Real World Data Utilization:

PMDA’s Approach to Pre-market

Review and Pharmacovigilance

Daisaku SATO, Ph.D.Chief Management Officer

& Associate Centre Director for Regulatory Science,

Pharmaceuticals and Medical Devices Agency, JAPAN

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© 2018 DIA, Inc. All rights reserved Page 2

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Center for Regulatory Science

2018/09/04

4

Center for Regulatory Science (Organization Structure)

5

Director of Center for Regulatory Science

Associate Center Director Associate Executive Director

Office of

Medical Informatics

and Epidemiology

Office of

Advanced

Evaluation with

Electronic Data

Office of

Research

Promotion

Coordination

Officer for

Evaluation of

Advanced

Science and

TechnologyEMRs database

(RWD) CDISC database

(Clinical trials)

Big-Data analysis in regulatory science

Closely working together with Office of New Drugs, Office of Safety etc.

Center for Regulatory Science established on Apr 1

Office of Research Promotion

Review Offices

Office of Medical

Informatics and

Epidemiology

Safety Offices

Office of Advanced

Evaluation with Electronic Data

(former Advanced Review with

Electronic Data Promotion Group)

2018/09/04

6

Regulatory Science Centere-study

database

MID-NET

etc.

PMDA initiatives for quantitative science

Several initiatives/activities for quantitative science are established and are in execution for new drug/device development and review in Japan.

We are considering how we can efficiently use those data that we will obtain in each stage of clinical development.

2018/09/04

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Drug development

Regulatory review

Post-marketing Surveillance

Approval

Advanced Review with Electronic Study Data

(CDISC)

Use of electronic patient registry data (e.g. Clinical Innovation Network)

Electronic medical records,claim data(e.g. MID-NET®)

Use of data standards is the key for all the initiatives

Premarketing Electronic Data Utilization

2018/09/04

8

Accumulation and utilization of data

2018/09/04

9

Submission of electronic

clinical study data has

started since Oct 1st 2016!

Timeline for implementation of e-data submission

2018/09/04

10

TaskJ-FY

2014

J-FY

2015

J-FY

2016

J-FY

2017

J-FY

2018

J-FY

2019

J-FY

2020

Guidance

and related

documents

Review

Consultation

for e-study data

submission

System

Development

2014 Pilot

2015 Pilot

Pilot

System Development /

Pilot for data

submission

Issuance of “Basic

Principles”

3.5 years of Transitional

period

FAQ

Briefing/Workshop

Eng.

Mar 31

End of the transitional period

Regular Update

Preparation for the

end of transitional

period

(Revision of the

notifications, etc.)

Today

WS

Oct 1

Portal Site

Open

New Consultation

framework

Issuance of “Notification on the consultation for the clinical e-data

submission”

Issuance of “Notification on Practical

Operations ”

Issuance of “Technical Conformance Guide”

Data Standards Catalog

PMDA Validation Rules

Initiation of e-study data

submission

WS WS

Q&A

Consultation for clinical e-data submission

169 consultation meetings have been requested by 49 companies as of July 31, 2018.

Multiple meetings have been held for the some products.

The number of consultation for NDA after transitional period is increasing.

Various characteristics– With/without official minutes

– Japanese/foreign company

– Oncology and other therapeutic areas

2018/09/04

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Year N of request

J-FY 2015 (May 15, 2015 – Mar 31, 2016) 13

J-FY 2016 (Apr 1, 2016 – Mar 31, 2017) 62

J-FY 2017 (Apr 1, 2017 – Mar 31, 2018) 65

J-FY 2018 (Apr 1, 2018 – Jun 30, 2018) 29

Total 169

Practical cases for utilization of submitted data in review process (as of now)

Supplementary analyses to the dataset by reviewers contributed to improvingthe efficiency of NDA review process (e.g. reducing enquiries to the sponsors)

Examples:

(1) Conduct subgroup analysis to check the sponsor’s idea of dose adjustment topatient weight. (and other factors which affect efficacy/safety of the product)(clinical data)

(2) Conduct subgroup analysis by baseline status to check the consistency of efficacy among subjects (clinical data)

(3) Perform supplementary analysis to check the robustness of the primary result(e.g. value transformation, assumption of distribution, model)(clinical data)

(4) Check whether PPK analysis result from multinational trial was valid for Japanese or not. (PK/PD data)

2018/09/04

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Analyses of CDISC data in review team

