What is Pharmacometrics (PM)?

Pharmacometrics: A Business Case
NJE-AAA Pharmacometrics: A Business Case Pharmacometrics Task Force May 25, 2010

What is Pharmacometrics (PM)?
Can include: Mathematical modeling and simulation Statistical analysis Often used to facilitate efficient drug development and approval process Needs multidisciplinary team consisting of quantitative clinical pharmacologists, statisticians, engineers, data management experts, and clinicians Pharmacometrics (PM) analyses are: Quantitative analyses of data pertaining to: Pharmacokinetics Biomarkers Clinical outcomes Disease characteristics Trial characteristics

Utilization of PM allows for a more efficient drug development process
Main advantages Propose best doses The cost of PM is marginal compared to the final cost of a trial. In most cases, the level of effort for PM is as low as 1 person for 2-6 months1 Estimate effect size Rescue discarding good drug Target patient selection Maximize value of prior data Drug approval Labeling 1 Depending on the complexity of analysis, the level of effort may be slightly lower or higher

Common questions about PM
Will it delay my NDA? Will it take a long time? Does it take many resources to deliver? No!

Example benefits (1/2) Scenario and impact examples Propose best doses
NJE-AAA Example benefits (1/2) Scenario and impact examples Propose best doses 1 Exposure – response relationships for organ rejection (effectiveness) and creatinine clearance (safety) were developed. Simulations explored alternative dosing regimens to optimize benefit – risk profile; level of effort was 1 person for less than 2 months. Pharmacometric analyses presented to Cardio-Renal Advisory Committee in 2005 with recommendations to conduct another clinical study using an optimized dosing regimen (pg 9) 2 Highly variable PK of Tacrolimus between ulcerative colitis patients and high trough concentrations in Phase II studies presented challenges to further development. Simulation of dose titration based on exposure-response was effective for identifying target trough concentration, demonstrating effectiveness and justifying Phase III studies (pg 13) 3 The exposure-viral load reduction model predicted the effect at different doses, resulting in a range of possible active oral doses used in the design of phase IIa trials (pg 15) Estimate effect size 4 Enhanced the trial success by increasing study duration; trial now suitable for registration (pg 21) Rescue discarding good drug 5 A new dosing regimen was selected based on pharmacometric analyses and evaluated in an additional clinical trial. Nesiritide was approved by FDA in 2001 (pg 30)

Example benefits (2/2) Scenario and impact examples
NJE-AAA Example benefits (2/2) Scenario and impact examples Target patient selection 6 Pharmacometric dose – response analysis identified the proportion of mildly diseased non-responders was the primary cause of lack of evidence of effectiveness. FDA’s approvable letter suggested that sponsor conduct a future study including patients with moderate and severe disease (pg 35) Maximize value of prior data 7 Approval of oxcarbazepine monotherapy in pediatrics was based on demonstrating similar exposure –response relationship for seizure frequency in pediatrics and adults using prior data from adjunctive therapy trials. No additional monotherapy pediatric trials were required (pg 38) Drug approval 8 Confirmatory evidence provided by significant dose–response (chorea score) relationship. Internal consistency of results across one positive, one negative trial and their extensions. (pg 40) 9 Clinical trial simulations a paricalcitol dosage regimen based on iPTH/80 was predicted to significantly lower the rate of hypercalcemia, compared to the iPTH/60 based regimen tested in clinical trials, without significantly impacting efficacy. Oral paricalcitol was approved by the FDA for use in CKD Stage 5 patients at a dose of iPTH/80 TIW without the conduct of further clinical trials in patients (pg 41) Labeling 10 Cleviprex dosing regimen used in clinical trials resulted in overshooting and oscillations around the target blood pressure. Simulations of the exposure – response relationship were used to optimize the dosing regimen to quickly achieve and maintain target blood pressure (pg 44)

Three big challenges exist
Perception that PM may slow the drug development timeline and raise costs Lack of awareness of full benefits and usage Lack of adequate training infrastructure in the US and abroad

Key success factors for enhancing adoption of PM
Educate professionals around cost and speed implications of PM, recognizing that “in some” cases it may take longer time in Phase 2 Increase awareness of PM benefits with clinical development executives and scientists and widely share successes Align structurally PM in large organizations to reflect multi-disciplinary nature and clinical decision-making implications Enhance number of trained professionals Develop standardized analysis and reporting

