Genome Editing & Gene Therapy by Eric Kelsic
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Slides from the Genome editing & gene therapy Impact.tech seminar, hosted by Eric Kelsic on June 11th, 2019. The seminar covers the experiments and inventions that led to the development of genome editing technologies. These inventions were derived from life itself: isolated from natural organisms and adapted for scientific and therapeutic goals. You will learn the history of how genome engineering tools, including CRISPR, and delivery technology, including AAV capsids, were created in their modern form. The seminar explores how genome editing and gene therapy technologies are giving individuals control over their own genomes, focusing on the treatment of genetic diseases. It will describe major companies and emerging trends in the gene therapy industry. Finally, the seminar will discuss how and where new discoveries, including accelerated algorithms for genetic engineering, will lead us in the near and distant future. Eric Kelsic, PhD, is the founder and CEO of Dyno Therapeutics, a VC-backed biotech located in Cambridge, Massachusetts. Dyno is leading a machine learning revolution to develop enhanced capsid proteins that enable new gene and genome editing therapies. Eric co-developed the technology underlying Dyno’s machine-guided protein engineering platform as a Staff Scientist in George Church’s lab at the Wyss Institute of Harvard Medical School. He holds a PhD in Systems Biology from Harvard University and a BS in Physics from Caltech.
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Genome Editing & Gene Therapy by Eric Kelsic
- 1. Genome Editing & Gene Therapy Eric Kelsic CEO Dyno Therapeutics
- 2. Genome Editing and Gene Therapy Eric Kelsic CEO Dyno Therapeutics Cambridge, MA Impact.Tech Seminar San Francisco, June 11, 2019 1
- 3. Outline of our seminar I: Genome editing new tools II: AAV gene therapy new cures III: Genetic engineering the actual IV: What next? the possible 2
- 4. Self introduction 3 Systems Biology PhD w/ Roy Kishony @ Harvard Physics BS @ Caltech Postdoc w/ George Church @ Wyss Institute CEO Dyno Therapeutics Puzzles Programming Startups AAV
- 5. The journey toward modern medicine 4 Galen’s surgeries based on animal dissection, dissection of human body is taboo Hippocratic Oath prohibits euthanasia and abortion, prohibits surgery for kidney stones (lithotomy), prohibits divulging of medical secrets Renaissance: human dissections and modern anatomy 1800 1600 400 BCE 200 CE Pasteur, Koch, Cell & Germ Theory 1900 Blood transfusions DNA structure 2000 Human genome
- 6. Millions of patients have untreatable genetic diseases 5 •10,000+ known monogenetic diseases •1% of births worldwide •Causing 20-30% of infant deaths in US Not treatable with current small molecule or biologic drugs WHO, Berry et al. 1987
- 7. I: Genome editing discovering new tools 6
- 8. Genetics 101 DNA à RNA à Protein 7 (AGCT) (ACGU) (ACDEFGHIKLMNPQRSTVWY)
- 10. The human genome is ~3 billion nucleotides ~20,000 genes and many more non-coding regulatory elements 9 Annunziato , Scitable, nature.com
- 11. Diverse gene sequences lead to different phenotypes 10
- 12. Gene malfunction causes genetic disease 11
- 13. Toward base editing: a case study in genome editing 12 Komor et al, Nature 2016
- 14. Bioprospecting: treasures in the biosphere • Nature has invented ~1e42 different protein sequences • 1e42 upper bound • 1e30 bacteria in the world • 1e3 genes/cell • Each gene changes each year for 1e9 years 13 see Dryden, J. R. Soc. Interface, 2008 David Goodsell
- 15. Genome editing pre-CRISPR 14 1990 TALEs Zinc Finger Nucleases 2000 2010 Kim, Nat Rev Gen 2014; David Goodsell Meganucleases
- 16. CRISPR-Cas is a bacterial immune system using an RNA-guided endonuclease 15 Rath, Biochimie 2015 Spacer sequences from invading phage are stored in the CRISPR module RNA guided complex identifies and cleaves protospacer sequences from invading phages, interfering with phage infection
