Label-free compound characterization in drug discovery and development

Applications from validation of hits to

selection and optimization of lead

compounds

2 / GE /

Which segments of the DD workflow?

Label-free compound characterization focused on applications in these two phases of the DD workflow

• What is most important during these stages?

CD2IN

Screen

to Hits

Hits to

Leads

Leads to

Optimiz . Leads

Leads to CD

selection

Primary

Screen

*L2CL2*LH2LS2HHTS

Investigational

new drug

Screen

to Hits

Hits to

Leads

Leads to

Optimiz . Leads

Leads to CD

selectionScreen

Investigational

new drug

INTERNAL USE SLIDE!!

affinity & kinetics

binding site data

early ADME data

kinetics

thermodynamics

selectivity

3 / GE /

Lead selection and optimization

”Hits to leads”: validated hits provide structural basis for selection of a small number of scaffolds (Impalcatura)

• Sub-libraries of related compounds generated from selected scaffolds (lead expansion)

• Can be difficult to distinguish between quality of multiple scaffolds

– requires detailed characterization of lead series (binding site info, affinity/kinetics, early

ADME)

“Leads to optimized leads”: extensive synthesis of hundreds of derivatives from lead scaffolds

• Aim to optimize target affinity/selectivity and ensure good PK properties

• Still a high failure rate of optimized leads in later development

– high quality characterization (kinetics, thermodynamics, selectivity) provides more

confident selection

INTERNAL USE SLIDE!!

4 / GE /

Recognized industry challenges

General problems for pharma industry

• Reduced productivity

• Increased costs

• Fewer new drugs to market

Challenges within lead selection area

• Current selection processes need to be improved

• Difficult to distinguish between quality of multiple scaffolds

5 / GE /

Label-free characterization of compound-target binding in lead selection/optimization

Increased productivity and reduced costs

• Comprehensive characterization of compounds during selection and optimization is vital to minimize downstream attrition of leads

More confident selection based on high information content data:

• Binding site information

• Kinetics and affinity

• Thermodynamics

• Binding selectivity

• Early in vitro ADME

efficacy/PD (Pharmacodynamics) issues

PK (Pharmacokinetics)/safety issues

Kinetic analysis of compound-target binding

7 / GE /

Kinetics in drug discovery and development

• Association (⇒ on-rate) and dissociation (⇒ off-rate ) are different processes that can be separately controlled

• Affinity depends on the ratio between on-rate and off-rate

– Steady-state data cannot discriminate between equal affinity compounds that may have

radically different binding characteristics

• Drug on- and off-rates may have different impacts on pharmacodynamics (PD) and pharmacokinetics (PK)

– Kinetic information can greatly faciltate selection and development of drug compounds

– Relevant for target binding in vivo (efficacy implications) and selectivity (safety implications)

8 / GE /

Equal affinity compounds with varying kinetics

• Example: 4 compounds:

– All 10 nM affinity – judged as ”equivalent” by equilibrium-based techniques

– On/off rates vary over 3 orders of magnitude – critical differences resolved by kinetics

kon koff

M-1s-1 s-1

106 10-2

105 10-3

104 10-4

103 10-5

Re

spo

nse

Time

Re

spo

nse

Time

Concentration = 100 nM Concentration = 1000 nM

*

**

*

– On-rate is concentration dependent: slow rates compensated by dose*

– Off-rate is concentration independent : compounds with fast off rates can

only be improved by chemical optimization

9 / GE /

Binding kinetics impact greatly on PK

2

0

100

4 6 8 h

% blocked target

0

Compound with rapid kinetics:- continuous supply needed to block target

- increasing concentration will not change profile much

- frequent adminstration of low doses required

Compound with slow kinetics:- will block a fraction of target sites long after injection

- increasing concentration will block a larger percentage of target sites for a long period

- infrequent adminstration of high doses required

This information is critical to therapeutic performance of drugs

Differences cannot be resolved using affinity alone!

10 / GE /

SAR/SKR/QSAR

• SAR = structure activity relationship

• Essential tool in lead optimization where chemists try to relate the ”activity” of compounds to their detailed structure

– activity usually assessed in terms of potency in an in vitro inhibition assay or cell-based

assay

INTERNAL USE SLIDE!!

