LogD and pKa enhancements: Applications in ADME profiling

ACD UK users meeting, Windsor, 2007

Jordi Munoz-Muriedas, GSK, Stevenage

logD and pKa in ADME

Related with almost every ADME process

Increases absorption, volume of distribution, plasma protein binding, Pgpefflux, CNS penetration Cytochrome inhibition, intrinsic clearanceDecreases solubility

Hydrophobicity generally:

Acidity generally:

Increases plasma protein binding, solubilityDecreases in vivo clearance, CNS penetration, PGP efflux, volume of distribution

Basicity generally:

Increases in vivo clearance, solubilityDecreases plasma protein binding

pKa predictors performance

ACD pKA vs. other software (n=1000)

0

20

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80

100

+/-0.25 +/-0.5 +/-1 +/-2 >+/-2 not_calcMeasured - Calculated pKa within specified range

Cum

ulat

ive

%

external 1 ACD_v4.1 external 2 ACD_v7 external 3

logD predictors performance

ACD logD vs. other software (n=4693)

0

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+/-0.5 +/-1 +/-2 >+/-2 not_calcMeasured - Calculated logD within specified range

Cum

ulat

ive

%

in-house method ACD_v4.1_logd external 1 ACD_v7_logd external 2

logD predictors performance

0

20

40

60

80

100

"+/- 0.5" "+/- 1.0" "+/- 2.0" "+/- >2.0" "not_calc"

ACD v8 ACD v9

ACD logD prediction: logD range [-1.5,3.0] n=17006

logD predictors performance

ACD logD prediction n=22903

Quick implementation in ADMET models

Absorption predicted by logD/CMR model

-12

-8

-4

0

4

8

0 5 10 15 20 25cmr (Daylight)

AC

D_L

ogD

pH

7.4

OK Poor

Quick Quick implementation in ADMET modelsin ADMET models

Necessity of good logD/pKa predictions

Expt Absorb: High

Pred. Absorb: Fail (high confidence)

Expt. logD= 1.89

ACD logD= -1.5

Expt Absorb: High

Pred. Absorb: Fail (Borderline)

Expt. logD= 0.75

ACD logD= -3.8

5 10 15 20

-2

-1

0

1

2

3

4

CMR

Exo.

logD

Colours based on predicted Absorption:

Green: Good

Yellow: Good (borderline)

Orange: Poor borderline

Red: Poor

Training Training pKapKa, , logPlogP and and logDlogD

ACD physchem and pKa accuracy extenders

pKalogPSolubility

pKA and logP trained values can be used together to increase accuracy in logD predcition

pKa training

pKa training

ApKa1 Prediction

62

84

93100

40

62

89

100

0

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60

70

80

90

100

<0.5 <1 <2 >=2

Error (log units)

Cum

ulat

ive

%

V9_UserV9

Global dataset training

BpKa1 Prediction

55

71

90

100

49

67

90

100

0

10

20

30

40

50

60

70

80

90

100

<0.5 <1 <2 >=2

Error (log units)

Cum

ulat

ive

%

V9_UserV9

N=369 N=684

pKa training: Basic compounds

0 2 4 6 8 10 12 14

2

4

6

8

10

12

Global dataset training: Basic pKas

K b10 2 4 6 8 10 12 14

2

4

6

8

10

12

Before Training After training

pKa training

Most acidic pKa: Measured vs. ACD v9

0 2 4 6 8 10 12 14

2

4

6

8

10

12

Most acidic pKa: Measured vs. ACD v9_user

K 10 2 4 6 8 10 12 14

2

4

6

8

10

12

Acidic compounds: New reaction center

R2R1N

OOH

R3

Before Training

After Training

pKa training

Expt_LogD vs. CMR

5 10 15 20

-2

-1

0

1

2

3

4

R2R1N

OOH

R3

Expt Absorb: High

Pred. Absorb: Fail (Borderline)

Expt. logD= 2.25

ACD logD= 1.20

ACD logD (pKa trained) = 2.84

New absorption prediction: High

Acidic compounds: Absortion predictionColours based on predicted Absorption:

Green: Good

Yellow: Good (borderline)

Orange: Poor borderline

Red: Poor

cmr

Exp_

logD

pKa training

logD correction using trained pKas (n=17006)

LogD prediction

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64

90

100

36

63

90

100

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<0.5 <1 <2 >=2

Error (log units)

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%

V9_UserV9

pKa training

Solubility correction using trained pKas (n=909)

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Error<0

.5Erro

r<1.0

Error<1

.5Erro

r<2.0

Error<3

.0Erro

r>3.0

v9v9_pKa_user

100.0%96.4%87.6%78.2%63.9%42.5%

909876796711581386v9_pKa_user

100.0%95.4%86.2%76.7%63.3%42.4%

909867784697575385v9

Error>3.0

Error<3.0

Error<2.0

Error<1.5

Error<1.0

Error<0.5

100.0%96.4%87.6%78.2%63.9%42.5%

909876796711581386v9_pKa_user

100.0%95.4%86.2%76.7%63.3%42.4%

909867784697575385v9

Error>3.0

Error<3.0

Error<2.0

Error<1.5

Error<1.0

Error<0.5

Error Sol(pH7)-Error Sol(pH1.2) / Error Sol(pH7)*- Error Sol(pH1.2)*

pKa training

Application to projects

ACD K i6.5 7 7.5 8 8.5 9 9.5 10 1...

