reducing safety-related drug attrition: the use of in … safety-related drug attrition: the use of...

Reducing safety-related drug attrition: the use of in vitro pharmacological profiling

Dr Joanne Bowes Global Safety Assessment AstraZeneca SPS Webinar, 16th May 2013

A cross-pharma view

Recent Publication AZ, GSK, Pfizer and Novartis

2 Joanne Bowes | May 2013 Global Safety Assessment | Innovative Medicines

December 2012 (pp.909-922)

• Objective: to share our collective knowledge and experience publically

The Challenge: reducing safety-related attrition Types of Adverse Drug Reactions


type A Dose-dependent; predictable from primary, secondary and safety pharmacology

Main cause of ADRs (~75%), rarely lethal

type B

idiosyncratic response, not predictable, not dose-related

Responsible for ~25% of ADRs, but majority of lethal ones

type C long term adaptive changes Commonly occurs with some class of drug

type D type E

Delayed effects e.g. carcinogenicity, teratogenicity Rebound effects following discontinuation of therapy

Low incidence Commonly occurs with some class of drug

Opportunity to avoid side effects in humans

Breckenridge, A. (1996) Br. J. Clin. Pharmacol. 42, 53-8; Lazarou, J. et al. (1998) JAMA 279, 1200-5;

What is in vitro Pharmacological Profiling? A method for Secondary Pharmacodynamics


Desired therapeutic effect

Secondary effects Beneficial, deleterious or neutral

Primary therapeutic target

Drug (or metabolite)

(Other effects)

Secondary targets “off-target interactions”

• Relevant for small molecules and some biologics e.g peptides • Application of a range of technologies

Why use diverse broad panels for profiling? Not predictable from the therapeutic target


Paolini et al, (2006) Nature Biotech 7:805

•If your primary therapeutic target is a GPCR, you need to screen more broadly Than other GPCRs

•Quote from Gaddum: “Every drug has two actions – the one you know about, and the one you don’t”

An Examples of a Target to Avoid 5-HT2B receptor agonists and cardiac valvulopathy


hERG and other cardiac ion channels (Dr Arthur Buzz Brown)

Current Regulatory Guidance and Pharma Practice Safety Pharmacology (ICHS7A, 2001)


• “Ligand binding or enzyme assay data suggesting a potential for adverse effects” should be considered in the “Selection and Design of Safety Pharmacology Studies” (II.B.3)

• What to test, when or how best to do it has not been defined • Pharma experience indicates Regulatory Authorities see data as important

• Most major Pharmaceutical companies do in vitro profiling during Discovery

- AZ, Novartis, Pfizer and GSK all utilise profiling panels not to just select and design in vivo safety pharmacology studies, but to identify potential safety liabilities of compounds and use data to design the liability/promiscuity out of the chemistry before candidate selection

• An average Lead Optimisation programme can cost up to $20M. Using profiling panels to de-select a promiscuous series in the Lead Generation phase easily gives a return on investment.

Why do in vitro Pharmacological profiling?

• Decision-making in Discovery • Profile large numbers of compounds in a turnaround time compatible with Discovery, in cost-

effective way • Target validation • Lead series selection • Build structure-activity relationships (SAR) to design the liability out • Candidate selection from a short-list • Profile competitor compounds for external benchmarking

• Predict and interpret pre-clinical and clinical effects in vivo • Use data to design the optimal safety pharmacology and toxicology studies • Test for activity at human targets and predict adverse effects not detected in pre-clinical models • Profile major human metabolites to assess potential effects in humans • Identify human biomarkers for use in clinical studies • Understand molecular mechanism driving in vivo effects • Key in vitro component of an integrated risk assessment before first time in human • Build patient risk management plan

• Build in silico tools • Predictive Secondary Pharmacology models • Interpretation tools

Major Advantages


When to do profiling? As Early as possible to maximise impact


• The use of in vitro pharmacology profiling data through early drug discovery to clinical development was found to be very similar between the 4 major pharma companies.

