Use of Informatics for Risk Assessments in 21st Century

Sandeep ModiNovember 2010

SEAC

Discovery Process : The challenges

Only 1-2 chemicals reach market out of hundreds of leads, which might come from thousands of chemicals synthesized. Currently the whole process may take about 14-15 years and ~ $800. million.

http://csdd.tufts.edu/

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in-vivo Decides

in-vitro Guides

in-silico Designs

Discovery / Role of Informatics in 20th Century

SEAC

Why use informatics tools?

HTS (“Fail fast, fail cheap”–new mantra for R&D)• need of decisions, more quickly

e.g. Library Design (can be done on virtual compds)

Need to do more than just screen molecules• need of understanding SAR relationships

e.g. how to “alter” undesirable properties

Commit to product

type

Commit to target

Tractablehit

Candidateselection

FTIM PoC

Target to lead

Gene-function-

targetassociation

FTIM to PoC Pre-clinical

/ Safety

Lead tocandidate

Target family

selection

Disease selection

Decision

points

Where in the discovery process informatics methods could be used?

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IdentifyProblem/disease

Isolate protein

Find compWhich binds target

Animal testing/Clinical Trials

How informatics can help in different steps for candidate selections

Genomics / Proteomics / Bioinformatics

Assay development, HTS screening, Analysis, Combinatorial chemistry / Libraries, Virtual screening

Structural BiologyXray structures,molecular modelling

In-vitro and in-silicoADMET models

PBPK modelling(Exposure / Populationdifferences)

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Genomics / Bioinformatics

● Genomics involves determining the entire DNA sequence of organisms and fine-scale genetic mapping efforts.

● Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques.– Sequence analysis– Genome annotation– Computational biology– Analysis of gene expression / regulation– Comparative genomics– Prediction of protein structures

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Known Structures (similar sequece to target) : MTIKEMPQPKTFGELKNLPL……

Unknown Structure (target) :MGLEALVPLAVIVAIFLLLV……..

Copy Conserved Region :

Add loops and calculate structure ofnon- conserved parts

Structural Biology (e.g. Homology Modelling)

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Structural Biology (in 21st Century)

21st Century:● We now have

access to more structures.

● And also computational methods are becoming better and more intelligent

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High throughput screening

● Assumptions– If we screen large no. of compounds, we will find right

chemicals– In-vitro data is good measure of reality

• We understand biology enough that hitting a given target will have desired effect on the disease.

● 20th Century– Far too many hits– False +ve rate due to expt errors / purity of sample– Bad ADMET profile (safety needs to be considered)

● 21st Century– Include safety in selection / screening.– Understanding of ADR.– Need to have smart screening instead of blind screening

• Use of informatics and QSAR models– Use of diverse library of compounds (diverse set)

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Combinatorial Chemistry : Chemical reactions in plate (Use of informatics approaches)

R3

R1

R2

R3

O

R1

R2

● Better design using ADMET / Safety considerations (coming later)

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Beside activity, it needs to be able to reach target, maintains its conc., doesn’t reaches toxicity levels & have no side effects

Balance of activity with safety (ADMET)

Good Potency towards desired TARGET

ABSORPTION (Gut-Blood)

DISTRIBUTION (Blood-Tissues)

METABOLISM (Enzymes)

EXCRETION (Urine, Bile, Faeces)

TOXICITY (Complex)

These issuesare importantfor allindustries

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Plasma Drug Concentrations Following Oral Dosing

1

10

100

1000

10000

0 4 8 12 16 20 24

Time (hours)

Toxicity

Activity

Need to be Safe, and also effective concentrations needs to be maintained in circulations

Depending on target/needs (e.g. infood or personal care Industrieswe may not like to have anyplasma levels.

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Therefore lots of efforts are going into in-silicomodelling in ADMET area

Reasons for termination of development of New Chemical Entities by 7 UK based companies

05

10152025303540

1991

2001

The continuing High Safety Failure, about 30%(Clinical Safety & Toxicology)

40% PK/Efficacy failure?

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QSAR

Experimental Data (E)

Description of Molecules (P1,P2...)

Statistical method Model e.g E=f(P1,P2...)

Validated

Released for useRefined basedon new data

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O

OH

NH

N2H

OO

OH

NH

NH

OH

OH

OH

propranololsalbutamol atenolol

LogP 0.11 -0.11 2.75PSA 80 93 43logD@pH7.4 -1.79 -2.21 0.59

Common Molecular Features

Property Salbutamol Atenolol PropranololClearance route renal/hepatic renal hepaticVd (lkg-1) 3.4 0.7 3Protein binding ~10% ~5% ~90%CNS penetration low low high

Different Properties

Descriptors: Relate Structure to Properties which can reflect expt data

QSAR

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Different Methods for Predictive Chemistry

SAR / alerts

● Simplest approach● Only works on +ves

QSAR

● Work equally on +ve & -ves● Can be a black box

Read across / kNN Prediction based on analogues from same chemical class with experimental data

● Can work on +ve & -ves● How to define “SIMILARITY”

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What is available currently ?