6.5

7

7.5

8

8.5

9

9.5NR1

R2

6.5 7 7.5 8 8.5 9 9.5 10 1...

6.5

7

7.5

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8.5

9

9.5Before training After training

pKa= 1.62pKa_acd -7.00

R2=0.72pKa= 1.07pKa_acd – 0.57

R2=0.87

N=16

pKa training

XX

X

XX

NR2

R1

5.8 6 6.2 6.4 6.6 6.8 7 7.2

6

6.5

7

7.5

Application to ADME

Bioavailability and CYP inhibition issues related with the pKa of the imidazopyridine groupPoor pKa prediction by ACDResults improved significantly after pKa AE training

pKa= 0.09pKa_acd + 6.03

R2=0.02

5 6 7 8 9 10

6

6.5

7

7.5

pKa= 1.14pKa_acd – 0.93

R2=0.63

Validation set

N=28

Before training After training

pKa training

2C19 pIC50 vs. Basic pKa (n=77)

4.5 5 5.5 6 6.5 7 7.5

4

4.5

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5.5

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6.5

7

R2=0.40 R2=0.09 R2=0.33

Experimental pKa Before Training After Training

Expt pKa ACD pKa ACD trained pKa

pIC

50

“logD training”

logD training

-1 0 1 2 3 4

-1

0

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3

-2 -1 0 1 2 3

-1

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Using logP training to improve logD prediction

N

NHR1

R2

Expt LogD = 0.68 ACD logD + 0.23R2 = 0.39Average error 0.17Overprediction

Expt LogD = 0.69 ACD logD + 0.57R2 = 0.64Average error -0.35Underprediction

N=445

Training process leads to a better correlation but to a systematic underestimation of logD

logD training

Exp LogD vs T rained ACD logD

-2 -1 0 1 2 3

-1

0

1

2

3

Relating error with specific chemical groups

N

R2O

NHR1

N=26

Red: Amides

logD training

Relating error with specific chemical groups

-1 0 1 2 3 4

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-2 -1 0 1 2 3 4

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N

R2O

NHR1

logP underpredicted for series with amidesTraining process corrected this partially

ACD_logD ACD_logD

ACD_logP ACD_logP

Exp_

logD

Exp_

logD

Exp_

logD

Exp_

logD

Before Training After Training

logD training

N

R2O

NHR1

-2 -1 0 1 2 3

-1

0

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-1 0 1 2 3 4

-1

0

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Relating error with pKa prediction

The training process leads to a underprediction of the logP. Probably due to a underprediction of the basicityof the compounds

ACD_logD ACD_logD

ACD_logP ACD_logP

Exp_

logD

Exp_

logD

Exp_

logD

Exp_

logD

Before Training After Training

-1 0 1 2 3 4

-1

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-2 -1 0 1 2 3 4

-1

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N=419

logD training

pKa underprediction

7 7.5 8 8.5 9 9.5

7.6

7.8

8

8.2

8.4

8.6

8.8

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9.2

N

NHR1

R2

Y=0.37x+5.46

R2=0.26

pKa influence in logP training

Experimental logD and predicted ACD pKa values are used to calculate estimation of “experimental” logPThese estimations are used to train logP fragmentsTrained logP can be used to calculate logDLogD predictions can be enhanced by using (indepently) trained pKasProblem: Errors in pKa prediction in the first step will affect logP training and logD prediction

It should be possible to introduce Experimental logD and experimental pKa (or at least trained pKas) to get a better prediction of logP in the first stepThis logP trained using pKa information and trained pKa should lead to a better logD prediction

∑∑=

−

Δ−

=−

−Δ

⎟⎟⎠

⎞⎜⎜⎝

⎛

++−⎟

⎟⎠

⎞⎜⎜⎝

⎛

++−=

Nb

ipHBpKa

BpKaNa

iApKapH

ApKa

i

Bii

i

iAi

PD11 101

101log101101logloglog

Current algorithm:

Ideal:

Conclusions

pKa accuracy extender is a very useful tool to enhance pKapredictions when applied to improve predictions for local sets.

pKa AE power would be increased and be of great help if it could beused simultaneously with the logP training algorithm.

logP training algorithm is very dependent of pKa prediction and should not be used with datasets where errors in pKa are expected unless experimental logP values could be provided.

Ackowledgements

ADMET In Silico Team, GSK, Stevenage:Anne Hersey, Paul Gleeson, Sandeep Modi

ACD Labs: Ed Kovolanov, Greg Pearl