Detailed business cases
NJE-AAA Detailed business cases Propose best doses Estimate effect size Rescue discarding good drug Target patient selection Maximize value of prior data Drug approval Labeling

Determining appropriate dosing for future trials
1 PROPOSE BEST DOSES NJE-AAA Determining appropriate dosing for future trials Probability of failure Percent Azathioprine PM approach and impact 50 Everolimus 3 ng/ml 45 Everolimus 6 ng/ml Sponsor NDA for everolimus tablets (a prophylaxis of organ rejection following heart transplantation) needed to provide safe and effective dosing regimens that would minimize renal toxicity Key questions included: Are the effectiveness and renal toxicity clinical outcomes related to drug exposure? What is a rational dosing regimen that would maximize effectiveness (benefit) and minimize nephrotoxicity (risk)? Analysis conducted by 1 person for <2 months FDA performed simulations based on heart transplantation study to project range of outcomes of altered dosing schemes Determined the appropriate dosing to test in future trials 40 Everolimus 9 ng/ml 35 Everolimus 12 ng/ml 30 25 20 100 200 300 400 Cyclosporine trough ng/ml Mean CrCL change from baseline ml/min 100 200 300 400 Cyclosporine trough ng/ml SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

Enhancing trials through prodrug dosing
PROPOSE BEST DOSES Enhancing trials through prodrug dosing Response PM approach and impact Toxicity Effectiveness Sponsor developing a prodrug (test) for the treatment of a life threatening disease in which a noninferiority comparison to the parent (reference) drug failed to establish effectiveness Proposed a fixed dosing strategy for patent drug (as approved) with key question: What is the appropriate dosing regimen (fixed vs. per kg dosing) for noninferiority comparison of the prodrug and parent drugs? Analysis conducted by 1 person for <2-4 weeks FDA analyzed data from failed test drug trials to develop the parent drug AUC, effectiveness, and toxicity relationships Proved dosing regimen for prodrug (mg vs. mg/Kg), which enhanced future trial to support an approval 10 20 30 40 50 60 70 Area under the curve (AUC) mg.hr/L Response Predicted effectiveness – weight-based dosing Predicted effectiveness – fixed dosing Predicted toxicity – weight-based dosing Predicted toxicity – fixed dosing 40 50 60 70 80 90 100 Weight kg SOURCE: Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng JJ and Gobburu JV (2008) Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during J Clin Pharmacol 48:

Recommending dosage warning label
PROPOSE BEST DOSES NJE-AAA Recommending dosage warning label PM approach and impact Probability of quitting at weeks 9-12 or nausea after treatment Percent Sponsor conducted five dose-finding and registration clinical trials to determine the appropriate dosing Key questions included: What is the optimal dose? Is there a need for dose adjustment in subjects with renal impairment? If so, by how much? Sponsor conducted population pharmokinetics analysis, model simulations and exposure-response analysis to determine basis for discussing the dose selection Led to recommendation of dosage warning to be included in current label 100 200 300 400 AUC Ng/ml SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

Clarifying dosing and regulatory expectations
2 PROPOSE BEST DOSES NJE-AAA Clarifying dosing and regulatory expectations NDA submission – Feb 29, 2008; approval – Dec 24, 2008 PM approach and impact Activity 2001 02 03 04 05 06 2007 Sponsor needed to determine the dosing for a drug 7 years in development for advanced prostate cancer patients Key questions were: Is a loading dose needed to suppress testosterone, and, if so how much? Is a maintenance dose and suppression regimen needed? Sponsor developed a mechanistic data model to explore dosing strategies via trial simulations Identified alternative dosing strategies and clarified regulatory expectations that led to approval CS02/N = 129, 6 mo CS06/N = 82, SD CS07/N = 172, SD CS12/N = 187, 12 mo Registration trial CS14/N = 127, 12 mo CS21/N = 610 EOP2A meeting CS21 dose/ regimen not finalized Mar 02 Mar 03 Mar 04 Mar 05 Mar 06 Mar 07 NOTE: Only dose-finding studies shown SOURCE: FDA, Drug Approval Package, Degeralix Injection, Ferring Pharmaceuticals, December 24, 2008

