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Serge Guzy; PhD President, CEO, POP-PHARM; Inc.

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Presentation on theme: "Serge Guzy; PhD President, CEO, POP-PHARM; Inc."— Presentation transcript:

1 Serge Guzy; PhD President, CEO, POP-PHARM; Inc.
Pharmacometric Tools in The Pharmaceutical Industry: Concepts and Application in Drug Development Serge Guzy; PhD President, CEO, POP-PHARM; Inc.

2 Pharmacokinetics What the body does to the drug
Distribute in circulation Distribute in tissue Eliminate drug by chemical degradation or filtering via kidneys

3 Pharmacodynamics What the drug does to the body
Interact with target protein in circulation or on a cell surface Reduce or enhance activity of circulating protein or cell Mitigate disease condition

4 Definition of Half-Life
The time required for the concentration of drug to decline by ½ Example: Drug is 20 ug/mL at 1 hour after dosing Drug is 10 ug/mL at 5 hours after dosing Drug is 5 ug/mL at 9 hours after dosing ½ drug cleared every 4 hours. Half-life is therefore 4 hours

5 Pharmacometrics Pharmacometrics
analysis and interpretation of data produced in pre-clinical and clinical trials. Inter-disciplinary field Biostatistics Computational methods Pharmacokinetic/Pharmacodynamic modeling.

6 Pharmacometric components
Population pharmacokinetic and pharmacodynamic modeling Disease progression modeling Clinical trial simulation

7 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.

8 Disease progression modeling
Mathematical models to describe, explain, investigate and predict the changes in disease status as a function of time. It incorporates functions of natural disease progression Drug action which reflects the effect of a drug on disease status

9 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

10 Impact of Pharmacometrics on Drug Approval and Labeling Decisions: Example 1
Drug nesiritide for the treatment of acute decompensated congestive heart failure Acute decompensated heart failure (ADHF) is a common and potentially serious cause of acute respiratory distress. PD marker used to measure severity of the disease: The Pulmonary wedge pressure (PWP) is the pressure measured in a pulmonary artery distal to an occlusion of that artery High in the presence of ADHF Drug for ADHF should decrease PWP Side effect: Hypotension In April 1999, the FDA issued a nonapprovable letter to the sponsor. A subsequent Pharmacometric analysis was performed to optimize dosing regimen of nesiritide to achieve a faster decrease in PCWP (benefit) and minimize undesired hypotension (risk)? Exposure (PK) and response (PD) data from the original submission were modeled. The developed model was used to explore various alternative dosing scenarios. Evidently, 2 µg/kg followed by 0.01 µg/min/kg infusion seems to offer a reasonable benefit-risk profile. The sponsor submitted the results in support of a revised dosing regimen. The FDA approved nesiritide for acute CHF in May 2001.

11 Impact of Pharmacometrics on Drug Approval and Labeling Decisions: Example 2
The sponsor sought approval of apomorphine (Apokyn), subcutaneous injection) for acute use in patients with Parkinson’s disease. Along with the registration studies, the sponsor submitted results from a dose-finding (2 to 10 mg) study with a suggested maximum recommended dose. In the renal-impaired apomorphine demonstrated a 50% increase in exposure. The FDA conducted exposure-response analysis to aid in evaluating the appropriate dosing instructions for labeling. Regulatory Questions Is the maximum recommended dose and the titration strategy proposed by the sponsor appropriate? Is there a need for adjusting dose in the renal impaired? Role of Pharmacometric Analysis The data from the dose-finding study indicated a concentration-dependent effect on Unified Parkinson’s Disease Rating Scale, which is desired, and blood pressure, which is undesired. Simulations using the exposure-response model suggested only minor additional benefits beyond 6 mg. The starting dose for patients with renal impairment was recommended to be 1 mg. Regulatory Action The dosing recommendations suggested by the Pharmacometric exposure-response analysis were incorporated in the labeling after discussions with the sponsor.

12 Detailed Case Study: TV1102 Phase 2a Study Design
Objective of the study prove the therapeutic concept and to determine the pharmacokinetic profile of ATL 1102 by subcutaneous injections in patients with multiple sclerosis Develop a PK/PD/Efficacy model that will allow optimally designing the next Phase 2 b study Study Design Cohort 1 – 40 patients to be s.c administered three ‘induction’ doses of 200 mg ATL 1102 each on days 1, 4, and 7 of study and then a ‘maintenance’ dose regimen of 200 mg twice a week (days 4 and 7 of the week) for seven weeks. Cohort 2 – 40 patients to be administered placebo injections s.c. according to the schedule of Cohort 1 Duration 16 weeks, 8 weeks treatment period (first week induction phase followed by seven weeks maintenance period) followed by 8 weeks without treatment. Efficacy variable modeled Cumulative Number of T1 Gd-Enhancing Lesions PK measurements 6 samples on Day 1, 28, 56 and 112 as follows: 1 sample before administration of medication and after 1, 2, 3, 4 and 6 hours MRI measurements Day 28,56,84 and 112

13 Population PK/T1 Modeling
The observed T1 lesions data suggest a significant difference between the Placebo and Treated group (see next slide) We modeled the average T1 lesion time profile using a Poisson (response is categorical) regression (T1 lesion changes with time) model and linked it to the PK model. This model can therefore simulate Placebo T1 time profile as well as treated T1 time profile. The model accounts also for the patients that did not show any active lesions during the course of the Phase 2a trial

