Placing microphysiological systems in the pharmaceutical R&D - PowerPoint PPT Presentation

Placing microphysiological systems in the pharmaceutical R&D strategy Dr Lorna Ewart FRSB FBPhS EMA workshop: challenges and opportunities for use of micro-physiological systems, London 5 October 2017

Outline of today’s presentation • Background • Introducing the AZ framework for MPS application • Bringing the framework to life through case examples • Closing remarks IMED Biotech Unit I EMA workshop 2

Background IMED Biotech Unit I EMA workshop

The need for improved mechanistic and predictive modelling: a well described pharmaceutical challenge The average drug takes It costs $2.6 billion to develop Safety and efficacy lead to 12 to 15 years to develop (DiMasi et al., 2016) failure (Cook et al., 2014) IMED Biotech Unit I EMA workshop 4

Microphysiological systems enable us to precisely tune cellular biology to produce an accurate model Blood or blood components Immune Cell shape and component cytoarchitecture Extracellular Aim: recreate the dynamic Mechanical matrix and cell cellular microenvironment in forces interactions which cells function in vivo IMED Biotech Unit I EMA workshop 5

Successful adoption and application is intimately linked to the correct context of use • Several potential scenarios for value proposition within drug discovery and development pipeline • Each scenario has: • a different set of technical standards or requirements • a standard against which success will be measured a threshold of confidence that would need to be achieved • • Uses are not mutually exclusive Adapted from Ewart et al., 2017 EBM Thematic Issue MPS IMED Biotech Unit I EMA workshop 6

Context of use within the value chain also requires understanding of the problem that needs to be solved Confirm presence of Baseline effect on Assess impact on Identify and assess Testing Requirements relevant targets physiology disease phenotype potential side effects Thousands of Tens to hundreds of Two to three One to two compounds compounds compounds compounds High Throughput Medium to high Low throughput Low throughput systems throughput systems systems systems Adapted from Ewart et al., 2017 EBM Thematic Issue MPS IMED Biotech Unit I EMA workshop 7

AZ framework for MPS application IMED Biotech Unit I EMA workshop

The AstraZeneca framework Decreasing throughput but increasing validation needed/increasing MPS complexity 4 1 2 3 Target Lead Generation Lead Optimization Clinical Pre Clinical Selection Routine in vitro screens Routine in vivo studies Regulatory studies Bespoke in vitro assays Bespoke in vivo studies 1 2 Enhance target selection and target biology Enhance compound progression with MPS that using disease relevant systems that are agnostic are “superior” to existing in vitro models to therapeutic modalities 4 3 Improve in vivo study design and/or reduce the Problem solving: Drive understanding of efficacy number of in vivo studies and/or safety; influence risk assessment and management IMED Biotech Unit I EMA workshop

Case examples IMED Biotech Unit I EMA workshop

1 Towards disease modelling: enhancing biological understanding Islets only: insulin rises unchecked Experimental set up Physiological scaling Islets plus liver: insulin levels rise but plateau maintained IMED Biotech Unit I EMA workshop 11 Bauer et al., In press Nature Scientific Reports

1 Introducing insulin resistance to the liver to explore the impact on beta cell proliferation • Insulin resistance and pancreatic beta cell dysfunction are key interrelated pathogenic factors in the pathogenesis of metabolic diseases such as diabetes • Pancreas and liver are affected by insulin resistance • AZ are building an insulin-resistant liver model in three ways: (1) elevated media glucose concentration, (2) pharmacological inhibition of the insulin receptor, (3) creation of hepatocyte cell lines without the insulin receptor using CRISPR Key organ systems in metabolic disease • Can MPS help identify factors that impact beta cell function and/or proliferation? IMED Biotech Unit I EMA workshop 12

“Superiority” to existing in vitro models: 2 Case example hematotoxicity assessment HSC proliferation/CFU In vivo MPS in vitro  High throughput  In vivo Bone Marrow PK  Human cells with potential to include patient cells  Dose scheduling to mimic  Human cells clinic  Long term cell culture  Small compound amounts enables investigation of  Monitor cell recovery (mg) required haematopoiesis  Need to translate to human  In vivo BM PK difficult to  Recapitulate in vivo  Use of large number of environment recapitulate animals  Kinetic data - potential for  Not amenable to dose  Large compound amounts enhanced opportunities for scheduling (g) required systems pharmacology and  Limited data output for Modelling and Simulation Modelling & Simulation etc. IMED Biotech Unit I EMA workshop 13