Simulating dose to justify Phase 3 design
, 2 PROPOSE BEST DOSES Simulating dose to justify Phase 3 design High trough concentrations in Phase II studies PM approach and impact Tacrolimus, a potent approved immunosuppressant, had a promising expansion as an oral therapy for ulcerative colitis (UC) Highly variable PK between patients and high trough concentrations in Phase II studies presented challenges to further development Logistic analysis of PK/response in late Phase II demonstrated trough concentration is a good predictor for response Simulation of dose titration based on PK/response was effective for attaining target trough concentration, demonstrating efficacy and justifying Phase III studies Simulated dose titration shows eliminated high trough SOURCE: Presented by Atsunori Kaibara (Astellas Pharma, Inc) at the “PK/PD Internal Symposium on Modeling and Simulation in Drug Development and Clinical Applications", Yonsei University Medical Center, Seoul, Korea (2006)

62 PROPOSE BEST DOSES NJE-AAA Promoting innovative trial designs to determine effective and safe dosing Genotype Biomarker PM approach and impact Response rate Percent Sponsor developing a new compound to treat type 2 diabetes Key questions include: Is this a once-a-day drug? Is dosing adjusted to match exposures reasonable to characterize the effectiveness and safety of the drug? Built a semimechanistic model, using parameters derived from FDA’s prior experience with 26-wk trials, to describe the time course and drug concentrations Provided the FDA team with the ability to discuss innovative trial designs and the sponsor with a model to apply to other similar drugs 05 10 20 10 20 40 BID QD SOURCE: Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng JJ and Gobburu JV (2008) Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during J Clin Pharmacol 48:

3 PROPOSE BEST DOSES NJE-AAA Accelerating drug development through a model-based approach to understanding dosing Simulated and observed viral load for 2 dosage regimens PM approach and impact IC50 = 5.75 ng/ml Solid lines: Measured Dotted lines: Simulated Bold: 100 mg 2x / day Narrow: 25 mg 1x / day Sponsor needed to select a range of active oral doses for clinical phase IIa trials of a novel anti-HIV drug, maraviroc PK/PD model to link plasma concentration to inhibition of viral replication was adapted for short-term treatment based on disease model parameters from literature and preclinical data The model predicted the effect on viral load of different doses, resulting in a range of possible active oral doses used in the design of phase IIa trials Diff log (BSL)-log (Day 6), copies/ml IC50 = 0.64 ng/ml Time, days SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Rosario MC, Jacqmin P, Dorr P, van der Ryst E, Hitchcock C. A pharmacokinetic-pharmacodynamic disease model to predict in vivo antiviral activity of maraviroc, (reference citation), 2005 Nov;78(5):508-19, copyright year of publication)

PROPOSE BEST DOSES NJE-AAA Promoting collaboration and more informed decisions to determine dosing 40 mg QD 10mg BID 20 mg BID PM approach and impact Chance of being the preferred dose Percent Sponsor seeking appropriate dosing and trial design for a new class of antiviral compound in conjunction with another approved drug Key questions included: Are the proposed dose and dosing regimen reasonable for the phase 2b trial? Is dose selection based on the first 1-mo data reliable? Applied a mechanistic viral–dynamic model, including prior knowledge of the approved drug combination, to describe the time course of viral load reduction driven by concentrations Incorporating prior knowledge to build a model that leads to more informed decisions and FDA/sponsor collaboration 100 100% 90 80 70 60 50 40 30 20 10 4 8 12 16 20 24 28 32 36 40 44 48 Time Week SOURCE: Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng JJ and Gobburu JV (2008) Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during J Clin Pharmacol 48:

