prediction model for absorptivity of drug-like compounds...
TRANSCRIPT
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Prediction Model for Absorptivity of Drug-like Compounds
Based on Structural Features and Interfacial Properties
Chihae Yang, Glenn Myatt, Paul BlowerLeadScope, Inc.
Jim RathmanThe Ohio State University
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Objectives
§ Compound Description by Structural Features– Selection of features
§ Compound Description by Physical Properties– Molecular parameters– Interfacial parameters
§ Prediction Models– Structure feature based– Property based– Structure feature and property based
Develop Prediction Model for Absorptivity:
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Structural Description of Dataset
143,4 ring system
21Pyridine (partially saturated)
65,6 or 6,6-fused rings
11SteroidsNatural Products
2Naphthalenes
9Pyrrolidone
375 membered ring
70N-containing heterocyclesHeterocycles
# of compoundsSub classificationClasses
L.G. Martini, et.al, European journal of pharmaceutics and biopharmaceutics 48 (1999) 259-263K. Palm, K.Lutman, et. al., J. Med. Chem, 1998, 41, 5382-5392W.L. Chiou, Pharmaceutical Research, Vol 17, No 2, 135-140, 2000P. Stenberg; U. Norinder, et. al., J. Med. Chem, 2001 44, 1927-1937
Total compounds ~100
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Structural Description of Dataset
37Ether
15Sulfide
38Halide
2Quinone
18Carboxylate and carboxylic acid
91Amines
56AlcoholFunctional Group
80Any 1-substitution
501,4 substitution
451,3 substitution
551,2 substitutionBenzenes
4Bases, nucleosides
14Amino acids
# of compoundsSub classificationClasses
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Distribution of % Fraction Absorbed Data
Fraction Absorbed after oral administration to humans*
* P. Stenberg; U. Norinder, et. al., J. Med. Chem, 2001 44, 1927-1937K. Palm et. al, Pharm Research 1997, 14, 568-571
K. Palm, K.Lutman, et. al., J. Med. Chem, 1998, 41, 5382-5392
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Clustering of Compounds Against %FA
90 – 99 %
71-89 %
100 % (removed from model)
0 –10 %
25-44 %
50-70 %
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Factors Affecting Absorption
§ Physical Properties: - Solubility- Dissolution rate- Molecular size- Partition coefficient
§ Physiological Properties:- Regional pH- Intestinal Permeability
§ Not considered:- Active transport, binding, complexation, etc.- Pericellular- Metabolism- Gastric and intestinal transit
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Compound Description By Physical Properties
§ Molecular weight
§ Hydrogen bond acceptors and donors
§ Log P
§ Log DCalculated at pH 1, pH 4, pH 7, pH8
§ pKa and solubility (at pH 1, 4, 7, 8)
§ Polar surface area
§ Thermodynamic solution/interfacial property- Activity coefficients at infinite dilution
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Property Distributions of Dataset
molecular weight rotatable bonds Hydrogen bond acceptors Hydrogen bond donors
polar surface area aLogP
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Relationships (or Lack of) between FA and Properties
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Prediction Based on Properties using NIPALS*
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
actual_FA
pre
dic
ted
_FA
Properties: MW, HBD, HBA, PSA, LogP, Log D(@ pH 1,4,7,8), solubility (pH 1,4, 7), pKa
Compounds: 93 compounds ranging %FA from 0 -100
R2 =0.40 R2 =0.49 (if 100 % absorption is excluded)
Nonlinear iterative partial least squares algorithm from Geladi and Kowalski, Analytica Chimica Acta, 185 (1986) 1-17.
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Prediction Based on Structural Features
§ Selection of representative features from the dataset– global – local neighbors
§ Scoring or extraction criteria § Reduction of dimensionality
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Feature Selection by Scoring Criteria
§ Method 1: Scoring of features (select 25 from ∼1600)- Coverage atoms - maximize- Partition of compound set
• prioritize features to partition the compound set to ~50:50- Complementarity of features
• minimize the overlap between features
§ Method 2: Extraction from principal components- diagnostic: influence function*
§ Compared features from method 1& 2 for selection
§ Used 25 feature counts per compound as fingerprint values
* Brooks, S.P. The Statistician (1994), 43, 483-494* Pack, P.; Jolliffe, I.T.; Morgan, B.J.T. Journal of Applied Statistics (1988), 15, 39-52.
