CPV y protocolos de monitorización de procesos. Adaptación al nuevo Anexo 15 de las GMPsDe la estadística al control de procesos.

Como integrar los requisitos del anexo 15 dentro de una herramienta informática robusta

Leon Rebolledo20-Oct-2015

2

IT should be responsible to deliver an integration framework and an application that should :

“Embrace the complexity of an OPV program delivering the simplicity required to run a day to day business application”

Quote on Process validation :

"It is so counter-productive when the 'process' becomes the job vs. the 'process' of doing the job.”, Unknown.

Quote on statistics:

“Statistics are like bikinis. What they reveal is suggestive, but what the conceal is vital”, Aaron Levenstein.

3

Objectives:

❖ Understand if your IT information network is ready to support OPV.

❖ Understand the required IT infrastructure to run a OPV program.

❖ Understand the system and data integration requirements to successfully implement an OPV application.

4

How to integrate the OPV requirements within a robust IT tool:- Traditional vs Continuous process validation approach.

- The IT role

- IT Integration evolution

- OPV Roadmap

- How IT integration are pivotal for a successful PV program.

- IT in the information pyramid.

- IT integration on the context of OPV

- Data model: “One ring to rule them all”

- Integration models supporting a traditional validation approach

- ISE : “Integrated Statistical Engine”

- Formal and Exploratory

5

How to integrate the OPV requirements within a robust IT tool:- Ongoing Process verification

- “Readiness check” from IT perspective.

- Dashboard-base monitoring vs Report-base monitoring.

- Integration between OPV and APR/PQR

- IT Systems:

- “Home-grown” vs “Product”

- 5 Forces that will prevent a successful implementation of an IT solution

- IT System scope

- Practical case – App demo -

- Implementation of IT applications to support PV.

- Internal manufacturing process performance collaboration.

- OPV Objects.

- CPV and Big Data

- Beyond step3

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Traditional vs Continuous process validation approach.

.

Pro

du

ct li

fe c

ycle

New productdesign space based on

science

Ongoing process verification

Ongoing process verification

Fragmented electronic, data based, process foot print.

Full electronic, data based, process foot print

Control strategy based on current process and product understanding.

Trad

itio

nal

pro

cess

val

idat

ion C

on

tin

uo

us

pro

cess

va

lidat

ion

7

Traditional vs Continuous process validation approach, the IT role

.

Ongoing process verification

Ongoing process verificationCo

nti

nu

ou

s p

roce

ss v

alid

atio

n

Fragmented electronic, data based, process foot print.

Full electronic, data based, process foot print

Trad

itio

nal

pro

cess

val

idat

ion

Pro

du

ct li

fe c

ycle

New productdesign space based on

science

Control strategy based on current process and product understanding.

To provide a validated system to

capture and integrate the details

of the design space into the OPV Application.

To provide a validated tool to

capture and integrate the details

of the control strategy into the OPV Application.

To enable and validate

an IT infrastructure,

systems and controls

to support the ongoing

process verification application.

An application and platform to maintain the

lasting process control evidence of a produce life cycle.

8

Evolution of IT integration in the Life Science industry

9

How IT integration are pivotal for a successful implementation of an OPV program.

•Data diversity

•Data volumes

•Data frequency

•Complex calculations

•Data Contextualization

•Data storing

•Data distribution

•Integration from CMO’s to Pharma

•Business processes

•APR/PQR Integration

Information Network OPV Application

10

Roadmap OPV

11

OPV Roadmap in summary...

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Step One CQA-based control

strategy

o Standardization of CPV processeso Deployment of enterprise

applicationo Alignment of statistical analysis

across the enterprise.o Integration with APR/PQR.o Based on risk assessments.