• SAR analyses in Biacore terms usually means relating structural differences or modifications to target-binding kinetics

– sometimes referred to as ”structure-kinetic-relationships”, SKR

– high information content facilitates advanced analysis where mathematical descriptors

are applied to SAR data – potential for predictive modeling

= ”QSAR” (quantitative structure activity relationship)

11 / GE /

Kinetic characterization greatly facilitates SAR studies

SAR assessment based on equilibrium methods of potency may be impossible in some cases

• Dramatic structure-related changes in kinetics may cancel each other out at the affinity level (e.g. faster on but faster off)

Kinetics-based SAR enables focus on improvement of specific key binding properties

• For example, if off-rates are considered crucial for therapeutic performance, this can be focused on from the start

High resolution kinetic SAR data offers a powerful tool for selection of scaffolds and indentification of compounds for further development

12 / GE /

Kinetic analysis of compound-target binding

• Principle: monitor real-time binding curves over a range of compound concentrations

– Applicable to a wide range of target classes and over a broad kinetic range

-3

1

5

9

13

17

21

25

900 1100 1300 1500 1700 1900 2100

Time (s)

Res

po

nse

(R

U)

HIV protease

-2

-1

0

1

2

3

4

5

6

-100 -50 0 50 100 150 200

Fc=1 Spot=1-r corr

Estrogen receptor Albumin

• Curves from concentration series of each compound fitted to binding model

• Rate constants & affinity rapidly derived

– ka/kon : association (on) rate constant

– kd/koff : dissociation (off) rate constant

– KD : equilibrium dissociation constant (affinity)

13 / GE /

Resolution of kinetic data: on/off-rate maps

• Rapid assimilation of kinetic data sets for compound assessmet and selection is provided by on/off-rate maps

– log scale plot of association rate constant versus dissociation rate constant - diagonals

show affinities

Re

co

gn

itio

n

Stabilitylog kd

affinity isotherms

2 compounds with identical affinity

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800But very different kinetics!

14 / GE /

Kinetic data facilitate better understanding of drug actions

The use of HIV-1 protease inhibitors in anti-HIV therapy is limited by rapid viral mutation leading to drug resistance

• How do mutations in the HIV protease affect drug action?

Shuman et al., Antiviral Research 58: 235-42 (2003)

Reduction in affinity (related to number of accumulated

target mutations) is driven by faster off-rates

15 / GE /

A critical role for kinetics in selection of a lead series for CD80 inhibitors

Avidex Ltd had 5 lead series of compounds in a rheumatoid arthritis project

• Inhibitors of CD80/CD28 signalling in T-cell activation (target = CD80)

• Label-free kinetic characterization of 259 compounds

• 5 structurally related lead series A-E

Data presented as an on/off plot for all compounds

• Excellent overview of lead series characteristics

• Enables identification of specific compounds of particular interest

Data courtesy of Avidex Ltd

16 / GE /

slower off

fast

er

on

higher affinity

Critical selection criteria provided by kinetic characterization

critical kinetic resolution: moderate affinity but very slow dissociation for group E

Data courtesy of Avidex Ltd

Affinity improvement going from group A to D

17 / GE /

Lead series with highly favored properties “rescued” by kinetic resolution

Lead series E showed no improvement over series A-D based on affinity for CD80

• Avidex would have discarded series E based on this assessment

Label-free kinetic characterization revealed very slow off-rates for series E compounds

• Highly valuable property for desired PD profile of drug

• Chemical optimization can be focused on improving on-rates

Series E was kept in the development process on basis of this off rate data

Data courtesy of Avidex Ltd

18 / GE /

Binding selectivity and kinetics in validation of DPP-IV lead compound series

Study at Roche (Basel) to characterize DPP-IV inhibitor lead series during hit expansion process

• 160 compounds characterized by label-free analysis

*Huber, W. (2005) J. Mol. Recog. 18, 273-281

Binding site selectivity – wild type and blocked binding site DPP-IV targets assayed in parallel

• Blocking reaction carried out on immobilized protein within the instrument

High resolution kinetic analysis of active site binders

• To distinguish between lead scaffold properties and make selection decisions

19 / GE /

-428

14202632384450

-100 200 500 800 1100 1400 1700 2000

-428

14202632384450

-100 200 500 800 110014001700 2000

125.0 nM62.5 nM31.2 nM15.6 nM

7.8 nMCurve Fit

wt DPP-IV Blocked DPP-IV

Time (s) Time (s)

Res

pons

e (R

U)

Res

pons

e (R

U)

-428

14202632384450

-100 200 500 800 1100 1400 1700 2000

-428

14202632384450

-100 200 500 800 110014001700 2000

125.0 nM62.5 nM31.2 nM15.6 nM

7.8 nMCurve Fit

wt DPP-IV Blocked DPP-IV

Time (s) Time (s)

Res

pons

e (R

U)

Res

pons

e (R

U)

Example data - concentration series of one compound analyzed for both targets:

