ALL RIGHTS RESERVED - ISPE · Pharma manufacturers are swimming in vast amounts of data streaming in from digital signals, and industry 4.0 technologies. ... • A Practical Examination

Thursday, 12 November 1100 – 1200 ET

Dan WilsonHead of Sales

ISPE Pharma Best Practices Webinar SeriesWhere AI Fits in the Pharma Value Chain

Webinar Sponsored by:

©2020 ISPE - ALL RIGHTS RESERVED

Connecting Pharmaceutical Knowledge ISPE.org

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Connecting Pharmaceutical Knowledge ISPE.org 4

Speaker

Daniel WilsonHead of Sales, North America Element AI

Dan brings nearly 20 years of experience and global leadership in enterprise software and services to Element AI. Throughout his career, Dan has led client-facing activities with the world’s leading manufacturing and pharmaceutical companies as they invest in advanced technology. He puts forward a value-oriented, approach to deeply understand his client’s needs in their endeavors to improve efficiency, implement more efficient business processes and adopt new business models. He focuses on strategic alignment with clients and colleagues to drive innovation.



IN A NUTSHELL

A few facts

20 years leading disruptive decision and data science-based software companies

JF Gagné CEO

Linkedin Top Startup 2019

LEADERSHIP

Godfather of Deep Learning, co-author of the book on Deep Learning, full Professor at UdeM and Head of Montreal Institute for Learning Algorithms

Yoshua BengioCo-founder

Turing Award 2019

COMPANY

Offices in Montreal (HQ), Toronto, London, Seoul and Singapore.

INDUSTRIES

GovernmentsManufacturingSupply ChainFMCG / RetailInsuranceCapital Markets

BankingPharma

5



R&D Manufacturing Distribution Dispensing1 2 3 4

The Pharmaceutical value chain is much more than just manufacturing

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R&D Manufacturing Distribution Dispensing1 2 3 4Machine learning and deep learning can play a crucial role in the pharmaceutical industry at various stages of the value

chain

The Pharmaceutical value chain is much more than just manufacturing

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POLL #1

Does your organization have AI and/or machinelearning applications already in production?

Yes

No

Not sure

8



What is AI?

Artificial Intelligence

Machine Learning

ArtificialNeural Networks

DeepLearning

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STEP 01

Fundamental Research

Deep algorithmic & theoretical research begins in the lab.

STEP 04

Solutions

Modular, accessible & secure products are created & built.

STEP 02

Applied Research

From the findings, core capabilities - each with different aptitudes - are built & developed.

STEP 03

Capabilities/APIs Library

Core capabilities are organized & made accessible.

AI journey to production

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Text Insight / OCR

CAPABILITIES/APIsSingle AI model

OPPORTUNITY SCENARIOSProduct and Capabilities/APIs

Anomaly Detection

Decision Support

Trustworthiness

Visual Insights

Visual Anomaly Detection

Deviation management

Forecasting

Visual anomaly detection

Forecasting supply chain & production

+

Why AI?

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POLL #2

Do you have an innovation group that’s solely dedicated to incorporating AI into your company’sprocesses?

Yes

No

Not sure

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Connected high accuracydigital sensors

Quality control lab

Detecting anomalies in parts, components and products


2

Insights from rich text and structured data for more efficient deviation investigations

Natural language processing

1

Source: McKinsey Insights: The future of pharma quality control

AI-powered parameter control to optimize yield and reduce deviations

Forecasting

3

AI can help improve outcomes in various stages of the pharma manufacturing line

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https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/digitization-automation-and-online-testing-the-future-of-pharma-quality-control

Opportunity Scenarios• Quality Control – Deviation management• Visual anomaly detection on the production line• Forecasting supply chain & production



POLL #3

Which of the following AI disciplines has your company experimented with:

Visual Anomaly detection

Text insights/Optical Character Recognition (OCR)

Forecasting

Other

None of the above

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PHARMACEUTICAL

Reducing investigation time of deviations is a major challenge in the industry which impacts both cost (e.g. equipment downtime, FTE time investment) and revenue (e.g. faster go to market)

● High number of data sources

● Tacit knowledge is required to make sense of all the data

● Institutional knowledge is not formalized

For example, in the pharmaceutical industry, solving critical quality investigations relies on a worker’s expertise to navigate through complex data sources (e.g. equipment, reports)

Quality Control – Deviation Management

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Multiple data sources are aggregated and combined with powerful NLU and search capabilities.

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Paper-based data

STRUCTURED & UNSTRUCTURED DATA

Relational databases

Document repositories

ERP QMS

PubMed Other client data MES/SCADA FDA Data.

Predefined ontology

Deep learning for semantic

similarity

Consolidated data extracts

Natural language query

AI-POWERED NATURALLANGUAGE PROCESSING

Extract entities

Illustrative

Quality Control – Deviation Management

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Assess recurrence

Perform initial impact assessment

Define CAPA plan

Assess, contain & classify

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Perform final impact assessment

1Implement remedial actions

2

Total time to resolve a deviation

Complete deviation report

Investigate

Plan root-causeinvestigative actions 5

3

2

2

2

4

4

-30%

Deviation resolution lead time, # days Example queriesQC agent identifies the most efficient way to contain and classify the deviation searching on similar reports

Show me the deviations where material Y failed the thickness out of bounds control.

