Use of modelling and simulation to assess and manage individualized risk of drug drug interaction

Use of Modelling and Simulation to Assess and Manage Individualized Risk of Drug-Drug InteractionHelen Humphries, Senior Research Scientist at Simcyp

© Copyright 2015 Certara, L.P. All rights reserved.

M&S to Assess and Manage Individualized Risk of DDI

• Importance of fm to risk of DDI

• Simcyp Approach

• Tablet and Smartphone App

– Static DDI and Population Representative

• Simcyp simulator

– Dynamic simulations and inter-individual variability

• The future – Virtual twin..

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• Systemic Clearance (L/h) = Metabolic Clearance (CLmet) + Renal Clearance (CLR)

• fm = fraction of the systemic clearance metabolised via enzyme

• fm is THE most important parameter contributing to inter-individual variability in the extent of DDI

Importance of fm to Risk of DDI

30% CYP3A4

29% CLR

41% CYP2C8

North European Caucasian PopulationMean of 1000 individuals

Inhibitor of CYP2C8

e.g., VICTIM DRUG X


Variability in DDI: Detecting Individuals at Risk

Av SubjectCL2C9 (L/h) 8CLRenal (L/h) 2CL (relative) 100%fm2C9 0.80

Two Elimination Pathways

0%

100%

200%

300%

400%

Sbj Avg Sbj 2 Sbj 3

CL

Sbj232 2

340%0.94

Sbj380.5

85%0.94

0%

100%

200%

300%

400%

Sbj Avg Sbj 2 Sbj 3

Dose

Usually the dose isadjusted to CL in theclinic (based onmonitoring response;e.g. Warfarin)

Subject 2 & 3 have same susceptibilityto inhibition of CYP2C9 however:

The higher maintenance dose (/CL)can be used as an early indication ofsusceptibility to strong inhibition ofCYP2C9 in Subject 2 BUT NOT inSubject 3.


Apps for Tablets and Smartphones

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• Static Inhibitor Concentration• Substrate: Ratio of Inhibited to Uninhibited

Clearance• Competitive and Mechanistic Inhibition• Induction

“Everything should be made as simple as possible, but not simpler”

Albert Einstein

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Simcyp Main Simulator Platform

HTML Screens•Data rich•Editable fields

Interfaces with MS Excel•Data rich•Input/output data (regulatory needs)

Teaching/e-learning•Simplified to key Input Fields•Interactive/Dynamic Screens•Animation/output on screen•PC/PDA-based

Simcyp EngineVirtual Subject Generation

Age

Gender

Blood FlowsOrgans & Tissues

Weight

Height

Albumin

AAG

Enzymes(abundance & phenotype & turnover)

Stomach Emptying

Int Transit Time

Renal Function

Virtual Population

Blood Flows


What is IVIVE? “New Tricks” for an “Old Hat” In Vitro to In Vivo Extrapolation


Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012

Systems Pharmacology

AgeWeightHeightSexGeneticsRaceDisease

Organ sizeBlood flowEnzymesTransportersPlasma proteinHaematocritTransit timepH

MWtpKaLog DH-bondingSolubilityPermeabilityKm, VmaxKbinding

SYSTEM SPECIFICFACTORS

DRUG SPECIFICFACTORS

++

CL,V,t½,DDI PD

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Physiologically-Based Pharmacokinetic Modelling (PBPK)

Rowland, Peck & Tucker – Physiologically-based pharmacokinetics in drugdevelopment and regulatory science – Ann Rev Pharmacol Toxicol 51: 45-73 (2011)


Inter-Individual Variability: IVIVE of Liver Clearance

WeightLiver×MPPGL×K

abundance Enzyme×V =[L/h]CL

n

1=j

n

1=i jim,

jimax,

int ∑ ∑

j CYP isoforms

i metabolic pathways

Rate of metabolismpmol/min/pmol

enzyme

e.g. for recombinant microsomal system

BSA (m2)

LV (L

)

Johnson et al, 2005. Liver Transplantation. 12: 1481

Rowland-Yeo et al., 2004. BCRP. 57: 687Barter et al, 2010. BDD. 31: 516 Barter et al, 2008. DMD. 36: 2405


Age(Distribution in Population)

Ethnicity Disease

Height

CardiacIndex

MPPGLHPGL

Plasma Proteins

&Haematocrit

SerumCreatinine

Body Surface

AreaWeight

LiverVolume

CardiacOutput

IntrinsicClearance

Renal Function

LiverWeight

Complex Covariate Effects: IVIVE of Clearance

QH∙fuB∙CLuintQH + fuB∙CLuint

CLH =

CLH+CLRfa∙FG∙FH

CLPO =

Jamei et al, 2009. DMPK. 24: 53

Relationships between covariatesbased on meta-analysis of published literature and otherdata sources includingHealth Survey for England data


Importance of fm to Risk of DDI

• Factors contributing to inter-individual variability in the extent of DDI do so through the impact on fm

• Cirrhosis Chalon et al., 2003. Johnson et al., 2010• Japanese Inoue et al., 2006• Chinese Barter et al., 2013• Obesity Ghobadi et al., 2011• Pregnancy Lu et al., 2012. Ke et al., 2012• Paediatric Salem et al., 2013 and 2014• Smoking Plowchalk & Rowland Yeo, 2012• Geriatric• Renal Impairment

