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Driving Step Changes in
Manufacturing Operations with
Predictive Insights
Practical Application of Pharma 4.0
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
CSO & co-founder of Bigfinite
Physics Degree, Master in Information and Knowledge Society, Post graduated in quality systems for
manufacturing and research pharmaceutical processes.
7 US Patents: encryption, transmission, storage and processing big data for regulated environments in the cloud
Articles & white papers based on cloud, pharmaceutical industry and data science
AI Health Xavier University of Cincinnati: AI Core Team and AI Manufacturing Team Lead
PDA. Scientific Committee & Europe co-chair
AI & big data SME for Sciences in the Spanish Parlament
Professor at the University Autonomous of Barcelona
Bioinformatics of Barcelona. Project Leader of the Data Integrity team
Toni
Manzano
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Pharma 4.0 ≈ Digitization + ICH Q10
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What does big data mean from a regulatory perspective?
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What does big data mean in GxP environments?
3600000 GB = 3600 TB per minute
Data transferred now
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Yet the adoption rate of big data, cloud technologies in Pharma is
lagging that of other industries
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Companies investing in AI by industry
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Opportunities enabled by digital and analytics are recognized
across sectors
Digital Quotient score. Points (out of 100)
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Pharma 4.0. Is this strategy well aligned?
2017: A Holistic Approach to Production Control: From Industry 4.0 to Pharma 4.0.
Herwig C., Wölbeling C., Zimmer T.
2018: Getting Ready for Pharma 4.0
Manzano T., Langer G.
2019: Pharma 4.0 – The New Frontier for the Pharma Industry
Minero T., Augeri A.
2019: The ISPE Pharma 4.0 Operating Models
Heesakkers H., Schmitz S., Kuchenbrod U., Wölbeling C., Zimmer T.
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing the world perspective regarding AI
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing in Pharma regarding AI
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing in Pharma regarding AI
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Area Priority
"Big Data" Leverage "Big Data" for regulatory decision-making
Biocompatibility Modernize biocompatibility and biological risk evaluation of device materials
Real-world evidence Leverage real-world evidence and employ evidence synthesis across multiple domains in regulatory decision-making
Clinical performance Advance tests and methods for predicting and monitoring medical device clinical performance
Clinical trial design Develop methods and tools to improve and streamline clinical trial design
Computational modeling Develop computational modeling technologies to support regulatory decision-making
Digital Health and
cybersecurity
Enhance the performance of Digital Health and medical device cybersecurity
Healthcare-associated
infections
Reduce healthcare associated infections by better understanding the effectiveness of antimicrobials, sterilization and
reprocessing of medical devices
Patient input Collect and use patient input in regulatory decision-making
Precision medicine and
biomarkers
Leverage precision medicine and biomarkers for predicting medical device performance, disease diagnosis, and
progression
CDRH Regulatory Science Priorities
CDRH's regulatory science priorities serve as a catalyst to improve the safety, effectiveness, performance, and
quality of medical devices and radiation-emitting products, and to facilitate introducing innovative medical
devices into the marketplace. Report of August 22th, 2019
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What is Artificial Intelligence?
“ AI can be thought of as simulating the capacity for
abstract, creative, deductive thought - and particularly the
ability to learn - using the digital, binary logic of computers ”
Artificial Intelligence (AI) is no longer some bleeding
technology that is hyped by its proponents and mistrusted by
the mainstream. In the 21st century, AI is not necessarily
amazing. Rather, it is often routine. Evidence for the routine
and dependable nature of AI technology is everywhere.
“Verification and Validation and Artificial Intelligence”, Tim Menzies, Portland
State University, Charles Pecheur, NASA Ames Research Center. July 2004
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Data must be prepared before to use it:
DS invest the 80% of their time
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Warning Letters including references to
data management and data integrity
Source: fda.gov & PharmaceuticalOnline
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Expected and required data quality
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
When the statistical results have huge impact in the users
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
More than just multivariable models...
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
5 AI concepts
● Training/Test data
○ Problem’s dataset
● Algorithm
○ Mathematical procedure that creates the Model from the training data
● Model
○ Mathematical system that has been created from the exploration of a data set. It’s created
after an extensive learning process referred as training.
● Prediction / Classification / Recommendation / Recognition
○ Single inference over a model with an unseen sample
● Evaluation
○ Score evaluation of the Test dataset
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
More than just multivariable models...
