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Pharmacometrics

an emerging tool to asses the efficacy of a potential molecule in a computer based model thus saving time and money

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Pharmacometrics

  1. 1. PHARMACOMETRICS DR PRAFULL DR ANIMESH •Please put your smartphones and mobiles on silent mode – They may wake somebody up •This session will be videographed for training purposes
  2. 2. “...The quick inference, the subtle trap, the clever forecast of coming events, the triumphant indication of bold theories — are these not the pride and the justification of our life’s work?” Sherlock Holmes in `The Valley of Fear’ (Sir Arthur Conan Doyle) 2
  3. 3. INFERENCES AND FORECASTS • Pharmacometrics Google search – 1,19,000 results Pubmed search - 162 items • Google search for: Prafull – 5,47,000 results Animesh – 7,66,000 results Are the presenters more popular than the topic ? • Pubmed search Prafull – 2 items Animesh – 39 items NOW ? 3
  4. 4. •Four limbs •One tail •Coarse skin •One trunk •Two ivory tusks May or may not be an Elephant Previous data: Every elephant has a trunk and ivory tusks Conclusion: •The animal is definitely an elephant •Chances of getting killed by poachers are high 4
  5. 5. The CAR paradigm •Mechanical engineer needs to know driving too •Driver also should know basic troubleshooting 5
  6. 6. DRUG DEVELOPMENT PROCESS Approx $1.8 Billion (How to improve R&D productivity: The pharmaceutical industry’s grand challenge . Nature Reviews:Drug Discovery 2010;9:203-14) The Food and Drug Administration (FDA) has expressed its concern in Challenge and Opportunity on the Critical Path to New Products published in March of 2004 6
  7. 7. DRUG APPROVALS PER YEAR 7 Malorye Allison Nature Biotechnology 30, 41–49 (2012) doi:10.1038/nbt.2083
  8. 8. Diabetes Increased HbA1C Increased blood sugar complications Disease treatment outcome 8
  9. 9. Quality of life, longevity, affordability safety, efficacy efficacy, safety, guidelines business potential, profit 9
  10. 10. MANAGE AND LEVERAGE KNOWLEDGE Knowledge Information PHARMACOMETRICS 10
  11. 11. PHARMACOMETRICS DEFINITION: Pharmacometrics is the science of developing and applying mathematical and statistical methods to characterize, understand, and predict a drug’s pharmacokinetic, pharmacodynamic, and biomarker–outcomes behavior1 FDA DEFINITION Pharmacometrics is an emerging science defined as the science that quantifies drug, disease and trial information to aid efficient drug development and/or regulatory decisions 1. P. J. Williams, A. Desai, and E. I. Ette, in The Role of Pharmacometrics in Cardiovas- cular Drug Development, M. K. Pugsley (Ed.). Humana Press, Totowa, NJ, 2003, pp. 365–387. 11
  12. 12. PK modelPD- biomarker- outcomes link model statistics Stochastic simulation PD model Data visualization Computer programming Pharmacometrics 12
  13. 13. HISTORY AND DEVELOPMENT • Peck- Ludden 87-95 Drug concentration development paradigm Individual PK forecasting & individualized Rx Population PK/PD applications Pharmacometrics derived evidence of efficacy/safety (e.g., Phase 2b-3) Randomized concentration-controlled trial 13
  14. 14. 1997 • Population PK guidance 2001 • End of Phase 2a Meeting idea emerged CDDS meeting 5 2002 • Clinical pharmacology subcommittee emphasizing pharmacometrics solutions • Drug approval decision based on PM analysis 2003 • Exposure-Response guidance 2007 • Disease model & trial design started (Parkinson’s disease) • QT trial design & concentration-response analysis 2011 • Strategic plan emphasizing PM • Centralized PM Data warehouse-Software environment Lesko -Woodcock- Galson –Murphy 95-present 14
  15. 15. CORNERSTONE OF PHARMACOMETRICS Bayesian statistics and Probability Modeling Simulation 15
  16. 16. BAYESIAN STATISTICS 16
  17. 17. CALCULATION OF POSTERIOR PROBABILITY FROM PRIOR PROBABILITY • Suppose someone told you he had a nice conversation with someone on the train • Assuming women constitute half of the population the probability of the event that the conversation was held with a woman P(W)= 0.5 • Then we come to know that the person had long hair • Suppose it is also known that 75% of women have long hair and 25% of men have long hair 18
