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QSARs to Predict Extent of Drug Biotransformation in Humans. Na’ngono Manga , Judith C. Duffy, Phil H. Rowe, Mark T.D. Cronin, School of Pharmacy and Chemistry Liverpool John Moores University. Introduction. Project failures mainly attributed to pharmacokinetic problems

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qsars to predict extent of drug biotransformation in humans
QSARs to Predict Extent of Drug Biotransformation in Humans

Na’ngono Manga, Judith C. Duffy,

Phil H. Rowe, Mark T.D. Cronin,

School of Pharmacy and Chemistry

Liverpool John Moores University

introduction
Introduction
  • Project failures mainly attributed to pharmacokinetic problems
  • Increasing effort placed into forecasting pharmacokinetic properties of drugs
  • Most work has focussed on absorption, transporters, metabolism enzymes and blood brain barrier
aims of study
Aims of Study
  • To model extent of drug biotransformation in humans as a composite process, using urinary excretion of unchanged drug
  • To develop a transparent QSAR model in rational manner accepting non-linear nature of data
methods
Methods
  • Source of Data
    • Training set: 160; Test set: 40 drugs
    • Molecular weight capped at 500
  • Response data
    • Cumulative amount of unchanged drug excreted in the urineexpressed as percent of i.v. dose
    • Data categorised into low, medium, high
    • No boundary values assigned a priori
methods5
Methods
  • Physico-chemical properties calculated:
    • log P, pKa, log D
    • Structural and topological parameters
    • Molecular orbital (AM1) properties
  • Statistics:
    • Stepwise LDA
    • Recursive partitioning
local models of the data
Local Models of the Data
  • Drugs with log D6.5 > 0.3 removed
  • Attempt to build local models on remaining classified data:
    • 1- Low vs. high urinary excretion compounds
    • 2 - Low vs. medium
    • 3 - Medium vs. high
low 10 vs high 75 urinary excretion
Low (< 10%) vs. High (> 75 %) Urinary Excretion

W = 1.11 {7.07 + 27.7 Sacid + 21.7 SHBD

– 23.6 SOH} – 17 SQ7 + 15.5

slide9

Modelling Whole Data Set

Compounds with log D6.5 > 0.3 assumed to have low urinary excretion

classifications from lda were subjected to recursive partitioning
Classifications from LDA were Subjected to Recursive Partitioning

For compounds with W 47 satisfactory decision tree resulted:

Urinary Excretion  38% if Total Energy > 15, or if HB < 2, or If –27919 < EE < -19320, OrIf IP < 8.96 or IP > 9.78

Otherwise Urinary Excretion > 38%

decision tree for whole data set
Decision Tree for Whole Data Set

> 0.3

Log D6.5

Excretion < 25%

> 47

Discriminant Function

Excretion > 38%

Recursive Partitioning

Excretion  38%

Excretion > 38%

classification and validation using test set

Decision Level

Decision Level

Ratio of correct

Ratio of correct

Percentage

Percentage

Compounds misclassified as extensively metabolised

Compounds misclassified as extensively metabolised

Compounds misclassified as moderately/poorly metabolised

Compounds misclassified as moderately/poorly metabolised

Level 1

Level 1

70/73 compounds

70/73 compounds

95%

95%

3

3

0

0

Level 2

Level 2

31/36

31/36

86%

86%

0

0

5

5

Level3

Level3

45/51

45/51

88%

88%

5

5

1

1

Overall

Overall

146/160

146/160

91%

91%

9

9

6

6

Classification and Validation Using Test Set
discussion
Discussion
  • Hybrid metabolism data can be modelled adequately
  • Model uses descriptors related to metabolism
  • Weakness in drugs with medium urinary excretion and drugs with long half-lives
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