2018/09/04

13

• Distribution of patient demographics

• Changes in laboratory data

• Adverse events rates

Common analyses to

many clinical trials

• Simple analyses depending on thecharacteristics of evaluation variables– continuous/categorical/time-to-event)

General analyses for efficacy and safety data

• Analyses with programing(innovative/complicated analyses)

• Simulations

Relatively complicated

analyses

Software: JMP Clinical, etc.Datasets: SDTM

Software: JMP, SAS etc.Datasets: ADaM

Software: SAS, etc.Datasets: SDTM, ADaM

Prospect of e-Study data utilization in Japan

J-FY2019 - 2021

• e-study data can bereceived andmanagedappropriately

• e-study data can beutilized in the review

• Industries’ workloadis reduced graduallywhile keeping thereview period

• More predictableefficacy/safety

• Consideration ofexpanding the scopeof e-data utilizationto toxicological studyand post-approvalclinical study

- J-FY2017

J-FY2018

J-FY2022 -

• Preparations ofguidelines and relateddocuments

• Earnest on cross-product analysis anddevelopment ofdisease models

• Establishment ofdisease models

• Publication ofdisease-specificguidelines

First-class review quality

Setup e-data management and utilization

Ordinary utilization of e-

data in the product review

Starting earnest cross-product

analysis

Publication of guidelines to

contribute to drug development

e.g. guidelines and disease

models based on data on Asian

populationPromotion of

paperless operation

Transitional period are taken until March 31st, 2020

Prospect As of Sep 2017(Subject to Change)

Start e-study data submission for NDA*

from Oct 1st, 2016

*NDA=New Drug Application

14

Utilization of study data in the future

2018/09/04

15

Utilization of study data for new drug review

- Improvement of predictability of efficacy and safety

- Reviewing M&S results

- Reviewing novel evaluation methods

- Swift and effective decision-making

Utilization of accumulated study data

- Information from cross-product analysis

- Active use of M&S

- Evaluation of innovative analysis methods based on the accumulated data

- Experiences of meta-analytic approach

Efficient new drug development

- Use of consultation meeting based on the cross-product information by PMDA

- Active use of M&S

- Use of innovative and appropriate methods for the purpose

- Consultation based on the cross-product

information

- Guidance for therapeutic areas

- Issuance of points to considers for methodology

Submission of standardized study data

- Data

accumulation

- Experiences

of data

evaluation

Use of various data sources in the future- Importance of study quality, data quality, and data

standardization

- Innovative methods for analyzing data from various data sources

- Consultation/guidance

about innovative analysis

methods

- Contribution to data

standardization

Post-marketing Real-world Electronic Data Utilization

2018/09/04

16

Clinical Safety

Data

CT start NDA Approval

Pre-approvalPost

-approval

Keep agreeable benefit-risk balance in the lifecycle

Real world, day to day

medicine

Clinical Trial

Phase

“Optimal use”, at each stage, from pre-marketing to post-marketing,

Pre-market Post-market

Consistent risk management

From rigorous CTs To complex real world after product

launch

Clinical

Effectiveness

Data

リスク最小化活動Risk Minimization Action Plan

安全性監視計画Pharmacovigilance Plan

Regular

Additional

安全性検討事項Safety Specification

Serious specified risk

Serious potential risk

Serious missing

information

Additional measure

No

Yes

Concept of structured RMPimplementation in line with ICH-E2E guideline

Spontaneous Rep.

Literature survey

Labelling

Commentary

EPPV

Medical experience survey

Controlled study

Pharmaco-epi study

Patient medication guide

Leaflet

Education programme

Distribution control

Labelling revision

Vigilance

and / or

minimisation？（evaluation）※

Additional vigilance

Additional minimisation

Regu

lar re

vie

w

Limited medical

institutions and limited

doctors

Patient selection

criteria

What will become possible by utilizing a large-scale medical information database?