- 17. There are a wide variety of biochemical mechanisms for CRISPR interference 16 Rath, Biochimie 2015
- 18. Cas9 sgRNA enables simple programmed genome editing 17 Jinek, Science 2012; see also Gasiunas, PNAS 2012
- 19. 18 Kim, Nat Rev Gen 2014
- 20. Adapting Cas9 for human genome editing – adding a nuclear localization sequence (NLS) 19 Mali, Science 2013 Cong, Science 2013 Jinek, eLife 2013
- 21. Applications of Cas9 for basic research 20 Wang, Science 2014; Shalem, Science 2014 Multiplexed programmable genome perturbations perhaps the most impactful initial application of Cas9: • Knockout • Activation • Repression • Epigenetics…
- 22. How will human therapeutics be developed? 21 Pro Con Germ cells Mutations are inherited Unknown risks of off-target mutations, ethics Somatic cells Mutations are not inherited Challenge of delivery ex vivo Enables screening for positives and removal of negatives Complicated manufacturing and transplantation procedures in vivo Easier to manufacture and administer Challenge of low efficiency editing, dangers of off- target editing Question#1Question#2 ✗ ✓
- 23. Industry snapshot (June 2019) 22
- 24. Editas 23 ex vivo in vivo in vivo
- 26. CRISPR Therapeutics 25 in vivo ex vivo
- 27. Main challenges facing human therapeutics? 26 HIGH EFFICIENCY EDITING LOW OFF TARGET EDITING
- 28. Why base editing? 27 HIGH EFFICIENCY EDITING LOW OFF TARGET EDITING
- 29. Cas9 base editors 28 Komor, Nature 2016 This is the simplest version, additional elements enable increased efficiency
- 30. High efficiency precision editing with base editing 29 Komor, Nature 2016
- 31. Expanding the capabilities of base editors: new PAMs 30 Hu, Nature 2018
- 32. Expanding the capabilities of base editors: new bases 31 Gaudelli, Nature 2017
- 34. Frontiers in base editing • Precision of edits • Reduced off-target edits • In vivo delivery • Reduced size (smaller Cas) for use in AAV • Or viruses/capsids with larger capacity 33
- 35. Summary • Excitement around genome editing driven by potential to understand and treat genetic diseases with high unmet need • Cas9 is an RNA-guided endonuclease enabling programmable genome cutting, nicking, binding, deletion, insertion, substitution, … • Base editing enables precision substitutions • Broad consensus around the benefits of somatic cell genome editing therapies, uncertainty around when germline editing will be acceptable • Genome editing therapeutics can be ex vivo cell therapy or in vivo gene therapy 34
- 36. II: AAV gene therapy creating new cures 35
- 37. Why gene therapy? 36 MOST DRUGS ARE INHIBITORS MANY GENETIC DISEASE ARE CAUSED BY LACK OF THE PROTEIN, SO THERE’S NOTHING TO INHIBIT GENETIC MALFUNCTIONS ARE CHALLENGING TO TREAT USING SMALL MOLECULES
- 38. Principles of in vivo gene therapy 37 Gene Therapy DNA Payload = Delivery + Replace or fix the malfunctioning gene
- 39. Why use viral capsids? 38 LIPID NANOPARTICLES ARE MATERIALS CAPSIDS ARE MACHINES • Flexible membranes • Variable stickiness, reacts to chemical properties • Piggyback on endogenous cellular machines • Combinatorial chemical synthesis • Strong shell • Senses and responds to environmental signals • Manipulates endogenous cellular machines • Genetic encoding enables multiplexing
- 40. Gene therapy: early years 39 Friedmann & Roblin, first proposal for gene therapy 1972 1980 1990 First attempts to treat humans in clinical trials For ADA-SCID, ex vivo via lentivirus delivery Development of viral delivery vectors: Adenovirus, Lentivirus Trials begin for in vivo gene delivery using Adenovirus 2000
- 41. Tragedy motivates shift toward safer delivery 40
- 42. Adeno-Associated Virus is a small non-replicative virus (dependovirus genus) 41 Kotterman, Nat. Rev. Gen. 2014
- 43. Adapting AAV for gene delivery 42 Kotterman, Nat. Rev. Gen. 2014
- 44. Advances in AAV therapeutic development 43 Dunbar, Science 2018
