Moderna, Inc. · NASDAQ:MRNA
Moderna — Investor Day (May 2022)
136 slides from Moderna's investor day presentation, May 17, 2022
Slide 1: Moderna Science & Technology Day
DNA mRNA Protein DNA messenger RNA Protein Storage Software Applications Moderna Science & Technology Day May 17th, 2022
Slide 2: Forward-looking statements and Disclaimer
Forward-looking statements and Disclaimer This presentation contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, as amended, including statements regarding: the potential for mRNA as a new class of medicines; the potential of Moderna’s mRNA-
Slide 3: mRNA is a unique opportunity to change medicine forever
mRNA is a unique opportunity to change medicine forever • mRNA is an information molecule • mRNA had never been a drug modality, so the pace of learning about mRNA would likely follow a S curve • Exponential increase of mechanistic understanding of disease knowledge, at the molecular level, is an in
Slide 4: Obsess over how fast we can expand its clinical applications
Obsess over how fast we can expand its clinical applications Better mRNA molecules Better manufacturing processes to make the medicines Ability to deliver mRNA to new cell types Slide 4 © 2022 M oderna
Slide 5: Our platform has incredible independent features to expand
Our platform has incredible independent features to expand Slide 5 © 2022 M oderna
Slide 6: Moderna is in a unique position to lead the emerging mRNA industry
Moderna is in a unique position to lead the emerging mRNA industry We are an mRNA- We have scale in Science We have a unique cross only company >700 platform research functional culture scientists Focused in MA to increase collaboration We are a digital company We have unparalleled Now layering AI t
Slide 7: Our commitment to be the best at mRNA science is core to who we are
Our commitment to be the best at mRNA science is core to who we are Performance Mindset 4 We obsess over learning We don't have to be the smartest—we have to learn the fastest. Time Slide 7 © 2022 M oderna
Slide 8: This year, we will focus on our newest modality and advances in our vaccine platform
This year, we will focus on our newest modality and advances in our vaccine platform Rebranded to Science & Technology Day to reflect investments across technical development Personalized OX40L/ cancer IL-23/IL-36𝛾 CM V vaccine (triplet) vaccine VEGF-A (no LNP) KRAS Biology risk cancer IL-12…
Slide 9: Today's Agenda
Today’s Agenda Stéphane Bancel, CEO Introduction Melissa J. Moore, Ph.D., Platform Research Focus on our Inhaled Pulmonary Therapeutics Jean Sung, Ph.D., Drug Product Development newest modality Improving Vaccine Stability Phil White, Ph.D., Chemistry, Manufacturing & Control Break (10 minutes) Adva
Slide 10: Science & Technology Day
DNA mRNA Protein Science & Technology Day Melissa J. Moore, Ph.D. Chief Scientific Officer, Scientific Affairs © 2022 M oderna
Slide 11: Moderna's Science and Technology Ecosystem
Moderna’s Science and Technology Ecosystem ~280 Employees mRNA, delivery system and Chemistry & Formulation Discovery, RNA Science, Platform Science Biological Science, Computational Science, gene editing discovery Immunology, Nonclinical Development, mGx ~450 Employees mRNA & delivery system develo
Slide 12: Publications from 2020 and 2021 Science Days
Publications from 2020 and 2021 Science Days An engineered T7 RNA polymerase that generates immune-silent mRNA Athanasios Dousis, Kanchana Ravichandran, Elissa M. Hobert, Melissa J. Moore , Amy E. Rabideau Nature Biotechnology (2022) in press Adv. Funct. Mater. (2022) 32, 2106727. Slide 12 © 2022 M
Slide 13: Publications from 2020 and 2021 Science Days
Publications from 2020 and 2021 Science Days Small Medium Large X-Large 10 4 Anti-Pentamer Titer 10 3 10 2 10 1 10 0 Small Medium Large X-Large LNP Slide 13 © 2022 M oderna
Slide 14: Today's Agenda
Biodistribution and safety of Today’s Agenda LNP our IM vaccine LNP mRNA Improving vaccine stability by understanding Log (Intact mRNA, %) mRNA inactivation Our new inhalable LNP pathways Mathematical modeling of vaccine immunogenicity and reactogenicity Slide 14 Time (Months) © 2022 M oderna
Slide 15: Moderna's mRNA delivery systems
Moderna’s mRNA delivery systems © 2022 M oderna
Slide 16: Moderna combines mRNA engineering, delivery system discovery, and clinical manufacturing platforms
Moderna combines mRNA engineering, delivery system discovery, and clinical manufacturing platforms within a single, fully-integrated organization mRNA Delivery Manufacturing Process Thr Gly Leu We define success in our platform as achieving the following properties: • Predictable dose response • The
Slide 17: Moderna's 2022 Q1 investigational pipeline
Moderna’s 2022 Q1 investigational pipeline mRNA-based investigational vaccines and medicines cover 7 different modalities Prophylactic vaccines Cancer Intratum oral Germline Zika vaccines im m uno- HIV VLP oncology Localized System ic Encoded regenerative secreted Nipah Checkpoint PCV therapeutics &
Slide 18: Different modalities require different routes of administration