  18. 18. CALCULATION OF POSTERIOR PROBABILITY FROM PRIOR PROBABILITY Now our posterior probability of the person being a woman given the fact that the person had long hair is or 19
  19. 19. 20 A SIMPLE EXAMPLE • Unknown parameter (): long-term systolic blood pressure (SBP) of one particular 60-year-old female • 4 measurements with a mean and a standard deviation =5 • Survey of the same population (60-year-old female): mean SBP =120 and standard deviation =10 130Y
  20. 20. 11/13/2008NONMEM Estimation Methods 21 ESTIMATION OF  • Frequentist • Point estimate: • Interval estimate (95%CI) • Bayesian • Prior probability • likelihood • Posterior probability
  21. 21. 11/13/2008NONMEM Estimation Methods 22 ESTIMATION OF  • Frequentist • Point estimate: • Interval estimate (95%CI) • Bayesian • Posterior distribution P(|Y) • Posterior mean • 95% credible interval 130ˆ  Y 5.2*96.113096.1  n Y  4.129|ˆ Y 4.2*96.14.129 
  22. 22. 23 PRIOR, LIKELIHOOD AND POSTERIOR xp pdens 80 100 120 140 160 0.00.050.100.15 Population Distribution (prior) Individual data (likelihood) Individual parameter (posterior) Long-term systolic blood pressure (SBP) of 60-year-old woman
  23. 23. BAYESIAN STATISTICS AND PHARMACOMETRICS • An important fact about a pharmacometrics based experiment is that the design of any experiment is conditioned upon the results and understanding of previous experiments. • The process is inherently Bayesian. 24
  24. 24. 25 DEFINITION OF MODELING Mathematical (conceptual) modeling is describing a physical phenomenon by logical principles characterized with quantitative relationships, e.g., formulas, whose parameters may be measured (or experimentally determined) http://www.hawcc.hawaii.edu/math/Courses/Math100/Chapter0/Glossary/Glossary.htm
  25. 25. 26 USES OF MODELS Yates FE (1975) On the mathematical modeling of biological systems: a qualified “pro”, in Physiological Adaptation to the Environment (Vernberg FJ ed), Intext Educational Publishers, New York. 1. Conceptualize the system 2. Codify current facts 3. Test competing hypotheses 4. Identify controlling factors 5. Estimate inaccessible system variables 6. Predict system response under new conditions
  26. 26. DISEASE MODEL • Mathematical models to i. describe ii. explain iii. investigate iv. Predict the changes in disease status as a function of time • . It incorporates • functions of natural disease progression • Placebo effect • Drug action which reflects the effect of a drug on disease status 27
  27. 27. POPULATION PHARMACOKINETIC AND PHARMACODYNAMIC MODELING Population modeling involves the analysis of data from a group (population) of individuals, with all their data analyzed simultaneously to provide information about the variability of the model's parameters. 28
  28. 28. OXCARBAZEPINE • Adjunct and monotherapy in adult patients and as adjunct therapy in pediatric patients with partial seizures • Exposure-seizure frequency data collected from adult and pediatric patients submitted originally was subjected to qualitative analysis and to build an exposure-response model to test: whether placebo responses in adult and pediatric patients were similar whether the exposure-response relationships in the 2 populations were similar Derive reasonable dosing recommendations for monotherapy in pediatric patients • Mixed-effects modeling indicated no important differences in the placebo and drug effects between adults and pediatric pts. Oxcarbazepine monotherapy in pediatric patients was approved without the need for specific controlled clinical trials (Bhattaram VA, Booth BP, Ramchandani RP. The AAPS Journal 2005; 7 (3)) 29
  29. 29. CLINICAL TRIAL SIMULATION • Simulation of a clinical trial can provide a data set that will resemble the results of an actual trial. • Multiple replications of a clinical trial simulation can be used to make statistical inferences • Estimate the power of the trial • Predicting p-value • Estimate the expected % of the population that should fall within a predefined therapeutic range 30
