Deploying Automated Workstreams and Computational Approaches for Generation of Toxicity Data Used for Hazard Identification

Robert T. Dunn, II, Ph.D., DABT

SLAS Annual Meeting: ADMET Special Interest Group

January 27, 2016

Presentation Overview

• Toxicity testing: Brief Historical Perspective

• Predictive Safety

• Deploying automated workstreams for key assay platforms:

• Mitochondrial toxicity

• Hepatobiliary transport

• Computational approaches

• Future Directions

2

Classical Approaches to Toxicity Testing

• Labor intensive, costly, and time consuming

• Animal-centric

• 2 test species required for most regulatory submissions

• Rodent and non-rodent

• Uses fully integrated mammalian systems

• Not fully predictive of similar toxicities in human

• Species differences may lead to unexpected toxicities in early human trials

• Species may be more or less sensitive than humans

• Immune-mediated toxicities are poorly predicted using non-human systems

3

The Advent of Predictive Safety Goal: Reduce Late-Stage Safety Related Attrition

• In Vitro Safety assays

• Recent notable successes in earlier hazard identification

• BSEP profiling

• hERG (cardiac ion channel) testing

• Binding very high throughput/early triage

• Functional lower throughput but good correlation to in-vivo drug effects

• Following the Success noted with In vitro ADME assays

• In use for decades

• High throughput

• Simple in vitro systems

• Reduced the number of drug failures due to poor pharmacokinetic properties

4

Predictive Safety

• 3 Examples of assay platforms in use:

• Mitochondrial toxicity: “Mitomics”

• Hepatobiliary transport: BSEP

• Computational Screening: using structural information from the drug and target to predict toxicity

5

Automation Example: Isolated Mitochondrial Function Platform (IMF)

Automated assay initiation and data capture

• Platform is comprised of 21 assays

• 16 test articles/run

• 5 Automation protocols per run

1. Test article dilution (5X in buffer and 10X in water)

2. Automated assay preparation: rat heart mitochondria isolated and placed on robotics deck

3. Stamping diluted 5X TA into 384 well plates

4. Stamping diluted 10X TA into 384 well plates

5. Full 21 assay run

• Total automation run time = ~15hrs (overnight) • FTE Costs: 2 FTE, 2 business days (fully automated)

• FTE Costs: 4 FTE, 5 business days (partially automated)

6

Isolated Mitochondrial Function Profiling Assays Overview

Pathway Assay

TCA Glutamate oxidation

Pyruvate oxidation

Succinate oxidation

Citrate Synthase

Fatty Acid Oxidation Acetyl-CoA oxidation

Acetyl Carnitine oxidation

Butyryl-CoA oxidation

Octanoyl-CoA oxidation

Palmitoyl Carnitine oxidation

Palmitoyl-CoA oxidation

Electron Transport Chain

NADH oxidation

Complex I

Complex II

Complex III

Complex IV

Complex V

Uncoupling Oxidative Phosphorylation

Uncoupling via respiratory burst

Mitochondrial Membrane Potential (MMP)

Mitochondrial Swelling Mitochondrial Permeability Transition (MPT)

Calcium Homeostasis Calcium Loading Potential

Oxidative Stress Aconitase

Dykens and Will, 2007

Qu, Y., et al., 2013

Hamilton-Based Automation Systems

Shakers

Readers

Reader

eSwap

Washer

• Liquid Handler: Hamilton, MicroLab Star

• Robotic Arm: Hamilton, MicroLab eSwap

• Plate Reader Spectrophotometer: Molecular Devices, SpectraMax plus384

• Plate Reader Luminometer: Molecular Devices, LMax

• Plate Reader (2): Multi-Mode: Molecular Devices, M5

• Plate Reader Electrochemiluminescence: MesoScale Discovery (MSD), Sector Imager 6000™

• Robotic Incubator: Thermo Scientific, Cytomat

• Plate washer: Biotek, ELX405

• Plate Shakers (5): Thermo Scientiic, VARIOMAG® Teleshake

8

Front view Rear view

Automation System Control

9

GATE

CPU

Intra-network

MOTHER

CPU

Robotic Arm

Electrochemiluminescence

Shakers

Liquid Handler

Isolated Mitochondrial Function Profiling Dataset Examples

• By comparing potencies, a primary mitochondrial target can be identified

• Compounds can be compared via potencies at the same target or by their collection

of targets

• Results are reported to teams in a standard template that enables prioritization

decisions

Heat maps are generated for each

compound (or series of compounds)

