High-content Analysis of Signaling Networks Enables Predictive Compound Pro� lingThomas Koblizek1, Jennifer Dias2, Zhengping Huang2, Graham Bilter2, Helen Chan2, Ann Sieho� 1, Anthony Pitt1, Jane Lamerdin2, Nicole Faust1, John Westwick2

1Lonza Cologne AG, Nattermannallee 1, 50829 Cologne, Germany2Odyssey Thera, Inc., 4550 Norris Canyon Road, San Ramon, CA 94583, USA

Abstract

Modern drug discovery work� ows seek to address the speci� city of drug / target interactions, but often fail to re� ect the inherent complexity of the protein complexes and biochemical networks within the live cell context missing key “o� -target” e� ects. A major challenge for drug discovery remains the need for assays that capture diverse targets and pathways in their native state delivering a picture of the global cellular response to a drug candidate. We have created a cell based systems biology platform that moves beyond classical o� -target pro� ling methods and enables us to learn much more about lead compounds, their mode of action and possible future dangers. The platform comprises a panel of cell based protein-protein interaction measurements facilitating analysis of known signalling pathways in a highly contextual fashion. Using this platform we assessed the system-wide activity of hundreds of diverse known drugs, failed drugs, toxicants, and genetic reagents. We observed that all drugs and toxicants, including closely-related chemical structures, generate a unique “signature” across the panel. Compounds with similar mechanisms of cellular toxicity generated reproducible signatures, enabling the development of algorithms which predict selectivity, safety, and e� cacy of novel therapeutic candidates

The Process

Arrays of customer compounds as well as reference drugs and toxicants are added to human cells that express the protein complex of interest. Then, pathway modulators may be added and cells are fixed and stained at assay-adapted time points. Data collection is carried out in an automated high-throughput-capable high-content analysis platform, and image analysis is run on-the-fly. Quantitative assay results allow for comparison to our extensive compound database comprised of diverse known drugs, failed drugs and known toxicants. This enables the delivery of in-depth data classification and interpretation.

The Panel

In vitro assays and target class selectivity panels are widely used and generally show compound promiscuity. However, because these do not include “undruggable” protein classes they will still under-estimate potential adverse drug reactions (ADR), because drugs and novel compounds are very likely to also bind and interfere with all sorts of protein classes. To solve this problem, our analysis captures drug impact on all relevant cellular processes independent of enzymatic activity. Over a decade, we have selected a panel of assays that delivers the maximal predictivity, investigating numerous cellular pathways.

Case Study

To investigate the effectiveness of our profiling, we have chosen real life examples of drugs that have shown unexpected ADR. Troglitazone, marketed as Rezulin, was introduced in 1997 as an oral treatment for type 2 diabetes. It was withdrawn in 2000 after its use was shown to be associated with a markedly increased risk of acute idiopathic liver injury and acute liver failure.Troglitazone belongs to the drug class of the thiazolidinediones, which work by activating peroxisome proliferator-activator receptors (PPARs). Troglitazone activates PPARγ and to a minor extent PPARα. Troglitazone also contains an a-tocopherol moiety presumably responsible for its anti inflammatory activity through decrease of nuclear factor kappa-B (NF-KB) levels.We have tested troglitazone for its on-target activity in our PPARγ/SRC-1 assay and with our profiling panel of assays for possible off-target effects. As expected, troglitazone does indeed activate PPARγ, but is also active in assays indicative of apoptosis and mitotic stress. In contrast, rosiglitazone, belonging to the same class of compounds shows even stronger activity in our PPARγ assay without having the liabilities of troglitazone.

Summary

New strategies are needed to improve drug discovery decision-making. Current selectivity profiling strategies (in vitro, functional) fail to adequately capture off-target activity and compound mechanisms. Diversity of pathway and target class coverage is required for identification of on- and off-target effects. Our contextual, systems-based analysis identifies unexpected compound mechanisms (desirable and undesirable) and the database of known drugs and toxicant signatures provides unique resource for identifying compound mechanisms and certain liabilities. Data classification simplifies the decision making with full data analysis and interpretation. We provide you with recommendations from senior scientists with decades of signal transduction analysis experience.

Figure 1. Protein-fragment Complementation Assay (PCA) technology. A reporter protein like GFP is synthesized in two complementary fragments which are fused in frame to two proteins that are expected to bind to each other. Assembly of the reporter protein from its fragments can only happen if the test proteins exist in a complex (no background). Drug activity is measured via changes in signal intensity and location of the reporter protein (e.g. � uorescence). Quantitative image analysis of every assay delivers a compound activity pro� le that can be compared to our extensive database.