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised and unsupervised algorithms
Learning Supervised Learning UnSupervised
➔ Linear Regressor
➔ Support Vector Machines
➔ Random Forests
➔ Neural Networks
➔ K-Nearest Neighbours
➔ Gradient Boosted Trees
➔ ...
vs
➔ K-Means Clustering
➔ Hierarchical Clustering
➔ Isolation Forests
➔ Graphical Lasso
➔ Bayesian Networks
➔ Markov Hidden Models
➔ ...
AI unsupervised Vision ExampleAI supervised Vision Example
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Decision Trees.
From a business decision point of view,
a decision tree is the minimum number
of yes/no questions that one has to ask,
to assess the probability of making a
correct decision, most of the time. As a
method, it allows you to approach the
problem in a structured and systematic
way to arrive at a logical conclusion.
Usually applied for root cause analysis
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Naïve Bayes Classication.
• P(A|B) is posterior probability
• P(B|A) is likelihood
• P(A) is class prior probability
• P(B) is predictor prior probability
Naïve Bayes classifiers are a family of simple
probabilistic classifiers based on applying
Bayes’ theorem with strong (naïve)
independence assumptions between the
features.
Some examples of usage:
• Deviations classification
• Clinical trials analysis
• Cause-effect analysis
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Naïve Bayes Classification.
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Logistic Regression.
Is a way of modeling a binomial outcome with
one or more explanatory variables. It measures
the relationship between the categorical
dependent variable and one or more
independent variables by estimating probabilities
using a logistic function, which is the cumulative
logistic distribution.
In general, regressions can be used in pharma applications such as:
• Predicting the quality of batches (ok / ko)
• Unexpected manufacturing stops
• Predicting the time to finish of a certain product
• Is there going to be a non conformity in a batch?
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Unsupervised Learning*. Principal Component Analysis
PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of
possibly correlated variables into a set of values of linearly uncorrelated variables called principal
components. It is not suitable in cases where data is noisy (all the components of PCA have quite a
high variance). Notice that domain knowledge is very important while choosing whether to go forward
with PCA or not.
Some of the PCA applications include:
• Compression
• Simplifying data for easier learning
• Dimensional reduction
• Identification of relevant dimensions
• Dimensionless analysis
* PCA can not be consiered as an AI algorithm
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Which types of software must be validated?
• All medical devices that contain software – even Class I devices – are subject to the Design
Control Provisions, and thus require software validation
• Any software used to automate any part of the device production process or any part of the
quality system must be validated for its intended use (21 CFR §820.70(i)).
• Computer systems used to create, modify, and maintain electronic records and to manage
electronic signatures (21 CFR §11.10(a)) must be validated.
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Not adequately validating software can lead to a Warning Letter...
4/12/2017
3. Failure to ensure that design verification shall confirm that the design output meets the
design input requirements, as required by 21 CFR 820.30(f). For example: Your firm has a
design input, (b)(4), of “the Remote Monitoring device shall only open network ports to
authorized interfaces” which is documented in Software System Requirements
Specification, Document. This is implemented as a design output in your firm’s DeviceName
Software Requirements Specification Uploads (b)(4).
This design output was not fully verified during your firm’s design verification activities.
According to your firm’s testing procedures, (b)(4), Final Configuration Test Procedures,
(b)(4) and Final Configuration Test Procedures Document (b)(4), the requirement was only
partially verified by testing that the network ports opened with an authorized interface. Your
testing procedures did not require full verification to ensure the network ports would not
open with an unauthorized interface.
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Robustness Fairness Explainability Lineage
Ex. Adversarial
training methods
Bias
• Data collection
• Processing
• Labelling
Explainer
•Data
•Metadata
•Catalog
•Hyperparameters
•Governance
•Processing
•Integration
•Privacy.
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Adversarial training methods example:
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Adversarial training methods example:
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Root Cause Analysis
?