Comparison with

other drugs

Comparison with

symptoms due to

underlying disease

Verification of effects

of safety measures

Can compare frequency of

ADR occurrence drugs in

the same class

Rate of occurrence of ADRs

(ADRs/number of patients who

used the drugs)

Treated with

Drug A

Treated with

Drug B

Rate of occurrence of

symptoms

(symptoms/number of patients

who used the drugs)

Treated with

Drug A Treated without

Drug A

Can ascertain whether

the occurrence of a

certain symptom is

increased by

administration of a drug

Rate of occurrence of ADRs

(symptoms/number of patients

who used the drugs)

Before safety

measures

After safety

measures

Can compare to see whether

implementation of safety

measures actually results in

a change in ADR frequency

Data sources for post-market safetyassessment of a drug

Spontaneous

ADR report

DBSafety

measure

DPC DB

Risk

communi

cation

Literatures

Overseas

regulatory

actions

Presentation in

Academic

Conference

etc

Claims

DB

Electronic

Healthcare Data

PMDA

Conventional

Information Sources

MHLWMedical

institutions

Launched in 2009

Collaboration

Expected Outcome：Prompt and precise safety actions

PMDAsafety information collection and

analysis

Researcher, MAHs

Prompt safety

action

site

sitesite

DB

DB

DBDB

Networking 10

sentinel sites of

23 hospitals

Data

analysis

Tohoku U, Tokyo U, Chiba U,

NTT Hosps、Kitasato Hosps,

Hamamatsu M U, Tokushukai

Hosp Group, Kagawa U,

Kyushu U, Saga U.

site

DBSentinel site

hospitals

EHRClaim Data

DPC

DataLab. test

Medical Information Database Network（MID-NET）

【History and way forward】●April 2010 ：「Revision of pharmaceutical administration etc. to prevent recurrence of pharmaceutical

disasters (final recommendation) 」● April 2011 - ： Start construction of MID-NET system

● April 2013 - ： Start data quality validation to assure precision and comprehensiveness of the collected data

● April 2015 - ： Start trial operations by PMDA and sentinel sites

● April 2015 - ： Setting utilization rules for full-scale operation and framework of operation cost / user fees.

● April 2018- ： Full scale operation, enable MAHs and researchers to use MID-NET

Promote safety measures by pharmaco-epidemiological method using medical information

database.

MHLW/PMDA have established a medical information database for collecting large-scale

medical data at sentinel site hospitals and have constructed analytical systems at PMDA

since FY 2011.

More than

4,000,000

patients included

Common Data Model of the MID-NET®

Database

Claims data

DPC data

HIS data

・Patient identifying data

・Medical examination history data

(including admission , discharge data)

・Disease order data

・Discharge summary data

・Prescription order/compiled data

・Injection order/compiled data

・Laboratory test data

・Radiographic inspection data

・Physiological laboratory data

・Therapeutic drug monitoring data

・Bacteriological test data

HIS data

Contents Standard Code

Disease ICD-10

Drug

YJ, HOT9

(JP specific codes)

Laboratory test

JLAC10

(JP specific codes)

Example of standard codes

Example: Data Consistency Check

Data Extraction

Storage Server based on HL-7(SS-MIX2)

data standard

MID-NET data server

Hospital Information System (HIS)

transfer

AnnonymizationData

extraction system

Primary data

calculation system

Data server

Data ExtractionCompare number of cases and

contents per data element per hospital for certain periods

At the beginning, approximately hundreds issues per site were identified for further investigation or consideration

Examples of data inconsistency

Lack of a unit

Wrong place of data storage

e.g.; single dose, daily dose vs total dose

transfer

Data Quality of MID-NET®

Disease order data

Prescription order data

EMR MID-NET

Laboratory test data

compare

99.1%

67.0%

55.8%

Disease order data

Prescription order data

EMR MID-NET

Laboratory test data

compare

99.9%

100%

100.0%

PMDA has worked with cooperative hospitals

for assuring data quality of MID-NET®.

Before quality

managementAfter quality

management

25

Standardized data coding process-Example; Laboratory test-

• Confirming appropriateness of a code for individual laboratory test bychecking a distribution of laboratory test results (Approximately 200 tests)

ALT, AST, BUN, K, Creatinine, LDH, Gamma-GT, Cl, ALP, MCHC, MCH, Uric Acid, cGFR, TG, Cholesterol, Amylase, Blood Glucose, LDL-C, Inorganic Phosphate, HDL-C, PT-INR, HbA1c, PT, APTT, CEA, Fe, FT4, IgG, TSH, Sedimentation rate, RPR, IgM, HbA1c（NGSP）, TPHA, AFP, Ferritin, Hb, Reticulocyte, Blood Gases（TCO2）, Blood Gases（pH）,etc

Distribution of laboratory test results among hospitalsp

rop

ort

ion

Original data (local unit)

Hosp. A

Hosp. B

Hosp. C

pro

po

rtio

n

Standardized data

Hosp. A

Hosp. B

Hosp. C

After quality check

Before After

Confirmed

Examples of available laboratory test

25

Further investigation were conducted in case of different distributions

for understanding a reason and identifying an appropriate code

26

Launched（2012.4.17）

Spontaneous ADR reports

・32 serious cases of hypocalcemiaincluding 2 death cases

（～2012.8.31）

MID-NET® pilot: Case 1

denosumab and severe hypocalcemia

Warning letter(Dear healthcare

professionals letter)

（2012.9.12）

A) More laboratory test on serum calcium etc.