Different modalities require different routes of administration Routes of administration Subcutaneous Intratumoral injection injection Intramuscular injection Inhaled delivery Intracardial injection IV infusion Slide 18 © 2022 M oderna
Slide 19: Most routes of administration require mRNA formulation into lipid nanoparticles
Most routes of administration require mRNA formulation into lipid nanoparticles (LNPs) mRNA: Codes for desired protein(s) Ionizable lipid: Interacts with RNA Phospholipid: Forms surface membrane Sterol: Enhances membrane fluidity PEG lipid: Prevents LNPs from fusing Cryoelectron in vial micrograph 1
Slide 20: LNP engineering requires optimization of many parameters
LNP engineering requires optimization of many parameters Components Composition & Process Structure Stability Routes of Administration Sterol Ionizable lipid Phospholipid Δ molecules Δ composition Δ process mRNA Chemical stability Physical stability Excipient PEG lipid Each new route of administrati
Slide 21: Our strategy to determine design rules for individual mRNA delivery challenges
Our strategy to determine design rules for individual mRNA delivery challenges Components Composition & Process Structure Analyze Sterol Δ molecules Ionizable lipid Phospholipid Δ composition Δ process mRNA Screen/test Design Excipient PEG lipid >2,000 Novel Delivery Components synthesized to date >
Slide 22: We have previously developed multiple proprietary LNPs for different delivery goals
We have previously developed multiple proprietary LNPs for different delivery goals Prophylactic Vaccines Intratumoral Cancer Vaccines Immuno-oncology Intramuscular injection Intratumoral injection IM LNP ITu LNP Systemic Therapeutics IV injection Sabnis … Benenato (2018) Molecular Therapy IV LNP 1
Slide 23: We've also been working to develop an inhalable LNP
We’ve also been working to develop an inhalable LNP https://news.vrtx.com/press-release/vertex-and-moderna-establish-exclusive-collaboration-discover-and-develop-mrna https://www.modernatx.com/research/product-pipeline Slide 23 © 2022 M oderna
Slide 24: We now have five proprietary LNPs for different delivery goals
We now have five proprietary LNPs for different delivery goals Prophylactic Vaccines Intratumoral Cancer Vaccines Immuno-oncology Intramuscular injection Intratumoral injection IM LNP ITu LNP Systemic Therapeutics Inhaled pulmonary therapeutics IV injection Inhaled delivery IV LNP 1 (MMA, PA) IV LNP
Slide 25: Pulmonary Delivery of mRNA LNPs
DNA mRNA Protein Pulmonary Delivery of mRNA LNPs Jean C. Sung, Ph.D. Senior Director, Respiratory Delivery, Drug Product Development
Slide 26: Vertex collaborations to address Cystic Fibrosis
Vertex collaborations to address Cystic Fibrosis Two parallel approaches to addressing the unmet mRNA for CFTR need mRNA for gene editing (Treating the approximately 5,000 CF patients who do not Formulation produce any CFTR protein) CFTR-encoded mRNA Formulation Cas9-encoded mRNA* Chloride ion CFTR
Slide 27: Inhalation of mRNA has the potential to address a range of unmet medical needs
Inhalation of mRNA has the potential to address a range of unmet medical needs For example: • Cystic Fibrosis (CF) • Alpha-1 Antitrypsin (AAT) • Idiopathic Pulmonary Fibrosis (IPF) • Lymphangioleiomyomatosis (LAM) • Primary Ciliary Dyskinesia (PCD) • Chronic Obstructive Pulmonary Disease (COPD) • CO
Slide 28: Challenges for inhaled delivery of mRNA LNPs
Challenges for inhaled delivery of mRNA LNPs m RNA LNP Airw ay LNP delivery to cell of interest Mucus Device Aerosol generation & inhalation LNPs inside of droplets Club cell Basal cell Goblet cell Ionocyte Tuft cell Intermediate Basement Ciliated Neuroendocrine ciliated cell membrane cell cell (PNE
Slide 29: Key questions
Key questions • Can we efficiently deliver mRNA to target pulmonary cells? • Does the delivered mRNA express protein? • Can we aerosolize LNPs and maintain their functionality? • Can a commercially-viable product be realized? Slide 29 © 2022 M oderna
Slide 30: Our strategy to determine design rules for individual mRNA delivery challenges
Our strategy to determine design rules for individual mRNA delivery challenges Components Composition & Process Structure Analyze Sterol Δ molecules Ionizable lipid Phospholipid Δ composition Δ process mRNA Screen/test Design Excipient PEG lipid >2,000 Novel Delivery Components synthesized to date >
Slide 31: Early studies using luciferase mRNA and existing LNP library
Early studies using luciferase mRNA and existing LNP library Mouse intratracheal (IT) dosing Whole body imaging Whole Body ## PBS Naked mRNA 1×10 5 Radiance (p/s/cm2/sr) 1×10 4 Test Article 2 Test Article 26 1×10 3 A 10 11 12 13 14 15 16 17 18 19 20 21 22 24 23 25 26 m BS 1 2 3 4 5 6 7 8 9 N ed P R
Slide 32: Early studies using luciferase mRNA and existing LNP library: ex-vivo lung imaging
Early studies using luciferase mRNA and existing LNP library Mouse IT Ex-vivo lung imaging Lungs ** *** #### #### 1×10 6 PBS Naked mRNA ## Radiance (p/s/cm2/sr) 1×10 5 Test Article 2 Test Article 26 1×10 4 1×10 3 S A 1 2 3 4 5 6 7 8 9 10 11 12 13 51 15 16 17 18 19 20 21 22 23 24 25 26 PB RN 1 m ed T
