Htg Molecular Diagnostics, Inc. · OTC:HTGMQ
Htg Molecular Diagnostics — Investor Presentation (December 2022)
29 slides from Htg Molecular Diagnostics's investor presentation, Dec 12, 2022
Slide 1: Corporate Overview December 2022
Corporate Overview December 2022 Exhibit 99.1
Slide 2: Forward-looking Statements and Other Information
Forward-looking Statements and Other Information This presentation contains forward-looking statements that involve substantial risks and uncertainties. All statements, other than statements of historical facts, contained in this presentation are forward-looking statements, including statements rega
Slide 3: Precision Medicine: Platform Assets and Drug Assets
PRECISION MEDICINE Platform Assets Platform based pharma partnering Drug Assets Transcriptome-informed small molecule drug discovery
Slide 4: The Power of RNA and Gene Expression Profiling in Precision Medicine
The Power of RNA and Gene Expression Profiling (GEP) in Precision Medicine RNA Transcriptome DNA Protein Proteome Making Precision Medicine More Precise M H S V I D Genome RNA is what is happening Proteins are the working molecules … but are very difficult to multiplex DNA represents the blueprint M
Slide 5: Strategy
Strategy Leverage proprietary technologies to help improve drug discovery/development efficiency and effectiveness Use captive RNA profiling and ML driven chemistry engine to build a pipeline of potentially best in class drug candidates to known targets and advance them into clinical development Sel
Slide 6: Proven Team with Rich, Relevant Expertise
Vidya Kankipati VP, Scientific Services Carl Kaub VP, Chemistry John Lubniewski President and Chief Executive Officer Stephen A. Barat SVP, Therapeutics Debrah Thompson VP, Scientific Innovation Christina Carruthers VP, Target Strategy Shaun D. McMeans Senior Vice President and Chief Financial Offi
Slide 7: Problems We Address
Problems We Address Drug Candidate Attrition Getting the Right Drug to the Right Patient Drug Discovery Preclinical 3-6 Years FDA Review Scale-up to MFG NDA Submitted .5-2 Years 20-100 100-500 1000-5000 6-7 Years Clinical Trial IND Submitted ~5,000 – 10,000 Compounds 250 Pre-Discovery Phase 1 Phase
Slide 8: HTG Positioned for High Value-add Throughout The Process
HTG Positioned for High Value-add Throughout The Process DRUG DISCOVERY CDx DEVELOPMENT NIH / Academia profiling tools Iterative Process Potential royalties and milestones or asset sale Objective: Patented development candidate out licensed or sold by HTG CDx Development Target Discovery Validation
Slide 9: Therapeutics: Transcriptome-informed approach to drug discovery and design
THERAPEUTICS Pioneering a transcriptome-informed approach to drug discovery and design Reducing to practice the use of transcriptomic profiling to understanding cellular perturbation. Our approach is unique and anchored in captive technologies Platform is an integration of captive profiling capabili
Slide 10: Literature concurs that integrating big data expression profiling can improve candidates
Literature concurs that integrating big data expression profiling can improve therapeutic candidates Early and often transcriptome profiling can potentially disrupt drug development
Slide 11: What Differentiates HTG for Drug Discovery?
What Differentiates HTG for Drug Discovery? Functional integration of full RNA profiling, in silico chemistry, and primary data-driven redesign: Potentially de-risked drug candidates FULL TRANSCRIPTOMIC PROFILING INTEGRATED ML DRIVEN CHEMISTRY TRANSCRIPTOME-INFORMED SAR Full mRNA, m6a and miRNA prof
Slide 12: Our Solution: The Value of Early Pre-clinical Insight
OUR SOLUTION The Value of Early Pre-clinical Insight Used too late in process Clinical Transcriptomic profiling Few levers: Dosing Post-facto patient selection TODAY Use early, during small molecule discovery and design Preclinical Full system transcriptomic profiling Medicinal chemistry Many levers
Slide 13: Proprietary Profiling Technology
Proprietary Profiling Technology HTG Epi- EdgeSeq Epi transcriptome mRNA transcriptome miRNA transcriptome EdgeSeq Platform Technology for Drug Discovery HTG’s Tech Enabled Drug Discovery Platform Neurology Cardiovascular Diabetes Liver Disease Oncology/IO Rare Disease Infectious Disease Immunology
Slide 14: Current Focus: Criteria for Target Selection
Current Focus Rapid path to clinical POC Clear patient selection strategy Amenable to HTG Platform Innovators Challengers Target is a driver of disease Clear patient selection strategy Unmet Medical Need Exists Favorable Competitive Landscape Clear Patient Selection Strategy Good IP Protection Possi
