Improving Viral Manufacture through Advanced PAT

Dr Michael DelahayeLead Scientist – Industrialisation

AMC October 2019

The Cell and Gene Therapy Catapult

£70m Development Facility

• 1,200m2 Custom designed cell and gene therapy development facility

• Prime location in the heart of the London clinical research cluster

• >200 permanent staff

£55m large scale manufacture center

• 7,200m2 manufacturing centre designed specifically for cell and gene therapies

• Located in the Stevenage biocatalyst

• 12 independent manufacturing modules

Artificial Intelligence

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ARTIFICIALIntelligence

Predictive analytics

Deep learning

Digital twins

Machine Learning

Translation

Data extraction

Natural Language processing

Image recognition

Machine vision

Diagnostics

Autonomousmachines

Planning & optimisation

AI application – cell therapy

4

In 2016 Google Deepmind health began collaborating with Moorfields eye hospital

Develop an AI platform which can analyse OCT images and diagnose the early stages of 50 eye disease and recommend treatment

Has a 94% accuracy rate (comparable to clinician with 20 years experience)

Also explains how it arrives at it recommendations to clinicians

In 2018 the FDA approved the marketing of the first medical device that uses artificial intelligence to detect diabetic retinopathy (DR) in adults with diabetes

Uses a deep learning system to analyse images and identify DR with an 89.5% accuracy.

Microsoft in collaboration with MIT have developed 2 systems which use machine learning to design guide RNA sequences for CRISPR based gene editing.

Elevation – predicts off target effects and provides a score for how likely a guide is to disrupt the cell

Azimuth - predicts which part of a gene to target for optimal KO

AI and big data

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• AI works best when large amounts of rich, big data are available. The more facets the data covers, the faster the algorithms can learn and fine-tune their predictive analyses.

• Big Data is the raw input – it needs to be cleaned, structured and integrated before it becomes useful,

• Artificial intelligence is the output, the intelligence that results from the processed data.

• To solve a specific set of problems, AI needs to be trained properly in order to build intelligence and allow systems to be validated

• IDx – 495,000 images to train and validate the AI system for identifying diabetic retinopathy

Data generation – biopharmaceutical production

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Upstream FormulationDownstream

Process and material insightsActionable data analytics>500 QC entries

>2000 batch record entries

>500 million continuous data point

Upstream FormulationDownstream

Data driven processing

Data driven decision making

Bus

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Complexity

Hindsight

InsightForesight

Descriptive AnalyticsWhat happened?

Diagnostic AnalyticsWhy did it happen?

Predictive AnalyticsWhat will happen?

Prescriptive AnalyticsHow can we make it

happen?

Data driven gene therapy bioprocessing

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• Temp• pH• DO• Raman spectroscopy• RI spectroscopy

