Methods and Approaches for Defining Mechanism Signatures from Human Primary Cell-Based Disease Models

Ellen L. Berg and Jian Yang, BioSeek, Inc., Burlingame, CA 94010

NFκB PathwayDose Response Characteristics

Figure 9. Relationship of oxidative / nitrosative stress and ER stress responses connected by chemical effects. Compounds shown affect diverse and/or unknown targets including SERCA (thapsigargan), mitochondrial complexes IV (sodium azide) and I (rotenone), but induce functionally similar BioMAP profiles (see Fig. 5). Literature references are indicated in parentheses.

Figure 4 Dose Response of Oligomycin A and Sodium Azide in BioMAP. BioMAP profiles of reference compounds oligomycin A (left) and sodium azide (right), tested at multiple concentrations in 4 BioMAP Systems. The biomarker readouts measured (Kunkel, et al., FASEB Journal, 18, 1279-81, 2004) are indicated along the x-axis. The y-axis shows the log10 expression ratios of the readout level measurements relative to solvent (DMSO buffer) controls. Each datapoint represents a single well. The grey area above and below the dashed line indicates the 95% significance envelope of DMSO negative controls. The BioMAP profiles of oligomycin A at different concentrations remain mathematically similar to one another over a dose range of >1000x (4nM – 3μM, data not shown), in contrast to sodium azide, which shows a sharp dose-response relationship between profiles at different concentrations. Oligomycin, an inhibitor of F(0)F(1) ATPase, and sodium azide, an inhibitor of Complex IV, are both inhibitors of mitochondrial electron transport chain function, but can be distinguished by BioMAP profiling (see Fig. 5).

Figure 7 Overlay of BioMAP profiles of colchicine, JNK Inhibitor IX (an inhibitor of JNK2/3) and paclitaxel. Compounds were tested at the indicated concentrations. JNK inhibitor IX (selective for JNK2/3) does not show similarity with the database reference pan JNK inhibitor, AS602801, but rather with colchicine, a microtubule destabilizer, and with paclitaxel, a microtubule stabilizer. Paclitaxel and colchicine are not significantly similar to each other: they do not cluster in Fig 5, left; and are not connected to each other in Fig 5, right). The similarity of JNK Inhibitor IX with anti-mitotic agents is consistent with biological activities that have been described for JNK2. Inhibition of JNK2 (but not JNK1) with dominant-negative mutant or RNA interference has been shown to disrupt spindle formation and chromosome formation during anaphase, a process involving microtubule function, resulting in polyploidy (MacCorkle, R.A. et al., J. Biol. Chem. 279, 40112-21, 2004).

Figure 8. Overlay of BioMAP profiles of chlorambucil and cycloheximide. Compounds were tested at the indicated concentrations. Cycloheximide is a protein synthesis inhibitor that interferes with peptidyl transferase of the 60S ribosomal subunit, by modifying of a sensitive sulfhydryl group. Cycloheximide has been shown to induce ER stress in vivo (Ito, K., et al. Toxicology. 219, 175-86, 2006). The mechanism of action of chlorambucil, which is a nitrogen mustard and DNA alkylating agent, as a chemotherapeutic in oncology may include alkylation of non-DNA targets and increased levels of reactive oxygen and nitrogen species (Begleiter, A., et al., Leuk. Lymphoma. 23, 187-201, 1996; Boldogh, I., et al. Toxicology 193, 137-52, 2003).

Figure 2. BioMAP Model Systems. Eight BioMAP Systems listed are comprised of the primary human cell types cultured and stimulated with the environmental factors (stimuli added along with test compounds) for 24 (proteins) or 72 hours (proliferation measurements). For each System, the levels of biomarker readouts listed are measured as described in Kunkel, E. et al., FASEB Journal, 18, 1279-81, 2004 2004 and Berg, E., et al., J. Pharmacol. Toxicol. Methods. 53, 67-74, 2006. “Vis” is a morphologic score and SRB is a measure of total protein levels

BioMAP Systems: Primary Human Cell Disease Models

Figure 1. BioMAP Platform. BioMAP systems are complex primary human cell-based disease models that contain specific combinations of inputs (stimulatory factors), cell types, and readout parameters, selected and optimized to enable the detection and discrimination of a broad range of compound classes and mechanisms of action. Regulatory networks within cells integrate signals from the environment and the test agent (e.g. compound) resulting in reproducible profiles that are compiled in a database. Custom informatics tools are used to mine the database of profiles generated for approved and failed drugs, experimental chemicals and biologics.

Predicting the health effects resulting from drug or chemical exposure from in vitro assays is important for prioritizing chemicals for in vivo toxicology studies. While primary human cell-based assays offer the opportunity to assess chemical effects in settings that are more relevant to human exposure, there are significant challenges. We will discuss the approaches that we have taken to address assay reproducibility, donor differences, selection of assay endpoints, and the statistical analysis methods we have developed for the generation of mechanism signatures as well as results from their application to the ToxCast phase I library.

