This document provides an overview of qPharmetra, LLC, an international pharmacometric consulting company. It introduces the 13 scientists who founded the company in 2010 and describes their services, which include population pharmacokinetic modeling, clinical trial simulations, and other quantitative analyses to support decision making across drug development. An example case study is presented showing how the company used modeling and simulation to determine the optimal dose level for an oncology drug candidate based on exposure-response relationships.

1. Meet qPharmetra, LLC
a pharmacometric consulting company
SMB / Health Valley event Sept 25, 2014
“from molecule to business”

2. Lars Lindbom, PhD
Anders Viberg, PhD
Klas Petersson, PhD
Anja Henningson, PhD
Eva Hanze, MSc
Jacob Brogren, PhD
Klaas Prins, PhD
Marita Prohn, MSc
Jan Huisman, BEng
Kevin Dykstra, PhD
Lee Hodge, MBA
Eric Burroughs, MSc
Jason Chittenden, MSc
qPharmetra LLC
• Founded in 2010 by 4 company owners: US (2), NL, SE
• 13 seasoned scientists with background in mainly pharmacy & engineering
• Serving ~25 innovative pharma companies (small biotech – large cap)
• Working as home- or office-based consultants
US-based, international, pharmacometric consulting company
"from Molecule to Business" 25 Sept 2014

3. Pharmacometrics
Pharmacometrics
Branch of science concerned with mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between xenobioticsand patients, including beneficial effects and side effects resultant from such interfaces.
Analogy: think of it as the pharmaceutical version of econometrics
Pharmacometriciansquantify in silicoany measured biological relationship arising from administering drugs to humans (and animal species)
Note: QSAR –quantitative structure activity relationships could fall under pharmacometrics, but as it comes often before study in any animal species, leave humans, it is considered a separate field.
What is that?
"from Molecule to Business" 25 Sept 2014

4. Pharmacometrics
Pharmacokinetics (PK)
What the body does to the drug
Pharmacodynamics (PD)
What the drug does to the body
Population pharmacokinetics (popPK)
The study of the sources and correlates of variability in drug concentrations among individuals who are the target patient population receiving clinically relevant doses of a drug of interest .
Population pharmacokinetics (popPK-PD)
The study of the sources and correlates of variability in drug exposure – response relationships among individuals who are the target patient population receiving clinically relevant doses of a drug of interest .
Further General Concepts
"from Molecule to Business" 25 Sept 2014

5. What’s so special about pharmacometricians?
"from Molecule to Business" 25 Sept 2014

6. these nerds talk the language of the statistician …
"from Molecule to Business" 25 Sept 2014

7. … and that of the MD …
"from Molecule to Business" 25 Sept 2014

8. Shared interests, different language
Different means to the same end
"from Molecule to Business" 25 Sept 2014
Gauss

9. Our expertise needs to be pretty broad
Data Manager
Preclinical pharmacologist
Statistician
Clinical pharmacologist
Formulation expert
Member of Data Monitoring Board/Committee
Pharmacokineticist
Disease Expert
Development Team member / lead
Etc…
"from Molecule to Business" 25 Sept 2014
Without being The Expert in one field we have sufficient expertise in all

10. Pharmacometric analyses contributions
"from Molecule to Business" 25 Sept 2014
drug
exposure
effect
filing
market
across entire (pre) clinical drug development phase
What formulation?
Plasma exposure?
Drug Accumulation?
Drug-drug interactions?
Impact of renal impairment?
…
Desired efficacy vs.
Adverse events
Pharmacokinetic and pharmacometric sections mandatory
What the minimum effective dose?
Pharmacometric can aid line extensions
Post –marketing clinical studies
We model the (measured) past to project out to the future

11. Patient C
Patient B
qPharmetra Services
"from Molecule to Business" 25 Sept 2014
We use integrated pharmacometric methods to help companies make the best drug development decisions
Decision
Mentoring / Partnering
StakeholdersManagement, External Decision Makers, Project Team, other R&D Functions
Efficacy
Patient A
Exposure
Time
Efficacy
Time
Our Drug’s Best Dose
Competitors
Clinical Utility
Dose
Tolerability
Dose
P(Success)
Trial Scenario
A
B
C
Scenario
B
success
(60%)
failure
Scenario
A
success
(20%)
failure
Clinical Utility
Efficacy 1
Ease of Use
Tolerability
Efficacy 2
Big trial, slow to market
Small trial, fast to market
$$$
$$
$
Scenario B
$ Net Present Value
A
B
PopPK
PK/PD
Meta- Analysis
Clinical Utility
Decision Analysis
Virtual Trials

12. "from Molecule to Business" 25 Sept 2014
Case Study
Predicting Survival as a function of Tumor Growth Inhibition in Oncology

13. The oncology model framework
"from Molecule to Business" 25 Sept 2014
Client question: what dose do I need to take forward into the next trial?
Dose
Exposure
PFS models and simulations
PK Model
Exposure
Time
Tumor Growth Model
Tumor
Exposure
Survival Model
Time
Survival
푆푡,퐷표푠푒=푓푇퐺퐼푡,퐷표푠푒
푇퐺퐼푡,퐷표푠푒=푓퐶푡
퐶푡=푓푡,푋
Pharmacodynamics
Pharmacokinetics

