CZ3253: Computer Aided Drug designCZ3253: Computer Aided Drug design

Lecture 6: QSAR part II Lecture 6: QSAR part II

Prof. Chen Yu ZongProf. Chen Yu Zong

Tel: 6874-6877Tel: 6874-6877Email: Email: csccyz@nus.edu.sgcsccyz@nus.edu.sghttp://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg

Room 07-24, level 7, SOC1, Room 07-24, level 7, SOC1, National University of SingaporeNational University of Singapore

22

Examples of QSAR ApplicationsExamples of QSAR Applications::

Application of Application of in silico in silico technology to technology to screen out potentially toxic compounds screen out potentially toxic compounds using expert and QSAR modelsusing expert and QSAR models

33

Commercial SoftwareCommercial Software

Commercially available toxicity estimation packages are available to predict a variety of toxic endpoints including mutagenicity, carcinogenicity, teratogenicity, skin and eye irritation and acute toxicity:

• DEREK (Deductive Estimation of Risk from Existing Knowledge)- www.chem.leeds.ac.uk/luk

• HazardExpert – www.compudrug.com/hazard

• CASE (Computer Automated Structure Evaluation) – www.multicase.com

• TOPKAT (Toxicity Prediction by Computer Assisted Technology) – www.accelrys.com/products/topkat

• OncoLogic – www.logichem.com

44

Pharma AlgorithmsPharma Algorithms

N10,00022,000

8,00020,000

5,5001,000

5001,000

90036,000

...

Log PDMSO Solubility

pKaStability at pH < 2Aqueous SolubilityPermeability (HIA)

Active TransportPgp Transport

Oral Bioavailability (Human)LD50 Intraperitoneal

...

Providers of Databases, Predictors and Development Tools

55

Pharma Algorithms Pharma Algorithms Development ToolsDevelopment Tools

Algorithm Builder development platform:

• Data storage and manipulation

• Generation of fragmental descriptors

• Statistical procedures: MLR, PLS, PCA, Recursive Partitioning, HCA

• Tools for predictive algorithm development

66

Generation of DescriptorsGeneration of Descriptors

...

...

...

...

Y

Structure1

Structure2

StructureN

...

F1 F2 FM

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

F3

77

““Causal” DescriptorsCausal” Descriptors

One-atom("topological")

Three-atom

Five-atom

Larger chains, Ring scaffolds

Atom chains Activity effects

Non-specific(size, PSA)

COOH, CONH

Ionization,H-bonding

Reactivity, internal interactions

Similarity to natural compounds

N

OOH

OH

N

OOH

OH

N

OOH

OH

N

OOH

OH

N NH

O

O

OCl O

Examples

Spec

ifici

ty

Frag

men

t Siz

e

88

Algorithm DevelopmentAlgorithm Development

• Graphical Interface provides easy to use tools for programming complex algorithms

• Combine fragmental, descriptor and similarity based methods

• Use logical expressions, conditions and equations based on descriptors, sub-fragments, internal interactions or any other chemical criteria

• Combine multiple sub-algorithms into general algorithms

• Rapidly develop ‘custom’ filters incorporating ‘expert’ in-house or project specific rules

99

Our focus

Tox Effects in Drug DesignTox Effects in Drug Design

Tox Effect

Acute (LD50)

Organ-specific effects

Mutagenicity

Reproductive effects

Carcinogenicity

Programs

Topkat, AB/LD50

AB/Tox* (next version)

Many programs, AB/Tox

Many programs, AB/Tox*

Many programs, AB/Tox*

1010

Existing ProgramsExisting Programs

QSAR

Expert

Other

DEREKHAZARD

TopKatQickProp

ADME LD50

AB/ToxAB/LD50AB/Oral %F

Mixed

Combinations of above

“Manually” derived skeletons

COMPACT

Combined

Descriptors

C-SAR M-CASE “Statistical” skeletons

META

Will consider these

1111

What Is What Is LDLD5050

A dose that kills 50% of animals during 24 hrs

In drug design, used at pre-clinical stage

In early stages, replaced with “reductionist” considerations

Some scientists question its utility

1212

Informatics Toxicologists PK Specialists

“Reactivity + log P ” Empirical

knowledge

Empiricalknowledge +simulations

Complexity of Complexity of LDLD5050

O ra l LD 50

Tox Effects

Alkyla tion

"N arcosis"

O rgan-specific

D istribution"B asa l" C N S, PN S

ATP Synthesis

Krebs C ycle

O ther targets

Excretion

Oral % F

1313

Is this good enough?