Glutamate

Pyruvate

Succinate

Acetyl CoA

Acetyl Carnitine

Butyryl CoA

Octanoyl CoA

Palmitoyl CoA

Palmitoyl Carnitine

Citrate Synthase

NADH oxidation

Complex II

Complex III

Complex IV

Complex V

MMP

Uncoupling

MPT

Calcium Loading

Aconitase

concentration

Series A Series B

Concentration-response curves are

created for each assay and compared for

each compound Pyruvate Succinate Acetyl Carnitine

Palmitoyl Carnitine

NADH Complex I Complex II Complex III Complex IV

Complex V

Acetyl-CoA

Butyryl-CoA Octanoyl-CoA Palmitoyl-CoA Citrate Synthase

MMP Uncoupling MPT Calcium Loading

Aconitase

Glutamate

Bile Salt Export Pump (BSEP) • Other names include:

• ATP-binding cassette transporter ABCB11

• sister of p-glycoprotein (SPGP)

• Originally discovered in 1995 as SPGP in pig

• Discovered to play a major role in hepatobiliary excretion of conjugated bile salts

• Mutations of ABCB11 gene (in humans) are associated with familial intrahepatic cholestasis

11

Morgan, R. et al (2013) Tox Sci

Bile Acid Trafficking: High Level

OSTβ

OST

Conjugated BA

IBAT Conjugated BA

NTCP

Conjugated BA

Conjugated BA

Po

rta

l ve

in

Ileocyte

Hepatocyte

BSEP Conjugated BA

Cholesterol

Bile acids

Un-Conjugated +

Un-Conjugated +

Un-Conjugated +

MR

P3

MR

P4

MRP2

12

Membrane Vesicle Assay for Transporter Function

- Sf9 insect cells transfected with BSEP/Bsep - BSEP Assay is a highly stable platform - History:

- Manual assay that was run as needed - Transferred to Amgen HTS group to assess 1000’s of compounds/year in-house - Now outsourced to a 3rd party

Inverted membrane vesicle

3H-T

ATP

Test article BSEP

BSEP

3H-T

3H-T

13

Morgan et al, 2010, ToxSci

Comparison of the Css (total)/BSEP IC50 ratio with the BSEP IC50 value alone for 109 marketed or withdrawn drugs

25 µM IC50

10x safety margin

Morgan et al, 2013, ToxSci 14 Human BSEP IC50 (µM)

Off-Target Screening Moving toward computational screens

• Small molecule drugs are developed against specific targets to elicit a desired pharmacological effect

• Receptor proteins

• Enzymes

• Nuclear targets

• However small molecules often interact with other entities

• So called “off-target” effects*

• Off-target effects are identified using HT binding assays

15

*Fan, F., et al., Tox. Sci., 2015

Off-Target Screening

• A retrospective analysis of ~6,000 small molecules tested against 140 off-target entities

• For certain targets, high hit rates were observed

• >30% of compounds had binding activity

• Follow up functional assays were rarely positive

• Low sensitivity and specificity

• Creates additional work and resource drain to follow false hits

• Can we derive a computational approach to prioritize early off-target screening?

16

Off-Target Screening

• Computational platform: “Maestro” by Schrödinger

• The present analysis may be used to:

• Generate SAR for chemistry to “design out” off-target activities

• Build target structure-based predictions

• Build QSAR ligand-based predictions

17

Using assay data to identify “features” GPCR receptor example

18

EC50 data from 235 Amgen compounds

Scaffold summary based on

235 Amgen compounds

Importance analysis

of key functional

groups

R5: most

important for

activities

Off-Target Screening

• To avoid possible “hits” on the GPCR of interest we can virtually screen prior to ordering expensive assays

• Enables Medicinal Chemists to build safer molecules

• Prioritizes investment in cleaner molecular scaffolds

• Avoids unnecessary spend on riskier scaffolds or molecules

19

The Future…

• Using computational tools, build in early screening assays based solely on “in-silico” tools

• Can rule out “risky” chemical structures in virtual space

• Prevents unnecessary investment in assays for molecules that have a low likelihood of advancement

• Share the structural information on safety endpoints with medicinal chemists to feedback into the design of safer molecules

• Construct reference database of chemical structures that may pose a risk for various off-target entities (receptors, enzymes, etc)

20

Acknowledgements • Patrick Cosgrove

• Rocio Hernandez

• Ryan Morgan

• Yuan Chen

• Fan Fan

• Cindy Afshari

• Hisham Hamadeh

• Craig Spruiell

• Paul Santana

• Jeff Lawrence

• Paul Acton

• Nianyu Li

• Padma Narayanan

• Jesse Campbell (Telos Scientific) 21