14. Tumor Growth Inhibition after Novanibadministration
"from Molecule to Business" 25 Sept 2014
Integrating individualized exposure as driver of tumor shrinkage
model:
Mean+/-95%CI and mean model prediction
푑퐴1푑푡=퐾퐿∙퐴1−퐾퐷∙푒−휆푡∙ 퐶푠푠 퐶푠푠 ∙퐴1
Tumor
1
KL
KD∙e-λt∙exposure
2
4
3
1
1
2
3
4
We established a significant relationship between exposure and tumor shrinkage

15. Progression-Free Survival Advantage vs. Exposure
"from Molecule to Business" 25 Sept 2014
Increased exposure to drug increases probability to survive
Increasing drug exposure in plasma
Concentration
quartiles

16. Among novanibpatients, there is a clear exposure-response relationship with PFS
Trend with increasing AUCSS, with q4 clearly superior to q1
coxph(formula = Surv(time = pfs, event = cens) ~
aucSS.q4, data = pfsData)
coefexp(coef) se(coef) z Pr(>|z|)
aucSS.q4(1.56,2.06] -0.3431 0.7096 0.2217 -1.548 0.121741
aucSS.q4(2.06,2.69] -0.4061 0.6662 0.2175 -1.867 0.061841 .
aucSS.q4(2.69,6.51] -0.7894 0.4541 0.2397 -3.294 0.000988 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Treating AUCSSas continuous:
coxph(formula = Surv(time = pfs, event = cens) ~
aucSS, data = pfsData)
coefexp(coef) se(coef) z Pr(>|z|)
aucSS-0.0003256 0.9996744 0.0001101 -2.958 0.00309 ** ---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Similar relationship with Cavg
"from Molecule to Business" 25 Sept 2014

17. Furthermore, predicted tumor shrinkage is a predictor of PFS
"from Molecule to Business" 25 Sept 2014
Increased exposure leads to tumor shrinkage which increases Pr(survival)
020406080100 0.00.20.40.60.81.0 Progression Free Survival by Quartiles of Predicted Tumor InhibitionTime Since First Dose (w) Fraction of Patients with PFS TGI,cfb Q4TGI,cfb Q3TGI,cfb Q2TGI,cfb Q1

18. Prediction of PFS as a function of novanib-induced TGI
"from Molecule to Business" 25 Sept 2014
Using the model to predict different scenarios – an example: doubling the dose
20 mg
10 mg
0 20 40 60 80 100
0.4 0.5 0.6 0.7 0.8 0.9 1.0
Time (d)
Fraction Patients Surviving
Tumor Shrinkage (% CFB)
tixladone 10 mg
tixladone 20 mg
-80 -60 -40 -20 0 20
novanib 20 mg
novanib 10 mg
95% CI

19. Prediction of PFS as a function of Novanib-induced TGI
"from Molecule to Business" 25 Sept 2014
Zooming in on 1 year survival cut the deal for taking 20 mg into phase III
tixlatinib 10 mg
Fraction Patients Surviving
Density
0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 1 2 3 4 5 6
tixlatinib 20 mg
Fraction Patients Surviving
Density
0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 2 4 6 8
Vertical line indicates standard of
care (SOC) 1-yr survival
The model allowed to evaluation of
different dose levels and regimens in
in-silico
Conclusion: phase III dose (10 mg) might
have been too low for optimal efficacy.
tixlatinib 10 mg
Fraction Patients Surviving
Density
0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 1 2 3 4 5 6
tixlatinib 20 mg
Fraction Patients Surviving
Density
0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 2 4 6 8
novanib 10 mg
novanib 20 mg
SoC
SoC
We recommend to study 20 mg vs
SoC in the next trial
(note: a separate adverse event analysis that was an
integral part of this recommendation supported this)

20. How do we turn our work into business
•In a landscape of other providers branding is essential
•Give clients a reason to go to you specifically
•For qPharmetra this branding theme is reproducible quality
•We believe that delivery of top-end quality products has led and will lead to repeat and new business
•How? SOPs, Automation, QC & QA on products delivered
•Our market is global with many companies US-based
•Flyeringin central Nijmegen not helpful
•In EU: UK, Germany, Switzerland
•The NL –Germany area is increasingly vibrant
•Here NovioTech Campus / SMB could play a role for us
"from Molecule to Business" 25 Sept 2014
“wiegoeddoet, goedontmoet”

21. "from Molecule to Business" 25 Sept 2014
In NovioTech Campus through SMB since Sept 1st2014
Thank you !

22. Data Exploration
"from Molecule to Business" 25 Sept 2014
Challenge: Graphically explore data, uncovering the interrelationships between variables and covariates.
The qP Solution
With standardized datasets in hand, we are able to efficiently construct attractive and informative graphics of endpoints vs. exposure and other covariates. Having a standardized toolbox of graphing programs available means we can spend more time in the creative aspects of exploring these visualizations for insights.
Analysis-Ready
PAT
DOSE
TIME
OBS
AGE
SEX
Efficacy
Tolerability
Dose
Efficacy
Covariate

23. Model-Building
"from Molecule to Business" 25 Sept 2014
Challenge: Develop mathematical framework quantifying the strength of and uncertainty in the relationships among endpoints and covariates
The qP Solution
For model-building, we don’t always have a standard, one-size fits all solution. Using our experience, we often define a structural model that describes the relationships among key variables and gives an appropriate distribution of random effects. We work to find models that are adequate for the task at hand, mechanistically appropriate, and capable of producing robust predictions.
Efficacy
Tolerability
Dose
Efficacy
Covariate
Efficacy
Tolerability
Dose
Observation
Prediction