The Utility of Modeling and Simulation in Understanding Locally Acting Drug Delivery

Liang Zhao, Ph.D.

Director, Division of Quantitative Methods and Modeling

Office of Research and Standards

Office of Generic Drugs

Center for Drug Evaluation and Research, FDA

AAPS, Denver November 13th, 2016

2

The Disclaimer

• This presentation represents the views and perspectives of the speaker and do not necessarily reflect the views of the FDA.

• The speaker may be contacted at Liang.Zhao@fda.hhs.gov

3

Everything is about a start and finish

4

Background• BE demonstration for locally acting products is challenging.

• Recent advancement in science created several innovative pathways for BE of locally acting products in addition to clinical endpoints BE studies.

– Better analytical tools

– Better product characterization

– Quantitative methods and modeling

• M & S Impact: Model based guidance development for complex locally acting products will ensure timely availability of high quality and affordable generics for patients.

5

Quantitative Approaches for Generics

Industry

• Limited inclusion in ANDAs

• Conventionally used in

– Model based product design

– BE study design and assessment

Regulatory

• Complex substance, e.g., Copaxone

• Locally acting products: topical, nasal, inhalation, ocular, and GI

• NTI

• Long acting injectable products

• Drugs with steep exposure-response relationships

• In vitro BE assessment

• Safety surveillance

• Others

6

Core DQMM Tool Set

Oral Drug

PK-PD model

Population model

Our Goal is to support Generic drug research Policy development Regulatory decisions

Release/ Absorption/PBPK Models

Pharmacometrics

Big Data

Non-Oral Drug

Analytics for complex mixturesSystems pharmacologyRisk modelsBusiness process models

7

Role of PBPK and Pharmacometrics for locally acting drugs

• PBPK models– Link in vitro performance with in vivo performance (PK)

– Predict the local tissue concentration in special populations including pediatric, geriatric, and disease populations

– Identify critical quality attributes for BE demonstration

– Define clinically relevant specification

• Pharmacometrics– Perform assessment of new BE criteria

– Evaluate clinical significance of failing BE

– Sensitivity of clinical endpoints

8

Currently Posted BE Recommendation for GI Locally Acting Drug Products

Method Drugs Comment

In vitro only calcium acetate, colesevelam HCl, sevelamer carbonate, sevelamer HCl

binding agents, no detectable PK

In vitro OR in vivo

acarbose (PD), lanthanum carbonate (PD), vancomycin HCl (clinical)

IR, no detectable PK

In vitro + PK budesonide, mesalamine, balsalazide disodium

PK as in vivo dissolution surrogate, in vitro testing as confirmative study

PD only orlistat no detectable PK

Clinical only rifaximin low solubility, no detectable PK

PK + clinical nitazoxanide, rifaximin , lubiprostone (clinical and PK)

9

Research Activities in PBPK Modeling and Simulation for Non-gastrointestinally Absorbed Drug Products

• Dermal absorption PBPK modelsAwarded to the University of South Australia (1 U01 FD005232 -01) and Simcyp, Ltd. (1 U01 FD005225-01)

• Ocular delivery PBPK modelsAwarded to Simulations Plus, Inc. (1U01FD005211-01) and CFD Research Corporation (1U01FD005219-01)

• Nasal deliveryAwarded to Applied Research Associates, Inc. (1 U01 FD005201-01)

• Pulmonary deliveryAwarded to CFD Research Corporation (1U01FD005214-01)

10

Case Studies

• PBPK model

– Case I: BE guidance development for mesalamineoral products

• PKPD model:

– Case II: Modeling and simulation evaluation for using residual levonorgestrel as one of BE metrics

11

Case I: BE guidance development for mesalamine oral products

12

Mesalamine Properties

• pH dependent solubility

• pKa: 2.7, 5.8, and 12

• Short half life

• Metabolized by N-Acetyltransferases

• Targets lower GIT and acts topically for ulcerative colitis

• Modified release dosage form

1 2 3 4 5 6 7 8 90

5

10

15

20

pH

So

lub

ilit

y (

mg

/mL

)

aqueous (French DL)

luminal fluids (Fadda HM)

ileostomy / colostomy (Fadda HM)

13

BE Challenges for Mesalamine MR Oral Products

• Multiple NDAs on the markets

• Insensitivity of clinical endpoints study– Similar response rate above dose of 2g

