smb 25092014 klaas prins q pharmetra

Meet qPharmetra, LLC

a pharmacometric consulting company

SMB / Health Valley event Sept 25, 2014

“from molecule to business”

Lars Lindbom, PhDAnders Viberg, PhDKlas Petersson, PhD

Anja Henningson, PhDEva Hanze, MSc

Jacob Brogren, PhD

Klaas Prins, PhDMarita Prohn, MScJan Huisman, BEng

Kevin Dykstra, PhDLee Hodge, MBA

Eric Burroughs, MScJason 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

Pharmacometrics

Pharmacometrics

Branch of science concerned with mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between xenobiotics and patients, including beneficial effects and side effects resultant from such interfaces.

Analogy: think of it as the pharmaceutical version of econometrics

Pharmacometricians quantify in silico any 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?


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


What’s so special about pharmacometricians?


these nerds talk the language of the statistician …


… and that of the MD …


Shared interests, different language

Different means to the same end


Gauss

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…


Without being The Expert in one field we have sufficient expertise in all

Pharmacometric analyses contributions


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 mandatoryWhat the minimum effective dose?

Pharmacometric can aid line extensions Post –marketing clinical studies

We model the (measured) past to project out to the future

Patient C

Patient B

qPharmetra Services


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

FunctionsEf

fica

cy

Patient A

Exp

osu

re

Time

Effi

cacy

TimeOur Drug’s Best Dose

Competitors

Clin

ical

Uti

lity

Dose

Tole

rab

ility

Dose

P(S

ucc

ess

)

Trial ScenarioA B C

ScenarioB

success(60%)

failure

ScenarioA

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/PDMeta-

AnalysisClinical Utility

Decision Analysis

Virtual Trials


Case Study

Predicting Survival as a function of Tumor Growth Inhibition in Oncology

The oncology model framework


Client question: what dose do I need to take forward into the next trial?

Dose Exposure PFS models and simulations

PK Model

Exp

osu

re

Time

Tumor Growth Model

Tum

or

Exposure

Survival Model

Time

Surv

ival

𝑆𝑡,𝐷𝑜𝑠𝑒 = 𝑓 𝑇𝐺𝐼𝑡,𝐷𝑜𝑠𝑒

𝑇𝐺𝐼𝑡,𝐷𝑜𝑠𝑒 = 𝑓 𝐶𝑡

𝐶𝑡 = 𝑓 𝑡, 𝑋

Pharmacodynamics

Pharmacokinetics

Tumor Growth Inhibition after Novanib administration


Integrating individualized exposure as driver of tumor shrinkage

model:

Mean+/- 95%CI and mean model prediction

𝑑𝐴1𝑑𝑡 = 𝐾𝐿 ∙ 𝐴1 − 𝐾𝐷 ∙ 𝑒−𝜆𝑡 ∙

𝐶𝑠𝑠

𝐶𝑠𝑠

∙ 𝐴1

Tumor1

KLKD∙e-λt∙exposure2

43

1

1 2

3 4We established a significant

relationship between exposure and tumor shrinkage

Progression-Free Survival Advantage vs. Exposure


Increased exposure to drug increases probability to survive

Increasing drug exposure in plasma

Concentrationquartiles

Among novanib patients, 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)

coef exp(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 AUCSS as continuous:coxph(formula = Surv(time = pfs, event = cens) ~

aucSS, data = pfsData)

coef exp(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


Furthermore, predicted tumor shrinkage is a predictor of PFS


Increased exposure leads to tumor shrinkage which increases Pr(survival)

0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Progression Free Survival by Quartiles of Predicted Tumor Inhibition

Time Since First Dose (w)

Fra

ctio

n o

f P

atie

nts

with

PF

S

TGI,cfb Q4

TGI,cfb Q3

TGI,cfb Q2

TGI,cfb Q1

Prediction of PFS as a function of novanib-induced TGI


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)F

ractio

n P

atie

nts

Su

rviv

ing

Tumor Shrinkage (% CFB)

tixladone 10 mg

tixladone 20 mg

-80 -60 -40 -20 0 20

novanib 20 mg

novanib 10 mg

95% CI

Prediction of PFS as a function of Novanib-induced TGI


Zooming in on 1 year survival cut the deal for taking 20 mg into phase III

tixlatinib 10 mg

Fraction Patients Surviving

De

nsity

0.3 0.4 0.5 0.6 0.7 0.8 0.9

01

23

45

6

tixlatinib 20 mg


De

nsity

0.3 0.4 0.5 0.6 0.7 0.8 0.9

02

46

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


De

nsity

0.3 0.4 0.5 0.6 0.7 0.8 0.9

01

23

45

6

tixlatinib 20 mg


De

nsity

0.3 0.4 0.5 0.6 0.7 0.8 0.9

02

46

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)

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

• Flyering in central Nijmegen not helpful

• In EU: UK, Germany, Switzerland

• The NL – Germany area is increasingly vibrant

• Here Novio Tech Campus / SMB could play a role for us


“wie goed doet, goed ontmoet”


In Novio Tech Campus through SMB since Sept 1st 2014

Thank you !

Data Exploration


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-ReadyPAT DOSE TIME OBS AGE SEX

Effi

cacy

Tole

rab

ility

Dose

Effi

cacy

Covariate

Model-Building


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.

Effi

cacy

Tole

rab

ility

Dose

Effi

cacy

Covariate

Effi

cacy

Tole

rab

ility

Dose

Ob

serv

atio

n

Prediction

smb 25092014 klaas prins q pharmetra

Documents

drugdrug interactions

exposurefrom molecule

endfrom molecule

drug accumulation

drug concentrations

general conceptsfrom

drug increases probability

plasma exposure