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Smb 25092014 klaas prins q pharmetra

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Smb 25092014 klaas prins q pharmetra

  1. 1. Meet qPharmetra, LLC a pharmacometric consulting company SMB / Health Valley event Sept 25, 2014 “from molecule to business”
  2. 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. 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. 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. 5. What’s so special about pharmacometricians? "from Molecule to Business" 25 Sept 2014
  6. 6. these nerds talk the language of the statistician … "from Molecule to Business" 25 Sept 2014
  7. 7. … and that of the MD … "from Molecule to Business" 25 Sept 2014
  8. 8. Shared interests, different language Different means to the same end "from Molecule to Business" 25 Sept 2014 Gauss
  9. 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. 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. 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. 12. "from Molecule to Business" 25 Sept 2014 Case Study Predicting Survival as a function of Tumor Growth Inhibition in Oncology
  13. 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. 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. 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. 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. 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. 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. 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. 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. 21. "from Molecule to Business" 25 Sept 2014 In NovioTech Campus through SMB since Sept 1st2014 Thank you !
  22. 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. 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

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