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System Validation –
The Simcyp Approach
Karen Rowland Yeo
Senior Scientific Advisor & Principal Consultant
© 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
© Copyright 2012 Certara, L.P. All rights reserved.
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.
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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)
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
• 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
© Copyright 2012 Certara, L.P. All rights reserved.
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
© Copyright 2012 Certara, L.P. All rights reserved.
• 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
© Copyright 2012 Certara, L.P. All rights reserved.
Thank you for your attention!