Jérôme and Raffaele, living with epilepsy

Implementation of multi-criteria decision making (MCDM) tools in early drug discovery processes

M. Ledecq

UCB Pharma

ACS spring meeting 2013

New Orleans

The "Many-Many" Challenge in Pharma

Find the compounds in a large multidimensional chemical space

… that maximize chances of future...

... efficacy and druggability (affinity, selectivity, toxicity, ADME, PK, etc.)

Under constraints (time, resources, budget)

Multi Criteria Decision Making

Implement new drug design tools

better extract knowledge from the generated data

Increasing amount of different data generated

Data Generation vs Data Use

Aim to capitalize on data to drive design/address issues How to reduce data to a granularity level to enable effective decision making?

Agenda

• MCDM in DMTA cycles • Derringer functions and Pareto front • Data analysis workflow • Examples in drug design • Conclusion

Agenda

Improving the drug design process

Use MCDM Tools

- Desirability Scores - Pareto Front

To be used on experimental and in silico data!

Make

Test

Design

Analyze

Objective:

Accelerate the drug discovery by

- Objectively following the progression of the projects

- Identifying chemistries with an optimal balance of properties

- Identifying earlier liabilities

- Stopping more rapidly series with non-optimizable liabilities

- Exploring more efficiently the chemical space diversity

Hit ID Lead Generation Lead Optimization CS

Implementing it in a continuous process: iterative DMTA cycles

Key success factors

Covering the targeted chemical space

• Regions of interest where the properties are optimizable

• Regions to avoid

• Unexplored regions to gain knowledge

Taking into account variability • To enhance our confidence in

the decision making process

Desirability scores

Desirability scores (DS): Derringer’s functions

Scores can be calculated for predicted/calculated properties as well as for experimental properties

Vectors all go in the same direction, on the same scale

Hard cut-off are avoided

Combination of the normalized responses in a global desirability score (desirability profile) can be done by applying:

• Arithmetic/geometric mean

• Probabilistic scoring

MW desirable values

undesirable values

1

0

D

350 500

Score MW = 0.8

• Normalisation of the response values

• Any math function can be applied between highest and lowest scores

Pareto Front

Pareto Front

Pareto Front vs hard cutoffs

1

0 1

propA

propB

« A compound is Pareto optimal if there is no other compound that is better in any response »

Property A

Pro

pert

y B

No compound is left by rules

Property A

Pro

pert

y B

Too much compounds left

x z

Property A

Pro

pert

y B

x z

Best for A & B: # of cpds increase

with # criteria

Best for A

Best for B

• To identify compounds with best trade-off of liabilities

• To follow the series evolution

• To do multi-objective optimization

How to extract maximum knowledge from available data?

Data access is not trivial: several protocols for one property, non alphanumerical properties, variability of the assays not always easily accessible, etc.

Derringer functions definition for each key data

Data variability incorporation

• For each experimental protocol/in silico model considered

Stardrop/Spotfire integration

Stardrop TIBCO Spotfire

Preformated data for desirability scoring

Desirability profiles via PLP protocols dev at Optibrium

Data access, treatment & analysis

Probabilistic Desirability scoring

Copy/paste

Several steps needed to allow MCDM

Example 1: Hit ID phase

Project in hit ID phase exploration of several series around HTS

hits

Screening cascade 1st focus on:

• Potency & selectivity Biological profile

• Lipophilicity & solubility Physico-chemical profile

Compounds ranking: Biological profile

Compounds significantly different from inactive cpds no series3 cpds

series1

Series2

Series3

undefined

Series1 Series2 Series3

Taking into account the variability of data can give you more confidence in the decision!

pIC50 : mean sd = 0.3

Selectivity : mean sd = 0.4

5 6.5

1

0.2

0.5 1

1

0.2

Series1 Series2 Series3

Biol_profile

Ph

ysC

hem

_p

ro

file

Pareto Front: biological profile vs physchem profile

is it possible to optimize physchem & biology?

