Lab of the Future CongressCambridge UK, November 2019

Lab 4.0: moving from siloed lab information systems to lab informatics platforms

• Lab 4.0 principles call for sensing, predicting and self-optimizing Laboratory processes in near real-time to produce results faster and more accurately.

• Current siloed instruments + software systems such as LIMS, Freezer Management, Sample Management, Quality systems, Ordering, Inventory Management etc. create data and information silos that inhibit the application of Lab 4.0 principles needed for high-throughput labs.

• Precision medicine, complex instruments, and more software analytics such as bio-informatics and AI/ML is creating a need for next generation lab informatics platforms that automate and integrate increasingly complex lab processes (scientific, analytical, operational and regulatory)

• Need to maintain sample data provenance, chain of custody and chain of identity across all the lab processes (including analytics) and across large volumes of heterogenous data types.

Overview

Lab 4.0

Industry 4.0 – Core Principles

• Information 3.0• Connectivity 3.0• Visibility 4.0• Transparency 4.0• Predictability 4.0• Self-optimization 4.0

• Visibility (see)• What is happening in the lab?• What is the status of all the experiments ?• Capacity bottlenecks and TAT.

• Transparency (understand)• Correlation between experiment failures and instrument status?• Correlation between scientists, reagents/vendors and experiments

• Predictability• Predict experiment success and/or failure?• Experiment completion time?• Instrument failure/refurbishment based on results and quality reports

• Self-optimization

• Match of results to protocols, instruments, reagents and people?• Automatic scheduling and inventory re-ordering?

Lab 4.0 principles and advantages

Why lab 4.0: Reduce research & development times

Clinical Trial Processes Bio-process Mfg. Personalized Therapies

CommercialClinical Trial Phase

R&D + Pre-Clinical

20 Years + $$$$Bench Bedside

R & D Processes

Sequencing

Analysis

Diagnosis orManufacturin

g

Shipment or

Diag. Repor

t

Wet

Lab

ClinicInfusion

Sequencing

Analysis

Diagnosis orManufacturin

g

Shipment or

Diag. Repor

t

Wet

Lab

ClinicInfusion

Sequencing

Analysis

Diagnosis orManufacturin

g

Shipment or

Diag. Repor

t

Wet

Lab

ClinicInfusion

Faster research and better data integrity improves time to market for drugs and diagnostics

• Molecular diagnostics (cancer, rare diseases and inherited diseases) and molecular companion diagnostics – both require time critical lab operations

Why Lab 4.0:

Rady Children’s Institute sets Guinness world recordBy Paul Sisson Feb. 12, 2018 6 AM

It took 13 years to build the first full set of genetic blueprints for the human race by sequencing the DNA inside our cells that governs everything from eye color to risk of debilitating disease.But a team at Rady Children’s Institute for Genomic Medicine, working closely with homegrown sequencing sensation Illumina Inc., just proved it’s possible to get the job done in just 19.5 hours.

The 19.5-hour achievement is considered a proof of concept and will need further

refinement before it’s ready to be used clinically. At the moment, the fastest the

institute’s sequencing lab can go is 37 hours.

But that’s still the fastest turnaround for genetic diagnosis in pediatric medicine. It’s

common for this kind of work to take weeks, but Kingsmore has been pushing for years

to decrease turnaround times, especially for babies whose conditions are particularly

dire.

Last year, he shared news of a child suffering from severe liver disease and whose

genetic workup showed a different diagnosis than the one the child was about to be

operated on for, and a frantic call to the child’s doctor prevented an unnecessary

surgery.

The article continues on to say….

problem: data silos + lack of process orchestration

Wet Lab Sequencing Analysis ResultsSample Reports

X

Functional silos reduce business velocity + increase business risk & cost

Regulatory Risk

Data Integrity

Costs

Quality

solution: data integration + process orchestration platform

Wet Lab Sequencing Analysis ResultsSample Reports

Regulatory Risk

Data Integrity

Costs

Quality

unified systems Increase business velocity + reduce business risk & cost

Enterprise Science Platform

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Life Science’s Lab Informatics Digital Criteria to Separate Vendor Leaders From Laggards

Gartner Article

Michael ShanlerVP AnalystPublished 20 December 2018Source: © Gartner, Inc 2018

Unified Platforms:• Common data models• Code• Tooling• Architecture• Business process that span

capabilities• Process intelligence

THE SOLUTION

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• Function centric software (point-solutions) creates data + process silos

• Need to move to specimen, sample & patientcentric software (data + process)

• Multiple functions can leverage the same data set and multiple data sets leverage the same function

• To control and predict behavior of any system you need to be able to model it: the concept of a digital twin

• A digital twin will support real-time visibility (now),prospective & predictive process models

• Digital twin process models enable shift from automating execution to prediction and control of outcomes

function-centric to (data + process) centric software

Sample

SampleRegistry

Location(where?)