Slide 33: For most LNPs, eGFP was either undetectable or localized to alveolar macrophages
For most LNPs, eGFP was either undetectable or localized to alveolar macrophages with microscopic evaluation… Mouse IT eGFP mRNA eGFP protein Test Article 2 Luciferase protein Test Article 26 Alveolar Luciferase protein Macrophage eGFP: enhanced green fluorescent protein Slide 33 © 2022 M oderna
Slide 34: …but a few gave weak protein expression in epithelial cells
…but a few gave weak protein expression in epithelial cells Mouse IT eGFP mRNA eGFP protein Further LNP design optimization needed: LNP-A • A mechanistic understanding of delivery limitations • Tools to enable a deeper dive with higher throughput LNP-B Slide 34 © 2022 M oderna
Slide 35: Mechanistic understanding of delivery limitations needed to inform LNP design
Mechanistic understanding of delivery limitations needed to inform LNP design Secreted LNP protein mRNA Transmembrane protein Target cell Protein Uptake chain Intracellular Ribosome protein Lysosome LNPs with unreleased Endosome Mitochondrial mRNAs escape protein Mitochondria Cytosol Nucleus Endosom
Slide 36: Human Bronchial Epithelial (HBE) cell model
Human Bronchial Epithelial (HBE) cell model: Higher throughput and human relevant Lung HBE cell model at air-liquid interface (ALI) Pseudostratified epithelium Transwell plate Slide 36 © 2022 M oderna
Slide 37: V5-tagged, nuclear-targeted luciferase protein: Better in situ protein reporter
V5-tagged, nuclear-targeted luciferase protein: Better in situ protein reporter Mouse eGFP protein Nascent Peptide Imaging (NPI) Luciferase (NPI-Luc) V5 NPI-Luc protein 5x V5 epitopes (V5) Adapted from Yan …..Tanenbaum (2016) Cell V5: Peptide tag for protein detection Slide 37 © 2022 M oderna
Slide 38: NPI-Luc mRNA containing LNPs with dye-label
NPI-Luc mRNA containing LNPs with dye-label: Quantification of both protein expression and LNP uptake HBE Rhodamine DAPI LNP Uptake • % LNP positive cells • LNP uptake per cell (rhodamine intensity) Fluorescent dye-labeled LNP Anti-V5 DAPI Protein Expression • % expressing cells • Protein expression
Slide 39: Nascent Peptide Imaging also allows us to count cytoplasmic mRNAs
Nascent Peptide Imaging (NPI) also allows us to count cytoplasmic mRNAs and assess their translational activity Adapted from Yan …..Tanenbaum (2016) Cell Slide 39 © 2022 M oderna
Slide 40: HBE cell results recapitulated in vivo observations
HBE cell results recapitulated in vivo observations and identified uptake versus protein expression differences LNP-C NPI-Luc mRNA NPI-Luc V5 Protein Mouse IT LNP Uptake NPI-Luc V5 Protein ~60% cells with uptake ~1% cells expressing HBE Blue: Nuclei (DAPI) Green: Anti-V5 IF 63X Yellow : Fluorescent
Slide 41: mRNA reaches the endosome, but very few mRNA molecules were translationally active
mRNA reaches the endosome, but very few mRNA molecules were translationally active in the cytoplasm Electroporation LNP-C Translating NPI-Luc mRNA Cytosolic NPI-Luc mRNA 700 cytosolic mRNA 600 translating mRNA Total uptake # NPI-Luc mRNA 500 400 300 200 Translating NPI-Luc mRNA 100 0 C ly n io P- on
Slide 42: Higher throughput HBE screening enabled rational LNP design
Higher throughput HBE screening enabled rational LNP design based on mechanistic understanding Components Composition & Process Structure HBE Sterol Ionizable lipid Phospholipid Δ molecules Δ composition Δ process mRNA Excipient PEG lipid NPI-Luc V5 Protein Expression: Fold Increase Relative to LNP-
Slide 43: Optimized LNP expression while maintaining cellular uptake was confirmed across systems
Optimized LNP expression while maintaining cellular uptake was confirmed across systems LNP-C Pulmonary LNP LNP Uptake NPI-Luc V5 Protein LNP Uptake NPI-Luc V5 Protein HBE NPI-Luc mRNA NPI-Luc V5 Protein NPI-Luc mRNA NPI-Luc V5 Protein Mouse IT Slide 43 © 2022 M oderna
Slide 44: Improved reporter with AI algorithm allowed high-throughput analysis of airway epithelium
Improved reporter in combination with AI algorithm allowed for high- throughput analysis of target airway epithelium across whole lung Supervised Lung learning Study XYZ Study XYZ Ms Lung Ms Lung Study XYZ Study XYZ Ms Lung Ms Lung Study XYZ Study XYZ Ms Lung Study XYZ Ms Lung Ms Lung Study XYZ Inpu
Slide 45: Aerosol delivery of LNPs showed widespread protein expression in rat airway epithelium
Aerosol delivery of LNPs showed widespread distribution of protein expression throughout the airway epithelium in rat Right lung middle lobe NPI-Luc V5 Protein Right lung middle lobe Rat Left lung Left lung • Positive V5 bronchial epithelium cells • Total bronchial epithelium cells ‾ Lymph region Sl
Slide 46: Aerosol delivery in non-human primate also showed widespread protein expression
Aerosol delivery in non-human primate (NHP) also showed widespread protein expression in the airway epithelium NPI-Luc V5 Protein • Positive V5 NHP bronchial epithelium cells • Total bronchial epithelium cells ‾ Lymph region Slide 46 © 2022 M oderna