• There was significant overlap in the molecular targets in the panels

What Dx Projects do


Make

Test

Design

Analyse

Design it out

Deliver compounds with minimal off target liability

“enhanced quality”

CODE NAME 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM 10µM219600 Deacetylase, Histone 12 -1 11 78 -1 82 86 78 43 -1 0 0 0 0200510 Peptidase Matrix Metalloproteinase-1 (MMP-1) 0 4 20 9 2 -4 2 -18 -8 -2 -13210030 Peptidase, Matrix Metalloproteinase-2 (MMP-2) 13 1 98 1 -1 6 1 -8 8 21 69 0 -6 4 1 12212610 Peptidase, Matrix Metalloproteinase-3 (MMP-3) 14 4 13 21 16 6 3 24 33 29 3200610 Peptidase Matrix Metalloproteinase-7 (MMP-7) -5 0 2 1 -7212620 Peptidase, Matrix Metalloproteinase-8 (MMP-8) 8 8 -3 4 -1214010 Peptidase, Matrix Metalloproteinase-9 (MMP-9) -7 5 5 7 -2204010 Peptidase, Matrix Metalloproteinase-12 (MMP-12) 10 32 14 35 1204110 Peptidase, Matrix Metalloproteinase-13 (MMP-13) 1 11 48 5 17 -2 12 -3 24 3 3210020 Peptidase, Matrix Metalloproteinase-14 (MMP-14) 3 5 92 -1 28 11 2 1 10 0 -13 -6 -3 2 10 13224010 Protein Tyrosine Kinase, EGF Receptor 16 -2 62 22 65 15 58 53 4 -4 93 61 66 4 3226010 Protein Tyrosine Kinase, FGFR1 22 9 26 96 7 102 19 96 52 12 -9 100 98 99 10 20226600 Protein Tyrosine Kinase, FLT1 (VEGFR-1) 28 -13 12 77 15 96 4 59 42 10 22 68 64 71 19 10228610 Protein Tyrosine Kinase, Fyn 1 -6 0 98 8 93 77 94 90 4 -1 97 86 90 -10 0232910 Protein Tyrosine Kinase, Insulin Receptor 32 -4 9 99 3 97 86 97 98 13 3 98 98 100 -20 5239000 Protein Tyrosine Kinase, MET (HGFR) 12 1 24 95 9 92 1 89 64 5 14 95 99 96 48 -11239610 Protein Tyrosine Kinase, SRC 6 -8 99 2 98 77 95 96 -2 4 98 96 99 -9 11239710 Protein Tyrosine Kinase, YES1 10 1 11 93 -6 51 14 78 40 29 31 4 99 95 13 19217100 Phosphodiesterase PDE3 9 3 12 12 -12 32 6 31 21 28 0 12 14 23 28 30217500 Phosphodiesterase PDE4 -8 20 15 96 91 84 16 28 5 19 15 3 23 25170020 Angiotensin AT1 12 7 -5 9 6 -2 -10 3 10 7 1 3 -4 10 -1 2287530 Xanthine Oxidase -6 -1 -14200720 Peptidase, Angiotensin Converting Enzyme -17 8 4 -6 -18 10 -4 45 6 -6 -5 8 30 23 10 3203100 Peptidase, Caspase 1 -1 3 4 5 0 -1 -2 5 1 1 -3 18 1 6 3 -8203400 Peptidase, Cathepsin B 4 1 -8 1 -3 -6 -7 19 5 -7 -9 18 -7 -5 -3 -15203710 Peptidase, Endothelin Converting Enzyme-1 (ECE-1) 44 10 1 40 22 93 49 98 93 12 10 68 14 48 69 26260210 Somatostatin sst4 -6 21 -11 6 1 3 -9 -3 -8 6 3 5 -17 -8 -19 -4170010 Nitric Oxide Synthase, Endothelial (eNOS) 2 0 -3 11 2 10 9 -13 -10 -3 -13 15 -7 -3 -7 -3171520 Nitric Oxide Synthase, Inducible (iNOS) -7 -8 -5172010 Nitric Oxide Synthase, Neuronal (nNOS) 19 12 9214020 Peptidase, Renin 12 1 -2 9 -3 35 21 35 6 11 20 18 8 7 19 23194020 Carbonic Anhydrase -5269500 Carbonic Anhydrase II -5 7 -8 37 2 -26 9 -12 1 12 26 29 -11 -2 -9 -11114910 Lipoxygenase 12-LO 75 105 88 97 48116020 Lipoxygenase 15-LO 14 1 67 63 31118010 Lipoxygenase 5-LO 80 84 99 99 76251350 Retinoid X Receptor RXRalpha -59 -28 27 6 4 56 -11 5 25 0 2 -8 -21 -11 11 3252200 Serotonin (5-Hydroxytryptamine) 5-HT1B 1 -15 5 5 3 32 18 24 3 39 3 10 31 21 5 27252810 Serotonin (5-Hydroxytryptamine) 5-HT2B 10 64 8 8 -3 -19 -4 15 11 14 16 10 -3 -3 1 -8271110 Thromboxane Synthase 19 57 -9271910 Transporter, Adenosine 5 10 -3 62 -7 50 25 59 27 92 16 60 17 66 3 84274030 Transporter, Dopamine (DAT) 3 56 7 64 -1 35 36 10 4 12 1 4 9 6 0 -2279510 Transporter, Norepinephrine (NET) 9 56 3 0 2 25 50 -11 -2 1 6 10 13 8 2 14175000 Acetylcholinesterase 3 3 -4 15 6 53 65 3 2 9 -5 5 35 2 5 8232030 Aldose Reductase 43 35 4 24 10 14 3 16 15 4 11 28 48 47 12 1104010 Cannabinoid CB1 -11 4 2 9 49 22 33 1 44 9 5 75 57 35 64204410 Cannabinoid CB2 8 -16 8 13 3 -5 -7 10 17 5 -4 -3 11 7 14 5123850 Melanocortin MC4 8 3 6 3 -1 11 13 -3 0 0 5 2 2 0 -1 2123860 Melatonin MT1 12 6 -8 10 1 6 1 3 -9 1 0 4 -7 -7 -3 5123870 Monoamine Oxidase MAO-A 17 3 5 35 14 46 53 53 41 95 -14 25 -6 3 40 60112000 Dopamine D2L 12 11 5 -3 8 49 13 3 -1 4 11 10 49 -3 13 -1226700 Glucocorticoid 9 14 -1 13 97 48 96 14 71 4 17 41 33 20 65