- 45. Immune suppression for gene therapy • Prednisone • Prevents inflammation • Prevents immune system from eliminating cells with viral capsids • Transient dosing schedule concurrent with AAV administration 44
- 46. Gene therapy for Leber’s Congenital Amaurosis (LCA) • Payload: RPE65 (Retinoid Isomerohydrolase, makes retinol) • Results in loss of vision then blindness during childhood • Prevalence: 1 in 200,000 • Subretinal AAV2 delivery • Dose: 1.5e11vg/eye in 0.3mL • Prednisone 3 days prior and 7 days after injection • Spark Therapeutics 45
- 47. Broadening the potential of AAV with new serotypes 46 Gao, J Virol. 2004DiMattia, J Virol. 2012 AAV2, 4, 8 & 9
- 48. New tricks: self-complementary vectors 47 Wang, Nat. Rev. Drug Disc. 2019
- 49. Gene therapy for Spinal Muscular Atrophy (SMA type 1) • Payload: SMN1 (Survival motor neuron protein) • Results in deterioration of motor neurons, loss of mobility and then death by 2 years • Prevalence: 1 in 10,000 • Intravenous AAV9 delivery, self- complementary vector • Dose: 2e14vg/kg in 10-20mL/kg • Prednisone as needed to control immune response • Avexis 48
- 50. Scaling up to meet manufacturing challenges 49 Vigene Baculovirus for large scale production Other systems using herpesvirus transduction, suspension cells, …
- 51. Clinical results • The Gene Doctors, LCA treatment (39:17-43:14) • https://www.pbs.org/video/the-gene-doctors-i1dd2h/ • Spinal Muscular Atrophy Treatment at Nationwide Children's -- Brett & Paige • https://www.youtube.com/watch?v=3FyainVRdRc • Gene Therapy for SMA Type 1: Evelyn's Story • https://www.youtube.com/watch?v=yRrqbvUv6gQ 50
- 52. Adeno-Associated Virus (AAV): 50 years 51 Wang, Nat. Rev. Drug Disc. 2019
- 53. Clinical snapshot 52 Wang, Nat. Rev. Drug Disc. 2019
- 54. 53 Selected AAV trials Wang, Nat. Rev. Drug Disc. 2019
- 55. In vivo genome editing with AAV 54 (Gene knockouts)
- 56. Gene therapy frontiers: payloads • Increased expression • Control of dynamics • Reduced size for large genes • Dual and multi-payloads • Combinatorial therapies 55
- 57. Gene therapy frontiers: capsids • Not all cells and organs are reachable • Limitations of cost and manufacturing capacity 56
- 58. Summary • In vivo gene therapy delivers a genetic payload into your body’s cells • Viral capsids are efficient machines for in vivo delivery • AAV capsids enables in vivo disease treatment • LCA, SMA, DMD, and many more… • Gene therapy field has consolidated around AAV technology • Early gene therapies are a roadmap for translation of in vivo genome editing therapies • With innovations on payloads and delivery, curing all genetic diseases is within reach in our lifetime 57
- 59. III: Genetic engineering expansion of the actual 58
- 60. Genetic engineering: how to develop new therapies for treating disease as quickly as possible? • Domesticating and improving our genetic natural resources 59
- 61. How can we make AAV capsids better for gene therapy? 60 Efficient delivery to target cells and tissues Reduced off-target delivery Robust evasion of pre-existing immunity Increased packaging size High-titer manufacturing capabilities Natural capsids ✓ ✗ ✗ ✗ ✓ Ideal ✓ ✓ ✓ ✓ ✓
- 62. AAV sequence space is vast and mostly unexplored Explored: 1-4 mutations between serotypes: 100-300 mutations AAV2 AAV5 AAV9 AAV8 61
- 63. Genetic engineering: recombinant DNA technology Tools for DNA manipulation • 1972: plasmid cloning to propagate DNA in bacteria • 1977: Sanger sequencing • 1980’s: Phosphoramidite DNA synthesis • 1983: PCR to selectively amplify DNA • 1975: Asilomar – voluntary moratorium on risky applications of recombinant DNA technology 62
- 64. Genetic engineering: synthetic biology New tools à New name • Parallel DNA sequencing (NGS) • Long read sequencing (Pacbio, Oxford Nanopore) • Short read sequencing (Illumina) • Today: read 600B bases for $10k • DNA synthesis • Primer oligos • Custom gene synthesis • Parallel oligo synthesis (oligo pools) • 250,000 oligos at 230-300nt • 75M bases for $5-20k 63