Slide 47: Demonstrated ability to develop an inhalation product
Demonstrated ability to develop an inhalation product Scale-up Stability Aerosol Delivery 100,000-fold scale increase Stable with long-term -70 °C storage Respirable droplet sizes across devices with retained LNP activity Protein Expression in HeLa Cells 6 mRNA purity (%) MMAD (μm) 4 2 0 0 3 6 9 12
Slide 48: Science Summary
Science Summary • We have developed a new LNP formulation that enables efficient delivery of mRNA to and protein expression in the pulmonary epithelium upon aerosol delivery • Our ability to do so was enabled by building in-house systems and assays for high throughput testing of rational LNP design
Slide 49: CF mRNA therapeutics collaboration with Vertex
CF mRNA therapeutics collaboration with Vertex mRNA for CFTR Formulation CFTR-encoded mRNA Chloride ion CFTR protein Encoded m RNA Protein chain Ribosome Vertex Pharmaceuticals First Quarter 2022 Earnings Call Cytosol https://investors.vrtx.com/events/event-details/vertex-pharmaceutical-q1-2022-conf
Slide 50: Improving Vaccine Stability
DNA mRNA Protein Improving Vaccine Stability Phil White, Ph.D. Vice President, CMC Lifecycle management © 2022 M oderna
Slide 51: Introduction
Introduction • Moderna has invested heavily in the mRNA-LNP platform for over 10 years • Historical and ongoing efforts to fundamentally understand the product enabled rapid scale-up for our COVID-19 vaccine • Our obsession with science and attention to details have led to industry- leading knowledg
Slide 52: Personalized Cancer Vaccine (PCV) workflow
Personalized Cancer Vaccine (PCV) workflow mRNA encoding up to 34 neoantigens 1 5 Dosing every 3 weeks for a Screening Tissue Samples) Administration Every 3 weeks total of 9 cycles Tumor (biopsy) and With pembrolizumab = 6 months total normal (blood Preference to store Drug Product at 2-8°C 2 4 Sta
Slide 53: The amount of mRNA recovered appeared to decrease upon storage at 2-8°C
The amount of mRNA recovered appeared to decrease upon storage at 2-8°C LNP sample Detergent mRNA Lipids Anion exchange mRNA chromatography quantitation The method is not stability indicating so it was unclear why mRNA appeared to reduce in content upon storage Slide 53 © 2022 M oderna
Slide 54: What could be causing these observations?
What, based on our understanding of the platform, and the tools available to us, could be causing these observations? Slide 54 © 2022 M oderna
Slide 55: New analytical method for mRNA: RP-IP HPLC
New analytical method for mRNA: Reversed-Phase Ion Pair (RP-IP) High Performance Liquid Chromatography (HPLC) Negatively charged backbone RNA Hydrophobic HPLC column Bead Main peak Hydrophobic rings Organic UV Intensity solvent gradient Degraded RNA Time Slide 55 © 2022 M oderna
Slide 56: Discovery of a late-eluting peak when switching to RP-IP HPLC
Discovery of a late-eluting peak (LP) when switching to an RP- IP HPLC method for purity UV Absorbance UV Absorbance UV Absorbance A late-eluting peak was observed when mRNA is isolated from LNPs Slide 56Packer … White (2021) Nature Communications. © 2022 M oderna
Slide 57: Characterization of the late-eluting peak (LP)
Characterization of the late-eluting peak (LP) RP-IP re-injection UV Absorbance UV Absorbance UV Absorbance Re-analysis of the isolated LP using RP-IP Showed the same Profile Slide 57 © 2022 M oderna
Slide 58: Traditional analytical methods for assessing RNA quality
Traditional analytical methods for assessing RNA quality: Capillary electrophoresis (CE) and size exclusion chromatography (SEC) Flow Charge Small Large Long Longer RNA Shorter RNA Short Shorter RNA Time Time Longer RNA Detection Light source Charge Capillary Electrophoresis Size exclusion chromatog
Slide 59: Characterization of the late-eluting peak (LP)
Characterization of the late-eluting peak (LP) Capillary Electrophoresis UV Absorbance UV Absorbance UV Absorbance Re-analysis of the isolated LP using CE did not reveal differences Slide 59 © 2022 M oderna
Slide 60: Characterization of the late-eluting peak (LP)
Characterization of the late-eluting peak (LP) Size exclusion chromatography UV Absorbance UV Absorbance UV Absorbance The LP was shown not to be an aggregate Slide 60 © 2022 M oderna
Slide 61: Characterization of the late-eluting peak (LP)
Characterization of the late-eluting peak (LP) RP-IP HPLC UV Absorbance UV Absorbance UV Absorbance The LP increased over time and with increased temperature Slide 61 © 2022 M oderna
Slide 62: Characterization of the late-eluting peak (LP)
Characterization of the late-eluting peak (LP) In-vitro expression UV Absorbance UV Absorbance UV Absorbance Ladder -ve +ve MP LP The LP does not express the encoded protein Slide 62 © 2022 M oderna
Slide 63: Other standard analytical techniques found no differences
Other standard analytical techniques found no differences NaOH digestion to nucleotides FT-IR Spectroscopy T1 digestion / Oligo Mapping NMR Slide 63 © 2022 M oderna
Slide 64: What is the late-eluting peak and where is it coming from?