  30. 30. NESIRITIDE • Drug nesiritide for treatment of acute decompensated congestive heart failure • PD marker: Pulmonary capillary wedge pressure (PCWP) • Nesiritide reduced PCWP but also reduced SBP. • Desired effects cannot be achieved without undesired effects, such as hypotension April 1999, FDA issued a nonapprovable letter to the sponsor Exposure and response data from the original submission were modeled and model was used to explore various alternative dosing scenarios 31
  31. 31. 2 mg/kg followed by 0.01 mg/min/kg infusion offered a reasonable benefit-risk profile. This dosing regimen was selected for additional investigation in the Vasodilation in the Management of Acute CHF (VMAC) trial. The results obtained from the VMAC trial and the simulations are in close agreement with those observed NESIRITIDE May 2001, FDA approved nesiritide for CHF Publication Committee for the VMAC Investigators. JAMA. 2002;287:1531-1540. 32
  32. 32. 33 TOOLS FOR MODELING AND SIMULATION • NONMEM (UCSF, Globomax) • SAS (SAS Institute Inc) • Splus (Insightful Corporation) or R (Free) • WinBUGS (MRC Biostatistics, Free) • ADAPT II (USC, Free) • WinNonLin/WinNonMix (Pharsight) • Trial Simulator (Pharsight)
  33. 33. 34
  34. 34. APPLICATIONS OF PHARMACOKINETICS IN CLINICAL TRIALS • Dose determination in pediatric trials • Optimizing dose in clinical trials (CT) • Faster drug development • Drug approvals without CT • Better trial designs • Risk reduction in CT • Dose finding in adults • Strengthening pharmacogenomics • Evolving new strategies 35
  35. 35. TRIALS IN CHILDREN Trials in adults • Based on mortality benefits • Large sample size • Homogenous population • Ethical issues Trials in children • Mainly to support dosing recommendations • Heterogeneous population • Smaller samples and sample sizes Do more with less 36
  36. 36. TIPRANAVIR • Tipranavir capsule, was approved as an HIV-1 protease inhibitor in adult patients • seeking an approval of APTIVUS oral solution (OS) and capsule for HIVinfected pediatrics 2 to 18 years of age • 48-week, open-label, parallel, randomized clinical trial with 2 doses of TPV OS with ritonavir (RTV). 290 + 115 mg/m2 (BSA adjusted adult dose) 375 + 150 (30% higher than lower dose Matched exposure with adult dose Increased virological success But: •Better efficacy only in pts with higher no of mutations •And Age of the patient directly correlates with no of mutations •Higher dose for children > 6ys •Lower dose for children < 6 yrs •(Salazar JC, Cahn P, Yogev R, et al. AIDS. 2008;22:1789-1798 •US Food and Drug Administration. Pediatric drug development 2009. http://www.fda.gov/cder/pediatric/index.htm) 37
  37. 37. TIPRANAVIR Contd…… •Higher dose for children > 6ys •Lower dose for children < 6 yrs But: In adult patients TPV is indicated for treatment experienced pts who are likely to have more mutations. In paed population too, target pt group is likely to have more mutations Higher dose range recommended for all children 38
  38. 38. TIPRANAVIR Contd…… Higher dose range recommended for all children But: Pharmacokinetics of TPV are a function of body wt and not BSA or age •Simulations were done for weight based dosing in place of BSA adjusted dosing •12/5 mg/kg and 14/6 mg/kg were new decided as lower and higher doses. TPV/RTV in the dose of 14/6 mg/kg approved for children For adults too, recommended dose was increased to 375/150 mg/m2 39
  39. 39. FENOLDAPAM • Systemic and renal vasodilator, approved in adults for in-hospital, short-term management of severe hypertension • Adult dose: 0.01 to 1.6 μg/kg/min • Seeking an approval of fenoldopam for the pediatric population (from 1 month to 12 years of age) for the same indication. • The sponsor studied doses of 0.05, 0.2, 0.8, and 3.2 μg/kg/min in pediatrics Doses upto 0.8 mcg/kg/min were approved (Hammer GB, Verghese ST, Drover DR, Yaster M, Tobin JR. . BMC Anesthesiol. 2008;8:6). 40