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Selected Features According to CriteriaCounts in the data setFeatures
14[benzene, 1-amino-] + [benzene, 1-amino-]
27
21
17
39
58
33
39
20
11
31
37
51
33
22
21
48
47
36
31
40
[amine, alkyl, acyc-] + [tert-amine, p-alkyl-]
[alcohol] + [ether, p-alkyl-]
[benzene, 1-(alkyl, cyc)-] + [benzene, 1-(alkyl, cyc)-]
[carbonyl, alkyl, acyc-] + [carboxamide]
[carbonyl] + [methane, 1-aryl-,1-carbonyl-]
[amine, alkyl, cyc-] + [carboxamide(NHR), alkyl-]
carboxamide
benzene, 1-chloro-
amine(NR), diphenyl
benzene, 1-(alkyl, acyc)-
ether
alcohol, alkyl-
benzene, 1-oxy-
benzene, 1-(alkyl, cyc)-
pyridine(H)
tert-amine
amine, alkyl, cyc-
alkene
benzene, 1-amino-
carbonyl, alkyl, acyc-
Counts in the data setFeatures
14[benzene, 1-amino-] + [benzene, 1-amino-]
27
21
17
39
58
33
39
20
11
31
37
51
33
22
21
48
47
36
31
40
[amine, alkyl, acyc-] + [tert-amine, p-alkyl-]
[alcohol] + [ether, p-alkyl-]
[benzene, 1-(alkyl, cyc)-] + [benzene, 1-(alkyl, cyc)-]
[carbonyl, alkyl, acyc-] + [carboxamide]
[carbonyl] + [methane, 1-aryl-,1-carbonyl-]
[amine, alkyl, cyc-] + [carboxamide(NHR), alkyl-]
carboxamide
benzene, 1-chloro-
amine(NR), diphenyl
benzene, 1-(alkyl, acyc)-
ether
alcohol, alkyl-
benzene, 1-oxy-
benzene, 1-(alkyl, cyc)-
pyridine(H)
tert-amine
amine, alkyl, cyc-
alkene
benzene, 1-amino-
carbonyl, alkyl, acyc-
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Fingerprint Table: Features and Counts
TemplateName Acebutolol Acetazolamine Alprazolam Amiodarone Amitriptylline .......carbonyl, alkyl, acyc- 2 1 0 0 0benzene, 1-alkylamino- 0 0 0 0 0benzene, 1-amino- 1 0 1 0 0alkene 0 0 0 0 1amine, alkyl, cyc- 0 0 0 0 0tert-amine 0 0 0 1 1benzene, 1-alkoxy- 1 0 0 1 0benzene, 1-(alkyl, cyc)- 0 0 0 0 2benzene, 1-oxy- 1 0 0 1 0alcohol 1 0 0 0 0alcohol, alkyl- 1 0 0 0 0ether 1 0 0 1 0benzene, 1-(alkyl, acyc)- 0 0 0 0 0amine(NR), diphenyl 0 0 0 0 0benzene, 1-chloro- 0 0 1 0 0carboxamide 1 1 0 0 0[tert-amine] + [pyridine(H)] 0 0 0 0 0.....
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Introduction of Solution/Interfacial Properties
§ Factors important for passive diffusion through lipid bilayer- Headgroup interaction- Hydrophobic tail interaction- Hydrophilic to lipophilic balance (HLB)
§ Partition model of drug molecules in lipid layer :
lipid Drug Drug
at equilibrium
partition coefficient:
:activity coefficient
bulk
Drug bulk Drug lipid
Drug-lipid Drug bulk
Drug bulk Drug lipid
a a
xK
x
γ
γ
γ
− −
−
− −
⇔
=
≈ =
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Partition and Activity Coefficients
Partition coefficient: (in dilute solution)
log log log
bulkDrug
lipidDrug
bulk lipiddrug drug
K
K
γ
γ
γ γ
≈
≈ −
tan tan
tan
tan
Compare with LogP:
(octanol-water)
log log log
Oc ol wateroc olDrug pure Drug
water oc olwaterpureDrug Drug
water oc oldrug drug
C CP
CC
P
γ
γ
γ γ
= ≈ ⋅
∝ −
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UNIFAC Activity Coefficient Model
molecular volume and surface area effects(size, shape, packing)
intermolecular energy effects (interaction)
“combinatorial” term
“residual” term
ln ln lnC Ri i iγ γ γ= +
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1
ln ln ln2
CC i i ii i i j j
ji i i
zq l x l
x xφ θ φ
γφ =
= + + − ∑
( ) ( )12i i i i
zl r q r= − − −where:
Combinatorial Termln γi
C is calculated using a group contribution approach:
• The drug and solvent molecules are decomposed into simple fragments.
• Volume (r) and surface area (q) parameters are computed for each molecule by summing values for the appropriate fragments.
• At a given mole fraction xi, the fraction of the total volume (φι) and total surface area (θi) due to compound i are calculated.
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Residual Termln γi
R is calculated using the same fragments:
• Pairwise interaction terms (Ψmn and Ψ nm) are available for the fragments.
