Prediction in silico of major clearance
pathway of drugs in vivo
Kota Toshimoto
Sugiyama Laboratory, RIKEN, Japan

Importance of predicting drug clearance pathways
Selection of multiple clearance pathways drug2

Problem for the prediction of drug clearance pathway• Drug clearance pathway is not determined only by theinformation whether drug is a substrate for each enzyme andtransporter!
Drug X
Substrate of metabolic enzyme A,
B, and C by in vitro experiment.

What is the in vivo major
clearance pathway?
Requirement of in silico prediction

・Contribution ratio(e.g. RAF method).

・Prediction whether drug ismetabolized for each enzyme.

・Prediction of urinary
excretion ratio.

・Quantitative estimation of intrinsic
clearance for each enzyme.

・Involvement of hepatic
uptake transporters

・ Quantitative estimation of renal
clearance.
・ Prediction whether drug issubstrate for each uptake transporter.

Too complex and difficult!!

3

Intuitive prediction of drug clearance pathway
Experts of pharmacokinetics can roughly predict the clearance
pathways of drugs only by looking at their structures.
Example…

OHHOOC
HOO

HO

Substrate of OATPs…?

HHO

We suppose that experts predict the clearance pathways of drugs using acertain characteristic (feature) which represents drug chemical structure
Charge: anion → a substrate of OATPs?
Lipophilicity:
low → renal excretion?
high → high permeability?

4Strategy of drug clearance pathway prediction
OHHOOCHOO

H
O

H

Calculate 4 descriptors (default descriptors)• MW (molecular weight; published data)• log D (lipophilicity; SciFinder)• fup (unbound fraction; ADME boxes)• Charge at PH7.0 (ADMET predictor)

HO

Chemical Structure

Classification
system？？
CYP3A4

CYP2D6CYP2C9OATPs

Renal

Establishment of in silico prediction system of major
drug clearance pathway using 4 chemical descriptors.

5CPathPred ver. 1 (Empirical approach)
Rectangular Method

Kusama M et al., Drug MetabDispos, 38, 1362-70 (2010)6

CPathPred ver. 1 (Empirical approach)Classification boundaries

7Kusama M et al., Drug Metab Dispos, 38, 1362-70 (2010)

CPathPred ver. 2 (Machine learning technique)Support Vector Machine (SVM)
The major shallow machine learning technique.
Create the complex classification boundaries.

Drugdata

CYP3A4 SVM

Yes or No

CYP2C9 SVM

Yes or No

CYP2D6 SVM

Yes or NoRenal SVM

Yes or No

OATP SVM

Yes or No

Each SVM perform theprediction independently.

Toshimoto K et al., Drug Metab 8
Dispos, 42, 1811-9 (2014)

Prediction performance

No. additionaldescriptor

RectangularmethodDefault 4
descriptors

CYP3A4: 4
CYP2D6: 3
CYP2C9: 2
Renal: 4
OATP: 2
Default
descriptors

All (885)
descriptors

Greedy featureselection

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Our project (CPathPred 3)(1) Expansion of clearance pathway (5→9).
CYP3A4CYP2C9CYP2D6Renal
OATPs

CYP3A4CYP2C9CYP2D6RenalOATPs＋CYP2C8CYP2C19
CYP1A2UGTs(2) Multiple clearance pathways prediction.
Predict the most majorclearance pathway (avoidthe drugs which havemultiple clearance pathway
in the training dataset).

Predict multiple clearance
pathways (>25% ofsystemic clearance).
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Two-step SVM approach
Single-step SVM
Use one SVM per one clearance
pathway

Better predictability

Two-step SVM
Use two SVMs per one
clearance pathway
1st SVM: predict clearance
pathway roughly.
2nd SVM: predict a single
target clearance pathway.

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Short summary• The purpose of this research is to expand the numberof clearance pathways (from 5 to 9, with metabolism
by CYP1A2, CYP2C8, CYP2C19, or UGTs newlyadded) and to evaluate the prediction performance ofthis system.
Plan and methods
1. Establishment of a prediction system for 9 major
clearance pathways
2. Evaluation of the prediction performance12