14 Observed T1 enhancing lesions over time: Placebo vs. Treated Group

15 PK/T1 Modeling: Processes
SC Poisson (C) ka T1 c k31 k13 Vm,Km Tissue 2 Linear Clearance

16 PK/T1 Modeling: Mathematical Model

17 Fitting Results Knowing the Efficacy parameters will help predicting the efficacy time profile for the upcoming Phase 2b Trial

18 Population PK/Platelet Modeling
An indirect response model was linked to the PK model in order to quantify the correlation between Platelet and PK time profile

19 PK/PD Modeling: Processes
SC kin(1-C/(C+ED50)) kout ka Platelets c k31 k13 Vm,Km Tissue 2 Linear Clearance

20 PK/Platelet Modeling: Mathematical Model

21 Average Population PD estimates
Knowing the PD parameters will help predicting the PD time profile for the upcoming Phase 2b Trial

22 Phase 2b Trial design and Goal
The Phase-2b tentative design is a 6 month treatment period, with 2-3 ATL dose groups and placebo. MRI would be observed once a month with the primary endpoint being the percent reduction in the average cumulative (starting on Month 4 and cumulated every month until Month 7) number of T1 lesions, relative to Placebo. The optimal regimen would have preferably the following characteristics. At least 60% reduction of cumulative T1 lesions compared to placebo Platelets with not more than 10% of subjects <150 (10^9/L) at any time, 5% of subjects <100 (10^9/L) at any time and no subject <50 (10^9/L) at any time The dosing to be explored are 100 mg, 200 mg and 400 mg with dosage intervals varying from weekly to every 4 weeks. The goal of the simulation exercise was to have insight to the following issues: What dosing regimens can provide the required outcome based on the boundaries of MRI and platelets? What is the outcome simulating the predefined regimens? Can drug loading provide any advantage?

23 Calculation of the Cumulative number of T1 lesions
The PK/PD model was used to predict on Month and 7 the expected average number of T1 lesions Therefore, the cumulative number of T1 lesions for the treated group is simply calculated by just summing up the number of T1 lesions starting on month 4 until Month 7. We call it cum_T1_drug The same calculation proceeds for the Placebo group (same model with a zero dose). We call it cum_T1_Placebo The % MRI improvement ( %MRI ) is directly computed using the following formula %MRI=(cum_T1_Placebo-cum_T1_drug)/cum_T1_Placebo x 100

24 Phase 2b Trial Simulation Results: Percent MRI improvement vs
Phase 2b Trial Simulation Results: Percent MRI improvement vs.Total Dosing per 4 weeks for dosage intervals between 1 and 4 weeks: MRI cumulated on Month and 7 Interval Week MRI Improvement is better for small Dosage intervals, given a fixed total dose

25 Phase 2b Trial Simulation Results: Estimation of the percent of Patients to reach platelet counts below a certain threshold value The PK/PD model that was developed and fit to the Phase 2a data lead to an estimate of both the PK/PD average parameters as well as the parameters quantifying the variability across the population The average parameter values were used to simulate the average PK/PD profile using the Phase 2b dosing conditions (shown next slide) Both average and variability information can be used to simulate hypothetical patients that would behave similarly than the actual population for any specific dosage regimen We simulated a large number of patients and recorded their platelet counts for each of them at the expected measurements times (usually predose), based on the potential Phase 2b Trial designs. Each patient that had at least one recorded platelet count less than X (X being either 150,100 or 50) was considered as passing the threshold value The percent of patients passing at least once the specific threshold value was then plotted versus the total dosing per 4 weeks for different dosage intervals.

26 Example of simulation of both PK and Platelet average time profile: 200 mg TV1102 given weekly
The predictions are predose up to the last dose, then every day for 30 days after last dose. PK and Platelets are mirror projections (PK going down, PD going up)

27 Phase 2b Trial Simulation Results: Percent subjects with Platelet <150 versus total dose per 4 weeks and dosage interval Dosage interval

28 Phase 2b Trial Simulation Results: Percent subjects with Platelet <100 versus total dose per 4 weeks and dosage interval Dosage Interval

29 Phase 2b Trial Simulation Results: Percent subjects with Platelet <50 versus total dose per 4 weeks and dosage interval

30 Conclusions PK and Platelet time profiles are highly correlated
The only safety concerns are with the percent of Patients expected to have platelet counts less than 150 which is larger than 10% for a total dose per 4 weeks exceeding 400 mg However, 200 mg every week (800 mg per 4 weeks) should not lead to more than 5% of the population with platelet counts less than 100 In Conclusion, for the tentative Phase2b Trial design, we have 200mg every week: safety concerns only for Platelet counts less than 150 (about 20% of the population) but less than 5% of the population will have platelet counts less than 100 for that regimen MRI reduction is expected to be about 60% 200 mg every two weeks (no safety concerns) MRI reduction is expected to be about 45% 200 mg every three weeks (no safety concerns) MRI reduction is expected to be about 35%

31 Conclusions The proposed design of 200mg every week, two and three weeks without a loading dose should lead to enough separation in the MRI response (60,45 and 35% MRI reduction) to model a dose response relationship Characterize a dose response relationship will lead to an optimal design of Phase 3. The safety concern for a 200 mg Dose every week has been addressed and quantified About 20% of the population is expected to have platelet counts less than 150 for that dosage regimen but only 2% would have platelet counts less than 100

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