Step Two3-deep down control strategy

o Integration of CPP datao Batch tree’s for CMA’so Integration of CIPC’so Integration with Equipment and

facilities.o Integration with Analytical

method validation

Step ThreeState of control achieved /

Design space exist

o Continued verification of the state of control.

o Single source of truth.o Integrated Manufacturing DWo Data-based and ISE-based

(Integrated statistical Engine) root cause analysis

o Basis to rollout the continuous process verification

Traditional Validation Continuous PV (CPV)

• Real-time Batch release

• Real-time OOS/OOE’s

• State of control monitored On-line.

• Process integrated APR/PQR

• And beyond.

Ongoing Process verification application

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How IT Integration stands in the information pyramid.

•Product Life Cycle•Supply Chain•Production Planning•EBR•Shop Floor integration•Manufacturing Process

Inte

grat

ion

Process understanding

IT

PLC’s

Scada’s

Shop-Floor systems

ERP

PLMSystems / QA events

Point to point

LAN

WLAN

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IT integration in the context of OPV

• OPV• App

•Risk assessment•ERP•Production process, Batch Tree and QC•Manufacturing Execution systems•PLCs and SCADA’s

CPP

’s /

CIP

C’s

Process understanding

CQ

A’s

/ C

MA

’s

Master DataInspection plansInspection ControlInspection resultsBatch tree

Process Data points

Manufacturing Data contextualization

Risk assessment

Monitoring

Risk assessment

Monitoring

IT integration maturity

Data model : “One ring to rule them all”

Step IIISample TextSample Text

CPV

Step ISample TextSample Text

Step IISample TextSample Text

Data ModelSample TextSample Text

Integration models supporting traditional validation approach

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ERP

LIMSLab

Equipment

SCADA

MES

Historian

PLC’s

ERP

Lab Equipment

SCADA

MES

Historian

PLC’s

ERP-Based Integration

MMDM

Lab Equipment

SCADA

MES

Historian

PLC’s

De-coupled Integrated

LIMS

Data Model

CPV Application

ERP-supported Integration

ISE : Integrated Statistical Engine

•Data collection

•Statistical Engine

•Presentation

What is an integrated statistical engine:

• It is a recognized statistical application or library set that delivers the required standardized calculations and plotting mechanisms to support OPV.

• It is integrated within the confines of the application itself, validated and intrinsically linked to the data acquisition.

• It delivers calculations and plotting mechanisms without jeopardizing or risking the data integrity.

• It is a tool that can provide extended statistical features, whilst connecting to the data sources, outside of a validated environment.

ISE : Integrated Statistical Engine : Formal / Exploratory, options and validation

Manufacturing Data Warehouse

MVDA tool

R- WidgetsOther ISE’s* &

Dashboarding

gxp

non gxp

Data sources

Formal OPV

Exploratory

OPV

OPV App

GMP Annex 15: Readiness check listIT tools

Ongoing(Continued)

Non-US marketStep I

Ongoing(Continued)US market

Step II

Continuous

Step III

An electronic integrated PAT system

A system to capture consistently inspection results in a unified manner

ISE for the cross organization agreed evaluations

Master data centrally managed in a validated single application (raw materials, Vendors, products)

An standardized reporting tool for ongoing verification.

A system to capture electronically quality events. (changes, deviations, etc.)

An EBR system or connectivity to PLCs / SCADA’s to collect PP’s

A historian / measurements archival system

Equipment management is electronically captured and integrated to the ERP and MES

An electronic record SOP’s system

An electronic project and change control system to maintain CSV details

Required Suggested

Report vs Cockpit based monitoring

PS Cockpit(Interactive)

Static (Report)

• Plot based analysis• PDF-generation• APR/PQR ready integration.

• Interactive.• Pre-assessed.• Risk based driven.• On-Demand PDF generation.• Integration / source for

routine product review boards.

OPV and APR/PQR Integration

Requirements

• The Annual Product Review (APR) - Product Quality Review (PQR) reports are the first documents reviewed by health authorities while inspecting a manufacturing facility.

• The importance of this report is such that even a small error in reported data may have severe impact on product quality and patient safety.

• Considering its criticality and importance, organizations must try to eliminate errors through process standardization and automation.