Parallel analysis of target variants clearly identifies active site binders

Targets: 1) wild-type PPV-IV, 2) blocked PPV-IV with phosphinate ester bound to serine in active site

no binding to blocked target

high quality kinetic data from selective active site binder

*Huber, W. (2005) J. Mol. Recog. 18, 273-281

20 / GE /

Kinetic analysis facilitates selection of lead series for PPV-IV

• Compounds cluster into three distinct regions on kinetic map

– correlate well with 3 structural classes

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0-1

0

1

2

3

4

5

6

7

log(off-rate)

log

(on

-ra

te)

1 nM 10 nM 100 nM

1 µM

10 µM

1 mM

100 µM

KD

•

• Class II

• fast on/fast off

• potential for optimization by reducing off-rates

• Class I

• fast on/slow off

• best potential for drug candidates

• Class III

• very slow on/slow off

• excluded from project

Adapted from Huber, W. (2005) J. Mol. Recog. 18, 273-281

21 / GE /

Kinetics enables discrimination between compounds/scaffolds of similar affinity

Two compounds from mapping study exemplify resolving power of kinetic analysis

• Different structural scaffolds with

totally different binding properties

• Would be judged as ”equivalent”

using equibrium-based methods

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.50

-1

0

1

2

3

4

5

6

7

log(off-rate)

log

(on

-ra

te)

1 nM 10 nM 100 nM

1 µM

10 µM

1 mM

100 µM

KD

• Affinities ~2-fold difference

• Compound 2 has ~100-fold faster on & off rates

(KD 200 nM)

(KD 400 nM)

1

2

Adapted from Huber, W. (2005) J. Mol. Recog. 18, 273-281

22 / GE /

Differential effects of side-chain modifications only resolved by kinetic SAR

B408

OHA016

A015

B376

B322Br

S N

B440

A038

O

HO

N

B429

P1

H

NN

H

H

NN

HO

O O H

O HO

O

O

O

B268

P2

HIV-1 protease inhibitor: 2 side-chains modified

Kinetic map

P1 expansion reduces off-rates

P2 expansion increases off-rates

Some major differences would be missed using affinity-based methods*

*

*

*

23 / GE /

Kinetic SAR for evaluation of alternative optimization strategies

Kinetic characterization of steroid compound derivatives binding to estrogen receptor – SAR alternative 1:

This group may not be optimal for targeted modifications

Data courtesy of GlaxoSmithKline, NC, USA

Modifications reduce both on & off rates

Effects balance out in 1B (similar affinity)

Effects on on-rate dominate for 1C & 1D -significant reduction in affinity

24 / GE /

Kinetic SAR for evaluation of alternative optimization strategies

Kinetic characterization of steroid compound derivatives binding to estrogen receptor – SAR alternative 2:

Addition of oxygen to these positions is a useful approach to increase

compound affinity via improved association characteristics

Modifications mainly increase on-rates

Changes for off-rates are relatively smaller

Overall effect of is to increase affinity

Data courtesy of GlaxoSmithKline, NC, USA

25 / GE /

Label-free thermodynamic analysis in lead optimization

• Label-free characterization of compound-target interactions can also be extended to include thermodynamic analysis

– influence of temperature on binding kinetics

• Provides high resolution differentiation among lead compounds and insights into binding mechanims

• Biacore systems provide high quality thermodynamic data with much lower protein consumption that alternative methods

– e.g. compared to ITC

26 / GE /

Fre

e e

ne

rgy

(G

)

∆G(∝1/KD)

Kinetics-based thermodynamics provides higher resolution data (1)

∆Gon

(∝1/kon)∆Goff

(∝1/koff)

Kinetics resolves free energies

separately for binding and dissociation

Interaction coordinate

∆G = ∆H -T∆S

∆G depends on changes in both enthalpy (H) and entropy (S)

∆G

∆H

-T∆S

A ∆G value can result from different combinations of enthalpy and entropy

Label-free characterization

resolves these differences

27 / GE /

Fre

e e

ne

rgy

(G

)

∆G(∝1/KD)

Kinetics-based thermodynamics provides higher resolution data (2)

∆Gon

(∝1/kon)∆Goff

(∝1/koff)

Kinetics resolves free energies

separately for binding and dissociation

Interaction coordinate

∆G = ∆H -T∆S

∆G depends on changes in both enthalpy (H) and entropy (S)

∆G

∆H

-T∆S

A ∆G value can result from different combinations of enthalpy and entropy

Label-free characterization

resolves these differences

D+T

DT*

DTD = drugT = target

28 / GE /

Thermodynamic analysis of HIV-1 protease inhibitors

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-100 -40 20 80 140 200 260 320 380 440 500