MW

microsol: mean sd = 10

350 500

1

0.2

10 100

1

0.2

CHI: mean sd = 2

0 40 80 120

1

0.2

Radar plot including DS for calculated properties

series1

series2 series3

0

0.2

0.4

0.6

0.8

1 chiral

HBD

sp3ratio

PSA

rotB

MW

lipCHI

microsol

selectivity

potency

median

q1

q3

0

0.2

0.4

0.6

0.8

1 chiral

HBD

sp3ratio

PSA

rotB

MW

lipCHI

microsol

selectivity

potency

0

0.2

0.4

0.6

0.8

1 chiral

HBD

sp3ratio

PSA

rotB

MW

lipCHI

microsol

selectivity

potency

Chemical space analysis: colored by series

Series 1

Series 3

Series 2

Dot size: biological profile score Low High

Chemical space analysis: colored by physchem scores

Series 1

Series 3

Series 2

Dot size: biological profile score Physchem profile score

Low High

Maybe a region to further explore?

Example 2: Transition in Lead Generation phase

Go/noGo decision for a transition in LG phase

Challenges: Narrow chemical space

Very low hit rate – slow progression

Conflicting vectors: biological activity vs lipophilicity

Focus on:

• Potency & efficacy Biological profile

• Lipophilicity & MW Physico-chemical profile

Pareto Front progression B

iol_

pro

file

PhysChem_profile

Cycle 1

hits Biol_profile: pEC50 & Erel Physchem profile: lip_CHI & MW

Potency: pEC50

Efficacy: Erel

5 6.5

0.2

0.2 0.5

1

0.2

1

MW

350 500

1

0.2

CHI

0 40 80 120

1

0.2

Bio

l_p

ro

file

Cycle 1

Cycle 2 hits Biol_profile: pEC50 & Erel Physchem profile: lip_CHI & MW

Pareto Front progression

PhysChem_profile

Bio

l_p

ro

file

Cycle 1

Cycle 2

Cycle 3 hits

Biol_profile: pEC50 & Erel Physchem profile: lip_CHI & MW

Pareto Front progression

PhysChem_profile

No more progression!

Bio

l_p

ro

file

Cycle 1

Cycle 2

Cycle 3

Cycle 4

hits Biol_profile: pEC50 & Erel Physchem profile: lip_CHI & MW

Pareto Front progression

PhysChem_profile

Project progression: optimizing conflicting vectors

Cycle 1 Cycle 2a Cycle 2b Cycle 3 Cycle 4

Biol profile (pEC50 + Erel)

Global profile

PhysChem profile (lip_CHI + MW)

No more improvement

Chemical plan to improve phys chem properties

Can easily be implemented in an automated process to follow project progression

Stardrop TIBCO Spotfire

Desirability profiles via PLP protocols dev at Optibrium

Data access, treatment & analysis

Probabilistic desirability scoring

Conclusions

Balancing properties is at the center of the drug design process

use of MCDM tools to enhance our chance of success

Key success factors

• Data access/ease of use

• Diversity

• Uncertainty

• Continuous/dynamic process

Some parameters are difficult to treat with desirability scores (in vivo exp., chemical tractability, IP space, etc…) but have to be considered to take enlightened decision

Good desirability profiles are not sufficient to ensure success: to be combined with all other drug design tools!

23

Acknowledgements:

Chemistry

• Joël Mercier

• Jag Heer

• Florence Lebon

Informatics

• Daniel Smaragd

CNS research

• Etienne Hanon

Optibrium

• Matthew Segall

Questions?

Back-up slides

Desirability scores

Desirability scores: Derringer’s functions

Arithmetic vs geometric mean

scoreBselectscoreAselectcombi __

MW desirable values

undesirable values

1

0

D

350 500

Score MW = 0.8

One score for each property (experimental or calculated) :

combination of them geometric mean

• To rank compounds

• To follow the series evolution

• To compare properties

Variable A

Vari

ab

le B

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Z = 0.1

Z = 0.2

Z = 0.3

Z = 0.4

Z = 0.5

Z = 0.6

Z = 0.7

Z = 0.8

Z = 0.9

Variable A

Vari

ab

le B

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Z = 0.1

Z = 0.2

Z = 0.3

Z = 0.4

Z = 0.5

Z = 0.6

Z = 0.7

Z = 0.8

Z = 0.9

• Give a broader distribution of scores: better discrimination • Penalize compounds with poor values in a response: quick alert! • Better to compare series of cpds • Ranking of cpds within a serie give similar results

Derringer’s functions (in silico properties)