Developing models that accurately identify dosing
PROPOSE BEST DOSES NJE-AAA Developing models that accurately identify dosing Workflow for the development of the pain relief and dropout models PM approach and impact SC capsule SC-75416 Oral solution Model IA Pain relief Sponsor needed to determine which dose of a pain compound would be needed to achieve superiority to 400 mg ibuprofen in a post-oral surgery model PK/PD models were developed relating plasma concentrations to pain relief scores Clinical trial simulations conducted to recommend a dose of 360 mg Dose of 360 mg was corroborated with placebo- and positive-controlled study evaluating various doses and 400 mg ibuprofen ModelIA/IB Goodness of fit Model IA/IB PR prediction Model IB Dropout Oral solution PK Valdecoxib Model IA Pain relief Model IIA/IIB TOTPAR6 predictions Model IIA/IIB Goodness of fit CTS results Model IB Dropout Study results Oral solution predictions and clinical trial simulations to support the design of the post–oral surgery pain study SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Kowlaski, KG, Olson S, Remmers AE, Hutmacher MM; Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain, (reference citation), June 2008 copyright (year of publication)

Determining best dosing to avoid failure
PROPOSE BEST DOSES NJE-AAA Determining best dosing to avoid failure PM approach and impact Patients with clinical failure Percent Sponsor seeking approval for an oral suspension product Key question was: What is the optimal dosing strategy to avoid clinical failure in the majority of patients? FDA conducted exposure-response analysis to compliment the sponsor’s pre-specified statistical analysis Enhanced the label and decided to conduct a Phase IV study to evaluate therapeutic advantages of monitoring and adjusting the dosing 10 20 30 Steady-state concentration Ng/ml SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

Determining age-appropriate dosage for labeling
PROPOSE BEST DOSES NJE-AAA Determining age-appropriate dosage for labeling Age factor PM approach and impact Sponsor seeking approval to use an adult tachycardia drug (already on the market) for pediatrics Key question was: is the pediatric dosing regimen proposed by the sponsor acceptable? FDA used data from two clinical trials and modified the sponsor’s exposure-response model to determine dosing regimen for neonates and infants, the subset of pediatrics in question Approved dosage based on outcome of age analysis and incorporated pediatrics-specific dosing into the label 0.1 0.2 0.3 1 2 3 10 20 30 Age Months 100 Age factor = for age >24 months SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Advancing decisions based on dual dose range
5 4 ESTIMATE EFFECT SIZE NJE-AAA Advancing decisions based on dual dose range Insomnia patients LPS % change from mean placebo response Y = 0.31x -32.5 r2 = 0.66 PM approach and impact Sponsor developing a drug to treat insomnia held an end-of-phase 2a meeting (EOP2A) Key questions discussed were: Is the dose range selected for the Phase 2b studies in insomnia patients reasonable? What should be the duration of the Phase 2b studies? Analysis conducted by 1 person for <2-4 weeks FDA used data from 14 studies of internal FDA submissions and insomnia drug literature Proved: Effective use of dual dose range (correlation between healthy subjects and insomnia patients) Shorter trials could produce results Healthy volunteers LPS, % change from mean placebo response Insomnia patients WASO % change from mean placebo response -5 -10 -15 -20 -25 -30 Healthy volunteers WASO % change from mean placebo response SOURCE: Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng JJ and Gobburu JV (2008) Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during J Clin Pharmacol 48:

Simulating clinical trials for successful design
ESTIMATE EFFECT SIZE NJE-AAA Simulating clinical trials for successful design PM approach and impact Sponsor needed to determine if a new treatment was effective for Alzheimer’s disease (AD) A crossover design was evaluated to establish proof-of-concept using smaller and shorter duration trials Clinical trial simulation model was built from Phase I data and literature reports including PK/PD and disease progression of other AD treatments Eight alternative trial designs were simulated to determine trial design Trial resulted in more efficient trial design and a conclusive decision for further development SOURCE: Lockwood, Peter; Ewy, Wayne; Hermann, David; and Holford Nick; Application of Clinical Trial Simulation to Compare Proof-of-Concept Study Designs for Drugs with a Slow Onset of Effect; An Example in Alzheimer’s Disease, Pharmaceutical Research, 2006

Determining precision in dose prediction
ESTIMATE EFFECT SIZE NJE-AAA Determining precision in dose prediction Concentration–response profiles for efficacy PM approach and impact Drug effect (fraction of baseline value) Sponsor evaluated pregabalin for pain treatment by determining how precisely pain reduction could be predicted and demonstrated PK/PD model relating pain relief to gabapentin plasma concentrations was derived from a phase 3 study and further modified to reflect pregabalin preclinical data Simulation data suggested that doses that identify predefined response may be imprecisely estimated Quantification of imprecision will drive phase 2 dose and trial design Gabapentin concentration Ug/ml Model 1 Model 3 Drug effect models based only on data within this concen-tration range Model 2 10-1.0 10-0.0 10-1.0 10-2.0 SOURCE: Lockwood, Peter A; Cook, Jack A; Ewy, Wayne E; and Mandema, Jaap W;The Use of Clinical Trial Simulation to Support Dose Selection: Application to Development of a New Treatment for Chronic Neuropathic Pain, Pharmaceutical Research, November 2003

