity.