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TRANSPARENT AI/ML TO DISCOVER NOVEL THERAPEUTICS FOR RNA SPLICING-MEDIATED DISEASES
- 1. Discovery of Splicing-Based RNA Therapeutics with AI/ML RNA Therapeutics Conference February 27-28 2023, Anaheim, CA
- 2. PARTNERS Envisagenics Overview § Technology partnered by biopharmaceutical companies for drug target discovery § Exon-centric approach for RNA-splicing analysis § Scalable HPC for high volume RNA-seq data § Proprietary algorithms of drug target prioritization § Drug targets in five therapeutic programs Primary therapeutic modalities Antisense & Immunotherapies Therapeutic areas of focus Neurodegenerative, Oncology, Metabolic Proprietary drug target discovery platform Driven by Artificial Intelligence & Machine Learning ENVISAGENICS AT A GLANCE FOUNDERS Founded in 2014 Spin out from Cold Spring Harbor Laboratory Maria Luisa Pineda, PhD CEO & CO-FOUNDER Martin Akerman, PhD CTO & CO-FOUNDER
- 3. Ribosome 25-30 nm ~80 proteins Verschoor et al NAR 1998. Frankenstein et al Structure 2012. Zohu et al PNAS 2002. Spliceosome 40-60 nm ~300 proteins Proteasome 11-16 nm ~60 proteins Kopp et al Biochem Biophys Acta 1986. Nuclear Pore 120 nm ~30 proteins Winey et al Mol Biol Cell 1997. The spliceosome is one of the largest, if not the largest, molecular machines in the cell – Valadhan S & Jaladat Y, Proteomics 2010
- 4. BRCA CRC LUSC AML/MDS CMML MPN Obesity NASH NAFLD ALS FTD PGD PRPF40B SF1 SF3A1 SF3B1 SFPQ RBM10 U2AF1 U2AF2 ZRSR2 SRSF1 SRSF2 SRSF3 SRSF4 SRSF5 SRSF6 SRSF7 TRA2β FUS hnRNPA1 hnRNPA2B1 hnRNPC hnRBPK hnRNPM PTBP1 SRSF10 TDP-43 SMN EWS 370 human diseases are caused or aggravated by splicing errors. ― Splicing errors are pervasive in cancer, metabolic diseases and neurodegeneration ― Mutated splicing factors alter spliceosomal function leading to disease-specific splicing errors ― Up/down deregulated splicing factors are equally harmful, they can alter spliceosome stoichiometry Adapted from: Urbanski et al. (2018) Pihlajamaki J et al. (2011) Bangru S et al. (2018) Del Rio-Moreno M et al. (2019) Wang H et al. (2019) Sephton CF & Yu G (2015) Exampleofpervasivesplicingerrorsacrossdiseases Auxiliary Factors Core Spliceosome Splicing Activators Splicing Repressors Solid Tumors Hemato Tumors Metabolic Disease Neuro degeneration Mutations Inclusions bodies Upregulation Downregulation
- 5. Role of splicing in neurodegenerative diseases. 8/10 top driver RNA-binding proteins (RBP) in neurodegenerative diseases have been shown to either regulate alternative splicing, participate in spliceosome biogenesis or co- precipitate with core spliceosome subcomplexes RNA-binding protein Disease Angiogenin (ANG) ALS, PD Ataxon-2 (ATXN2) ALS, SCA2 Ewin Sarcoma Protein (EWS) ALS, FTD Fragile X mental retardation protein (FMRP) FXS Fused in Sarcoma (FUS) ALS, FTD, PQE Het. nuclear ribonuclear protein (hnRNPA2B1) ALS, FTD, PGD Het. nuclear ribonuclear protein (hnRNPA1) ALS, PGD Survival of motor neuron (SMN) ALS, SMA TATA-binding protein assoc. factor 15 (TAF15) ALS, FTD TAR DNA-binding protein (TDP-43) ALS FTD, AD, HD AD Alzheimer’s disease; ALS Amyotrophic Lateral Sclerosis; FTD Frontotemporal Dementia; FXS Fragile X syndrome; HD Huntington Disease; PD Parkinson’s Disease; PGD Paget disease; PQE PolyQ Expansion disease; SCA2 Spinocerebellar ataxia type 2; SMA Spinal Muscular Atrophy Splicing Related RBPs Mutated in Neurodegeneration Sephton CF & Yu G. Cell. Mol. Life Sci. (2015) Mutations Inclusions bodies Mut. & IB. PolyQ Expansion
