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System Validation –

The Simcyp Approach

Karen Rowland Yeo

Senior Scientific Advisor & Principal Consultant

[email protected]

© Copyright 2012 Certara, L.P. All rights reserved.

Systems Qualification: Development Process

Implementation Documents

Science Overview Software Test Sets Training

Model developed in Matlab and results are compared with

results obtained using model implemented in the software


Support &Transparency: Models

•Jamei M, Bajot F, Neuhoff S, Barter Z, Yang J, Rostami-Hodjegan A, Rowland-Yeo K. A

Mechanistic Framework for In Vitro-In Vivo Extrapolation of Liver Membrane

Transporters: Prediction of Drug-Drug Interaction Between Rosuvastatin and

Cyclosporine. Clin Pharmacokinet. 2014 Jan;53(1):73-87

•Polak S, Ghobadi C, Mishra H, Ahamadi M, Patel N, Jamei M, Rostami-Hodjegan A. Prediction

of concentration-time profile and its inter-individual variability following the dermal drug

absorption. J Pharm Sci. 2012;101:2584 - 95.

•Jamei M, Turner D, Yang J et al. Population-based mechanistic prediction of oral drug

absorption. AAPS J 2009; 11, 225-237.

•Rowland-Yeo K, Jamei M, Yang J, Tucker GT and Rostami-Hodjegan A. Physiologically

based mechanistic modelling to predict complex drug–drug interactions involving

simultaneous competitive and time-dependent enzyme inhibition by parent compound

and its metabolite in both liver and gut - the effect of diltiazem on the time-course of

exposure to triazolam. Eur J Pharm Sci 2010; 39:298-309.

•Yang J, Jamei M, Rowland-Yeo K, Tucker GT and Rostami-Hodjegan A. Prediction of

intestinal first-pass metabolism. Curr Drug Metab 2007; 8:676-684.

Some of the key algorithms have been reported in the following publications……..

………and referenced in the help file and presented at our workshops.


Internal Testing Process

Excel Compare

Program

Automated Regression Testing

Multiple

Workspaces

Autotest

Program

Excel

Outputs Summary

Results

Metrics

All software produced is continuously tested against known good results for verification

and so performance is benchmarked

Expected results

In database


Performance Verification

Simcyp supply a series of workspaces and expected results sets in Excel format.

The user then has the ability to satisfy themselves that Simcyp Simulator is performing

as expected.

Excel

Outputs

Simcyp

Simulator

Manual

Comparison

with stock results

Sample

Workspaces

External Testing Process


Paroxetine

Paroxetine

Paroxetine

Table: 21.2.1.1 Summary of changes to existing compound files in Version13

Compound Parameter Value Comments

V12 V13

SV-Paroxetine fumic for CYP2D6 inactivation parameters

0.2 1 Value modified in line with in vitro data

SV-Rosuvastatin

BCRP-CLint,T (uL/min) 18 35 Value re-optimised using Simcyp PE module to recover the tmax. Performance verification of the file is reported in Jamei et al., 2013.

Rosuvastatin

V13 vs. V12.2 Comparisons


Consortium Member Poll/Initial Shortlist

Lit searching:- clinical data (e.g. SAD/MAD/DDI)

Lit searching:- in vitro data

Extensive lit searching/meta-analyses – compound fields

Comparison to in vivo

Simple – CL, C-T profile

input to database/peer review -QA

auto generation of files within the installer

Auto testing – compound comparisons to in vivo in multiple builds during

software development/release candidate/released version

Victim vs. perpetrator

Key mechanisms

Data availability

Final Prioritisation based on feasibility

Focus on key data

e.g. Fm for victim

Project initiation

VX Release

Library File Qualification: Compound File Development

Development of SV-file Development of Sim-file

Sim-file in vitro data

available

SV-file optimisation to fill

data gaps


Q: Will the new file be a victim or perpetrator or both (auto-inhibition/auto-induction)

The Focus on Key Data for a Compound File

1. Development of a victim file

Q: Which are the key data to include

fmperpetrated

•rCYP/rUGT

•HLM/HHEP with inhibitor

•Clinical DDI/null alleles/mass balance

fmperpetrated e.g. CYP3A4

40%

85%

Fg

•CLint,g- Qgut

•rCYP/rUGT/HIM/permeability data

•GFJ studies

•iv and po in same subjects (fa x Fg)