B) Co-administration of calcium/vitamin D

C) Special caution to patients with severe

impairment of renal function

D) Prepare for emergency situation

At the post-market, the label change and

warning letter were issued for awaking the risk

of hypocalcemia associated with denosmab

Monthly transition of the incidence of hypocalcemia

27

0.0

1.0

2.0

3.0

4.0

5.0

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

Ris

k r

ati

o

Inci

den

ce P

rop

ort

ion

(%

) ランマークゾレドロン酸水和物リスク比

Blue

letterLabelling

change

Denosumab

Zoledronate

Risk ratio

MID-NET® pilot:

denosumab and severe hypocalcemia

Pilot study

・Calculate the incidence of hypocalcemia during 28 days from a prescription date.

・Perform segment regression analysis based on the incidence of hypocalcemia / month.

■ObjectiveTo examine impacts of label change and warning letter in clinical practice for the risk of hypocalcemia

associated with denosmab

28

MID-NET® pilot:

Risk of Acute Myocardial Infarction Associated with

Anti-Diabetes Drugs

Pilot study

Incidence Rateper 1,000 Person-

years

Adjusted

Rate Ratio

（95%CI）

Adjusted

Hazard Ratio

（95%CI）Non-sulfonylurea insulin

secretagogues2.4 1［ref］ 1［ref］

DPP-4 inhibitors 2.10.86

(0.25-2.90)

0.93

(0.08-10.80)

Table. Adjusted rate ratio and

adjusted hazard ratio for AMI in

the standardized population.

Objective

Cardiovascular events associated with anti-diabetes drugs

are common risk in post-marketing phase

To compare the risk of acute myocardial infarction (AMI)

associated with DPP-4 inhibitors monotherapy to other

anti-diabetes drugs monotherapy.

Exposed GroupDPP-4 inhibitors

(n=2,578)

MID-NET®

(2010～2015）

DPP-4 inhibitors（n=2,952）

Cohort：New users of anti-diabetes drugs monotherapy

Propensity score standardization

(SMRW)

■Outcome definition（AMI）

Definitive diagnosis of AMI, Admission* and Elevation of cardiac biomarker values*（CK or CK-MB：≧URL ×2 or Troponin T：≧0.1ng/mL）

*during 30 days before and after the diagnosis date of AMICases of AMI Cases of AMI

Occurrence of AMI

Control groupGlinides

(n=2,717.2)

Glinides(n=237)

29

Cohort Case (people) Users (people) frequency (%) 95% CI

Whole cohort 24 7,267 0.3 0.2-0.4

subgroup①（under 12y.o.）

-*2 209 -*2 0.0-1.0

subgroup②（12y.o.～18y.o.）

0 199 0 0.0-0.0

subgroup③（above 18y.o.）

-*2 6,859 -*2 0.2-0.5

※1The case where the respiratory depression has been developed is defined below.

1) prescription of therapeutics for respiratory depression (levarophan, naloxone), or,

2) Implementation of diagnosis related to respiratory depression (dyspnea, acute respiratory failure,

respiratory failure) and oxygen inhalation

※2 When the number of cases in the subgroup is less than 10 people, concrete numerical values are not

disclosed in accordance with personal information protection rules

Method：Among patients in MID-NET (976,859 patient records of 7 sites

from 2009 to 2015), evaluate those who were prescribed codeine-

containing products and hereinafter suspected to develop

respiratory depression in each age group.

呼吸抑制の発生割合

Using MID-NET®, to evaluate the quantity of prescriptions of codeine-

containing products and the risk of developing respiratory depression in

Japan.