Inventorycapacity

(how much?)

Experiments(what?)

SampleAnalytics(Bioinfo/AI/ML)

SampleNew

Insights, data,

characteristics

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Project ManagementBatch Records

Inventory

LIMS

Connector Service

from point software to a unified informatics platform

API

Security

Cloud Execute

SampleService

ProjectService

ExperimentService

WorkflowService

ProtocolService

PipelineService

InstrumentControl

ReportingService

AnalysisService

UserService

Point Solutions

LIMSLocation Inventory Analysis

Freezer Management

Informatics Platform

ELN

Bio-InformaticsAI & ML

Sample Mgmt.

ReportingDashboard

Capacity

Sample Capacity

reduces complexity and provides a unified data + process business platform

Search knowledge

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Software + Instrument + Equipment + Automation + BMS Connector Service

Data & Meta-Data & Tag Catalog service

Process Catalog service

Analytics Catalog Service

Knowledge Catalog Service

ESP REST APIPython SDK

Sample Registry

Security

Cluster Execute

FileRegistry

Cloud Execute

InstrumentMonitor

Regulatory

Auditservice

SampleService

ProjectService

ExperimentService

WorkflowService

ProtocolService

PipelineService

InstrumentControl

ReportingService

AnalysisService

UserService

Custom Connecters & AppsApps

Data Model Content Library

Protocol/Method/Experiment/Analytics Content Library

Workflow Chains (Experiment treatment) Content Library

Connector Content Library

unified lab informatics platform: content vs platform

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solution enables embedded and process-agnostic analyses: current + retrospective + predictive

Data analysis

Data collection and provenance across various vendors

Data visualization and ad-hoc reporting

Business Intelligence Artificial Intelligence

123

Statistical Analysis Bio-Informatics

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Software + Instrument + Equipment + Automation + BMS Connector Service

Data & Meta-Data & Tag Catalog service

Process Catalog service

Analytics Catalog Service

Knowledge Catalog Service

ESP REST APIPython SDK

Sample Registry

Security

Cluster Execute

FileRegistry

Cloud Execute

InstrumentMonitor

Regulatory

Auditservice

SampleService

ProjectService

ExperimentService

WorkflowService

ProtocolService

PipelineService

InstrumentControl

ReportingService

AnalysisService

UserService

Custom Connecters & AppsApps

Data Model Content Library

Protocol/Method/Experiment/Analytics Content Library

Workflow Chains (Experiment treatment) Content Library

Connector Content Library

Solution enables

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• FAIR – Data architectures• Various data/metadata elements in life

sciences to describe human biology, patient health etc. HPO, FHIR, SNOMED

• Instrument integration standards, Allotrope

• Methods standards Pistoia Alliance

standards are important

Platform Catalog Services

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ESP data service – acquisition of data

Data model versioningData ontologies and standards

Phenotype/Genotype/scientific data and meta-dataAutomated data collection via connectors

Operational data: cost/inventory/capacityData Provenance

Regulatory complianceAutomated Data calculations

Mapping to external data sources

OrdersSample Records

Instru-mentsInventory File DataAnalysis

PipelinesContent SourcesDatabaseLibrariesSamples

Receiving Wet Lab Informatics Reports Publication

Sam

ple

Rep

osito

ry Cohort 1

Cohort 2

RNAseq

Mass Spec

BWA

bcbio

GATK

XNATMass-Quant

Imaging

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process service – a library of experiments, trials etc.

Process execution engine with sample provenanceRegulatory Compliant

Operational + scientific reportingBottleneck identification

Process/pipeline creation & maintenance

Process versioningProcess export

Process provenanceReal-time monitoring

API access

Experiment 1,Experiment 2.

Trial 1,Trial 2.

Treatment 1,Treatment 2,

Receiving

Trail Recruit

Diagnostic

Wet Lab Informatics Reports Publications

Treatment selection

Measure outcomes

Review outcomes

Publish metrics

Review Pathway

Modify Pathway

Inc/Exclusion Dose 1 Measure Dose 2 Measure Dose 3 Outcome

1 44 10 5

4

10

15

10 1020 10 10 10 10

10 30 10 5 2

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Analytics service

• Bioinformatics software• Natural Language processing• Artificial Intelligence• Genetic Programming• Machine Learning• Statistical Analysis

• A standard, platform independent, relocatable package structure enables easy search, usage & deployment.