Derringer’s functions to derive the desirability scores for

calculated/predicted in silico properties

A

b

a

d

MW

350 500

PSA(NO)

1

3 30 90 125

#HBD

1

2 4

#C*

1

2 4

clogD

1

-0.5 1 3 4.5

#RotB

1

0 12 1 6

1

Fsp3

0.1 0.4

1

Properties

Phys Chem

Derringer’s functions protocols Treatment of non

alphanumerical values

logD

LOGD_pH7.4_Octanol >4 DS=0.1

<0.01 DS=0.1

CHI

CHI_C18_PH7

Log Solubility

Sol_pH7.4_AQ_24h log

transf of the result value

Sol >1.5 DS=1

Sol<0.0005 DS=0.1

Nephelometry No alphanumerical value Sol_PH7.4_NEPH_1mM

And

Sol_PH7.4_NEPH_0.1mM

Sol_pH7.4_NEPH

0,1 mM 1 mM DS

Soluble Soluble 1

Soluble Insoluble 0.75

Insoluble Insoluble 0.1

pKa MAX EXP(pKa1,2,3) (basic

cpds)

>10 DS=0.1

<8 DS=1

Definition of generic Derringer’s functions

1

0 1 2.5 4

1

8 10

1

0 40 80 120

1

-3 -1

Definition of generic Derringer’s functions

Properties

DMPK

Derringer’s functions protocols Treatment of non

alphanumerical values

Clearance

usome_Hum

(µl/min/mg Prot)

CLEARANCE_MICR_Human

Clearance usome_Rat

(µl/min/mg Prot)

CLEARANCE_MICR_Rat

Clearance hep_rat

(µl/min/106cells)

Direct scoring

X<10 DS=1

10<x<35 DS=0.8

X>35 DS=0.5

CLEARANCE_Hep_Rat

Caco2_Papp_AB

(nm/s)

PERM_CACO2_Cerep Papp

A-B

Caco2_ER PERM_CACO2_Cerep ER

hPPB (Fu)(%) PB_PL_Human (Fu)

1

0.1

15 45

1

0.1

25 100

1

0.1

30 300 1

0.1

1.1 3

1

1 5

0.1

Definition of generic Derringer’s functions

Properties

Tox

Derringer’s functions protocols Treatment of non

alphanumerical values

hERG

(IC50, µM)

HERG_001

HERG_002

HERG_003

>30, >100, inactive DS=1

<0.1 DS=0.1

Genotox

(HGS +/- S9)

No alphanumerical value GS+S9_HUM_001

GS+S9_HUM_001

+ (in at least 1 of both assays)

n DS=0

- (in both assays) DS=1

1

5 30

0.1

Ex1 Library design: Full library enumeration #

CNS MPO score

Final library

selection

Low High

Ex1 Library design: evaluation

inh >50%

inh <50%

Ex1 Library design: Second cycle

Active 1st cycle

2d cycle selection

34

Ex2 Pareto Front progression by series

Bio

l_p

ro

file

PhysChem_profile

cycle1

cycle2

cycle3

cycle4

hits Biol_profile: pEC50 & Erel Physchem profile: lip_CHI & MW

STOP

STOP STOP

STOP

35

Ex3 Comparison of optimization potential of 2 lead series: Diversity selection

Global chemical space of Series 1, 2 & 3

Chemical space clustered for each series

15-20 cpds selected to cover the various clusters :

• Availability, binding, solubility

Series 3

Series 1 Series 2 Series 3

36

Ex3 Comparison of optimization potential of 2 lead series: Ranking of compounds

Binding:

• Good affinity & selectivity with both series

Physchem:

• Better physchem profile for series 1

In vitro DMPK:

• Better DMPK profile for Series 3

• Overall very low desirability for series 1

• Major difference between both series

Global Desirability profile:

• High scores achievable with both series

• Slight trend for an overall better desirability with series 3

37

Ex3 Comparison of optimization potential of 2 lead series: Profiles of individual properties

Overall desirability quite similar

Major weakness of series 3 is high MW • High impact on overall physchem

profile

Major weaknesses of series 1 are low solubility and high µsomes clearance

Decision: • Provided that a strategy is identified

to fix the issues of series 1,

progressing both series will mitigate the risk associated with the physchem profile of series 3.