, ESTIMATE EFFECT SIZE Conducting exposure-response analysis to prevent the need for additional trials Mean pain score Mean pain score PM approach and impact Clinical trial data of gabapetin for postherpetic neuralgia was comprised of single replicates of various doses Exposure-response analysis was used to avoid additional replicate trials for the representative doses A random-effects model was applied to the submitted studies, demonstrating predictability and establishing exposure-dependent decrease in pain scores and cross-confirmation of trials Regulatory approval was granted without the need for additional clinical trials Time Days Time Days Mean pain score Mean pain score Time Days Time Days Mean pain score Placebo (observed) Placebo (predicted) 600 mg daily (observed) 600 mg daily (predicted) 1,200 mg daily (observed) 1,200 mg daily (predicted) 2,400 mg daily (observed) 2,400 mg daily (predicted) 3,600 mg daily (observed) 1,800 mg daily (predicted) 3,600 mg daily (predicted) 1,800 mg daily (observed) Time Days SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Lalonde RL, Kowalski KG, Hutmacher MM, Ew W, Nichols DJ, Milligan PA, Corrigan BW, Lockwood PA, Marshall SA, Benincosa LJ, Tensfeldt TG, Parivar K, Amantea M, Glue P, Koide H and Miller R; Model-based Drug Development, (reference citation), July 2007 copyright (year of publication)

y ESTIMATE EFFECT SIZE NJE-AAA Using quantitative decision criteria to understand when to terminate development of drug Example of a decision rule based on dual criteria PM approach and impact Gemcabene, a cholesterol-lowering drug, was evaluated for market potential versus existing therapy using quantitative decision criteria based on estimated effect and distribution of trial results A lower reference value (LRV) of competitive efficacy and target value (TV) of estimated commercial viability were used for risk criteria PK / PD and disease model data were used to simulate trials using the uncertainty in treatment effect Both probability of success (a go decision) and probability of a correct decision were used to evaluate trial performance metrics, leading to a decision to terminate development Go Pause Stop Example of a design performance summary Data-driven decision Go (success) PCT20>LRV and PCT90>TV Stop (fail) PCT20≤LRV or PCT90≤TV Truth (desired decision) Total ∆>TV (Go) Prob(Go and ∆>TV) Prob(Stop and ∆>TV) Prob(∆>TV) ∆≤TV (Stop) Prob(Go and ∆≤TV) Prob(Stop and ∆≤TV) Prob(∆≤TV) Total Prob(Go) Prob(Stop) 1.0 SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Lalonde RL, Kowalski KG, Hutmacher MM, Ew W, Nichols DJ, Milligan PA, Corrigan BW, Lockwood PA, Marshall SA, Benincosa LJ, Tensfeldt TG, Parivar K, Amantea M, Glue P, Koide H and Miller R; Model-based Drug Development, (reference citation), July 2007 copyright (year of publication)

Demonstrating drug dosages to allow early trial discontinuation
ESTIMATE EFFECT SIZE NJE-AAA Demonstrating drug dosages to allow early trial discontinuation Dose–effect curve of effect over placebo PM approach and impact Proof-of-concept trial of acute stroke therapy used an early termination rule to end trial at the earliest point of establishing futility or efficacy An adaptive dose allocation and sample size scheme was continually updated to allocate patients to placebo or adaptively to best model the minimal dose providing maximal treatment effect Termination was based on standard deviation of response Dose-response relationship was flat, showing that only 3 of the 15 doses were needed to demonstrate efficacy over placebo, allowing early trial discontinuation Evaluable population E F Effect over placebo 10 16 22 27 33 38 45 52 59 67 76 84 96 108 120 Dose of UK-279,276 (mg) The horizontal line at 0 indicates the line of no change, at 1 the futility threshold (F), and at 2 the efficacy threshold (E) SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Lalonde RL, Kowalski KG, Hutmacher MM, Ew W, Nichols DJ, Milligan PA, Corrigan BW, Lockwood PA, Marshall SA, Benincosa LJ, Tensfeldt TG, Parivar K, Amantea M, Glue P, Koide H and Miller R; Model-based Drug Development, (reference citation), July 2007 copyright (year of publication)