- 65. What challenges face a genetic engineer? • Proteins are complex • At present we can’t simulate or model that complexity with low-level atomic models • Protein interactions are poorly characterized • Parameters for mid-level systems biology models are not accurately measured and many interactions are unknown • Many higher levels of complexity • From cells to tissues to organs to bodies 64 MAPK, cellsignal.com
- 66. Well then how does Nature engineer? 65 Influenza, David Goodsell
- 67. Evolution is an algorithm • With a goal of quickly finding improved sequences • In nature: without any information whatsoever about how these sequences work 66
- 68. Nature’s algorithm: Darwinian evolution 67 Random mutation Natural selection Natural rates of mutation & recombination Boltzmann function of fitness
- 69. Directed evolution uses artificial selection 68 Lab control over rates of mutation & recombination Often defaults to greedy selection Random mutation Artificial selection
- 70. Engineering AAV capsids with directed evolution 69 Wang, Nat. Rev. Drug Disc. 2019 Random mutation Artificial selection (mice, cells, primates) Random recombination
- 71. Why is AAV capsid engineering so challenging? • As number of mutations increases, probability of viability with random search decreases (exponentially) • And … AAV needs to be optimized across multiple properties Distance from WT 1 2 3 4 5 Probabilityofviability Distance from WT 1 2 3 4 5 Probabilityofviability A&B&C AB C 70
- 72. Random AAV libraries are typically “shallow” Serotype A Serotype B 100-300 mutations 1-4 changes (~1e18 mutants) 1-4 changes What’s needed is an efficient algorithmic exploration of this space Most useful areas of sequence space are unexplored 71
- 73. New technologies enable Super-Darwinian algorithms 72 Fitness Number of library members tested Super Darwinian evolution Darwinian evolution
- 74. Building smarter libraries with direct DNA synthesis 73 Print any DNA sequence Currently: batch size is ~250,000 sequences up to 300nt Limitation in length due to low tolerance for deletions
- 75. 74 Open-loop physicallayer Mutation Selection by Enrichment Mutation Selection by Enrichment Round 1 Round 2 Closed-loop physicallayer DNA Synthesis datalayer Library Design Machine Learning Sequencing Enrichment #3 Enrichment #1 Enrichment #2 Round 1, 2, … Selection New technologies enable closed-loop workflows
- 76. Balancing exploration and exploitation 75 Random mutation Artificial selection Optimize for exploration vs exploitation depending on how many rounds of selection remain Sampler
- 77. Modeling fitness landscapes with machine learning 76 Random mutation Artificial selection Machine learning models evaluate a large number of sequences Sampler Model evaluation
- 78. Understanding AAV fitness landscapes for capsid engineering confidential
- 79. An outcome-focused approach to protein engineering Mechanistic approach Outcome-focused approach confidential 78 Mechanistic understanding Experiment HypothesisResults Machine Learning Experiment SequenceOutcomes Experiment SequenceOutcomes Experiment SequenceOutcomes Experiment SequenceOutcomes
- 81. At Dyno Therapeutics we are building a machine-guided AAV capsid engineering platform 80 Founded 2018, VC funded, based in Cambridge, MA
- 82. In contrast to traditional random approaches, Dyno is systematically searching sequence space Before Dyno Random High throughput DNA Synthesis Enrichment via passaging High throughput DNA Sequencing Single property Multiple properties Library design Selection strategy Optimization Machine Learning 81
- 83. DNA synthesis DNA sequencing Biological assays Y Y Y Y Y Y Capsid packaging Immune evasion In vivo delivery Tissue specificity Machine learning Dyno’s high-throughput screening platform 82 Selection
- 84. Wide search Deep search 0 mutations (WT) 1 mutation 0 # mutations 1 2 3 4 5 (WT) Dyno Platform: Two algorithmic approaches 83