Time (s)

No

rma

lize

d r

esp

on

se (R

U)

saquinavir5°C

35°C5°C

35°C

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-100 -40 20 80 140 200 260 320 380 440 500

Time (s)

No

rma

lize

d r

esp

on

se (R

U)

lopinavir

Shuman. et al (2004) JMR

5°C

35°C5°C

35°C

• Association and dissociation rates increased with temperature

– Analyzed from 5 to 35°C

Temperature effect on interactions varied significantly among inhibitors

29 / GE /

Thermodynamics resolves inhibitor differences not seen using single temperature kinetics (1)

• Affinity increases with temp, i.eon-rates increase more than off-rate

• Lopinavir shows very small temp effect

• Amprenavir, ritonavir and indinavir show nonlinear temp effects

• Atanzanavir forms an extremely stable complex below 25oC

Shuman. et al (2004) JMR

2

2

1

1

30 / GE /

∆G = ∆H -T∆S

Thermodynamics resolves inhibitor differences not seen using single temperature kinetics (2)

Plots of ∆G and enthalpy/entropy characteristics for HIV protease inhibitors at equilibrium and during association:

Equilibrium ∆Gs very similar among different inhibitors

Lopinavir has a different ∆Gon: label-free kinetic characterization indentifies this unique binding property

Shuman. et al (2004) JMR

Binding site analysis

32 / GE /

Label-free analysis offers multiple approaches to binding site studies

In many screening approaches, analysis or confirmation of compound binding site in relation to target is important

Label-free compound characterization can be applied in a number of ways to address this issue:

• Use of site-directed mutations for parallel selectivity analysis

• Comparative binding to active/inactive forms of target

• Inhibition-type assays (e.g for allosteric site binders)

• Competitive kinetic analysis of inhibitor and natural substrates

33 / GE /

Binding site mutants validate in silico pre-selection of libraries

• Roche: selected 250 compounds based on structural data

– represented 10 different structural classes

– binding to wild-type and binding site mutant targets assayed in parallel

*Huber, W. (2005) J. Mol. Recog. 18, 273-281

sensor surface

wild-type HPPK binding site mutant

compound

binding?

identifies site-specific bindersidentifies all binders

34 / GE /

Parallel analysis of binding site mutant provides critical information for selection

Compound analysis based directly on binding site selectivity

5

25

45

65

Resp

on

se (R

U)

Compound

1 1514139 10 11 125 6 7 832 4 16

a)

Binding responses to both proteins

wild-typemutant

0

5

10

15

20

Compound

1 1514139 10 11 125 6 7 832 4 16

b)

Subtracted responses (wt-mutant)

• Highest responder (#5) revealed as worthless by binding site mutant

• Moderate responder (#14) revealed as best specific compound in set

35 / GE /

Binding selectivity to differentially phosphorylated kinase targets

Kinetic analysis of ATP and inhibitor binding to different forms of p38αactive p38α inactive p38α

inhibitor

(0.01-0.25 µM)• binds to both forms

• readily visible dissociation

ATP

(3-100 µM)

• specific for active form

• rapid ”square wave” kinetics

Label-free characterization shows key differences in both binding preferences and kinetics

36 / GE /

Label-free competition assays validate binding sites for hit compounds

• Do hit compounds really bind to the desired allosteric site in a specific target subunit, and not to a related site in another subunit?

– label-free competitive binding assay desiged to answer this question

Data courtesy of Hoffman La-Roche

immobilized HTPtest compound in solution

reference binder in solution

(saturation concentration)

37 / GE /

Distinguishing true allosteric site binders

Allosteric site binders Binders to other sites

Allosteric binders showed activity in cell assays

competitive binding: same site

responses < additive

non-competitive binding: different sites

responses are additive

180 & 203 bind allosteric site 165 does not bind allosteric site

Data courtesy of Hoffman La-Roche

38 / GE /

Kinetic profile-based competitive assays provide binding site information

An option when binders exhibit clearly distinct kinetics for the target

• Example – 4 inihibitors and ATP binding to a protein kinase target*

* all Schering AG proprietary compounds

focus on ATP and inihibitors A & B

- clearly distinct profiles

39 / GE /

Kinetic analysis defines binding site specificity (1)

Important to determine if inhibitors target ATP-binding site on kinase

• Competitive assay - increasing amounts of ATP added to a constant concentration of inhibitor A

Indicates that inhibitor A & ATP compete for same or overlapping sites

More ATP → faster“ATP-like”dissociation profile

40 / GE /

Kinetic analysis defines binding site specificity (2)