Using quantitative data to understand when to discontinue development
y ESTIMATE EFFECT SIZE NJE-AAA Using quantitative data to understand when to discontinue development Clinical utility index for lead and backup compounds. PM approach and impact Sponsor wanted to compare two potential insomnia compounds Clinical Utility Index (CUI) was developed using various measures of residual sedation and efficacy Dose-response analyses were conducted on each end point, using sensitivity analysis to determine the degree of clinical meaning in CUI Peak CUI values were observed at doses not considered viable, leading to an expedited and quantitative decision to discontinue development Lead compound Back-up compound Line: median CUI Shaded: 80% conf. Symbols: observed CUI Dose, mg Dose, mg Probability of observing a difference at peak clinical utility index (CUI) value Difference in CUI SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Ouellet D, Werth J, Parekh N, Feltner D, McCarthy B and Lalonde, RL; The Use of a Clinical Utility Index to Compare Insomnia Compounds: A Quantitative Basis for Benefit–Risk Assessment (reference citation), March 2009 copyright (year of publication)

Evaluating competitive potential of a drug to decide development
y ESTIMATE EFFECT SIZE NJE-AAA Evaluating competitive potential of a drug to decide development Dose-response relationship for gemcabene when combined with placebo PM approach and impact With placebo (A 0) 10 mg atorvastatin (A10) 40 mg atorvastatin (A 40) 80 mg atrovastatin (A 80) Sponsor needed to evaluate the efficacy of a gemcabene, novel cholesterol-lowering drug Dose response model was developed from publicly available data and proprietary patient data, using the Pharsight Drug Model Explorer visualization technology Unlike competitor ezitimibe, gemcabene was found to have little additional LDL-C lowering in combination with high statin doses Use of the model after the first phase II trial facilitated a quick decision to stop development LDL % change from baseline Dose, mg Dose-response relationship of atorvastatin monotherapy and in combination with gemcabene and ezitimibe Gemcabene Ezitimibe Atorvastatin alone (top) With agent (bottom) LDL % change from baseline Atorvastatin dose, mg SOURCE: Mandema, Jaap W; Hermann, David; Wang, Wenping; Sheiner, Tim; Milad, Mark; Bakker-Arkema, Rebecca; and Hartman, Daniel; Model-based development of Gemcabene, AAPS Journal, 2005

NJE-AAA Detailed business cases Propose best doses Estimate effect size Rescue discarding good drug Rescue discarding good drug Target patient selection Maximize value of prior data Drug approval Labeling

Revising dosing strategy for quicker approval
5 RESCUE DISCARDING GOOD DRUG NJE-AAA Revising dosing strategy for quicker approval Time course of nesiritide plasma concentrations Indicates predicted Indicates observed ` Typical time course of nesiritide plasma concentrations and the effects on the pulmonary capillary wedge pressure after a 2 μg/kg bolus followed by a fixed-dose infusion of 0.01 μg/kg per minute (data for the initial 3 hours) PM approach and impact Nesiritide plasma concentrations ug/l Placebo-corrected hemodynamics mmHg Sponsor seeking approval for a drug to treat acute decompensated congestive heart failure (CHF) Key question: what is the optimal dosing regimen of nesiritide to achieve faster benefits and minimize risk (i.e., undesired hypotension)? Developed model based on exposure and rate data from original submission to explore alternative dosing strategies Sponsor revised dosing strategy based on PM results and received quicker approval -1 Systolic BP -2 -3 PCWP -4 -5 0.5 1.0 1.5 2.0 2.5 3.0 Time Hours SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Incorporating dosing strategies into labeling
RESCUE DISCARDING GOOD DRUG NJE-AAA Incorporating dosing strategies into labeling Observed ` Predicted Risk of renal deterioration increases with decreasing renal function (assessed based on baseline creatinine clearance) following 4 mg infusion of zoledronic acid over 15 min and placebo in solid tumor and prostate cancer patients PM approach and impact Risk of renal deterioration Percent Sponsor seeking approval for a drug to treat hypercalcemia of malignancy and osteolytic bone metastases Key questions included: Is there a need to adjust dosing in patients with renal impairment? If so, what doses should be recommended? Early phase PK studies were used to develop a population model leading to FDA recommendation for dose adjustment in mild and moderate renal impairment patients Incorporated FDA’s dosing strategies into label Zoledronic acid Placebo 30 40 50 60 70 80 90 100 Creatinine clearance ml/min SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Eliminating need for additional trials
RESCUE DISCARDING GOOD DRUG Eliminating need for additional trials Symptom score PM approach and impact Sponsor had inconclusive results from two registration trials in patients with a debilitating neurological disorder without approved treatments Key question was: is there adequate evidence of effectiveness in the current clinical trial database? Analysis conducted by 1 person for <2-4 weeks FDA analyzed data across studies to investigate whether there was a consistent effectiveness signal Based on results and need to supply treatment for this disease, additional clinical trials were alleviated Week SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