- 85. All experiments and data from Harvard (2015-2018) with George Church and collaborators 84 George Church Pierce Ogden Sam Sinai
- 86. • All possible codon changes • Insertions • Substitutions • Deletions • Includes barcode replicates and internal controls • Testing ~200,000 mutant genomes in a single animal • Emphasis on maximizing information gained in the first round of experiments 0 mutations (WT) 1 mutation Dyno platform: Wide search 85
- 87. AAV Capsid GeneMutation Barcode Wide search: Maximizing information gained from a single experiment through multiplexing YY Y Y Y Y Capsid packaging Immune evasion In vivo delivery Tissue specificity NGS NGS NGS NGS 86
- 88. Case Study: Production Wide Search confidential
- 90. Rep Cap AAPMAAP new gene Landscape reveals new AAV biology
- 91. Dyno platform: Deep search 0 Distance from WT 1 2 3 4 5 (WT) 90 • Explore far away from natural capsids by introducing many mutations in combination • Emphasis on improving function based upon information gained from previous rounds of screening
- 92. AAV2 Can we explore deep into sequence space? Results: still viable at 10-15 mutations away from WT = 50-64% similarity within library region Deep search pilot • 80,000 mutants • 28 amino acid region • ~10,000 random • ~15,000 internal replicas • 1-42 mutations Deep Search 91
- 93. Deep Search accelerates AAV engineering Deep search library Random library Probabilityofviablepackaging Distance from WT 92 Deep Search
- 94. Deep Search accelerates AAV engineering Deep search library Random library Probabilityofviablepackaging Distance from WT + deep learning + future rounds 93 Deep Search
- 95. Frontiers: Machine-guided capsid engineering 94 Efficient delivery to target cells and tissues Reduced off-target delivery Robust evasion of pre-existing immunity Increased packaging size High-titer manufacturing capabilities Natural capsids ✓ ✗ ✗ ✗ ✓ Ideal ✓ ✓ ✓ ✓ ✓ All of these will be solved Up to 6kb for AAV
- 96. Frontiers in genetic engineering for therapeutics • Payload engineering • Cell and tissue specific promoters • Control over expression dynamics • Machine-guided engineering • Exploration strategies • Machine learning architectures • Transfer learning from other proteins • Integration of data from diverse properties • Technology development • Advanced methods for DNA assembly and sequencing 95
- 97. Summary • Evolution is an algorithm • Darwinian evolution is random mutation + natural selection • Directed evolution means artificial selection • New technologies enable Super-Darwinian evolution • Dyno Therapeutics is developing a machine-guided capsid engineering platform • Now hiring RA’s, Software engineers and experts in AAV gene therapy • Major challenges in delivery with AAV capsids will be solved soon • Machine-guided approaches will soon dominate genetic engineering 96
- 98. IV: What next? exploration of the possible 97
- 99. Focusing on predictions rather than opinions • Genome editing and gene therapy give us the ability to cure genetic diseases and improve health • Therapies that improve health may also enhance capabilities • Based on today’s evidence, what is likely to come next? 98
- 100. Questions to consider • Will genome editing and gene therapy become mainstream components of a healthy lifestyle? • What are the ethical concerns? • When will these treatments be safe? • When will benefits outweigh risks? • When will benefits and risks be distributed justly and equitably? 99
- 101. Technological revolutions • Once radical technologies are now common • Agriculture, Writing, Metals, Medicine, Machines, Computers, Internet • Once radical behaviors are now acceptable • Ethical concerns on risks and resources often have technical solutions • Technical revolutions flourish under the current model of economic liberalism 100