Acute Tox in Drug Acute Tox in Drug DesignDesign

Lead Selection

No tests performed

Reactive groups discarded

Lead Optimization

Basal cytotoxicity tested

Intra-cellular effects considered

Pre-clinical Stage

Animal tests are required

ADME effects considered

1414

Acute Tox in Drug DesignAcute Tox in Drug Design

An LD50 Model for mouse (intraperitoneal administration) was developed using data from the RTECS database (35,000 compounds)

1515

Distribution of Acute EffectsDistribution of Acute Effects

Extra-cellular effects - may be “invisible” in cytotoxic assays

RTECS DB: mouse, intraperitoneal administration

LD50 < 50 mg/kg(N = 4,099)

All compounds(N ~ 35,000)

Narcosis

32%

7%

55%6%

23%

25%

14%

38%

Natural toxins

Nervous system s(hydrophobic bases)

Reactivity

Other

Reactivity

Other

1616

In VivoIn Vivo vsvs. . In VitroIn Vitro

Log LD 50

Log IC 50

IC50 cannot model LD50 when extra-cellular effects occur

NN

O

LD 50 = 750 m g/kg

Intra-cellular

Natural toxins

N

NN

NOO

N

N

O

ON

H

LD50 = 0.008 m g/kg

ADM E Factors

N

NLD 50 = 51 m g/kg

Extra-cellular

In testina l permability,

1st pass metabolism

- Log LD 50

1717

How to Predict These Effects?How to Predict These Effects?

Quality of Predictions = Knowledge of Specific Effects

How much knowledge do we get?

“Reductionist” QSARs do not work

LD50 involves much more than “log P + reactivity”

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How Much Knowledge?How Much Knowledge?

QSAR Model

Log 1/LD50 = ai xi

Knowledge

Expert Deduction Little KnowledgeNCl N

Cl

Active Inactive

More KnowledgeC-SAR + DeductionNCl N

Cl

Active InactiveCl

CN CNCl

Active InactiveStruct. Space

1919

C-SAR + DeductionC-SAR + Deduction

LD50 values are split into groups using fragmental descriptors from AB

n = 7588avg. = 1.048

sd = 0.641N

N o Yes

F81 >= 1N o Yes

n = 7165avg. = 0.999sd = 0.583

N0

N o Yes

F44 >= 1N o Yes

n = 6844avg. = 0.978sd = 0.562

N00

N o Yes

F36 >= 1N o Yes

n = 5918avg. = 0.936sd = 0.526

N000

n = 926avg. = 1.245

sd = 0.694N001

n = 321avg. = 1.46sd = 0.805

N01

N o Yes

F7 >= 1N o Yes

n = 169avg. = 1.221sd = 0.737

N010

n = 152avg. = 1.725sd = 0.797

N011

n = 423avg. = 1.878sd = 0.943

N1

N o Yes

F56 >= 1N o Yes

n = 184avg. = 1.61sd = 0.86

N10

n = 239avg. = 2.085sd = 0.953

N11

OO ONO

PO

NHal

The most significant skeletons are “potential toxicophores”

2020

Specific Effects in AB/Specific Effects in AB/LDLD5050

> 33,000 Compounds with LD50 from RTECS DB

Natural toxins

Cholinesterase DNA Alkylation

AT P Synthesis

ON

O

ON

O

PO O

PO

O

SO

O

HONNCl

NCl

Cl

CN

CNCl

FO

FO

OCl

OF

O SO

O

OH SO

O

O

O

O

O

O O

OH H

N

N

NO

PO

OO

N

N

OO

O

O

O

O

Toxicity classes

Active:

Inactive:

Active:

Inactive:

2121

Low-Specific EffectsLow-Specific Effects

Small non-bases are least toxic.Hydrophobic amines are most toxic

Arrows denote increasing toxicity

Base p K a

Lo g P

3.5 7.0 8.5

3.2

1.2

M W

230

300

N arcos is N ervous syst.

N

N

LD 50 = 51 m g/kg(CNS effect)

NNO

LD 50 = 750 m g/kg("narcosis")

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C-SAR + Deduction

To get new knowledge, statistics must help deduction.To use QSAR models, they must work in narrow structural spaces.

Efficacy ComparisonEfficacy ComparisonK

now

ledg

e

Struct. DiversityEffo

rt

Expert Deduction

QSAR Model

2323

QSAR Models in AB/QSAR Models in AB/LDLD5050

NH

OCN

O

NH

OCN

O

NH

OCN

O

NH

OCN

O

NH

OCN

O

NH

OCN

O

NH

OCN

O

F ive -a tom cha ins

R eactive ske le ton

1. Narrow struct. spaces2. Dynamic fragmentation3. “Causal” parameters

- Log LD50 = a i F i

* S imila rity a lgorithm based on M ACCS II key

ClassS-1 Specific toxinsS-2 O rganometa llicsS-2 Covalent cationsS-4 Cho linesteraseS-5 A lkylating agentsS-6 ATP SynthesisL-1 L ipoph ilic basesL-2 Non-lipophilic basesL-3 W eak basesL-4 Hydrophilic basesN-1 Large non-basesN-2 Very weak basesN-3 M id-size non-basesN-4 Small non-basesAll compounds

N260120

1,3001,100

800600

4,0003,8004,6003,0003,3002,8004,3003,700

33,680

R---*---

0.860.890.820.790.750.750.750.820.840.830.800.760.83

pLD 50

+1.0 ... +6 .5-0.5 ... +2.5-0.5 ... +4.5-1.5 ... +4.0-0.5 ... +2.5+0.0 ... +2 .4-0.5 ... +1.5-1.0 ... +1.0-1.0 ... +0.9-1.2 ... +0.8-1.5 ... +1.0-1.5 ... +0.8-1.5 ... +0.5-2.0 ... +0.5-2.0 ... +6.5

S - Specific effects, L - Low-specific, N - Non-specific.

