Real World Trials: Uncovering the

demand for outsourcing post clinical

testing

Wednesday 1st July 2015

OCT UK London

Dr Amr Radwan European Medical Director

Norgine Ltd.

Overview

Real World Evidence

What it is and

How it differs from the RCT generated data

Key differences and similarities vis the RCT processes

Why do we need real world evidence ?

Success factors for real world trial process

From concept to reporting and evidence utilisation

Examples from the Real World

RCT data vs Real World Evidence

RCT

Gold Standard of research methodologies

Highly resource intensive

Randomisation a key feature

Tightly defined patient population (multiple inclusion / exclusion criteria)

hence potentially limited applicability of results

Focus on establishing safety & efficacy (esp for licence supporting

submissions)

Use of placebo control / active comparator often needed

RCT data vs Real World Evidence

RWE

More representative of real effects seen in standard clinical practice

(inclusion / criteria minimal compared to RCT)

Relatively low cost and complexity

No randomisation (generally)

Focus on epidemiology / disease and patient characterisation /

effectiveness / cost-effectiveness

No use of placebo comparators can be the patient themselves (pre-

post designs) or cohort matched controls

Use of statistical methods

Why Real World Evidence

Increasing realisation that RCT data is necessary but often not practically

achievable and certainly not sufficient for important clinical and particularly

pricing & reimbursement decision making

Need to make use of large clinical and health system data bases to gather

insights on specific diseases, patient pathways and natural history, identify

unmet needs and characterise patient populations

Availability of increasingly powerful computational tools and analytics

Increasing focus of reimbursement bodies and payors on systematic initial

evaluation of effectiveness and cost-effectiveness of new interventions in

their healthcare systems and monitoring this post reimbursement.

Need for systematic approach to safety signal detection and evaluation to

optimise Benefit-Risk ration for medicinal products and devices.

Real World Evidence why we need it..

Pricing

reimbursement

& Market

Access

HEOR &

Medical Affairs

Patient Safety

Signal

Detection and

Evaluation

Patient

Journey

Resource

Use

DUS

Epi

Studies

Pre /

Post

Studies

Cohort

control

studies

EMR /

database

studies

Registry

Patient Popn

Characterisation

Real World Evidence Generation Examples from the real world 1

Data base interrogation to understand the impact of a condition on disease

progression / natural history / epidemiology. [eg. Use of CPRD &

HES anonymised patient clinical database to evaluate effect of

decompensations on mortality and hospital bed days.

Real World Evidence Generation Examples from the real world 2

Drug Utilisation study, prescription database interrogation

characterisation of patients receiving therapy in the real world.

[eg. Use of pharmacy prescription database to evaluate use in unlicenced

population (eg. children, off-label use, etc.. )

Retrospective real world non-interventional study to evaluate the real

world impact on resource use. (pre/post design)

Prospective real world non-interventional study to evaluate the real world

impact on key patient outcomes and long term resource use. (combined

pre/post and matched cohort design)

APEX Study - Retrospective data collection Pre/Post design

Data captured retrospectively from patient records and other linked data bases (e.g. HES) :

Evaluation of frequency and duration of hospitalisation

12 months 12 months

Patient not on Xolair (PRE) Patient on Xolair (POST) Data

collection

start

PROS

Relatively quick

Good patient characterisation

Better suited to well recorded

data

Patient acts as their own

control

CONS

High risk of missing data in

historical patient notes

Risk of selection bias need to

be carefully managed

Better suited to well recorded

data

PROSPER Study prospective & parallel data collection

Data will be captured from patients on:

Rifaximin- (treatment arm)

Not receiving rifaximin- (comparator arm)

Collect retrospective data on ALL patients once enrolled in the study

The benefit of this design is that two data analyses can be planned:

Prospective comparisons across treatment groups, with matched control for stage of disease progression (the cohort control analysis)

Historical longitudinal comparisons within individual patients (the pre/post analysis)

12 months 24 months

Prospective Data - Control

Study Entry

Retrospective Data

Prospective Data - Treatment Retrospective Data

Analysis 1 Analysis

2a

Analysis

2b

Analysis 3

Market Access in todays pharmaceutical market is driven by the ability of a product to demonstrate value

Value = Outcome / Cost

Payers are increasingly implementing value-based programs: Value-based pricing for pharmaceutical companies

Value-based purchasing for hospitals and providers

As such, value needs to be defined, measured, predicted and optimised by all stakeholders, especially manufacturers

Developing a positive value proposition for products early may help ensuring financially successful development in line extensions

A centerpiece of Market Access is around the concept of Value where RWE can add important information.

Spend time defining your evidence needs first What will you use the data for. What is the minimum you require and what adds value

Evaluate fitness for purpose of potential provider

Local/national focus vs international capability

What will fulfil the need vs bells & whistles

Speed of delivery / Quality of output / Cost triangle

Prioritisation

Clearly brief the providers on the expected data needs and timelines

Does the provider understand the differences in approach in RWE vs RCT ? / level of experience ?

Successful outsourcing in RWE generation key principles

Have a robust process in place to access capability and engagement Outsource, but take responsibility for process & outcomes

Ensure appropriate level of governance in place.

Statistical methods to minimise bias are appropriately well defined in SAP (eg propensity scoring, matching ratios, estimated sample size calculations etc.)

Formal ethics review often not a requirement but should be sought where applicable.

Manage joint ownership of sites throughout the study periods and maintain close comms with all relevant stakeholders

Have a communication / publications plan draft one early in the process and refine as time and needs progress

Successful outsourcing in RWE generation key principles

CASE Study

A personal perspective from recent experience TARGAXAN (rifaximin-a) for Hepatic Encephalopathy

Very little known about this severe complication of liver disease (often an indication for transplantation!)

Standard of Care Lactulose (>40years old)

Relatively little general interest in disease area (compared to other novel agents e.g. New Oral Hep C therapies )

1 High Quality pivotal study in US/Russia/Canada Orphan indication in the USA EU licence based on this but no EU patients in the data set.

First true specialist product launch for the company No previous NICE experience

Success factors

True collaboration of internal functions (Medical, MAx and Commercial) to identify key data gaps for the different work-streams

Scientific / KOL / patient org. engagement converting what we dont know we dont know, to what we know we dont know (defining the gaps)

Significant generation (and publication ) of Key Epidemiology data (mortality of the disease / impact on resource use) Real World Evidence of resource use reductions associated with Targaxan

use in recurrent HE retrospective data 1 year pre & post analyses

Informed the building of a fit for purpose Health Economic Model to more closely reflect the natural history of the condition

One stage Markov model 2 stage Markov model

CPRD analyses (combined with pivotal study data review)

helped informed the building of a fit for purpose Health

Economic Model

One stage Markov model 2 stage Markov model

Stage 1

Stage 2

Stage 1 Stage 2

Impact of RWE on modelling approach for HTA

The Journey

Just over 2 years from early scoping to FAD

4 x TAC Technology Appraisal Committee meetings (D)

Significant peri-licencing experience

generating compelling real world evidence of value (hospitalisation frequency