Objective • The objective of an APR-PQR report is to document key aspects of drug manufacturing process and derive

improvement areas to assure continued suitability and capability of processes

Current reporting process

How to integrate4 logical steps are required:

• Data Standardization• Define Core business processes• Data requirements identification

• Data unification• Data transformation Logic• A single view, understanding and contextualization of your

data.

• Statistical engines• Integration with different source systems and statistical

analysis tool.• Report management

• Define global standard• Define roles and approval role• Define templates • Report scheduling and monitoring.

Data conextualization

UI Application

• Report Specs**

• Analytics **• Scheduling **• Report

sectioning and process flow

• Configuration

CPV

Ap

plic

atio

n

* Design available. To be develop as part of project** Currently available and developed for Novartis. –To be Demo-

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Contextualización de datos

Risks• Planned Reports vs. Actual Production: Manufacturing site must produce one report per product for a

review period. Since planning process is generally manual there is always a possibility of missing some products from the planning list

• Data Integrity: The data for reports is extracted from different source systems and then transformed into report sections. In most of the cases the process of data transformation is manual and causes significant doubt on its integrity. There is no ready solution to prove that data is not manipulated while preparing the report

• Due date non-compliance: Completion of reports is dependent on many stakeholders. e.g. input from specialists for different sections, reviewer’s comments and signature, approvers comments and signature etc. in Manual scenario, due to these dependencies it is really difficult for PQR Author to complete the report in time

• Errors in statistical analysis of data: Since PQR authors are not expert statisticians; it’s difficult for them to analyze varied set of data in different circumstances, leading to errors. I have seen multiple warning letters from health authorities pointing towards the same problem

OPV and APR/PQR Integration

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IT Systems: “Home grown” vs “Off the shelf”

•Home Grown•Make-to own requirements.•Longer knowledge curve•Continuous maintenance, changes and enhancements.•Apparent initial low cost. Lowe ROI•Could run risk of localized solutions.•90-100 requirements met

•Off-the-shelf•Standard functionality•80-20 requirements met•Higher cost up front. Better ROI.•Higher cost on customizations, if required.•Maintenance cost•Upgrades delivered as part of license.

5 forces that prevent a successful implementation of a CPV Program

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The absence of an enterprise IT solution

for CPV

Data Dispersion and

lack of integrity

Lack of a consistentData model

Not cleared ownership

Business Process not

aligned.

Lack of governance

Why a CPV program can fail:

• Lack of an enterprise IT solution:• Local/customized solution support the

execution of individual local reports, but prevents growth and strategical thinking around CPV.

• Process improvements and savings are lost in the details

• Data dispersion and lack of integrity:• Information is not concentrated in fit for

purpose solutions.• Home-grown solutions.• Lack of IT tools.• Manual processes.

• Lack of standards:• Agreed enterprise wide statistical analysis.

• Ownership : • Organizational gaps and lack of clarity.

• Business process not aligned, lack of governance

CPV Program

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Standardization

Data Source mapping,

correlation, transitory conversion

Data unification

Smart Data Contextualization.

Metadata

Cross functional correlation

Data Sources

RFC

Messaging

CSV upload

ODBC

OLAP, etc.

Data integration

to continuous

process monitoring

Data standardization for continued

monitoring (APR/PQR and

CPV)*

IT Systems scope

Demo

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IT Systems: Demo

Home grown : Novartis MONITOR system, r5.0.

Off the shelf : PharmaXpert™ mCQA ready / Discoverant / Atris. Panda

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How to start an IT Program for CPV.

•Inventory your IT landscape

•Determine your integration architecture to support OPV

•Adopt the OPV Data model.

•Define, and scope, your statistical requirements.

•Select an OPV application.

•Align business to the program.

•Run a Pilot.

•Deploy solution enterprise wide.