– A test formulation passed clinical endpoints but failed PK

• Relevance of plasma PK with local concentration – Correlation between GI local exposure and plasma profile

DR capsules NDA 204412 (Delzicol)

ER capsules NDA 20049 (Pentasa), NDA 22301 (Apriso)

DR tablets NDA 19651 (Asacol, discontinued), NDA 21830 (Asacol HD), NDA 22000 (Lialda)

14

Mesalamine BE Recommendation: Past and Present

• BE recommendation history

• Current BE recommendation– In vivo PK fasting and fed in healthy subjects

– Partial AUCs• AUC0-3 and AUC3-t for ER capsules

• AUC8-48 for DR tablets and DR capsules

– In vitro comparative dissolution• pH 4.5, 6.0, 6.5, 6.8, 7.2, and 7.5

PK or clinical endpoints for different products

PK endpoints + in vitro comparative dissolution

15

Commonly Asked Questions• Why PK?

– PK as a surrogate of in vivo dissolution

• Why pAUC?– Ensure similar distribution in different GI segments

• Why in vitro dissolution?– Capture pH range not covered in healthy populations

– Capture the range of trigger pH of the coating polymer

• Why not in patients?– Comparing formulation differences not performance

differences in different populations

– Extremely high variability in the inflammatory response both inter and intra patient.

Physiologically and pathologically relevant time

Partial AUC Search

Collect PK data after administration for 5 products

Calculate the PK metrics for each subject (dose normalized to 1.2mg)

Design the combinations of PK metrics to be analyzed

Linear Discriminant Analysis (LDA) for each combination of metrics

Randomly split the whole data set into training and testing.

Construct the model using training data and apply the model to the testing set.

AUC0-3,

AUC3-t

16

17

PBPK Absorption Modeling Approaches

Model was developed

based on i.v., suspension, and suppository PK

data.

Fit pH dependent dissolution

profiles as model input for in vitro

dissolution.

Adjust pH in the GI lumen against

observed PK profiles for each

subject.

Perform simulation to

answer specific questions.

18

Model Development and Validation

Suspension

ER capsule

Suppository

i.v.

19

Colon and plasma exposure correlation for ER capsules

Partial AUC3-t was identified

20

GDUFA research study supports BE recommendation

• Study Objective: To study the correlation between GI and plasma concentrations

• Treatment 1– Pentasa ER capsules: 500 mg capsule x 2

– Apriso ER capsules : 375 mg capsule x 3

– LialdaDR tablets: 1200 mg tablet x 1

• Treatment 2: – Mesalamine oral solution: 100 mg/125 mL water, followed by 125 mL water

• Each subject will go through treatment 1, then treatment 2

Randomized: Pentasa, Apriso, or

LialdaSolution

GI and plasma concentration was measured

Only plasma concentration was measured

21

Human Subjects

Number of subjects screened 65

Number of subjects participated 34

Number of procedures conducted

38 treatment 1 (MR arm)20 treatment 2 (IR arm)

Number of procedures completed

26 treatment 1 (MR arm)20 treatment 2 (IR arm)

Fasting 10 hours prior to study drug administration

Mean tube depth 170 cm

The study was approved by local IRB and FDA Research Involving Human Subjects Committee

22

0 2 4 6 8 10 120

20

40

60

80

100

Time (hr)

% D

isso

lved

dissolution comparison of Apriso, Pentasa, and Lialda

Apriso, pH6.8

Lialda, pH7.2

P, pH1.5

P, pH4.0

P, pH6.0

P, pH6.5

P, pH6.8

P, pH7.5

Dissolution vs. pH

23

Location Pentasa Apriso Lialda

Plasma

StomachpH ~ 1.5

DuodenumpH ~ 5-6

Proximal Jejunum

pH ~ 6-6.2

Mid JejunumpH ~ 6.4

Distal JejunumpH ~ 6.4

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

1000

2000

0 2 4 6 8

23

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

200000

400000

600000

0 2 4 6 8

0

1000

2000

3000

0 2 4 6 8

0

2000

4000

6000

0 2 4 6 8

0

2000

4000

6000

0 2 4 6 8

0

2000

4000

6000

0 2 4 6 8

0

2000

4000

6000

0 2 4 6 8

0

2000

4000

6000

0 2 4 6 8

0

500

1000

0 2 4 6 8

Time (hr)

Co

nce

ntr

atio

n (

ng

/mL)

24

Conclusions

• Based on modeling and simulation, partial AUC were identifedthat correlates with colon exposure.