Specifying additional clinical trial needs to avoid trial failure
RESCUE DISCARDING GOOD DRUG NJE-AAA Specifying additional clinical trial needs to avoid trial failure Best fit 95% confidence limits PM approach and impact Relationship between the relative risk of the clinical event and the percent change in the biomarker (laboratory concentrations). Sponsor seeking approval for a drug for patients with an unmet life threatening rheumatologic disorder was unable to demonstrate additional evidence of effectiveness after two trials Key questions included: Is the laboratory concentration predictive of the clinical outcome? What dose should be approved? Expanded analysis by simulating the estimated reduction required to achieve a clinical benefit resulting in a dose-response relationship Recommended to explore the maximally tolerated dose or a dose selected to achieve a greater reduction in this laboratory value Relative risk of primary end point Series -100 -50 50 100 Change in biomarker Percent SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Selecting patient population
6 TARGET PATIENT SELECTION Selecting patient population Placebo-subtracted change in score A at Week 12 PM approach and impact Sponsor proposed a drug to treat patients with mild, moderate or severe life-threatening disease with inconsistent results during three trials Key questions included: What is the reason for the inconsistent results across the three registration trials? How can the success rate of future trials be improved? Analysis conducted by 1 person for <2-4 weeks FDA developed a new exposure-response model based on analysis across the three studies Led to suggestion to conduct future study in patient segmentation (moderate and severe disease patients only) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Dose mg Placebo-subtracted change in score A at Week 12 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Dose mg SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

y TARGET PATIENT SELECTION NJE-AAA Saving time and allowing rapid development decisions by targeting patients Linear dose response model CI-1017 tid dose (mg) Hyperbolic (Emax) dose response model CI-1017 tid dose (mg) PM approach and impact 3 6 9 12 15 18 21 24 27 30 33 36 3 6 9 12 15 18 21 24 27 30 33 36 Early development decisions were needed for Alzheimer’s Disease compound to evaluate efficacy and dose-response versus competitor Original 12-week trial design had 6 parallel groups, 5 dose levels, and patients per group PK/PD and disease data were simulated in various dose-response relationships, using the slowest time patterns to evaluate crossover The most robust design demonstrated that while hundreds of patients were still needed, shorter trials were sufficient, resulting in time savings Negative results allowed rapid termination of development ED50 ~8.5 mg EC50 ~21 ng/ml ½ maximal effect = -2 Change in ADAS-cog score Effect size at 25mg = -3 Effect/dose slope = Effect/conc slope = Change in ADAS-cog score Effect size at 25 mg tid = -3 = 75% Emax CI-1017 average steady state plasma concentration (ng/mL) CI-1017 average steady state plasma concentration (ng/mL) Combined agonist-antagonist dose/response Agonist (Emax) dose/response Antagonist (Imax) dose/response Sigmoidal (Smax) dose response model CI-1017 tid dose (mg) U-shaped dose response model CI-1017 tid dose (mg) 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 ED50 ~ 8 mg EC50 ~ 21 ng/mL Hill coefficient = 4 Change in ADAS-cog score Change in ADAS-cog score Overall effect size at 10mg tid = -3 ED50 Emax ~ 7 mg ED50 Imax ~ 15 mg Effect size at 25 mg = 100% Emax = -3 CI-1017 average steady state plasma concentration (ng/mL) CI-1017 average steady state plasma concentration (ng/mL) SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Lalonde RL, Kowalski KG, Hutmacher MM, Ew W, Nichols DJ, Milligan PA, Corrigan BW, Lockwood PA, Marshall SA, Benincosa LJ, Tensfeldt TG, Parivar K, Amantea M, Glue P, Koide H and Miller R; Model-based Drug Development, (reference citation), July 2007 copyright (year of publication)