What is the late-eluting peak (LP) and where is it coming from? Slide 64 © 2022 M oderna
Slide 65: Digestion and MS revealed lipid modifications on nucleobases
Digestion and MS revealed lipid modifications on nucleobases Adenine (A) Guanine (G) MP LP Base Sugar Digestion Cytosine (C) Uracil (U) Lipid component 526.30 x103 LC-MS/MS 2 Signal counts x107 A Lipid-modified nucleosides ∆132.03 150X y-axis magnification 204.08 1 N1Meψ 1 112.05 Signal counts 376.2
Slide 66: Binary assays with components of our vaccine LNP
Binary assays with components of our vaccine LNP Buffered mRNA solution Precipitation Extraction and Ionizable and RP-IP HPLC Lipid Lipid solution in incubation analysis ethanol Sterol Phospholipid PEG lipid mRNA % LP in Sample Slide 66 © 2022 M oderna
Slide 67: Nucleobases are known to react with aldehydes
Nucleobases are known to react with aldehydes R Aldehyde HCRO [HTML] NMR analyses on N-hydroxymethylated nucleobases–implications for formaldehyde toxicity and nucleic acid demethylases Slide 67 © 2022 M oderna
Slide 68: Diverse toolset led us to broadly applicable mechanism for adduct
Diverse toolset led us to broadly applicable mechanism for adduct • Oxidized ionizable lipid forms large amount of adduct in binary compared to parent lipid UV Absorbance Ionizable lipid Ionizable lipid N-oxide Slide 68 Packer … White (2021) Nature Communications. © 2022 M oderna
Slide 69: Diverse toolset led us to broadly applicable mechanism for adduct
Diverse toolset led us to broadly applicable mechanism for adduct • Oxidized ionizable lipid forms large amount of adduct in binary compared to parent lipid • In acidic buffer used for binary and LNP precipitation, N-oxide degrades to generate aldehydes Ionizable lipid UV Absorbance N-oxide Oxidatio
Slide 70: Diverse toolset led us to broadly applicable mechanism for adduct
Diverse toolset led us to broadly applicable mechanism for adduct • Oxidized ionizable lipid forms large amount of adduct in binary compared to parent lipid • In acidic buffer used for binary and LNP precipitation, N-oxide degrades to generate aldehydes • Spiked binary series with two synthetic alde
Slide 71: Oxidation of a tertiary amine can result in an aldehyde
Oxidation of a tertiary amine can result in an aldehyde Oxidation Ionizable lipid Ionizable lipid N-oxide Hydrolysis H2O Acid-base catalysis Forms an “Adduct” on the mRNA molecule Hemi-aminal Secondary amine Aldehyde Slide 71 © 2022 M oderna
Slide 72: All ionizable lipids tested gave a late-eluting peak when combined with mRNA
All ionizable lipids tested gave a late-eluting peak when combined with mRNA Ionizable Lipid A B C D E F 100s of Moderna compounds screened, and all that contain a tertiary amine group demonstrate adduct formation Slide 72 © 2022 M oderna
Slide 73: Ionizable tertiary amine appears in other RNA LNP vaccine products
Ionizable tertiary amine appears in other RNA LNP vaccine products Moderna SARS-CoV-2 mRNA vaccine Pfizer/BioNTech SARS-CoV-2 mRNA vaccine The proposed adduct formation mechanism is likely to be widely applicable to RNA LNP products Slide 73 © 2022 M oderna
Slide 74: Why is adduct formation a problem for mRNA?
Why is adduct formation a problem for mRNA? mRNAs are partially single-stranded, meaning that many nucleobases are available to react A single modification in the coding region will stall the ribosome and inactivate the entire mRNA molecule Slide 74 © 2022 M oderna
Slide 75: Manufacturing controls required to abrogate adduct formation
Manufacturing controls required to abrogate adduct formation • Lipid synthetic schema • Control of oxidation • Purification processes Impact on final product Raw Material Manufacturing Controls • Improved Shelf-life ✓ • Higher Storage Temperature ✓ • Maintenance of Activity ✓ DP • Consistency of Man
Slide 76: Tris buffer acts as an aldehyde sink and enables longer term storage
Tris buffer acts as an aldehyde sink and enables longer term storage at 2-8°C 50% 45% PBS, 5°C 40% Aldehyde Tris Tris adduct % mRNA Lipid Adduct 35% 30% 25% 20% 15% 100mM Tris buffer, 5°C 10% 5% 0% 0 10 20 30 40 50 60 70 80 90 100 Days Slide 76 © 2022 M oderna
Slide 77: Does adduct formation explain the original observation?
Does adduct formation explain the original observation? Slide 77 © 2022 M oderna
Slide 78: The amount of mRNA recovered appeared to decrease upon storage at 2-8°C
The amount of mRNA recovered appeared to decrease upon storage at 2-8°C LNP sample Detergent mRNA Lipids Anion exchange mRNA chromatography quantitation Slide 78 © 2022 M oderna
Slide 79: Analysis by anion exchange (AEX) quantitation
Analysis by anion exchange (AEX) quantitation Control MP LP Dramatic back shoulder in late-eluting peak (LP), split peak and poor recovery in method Slide 79 © 2022 M oderna
Slide 80: What else is driving our product shelf-life?
