Determining the Predictive Validity of 3D Models & Achieving Standardization for Preclinical Efficacy Testing

Transcription

1 Determining the Predictive Validity of 3D Models & Achieving Standardization for Preclinical Efficacy Testing August Jason Ekert, Head of Complex In Vitro Models Platform Technology and Sciences GlaxoSmithKline (GSK)

2 Overview Background on pharma industry and success rate What are the greatest opportunities and challenges in adoption of more predictive 3D models? What key components should be considered when developing a predictive 3D model? What constitutes a validated model from an industry standpoint? How do we scale and automate 3D models in preclinical drug discovery?

3 The major reasons behind failure: Efficacy and Toxicity ~5% overall drug development success rate ~90% of the overall cost for a drug approval from Phase III -> Still ~40% of Phase III trials fail Ledford H. Nature 477, (2011) Current Models: Non-humanized animal models: systemic, but not human In vitro human assays: neither organotypic nor systemic Need better predictive, more human relevant preclinical models 3D Cell Culture Draws Drug Developers Interest Genetic Engineering and Biotechnology News, September 15, 2016, Zimmermann, A

4 Convergence of multiple disciplines to develop standardized and validated models of human diseases with clinical relevance Innovative Technology ipscs, CRISPR/Cas9 Gene editing omics High content imaging Computational Modeling (i.e. Deep Learning) Bioprocessing Bioreactor Microfluidics Organs-on-chip Biofabrication Automation/Robotics 3D Bioprinting Organoid Biomaterials Tissue Engineering Regenerative Medicine Innovation brings opportunity! Current Opinion in Biotechnology, Volume 35, 2015,

5 Opportunities for more predictive models earlier in R&D Reduce/replace reductionist screening models Reduce/replace animal models Presentation title in footer 5 Marx etal 2016; Altex;33(3):

6 Opportunities for more predictive models earlier in R&D translational and predictive value of models earlier in discovery When possible, combining safety and efficacy predictive models Presentation title in footer Marx etal 2016; Altex;33(3):

7 Filling the Gaps! Game-changers in drug development: the technologies that are revolutionizing research Hesperos Printed Muscle Fibers LAB-ON-A-PRINTER TM GSK is not advocating or promoting any of these but sharing these as examples for the different technology platforms

8 Current Complex In Vitro Models Fit & Potential Impact One-two cell types Simple Structure Multicellular Complex Structures Target ID & validation (MoA) Hit/lead discovery Lead optimisation Candidate selection Preclinical safety (efficacy/moa) Clinical assessment #compounds 1000 s 100 s 10 s 1-3 Lower Throughput More complexity GSK is not advocating or promoting any of these but sharing where the companies themselves have positioned their models

9 Challenges in adoption of more predictive 3D models $$$ and timelines can determine scope of validating/qualifying new model Need to establish confidence in both specificity and sensitivity to identify relevant endpoints Need to identify value in generating critical data and specific context of use for models Fee for service vs in house implementation Target validation vs High throughput vs lead optimization (LO) Presentation title in footer 9

10 Key components that should be considered when developing a predictive 3D model scientific due diligence 1. Application of 3D model (context of use): stage of drug development level of complexity of the model adequate for the information provided 2. Platform potential 3. Clinical translatability capability determine what aspect of the disease will be recapitulated and the level of complexity required to adequately model the disease 4. Histopathological evaluation: Cell morphology and viability Cell functionality Gene expression 5. How the 3D model has been/is being validated? Presentation title 10

11 Validated model from an industry standpoint Evidence of prediction of clinical efficacy or toxicity Strong proven track record from peer-reviewed literature Human-relevant functionalities (endpoints) and bio-markers Modulate disease target or pathway Robustness and reproducibility Additional : Phenotype reversal- demonstrate that the disease phenotype can be reversed toward a healthy phenotype Presentation title in footer 11

12 Current Complex In Vitro Models Fit & Potential Impact One-two cell types Simple Structure Multicellular Complex Structures Target ID & validation (MoA) Hit/lead discovery Lead optimisation Candidate selection Preclinical safety (efficacy/moa) Clinical assessment #compounds 1000 s 100 s 10 s 1-3 Lower Throughput More complexity GSK is not advocating or promoting any of these but sharing where the companies themselves have positioned their models

13 Example of validated model for LO- lung on a chip Wyss in collaboration with GSK Recapitulating pathological changes in barrier integrity Clinical translatability LO testing Dongeun Huh et al., Sci Transl Med 2012;4:159ra147 13

