Jashvant (Jash) Unadkat Milo Gibaldi Endowed Professor

Dept. of Pharmaceutics School of Pharmacy

University of Washington Seattle, WA

jash@uw.edu 1

Pursuing the holy grail of predicting and verifying tissue drug concentrations: A proteomics and PET

imaging approach

https://sop.washington.edu/people/jashvant-unadkat/

ATP ADP + Pi

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Failure Rate and Reasons for Failure in Drug Development

Smietana et al., Nature Reviews Drug Discovery 15, 379–380 (2016) Harrison, Nature Reviews Drug Discovery 15, 817–818 (2016) SSX

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One Possible Reason for Lack of Drug Efficacy & Safety

• Unable to measure or predict tissue conc. of drugs

• Unbound plasma conc. ≠ unbound tissue conc. if transporters are involved, i.e. asymmetry between blood and tissue drug conc. (e.g. liver:blood due to OATPs)

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4 Eyal, Hsiao & Unadkat Pharmacol. Ther., 2009

Asymmetry In Brain:Blood Drug Conc. Due to Blood-Brain Barrier Efflux Transporters

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P-gp at the Rat BBB Results in Asymmetry in Brain:Blood Conc. of Verapamil (P-gp substrate)

0%

500%

1000%

1500%

0 5 10 15 20Blood CsA concentraion (μM)

Human PET study

Mouse KO study

Perce

nt inc

rease

in bra

in: bl

ood

[3 H]-ra

dioac

tivity

Hsiao et al J Pharmacol Exp Ther. 2006 SSX

6 Eyal et al., Clin Pharmacol Ther. 2010

Time (min)

0 10 20 30 40 50

Brain

11C-

radio

activ

ity / D

ose (

L-1)

0.01

↑~2-fold

Time (min)

0 10 20 30 40 50

Plas

ma 11

C-rad

ioacti

vity /

Dos

e (L-1

)

0.01

0.1Plasma

Brain

- CsA + CsA

+ CsA -CsA

Asymmetry in Drug Conc. at the Human Brain:Blood Barrier: P-gp Efflux of 11C-Verapamil

11C-verapamil AUCbrain:AUCblood (20 min) - 0.42±0.04 SSX

ATP ADP + Pi

Asymmetry in Tissue:Blood Drug Conc. - Liver

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CLbile CLsin

CLsef

Bile Blood

[D] [D]

CLmet

e.g. OATPs

e.g. P-gp, BCRP, MRP2

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ATP ADP + Pi

Asymmetry in Liver Tissue:Plasma Conc. when Clsin is the Rate-Determining Step

i.e. CLmet+bile >> CLsef

0

0.5

1

1.5

2

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

0

0.2

0.4

0.6

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

CLins CLefs

CLmet CLbile

CLins CLefs

CLmet CLbile

0

0.5

1

1.5

2

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

0

0.2

0.4

0.6

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

CLins CLefs

CLmet CLbile

0

0.5

1

1.5

2

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

0

1

2

3

0 10 20 30 40 50 60

Con

cent

ratio

n (m

g/L

)

Time (min)

Inhibition of sinusoidal uptake CL

Inhibition of metabolism and biliary efflux CL

No inhibition

Assumptions: > Liver is the only

eliminating organ > 90% inhibition

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Liver

Blood

Patilea-Vrana & Unadkat. CPT. 2016

↑

↔

↔

↑

ATP ADP + Pi

Asymmetry in Hepatic:Blood Conc. of 11C-Rosuvastatin in the Rat

Coronal 2 min SUV images of 11C-Rosuvastatin

He et al., Mol Pharm., ‘14

Bile Blood

ROS

Hepatocyte

OATPs BCRP/MRP2

-RIF +RIF

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Hepatic Uptake and Biliary Excretion of 11C-Rosuvastatin in the Rat

He et al., Mol Pharm., ‘14 10 SSX

Changes in Rat Liver Exposure to Rosuvastatin ± Rifampin

11 He et al., Mol Pharm. 2014

2.3 fold increase in RSV plasma AUC but NO significant increase in RSV liver AUC

Rat Blood Rat Liver

% o

f Dos

e

% o

f Dos

e

Time (min)

Time (min)

OATPs

Bile Blood

ROS

Hepatocyte

BCRP/MRP2

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Biodistribution of 11C-rosuvastatin in humans

Billington et al., In progress SSX

How Can we Predict Tissue Drug Conc. in Humans?