C-T profile SD

•Classic DDI study design – victim SD

Q: What is the main purpose of the file (e.g. Probe substrate for CYP-X)

A B CYPs

+

UGT

CLuint gut

Pgp

Enterocyte Lumen

Qvilli

0.00

0.00

0.01

0.01

0.01

108 120 132 144

Syst

em

ic C

on

cen

trat

ion

(m

g/L)

Time(h)


Extensive lit searching/meta-analyses – compound fields

Comparison to in vivo

Simple – CL, C-T profile

Development of SV-file Development of Sim-file

Sim-file in vitro data

available

SV-file optimisation to fill

data gaps

Think about CL as an example...

e.g. Scenario:

Initial scaling of rCYP to CL under predicts the clearance that is observed for CYP2D6 probes in vivo

(e.g. Dextromethorphan).

mechanism unknown (DATA GAP)

under prediction of CL is evident in CYP2D6 EMs not PMs

CLint back calculated from in vivo CL – additional CLint added to CYP2D6

Transparency - Sim-file vs. SV-file

Verdict: SV- file

based on in vivo CL

EM vs. PM evidence gives confidence in the new ‘optimised’ CLint for CYP2D6 ‘mechanistic’

Data required:

If mechanism was elucidated (e.g. As with BSA effect on CYP2C9)

‘in vitro correction factor’ can be applied

Sim-file More details Clin PK inputs V10 release webinar


Summary of Rheumatoid Arthritis Population Data Requirements

10

Demographic ADAM PBPK Metabolism Excretion Brain Skin Biologics

Age Gastric emptying Cardiac output Haematocrit Kidney weight Brain weight Thickness SC

and VE Lymph flow rate

Height ITT Liver volume CYPs Kidney blood flow Brain blood flow Fat amount SC

and VE Lymph volume

Weight IMMC cycle Liver blood flow

Portal blood flow UGTs GFR CSF flow/Turnover Pancreatic BF

Gallbladder RV Albumin SULT Nephrons/g CSF volume Pancreatic size

% bile entering GB AAG MPPGL PTCPGK Transporters IgG conc

Stomach pH after

food Heart weight HPGL

Nephron parameters:

PT L&D; Henle Loop

L&D; DT LD IC blood flow

% achlorhydria Heart blood flow Kidney transporters

Pgp and other

transporters Lung weight

Spleen weight

■ Data collated;

■ No data found;

■ Data collation on-going;

□ Not looked for yet

Spleen blood flow

Adipose volume

Adipose blood flow

Blood volume

Muscle mass

Muscle blood flow

Skin mass

Skin blood flow

Bone mass

Bone blood flow

GI tract weight

GI tract blood flow


System Qualification: Impact of IL-6 on Simvastatin Exposure in RA patients

Healthy RA

Predicted simvastatin exposure was comparable

to observed clinical data (AUC; 59 vs. 58%).

Observed

Predicted —

∆

Machavaram KK et al., CPT, 2013


• Barter ZE, Bayliss MK, Beaune PH, Boobis AR, Carlile DJ, Edwards RJ, Houston JB, Lake BG, Lipscomb

JC, Pelkonen OR, Tucker GT and Rostami-Hodjegan A. Scaling factors for the extrapolation of in vivo

metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal

protein and hepatocellularity per gram of liver. Curr Drug Metab 2007; 8:33-45.

• Yang J, Liao M, Shou M, Jamei M, Rowland-Yeo K, Tucker GT and Rostami-Hodjegan A. Cytochrome

p450 turnover: regulation of synthesis and degradation, methods for determining rates, and

implications for the prediction of drug interactions. Curr Drug Metab 2008; 9: 384-394.

12

Support & Transparency: System Parameters & Populations

• Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and

associated variability in neonates, infants and children. Clin. Pharmacokinet. 45(9), 931-956 (2006).