Purpose

30

Various kinds of data including laboratory test

results

High data quality (confirmed consistency with

the original data source)

Real-time data update (every 1-4 weeks)

Advantages and Limitation of MID-NET®

Advantages

• May be not enough sample size (currently 4M)

• No linkage of a patient among hospitals

• Need to consider data generalizability due to

limited cooperative organizations (mainly mid-

large size hospitals like University hospitals)

Limitations

Characteristics of MID-NETⓇ and NDB

© 2018 DIA, Inc. All rights reserved

Data Type Electronic Medical Records Health Insurance Claims

Data Provider23 hospitals of 10 Medical

institutions

All health insurers in Japan

Covered patients

People provided medical

service by each institution (~4

Million)

Entire Japanese population

(120 Million)

Obtainable Health

Information

Detailed information in

medical practices by each

institution

Standardized information

relevant to reimbursement

Diagnosis YES YES

Medical procedure YES YES

Pharmacy Dispensing YES (on-site pharmacy) YES

Laboratory test result YES NO

OTC Drug NO NO

National Claims DB

Lessons Learned in utilizing RWD

High data quality is pre-requisite in utilizing real world data such as claims and electronic medical records database.

To obtain clinically meaningful results, it is important to – understand characteristics of the database in details

– validate outcome definitions

– utilize appropriate methods for controllingconfounding (e.g., propensity score matching)

– conduct sensitivity analysis

32

Clinical Innovation Network (CIN)

Study group for epidemiological methods and data quality standards

Study group for ethical issues for registries and relationships with industries

AMED

Advice,Cooperation

MHLW

Utilizing registry data for promoting cost effective clinical

studies, accelerating drug development, and B/R assessment

Output

PMDACIN-Working Group About 20 members

from New drugs & Safety Offices

Muscular dystrophyRegistry

by NCNP

ALS(Antilymphocyticserum) Registry

By Nagoya Univ.

Cancer registry

By National Cancer Center Japan

Cerebral surgery

By Japan neurosurgical

society

34

Revised GPSP

Good Post-marketing Study Practice(The Ministerial Ordinance, Implemented on April 1st 2018)

Intervention

Primary data

collection

“Post marketingclinical trial”

Observation

DB

“Post marketingdatabase study”

Observation

Primary data

collection

“Drug use result survey”

Study frames in GPSP

Newly created

Revised GPSP clearly mentions that safety study based on database is

acceptable for re-examination under the Japanese Pharmaceuticals and

Medical Devices Law

General steps for considering a plan of post-market studies (January 23, 2018)

35

Step 1. What is a concern to be clarified in post-

approval?

Step 2. What is a suitable approach(i.e.; routine or

additional PV)? If additional, what is the

research question and suitable data

source?

Step 3. If additional, which GPSP frame must be

complied with?(clinical trial, observational study

with primary data collection, database)

Step 4. If additional, creating a study protocol

Afte

r a

pp

rova

lN

DA

revie

w

Step 1～4 per each safety specification

Describes basic

principle on how to

plan a post-market

study under Japanese

pharmaceutical

regulation

• Four steps approach

to plan an appropriate

post-market study

Related guideline

Guideline on pharmacoepidemiological study for drugsafety assessment based on medical information database(March 2014)

Basic Principles on the utilization of health informationdatabases for Post-Marketing Surveillance of MedicalProducts (June 2017)

General steps for considering a plan of post-market studies(January 2018)

Points to consider for ensuring data reliability on post-marketing database study for drugs (February 2018)

https://www.pmda.go.jp/safety/surveillance-analysis/0011.html

Many related guidelines focusing on Real World Data utilization

were recently published in synchronization to the GPSP revision

Active utilization of EHR databases toward advanced medical care

37

RMP implementation utilizing EHR databases• Efficient risk management

• Better quality of safety information

Better quality of Medical Care

• Maximize benefit/risk ratio

Provide leading-edge Medical Therapy with

ensuring Safety

Regulatory decisions based on better scientific

evidences• Proper safety assessment utilizing HER databases in

addition to the traditional approaches

• Scientific and speedy safety measure

Medical Institutions

Public

Industries

MHLW/PMDA

Our Goals

Make new drugs and medical devices, developedaround the world, available to patients available in atimely manner (maximize benefits)

Detect unknown risk emerged throughout fromdevelopment to post-marketing as early as possible,to minimize the damage of the patient as a result oftimely action（minimize risk）

Encourage efficient R&D with less waste of cost, andimprove safety measures, so as to promote themedical innovations （cost optimization）

38

PMDA web site

http://www.pmda.go.jp/english/index.html

Thank you very much for your kind attention !!

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