Rohan ShirwaikerAssociate Professor, Industrial & Systems Engineering

Associate Faculty, Biomedical EngineeringDirector, 3D Tissue Manufacturing Research Team

North Carolina State UniversityEmail: rashirwaiker@ncsu.edu

Engineered Tissue “Factories”to Enable Bench-to-Bedside Translation

Tissue “Manufacturing”1995

Massachusetts General Hospital (Vacanti et al.)

2016

University of Tokyo and Kyoto University (Takato and Tsumaki et al.)

• Significant progress in relevant fundamentalbiomedical sciences not matched by advances inmanufacturing science to enable scale-up andscale-out.

• Other industries are evolving towards Industry5.0, but engineered tissue technology is stillstriving to make it to 2.0, for the most part.

https://www.smithsonianmag.com/science-nature/history-lab-rat-scientific-triumphs-ethical-quandaries-180971533/

https://www.medicaldaily.com/scientists-use-stem-cells-grow-human-ear-rats-back-371176

Congenital Disorders Trauma & Injuries Chronic Diseases

Pharmaceuticals Surgical Repair Devices & ImplantsGrafts & Transplants

Engineered Tissues: Clinical Needs

Engineered Tissue Technology: Proofs of Concept

Able to grow cells onto biomaterials…..

in patient-specific 3D geometries.….with tissue-specific characteristics…..

https://www.nature.com/articles/nbt.3413 https://iopscience.iop.org/article/10.1088/1758-5090/ab15cf https://science.sciencemag.org/content/364/6439/458

Current State of Tissue “Manufacturing”

Bioinks (Cells + Biomaterials)

Biomodeling(Imaging → CAD/CAM)

Bioprinting Process Modalities

Current State of Tissue “Manufacturing”

Bioinks (Cells + Biomaterials)

Biomodeling(Imaging → CAD/CAM)

Bioprinting Process Modalities

Bioprinting≠

Tissue Manufacturing

Biopsy Processing

Tissue Maturation

Quality Assessment

Packaging &

Shipping

Cell Expansion

Biomaterial Processing

Bioprinting

High-Level Map of Tissue “Manufacturing”

Example: Upstream Challenges

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

0 10 20 30 40 50 60 70

Num

ber o

f cel

ls (l

og s

cale

)

Time (days)

TARGET

0E+00

2E+04

4E+04

6E+04

8E+04

1E+05

1E+05

1E+05

2E+05

No.

of i

sola

ted

cells

Biopsy#

High upstream variability leads to more significant downstream disruptions (bullwhip effect)⇒ Wasted resources⇒ Scheduling issues in subsequent stages of bioprinting and implantation

For implantationFor quality inspection

Example: Downstream Challenges

Vision for Tissue ManufacturingModular Scalable Smart Factories for

Mass Production of Patient-specific Tissuesat Point-of-Care Pn Biopsy

Pn Data

Pn tissue

• Mass production with lot sizes of one• End-to-end sterile environment with no cross-contamination

between lots• Stochastic processes with high variabilities (pre-dominantly biology

driven)• Process cycle times and production lead times spanning up to

several weeks• Transient properties of living raw material, WIP, and finished product

leading to inventory constraints• 100% inspection• Continuous process improvement challenging due to regulations

Pn Digital Twin

Central QC/QA

Pn Biopsy

• Sterile coverage

• Tissue-neutral

• Self-contained (matl. hand. + data proces.)

• Reconfigurable• AutonomousPn Data

P2tissue

P1tissue

Pn Digital Twin

Vision for Tissue Manufacturing

Cell Seeding

Extrusion Printing

Vision for Tissue Manufacturing

Biopsy Processing

Waste

MincingCell

Isolation

Cell Passaging Pod#2

QC/QA

Cell ExpansionPod#2

Pod#1

BioprintingPod#3

DLP Printing

Tissue Maturation

Pod#4 Incu

batio

n

Incubation

Media/

G. Factors

Media/

G. Factors

Strain Bioreactor

Flow Perfusion Bioreactor

Waste

P2tissuePn Biopsy

P1tissue

Pn Digital Twin

Pn Data

Vision for Tissue Manufacturing

• Taxonomy & Ontology• Cyber-physical System Architecture• Machine Learning & AI• Logistics & Supply Chain Design• Data & Waste Management• Economics & Decision-making Modeling

R&D of Critical Enablers

• Design for X (Tissue Manufacturing, Implantability) • Process Design for Biomimicry• Automation & Robotics• Multi-variate Sensing

• Cells• Biomaterials

Education

Regulations Standards

Collaborations

Acknowledgements

Students, Collaborators & Mentors:

• Dr. Molly Purser• Dr. George Tan• Dr. Pedro Huebner• Dr. Lokesh Narayanan• Parth Chansoria• Karl Schuchard• Priyanka Sheshadri• Claudia Alvarado• Annie Lin• Brent Goldstein

• Dr. Binil Starly• Dr. Paul Cohen• Dr. Richard Wysk• Dr. Ola Harrysson• Dr. Jorge Piedrahita• Dr. Matthew Fisher• Dr. Elizabeth Loboa• Dr. Jeffrey Spang• Dr. Behnam Pourdeyhimi• Dr. Anthony Atala

and others….