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• Preparation of the user stories.• Review previous reports, identify contributors• Identify business process work flows and dependencies• Review statistical analysis methods used• Map relevant IT landscape, including network and resources• Identify data sources and options for data retrieval• Map data transformation and aggregation requirements• Create data validation plan• Identify pilot users• Create User Acceptance plan

• Install & configure needed IT resources• Implement data capture flows• Configure data mapping in PharmaXpert • Work with pilot users / product stewards to create report spec definition.• Run trial data analysis & plotting; review, evaluate, revise data aggregation, analysis & presentation• Configure workflow processes• Execute workflow with simulated reviewers/approvers• Review, evaluate, revise workflow and resulting final reports• Complete User Acceptance review

• Execute and monitor operations.

• Determine list of improvements for follow up releases

• Schedule regional and global release

• Kick off implementation project.

- Pilot with a single product & site- Go-live and rollout to additional products/sites not in scope

Implementation Step (est. 3 months)Preparatory Step (est. 2 months) Pilot Release and monitoring.

Scope of potential Pilot Project

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Internal manufacturing process performance collaboration.

OPV Reports

OPV Cockpit

Product Performance Review Board

Participants :• QA• Production• Product Stewards

or Product Steward head

Statistical

calculation results

integrated to the

board

Electronic recording of evaluations.

Electronic record of the overall evaluation.

APR/PQR

Annual OPV commented Report,

signed by QA.

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Technical view of OPV by objectCQA’s CPP’s CMA’s CIPC’s Events (incl. Change

controlEquipment Facilities Test

MethodsSystem validation

Transport Cleaning

Source LIMSERP

MESSCADA’s

ERPBatch Tree

LIMSERPMES

Track-wiseOther quality event tracking systems

ERPMaximo Other

Change control systems

Change control systems

Change control systems

ERPLogistic partners.

ERPMESOthers

Complexity Medium High High Medium Medium Medium Low Low Low High Medium

Challenges Data understanding.

No MES

Not connected to ERP

Availability of integrated Batch tree.

Access to MES data.

If in ERP, as per CQA’s/

Not standardized IT system.

Capture of events is not performed on a timely manner.

Paper based recording.

Paper based.

No plans for qualification of equipment.

Paper based qualification.

Paper based.

.

Paper based qualification.

Logistic partners can't deliver electronic records.

Paper based.

No plans for qualification of equipment.

Integration Data already contextualized

Data needs to be contextualized.

Identification of data source points

Data context in place.

From ERP.

Data in context.

From MES or ERP.

OPV app manual upload.

Direct connection to events system.

If in system, over direct connection.

Manual, direct flag in OPV app.

If in system, over direct connection.

Manual, direct flag in OPV app.

If in system, over direct connection.

Manual, direct flag in OPV app.

If in system, over direct connection.

Manual, direct flag in OPV app.

If in system, over direct connection.

Manual, direct flag in OPV app.

If in system, over direct connection.

Manual, direct flag in OPV app.

Risks Data integrity Data volumes prevents clarity.

None. Data is not verified before is plotted.

Data entry.

System is not stringent enough to force batch relevancy info.

No full verification on the qualification of equipment.

No full verification on the qualification of facilities.

No full verification on test methods.

No full verification on System validation.

No full verification on transportation.

No full verification on Cleaning status.

Feasibility Step 1 Step 2 Step 2 Step 1 Step 1 Step 2 Step 2 Step 3 Step 3 Step 3 Step 3

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CPV and Big Data

CQA’s

100-150 GB

CQA’s, CMA’s, CPP’s and CIPÇ’s

1 -3 TB

Full blown CPV

7-10 TB

Full blown CPV with images (NRI)

10-15 TB

Estimation of a big Pharma, all divisions.

It is estimated that for a full CPV, including OPV app, in a large Pharma, this program will take the next 3-5 years.

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Beyond CPV Step III.

It is estimated that for a full CPV program, including OPV app, in a large Pharma, will take the next 3-5 years.

Product lifecycle“Hearth beat”

2015

2020

2023

2025

Real-time release is a applicable.

Design-space based products are in continuous manufacturing.

Product life cycle monitoring is achieved.

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