Radar plot Series 1 vs Series 3

0.00

0.20

0.40

0.60

0.80

1.00

M.W.

LE

LogD

Sol. (neph)

pKi Aff inity

Full Select.

mµsomes Cl

hµsomes ClrHep. Cl

CYP450s

Caco-2 (Papp A-B)

hERG inhib

Genotox

Cytotox

PLD

Desirability scores Series 1 mean

Desirability scores Series 3 mean

Example 3: Transition in Lead Optimization phase

LG to LO transition: assessing the optimization potential of 2 lead series

Increased number of transition criteria :

• Physico-chemical parameters (MW, lipophilicity, solubility, …)

• Binding affinity, selectivity

• In vitro DMPK (clearance, CYP450 inhibition, membrane permeability, …)

• In vitro toxicology (genotoxicity, cytotoxicity, hERG, …)

• Other criteria that cannot be scored (in vivo activity, in vivo DMPK, SAR understanding, IP, synthetic accessibility, …)

Multi-variable analysis have to be performed

Multi Variate Analysis: covering the chemical space

Diversity selection: Chemical space generation

Liquid/solid stock availabilities Existing knowledge on properties

Full experimental profiling Binding & selectivity Phys Chem DMPK Tox

Id pKb sel logD hERG

UCB001 8.7 1.7 0.7 >30

UCB002 8.6 0.0 0.3 >30

UCB003 10.0 1.8 1.1 1.4

UCB004 7.2 2.1 1.4 13.5

UCB005 <6.5 1.6 1.1 1.7

Id pKb sel logD hERG mean

UCB001 1.0 1.0 0.7 1.0 0.9

UCB002 1.0 0.1 0.3 1.0 0.4

UCB003 1.0 1.0 1.0 0.1 0.6

UCB004 0.8 1.0 1.0 0.6 0.8

UCB005 0.1 1.0 1.0 0.1 0.3

Series comparison Global scoring/Pareto Front to rank cpds and follow progression Heat map Radar plot Hierarchical clustering

DS generation

0.0

0.2

0.4

0.6

0.8

1.0

hep_ra

cyp1a2

cyp2c8

cyp2c9

cyp2c19

cyp2d6

cyp3a4

caco2_d

cytotox

pld

genotox

pki_d

pkb_d

sel_d

mw_d

lig_effi_d

lipo_effi_d

usome_hu_d

usome_mo_d

herg_d

q1

q3

Make

Test

Design

Analyse

Hierarchical clustering to address liabilities

Hierarchical clustering of data from desirability scores

• Hierarchy identify related properties

• Hierarchy may relate unrelated structures

provokes ideas to address liabilities

co

mp

ou

nd

s

New logD DS

Hierarchical clustering to address liabilities

logD 1

0.1

0 1 2.5 4

logD 1

0.1

-0.5 0 1.5 2

Identifying liabilities: microsomes clearances

Adaptation of the logD Derringer function to focus the design on less lipophilic compounds

DMPK optimization

First LO cycle:

• logD in accordance with the

design hypothesis

• drastic improvement of the in

vitro clearance

Entry in LO

2d LO cycle

Second LO cycle:

• refinement of logD range

• soft points protection

• compounds with improved in

vivo PK half-life (from 0.2h to

1.2h).

Time

Exp logD

logD_DS

µsome_Cl_DS

43

Desirability scores in spotfire: Clustered profile charts

Profile chart display profiles of compounds from property clusters

Compounds’ evolution between related clusters

Does the best property cluster correlate with a specific chemotype ?

hCl hCl hCl hCl pKi pKi pKi pKi Sel Sel Sel Sel

44

29 April 2013

Desirability scores in spotfire: Property vs chemical clustering

Property clusters relate unrelated chemical clusters

Identification of chemical clusters enriched with compounds having the best properties

e.g. suggests further exploration of chemotypes

of clusters 4 & 7

Identification of chemotypes with poor property profile

e.g. chemotype of cluster 9

Pfizer analysis: CNS MPO score

UCB in silico desirability profile

• DS defined for MW, PSA, clogD, #HBD, #rotB, #C*, sp3 ratio

• Used for the synthesis prioritization, selection of compounds after a

virtual screening, characterization of HTS deck, library design, etc.

Wager et al, ACS Chem. Neurosci. (2010), 1, 435-449

Desirability scores