html5-img
1 / 15

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. Project failures mainly attributed to pharmacokinetic problems

ghazi
Download Presentation

QSARs to Predict Extent of Drug Biotransformation in Humans

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. 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

  5. Methods • Physico-chemical properties calculated: • log P, pKa, log D • Structural and topological parameters • Molecular orbital (AM1) properties • Statistics: • Stepwise LDA • Recursive partitioning

  6. Urinary Excretion against log D6.5

  7. 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

  8. 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

  9. Modelling Whole Data Set Compounds with log D6.5 > 0.3 assumed to have low urinary excretion

  10. Discriminant Function Developed for Drugs with log D6.5 < 0.3

  11. 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%

  12. Decision Tree for Whole Data Set > 0.3 Log D6.5 Excretion < 25% > 47 Discriminant Function Excretion > 38% Recursive Partitioning Excretion  38% Excretion > 38%

  13. 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

  14. 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

More Related