Qsars to predict extent of drug biotransformation in humans
Download
1 / 15

QSARs to Predict Extent of Drug Biotransformation in Humans - PowerPoint PPT Presentation


  • 131 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'QSARs to Predict Extent of Drug Biotransformation in Humans' - ghazi


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Qsars to predict extent of drug biotransformation in humans l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg

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 l.jpg
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 l.jpg
Decision Tree for Whole Data Set Partitioning

> 0.3

Log D6.5

Excretion < 25%

> 47

Discriminant Function

Excretion > 38%

Recursive Partitioning

Excretion  38%

Excretion > 38%


Classification and validation using test set l.jpg

Decision Level Partitioning

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 l.jpg
Discussion Partitioning

  • 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