Find ideal investigation approach and narrow down the potential root causes

Show me the investigative actions I should perform for deviations with material issue Y.

Evaluate whether the deviation is recurrent and aim to address the true root-cause

Does the batch typically get released for deviations with issues in material Y?

Identify CAPAs that were effective for similar deviations to address true root-cause

Which were the effective CAPAs for deviations with material issue Y?

ILLUSTRATIVE

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Quality Control – Deviation ManagementAI helps shorten key steps in the deviation investigation and remediation process



A clinical researcher (or a different user) asks a question

1

2

adverse_reactions

patient_genderadverse_events

active_ingredientlocation country

patientNatural language models detect and connect data entities based on a domain-specific ontology

3 User intent is classified and the computed results draw on structured and unstructured data sources

How many adverse events involving chloroquine occurred in male patients in the US, sorted by reaction?

Quality Control – Additional application: investigating adverse events

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PHARMACEUTICAL

Product anomalies and defects are commonly detected through visual human inspection or traditional machine vision based technologies. Some more sophisticated setups use supervised deep learning vision. Challenges with these methods range from:

● Cost and time of setup

● Regular maintenance and monitoring

● Extensive quantities of training data are needed

Capsules - scratch, cracks

Visual Anomaly DetectionIdentifying anomalies on products relies on highly accurate detection and inspection methods.

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HumanInspection

Traditional Machine Vision

Supervised Deep Learning Vision

AD

VAN

TAG

ESLI

MIT

ATIO

NS

Proven approach

Contextual and spatial awareness

Off the shelf availability (hardware and software)

Scales to large volumes with Automation

Reduced model development time & system maintenance

time

Expensive

Human inspection does not scale

Inconsistent quality control between operators

Images of complex parts or defects are invisible to the

human eye

Set up cost & time (rule definition, programing, testing)

Monitoring and maintenance (calibration & tuning)

Upper limit to ability to find defects

Extensive data required to train models

Data preparation needed for model training (i.e., labeling,

classification)

Models must be monitored and periodically re-trained to

avoid model drift.

Some defects still undetectable

Unsupervised Deep Learning Vision

Improved accuracy

Finds defects overlooked by other approaches

Robust to imperfect environments

Train model with minimal set up data

Low cost, highly scalable

Image capture and management system should already be in place

Requires more field testing


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PHARMACEUTICAL

The supply chain is closely tied with dynamic demand in the marketplace and alignment with public health needs. Accurate forecasting is essential to minimize risk of incurring drug shortages. The predictive aspect can be applied to:

● Ingredient shortages

● Drug shortages

● Public health emergencies

● Yield forecasting

● …

Forecasting supply chain & productionSupply chain is closely tied with dynamic demand in the marketplace & alignment with public health needs

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PHARMACEUTICAL

Pharma manufacturers are swimming in vast amounts of data streaming in from digital signals, and industry 4.0 technologies. Leveraging these rich datasets for forecasting purposes can allow manufacturers to:

● Predict quality using real-time time series data

● Predict asset failure using iOT signals

● Forecast parametric batch release

● …

Forecasting supply chain and productionManufacturing & maintenance

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Connecting Pharmaceutical Knowledge ISPE.org 24

Accuracy

Predicting quality in pharma environment

No feature engineering required

Reduced development time and rapid incorporation of new data sets i.e. R&D notes, factory floor systems, time-series systems, documents etc.

Generic deep-learning architecture

Model can be easily adapted to multiple use cases within your pharma organization

Explainable & transparent results

Deep learning architecture can be applied to various scenarios within the pharmaceutical environments.

Advantages of deep learning based forecasting

Forecasting



POLL #4

Do you see opportunities to address these use cases within your organization?

Yes

No

Not sure

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Key Takeaways

Deep learning based forecasting and time-series data analysis enable agility and responsiveness. In a manufacturing environment, this means adapting to dynamic demand signals to better predict and capitalize on market opportunities.

AI-powered vision systems that leverage deep learning techniques can improve accuracy and accelerate the adoption of visual detection and inspection solutions.

AI-powered enterprise software can help forge connections between users and improve knowledge management across multiple functions in an organization.

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Q&A

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Contact Information

Topic Ideas or Feedback? Send to [email protected]

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Upcoming Webinars• A Practical Examination of How to Use Risk Assessments in the CSV

Process(Sponsored by Agilent) Thursday, 19 November 2020, 1100 – 1200 Eastern

• How Digital Technology is Reshaping Assembly Processes for Medical Device Manufacturing(Sponsored by Stevanato GroupTuesday, 1 December 2020, 1100 – 1200 Eastern

• Annex 1 “Manufacture of Sterile Products” What’s next?Thursday, 3 December 2020, 1100 – 1230 ET

Visit ISPE.org/webinars for the full calendar

Webinar Sponsored by:

Dan WilsonHead of Sales [email protected]

Extended Learning

For more information, visit:https://www.elementai.com/industries/pharma/contact


Documents

ALL RIGHTS RESERVED - ISPE · Pharma manufacturers are swimming in vast amounts of data streaming in from digital signals, and industry 4.0 technologies. ... • A Practical Examination