• Based on the in vivo GI tube study, GI local concentration measurement results are qualitatively consistent with formulation design and in vitro dissolution.

• Based on the in vivo GI tube study, GI local concentration measurement results are qualitatively correlated with PK metrics (certain partial AUCs).

• In vivo GI tubing study and absorption modeling and simulation confirmed that PK endpoint study is a reasonable surrogate to reflect GI local concentration.

25

Case II: Modeling and simulation evaluation for using residual levonorgestrel as one of BE metrics

26

Background

• Active ingredient: Levonorgestrel (LNG)• Nominal release rate: from 20 μg/day at beginning

to 11 μg/day after 5 years • Indication: Contraception up to 5 years, treatment

for heavy menstrual bleeding• Features: the most effective; long-acting;

reversible; contraception• Modeling and simulation based evaluation of

residual Levonorgestrel can be used for an exploration of BE metric and the associated criteria.

• PK/PD modeling approach to assess the feasibility to use plasma concentration as a BE metrics

27

Questions to be Addressed and Challenges

• Conventional plasma pharmacokinetic (PK) endpoints to establish bioequivalence challenging:

– Long-term use of up to 5 years

– Locally acting site

– Highly variable plasma levonorgestrel (LNG) concentrations

• M & S was used to identify and assess potential BE metrics and statistical criteria to promote development of generic LNG IUS:

– Time

– # of Subjects

28

Source Data for Analysis• Residual Levonorgestrel (LNG) data from an array of IND and NDAs with

study durations of 1, 3, and 5 years

• Product Specification: No more than 1 of 10 results lies outside of 47 – 57 mg/system and no result lies outside of 45 – 60 mg/system.

• Created virtual population (n = 1000) incorporating the variability, under following assumptions:

1. SD of 1.56 (CV of 3%) for A (mean 52 mg) provided a range of initial LNG in 47 – 57 mg/system.

2. For release rate constant different CVs (i.e. 5 %, 10% and 15%) for k were tested and a CV of 15 % (SD of 5.835 × 10-5) with a mean release rate constant of 3.89 × 10-4 per day provided a reasonable explanation of observed residual LNG. Additive error model for release rate constant was used.

NDA 21-225 S-030 CMC Review

29

Study Design

• BE analysis was performed on residual LNG from virtual subjects ( n = 16) for RLD and hypothetical generics with different mean release rate constants (k) varying from 5% to 100% at 6 months, 1, 2, 3, 4 and 5 years using 80% - 125% BE limit.

• BE analysis was also performed between formulation D vs C and D vs Another Drug X to check if they would have passed current BE criteria at the end of 5 years.

• Phoenix/R/Berkeley Madonna software were used for the analysis.

30

Residual LNG from Virtual Population (n = 1000) and Observed data

A (CV = 0.03), K (CV = 0.15)

31

Study Simulations

• BE analysis was performed on residual LNG from virtual subjects ( n = 16) for RLD and hypothetical generics with different mean release rate constants (k) varying from 5% to 100% at 6 months, 1, 2, 3, 4 and 5 years using 80% - 125% BE limit.

• Phoenix/R/Berkeley Madonna software were used for the analysis.

32

Hypothetical Generics with Different Release Rate Constants

Rate Constant Change % Value

K RLD 3.89

K + 5 % 5% 4.0845

K + 10 % 10% 4.279

K + 20 % 20% 4.668

K + 40 % 40% 5.446

K + 60 % 60% 6.224

K + 80 % 80% 7.002

K + 100% 100% 7.78

K - 5 % 5% 3.6955

K - 10 % 10% 3.501

K - 20 % 20% 3.112

K - 40 % 40% 2.334

K - 60 % 60% 1.556

K - 80 % 80% 0.778

K - 100% 100% 0

33

Parallel BE Study Results at Different Time Points for Hypothetical Generics with Increase in Release