EnzymeInhibition1A2, 2D6, 2C9, 2C19, 3A4

Metabolic(P450 Mediated)

Biliary

Systemic Exposure Bioavailability

First Pass Met AbsorptionDistribution Clearance

PPB Vol Tissue(e.g CNS)

Renal Hepatic

Gut Stability

Solubility

Permeation

Drug-druginteractions

EnzymeInduction

Pgp (Transporters)PXR (induction)hERG (Tox)Genetic Tox hepatoTox

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It has now been possible to suggest changes for desired ADMET property

•Predicted as:• Pgp non-substrate • high brain penetration

N

O

F

NH

F

compA New Suggestion

•Pgp non-substrate•Low brain penetration

BB ratio of < 0.05:1 BB ratio of 1.8:1

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QSAR / Read-across in 21st Century

● Data availability and integration

● Role of integrated approaches

● Validation sets / models applicability domain

● Move away from black box methods

● Building on gaps in Models

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QSARsData and

Text Mining

StructureAlerts

BioinformaticsTools

Safety RiskAssessments

MetabolitesIn-vitroAssays

ADMETProfile

PhyschemProperties

HazardIdentification

HazardCharacterisation

QSAR / Read-across in 21st Century

ToxPathways Exposure

PKPDModelling

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0

10

20

30

40

50

60

70

80

90

100

3Q01 4Q01 1Q02 2Q02 3Q02

% cpds with poor AUC median AUC/20

0

10

20

30

40

50

60

70

80

90

2Q01 3Q01 4Q01 1Q02 2Q02 3Q02 4Q02 1Q03 2Q03

Time

% t

este

d low IC50

medium IC50

high IC50

Project1 (oral PK)

Time

Time

Project2 (CYP2C9)

Project3 (AUC)

0

5

10

15

20

25

30

35

40

45

Mar01–Jul01 Aug01–Nov01 Dec01-Jan02 Feb02–Mar02Date

Average AUC (rat po)

% of Cmpds. with AUC=0

0

0.2

0.4

0.6

0.8

1

1-2Q03 2-3Q03 3-4Q03 4Q03-1Q04 1Q04-2Q04

L

M

H

Project4 (CNS)

Time

Time

H

L

M

Application of informatics in 20th Century:1D approach

SEACCould also highlight the potential

problems at very early stage

Multi-optimisation (21st Century)

Solubility

Absorption

Metabolicstability

PotencySafetyX

Lead

XDrug

Property 1

Pro

per

ty 2

Skinpenetration

Reactivity

PeptideDepletion

SafetyDesiredEffect

A possible scenarioin case of consumerproducts

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Absorption

Solubility

Metabolicstability

Potency

Safety

X

X

Lead

Drug

Property 1

Pro

per

ty 2

Assessing the path of Lead Optimisation

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AUC

T1/2

CYP

Plasma Binding

Potency

Profile plot shows that the compounds with the highest scores have good properties for multiple endpoints

Ranking using Multi-optimisation

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Ability to visualize multiple databases

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Need for all steps to come together (21st Century)

IdentifyProblem/disease

Isolate protein

Find compWhich binds target

Safety RiskAssessments

Genomics / Proteomics / Bioinformatics

Assay development, HTS screening, Analysis, Combinatorial chemistry / Libraries, Virtual screening

Structural BiologyXray structures,molecular modelling

In-vitro and in-silicoADMET models

PBPK modelling(Exposure / Populationdifferences)

SEAC

Linking biology with chemistry

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Exposure

Internal Real Dose

Biologically Effective Dose

Early Biological Effects

Metabolites(Altered Structures)

Clinical Disease

Route / BioavailabilityPPB / Transporters

Exposure-Dose Response Paradigm

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Use of PBPK models

78k

77k 83c79k

76k

83c

82m

82m

81m

81m

80r75k

75k

83c

RESPONSEAPPLIED DOSE

BBDR MODELPBPK MODEL

Chemical Disposition (bodies effect on the chemical)

Information to Develop the PBPK Model • Target site (s) (organ, tissue, cell).• Chemical specific ADME rates.• Species specific parameter values (tissue

volumes, blood flow rates.• Which internal dose metric to use (based on

mode of action).

0.1

1

Biological Response (chemical’s effect on the body)

Information to Develop BBDR Model• Target site.• Adverse effect (what constitutes a significant

deviation from normal).• Mode of Action (i.e., key events leading to an

effect). • Best measure of effect (s).

INTERNAL DOSE AT TARGET (e.g., TISSUE, ORGAN)

0.1

1

Slide adopted from Kenyon et al, EPA

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MeS

O

S

N

O

Important structural features

Chemistry

Structural Biology

Linking biology/chemistry with other data

•Auruus•WOMBAT•GVKBio•DrugEBIlity (soon to be public)

Tox end PtQSAR

Target specificQSAR

X-ray/NMR Homology

Information

Biology Assays

Activity, e.g, pos/neg

Text/Data Mining

Exposure

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• Good/bad Chemical Features• Mechanism / mode of action• QSAR predictions

How Chemical is bound to Tox target Pathway Analysis

Chemical / Biological similarity

Linking biology/chemistry with other data

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GENETGENOMPROTEOMBIOINFORMATMEDINFORMATCHEMOGENOMCHEMOINFORMATPROTEOCHEMOMETR-

“-ics” – an old Latin suffix that means “way too much / organised knowledge”

-ICS

One of the challenges in 21st Century is how we convert this information rich –ICS technologies, to knowledge

Question AnswerProcess

Information

MethodsIn-slicoIn-vitroExpert Opinon

Knowledge

Information Rich “-ICS” approaches

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Need for Intelligent Information Harvesting

Integrated Information

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Safety

Integrated approach

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in-vivo Decides

in-vitro Guides

in-silico Designs

ADMET in 21st Century (where would like to be)

20th Century

Decides

21st Century

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● “Welcome in-silicoids to the ‘real world, real time zone’; get this right and do it now, and we’ll make you the President.”

And Finally - our challenge(Dennis Smith (Pfizer), DDT, 7, 2002, 1080-1081)

● “Hello…. I am from Insilico, take me to your President”

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Acknowledgements

“Part of Unilever’s ongoing effort to develop novel ways of delivering consumer safety”

● Andy White● Andrew Garrow● Michael Hughes● Yeyejide Adeleye● Matt Dent● Paul Carmichael● Jin Li● Carl Westmoreland● And other members of Unilever, SEAC