In addition to adduct formation, what else do we know is driving our product shelf-life? Slide 80 © 2022 M oderna
Slide 81: mRNA Integrity
mRNA Integrity A critical quality attribute and the primary determinant of shelf life mRNA must be fully intact to express the intended product Slide 81 © 2022 M oderna
Slide 82: RNA is inherently unstable due to strand scission via trans-esterification
RNA is inherently unstable due to strand scission via trans- esterification Org Biomol. Chem 2009 May 7;7(9):1904-10. Slide 82 © 2022 M oderna
Slide 83: Protein expression is impacted by mRNA Integrity
Protein expression is impacted by mRNA Integrity HPLC Chromatograms Non-Degraded mRNA Degraded mRNA • Intact mRNA readily expresses when transfected into cells in culture • The purity of mRNA is essential for protein expression Slide 83 © 2022 M oderna
Slide 84: COVID-19 vaccine – establishing the shelf life
COVID-19 vaccine – establishing the shelf life Common-slope models indicate consistent and predictable stability profiles -20°C 5°C 25°C Log (Intact mRNA, %) Log (Intact mRNA, %) Log (Intact mRNA, %) Time (Months) Time (Months) Time (Hours) Slide 84 © 2022 M oderna
Slide 85: Predictable stability profile across our vaccine platform
Predictable stability profile across our vaccine platform Longer mRNAs have a faster degradation rate and hence shorter shelf-life Slide 85 © 2022 M oderna
Slide 86: Nature Communications publication
Nature Communications publication Packer M, Gyaw ali D, Yerabolu R, Schariter J, White P. Nat Commun. 2021 Nov 22;12(1):6777. doi: 10.1038/s41467-021-26926-0.PMI D: 34811367 Slide 86 © 2022 M oderna
Slide 87: Conclusions
Conclusions • Deep knowledge of platform through investments in fundamental science led to key observations • Broad analytical toolkit and state of the art methods uncovered a critical quality attribute • Formation of adduct is a universal impurity in tertiary amine containing LNPs • Strand scission
Slide 88: Today's Agenda
Today’s Agenda Stéphane Bancel, CEO Introduction Melissa J. Moore, Ph.D., Platform Research Focus on our Inhaled Pulmonary Therapeutics Jean Sung, Ph.D., Drug Product Development newest modality Improving Vaccine Stability Phil White, Ph.D., Chemistry, Manufacturing & Controls Break (10 minutes) Adv
Slide 89: Biodistribution and Safety of our IM Vaccine LNPs
DNA mRNA Protein Biodistribution and Safety of our IM Vaccine LNPs Melissa J. Moore, Ph.D. Chief Scientific Officer, Scientific Affairs © 2022 M oderna
Slide 90: Moderna's proprietary vaccine LNP improves local expression and tolerability
Moderna’s proprietary vaccine LNP improves local expression and tolerability without impacting immunogenicity Improvement of reactogenicity Generic Vaccine-optimized LNP LNP Expression Tolerability Immunogenicity Hassett. . . Brito (2019) Molecular Therapy Nucleic Acids Slide 90 © 2022 M oderna
Slide 91: Common questions
Common questions Where does the mRNA go after IM administration? • What tissues and cell types? How long does the mRNA persist? Where is the protein expressed? • What tissues and cell types? Are there any concerns relative to fertility and pregnancy? Slide 91 © 2022 M oderna
Slide 92: Measurement of mRNA biodistribution over time after IM injection
Measurement of mRNA biodistribution over time after IM injection of Vaccine LNP Hour 02 8 24 48 72 120 Chemiluminescent 100ug 6 vaccine mRNAs in IM vaccine LNP substrate Analysis (SM-102) (tissue b-DNA assay) Liver Homogenize Lysate Muscle Signal enhancing b-DNA assay for detecting mRNA in tissue ho
Slide 93: Example preclinical IM vaccine distribution study
Example preclinical IM vaccine distribution study Mean Tissue mRNA Concentration Injection • mRNA distributes site predominantly to injection site and draining lymph nodes. 1400 1200 • mRNA is undetectable Concentration (ng/mL/Tissue) after 120 hours (5 days) 1000 800 Draining Lymph Node 600 Injecti
Slide 94: Skeletal muscle anatomy
Skeletal muscle anatomy Cross-section of skeletal muscle fibers LNP Neutrophil Connective tissue Muscle cell mRNA Dendritic cell Macrophage Blood vessel Monocyte Muscle Slide 94 © 2022 M oderna
Slide 95: Assays for detecting mRNA and protein in situ
Assays for detecting mRNA and protein in situ RNAScope ® (in situ hybridization) assay GFP mRNA in IM vaccine Analysis LNP (SM-102) Hour 0 8 24 72 120 168 LNP mRNA mRNA Muscle Protein Immunohistochemistry (IHC) Slide 95 © 2022 M oderna
Slide 96: Both mRNA and protein are readily detectable in the connective tissue at the injection site
Both mRNA and protein are readily detectable in the connective tissue at the injection site Cross-section of muscle, RNAScope ® (in situ hybridization) assay 24 hr (1 Day) mRNA (RNA Scope) mRNA Protein (IHC) muscle fibers connective tissue Protein Immunohistochemistry (IHC) Slide 96 © 2022 M oderna
Slide 97: mRNA is undetectable at injection site after 3 days
mRNA is undetectable at injection site after 3 days 8 hr 24 hr (1 Day) 72 hr (3 days) 168 hr (7 days) muscle fibers connective tissue Slide 97 © 2022 M oderna
Slide 98: Protein is undetectable at injection site after 3 days