Rate ConstantChange Plus 0 %

TIME 6 Months 1 Year 2 Year 3 Year 4 Year 5 Year

GMR 100 100 100 100 100 100

Lower 98.04 97.69 96.81 95.81 94.78 93.73

Upper 102 102.36 103.3 104.37 105.51 106.69

Change Plus 5 %

GMR 99.64 99.28 98.56 97.85 97.14 96.44Lower 97.68 96.97 95.37 93.68 91.97 90.27Upper 101.64 101.64 101.86 102.21 102.61 103.04

Change Plus 10 %

GMR 99.28 98.56 97.14 95.75 94.37 93.01

Lower 97.32 96.25 93.95 91.59 89.24 86.93Upper 101.28 100.93 100.45 100.1 99.79 99.52

Change Plus 20 %GMR 98.56 97.14 94.37 91.68 89.06 86.51

Lower 96.6 94.82 91.17 87.54 84.02 80.62Upper 100.57 99.52 97.69 96.01 94.4 92.85

Change Plus 40 %

GMR 97.14 94.37 89.06 84.04 79.31 74.85

Lower 95.18 92.03 85.84 79.96 74.45 69.3

Upper 99.15 96.77 92.4 88.34 84.5 80.84

Change Plus 60 %

GMR 95.75 91.68 84.04 77.05 70.63 64.75Lower 93.77 89.31 80.81 73.02 65.94 59.54Upper 97.76 94.1 87.41 81.3 75.66 70.42

Change Plus 80 %

GMR 94.37 89.06 79.31 70.63 62.91 56.02

Lower 92.39 86.67 76.06 66.66 58.4 51.15

Upper 96.4 91.52 82.71 74.84 67.76 61.36

Change Plus 100 %

GMR 93.01 86.51 74.85 64.75 56.02 48.47

Lower 91.02 84.1 71.58 60.85 51.71 43.93Upper 95.05 89 78.26 68.91 60.7 53.48

34

Observed Residual LNG from Different Formulations and Model Simulated Residual LNG in Virtual Population (n = 16) with

Hypothetical Generic Formulations

35

Parallel BE Study Results at Different Time Points for Hypothetical Generics with Decrease in

Release Rate ConstantChange Minus 0 %

TIME 6 Months 1 Year 2 Year 3 Year 4 Year 5 YearGMR 100 100 100 100 100 100

Lower 98.04 97.69 96.81 95.81 94.78 93.73

Upper 102 102.36 103.3 104.37 105.51 106.69Change Minus 5 %

GMR 100.4 100.7 101.5 102.2 102.9 103.7

Lower 98.4 98.42 98.27 97.99 97.67 97.32

Upper 102.36 103.09 104.75 106.58 108.49 110.47Change Minus 10 %

GMR 100.7 101.5 102.9 104.4 106 107.5

Lower 98.77 99.16 99.75 100.22 100.65 101.05

Upper 102.73 103.82 106.23 108.84 111.56 114.39Change Minus 20 %

GMR 101.5 102.9 106 109.1 112.3 115.6

Lower 99.5 100.64 102.77 104.83 106.86 108.91

Upper 103.46 105.29 109.26 113.51 117.98 122.67Change Minus 40 %

GMR 102.9 106 112.3 119 126.1 133.6

Lower 100.97 103.66 109.07 114.63 120.41 126.44

Upper 104.94 108.32 115.59 123.5 132.03 141.18Change Minus 60 %

GMR 104.4 109.1 119 129.8 141.6 154.4

Lower 102.47 106.77 115.73 125.29 135.56 146.64

Upper 106.45 111.44 122.34 134.45 147.85 162.64Change Minus 80 %

GMR 106 112.3 126.1 141.6 159 178.5

Lower 103.99 109.95 122.74 136.85 152.49 169.86

Upper 107.98 114.67 129.52 146.46 165.72 187.57

Change Minus 100 %

GMR 107.5 115.6 133.6 154.4 178.5 206.3

Lower 105.52 113.22 130.13 149.38 171.38 196.54

Upper 109.54 118 137.17 159.64 185.92 216.59

36

Observed Residual LNG from Tested Formulations and Model Simulated Residual LNG in Virtual Population (n =

16) with Hypothetical Generic Formulations

37

Proposed BE Criteria for Ex-Vivo Study

80

85

90

95

100

105

110

115

120

125

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Co

nfi

de

nce

Inte

rval

Time (Yrs)