Multivariateanalysis

100’s samples

• 2 production processes• 12 production runs

• ~10 million data point

• Metabolomics• Transcriptomics

Cell metabolism

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Glycine

Cysteinyl disulfide

Cysteinyl glycine

Cysteinyl disulphide

Cysteinyl glycine disulfide

Cystathionine

Cysteine

Serine

Isoleucine

.γ -glutamyl serine

Cysteine sulfinic acid

Lipoic acidGlucose

Pyruvate

Acetyl CoA

Mevalonate N-butyryl isoleucine

Cystathionine

Citrate

.α -ketoglutarate

SuccinateFumarate

Malate

.α -lipoate

TCA CycleCholosterol

N-aspartyl glutamic acid

N-acetyl aspartate

N-acetyl glutamate

N-acetyl glutamine

Histidine N-acetyl histidine

4-imadazoleacetate

Imadizole lactate

.γ -glutamyl glutamine

.α -ketoglutaramate

3-hydroxyisobutyrate

N-acetyl valine

.γ -glutamyl methionine

Xanthine Purine Metabolism

Aspartate

Proline

Glutamate

Aconitate

Glutamine

Valine

Methionine

Betaine

Homocystine

.γ -glutamyl valine

Alanine

Lactate

Isoleucine

Trytophan

lysine

Leucine

Carboxyethyltyrosine

Tyrosine

Thymine

Thymidine

DNA Synthesis

Petrin

Phenyl-pyruvate

4-hydroxyphenylpyruvate

1-carboxyethylphenylalanineArginosuccinate

Phenylalanine

Aspartate

Asparagine

2-aminoadipate

Methyl-lysine

Aminoadipic acid

Pipecolic acid

N-butyryl-leucine

.γ -glutamyl Isoleucine

N-carbomylalanine

Cholesterol

Glycerophosphoserine

Spermidine

Membrane formation

Glycerophosphocholine

Phosphocholine

Choline

Phosphate

Phosphoethanolamine

N-acetylputrescine

Putrescine

Cadaverine

N-acetyl-cadaverine

Process 1

Process 2

Supervised learning: model performance and optimisation

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All data (144 samples, 300 metabolites)

Training (70%) Test (30%)

Model performance

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 =𝑁𝑁𝑁𝑁. 𝑁𝑁𝑜𝑜 𝐴𝐴𝑁𝑁𝐴𝐴𝐴𝐴𝑐𝑐𝐴𝐴𝑐𝑐 𝑝𝑝𝐴𝐴𝑐𝑐𝑝𝑝𝑝𝑝𝐴𝐴𝑐𝑐𝑝𝑝𝑁𝑁𝑝𝑝𝑝𝑝𝑇𝑇𝑁𝑁𝑐𝑐𝐴𝐴𝑇𝑇 𝑝𝑝𝑁𝑁. 𝑁𝑁𝑜𝑜 𝑝𝑝𝐴𝐴𝑐𝑐𝑝𝑝𝑝𝑝𝐴𝐴𝑐𝑐𝑝𝑝𝑁𝑁𝑝𝑝𝑝𝑝

• Model performance measured on “test” data to assess ability to generalise (stop overfitting)

• Model parameters estimated using “training” data

x10

Model optimisation usingk-fold cross validation Parameter Optimisation

• Model parameter optimisation carried out using repeated k-fold cross validation

• Provides most mileage on training data• Accuracy averaged across 3 repeats and

10-folds

Cross Validation - Training Cv- Test

Machine learning algorithms

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What to measure –• Application of machine learning to select markers for process monitoring• Combined with metabolomics to incorporate biological function

Marker Function

1 Proline Amino acid 812 Glucose Glycolosis 753 Hydroxyphenyl lactate lactic acid 714 Asparegine Nitrogen donor 675 Histidine Amino acid 666 Spermine Cholesterol metabolism 647 Malate TCA cycle by-product 568 Glutamine Amino acid 529 Glycine Formation of Serine 47

10 Lactate Glycolosis by-product 4611 Serine Amino acid 4412 Pyruvate Glycolosis 33

Variable importance (%)

Data driven decision making

Bus

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Complexity

Hindsight

InsightForesight

Descriptive AnalyticsWhat happened?

Diagnostic AnalyticsWhy did it happen?

Predictive AnalyticsWhat will happen?

Prescriptive AnalyticsHow can we make it

happen?

Process Analytical Technologies (PAT)

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PAT is a framework for

“designing, analysing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality”

The aim of PAT is to obtain better process control by

• identifying and managing sources of variability,

• reducing cost by optimising the use of raw materials

• minimising product cycle times through the use of measurements that are • in-line (analysed in place),

• on-line (sample removed, analysed and returned to the process stream)

• at-line (sample removed and analysed close to the process stream)

FDA - PAT—a framework for innovative pharmaceutical development, manufacturing and quality assurance. 2004

Real time data acquisition

Raman SpectroscopyRaman Spectroscopy is a technique used to observe molecular vibrations that can identify and quantitate molecules

By measuring changes in the wavelength of laser light its possible to identify what molecules are present in the cell culture media

• Spectra collected at regular intervals.

• Matched to nearest offline / at-line sampling time points.

• Pre-processed using a number of algorithms.

• Modelling of at-line and offline data performed using multivariate chemometric methods such as projection to latent structures (PLS) and artificial neural networks.