– Tortorici et al., 2014. Rowland Yeo et al., 2011– Key PBPK parameters related to kidney disease

• Renal function, Cardiac output, CYP and transporter abundance, Gastric emptying, Albumin and Haematocrit


Paediatric Population – Impact of Age on fm


Importance of fm in Assessment of DDI risk

Systemic Clearance (L/h) = Metabolic Clearance (CLmet) + Renal Clearance (CLR)fm – fraction of the systemic clearance metabolised via each enzyme

30% CYP3A4

29% CLR 41%

CYP2C8

Mean fm for DRUG X (control)North European Caucasian Population

+ Gemfibrozil and Itraconazole(Metabolites)

AUC Ratio = 6.86 (5.81-6.75) 8%

CYP3A4

38% CLR 54%

CYP2C8

Values are geometric mean (90% CI)

Substrate for OATP1B1CLint,T = 246 µl/min/million hepatocytes

+ Itraconazole(Metabolites)

Competitive Inhibitor of CYP3A4

8% CYP3A4

38% CLR

54% CYP2C8

AUC Ratio = 1.57 (1.48-1.59)

+ Gemfibrozil(Gemfibrozil

1-O-β Glucuronide)

Mechanistic Inhibitor of CYP2C8Inhibitor of OATP1B1

29% CYP3A4

30% CLR 41%

CYP2C8

AUC Ratio = 3.89 (3.34-3.85)

fm values are staticAUC Ratio is based on dynamic concentrations


Individualised Risk of DDI

NEC NEC NEC NEC Subject 1 Subject 2

CYP2C8 *1/*1 *3/*3 *1/*1 *3/*3 *3/*3 *3/*3

OATP1B1 ET ET PT PT ET ET

Subject 1: 75 year old female with renal failure, 60kg weight, height 158 cmSubject 2: 45 year old male with cirrhosis CP-C, 80kg weight, height 175 cm

AUC Ratio

Simulated data: 10 trials of 12 subjects for each population

Values are geometric mean (90% CI)


The U.S. Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically-based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, the predictive performance of PBPK modeling in predicting CYP-mediated DDI was evaluated.

In 21/26 (81%) and 20/26 (78%) cases, respectively, AUC and Cmax ratios were within a pre-defined threshold of 1.25-fold.

These results suggest that, for submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibitors on the exposure of drug substrates.

Experience on DDI - Possibility to Take a Position


PBPK Model: Influenced Drug Label Approvals

Company Drug Name Indication No Clinical Studies

Revatio (Sildenafil) Pulmonary Arterial Hypertension 1

Xarelto (Rivaroxaban) Deep Vein Thrombosis - Pulmonary Embolism – hip/knee replacement and surgery 4

Edurant (Rilpivirine) HIV infection 1

Iclusig (Ponatinib) Chronic Myeloid Leukemia 0

Olysio (Simeprevir) Hepatitis C 7

Imbruvica (Ibrutinib) Mantle cell lymphoma & chronic lymphocytic leukemia 24

Opsumit (Macitentan) Pulmonary Arterial Hypertension 2

Movantik (Naloxegol) Opioid Induced Constipation 10

Cerdelga (Eliglustat) Gaucher Disease 12

Zykadia (Certinib) Metastatic Non-Small Cell Lung Cancer 2

Jevtana (Cabazitaxel) Metastatic hormone refractory prostate cancer 1

Bosulif (Bosutinib) Chronic, accelerated or blast phase Ph & chronic myelogenous leukemia 0

Blincyto (Blinatumomab) Philadelphia chromosome-negative relapsed or refractory B-cell precursor acute lymphoblastic leukemia (ALL 1

Farydak (Panobinostat) Myeloma 2

Lenvima (Lenvatinib) Metastatic, progressive, radioactive iodine-refractory differentiated thyroid cancer 1


• Match characteristics of a real patient with his or her virtual twin – Age, Weight, Height, Sex, Race– Current drug dosage, Comedications– Activity of metabolic enzymes and transporters

• Genotype• Biomarkers

• Exploration of likely impact of co-medication and changes in organ function

• Management of drug dosage

• Important step on the way to truly ‘personalised/stratified’ medicine

The Future – The Virtual Twin

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• Global profiling of ADME proteins (enzymes and transporters) in different organs (individual fingerprint)

• LC-MS/MS Methods for proteomics– AQUA, QconCAT, PSAQ and Label-Free– Achour et al., 2014. Russell et al., 2013. Al Feteisi et al., 2015– Harwood et al., 2013 and 2014

The Future of Proteomics-PBPK

• Use of enzyme expression patterns in blood cells as a surrogate for organ levels (e.g., liver)

• Use of blood circulating mRNA and microRNA as a biomarker for disease (e.g., hepatitis in Zhang et al., PLOS One, 2014)

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Mobile application or web based interface

Physician enters dataonto tablet device

Patient Details

Virtual Twin + Variances

PBPKPDPredictor

Virtual Twin Generation

Patient characteristics from 3rd party profiling

Data sent to PBPKPD Predictor in the Cloud

Required metrics returned ie predicted plasma

drug concentration – timeprofile with confidence

limits, relative to known‘therapeutic range’.

Any recommended dosageadjustment.

Health & Medicine

Use of modelling and simulation to assess and manage individualized risk of drug drug interaction