CQA: All of them under specs
CPP: Equivalent results except culture
duration
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Getting knowledge from AI in primary packaging
LC BCO Set-up Warm-up WO Reworks Reconcilliation
•Pens / minute
•Product / format
•Batch size
•Day of the year
•Time of the day
•Shift
•Downtime
•Stops
•Affected units by stops
Theoretical OEE Realistic OEE Deep process knowledge
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Unpurified Bulk bag selection
end
Start
unpurified bulk
manufacturing
drug substance
purification
From cell bank
To CMO for
formulation and filling
2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Anomaly detection
Predict TTNF (Time To Next Failure)
Recommend actions to avoid Next Failure
Section Divider
Slide Layout
Section divider subhead

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ISPE 2019 Driving Step Changes in Manufacturing Operations with Predictive Insights

  • 1. Driving Step Changes in Manufacturing Operations with Predictive Insights Practical Application of Pharma 4.0
  • 2. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas CSO & co-founder of Bigfinite Physics Degree, Master in Information and Knowledge Society, Post graduated in quality systems for manufacturing and research pharmaceutical processes. 7 US Patents: encryption, transmission, storage and processing big data for regulated environments in the cloud Articles & white papers based on cloud, pharmaceutical industry and data science AI Health Xavier University of Cincinnati: AI Core Team and AI Manufacturing Team Lead PDA. Scientific Committee & Europe co-chair AI & big data SME for Sciences in the Spanish Parlament Professor at the University Autonomous of Barcelona Bioinformatics of Barcelona. Project Leader of the Data Integrity team Toni Manzano
  • 3. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Pharma 4.0 ≈ Digitization + ICH Q10
  • 4. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas What does big data mean from a regulatory perspective?
  • 5. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas What does big data mean in GxP environments? 3600000 GB = 3600 TB per minute Data transferred now
  • 6. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Yet the adoption rate of big data, cloud technologies in Pharma is lagging that of other industries
  • 7. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Companies investing in AI by industry
  • 8. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Opportunities enabled by digital and analytics are recognized across sectors Digital Quotient score. Points (out of 100)
  • 9. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Pharma 4.0. Is this strategy well aligned? 2017: A Holistic Approach to Production Control: From Industry 4.0 to Pharma 4.0. Herwig C., Wölbeling C., Zimmer T. 2018: Getting Ready for Pharma 4.0 Manzano T., Langer G. 2019: Pharma 4.0 – The New Frontier for the Pharma Industry Minero T., Augeri A. 2019: The ISPE Pharma 4.0 Operating Models Heesakkers H., Schmitz S., Kuchenbrod U., Wölbeling C., Zimmer T.
  • 10. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Something is changing the world perspective regarding AI
  • 11. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Something is changing in Pharma regarding AI
  • 12. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Something is changing in Pharma regarding AI
  • 13. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Area Priority "Big Data" Leverage "Big Data" for regulatory decision-making Biocompatibility Modernize biocompatibility and biological risk evaluation of device materials Real-world evidence Leverage real-world evidence and employ evidence synthesis across multiple domains in regulatory decision-making Clinical performance Advance tests and methods for predicting and monitoring medical device clinical performance Clinical trial design Develop methods and tools to improve and streamline clinical trial design Computational modeling Develop computational modeling technologies to support regulatory decision-making Digital Health and cybersecurity Enhance the performance of Digital Health and medical device cybersecurity Healthcare-associated infections Reduce healthcare associated infections by better understanding the effectiveness of antimicrobials, sterilization and reprocessing of medical devices Patient input Collect and use patient input in regulatory decision-making Precision medicine and biomarkers Leverage precision medicine and biomarkers for predicting medical device performance, disease diagnosis, and progression CDRH Regulatory Science Priorities CDRH's regulatory science priorities serve as a catalyst to improve the safety, effectiveness, performance, and quality of medical devices and radiation-emitting products, and to facilitate introducing innovative medical devices into the marketplace. Report of August 22th, 2019
  • 14. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas What is Artificial Intelligence? “ AI can be thought of as simulating the capacity for abstract, creative, deductive thought - and particularly the ability to learn - using the digital, binary logic of computers ” Artificial Intelligence (AI) is no longer some bleeding technology that is hyped by its proponents and mistrusted by the mainstream. In the 21st century, AI is not necessarily amazing. Rather, it is often routine. Evidence for the routine and dependable nature of AI technology is everywhere. “Verification and Validation and Artificial Intelligence”, Tim Menzies, Portland State University, Charles Pecheur, NASA Ames Research Center. July 2004
  • 15. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Data must be prepared before to use it: DS invest the 80% of their time
  • 16. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Warning Letters including references to data management and data integrity Source: fda.gov & PharmaceuticalOnline
  • 17. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Expected and required data quality
  • 18. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas When the statistical results have huge impact in the users
  • 19. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas More than just multivariable models...
  • 20. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas 5 AI concepts ● Training/Test data ○ Problem’s dataset ● Algorithm ○ Mathematical procedure that creates the Model from the training data ● Model ○ Mathematical system that has been created from the exploration of a data set. It’s created after an extensive learning process referred as training. ● Prediction / Classification / Recommendation / Recognition ○ Single inference over a model with an unseen sample ● Evaluation ○ Score evaluation of the Test dataset
  • 21. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas More than just multivariable models...