• Versioned binary releases of all tools aid in validation and reproducibility.

• Vendor optimized versions of specific tools ensure high performance on cutting edge hardware.

• Inclusion of all supporting libraries ensures tools “just work”.

• A single source of provenance for all tools streamlines certification processes (e.g., FDA,CLIA).

Analytics can be cloud based / On-premise / Home-grown/ open source plug-ins

https://www.l7informatics.com/resources/biobuilds-2017-11/

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ESP: Knowledge service

Data Curation Knowledge Curation

ESP0001 is-stored-in Freezer-001

ESP0001 has-variant gene-Var

ESP0001 is-derived from ESP0000

ESP0000 is-a-sample-type Sample-001

Sample-001 is-a-strain AXEP200

LIMS

Freezer management

Vendor management

Knowledge system

DM/ML/AI Tools

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ESP: pre-built Software/Instrument connectorsSoftware

1. Shipstation – shipping software2. Benchling – ELN3. Clarity – LIMS4. Foundation Medicine – Tumor Variant File 5. BioPartners – Sample metadata6. Conversant Bio – Sample metadata7. US4Cure – Sample metadata8. Bioconductor – Genomic Analysis9. Microsoft Genomics – Hi-Thruput Genomics Analysis10. Lifemap – Genomic content11. Plotly – scientific Visualization software12. KeyCloak – Security Management13. Azure – Microsoft Cloud14. AWS – Amazon Cloud15. BlueMix – IBM Cloud16. Job Schedulers - LSF, Slurm, OGE/SGE, PBS17. CBIO portal – oncology cohort management18. GeneGlobe – Qiagen Ordering19. Sharepoint – Document management20. Box – Document Management21. CLC Workbench – Genomic Analysis (Qiagen)22. Rosalind – Genomic Analysis (on-ramp.bio)23. SAP – ERP24. Great Plains – ERP25. Microsoft Dynamics – ERP26. EPIC - EMR

Instruments

1. Illumina – NextSeq, 2. Illumina – HiSeq3. Illumina - MiSeq, 4. Illumina – iSeq5. Illumina - Novaseek6. Thermo - ION Torrent7. Thermo – Sanger8. Thermo - NanoDrop9. Thermo - QuBit10. PacBio - RS II11. PacBio - Sequel12. Roche - LightCycler13. Perkin Elmer – DropSense14. Perkin Elmer - LabChipGX15. Molecular Devices - Spectramax16. Beckman Coulter - Biomek17. Agilent – TapeStation 2200 18. Agilent – TapeStation 420019. Agilent – BioAnalyzer20. Agilent – Fragment Analyzer21. Zebra Label printer

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sample-to-answer provenance for all data

• Full and immediate traceability and auditability across science + health processes and analytical procedures

• Workflow, Protocol, Pipeline, and Task versioning ensures reproducibility• Sample Parent/Child relationships enable complex lineage patterns

25

Platform Content Services

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Content tree

workflows

Composed of Tasks to process and analyze data (AI/ML/Statistical)

To manage

To run

Composed of

Composed of Steps to track Samples/Patients associated with or on a protocol and the associated input and output data

PipelinesConnectors

projects

ExperimentsClinical Trials

DiagnosisTreatments

Data Collection

protocols

workflow chains

EntitiesPatientsSamples

DocumentsEquipment

Instruments

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Content models include: data + process + analytics + reporting + instrument integration

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• create a knowledge base of reusable data models, connectors, experiments (treatment pathways & protocols & workflows)

• separation of content standards and platform is critical to enable innovation• enable scientists/researchers/providers to create content • enable scientists/ /researchers/providers to share content• content becomes a key enterprise asset just like data• separation between user content and platform supports rapid innovation

content + platform = solution

SOLUTIONData Models

ExperimentsPathways

Connectors

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• Apply FAIR principles to content not just data

content library: data models, experiments (methods, protocols, workflows), connectors

Data Models

ExperimentsTreatments

Connectors

SongsMoviesTV

>100,000

>10,000

>1000

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collaboration at the protocol, process, and experiment level like other digital content (PDF, JPEG and other emerging digital standards)

intra + inter company content sharing + collaboration

Company A Company B

Send ReceiveExperimentTreatmentExperiment

Treatment

ExperimenTreatmentt

upload and “play”Save

1

2 3

4

Content should be

Putting it all together

data silos and information chaos before unified platformPatient Sample Flow – “Traditional” Approach

data integrity & synchronized flow after unified platformPatient Sample Flow – Operational Approach

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Case Study: Translational Oncology

• Sample accessioning and tracking

from biobank and external sources

• Disjointed data management

systems

• Manual data transfer to/from

external vendors and sources

• Manual bioinformatics pipeline

linkage to wetlab

• Poor data provenance

Demand for ESP: disjointed systems for Translational research workflows

35

ESP tracks sample metadata from biobank and feeds to ClarityLIMS

ESP monitors ClarityLIMS wet lab processes

ESP prepares forms and sample metadata for transfer to Foundation Medicine

ESP manages data transfer back to AZ

ESP integrates multiple informatics solutions (Foundation Medicine, bcbio etc.)