• Ψ values are directly related to intermolecular potentials:
•Ψmn = exp[(unn – umn)/RT] Ψnm = exp[(umm – umn)/RT]
• Although in theory these can be calculated from intermolecular potential functions, in practice they are based on experimental data (from primarily petrochemical and polymer databases).
ln 1 lnθγ θ
θ
Ψ ∝ − Ψ − Ψ
∑ ∑∑R m kmi k m mk
m m n nmn
q
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UNIFAC Group Contribution
CH3CH2CHCCH2=CHCH=CHCH2=CC=CArHArCArCH3ArCH2ArCH
OHCH3OHH2OArOHCH3C(O)CH2C(O)CH(O)CH3C(O)OCH2C(O)OHC(O)OCH3OCH2OCH-ORing-CH2O
CH3NH2CH2NH2CHNHCH3NCH2NArNH2C5H5NC5H4N C5H3N CH3CNCH2CNCOOHHCOOH
CH2ClCHClCClCH2Cl2CHCl2CCl2CHCl3CCl3CCl4ArClCH3NO2CH2NO2CHNO2ArNO2
CS2CH3SHCH2SHCF3CF2CF (CH2OH)2FurfuralCl(C=C)Me2SOC(O)N(Me)2C(O)N(Me)CH2C(O)N(CH2)2
The properties of Gases & Liquids, 4th ed., R. Reid, J. Prausnitz, B. Poling, McGraw Hill, 1987
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Lipid As A Solvent Phase
POO
O
O
O
OO
NO
O
O
O
O
O
O
O
O
OO
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Example of Activity Coefficients in Various Environment
O
O H
0.73Hexadecane
0.12Glycolipid
-0.40Lipid tail
0.05Octanol
5.23Water
Log10 γ∞Solvent
Due to its origin in petrochemical applications, standard UNIFAC tables do not include a few of the basic drug-like fragments present in this preliminary study.
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030
6090
0
5
10
15
-4
-1
2
5
-6-303
-505
15
0
20
40
FA
0 20 50 80110
water
0 5 10 15
octanol
-4 -2 0 2 4 6
glycolipid
-6 -4-2 0 2 4
tails
-5 0 5 10 15
hexadecane
0 10203040
Pairwise Correlations of Variables
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Model Comparisons
0.670.69Structure features + PSA + HBA
0.34
0.32
other
0.4011 Properties only (LogP, PSA, LogD, pKa, MW, HBA, HBD, etc.)
R2Model
0.72
0.73
0.69
0.70
0.69
0.67
20 factors
0.68Structure feature + activity coefficients + HBA
0.68Structure features + PSA
PSA only1
0.70Structure feature + activity coefficients + PSA
0.66Structure features + HBA
0.66Structure features + activity coefficients
0.65Structural features only
Activity coefficients only1
7 factors
1 By a simple linear regression; all other by nonlinear iterative partial least squares (NIPALS).Order of importance: Features>activity coefficient ≈ PSA >H-bond acceptors
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Feature and Interfacial Property Based Prediction Model
0
20
40
60
80
100
pred
icte
d
0 10 20 30 40 50 60 70 80 90 100actual
Model: Structural features, Activity coefficients, PSAMethod: nonlinear iterative partial least squares (NIPALS) with 7 factors
R2 = 0.70
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Preliminary Prediction
§ Test set: 5 compounds were randomly selected (one from each cluster of the FA values) and were not included in the model building§ Training set: 66 compounds were used as the training set using
NIPALS method with 7 factors. The model was based on structural features, PSA, 5 activity coefficients
5920Penicillin-G
7170Mianserine
8395Metoprolol
85Doxorubicine
4450Acebutolol
Predicted (%)Actual (%)Drug name
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Conclusions
§ Assuming passive diffusion to be the most critical factor for small molecule absorption in the GI tract, structural features extracted from the compound dataset described %FA much better than any properties.
§ Activity coefficient calculations may explain why LogP does not correlate well with absorption: partitioning into in a highly hydrophobic environment (lipid tail region) is not modeled properly using octanol.
§ This preliminary study shows that models based on structural features may be further improved by addition of interfacial properties such as activity coefficients and polar surface area.
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Next Steps§ Apply to larger dataset§ Further elaborate the scoring function for feature selection§ Method refinement of UNIFAC to model drug-like compounds
– Calculate R and Q values for the selected features from this dataset.
– Calculate activity coefficients at infinite dilution– Explore activity coefficients in multicomponent environments
§ Model can be applied to Caco-2 cell permeability studies– Human or animal absorption data may be too complicated to
model with predictive accuracy§ The model will also be compensated for transport phenomena.
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Acknowledgement
§ Julie Roberts, LeadScope, Inc.– building structures
§ Kevin Cross, LeadScope, Inc.– calculation of LogP and PSA
§ Tim Sötherlund, Kibron, Inc.– application of surface (air-liquid) properties to ADME properties