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What is novel?What is novel? The novel features of the Pharma Algorithms approach are:

• Combination of approaches used separately in earlier software i.e. Expert Rues (e.g. DEREK), C-SAR (e.g. CASE) and QSAR (e.g. TOPKAT)

• Reliable Confidence Intervals are generated from QSAR models (class specific and global) that are

derived using an automated multi-step process:1. Chain fragmentation and PLS with multiple bootstrapping2. Selection of best fragments with ‘stable’ increments3. Derivation of multiple models from subsets of the training set

to produce ranges of predictions4. Selection of the best model to use for a particular compound

by comprison of the different ranges5. Calculation of the confidence interval from the range of

predictions produced by the most appropriate model

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Screening the Specs DBScreening the Specs DB

SPECS are a supplier of diverse compound screening collections

A set (N = 14,902) was randomly selected (from > 200,000) and

screened using the AB/LD50 toxicity predictor.

Calculation of LD50 for the set takes about 30min on a standard Windows laptop

Compounds were deemed “Toxic” if LD50 < 50 mg/kg

Results:Overall only 2.7% were “toxic” (i.e. 310 of 14,902)

As expected a higher proportion (3.9%) of the bases (i.e alkylamines) were toxic (i.e. 92 of 2,351)

2626

Most significant

Toxic SkeletonsToxic Skeletons

N

NN

M W > 34068% (86/127)

N

NS

38% (60/158)

N

NN

MW < 26030% (3/9)

NH

NN

21% (7/33)

NH

OO30% (6/20)

NN

O25% (6/24)

N

N

N

CF3

67% (4/6)

CN

CN

31% (16/52)

NH

O

O

15% (4/26)

O O

37% (6/16)

O O

100% (4/4)Natural tox in? Alkylation, ox idation CholinesteraseCyanide relase

Artefacts?

Exp. verification required

2727

What We Have Learned So FarWhat We Have Learned So Far

Screening for basal cytotoxicity is not enough

The “C-SAR + Deductive” method opens new possibilities

The extra-cellular effects can be estimated in silico

Can we model in vivo toxicity?

2828

Administration vs. ADME

OR IVSc IP

ADME Effects

OR – OralSc – SubcutaneousIP – IntraperitonealIV – Intravenous

Stomach

Intestine

Vein

Liver

Toxicaction

Dissolution, permeation,hydrolysis, metabolism

IV

OR

Tissue,organs

2929

Informatics ADME Specialists

“Simple descriptors” “Simulations”

Complexity of ADMEComplexity of ADME

Absorption

G u t 1 st P ass

Solubility

Permeability

T ransporters

Liver 1 st Pass

O ral % F

“Simple descriptors” disregard many factors.Can we simulate them in HT mode?

3030

Oral %Oral %FF Prediction in HT Mode Prediction in HT Mode

Reliability validated by the consistency of independent predictions

Non-Batch Interface:

3131

Cost/Benefit ConsiderationsCost/Benefit Considerations

In silico Bioavailability and Toxicity predictions for compound collections are inexpensive to perform

The value of predictions is variable- Decisions still need to be made by expert scientists in a project context

In silico tools can assist the expert in a detailed evaluation of ‘hits’, ‘leads’ and ‘candidates’ but there is a need for:1. Predictions for a range of toxicity types:

LD50 (oral, i.v.,s.c.) Genotoxicity and Carcinogenicity Organ specific Effects (e.g. hepatotoxicity)

2. Integration of the prediction software with databases containing the training data so that the availability and behaviour of similar compounds can be checked

3232

Drug DesignDrug Design

General Principles

Aim for low logP

Aim for low M.Wt.

C. Hansch et. al. ‘ The Principle of Minimal Hydrophobicity in Drug Design’ J. Pharm. Sci., 1987, 76, 663

M.C. Wenlock et. Al. ‘Comparison of Physicochemical Property Profiles of Development and Marketed Oral Drugs’ J. Med. Chem., 2003, 46, 1250

3333

Simulations in HT ScreeningSimulations in HT Screening

ActivityTox

%F

“Reductionist” Methods:

High Activity = Low %F + High Tox

HT Simulations aim at:

High Activity = High %F + Low Tox

ActivityTox

%F

Very rough estimations, assuming that activity increases with increasing log P and MWt