- 102. 1. Germ-line editing with present day tools will not become mainstream • The option for embryo selection make germline editing unnecessary • Germline editing has risks • Our individualist society will most likely not support experiments that violate genetic agency 101
- 103. 2. Somatic therapies will predominate • Somatic gene therapies are already successful and many more are under development • Gene therapy technology will develop faster than human generation time • There will be no advantage to “locking in” a genetic change using germ-line genome editing 102
- 104. 3. Somatic therapies will redefine healthy • Mono-genetic diseases will be cured through embryo selection, genome editing and gene therapy • Treatments will expand to polygenic and non-genetic diseases • Mainstream therapies will lower costs and reduce risks • Our individualistic society will tolerate genetic experimentation as a form of self expression • DIY somatic gene therapies will pioneer new genetic lifestyles • The free market will develop mainstream somatic gene therapies 103
- 105. 4. Genome synthesis will gradually replace ex-vivo genome editing technologies Surgery is to small molecule drugs as ex vivo cell therapy is to in vivo gene therapy 104 making a small number of changes complete rewriting Editing Synthesis With time, editing will introduce more changes, until eventually we may skip editing in favor of synthesis
- 106. But applications of whole genome synthesis technologies will depend on many factors • Whole genome synthesis obsoletes the function of the germ line • Disruptive potential of whole genome synthesis is far beyond that of germ-line genome editing • Whether genome synthesis will replace sexual recombination is challenging to predict • When will society embrace somatic therapies aimed at lifestyle improvement? • When will whole genome synthesis capabilities be developed? 105
- 107. 5. Genetics will become self-expression • Humanity has always been defined more by behaviors than by genetics • Genome editing, gene therapy and whole-genome synthesis technologies will make genomics a personal choice 106
- 108. How to get involved? 107
- 109. Advice for scientists • Move to Cambridge/Boston • Why? • Power laws: the best people want to work with the best people • Boston area = Highest density of talent, capital and biotech innovation in the world 108 Especially in gene therapy and genome editing: Most big Pharma have R&D headquarters, likewise for CRISPR companies
- 110. Advice for entrepreneurs • Major challenges limiting the high potential of these technologies: • Payment • Distribution • Manufacturing • Delivery • Redose • Packaging size • Payload • Control over dynamics • Sensing and actuation • Polygenic diseases and beyond 109
- 111. Advice for investors • Plenty of opportunities will be revealed by learning the science • ASGCT and ESGCT conferences are a good place to start • More smaller bets on disruptive technologies (vs incremental technologies) 110
- 112. Final conclusions • Millions of patients have genetic diseases that are untreatable today • Genome sequencing, genome editing and gene therapy technologies give us the tools to cure these diseases • Somatic therapies will predominate over germline editing • Progress in treating mono-genetic diseases will lead to advanced treatments for polygenic and non-genetic diseases • In the long term, individual experimentation, market forces, reduced cost and reduced risk will make cell therapy and gene therapy part of a mainstream healthy lifestyle 111
- 114. Recommended reading • Genome editing with CRISPR • https://www.addgene.org/crispr/guide/ • Gene Therapy • Wang et al., 2019, https://www.ncbi.nlm.nih.gov/pubmed/30710128 • Machine-guided capsid engineering • Kelsic & Church, 2019, https://insights.bio/cell-and-gene-therapy- insights/journal/articles/challenges-and-opportunities-of-machine-guided- capsid-engineering-for-gene-therapy/ • Upcoming publications from Church lab: Ogden at al., Sinai et al., etc. • Dyno Therapeutics, www.dynotx.com 113