- 6. SpliceCore finds new splicing errors, stratifies sporadic ALS patients. 6 Therapeuticsspecificallydesigned forstratifiedsubpopulations SporadicALS(90%) FamilialALS(10%) ― 10% of ALS patients are “familial,” of known genetic cause ― 90% of ALS patients are “sporadic,” of unknown genetic cause ― Sporadic patient are stratified by their recurrent patterns of deregulated splicing factors, and prioritized for target discovery 4 Patient batches 4 Brain tissue types 1,556 RNA-seq samples
- 7. Target ID Data Input ML Algorithms Outcome Modular Software Process Overview – Horizontal Application RNA-seq data + metadata Exon-centric identification of disease- specific splicing events Prioritizing splicing events using ML Combine ML algorithms to prioritize targets for drug modality Qualify assets in lab In-house Public Partners RNA-seq Splicing modulators àdisease driver àdruggable Antigenic peptides àprotein translated àelicit immune response SpliceImpact Protein isoforms àhorizontal data search àtargeted analysis Small Molecules 7 Antisense Oligos Immuno therapy Proprietary exon-centric transcriptome assembly SpliceDisco Patient Stratification ASO discovery IO discovery Biological interpret. Artha Horizontal target studies SpliceIO SpliceLearn SpliceSlice Disease Control
- 8. PATIENT STRATIFICATION Identification of sporadic ALS subpopulations, based on spliceosome profiling
- 9. RNA-seq data, Spliceosome Stratified patients SpliceSliceTM: Spliceosome Profiling for Patient Stratification ― Utilizes SpliceCore exon-centric representation of alternative splicing ― Identifies splicing-derived patient subpopulations without labels ― Integrates multiple algorithms to select optimal parameters ― Isolates biomarkers by applying feature importance to splicing factor profiles SpliceSlice TM
- 10. SpliceSlice Explains 37% of Sporadic ALS Patient Samples, Predicts Splicing Errors in Five Subpopulations INPUT: RNA-seq data from ALS patients OUTPUT: ALS sub-populations sorted by deregulated modules SpliceSlice identifies spliceosomal deregulation and sorts RNA-seq samples accordingly
- 11. SpliceSlice Identifies ALS Subpopulation with Increased Target Prevalence CONFIDENTIAL HSPA8 HSPA1A HNRNPF PPIL2 ILF3 ZC3H18 CLASRP SNRNP70 NCBP1 SMNDC1 HNRNPH2 SMU1 SLU7 PPIL4 DHX15 CDC5L SREK1 U2SURP ARGLU1 SRRM1 SRSF11 RBM25 Spinal Predictive RBPs 0 50 100 Mean |SHAP| Cluster (%) 0 5 10 Mean |SHAP| 0 25 50 75 100 1 n74 6 n54 3 n70 4 n49 2 n81 7 n86 5 n28 Cluster (n# of samples) (%) −2 −1 0 1 2 Median Z−Score Cluster 5 7 2 4 3 6 1 Batch NYGC−HiSeq2500 NYGC−NovaSeq6000 TargetALS−HiSeq2500 TargetALS−NovaSeq6000 SpliceSlice