Protein is undetectable at injection site after 3 days 8 hr 24 hr (1 Day) 72 hr (3 days) 168 hr (7 days) muscle fibers connective tissue Slide 98
Slide 99: Both mRNA and protein are detectable in situ in the draining lymph node
Both mRNA and protein are detectable in situ in the draining lymph node mRNA protein Draining Lymph Node Slide 99 © 2022 M oderna
Slide 100: Antigen Presenting Cells capture, process and present foreign antigens
Antigen Presenting Cells (APCs) capture, process and present foreign antigens and help activate the immune system Antigen presentation and innate Antigen capture and processing by APCs immune signaling lead to help trigger an INNATE immune response ADAPTIVE immune responses Professional APCs B cell
Slide 101: Protein is expressed in draining lymph node macrophages
Protein is expressed in draining lymph node macrophages protein (fluorescent) CD169 (macrophages) Draining lymph node Germinal center Medullary sinus Subcapsular sinus Slide 101 © 2022 M oderna
Slide 102: Flowcytometry demonstrates that lymph node protein expression is primarily in APCs
Flowcytometry demonstrates that lymph node protein expression is primarily in APCs High-throughput multiparameter flow Draining Lymph Node Non-Draining Lymph Node cytometry to interrogate cell type 80 1.5 60 1.0 %GFP+ Lymph Node Single-cell 0.5 suspension 40 0.0 20 lls lls ce ce T B 0 s es lls lls l
Slide 103: Common questions
Common questions Where does the mRNA go after IM administration? • What tissues and cell types? How long does the mRNA persist? Where is the protein expressed? • What tissues and cell types? Are there any concerns relative to fertility and pregnancy? Slide 103 © 2022 M oderna
Slide 104: Example preclinical repeat dose toxicity study in rats
Example preclinical repeat dose toxicity study in rats 6 vaccine mRNAs in IM Vaccine LNP (SM-102) at clinically-relevant doses Dose1 Dose 2 Dose 3 Dose 4 Recovery Dose (no dosing) Day 1 15 29 43 57 Analysis (Clinical pathology; tissue histopathology) • Transient injection site and systemic inflammat
Slide 105: Example fertility and developmental study in rats
GD: Gestation Day Example fertility and developmental study in rats LD: Lactation Day 6 vaccine mRNAs in IM Vaccine LNP (SM-102) at clinically-relevant doses Dose1 Dose 2 Dose 3 Dose 4 Dose Day Relative +1 +13 +21 +42 to Mating -28 -14 0 (GD1) (GD13) (GD21) (LD21) . Mating Pregnancy Birth of Post-na
Slide 106: COVID-19 mRNA vaccination in pregnant persons
COVID-19 mRNA vaccination in pregnant persons • Antibody and CD4/CD8 T-cell responses were present in pregnant and lactating persons following mRNA vaccination1-7 – Vaccine-elicited antibodies were detected in infant cord blood and in breastmilk – T cells targeting SARS-CoV-2 spike protein have also
Slide 107: CDC recommends vaccination for people who are or may become pregnant and breastfeeding people
CDC recommends vaccination for people who are or may become pregnant and breastfeeding people https://www.cdc.gov/mmwr/volumes/71/wr/mm7107e3.htm#:~:text=small%20sample%20size.- ,Completion%20of%20a%202%2Ddose%20primary%20mRNA%20COVID%2D19%20vaccination,were%20vaccinated%20later%20in%20pregnancy Sli
Slide 108: Summary
Summary After IM administration of our vaccine LNP, the mRNA is found almost exclusively at the injection site and in the draining lymph nodes The mRNA is >95% gone after 48 hours Protein expression occurs in immune cells in the muscle interstitial space and draining lymph nodes No adverse consequen
Slide 109: Modeling and Simulation to Guide Dose Selection for Vaccines
DNA mRNA Protein Modeling and Simulation to Guide Dose Selection for Vaccines © 2022 M oderna
Slide 110: Pharmacometric models have been widely utilized for dose decisions
Pharmacometric models have been widely utilized for dose decisions across the biopharma industry for therapeutics • Pharmacometrics is the science of interpreting and describing pharmacology in a quantitative fashion • Pharmacokinetics (PK) is mathematical understanding of rate of absorption/distrib
Slide 111: PK/PD model for Chikungunya antibody program predicted PD responses in humans
We developed a PK/PD model using preclinical data for Chikungunya antibody program and successfully predicted PD responses in humans Solid line = Median predicted; Shaded area = 90% prediction interval; Shapes = Individual observations Solid line = Median predicted; Shaded area = 90% prediction inte
Slide 112: Modeling and Simulation to Guide Dose Selection for Vaccines
DNA mRNA Protein Modeling and Simulation to Guide Dose Selection for Vaccines Husain Attarwala Sr. Director, Pharmacometrics & Clinical Pharmacology © 2022 M oderna
Slide 113: Adapting PK/PD modeling to vaccines with IS/ID models
We adapted traditional PK/PD modeling approach to vaccines by developing immunostimulation/immunodynamic (IS/ID) models Antibody Titer IS/ID models are semi-mechanistic mathematical models that describe the immune response stimulated by vaccination (IS), and the resulting immune response dynamics (I
Slide 114: Typical time course of immune response following vaccination
Typical time course of immune response following vaccination Antibody Titer Time (Days) Slide 114 © 2022 M oderna
Slide 115: What is an IS/ID Model?