One approach to a Bone Marrow (BM) MPS 2 In vivo -like microenvironment (3D scaffold) important 3D microenvironment similar to Cytokine-free media for for cell proliferation and differentiation in vivo autonomous differentiation Scaffold Scaffold Human BM Human BM Thrombopoietin (TPO) and Flt3, to encourage autonomous cell differentiation Fluidic system for Ceramic scaffold Mesenchymal Stem Cell extended cell culture mimics human BM (MSC) growth similar to structure in vivo Microenvironment and flow are important for extended viable cell culture Fluidic system for dynamic cell sampling Enables monitoring of cell proliferation and differentiation over time 14 IMED Biotech Unit I EMA workshop

Model characterisation data and preliminary 2 toxicological data are encouraging Data redacted IMED Biotech Unit I EMA workshop 15

Future: Modelling & Simulation using BM MPS will drive 2 clinical use strategies Stemness Neg Feedback Lineage diff. Transit time IMED Biotech Unit I EMA workshop 16

3 Improve in vivo study design and/or reduce the number of in vivo studies • Despite comprehensive cardiac safety screening, cardiotoxicity sometimes remains undetected until in vivo testing, in part because cardiotoxicity can also be driven by exposures to metabolites instead of the drug itself • Hypothesis: a heart “chip” connected to a metabolically competent liver “chip” can distinguish parent and metabolite mediated cardiotoxicity in vitro IMED Biotech Unit I EMA workshop 17

3 MPS detects Terfenadine mediated cardiotoxicity QT Interval (Normalized to Control) The anti-histamine terfenadine is • cardiotoxic but is metabolized to 12 fexofenadine which is not cardiotoxic 10 8 • In the heart “chip” cardiotoxicity is detected (EC 50 1.3 µM) but when 6 connected to a metabolically 4 competent liver “chip” the response is right shifted (EC 50 >10 µM) 2 0 10 -12 10 -10 10 -8 10 -6 10 -4 • In the presence of a CYP inhibitor at a [Terfenadine] M concentration that reduces metabolism Heart Heart + Liver by 50% the response is left shifted (EC 50 5.4 µM) Heart + Liver + 10  M Troleandomycin IMED Biotech Unit I EMA workshop 18 McAleer et al., Manuscript in preparation

3 MPS detects Terfenadine mediated cardiotoxicity • Real time bioanalysis from heart-liver chips supports the pharmacology with a reduction in the terfenadine concentration over time and a subsequent increase in fexofenadine concentration • Terfenadine concentration is constant in heart only chips IMED Biotech Unit I EMA workshop 19 McAleer et al., Manuscript in preparation

3 Application of modelling and simulation to MPS data predicts literature in vivo data Monkey (Ando et. al) Observed (Ando et al.) MPS Readout Predicted Time (hr) IMED Biotech Unit I EMA workshop 20 McAleer et al., Manuscript in preparation

4 Application in risk assessment within preclinical safety • Prior to first time in human administration, new chemical entities are tested in 2 preclinical species • Translating the relevance of a signal to human is critical to risk assessment • Development of species “chips” will enhance our confidence in the risk assessment • AZ in partnership with Emulate have developed rat and dog liver chips IMED Biotech Unit I EMA workshop 21 Jang et al., Manuscript in preparation

4 Application in risk assessment within preclinical safety Data redacted IMED Biotech Unit I EMA workshop 22 Jang et al., Manuscript in preparation

Cytotoxicity confirmed by automated confocal and live 4 cell imaging Data redacted Peel et al., Manuscript in preparation IMED Biotech Unit I EMA workshop 23 Jang et al., Manuscript in preparation

Closing remarks IMED Biotech Unit I EMA workshop

• Partnership between the chip innovators and the end users will be essential to drive this technology deeper into our strategies Ewart et al., 2017 EBM Thematic Issue MPS Figure created by Kyle Brimacombe & Kristin Fabre