Profile a number of compounds in the series in broad panels

Is the series promiscuous? Is it inherent in the series? Are these off-targets of concern for this project? Is there SAR emerging? How confident are we that we could design it out?

project specific

/SAR panel

Define project SecP follow up strategy

Which targets should be included in the panel? Minimum panel proposed


Bowes et al: Nature Rev. Drug Discov. 11:909-922 (2012) – Table 1

Which technology should be used? A combination of binding, enzyme and functional is optimal


Human targets vs Non-human targets Selective assays vs Non-selective assays Functional assays vs Binding assays Number of targets, data points vs Cost and Benefit Recombinant vs Native Internal vs External CRO

Radioligand binding assays Enzyme activity assays Quantitative affinity data

Cell-based assays Tissue bath assays

Mode of action information

How to design the optimal study

• Some Pharma initially run single concentrations and follow up actives with

IC50 determinations, others test all compounds with full concentration response curves

• 10 µM is a common single shot test concentration - It is important to screen at a concentration relevant to the predicted

therapeutic exposure • Anti-infectives can reach high µM free in plasma • Inhaled compounds by design have low exposure and free plasma level could

be low nM • Consider

• Solubility of the compound in the assay buffers • Protein binding in the assay buffers

• Consider running binding assays and functional assays in parallel to

maximise the sensitivity of the panel for detecting off target interactions

Technical Considerations



Primary target GPCR antagonist

GPCR antagonist GPCR antagonist

transporter binder GPCR antagonist NHR antagonist

GPCR antagonist GPCR antagonist

GPCR antagonost NHR binder

GPCR antagonist GPCR agonist

kinase inhibitor GPCR antagonist NHR binder Ion cannel blocker enzyme inhibitor Transport inhibitor

GPCR antagonist

1o target Potency

Human free Cmax Dog Cmax)

0.001 0.01 0.1 1 10 100 1000

Binding Ki or Enzyme or cell functionaI IC50 1o Potency

Human free Cmax

Margin (dog Cmax)

100-fold margin

Predicting chance of off target effects in Humans Occupancy and Effect

Case Study Examples


Example of Impact Reducing promiscuity increases success?


Bowes et al: Nature Rev. Drug Discov. 11:909-922 (2012) – Fig. 2

Example of Impact Influencing Chemical Design


Bowes et al: Nature Rev. Drug Discov. 11:909-922 (2012) Fig. 3 taken from Fryer et al JPET 340:492-500 (2012)

Example of Impact Integrated Risk Assessment


Bowes et al: Nature Rev. Drug Discov. 11:909-922 (2012) – Box 3

Example: Cardiac sodium channel (Nav1.5) inhibition resulting in a block in ventricular conductance and potentially serious arrhythmias

Summary

• A valuable tool in drug discovery and development - Early identification of off target pharmacological interactions that could cause

adverse drug reactions in man - Aids decision-making in Dx project teams - Part of integrated risk assessment before first time in humans - Used to understand molecular mechanisms driving in vivo effects

• Future challenges

In vitro pharmacological profiling


20 000 genes 350 assays

Novel Therapies

Translation Predictivity to human


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Acknowledgements

• Steven Whitebread • Jacques Hamon • Andrew Brown • Arun Sridhar • Wolfgang Jarolimek • Gareth Waldron • Duncan Armstrong • Mike Rolf • Lyn Rosenbrier Ribeiro • Chris Pollard • Jean-Pierre Valentin

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