  40. 40. CANDESARTAN• Candesartan cilexetil, an ARB, approved for the treatment of hypertension and heart failure in adults. • Two 4-week dose-ranging safety and efficacy studies were conducted in hypertensive pediatric participants • 2 age groups (6 to <17 years and 1 to <6 years) were studied separately 1 < 6 yrs 6 yrs – 17 yrs Beneficial Not Beneficial Placebo corrected versus placebo anchored analysis US Food and Drug Administration. Review. http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/UCM189129.pdf 41
  41. 41. FLUCONAZOLE• Triazole antifungal drug that is used to treat invasive candidiasis in neonates in intensive care • Pharmacokinetic study to assess the dose- exposure relationship in neonatal infants who were 23 to 40 weeks gestational age • 55 infants who contributed 357 plasma samples; 60% were from specified study sample times, 40% were scavenged samples • Investigators developed a detailed population model that showed the influence of a number of clinical covariates on differences in pharmacokinetics (Wade KC, Wu D, Kaufman DA, et al. Population pharmacokinetics of fluconazole in young infants. Antimicrob Agents Chemother. 2008;52:4043-4049) Dose needed for a 24-week-old infant is approximately 67% lower on an mg/kg/d basis than that necessary to treat a 32-week-old or a 40-week-old infant 42
  42. 42. SOTALOL • Approved for ventricular and supraventricular tachycardia in adults • sponsor conducted 2 clinical trials to investigate the antiarrhythmic potential in pediatrics ages 1 month to 12 years • Dosing recommendation: 30 mg/m2 three times daily as a starting dose with subsequent titration to a maximum of 60 mg/m2. • The PD effects of sotalol in pediatrics were similar to those in adults for a given exposure. Hence, the exposure in the adults was a reasonable target in pediatrics • Clearance of sotalol increases until the patient reaches 2 years of age independent of body-size; after 2 yrs it depends only on body size. 43
  43. 43. • FDA proposed a dose in patients <2 years of age that included an age factor • The dosing recommendations for sotalol in pediatrics aged 1 month to 12 years old were incorporated in the labeling • modeling efforts led to the specific dosing instructions, which were not directly studied in trials, in patients <2 years of age. SOTALOL (Shi J, Ludden TM, Melikian AP, Gastonguay MR, Hinderling PH. J Pharmacokinet Pharmacodyn. 2001;28:555-575). 44
  44. 44. LEVOFLOXACIN • Approved by FDA in 2008 as treatment for children following inhalational exposure to anthrax. • Pharmacokinetic (PK) data from 90 pediatric patients receiving 7 mg/kg and two studies of 47 healthy adults receiving 500 and 750 mg/kg levofloxacin were used for the pharmacometric analyses. • Body weight:covariate for levofloxacin clearance and the volume of distribution • Clearance: reduced in pediatric patients under 2 years of age due to immature renal function • Different dosing regimens were simulated to match adult exposure • Dose of 8 mg/kg twice a day was found to match the exposure of the dose approved for adults Indication added without actually conducting clinical trial (Li F, Nandy P, Chien S, Noel GJ, Tornoe CW. Antimicrob Agents Chemother. 2010 Jan;54(1):375-9) 45
  45. 45. IDENTIFICATION OF BIOMARKERS (Bhattaram VA, Booth BP, Ramchandani RP. The AAPS Journal 2005; 7 (3)) 46
  46. 46. PK OF DABIGATRAN • Dabigatran etexilate is the orally bioavailable pro-drug of dabigatran, a direct thrombin inhibitor • Data from eight clinical studies in healthy volunteers and patients population pharmacokinetic (PK) and pharmacodynamic (PD) models were developed to investigate whether the PK and PD of dabigatran differ across different populations • Renal function was the only covariate shown to have a clinically relevant impact on dabigatran exposure • PK of dabigatran is sufficiently consistent to allow extrapolation of data generated in healthy volunteers to patients with AF or undergoing OS. (Dansirikul C, Lehr T, Liesenfeld KH, Haertter S, Staab A. Thromb Haemost. 2012 Apr;107(4):775-85) 47