For further information, please contact: Thomas.Koblizek@lonza.com

Table 1. Rezulin and Avandia similarity to other drug pro� les. Based on the pro� ling results, we searched the database for similarities. While Reszulin shows similarities to various compounds in di� erent pathways including some with severe liabilities, Avandia shows similarities only to its own group of PPARγ agonists.

Drugs rank ordered by similarity to RezuIin Drugs rank ordered by similarity to AvandiaTroglitazone (Rezulin; failed drug) Rosiglitazone (Avandia; marketed drug)Cisapride (serotonin receptor inhibitor; failed drug) GW 1929 (PPARγ agonist)Entacapone (COMT inhibitor) Rosiglitazone (Avandia; different dose)KRIBB3 (mitotic kinesin inhibitor) Indomethacin (anti-inflammatory)Radicicol (HSP90 inhibitor) GW 1929 (PPARγ agonist, different dose)Wee1 Inhibitor II (cell cycle/mitotic kinases) GW 1929 (PPARγ agonist, different dose)Deferasirox (Iron chelator; renal tox; cytopenia) Rosiglitazone (Avandia; different dose)YIC C8 434 (ACAT inhibitor) Rosiglitazone (Avandia; different dose)Fluvastatln (HIVIGCOA Reductase) GW 1929 (PPARγ agonist, different dose)Taxol (tubulin) Rosiglitazone (Avandia; different dose)Tubulin Polymerization Inhibitor (tubulin) Rosiglitazone (Avandia; different dose)Vinblastine (tubulin) SB 218078 (Chk1 inhibitor)Cerivastatin (HIVIGCOA Reductase; failed drug) GW 1929 (PPARγ agonist, different dose)Idarubicin (cytotoxic antitumor antibiotic) Rosiglitazone (Avandia; different dose)

Figure 4. Compound library. Drugs, withdrawn drugs, known toxicants and biologically active compounds in our library cover a wide variety of functional biological and chemical classes as well as therapeutic areas.

Figure 3. Quantitative analysis of PCA panel is compiled into compound activity pro� le. Validated assays are run and analyzed on a quantitative image analysis platform. Comparison to control for each assay results in a comprehensive pro� le.

Compound pro� le:activity across assay panel

Quantitative image analysis

Mdm2/p53

Vehicle Bortezomib

Figure 2. Image analysis platform. Customer and reference compounds are arrayed together and added to human cells plated in 384-well plates. Plating, drug addition, washing and � xation are carried out in an automated process. Image acquisition and data collection are carried out on our HTS capable high-content platform, collecting hundreds of thousands of images per day with scalable on-the-� y image analysis.

Array customer compounds at multiple concentrations

Add drugs to cells with pathways/ protein complex of interest

+/- pathway modulator Fix, stain and acquire data at multiple time points

Plating and transfection of cells

Custom Image and Data Analysis Pipeline

Highly Parallel Linux Processing Environment

Image Servers/Storage

Drug additions, washes, staining and � xation

Data collection

Data Servers Fully relational Oracle Database

Figure 5. PPARγ/SRC-1 PCA assay. The PPARγ nuclear receptor functions as a transcription factor as part of a large protein complex. Stimulation with a selective PPARγ agonist, here shown with GW1929 in the lower panel right hand picture, increases PPAR’s a� nity for speci� c co-activators like SRC-1/NCoA1. In the basal state (shown in the upper left picture), � uorescent level is very low. Upon stimulation, increase in mean � uorescence is measured (PCA depicted in green, DRAQ5 nuclear stain depicted in red). Troglitazone is active in this assay (upper right), Rosiglitazone is an even more potent activator (lower left).

DMSO Troglitazone 30 µm

Rosiglitazone (30 µM) GW 1929 (5 µM)

Figure 6. Comparison of Rezulin and Avandia pro� les. The activity over the entire panel is depicted on the left side, and a subset is magni� ed on the right hand side. Rezulin (shown in the upper panel) shows activity in assays for PPARγ, but also in assays indicative of apoptosis and mitotic stress. Avandia (as shown in the lower panel) is even more active in the PPARγ assay, but lacks the o� target actvities.

Mitotic stressRezulin (Troglitazone) 30 uMApoptosis Mitochondrial

disruption

PPARγ/SRC-1

PPARγ/SRC-1

Avandia (Rosiglitazone) 30 uM