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glucose

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lactate

00.20.40.60.8

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glutamine

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ammonia

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conc

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Raman spectroscopy

Process run 1Process run 2Process run 3Process run 4Raman Data

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glucose

Offline Data

Raman model development

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Run 1Run 2Run 3Run 4Run 5Run 6Run 7Run 8Run 9Run 10Run 11Run 12

Metabolomics Data Raman spectroscopy

Random selection of ~20% of the data to allow Raman model development

Raman model development

Run 1Run 2Run 3Run 4Run 5Run 6Run 7Run 8Run 9Run 10Run 11Run 12

Training data Test data Training data Test data

x50PLS regression modelling for each metabolite

Model building was performed using 5-fold cross-validation.

Raman model development

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Example of model performance for main metabolic markers:

𝑅𝑅2 = 1 −𝑅𝑅𝑐𝑐𝑝𝑝𝑝𝑝𝑝𝑝𝐴𝐴𝐴𝐴𝑇𝑇 𝑆𝑆𝐴𝐴𝑆𝑆 𝑁𝑁𝑜𝑜 𝑆𝑆𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴𝑐𝑐𝑝𝑝𝑇𝑇𝑁𝑁𝑐𝑐𝐴𝐴𝑇𝑇 𝑆𝑆𝐴𝐴𝑆𝑆 𝑁𝑁𝑜𝑜 𝑆𝑆𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴𝑐𝑐𝑝𝑝

• ~55 metabolites have R2 >0.85.

• ~88 metabolites have R2 >0.80.

Analyte Raman R2 Raman SD

Glucose 0.8354 0.0842

Lactate 0.9166 0.0440

Glutamine 0.8330 0.0846

Glutamate 0.8490 0.0602

Raman model development

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Performance of Raman models for marker selected using machine learning algorithm

Marker Function

1 Proline Amino acid 81 0.942 Glucose Glycolosis 75 0.843 Hydroxyphenyl lactate lactic acid 71 0.894 Asparegine Nitrogen donor 67 0.905 Histidine Amino acid 66 0.826 Spermine Cholesterol metabolism 64 0.587 Malate TCA cycle by-product 56 0.928 Glutamine Amino acid 52 0.839 Glycine Formation of Serine 47 0.92

10 Lactate Glycolosis by-product 46 0.9211 Serine Amino acid 44 0.8812 Pyruvate Glycolosis 33 0.87

Variable importance (%)

Raman Mean R2

Real time monitoring of viral titre

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Data driven decision making

Bus

ines

s V

alue

Complexity

Hindsight

InsightForesight

Descriptive AnalyticsWhat happened?

Diagnostic AnalyticsWhy did it happen?

Predictive AnalyticsWhat will happen?

Prescriptive AnalyticsHow can we make it

happen?

Going forward

Device manager

Data Parser

Digital twin

Data Processing

Manual upload

Data store

AnthaHubfile watcher

Data acquisition and processing using Antha

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Real time monitoring of metabolites

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Real-time online monitoring

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Future looking - In-silico digital twin to support process improvement

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Targeted and reduced experimentation

Robust, optimised processes

Improved process understanding

Soft sensors and augmented reality

In-silico modelling, process simulation, CFD

Digital Twin

Acknowledgements

John ChurchwellOliver DewhirstIsobelle Evie Marco BaradezAnna Williams

Peter JonesNicholas Clarkson Laura McCloskeyLee DaviesRui SanchesThomas WilliamsRobert LawAnn Justino

Markus GershaterJames ArpinoJames RutleyAllen Shaw

Improving Viral Manufacture through Advanced PATThe Cell and Gene Therapy CatapultArtificial IntelligenceAI application – cell therapyAI and big dataData generation – biopharmaceutical productionData driven processingData driven decision makingData driven gene therapy bioprocessingCell metabolism Supervised learning: model performance and optimisation Machine learning algorithmsData driven decision makingProcess Analytical Technologies (PAT)Real time data acquisitionRaman spectroscopyRaman model developmentRaman model developmentRaman model developmentRaman model developmentReal time monitoring of viral titreData driven decision makingGoing forwardData acquisition and processing using AnthaReal time monitoring of metabolitesReal-time online monitoring Future looking - In-silico digital twin to support process improvementAcknowledgements