  47. 47. DABIGATRAN HEMODIALYSIS • Hemodialysis a useful method of decreasing dabigatran plasma levels. • Seven patients with ESRD were investigated in an open-label, fixed- sequence, two-period comparison trial • A population pharmacokinetic model was developed to fit the data and then used for various simulations • Dialysis duration had the strongest impact on dabigatran plasma concentration • Dialysis settings such as filter properties or flow rates had only minor effects • The final model was successfully evaluated through the prediction of plasma concentrations from a case report undergoing dialysis. (Liesenfeld KH, Staab A, Härtter S, Formella S, Clemens A, Lehr T. Clin Pharmacokinet. 2013 Mar 26. [Epub]) 48
  48. 48. PHARMACO – GENOMICS AND METRICS • Patients from a pharmacokinetic sub study, were reconsented and reenrolled into a clinical trial for genotyping analysis • 198 single nucleotide polymorphisms were genotyped • 1260 nevirapine plasma concentrations obtained from 271 genotyped patients • Nevirapine clearance was 19.4% reduced in Asian/Black patients, compared with Caucasian/Hispanic patients • By integration of high-throughput genotyping data into a pharmacometric analysis of nevirapine, the impact of the CYP2B6 516G>T polymorphism on nevirapine's exposure was confirmed and quantified (Lehr T, Yuan J, Hall D, Zimdahl-Gelling H. Pharmacogenet Genomics. 2011 Nov;21(11):721-30) 49
  49. 49. SWOT ANALYSIS Strength: quantitatively explore relationships among different disease targets, quantify risk and benefits • Platform for communication for decision makers • Portability across modelers and regulatory agencies Threats: Protection of intellectual property Lack of regulatory guidelines Lack of data warehouse and IT infrastucture Opportunities: Engage project team at discovery stage, Integrate knowledge across phases • Create a shared vision • Develop standard definitions Weakness: Shortage of trained experienced leaders • No shared strategic vision • No standard process and definition • No defined roles, responsibilities 50 Model-Based Drug Development : Strengths, Weaknesses, Opportunities, and Threats for Broad Application of Pharmacometrics in Drug Development Pharmacol 2010 50: 31S Jeffrey D. Wetherington, Marc Pfister, Christopher Banfield, Julie A. Stone, Rajesh Krishna, Sandy Allerheiligen
  50. 50. KNOWLEDGE SHARING IN PHARMACOMETRICS Pharmacometrics now entering Industrialization phase Similar analyses to be performed over and over Access to prior knowledge are needed ---- Knowledge Sharing 51
  51. 51. The demand for scientists with pharmacometrics skills has risen substantially. Likewise, the salary garnered by those with these skills appears to be surpassing their counterparts without such backgrounds (Barrett JS, Fossler MJ, Cadieu JS. J Clin Pharm 2008;Volume 48 (5): 632–649) 52
  52. 52. 53 Frequentist conclusion Bayesian conclusion
  53. 53. SUGGESTED READING • Pharmacometrics: The Science of Quantitative Pharmacology Edited by Ene I. Ette and Paul J. Williams John Wiley & Sons, Inc • Pharmacometrics 2020DOI: 10.1177/0091270010376977 J Clin Pharmacol 2010 50: 151S Jogarao V. S. Gobburu • Exposure-Response Modeling of Darbepoetin Alfa in Anemic Patients With Chronic Kidney Disease not Receiving DialysisDOI: 0.1177/0091270010377201 J Clin Pharmacol 2010 50: 75S Sameer Doshi, Andrew Chow and Juan José Pérez Ruixo 54
  54. 54. SUGGESTED READING • Pharmacometrics as a Discipline Is Entering the ''Industrialization'' Phase: Standards, Automation, Knowledge Sharing, and Training Are Critical for Future Success J Clin Pharmacol 2010 50: 9S R. Gastonguay, Bernd Meibohm and Hartmut Derendorf • Model-Based Drug Development : Strengths, Weaknesses, Opportunities, and Threats for Broad Application of Pharmacometrics in Drug DevelopmentJ Clin Pharmacol 2010 50: 31S Dennis M. GraselaJ Clin Pharmacol 2010 50: 31S Jeffrey D. Wetherington, Marc Pfister, Christopher Banfield, Julie A. Stone, Rajesh Krishna, Sandy Allerheiligen 55
  55. 55. Thank You 56

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