Classification SpliceCore Target Selection HSPA8 HSPA1A HNRNPF PPIL2 ILF3 ZC3H18 CLASRP SNRNP70 NCBP1 SMNDC1 HNRNPH2 SMU1 SLU7 PPIL4 DHX15 CDC5L SREK1 U2SURP ARGLU1 SRRM1 SRSF11 RBM25 Spinal Predictive RBPs 0 50 100 Mean |SHAP| Cluster (%) 0 5 10 Mean |SHAP| 0 25 50 75 100 1 n74 6 n54 3 n70 4 n49 2 n81 7 n86 5 n28 Cluster (n# of samples) (%) −2 −1 0 1 2 Median Z−Score Cluster 5 7 2 4 3 6 1 Batch NYGC−HiSeq2500 NYGC−NovaSeq6000 TargetALS−HiSeq2500 TargetALS−NovaSeq6000 Four patient batches 22 Top deregulated splicing factors Highly predictive splicing factor snRNP70 exon skipping is a marker of ALS patients with increased oxidative stress and altered synaptic signaling (ALS-ox) Nakaya T. Gene 2022 Exon skipped in ALS Exon included in healthy Sporadic ALS SpliceSlice “group 6” snRNP70 exon skipping is more prevalent in the spliceosome-defined “group 6” vs general sporadic ALS patients and the ALS-ox subgroup 94.4% 38.9% Target Prevalence Increased spliceosomal modules snRNP70 splicing in ALS Spinal Cord ALS-ox subgroup Tam OH, Cell Rep. 2019 61% 7 9 7 9 8 8
- 12. TARGET PRIORITIZATION AI-based selection of disease-specific, functionally relevant, druggable targets
- 13. Disease Specificity Function Druggability Prevalence Tumor Specificity RNA/protein stability, Subcellular Localization, Regulation, Pathways Drug response, Drug Accessibility, MOA Cohort 1 Cohort 2 Cohort 3 Prediction 1 Prediction 2 Prediction 3 Literature Prediction 1 Prediction 2 Prediction 3 Clinical Trial Known Novel Targets Diversified Approach to Mitigate Risk in Target Selection 13
- 14. Multiple Shots On Goal: In-silico & In-vitro Pipeline SpliceCore Analysis Drug/Target Verification Drug/Target Investigation Diversified approach RNA-seq Transcriptomics Biologically relevant ML Novel splicing events Therapeutic Optimization Confirm splice event and novel protein present in cell lines and 1o disease tissue Confirm therapeutic value and druggability with ASOs in iPSC cell line models 1.5 months 0.5 month 10 months Cellular assays in iPSC derived models Organoids, mice models MoA confirm ASO tool inhibitors Druggability Specificity PK-PD Efficacy TI / tolerability 18- 24 months 18- 24 months 1,000 Splicing Events 80 Splicing Events 20 Splicing Events 10 Splicing Event 3 Splicing Event Function Disease-specificity Druggability/ antigenicity 14
- 15. DRUG DESIGN AI-assisted antisense drug design, In-silico MOA studies
- 16. Splicing Activators and Repressors at the Core of Antisense Drug MOA 16 - + - + - + Normal Splicing Regulation Exon inclusion is regulated by the interplay between RBPs that activate and repress splicing Disease Splicing Errors Certain mutations can delete activator binding sites, shifting the balance towards unproductive splicing -like MOA Splicing modulation Blocks the binding of repressor RBPs to restore exon inclusion