Using data to enhance trial design when expanding drug usage
y 7 MAXIMIZE VALUE OF PRIOR DATA NJE-AAA Using data to enhance trial design when expanding drug usage PM approach and impact Sponsor seeking approval to use an adult seizure drug (already on the market) for pediatrics Key questions included: Is there adequate evidence for approving drug in pediatrics without the need for additional controlled clinical trials? What are the appropriate dosing instructions for this indication? FDA used originally submitted data to build an exposure-response model for quantitative analysis to: Test whether placebo responses in adult and pediatric patients were similar Test whether exposure-response relationship in the two populations were similar Derive reasonable dosing recommendations in pediatrics Proved dosing recommendations in pediatrics matched adults providing an approval without additional trials SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Identifying treatment dosage and potential harmful effects
8 DRUG APPROVAL NJE-AAA Identifying treatment dosage and potential harmful effects Series Series Proportion of patients with favorable endoscopic response Percent PM approach and impact Number of patients, alkaline phosphatase 3x ULN Sponsor needed further analysis for an antifungal drug Key question is: what is the appropriate dose of micafungin for the treatment? FDA reviewer used data from two studies to model the relationship between dose and effectiveness Dose–response analysis recommended the treatment dose needed for approval as well as a package insert indicating harmful effects with greater dosage 20 16 12 8 4 50 100 150 Dose mg SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81: , copyright 2007 (year of publication)

Determining dosing for drug approval
9 DRUG APPROVAL NJE-AAA Determining dosing for drug approval PM approach and impact % of subjects Model simulated Sponsor conducted 3 trials in chronic kidney disease Stage 5 patients with hyperparathyroidism for an oral paricalcitol drug Used a validated exposure-response model to define new dosing regimen that reduces the rate of hypercalcemia while maintaining acceptable therapeutic response Results of trial confirmed predicted results from exposure-response modeling and simulations, maintaining a pre-defined efficacy rate of over 80% and limiting observed rate of hypercalcemia Drug approved by FDA without need for further clinical trials in patients Parameter Observed Efficacy (2 consecutive serum iPTH measurements decreased by 30% from baseline) 81 87.9 Safety (2 consecutive serum calcium measurements over 11 mg/dL; hypercalcemia) 1.5 1.6 SOURCE: Zemplar Capsules United States Package Information, June Accessed at 18March 2010.

Incorporating dosing recommendations into labeling
y LABELING NJE-AAA Incorporating dosing recommendations into labeling PM approach and impact Sponsor sought approval for Parkinsons’ disease drug for acute use in patients Key questions included: Is the maximum recommended dose and the titration strategy proposed by the sponsor appropriate? Is there a need for adjusting dose in the renally impaired? Used data from the dose-finding strategy for simulations using an exposure-response model Dosing recommendations suggested by the exposure-response analysis were incorporated in the labeling after discussions with the sponsor SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

Improving dosing regimen to incorporate into label
y 10 LABELING NJE-AAA Improving dosing regimen to incorporate into label PM approach and impact Drug evaluated in the pre-, during and post- surgical settings for patients with mild to moderate hypertension Cleviprex dosing regimen used in clinical trials resulted in overshooting and oscillations around the target blood pressure PK/PD modelling used to simulate the exposure–response relationship to optimize the dosing regimen to quickly achieve and maintain target blood pressure Resulted in a safer and more effective dosing regimen than employed in clinical trials, which was favored to be incorporated into the label SOURCE: Drug Approval Package. Cleviprex® (clevidipine butyrate) Application No b.

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