What is an IS/ID Model? Dose 1+ Dose 2+ Plasma Boost Naïve B cells Memory δ2 Antibody Titer Cells δ (Short B Cells liv ed) Kmab Elimination/ deactivation KLL2 Antibody Plasma Titer cells Kab Time (Days) (Long liv ed) Ab waning Elimination/ deactivation Slide 115 © 2022 M oderna
Slide 116: What is an IS/ID Model
What is an IS/ID Model Dose 1+ Dose 2+ Plasma Boost Naïve B cells Memory δ2 Cells δ (Short B Cells liv ed) Kmab Elimination/ deactivation KLL2 Antibody Plasma Titer cells Kab (Long liv ed) • 10 differential equations Ab waning Elimination/ • 17 parameters deactivation Slide 116 Adapted from Rhodes e
Slide 117: Key questions IS/ID models help address
Key questions IS/ID models help address What vaccine dose should be How do we select doses for used in clinical studies? different age groups? Slide 117 © 2022 M oderna
Slide 118: IS/ID models to predict dose-response relationship for clinical studies
We can use the IS/ID models to predict dose-response relationship for clinical studies IS/ID and Reactogenicity Modeling IS/ID Modeling for for CMV Vaccine mRNA-1273 Vaccine CMV Phase 3 dose mRNA-1273 pediat ric selection dose selection CMV COVID-19 (Pentamer & gB) (spike protein) Slide 118 © 2022 M
Slide 119: CMV vaccine Phase 1 data used to develop first IS/ID model
We used data from our CMV vaccine Phase 1 clinical study to develop our first IS/ID model CMV vaccine (mRNA-1647) includes 6 mRNAs 5 encode the pentamer, 6t h encodes gB ant igen Slide 119 © 2022 M oderna
Slide 120: CMV vaccine Phase 1 data used to develop first IS/ID model
We used data from our CMV vaccine Phase 1 clinical study to develop our first IS/ID model CMV Vaccine Phase 1 Data CMV vaccine (mRNA-1647) Dat a used for model development in 2019/2020 includes 6 mRNAs 5 encode the pentamer, 6t h encodes gB ant igen Arrows indicate time of dose Slide 120 © 2022 M od
Slide 121: CMV vaccine Phase 1 data used to develop first IS/ID model
We used data from our CMV vaccine Phase 1 clinical study to develop our first IS/ID model CMV Vaccine Phase 1 Data CMV vaccine (mRNA-1647) Dat a used for model development in 2019/2020 includes 6 mRNAs 5 encode the pentamer, 6t h encodes gB ant igen Slide 121 © 2022 M oderna
Slide 122: Our model adequately described the observed data
Our model adequately described the observed data Solid blue line = Fitted GMT; Shaded area = 90% Prediction interval Slide 122 Dots = Observed data © 2022 M oderna
Slide 123: Final model described time-course and dose relationship for CMV vaccine
The final model adequately described the time-course and relationship between dose and neutralizing antibody titers for our CMV vaccine Ab Titers Against Epithelial Cell Infection (Pentamer) Ab Titers Against Fibroblast Cell Infection (gB) Observed data: Dots Observed data: Dots Model predictions: l
Slide 124: What dose level is predicted to yield nAb titers above natural infection benchmark?
What dose level is predicted to yield nAbs titers above the benchmark of natural infection in the majority of subjects? nAb response against epithelial cell infection (Pentamer) nAb response against fibroblast infection (gB) GMT benchmark for natural infection GMT benchmark for natural infection Mod
Slide 125: Modeling the relationship between dose and adverse reactions
Modeling the relationship between dose and adverse reactions Probability Slide 125 © 2022 M oderna
Slide 126: Reactogenicity modeling supported selection of 100 µg dose level
Reactogenicity modeling supported selection of 100 µg dose level for the Phase 3 clinical study • Composite risk of adverse reactions was derived using probabilities of individual adverse reactions – 0 is no risk and 1 is maximum risk • Risk of adverse reactions (especially Grade 3) is predicted to
Slide 127: The 100 µg dose was selected to maximize immunogenicity and minimize adverse reactions
The 100 µg dose was selected to both maximize immunogenicity and minimize adverse reactions nAb response against epithelial cell infection (Pentamer) nAb response against fibroblast infection (gB) GMT benchmark for natural infection GMT benchmark for natural infection 100 µg dose was predicted to yi
Slide 128: IS/ID models to predict dose-response relationship for clinical studies
We can use the IS/ID models to predict dose-response relationship for clinical studies IS/ID Modeling for mRNA-1273 Vaccine mRNA-1273 pediat ric dose selection COVID-19 (spike protein) Slide 128 © 2022 M oderna
Slide 129: Model was developed using data pooled across various clinical studies
Model was developed using data pooled across various clinical studies Pooled primary vaccination data IS/ID Structural Model (N=2567*) Phase 2 Studies Model Development: Adapted the IS/ID modeling framework • P201 - Adults developed for CMV to COVID-19 via optimizing antigen specific parameters usin
Slide 130: Goal: predict doses for young children and infants with titers similar to young adults
Our goal was to predict which dose levels in young children and infants would yield neutralizing antibody titers that are similar to young adults 𝐺𝑀𝑡𝑒𝑠𝑡 𝐺𝑀𝑅 = 𝐺𝑀𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 Reference (young adult immunogenicity data) Fail Pass Slide 130 © 2022 M oderna
Slide 131: Primary series at 25µg dose predicted to meet non-inferiority criteria in young children
Primary series at 25µg dose was predicted to meet non-inferiority criteria in young children (2-5y) and infants (6-23m) Model Predicted GMRs in Different Age Groups Observed GMRs in Pediatrics GMR (95%CI) compared to Age group N Dose young adults (18- 25yo) Infants: 1.28 6m-23m (P204) 230 25 µg (1.1
Slide 132: Summary
Summary • Modeling and simulation helps drive data-driven decision making for clinical dose selection • We adapted the traditional PK/PD modeling approach for therapeutics to develop our IS/ID models for vaccines • IS/ID models can be used to successfully predict neutralizing antibody responses acro
Slide 133: Concluding Remarks and Q&A
DNA mRNA Protein Concluding Remarks and Q&A Stephen Hoge, M.D. President © 2022 M oderna
Slide 134: Our platform has incredible independent features to expand
Our platform has incredible independent features to expand mRNA Delivery Manufacturing Process Thr Gly Leu Slide 134 © 2022 M oderna
Slide 135: Today's topics offer glimpses into two modalities
Today’s topics offer glimpses into two modalities Expansion of our mRNA Increased depth and platform understanding across our vaccine modality A broad analytical toolkit and state of the art methods uncovered the formation of adduct in the manufacturing process Developed a new LNP formulation that e
Slide 136: Our mission
Our mission To deliver on the promise of mRNA science to create a new generation of transformative medicines for patients. Slide 136 © 2022 M oderna