• PET imaging (MRI and other imaging modalities do not have the required sensitivity): – Requires sophisticated equipment and

radiochemistry – Costly (about $20-40K/experiment/subject)

• Therefore we need alternative methods that will allow us to predict tissue conc. of drugs in humans 13

APPLICATION OF PROTEOMICS DATA TO PREDICT PK AND TISSUE CONC. OF DRUGS

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Hypothesis: Predict transporter-mediated in-vivo CL and tissue concentration of drugs in humans from in-vitro studies

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Predict in-vivo CL and tissue conc. using transporter scaling factor

1. transporter-mediated drug CL

2. transporter abundance using quantitative proteomics

3. Obtain transporter scaling factor

Transporter-expressing cells

Verify predictions using PET imaging

ATP ADP + Pi

Hepatic Uptake and Biliary Excretion of 11C-Rosuvastatin in the Rat

Boston 16

Coronal 2 min SUV images of 11C-Rosuvastatin

He et al., Mol Pharm., ‘14

Bile Blood

ROS

Hepatocyte

OATPs BCRP/MRP2

-RIF +RIF

ATP ADP + Pi

0

20

40

60

80

Clea

ranc

e (m

L/m

in/k

g bo

dy w

eigh

t)

Previous PET imagingstudy

SCRH not corrected forOatp expression

p<0.05

CLs,uptake CLs,efflux CLbile

Successful prediction of the hepatobiliary clearance of rosuvastatin using cell lines, sandwich-cultured rat hepatocytes and quantitative proteomics

CLs,uptake; sinusoidal uptake CLs,efflux; sinusoidal efflux CLbile; canalicular efflux

0

2

4

6

8

10

Oat

p ex

pres

sion

(fmol

/µg

mem

bran

e pr

otei

n)

SD rat liverSCRH

p<0.05

p<0.05 p<0.05

Ishida et al., DMD, 2018 17

0

20

40

60

80

100

120

CLs,

upta

ke(m

L/m

in/k

g bo

dy w

eigh

t)

ATP ADP + Pi

Rat Hepatic Rosuvastatin Conc. well Predicted

18 Ishida et al., DMD 2018

0

500

1000

1500

2000

2500

3000

3500

0 3 6 9 12 15

Hepa

tic c

once

ntra

tion

(kBq

/mL)

Time (min)

Observed valuepredicted value95% CI of observed data

Can Rosuvastatin Hepatobiliary CL and Hepatic Conc. be Predicted in Humans?

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Total transporter abundance in suspended (SH), plated (PH), sandwich-cultured (SCH) hepatocytes and liver tissue

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Kumar et al., Unpublished data

Data removed as not published

ATP ADP + PiATP ADP + Pi

Plasma membrane transporter abundance in suspended (SH), plated (PH), sandwich-cultured (SCH) hepatocytes cf liver tissue

Kumar et al., Unpublished data

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Data removed as not published

ATP ADP + PiATP ADP + Pi

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Transporter-expressing cells better predict in-vivo (IV ) human rosuvastatin hepatic uptake clearance than hepatocytes

RSV uptake CL scaled on the basis of hepatocellularity *(Classical method)

*88 mg protein/g human liver

*Assuming sinusoidal uptake is the rate determining step in

RSV plasma CL

Patilea-Vrana G and Unadkat JD, Clin Pharmacol Ther, 2016

Martin et al., Clin Ther. 2003 Oct;25(10):2553-63. Karlgren et al., J Med Chem. 2012 May 24;55(10):4740-63.

Data removed as not published

ATP ADP + Pi

Metformin renal clearance is reasonably well-predicted using OCT2 expressing

cells

Kumar et al., DMD 2018 23

Observed metformin renal secretory clearance in humans: 432 (range 215-643) mL/min

0

100

200

300

400

500

600

700

Total abundance PM abundance PM abundanceand PM potential

Met

form

in re

nal s

ecre

tory

cl

eara

nce

(mL/

min

)

215

643

432

ATP ADP + Pi

• Predicting tissue concentration and therefore efficacy and toxicity of a drug is the next frontier in ADME research

• The hepatic ECL model clarifies when transporters will or will not affect the systemic and tissue PK of a drug

• Tissue conc. measurement is possible using PET. However, this method cannot be routinely applied

• IVIVE using transfected cells and quantitative transporter proteomics is a promising technique to predict tissue drug conc.

• These predictions should be validated using PET imaging probes that interrogate multiple drug transporters

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Summary

ATP ADP + Pi

Bhagwat Prasad Anand Deo 25

YuYang Jiake He

Major Contributors

Sarah Billington Li Wang Gabriela Patilea-Vrana

Kazuya Ishida Vineet Kumar SSX

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Genentech, Merck, Biogen, Gilead, BMS, Takeda, Pfizer

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ATP ADP + PiOther Collaborators

Dept. of Radiology: Jeanne Link, David Mankoff, Todd Richards, Janet Eary, Satoshi Minoshima, Ken Maravilla, Mark Muzi, Steve Shoner, David Lewis, Jean Lee and the PET suite team Dept. of Medicine: Ann Collier and her team; Scott Lee and his team Dept. of Anesthesiology: Karen Domino, Matthew Pennington Dept. of Pharmaceutics: Bhagwat Prasad, Edward Kelly, Carol Collins, Joanne Wang Kidney Research Institute: Jonathan Himmelfarb Univ. of Greifswald: Stefan Oswald and team Children’s Mercy Hospitals: Steven Leeder and team Acknowledgement: NIH P01DA032507, MH63641, P50 HD44404, RR 00166, HD47892, AG031485, RC1NS068904, Solvo Biotech., UWRAPT funded by Genentech, Merck, Biogen, Gilead, BMS, Takeda, Pfizer

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