- 17. Spliceosomal circuit targeted by Splicing Regulatory Information: From Molecular Interactions to Predictive Features U4/U6 Prp19 Bact EJC U1/U2 hnRNP SMN U4/U6 SR Differential connectivity of splicing activators and repressors to the human spliceosome Akerman et al. 2015 Genome Biol Spliceosome Spliceosome as information Information as predictive feature SR (+) hnRNP (-) Other 31 regulatory circuits useful as ML features to predict splicing modulators 17
- 18. Predicts ASO Drug Targets Using Splicing Regulatory Information Splicing effect labels Splicing regulatory circuits Productive blocking Unproductive classifier SFs Predict ASO modulation SSMs sfB sfA sfB sfA 18 Development and validation of an AI/ML platform for the discovery of splice-switching oligonucleotide targets Fronk et. al., BioRxiv (2022) + - Test Sensitivity Specificity AUC Cross-validation 92% 93% 0.95 Independent validation 100% 90% 0.90 SpliceLearnTM
- 19. SpliceLearn Uses Disease-Specific Regulatory Information to Interpret MOA SF-weighted predictive features 19 HSPA8 HSPA1A HNRNPF PPIL2 ILF3 ZC3H18 CLASRP SNRNP70 NCBP1 SMNDC1 HNRNPH2 SMU1 SLU7 PPIL4 DHX15 CDC5L SREK1 U2SURP ARGLU1 SRRM1 SRSF11 RBM25 Spinal Predictive RBPs 0 50 100 Mean |SHAP| Cluster (%) 0 5 10 Mean |SHAP| 0 25 50 75 100 1 n74 6 n54 3 n70 4 n49 2 n81 7 n86 5 n28 Cluster (n# of samples) (%) −2 −1 0 1 2 Median Z−Score Cluster 5 7 2 4 3 6 1 Batch NYGC−HiSeq2500 NYGC−NovaSeq6000 TargetALS−HiSeq2500 TargetALS−NovaSeq6000 Deregulated Splicing Factors HSPA8 HSPA1A HNRNPF PPIL2 ILF3 ZC3H18 CLASRP SNRNP70 NCBP1 SMNDC1 HNRNPH2 SMU1 SLU7 PPIL4 DHX15 CDC5L SREK1 U2SURP ARGLU1 SRRM1 SRSF11 RBM25 Spinal Predictive RBPs 0 50 100 Mean |SHAP| Cluster (%) 0 5 10 Mean |SHAP| 0 25 50 75 100 1 n74 6 n54 3 n70 4 n49 2 n81 7 n86 5 n28 Cluster (n# of samples) (%) −2 −1 0 1 2 Median Z−Score Cluster 5 7 2 4 3 6 1 Batch NYGC−HiSeq2500 NYGC−NovaSeq6000 TargetALS−HiSeq2500 TargetALS−NovaSeq6000 Four patient batches 22 Top deregulated splicing factors Highly predictive splicing factor (Modules) “spliceosomal state” SF-specific ASO blockers 0 0.2 0.4 0.6 0.8 1 SpliceLearn Score 0 0.2 0.4 0.6 0.8 1 SpliceLearn Score 0 0.2 0.4 0.6 0.8 1 SpliceLearn Score P(ASO|sfA ) P(ASO|sfb ) P(ASO|sfc )
- 20. Lim et al. Nat Comm. 2020 Target: SYNGAP1 ; blocked SF: SRSF5 Centa et al., Nature Medicine 2020 SpliceLearn Finds Productive ASO binding sites in Retrospective Studies Target: PCCA ; blocked SF: hnRNPM Target:CD274 ; blocked SF: ILF2 Target: CLN3; blocked SF: KHDRBS3 retrospective retrospective retrospective retrospective retrospective retrospective Target: SMN1 ; blocked SF: hnRNPK Target: CFTR ; blocked SF: hnRNPA1 Hua et al., AM J Hum Genet, 2008 Kim et al., PNAS 2022
- 21. Fronk et al., BioRxiv 2022 SpliceLearn Discovers ASO binding sites in Prospective Study B B EXON A A B A B A Target: ENV-02 / Blocked SF: hnRNPL, SRSF7 hnRNPL hnRNPL SRSF7 SRSF7 QKI QKI A: p-val = 0.3 B: p-val = 1 A: p-val = 0.003 B: p-val = 0.001 A: p-val = 7.9E-5 B: p-val = 0.01 Tumor n=148 Normal N=30 PCR CLIP-seq RNA-seq P-val = 0.44 P-val = 0.001 P-val = 2.2E-9
- 22. Therapeutic Pipeline – Horizontal Application of SpliceCore 23
- 23. Thank you! Kendall Anderson Director – Target Discovery kanderson@envisagenics.com Using AI to deliver therapies for RNA splicing diseases