  • 22. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Supervised and unsupervised algorithms Learning Supervised Learning UnSupervised ➔ Linear Regressor ➔ Support Vector Machines ➔ Random Forests ➔ Neural Networks ➔ K-Nearest Neighbours ➔ Gradient Boosted Trees ➔ ... vs ➔ K-Means Clustering ➔ Hierarchical Clustering ➔ Isolation Forests ➔ Graphical Lasso ➔ Bayesian Networks ➔ Markov Hidden Models ➔ ... AI unsupervised Vision ExampleAI supervised Vision Example
  • 23. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Supervised Learning. Decision Trees. From a business decision point of view, a decision tree is the minimum number of yes/no questions that one has to ask, to assess the probability of making a correct decision, most of the time. As a method, it allows you to approach the problem in a structured and systematic way to arrive at a logical conclusion. Usually applied for root cause analysis
  • 24. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Supervised Learning. Naïve Bayes Classication. • P(A|B) is posterior probability • P(B|A) is likelihood • P(A) is class prior probability • P(B) is predictor prior probability Naïve Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naïve) independence assumptions between the features. Some examples of usage: • Deviations classification • Clinical trials analysis • Cause-effect analysis
  • 25. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Supervised Learning. Naïve Bayes Classification.
  • 26. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Supervised Learning. Logistic Regression. Is a way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution. In general, regressions can be used in pharma applications such as: • Predicting the quality of batches (ok / ko) • Unexpected manufacturing stops • Predicting the time to finish of a certain product • Is there going to be a non conformity in a batch?
  • 27. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Unsupervised Learning*. Principal Component Analysis PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is not suitable in cases where data is noisy (all the components of PCA have quite a high variance). Notice that domain knowledge is very important while choosing whether to go forward with PCA or not. Some of the PCA applications include: • Compression • Simplifying data for easier learning • Dimensional reduction • Identification of relevant dimensions • Dimensionless analysis * PCA can not be consiered as an AI algorithm
  • 28. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Which types of software must be validated? • All medical devices that contain software – even Class I devices – are subject to the Design Control Provisions, and thus require software validation • Any software used to automate any part of the device production process or any part of the quality system must be validated for its intended use (21 CFR §820.70(i)). • Computer systems used to create, modify, and maintain electronic records and to manage electronic signatures (21 CFR §11.10(a)) must be validated.
  • 29. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Not adequately validating software can lead to a Warning Letter... 4/12/2017 3. Failure to ensure that design verification shall confirm that the design output meets the design input requirements, as required by 21 CFR 820.30(f). For example: Your firm has a design input, (b)(4), of “the Remote Monitoring device shall only open network ports to authorized interfaces” which is documented in Software System Requirements Specification, Document. This is implemented as a design output in your firm’s DeviceName Software Requirements Specification Uploads (b)(4). This design output was not fully verified during your firm’s design verification activities. According to your firm’s testing procedures, (b)(4), Final Configuration Test Procedures, (b)(4) and Final Configuration Test Procedures Document (b)(4), the requirement was only partially verified by testing that the network ports opened with an authorized interface. Your testing procedures did not require full verification to ensure the network ports would not open with an unauthorized interface.
  • 30. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Trust in AI Robustness Fairness Explainability Lineage Ex. Adversarial training methods Bias • Data collection • Processing • Labelling Explainer •Data •Metadata •Catalog •Hyperparameters •Governance •Processing •Integration •Privacy.
  • 31. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Trust in AI Adversarial training methods example:
  • 32. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Trust in AI Adversarial training methods example:
  • 33. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Use cases in Pharma: Root Cause Analysis ? CQA: All of them under specs CPP: Equivalent results except culture duration
  • 34. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Getting knowledge from AI in primary packaging LC BCO Set-up Warm-up WO Reworks Reconcilliation •Pens / minute •Product / format •Batch size •Day of the year •Time of the day •Shift •Downtime •Stops •Affected units by stops Theoretical OEE Realistic OEE Deep process knowledge
  • 35. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Use cases in Pharma: Unpurified Bulk bag selection end Start unpurified bulk manufacturing drug substance purification From cell bank To CMO for formulation and filling
  • 36. 2019 ANNUAL MEETING & EXPO 27-30 October | Las Vegas Use cases in Pharma: Anomaly detection Predict TTNF (Time To Next Failure) Recommend actions to avoid Next Failure