Slide 15: Target Strategy Approach – Why we are different
Role of the target in the biology of the given disease. Location/expression of target, liabilities, etc. Preclinical proof of concept Target modulation in pharmacologic or disease model? Is the target druggable? Factors that influence target expression Age, comorbidities, etc. Potential modality Com
Slide 16: HTG EdgeSeq Data Generation
Sequencing Library prep Data Generation: HTG EdgeSeq Sequencer Agnostic Illumina Thermo Fisher Scientific Sample prep Patented and Proprietary Chemistry (U.S. Patent No. 8,741,564) Targeted RNA sequencing, 48 hour total time m6A RNA modifications (“epi-transcriptome”) mRNA Transcriptome miRNA Transc
Slide 17: HTG Chemistry Platform
2 HTG Chemistry Platform Synthesis In Vitro/Vivo Testing Normalization and Formatting Property and Attribute Calculation Filtering Evaluation, Prioritization and Iterative Design Data Extraction Model Selection , Training, Scoring, and Structure Generation Papers, patents, commercial monomers, scree
Slide 18: A Deeper Dive: Transcriptomic Informed Drug Discovery and Design
Analyze disease vs normal with mRNA, miRNA, m6A and other modifications. Map disease states Identify potential targets Knock on/knock off CRISPR Target validation Profiling Pharmacophore, structure and ligand based structures Binding site assessments In silico screening Targets are screened with tar
Slide 19: HTG Drug Discovery Engine
RNA Profiling Internal Experimental Data Data Sources Target Specific Data HTG Drug Discovery Engine Convolutional Neural Network Deep Neural Network LSTM Neural Network Deep Reinforcement Learning Graph Neural Network Convolutional Neural Network Deep Neural Network Engine ML Algorithms Chemical St
Slide 20: Proof of Approach Work
Proof of Approach Work HTG Therapeutics White Papers Proof of Approach
Slide 21: Compound to Profile Relationships in Discovery
Compound to Profile Relationships in Discovery An intriguing difference between mTOR inhibitors is the differential regulation of the ferroptosis pathway. Our RNA data show differential regulation of this key pathway across a range of these structurally similar compounds. C03 C02 In the discovery se
Slide 22: Profile to Compound
Profile to Compound We expect our machine learning based engine technology to allow generation of molecules from desired RNA profiles.
Slide 23: IP Strategy – Two Pronged
IP Strategy – Two Pronged Protecting the “Engine” Patent protection EdgeSeq assay technology 6 issued patents, including US, AU, CA, CN, JP, and EP (GB, FR, IT, DE, ES) m/miRNA codetection assay technology 3 issued patents, including US, AU, and CA EdgeSeq v2.0 assay technology 5 pending application
Slide 24: Profiling and Diagnostics
Profiling and Diagnostics Advantaged technology in RNA translational profiling Enables broad application of high quality RNA GEP in translational research Robust technology for real world issues Very sample efficient, high assay pass rates and quick turn around times Highly complementary in drug dev
Slide 25: What Differentiates HTG for RNA Analysis?
What Differentiates HTG for RNA Analysis? Faster…easier…higher expected success rate…lower sample requirement SAMPLE FRIENDLY SIMPLIFIED WORKFLOW CUTTING-EDGE PERFORMANCE 5-10x less starting sample required Compatible with both liquid and tissue samples Maintain effectiveness with degraded samples A
Slide 26: Momentum in Publications Referencing EdgeSeq
Momentum in Publications Referencing EdgeSeq
Slide 27: HTG human mRNA transcriptome product
Provides the mRNA transcriptome with the same value proposition as previous EdgeSeq targeted panels Smaller sample load High success rate with FFPE Simpler workflow Faster turnaround time Easy bioinformatics Helping to enable the next round of growth HTG’s new human mRNA transcriptome product, appro
Slide 28: Assay comparison and eRNA/FFPE performance vs RNASeq
Assay comparison and eRNA/FFPE performance vs RNASeq Technical Highlights – HTP System Performance * Only 1 sample required 2 sections ** RNA-Seq samples failed to generate sufficient extracted RNA to process samples. HTG works with smaller samples, is less sensitive to sample age, has a faster turn
Slide 29: Creating Two Categories of Potential Value: Rx/Platform
Creating Two Categories of Potential Value: Rx/Platform Rx VALUE (Patented Novel Therapeutics) Therapeutic pipeline based on transcriptome-informed approach to drug discovery - Licensable drug candidates (near term) - HTG early development pipeline (longer term) Upfronts and milestone payments on le
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