14 Current Complex In Vitro Models Fit & Potential Impact One-two cell types Simple Structure Multicellular Complex Structures Target ID & validation (MoA) Hit/lead discovery Lead optimisation Candidate selection Preclinical safety (efficacy/moa) Clinical assessment #compounds 1000 s 100 s 10 s 1-3 Lower Throughput More complexity

15 Neuratect Crack-it Challenge Sponsors: GSK, Sanofi, Abbvie, BASF, NC3Rs Key Collaborators: Mimetas, Institute of Risk Assessment Sciences (IRAS; Utrecht University, Netherlands) and CDI Key Objectives: Optimise 2D- and 3D-culture of mixed neuronal subtypes using selected human ipsc-derived cell types. Morphologically- demonstrate the presence and maturity of different neuronal subtypes in 2D and in 3D cultures. Functionally- characterize using endpoints that are involved in neuronal function and toxicity using high content imaging (3D) and multi-electrode array (MEA) plates (2D). Pharmacological proof of concept- demonstrate sensitivity of selected endpoints using reference compounds in both the 3D OrganoPlates and the 2D MEA-plates. Mimetas Organoplate

16 Physiologically relevant human stem cell based model(s) to identify neurotoxicity in vitro CDI Glutaneuron(70%)/ CDI icell(15%)/ Astrocyte(15%) co-culture (75k) DIV 16 β(iii)tubulin / S100β / DAPI DIV 21 β(iii)tubulin / S100β / DAPI DIV 23 β(iii)tubulin / S100β / DAPI DIV 25 β(iii)tubulin / S100β / DAPI DIV 7 MAP2/ Synapsin-I/ DAPI DIV 5 MAP2/, vglut/ vgat / DAPI Milestones Reached: Optimal ratio for 3D culture longevity and reproducibility Prototype- ephys (MEA) and imaging readouts in the Mimetas OrganoPlate Calcium imaging

17 Current Complex In Vitro Models Fit & Potential Impact One-two cell types Simple Structure Multicellular Complex Structures Target ID & validation (MoA) Hit/lead discovery Lead optimisation Candidate selection Preclinical safety (efficacy/moa) Clinical assessment #compounds 1000 s 100 s 10 s 1-3 Lower Throughput More complexity

18 Scale and automation of 3D models Develop a flexible high-throughput 3D lung tumor model Background Tumor spheroids better model physiological organization of cells (takes cell-cell and cell-matrix interactions into account) develop flexible high throughput 3D tumor platform Small tumor filling alveolus of the lung Lung tumor spheroid Ekert etal 2014 PLoS One Mar 17;9(3):e

19 Scale and automation of 3D models Generation of HTS 3D tumor assay Cells Tumor (cell line, organoids, primary tumor) TME (CAF, endothelial, blood cells) Monotypic vs heterotypic Tissue Architecture Collagen, Matrigel Hydrogel Nanoculture plates Microfluidics Matrix-free Readouts Cell Viability Cell death, apoptosis Spheroid size, number, width Morphology Instrumentation Acumen Operetta Incucyte Envision BioMek SelecT Natasha Cover, Hai Sheng, BR assay group 19

20 3D Lung Tumor Cell Model High-throughput format of single 3D spheroid formed in each well of 96 well plates We suggest that such subtle microenvironment-induced changes in lung tumor cell physiology add a level of complexity to cell-based assays that may be more representative of the in vivo tumor microenvironment. Ekert etal 2014 PLoS One Mar 17;9(3):e

21 Development of a flexible high-throughput 3D lung tumor model Spheroid formed from lung tumor cells ULA plate Signal transduction Proliferation Migration T cell killing Compound libraries Target discovery Ekert etal 2014 PLoS One Mar 17;9(3):e92248 Target validation 21

22 Conclusions Evaluation of the predictive value of models starts with the disease No standards or thorough scientific due diligence lists/criteria for model qualification/validation Predictive / human relevant models should be used at every step of drug development The predictive value of a model is more important than cost of the model, throughput, the ease of use or the gold standard.

23 Acknowledgements GSK Complex In Vitro Models Team Gianni Dal Negro Claire Jeong Neuratect-CRACK-IT GSK- Matthew Cato and Wayne Rycroft Mimetas Jos Joore IRAS Remco Westerink CDI Giorgia Salvagiotto Thank you for your attention! Questions? contact: