In collaboration with the University of Lyon, we presented a 2-hour session in Clinical Trial Simulation. This session fitted nicely within the Master's program curated by Emilie Henin (Calvagone). The 10 students managed to explore the PK model for Sunitinib. 4 students struck gold at the end, managing to create an efficient design that proved biosimilarity.

1. Clinical Trial Simulations
with Simulo
Ruben Faelens, Quentin Leirens, Emilie Hénin,
Marc-Antoine Fabre

2. About me
2
 Ruben Faelens
• Belgian, living in Leuven
• call me Ruben
 Computer Scientist, graduated in 2010
 Started in PK/PD modeling & simulation in 2012
 Wrote Simulo, a Clinical Trial Simulation software
 Clinical Trial Simulation expert
 Working at SGS Exprimo

3. We are SGS Exprimo
3
Per Olsson Gisleskog Andreas Lindauer Nancy Smets Marc-Antoine Fabre Eric Snoeck
(external)
Julia Winkler Bernardo de Miguel Lillo Isabelle Delor
(external)
Koen JollingDaniel Röshammar
Quentin Leirens Erno van Schaick
(external)
Ruben FaelensPhilippe Jacqmin
(external)

4. Who are we?
4
• founded in 2002, part of SGS since 2012
• company registered in Belgium (Mechelen),
home-based offices
• team of >10 experienced modellers,
currently recruiting
• strong academic/industrial backgrounds in
pharmacometrics, clinical pharmacology,
statistics and engineering
• we help clients answering key questions
throughout drug development
• >300 projects performed across a range of
therapeutic areas – very well received by
clients and regulatory authorities

5. Clinical Trial Simulation
5
https://www.youtube.com/watch?v=dW4fek6plP4

6. Clinical Trial Simulation
6
 Risk management tool
 Pick the most effective design
 Models + design elements = prediction
 Cost-effective

7. Backup: introduction
7
 M&S: Modeling & Simulation
• Modeling: explain what we see
• Simulation: predict what will happen next
 Why do we use M&S?
• They help answer questions
• They help support decisions
 CTS: Clinical Trial Simulation
• Simulate a full clinical trial
• Drug Model, Protocol, Dose Adaptations, Dropout

8.  We want to optimize the clinical trial together with the
development strategy to get the best treatment as
an outcome
Background
8
Dose Concentration Effect
Clinical
response
PK PD
What the body does to
the drug
What the drug does to
the body

9. Drug development
9
Simulations support

10. What kind of questions can we address?
10
 What is the best dose and administration interval?
 What is the best sampling schedule?
 Which protocol design will we choose?
 How many subjects do I enrol in the next phase study?
 Which proportion of my population will be well treated?
 What is the best population targeted by the drug?
 Will you continue this drug development?
 Can we look at another dosage form?
 Having information on other doses, what is the mean
effect of a 100mg dose after 2 weeks of treatment?
 What is the probability of success in phase III?
 …

11. Limitations
11
 Explain everything
 Give you the answer you want
 Find an effect where there isn’t one
 Provide one “true” answer
 Make good studies unnecessary
 Make a silk purse out of a sow’s ear
 Make your decisions for you

12. Simulo for quantitatively informed discussions/decisions
Time since study entry, year
Hippocampusvolume 0.0 0.5 1.0 1.5 2.0 2.5 3.0
100020003000400050006000
NL
MCI
AD
Hippocampal volume
versus time since study entry
Estimated time since disease onset, year
RHPNM
-4 -2 0 2 4 6 8 10
0.40.60.81.01.21.41.6
o NL
o MCI slow
o MCI fast
o AD
Males
Females
Hippo. vol. norm. to NL value for same
age and head size versus DOT
model-based simulation of various potential experimental scenarios enables;
1) visualization and summary of overall results/conclusions
2) quantitatively informed discussions/decisions (internal & regulatory)
3) optimized study designs, better investment-decisions, regulatory success
14

13. Sunitinib: our example
15
 Sunitinib is a multi-targeted tyrosine kinase inhibitor
used in the treatment of advanced renal cell carcinoma
(RCC) and imatinib-resistant/intolerant gastrointestinal
stromal tumors (GIST).
 Sutent - Pfizer (2006)
 Reference:
Reza Khosravan et al. Population Pharmacokinetic/Pharmacodynamic
Modeling of Sunitinib by Dosing Schedule in Patients with Advanced Renal
Cell Carcinoma or Gastrointestinal Stromal Tumor. Clin Pharmacokinet
(2016) 55:1251–1269

14. Focus on Sunitibib PK
16
Dose
Elimination
KA
CL/VC
Q/VC
Q/VP

15. PK parameters
17
 PK parameters are taken from
the publication. We don’t take
into account any covariate
effects or lag time.

16. IIV vs. RE
18

17. What do we need to know?
19
 What is the best dose and administration interval?
• Which dose gives a Cp of >0.01 mg/L at day 4 post-dose?
 Which proportion of my population will be well treated?
 What is the best sampling schedule?
 How many subjects do I need?

18. Translation in Simulo Drug Model
20

19. Translation in Simulo Drug Model (2)
21

20. Access to Simulo on web
24
 Then click Login to enter as a guest

21. Open a study
25
 Select ‘PK_sunitinib’ and click ok

22. When selecting a tab, 2 panels are displayed
26
The left one enables to
create, delete, move,
and select elements
The right one enables
to edit a selected
element

23. Demo of Live Simulation View
28
Interactively show how:
- to set up a single dose of 50mg
- to plot lines for 100 hours
- How A0, A1 and A2 behave
- to change the number of subjects
- To deactivate variability

24. A) Play with typical values
29
 Change THETA_VC, THETA_CL or THETA_KA
• What happens?
 Experiment with different doses
• What dose would you recommend to reach a target
concentration of 0.01 mg/L at day 4 ?
 Double THETA_VC
• How do you adapt the dose to different Vc?
• When could this happen?

25. B) Play with different levels of variability
31
 Plot the histogram of model parameter VC
• Activate variability
• What happens when you change THETA_VC ? (i.e. the population
parameter)
• What happens when you change ETA_VC ? (i.e. the subject
variability)
 Plot concentration in central compartment versus time.
• How does each parameter influence the curve?
 Let’s find the population-recommended dose
• Keep the dose previously found
• Use the default script as CUSTOM chart
• What dose would you recommend to reach a target concentration
of 0.01 mg/L at day 4 ? We want to treat well at least 95% of our
population.
 Add the actual observed concentration to plot
• What happens when you change EPS ? (i.e. the residual variability)

26. Any questions so far?
32
 Any questions so far?
 My own questions
• THETA?
• Parameter uncertainty
• ETA?
• Residual Error?

27. Let’s run a proper clinical trial simulation
33

28. 34

29. 35

30. AUC and CMax
36
N median_auc q05_auc q95_auc median_cmax q05_cmax q95_cmax
1000 1.56 0.99 2.37 0.025 0.0089 0.048

31. Changing the schedule
37

32. What is the best schedule?
38
 Every blood sample
costs…
• Collection
• Storage
• Bio-assay
 Can you use 10
observations and still
be accurate?
• median_AUC = 1.56
• Your design should
get a value between
+25% and -20%
= [1.42, 1.74] Note: optimal design

33. How many subjects?
39

34. How many subjects?
40
 Every subject costs
• Screening visit
• Follow-up
• Remuneration fee
 Why include more subjects?
• Residual Error vs. Inter-Individual Variability
• Standard Error of the Mean

35. Extra info
41
 What is the recommended dosage of sunitinib ?
• For RCC/GIST: 50mg 28 days on q1d, then 2w off
• For pancreatic cancer: 37.5mg every day
 Dose adaptation?
• Up to maximum tolerable dose
• Side effects: liver toxicity, cardiomyopathy, arrhythmia,
cardiovascular, …

36. How to ensure good data?
42
 It is 2021 and Sunitinib is off-patent. Let’s make a
biosimilar!
 Do a single trial with N=50!
Who established PK bio-equivalence with the originator
compound ?
• Median_AUC should be [1.42, 1.74]
• Q05_AUC should be [0.8, 1.25]
• Q95_AUC should be [1.99, 2.94]
 11% trials failed
• Probability of success / study power
• Publication bias / meta-analysis
• Lucky phase 2

37. 43
11% is predicted to fail

38. 44
53% is predicted to fail!

39. Key learning
45
 Clinical trial simulation is a risk management tool
• Allows to simulate different trial designs
• Calculate study power
• Optimize design, test robustness
 But within limits
• Available data, how good is the model?
• Based on assumptions (does the compound work ?)

40. Life Science Services Ruben Faelens
Scientist - Modeling & Simulation
SGS Exprimo NV
Generaal De Wittelaan 19A Bus 5
B-2800 Mechelen, E-mail : ruben.faelens@sgs.com
BELGIUM
Web : www.sgs.com/lifescience
THANK YOU FOR YOUR ATTENTION
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