• Salem F, Johnson TN, Abduljalil K, Tucker GT, Rostami-Hodjegan A. A re-evaluation and validation of

ontogeny functions for Cytochrome P450 1A2 and 3A4 based on in-vivo data. Clin Pharm . (Epub

ahead of print)

• Johnson TN, Boussery K, Rowland-Yeo K, Tucker GT, Rostami-Hodjegan A. A semi-mechanistic model

to predict the effects of liver cirrhosis on drug clearance. Clin. Pharmacokinet. 49(3), 189-206 (2010).

• Darwich AS, Pade D, Ammori B, Jamei M, Ashcroft DM, Rostami-Hodjegan A. A mechanistic

pharmacokinetic model to assess modified oral drug bioavailability post bariatric surgery in

morbidly obese patients: Interplay between CYP3A gut wall metabolism, permeability and

dissolution. J Pharmacy Pharmacol. 2012 Jul; 64 (7):1008-1024

• Abduljalil K, Furness P, Johnson TN, Rostami-Hodjegan A. Soltani H. Anatomical, physiological and

anatomical changes with gestational age during normal pregnancy: a database for parameters

required in physiologically based pharmacokinetic modelling. Clin Pharmacokin. 2012

Jun;51(6):365-96

© Copyright 2012 Certara, L.P. All rights reserved. 13

Support for Simulation Qualification: Compound File Summaries

• Purpose of the model

• Examples of model performance

• Summary of key PK features considered in the model

• Details of Simcyp workspaces mimicking the design of reported clinical studies


Almost 50:50 split between external

papers and Simcyp authored/co-authored

Support & Transparency: Publications Using Simcyp

Total publications referring to Simcyp use (n = 170)

Simcyp

External


• Johnson T, Zhou D, Bui KH. Development of physiologically-based pharmacokinetic model to

evaluate the relative systemic exposure to quetiapine after administration of IR and XR

formulations to adults, children and adolescents. Biopharm Drug Dispos (In Press)

• Patel N, Polak S, Jamei M, Rostami-Hodjegan A, Turner D. Quantitative prediction of formulation-

specific food effects and their population variability from in vitro data with the physiologically

based ADAM model: A case study using the BCS/BDDCS Class II drug Nifedipine. Eur J Pharm

Sci. 2014 Jun;57:240-9

• Kostewicz ES, Aarons L, Bergstrand M, Bolger MB, Galetin A, Hatley O, Jamei M, Lloyd R, Pepin X,

Rostami-Hodjegan A, Sjögren E, Tannergren C, Turner DB, Wagner C, Weitschies W, Dressman J.

PBPK models for the prediction on in vivo performance of oral dosage forms. Eur J Pharm Sci.

2014 Jun;57:300-321

• Siccardi M, Almond L, Schipani A, Csajka C, Marzolini C, Wyen C, Brockmeyer H, Boffito M, Owen A,

Back D. Pharmacokinetic and pharmacodynamic analysis of efavirenz dose reduction using an

in vitro-in vivo extrapolation model. Clinical Pharm Ther. 2012 Oct;92(4):494-502

• Jamei M, Bajot F, Neuhoff S, Barter Z, Yang J, Rostami-Hodjegan A, Rowland-Yeo K. A Mechanistic

Framework for In Vitro-In Vivo Extrapolation of Liver Membrane Transporters: Prediction of

Drug-Drug Interaction Between Rosuvastatin and Cyclosporine. Clin Pharmacokinet. 2014

Jan;53(1):73-87

• Neuhoff S, Yeo KR, Barter Z, Jamei M, Turner DB, Rostami-Hodjegan A. Application of

permeability-limited physiologically-based pharmacokinetic models: Part II - prediction of p-

glycoprotein mediated drug-drug interactions with digoxin. J Pharm Sci. 2013 Sep;102(9):3161-

73

• Yeo KR, Kenny JR, Rostami-Hodjegan A. Application of in vitro-in vivo extrapolation (IVIVE) and

physiologically based pharmacokinetic (PBPK) modelling to investigate the impact of the

CYP2C8 polymorphism on rosiglitazone exposure. Eur J Clin Pharmacol. 2013 Jun;69(6):1311-20

Simulations Qualification: Peer Reviewed Publications


Thank you for your attention!