Lower

Upper

TIME 6 Months 1 Year 2 Year 3 Year 4 Year 5 Year

Lower 96 95 90 87 84 80

Upper 104 105 111 115 119 125

• Tighter BE Limit Criteria for Residual LNG can be proposed for different time duration (up to 5 Year)

38

Kt = Kr+0.20*Kr

Sample Size BE Limits at 1 year(%) # Passing BE Limits at 5 years(%) # Passing

(20,20) 93 – 100/0.93 998 80-125 687

94 – 100/0.94 954 80-125 687

95 – 100/0.95 651 80-125 687

96 – 100/0.96 164 80-125 687

Kt = Kr-0.20*Kr

Sample Size BE Limits at 1 year(%) # Passing BE Limits at 5 years(%) # Passing

(20,20) 93 – 100/0.93 1000 80-125 859

94 – 100/0.94 992 80-125 859

95 – 100/0.95 804 80-125 859

96 – 100/0.96 219 80-125 859

Potential Selection of BE Limit at One Year

Initial LNG content (A, mean = 52 mg, CV=0.03), Release Rate Constant (k, mean = 3.89 × 10-4 per day, CV = 0.15)

• Purpose of this approach was to find BE limit at one year which will ensure similar passing rate at 5 years, assuming conventional 80% - 125% BE criteria applied to residual LNG at 5 years.

• Then 90% confidence interval of geometric mean ratio of the RLD and TEST at 1 year and 5 years were computed and the procedure was repeated 1000 times simulating 1000 studies.

39

Evaluation of the Proposed (95% – 100/0.95%) BE Criteria at 1 Year

• Two approaches: 1. Modeling and simulation as described above was utilized to create

virtual subjects and parallel BE comparison was conducted at 1 and 5 years. Release rate constants retrieved from relevant submitted applications were utilized.

2. In the second approach observed residual LNG data at one year was retrieved and parallel BE comparison was conducted. Residual LNG data in between 330 to 390 days were considered for one year analysis.

• For both approaches two cases were evaluated and BE analysis showed that criteria were met:– Formulation C vs formulation D

– Formulation D vs Another Drug

40

Summary of Case II

A lower limit of 95.00% and upper limit of 100/0.95% (105.26) seem to be appropriate BE limits at 1 year, which can be used to ensure equivalence of 80% - 125% at 5 years for residual LNG parameter.

41

Overall Summary

• PBPK or PKPD models are critical techniques to inform decision making regarding locally acting drug delivery

• Relevant BE metrics and assessment criteria can be established accordingly

• Modeling based recommendations can be confirmed by observed outcomes

42

Acknowledgments

OGD/OB

• Dale Conner

• Ethan Stier

• Xiaojian Jiang

• Nilufer Tampal

• Hongling Zhang

• Xiaofei Wang

• Minglei Cui

• Li Xia

• Yuzhuo Pan

• Kuldeep Dhariwal

• Moheb Makary

• Patrick Nwakama

• Mahadevan Chitra

OGD/ORS

• Xinyuan Zhang

• Jianghong Fan

• Satish Sharan

• Edwin Chow

• Arjang Talattof

• Thushi Amini

• Minori Kinjo

• Yuan Zou

• Yan Wang

• Yih-Chain Huang

• Wenlei Jiang

• Rob Lionberger

OGD Policy

FDA

• OPQ

• OTS

• OND colleagues

• ORP colleagues

University of Michigan Study Team

• Duxin Sun, Ph.D.

• William L. Hasler, M.D.

• Ann Frances Fioritto, B.S.Chem.

• Jason R. Baker, M.S.

• Barry Bleske, Pharm.D.

• Mark Koenigsknecht, Ph.D.

• Hiro Tsume, Ph.D.

• Jeff Wysocki, R.N. Bo Wen, Ph.D.

• Ying Wang, Ph.D.

• Ruijuan Luo, Ph.D.

• Siwei Li, Ph.D.

• Ting Zhao, Ph.D.

• Mark Johnson, B.S.

• MICHR nurse team

43

44

Tube Placement Procedure in Human GI Tract

Port Locations:1. Distal Jejunum/

Proximal Ileum2. Proximal Jejunum3. Duodenum4. Stomach

Fluoroscopic photo of GI tube placement. Shown are 3 aspiration ports located in the stomach, proximal jejunum, and distal jejunum.

1

4

3

2