Abstract

In conclusion, we have demonstrated the ability to classify chemicals according to mechanisms of action by profiling in primary human cell based systems, BioMAP systems. In addition to distinguishing compounds based on target mechanisms, compounds could also be classified by their pathway relationships (e.g. hsp90 and NFkB, etc.), and toxicity modes of action (e.g. ER stress). Analysis of reference compounds with known mechanisms and comparison with known toxicities should help provide an approach for prioritizing chemicals likely to be of concern for further analysis. In addition, while assessment of cellular mechanisms can help prioritize compounds with respect to their potential to induce toxic outcomes, the outcomes of pharmacokinetics, tissue distribution and in vivo metabolism studies are critical parameters in determining human responses to molecule exposure.

Summary

Figure 6. Overlay of BioMAP profiles of dimethyl fumarate and IKK-2 inhbitor IV (TPCA-1), both inhibitors of NFκB function. Compounds were tested at the indicated concentrations. Biomarker measurements, listed on the x-axis, were made as described (Kunkel, et al., FASEB Journal, 18, 1279-81, 2004). The y-axis shows the data are presented as log expression ratios (log10[drug/ buffer control]) of treated samples relative to solvent controls. The gray envelope around the origin shows the 95% confidence envelope of historical controls. Although compounds share many activities and are highly similar when 4 systems are compared (Fig. 5 left), they are more readily distinguished when compared across 8 systems (Fig. 5, right).

Figure 5. Function Similarity Maps of Reference Compounds: Comparison of Assay Formats. Left: Compound profiles generated from multiple experiments (3 experiments, n=3 wells each) in 4 BioMAP systems (3C, 4H, LPS and SAg) were compared by pairwise correlation and correlations analyzed for significance and subjected to non-linear projection (see Kunkel, et al., FASEB Journal, 18, 1279-81, 2004). Compound profiles that are similar to one another are close together in the graph (in clusters) and compound profile similarities that are above the selected thresholds (Pearson’s correlation of r > 0.7 and tanimoto > 0.5) are shown as connected lines or edges. Compound shading indicates relative concentration (darker shading indicates higher concentrations). Right: Function Similarity Map for compounds tested in screening format (one experiment, n=1 well) in 8 BioMAP Systems (Figure 1). In both assay formats, compounds can be classified according to target mechanisms of action.

BioSeekAssays

Human primary cells Disease-like culture conditions>25 systems

LPS

BF4T

SM3C

Profile Database Informatics

Biological responses to drugs and stored in the database

Specialized informatics tools are used to mine and analyze biological data

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Readout Parameters (Biomarkers) Readout Parameters (Biomarkers)

Oligomycin A Sodium Azide

Sodium Azide Chlorambucil

Cycloheximide A23187 Thapsigargin

Oxidative /Nitrosative Stress

ER Stress Unfolded Protein Response

Rotenone

(Ogino, 2001; Ajith, 2006)

(Begleiter, 1996; Boldogh, 2003; Yaren, 2007) (Larsen, 2008;

Sherer, 2007)

(Ryu, 2002)

(Dickhout, 2005)

(Ito, 2006)

(Deniaud, 2008)

(Deniaud, 2008)

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Readout Parameters (Biomarkers)

BioMAP Systems

Colchicine

Paclitaxel

JNK Inhibitor IX

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Readout Parameters (Biomarkers)

BioMAP Systems

Chlorambucil

Cycloheximide

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Control (buffer)

BioMAP Systems

Readout Parameters (Biomarkers)

Cytotoxicity Readouts

Dimethyl fumarate

IKK-2 inhibitor IV

Microtubule Function Endoplasmic Reticulum Stress

Pearson Correlation Coefficient

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

R1 1

R2 0.95 1

R3 0.96 0.94 1

R4 0.98 0.98 0.96 1

R5 0.93 0.94 0.91 0.94 1

R6 0.96 0.96 0.93 0.97 0.98 1

R7 0.94 0.91 0.9 0.93 0.89 0.9 1

R8 0.95 0.98 0.94 0.98 0.94 0.98 0.92 1

R9 0.91 0.92 0.88 0.92 0.89 0.91 0.93 0.93 1

R10 0.88 0.9 0.81 0.89 0.93 0.93 0.85 0.91 0.83 1

R11 0.94 0.97 0.9 0.94 0.91 0.93 0.94 0.96 0.91 0.89 1

R12 0.92 0.9 0.84 0.89 0.96 0.96 0.89 0.91 0.87 0.92 0.91 1

Reproducibility

Comparison of Assay Formats: Four Systems Vs. Eight Systems

Figure 3. Reproducibility of BioMAP Profiles. A test compound, BSK-714, at 5 µM, was profiled in 4 BioMAP systems in 12 experiments performed over a period of several months using cells derived from different doners. Data from each experiment were compared by pairwise correlation and show high correlation (Pearson’s correlation of r > 0.8) across experimental repeats. While the amplitude of biomarker changes varies between experiements and with cell donor, the overall profile shape is highly reproducible and diagnostic of mechanism of action.

MicrotubuleStabilizers

Mitochondrial ET chain

Retinoids

Hsp90

CDK

NFκB

MEK

DNAsynthesis

JNK

Proteinsynthesis

MicrotubuleDestabilizers

Estrogen R

PI-3K

Ca++

Mobilization

mTOR

PKC Activation

p38 MAPK

HMG-CoAreductase

Calcineurin

Transcription

ER Stress

NFκB

ER Stress

Microtubule