Similar approach taken to see if inhibitors A & B had the same binding site on the kinase:

Indicate that inhibitors A & B (and therefore ATP) compete for same or overlapping sites

More B → faster“B-like”dissociation profile

ADME assays: (short version)

42 / GE /

Label-free characterization of early ADME properties

• Poor pharmacokinetic profiles remain a significant cause of compound failures during drug development

– Earlier assessment of ADME properties offers more efficient compound selection and optimization

• Label-free compound screening and characterization can readily accommodate ADME-related analyses:

• Binding to multiple plasma proteins

– Can measure ”problem” compounds with high affinity for plasma proteins

– Risk assessment of plasma binding properties in relation to target affinity

– Comparison of plasma proteins from different species – animal model issues

• Binding to liposomes of different compositions

– indications for Fraction absorbed (Fa)

43 / GE /

Binding to plasma proteins identifies potential risks for drug development

S6

Pyrimetamine

S7

S8

Quinine

S3 S5

S4 Warfarin

Naproxen

S1

S2

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60

Response - HSA (100*RU/Da)

Resp

on

se –

AG

P (100*R

U/D

a)

Problems expected? - depends on affinity of

drug for target in relation to plasma proteins

Simultaneous analysis of compound binding to major human serum proteins, HSA and AGP

highest risk compound – unlikely

candidate for further development

44 / GE /

Liposome assays have predictive value for ADME properties

Fa ranking example using two types of liposome

2D plot of binding responses to 2 liposomes

Compounds with low, medium and high Fa values from literature cluster distinctly into corresponding areas of liposome plot

- set includes deviating compounds with known active transport mechanisms

Compounds with clear preference for one type of liposome

45 / GE /

-5

0

5

10

15

20

-60 -10 40 90 140

Kinetics: Fc=2 Spot=1-r corr

Res

po

nse

RU

Time s

K4

-2

0

2

4

6

8

10

12

14

16

-60 -10 40 90 140

Kinetics: Fc=2 Spot=1-r corr

Res

po

nse

RU

Time s

K9

-5

0

5

10

15

20

-60 -10 40 90 140

Kinetics: Fc=2 Spot=1-r corr

Res

po

nse

RU

Time s

K3

-2

0

2

4

6

8

10

12

-60 -10 40 90 140

Kinetics: Fc=1 Spot=1-r corr

Res

po

nse

RU

Time s

K5

-4

-2

0

2

4

6

8

10

12

-100 0 100 200 300 400 500

Kinetics: Fc=1 Spot=1-r corr

Res

po

nse

RU

Time s

K10

-20s

-50510152025

-20 0 20 40 60 80 100 120

RU

K2

-5

0

5

10

15

20

25

30

-20 0 20 40 60 80 100

Kinetics: Fc=1 Spot=1-r corr

Res

po

nse

RU

Time s

K7

-60 -10 40 90 140s

-5051015202530

-60 -10 40 90 140

RU

s

K11

K8

-5

0

5

10

15

20

25

30

-50 0 50 100 150 200 250 300

Kinetics: Fc=2 Spot=1-r corr

Res

pons

e

RU

Time s

K1

High plasma protein binding

Low liposome binding

Low plasma protein binding

-1

0

1

2

3

4

5

6

7

0 1e-6 2e-6 3e-6 4e-6 5e-6 6e-6 7e-6

Res

po

nse

RU

Conc M

Steady State: Fc=2 Spot=1-r corr

K6

K1, K7 & K8 show poor ADME properties

K2, K3, K10 & K11 show promising profiles

√√√√

√√√√

√√√√

√√√√

K4, K5, K6 & K9 dissociate rapidly

Combined target binding/ADME profilingReducing the risks of future PK failures for selected compounds

Kinase inhibitors: kinetic characterization of target binding combined with plasma protein and liposome binding data

46 / GE /

ADME assays - summary

Rapid, simple and reliable label-free measurement of ADME-related properties early during drug discovery

• plasma protein binding

• liposome binding

Real-time interaction analysis

• readily distinguish specific binding events

• discriminate among even high-affinity binders via dissociation signals

47 / GE /

ADME assays - summary

Multiple targets addressed simultaneously

• Higher information content for better decisions

Additional assay benefits

• Low compound consumptionThroughput tailored to specific application needs

48 / GE /

Summary: Label-free compound characterization for more efficient lead selection/optimization

Confident decision making based on high information content, real-time data:

• Binding selectivity

• Binding kinetics and affinity

• Binding site information

• Early in vitro ADME

Minimize downstream attrition of leads through better-informed selection based on high quality, information-rich data