ESP deploys directly onto AZ’s existing compute infrastructure

solution: integrated metadata and process

management

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Translational research use-case

ESP

Sample Vendors Freezers Clarity LIMS BCBIO pipelines

TranslationalResearchPathway

Sample phenotype Data Sample Storage Wet Lab QC Sequencing Bio-Informatics Pipelines

Reporting – downstream Drug Discovery

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Demand for ESP: GMP facility preparing for commercial launch

Case Study: Gradalis GMP cell and gene therapy Process

Gradalis is entering Phase III clinical

trials for Vigil®, its fully personalized,

patient-specific cancer

immunotherapy. The company

requires a robust regulatory-

compliant platform to manage its

process for manufacturing transfected

immune-stimulated cells.

Solution: integrated process

management

Batch records

Process management

Instrument

connectors

Audit trails

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Cell and gene therapy use case (GMP)

ESP

Bio-processEquipment LIMS/Instruments Freezers/Shipping

Cell & genetherapyPathway

Patient Consent & tumor procurement Tumor Dissection Plasmid Transfection Cell Counting

Fill VialsQC Testing Shipment for Treatment

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Case Study: Gradalis Cell Therapy manufacturing process

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Case Study: hereditary disease dx

The process begins with a Patient, and adynamic (i.e. user defined on the fly)number of children samples can beassociated to the patient. The childrensamples include Embryos or Fetuses,Blood and/or Buccal Swabs collected fromextended family and/or ova/ sperm donors,DNA is extracted from these samples andare used for different diagnostic tools, suchas Sureplex, Repli-g, Karyomapping,SNaPShot, Sanger, Nanopore MINion,Veriseq, Identifiler, etc.

As the technicians process these samples,they record critical sample metadata that isused to better assist the patient inbecoming pregnant.

Fig. 1 ESP workflow for DNA sequencing to identify and diagnose hereditary diseases in unborn babies (embryo & Fetus)

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MDx/CDx use-case (GLP/CAP/CLIA)

ESP

EMR Instruments Bio-Inf / Analytics Clinical Outcomes

HereditaryDiseasePathway

Patient Consent & Phenotype

Sample collection + prep Sequencing Bio-InfAnalysis

Diagnosis Treatment and monitor and outcomes

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Case Study: rare disease dx + cancer dx

Fig. 1 ESP workflow for whole genome sequencing testing & process for diagnosis of rare diseases in pediatric patients

Large Children's hospitalLarge Cancer Center

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MDx/CDx use-case (GLP/CAP/CLIA)

ESP

IOT EMR PACS LIS PATH INSTRUMENTS

Rare DiseasePathway

Patient Consent & Phenotype

Sample collection + prep Sequencing Analysis Diagnosis Treatment and monitor and outcomes

CancerPathway

Patient Consent & Phenotype

Biopsy + Sample prep NGS Sequencing Bio-informatics Molecular Tumor board Diagnosis + Treatment

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Connector Catalog

life sciences research architecture using lab 4.0 principles

CROsSample

vendors

Ref.

Labs

Seq.

Labs

External Partner Data SourcesBio-banks

& Freezer

Management

Wet Lab &

Sequencing

Instrumentation

LIMS &

other scientific

software

systems

Bio-informatics

Life-sciences AI &

Machine-learning

Statistical tools

Bio-marker

Content and

Pathway

databases

ERP

Inventory

Billing

apps for science roles apps for IT & ops roles apps for mgmt.roles

Freezer

Mgmt.

Process

Wet Lab

Sample

Prep.

Sample

Processing

Sample

Sequencing

Sample

Mgmt.

Process

Sample

Data

Analysis

Translational

Research &

Bio-marker

discovery

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Lab 4.0 for lab management and continuous improvement

Real time experiment status (Mgmt.)Cycle time tracking (OpEx)Schedule management (Planning)Process data monitoring (Tech Ops)Alert notifications (QA/QC)Inventory / Capacity tracking (Mgmt.)