Simulations Plus, Inc. · NASDAQ:SLP
Simulations Plus — Conference Presentation (September 2023)
26 slides from Simulations Plus's conference presentation, Sep 19, 2023
Slide 1: QSP: The Best Kept Secret for Increasing Technical Probability of Success in Clinical Trials
QSP: The Best Kept Secret for Increasing the Technical Probability of Success in Clinical Trials & Enhancing Regulatory Submissions Lisl Shoda and Steven Chang 19 September 2023 1 NASDAQ: SLP
Slide 2: Webinar Agenda
Webinar Agenda • Brief Introduction to Simulations Plus and QSP • Case Studies on QSP Impact in Support of Clinical Development • Developing a QSP Standard at Simulations Plus • Deeper Dive into QSP Capabilities in Multiple Myeloma 2 NASDAQ: SLP
Slide 3: Who We Are
Who We Are NASDAQ: SLP Regulatory Agencies Trained on our Technology 190+ Employees Worldwide >25 yrs. >280 Pharmaceutical, biotechnology, chemicals, cosmetics, and consumer goods companies Established in the U.S., Europe, Asia, and South America In 1996 3 NASDAQ: SLP
Slide 4: Pharma Increasingly Adopting Modeling
Pharma Increasingly Adopting Modeling Overall Phase 1 Drug development is plagued by: • Drugs failing in clinical trials despite indications of preclinical efficacy • Insufficient patients enrolling in trials (especially in immuno-oncology) Phase 2 Phase 3 Pfizer moved from a 2% clinical success rat
Slide 5: QSP Submissions to FDA are Accelerating & Attracting Attention
QSP Submissions to FDA are Accelerating & Attracting Attention 157 Submissions Containing QSP as of Dec 2020 2013 - 1st FDA Submission in 2013 Pre-IND in Oncology 2014 - rH parathyroid hormone QSP Model applied in NDA review to support dose selection - ASCPT Workshop Recognizes 2019 Increased Regula
Slide 6: Leveraging QSP to Impact Drug Development Programs
Leveraging QSP to Impact Drug Development Programs Trial Design Predicted Phase I and II Responses • Contributed to the trial design of over 40 large scale • Successfully predicted first-in-human efficacy failures clinical trials based on successful preclinical results • Simulation analysis sufficie
Slide 7: QSP is the Mechanistic Representation of Health and Disease to Predict Drug Efficacy and Safety
QSP is the Mechanistic Representation of Health and Disease to Predict Drug Efficacy and Safety Fit-for-Purpose Models Platform Models Disease Progression Models Model that generates the required results to Interplay of multiple drugs, pathways, tissues Build upon platform models the necessary
Slide 8: Simulations Plus Has A Library of Existing QSP and QST Models
Simulations Plus Has A Library of Existing QSP and QST Models to Address Your Questions QSP: Inflammatory and Fibrotic Diseases QSP: Immuno-Oncology • Non-alcoholic fatty liver disease / • Acute myeloid leukemia (AML) steatohepatitis (NAFLD/NASH) • Multiple myeloma (MM) • Idiopathic pulmonary fibros
Slide 9: Case Study: QST Informed Safe Dose Selection of Emvododstat in AML Patients
Case Study: QST Informed Safe Dose Selection of Emvododstat in Acute Myeloid Leukemia (AML) Patients • Emvodostat for the treatment of solid tumors initially terminated after two patients experienced drug-induced liver failure • Potential for AML efficacy using lower dosing regimens • Application of
Slide 10: DILIsym Retrospectively Reproduced Observed Liver Safety Signals and Prospectively Predicted Safety
DILIsym Retrospectively Reproduced Observed Liver Safety Signals and Prospectively Predicted Safety with Alternate Regimens • PBPK model-informed liver 100 mg BID 16 wk 160 mg BID 16 wk 200 mg BID 16 wk exposure to emvododstat and its metabolite • Exposure-dependent effects on bile acid transporters
Slide 11: Prospective DILIsym Simulations of Emvododstat Validated with Recent Clinical Trial Results
Prospective DILIsym Simulations of Emvododstat Validated with Recent Clinical Trial Results • PK exposure and liver safety outcomes from recent clinical trials of Emvododstat are consistent with simulation results, validating DILIsym predictions – Among 33 patients who participated in the AML clinic
Slide 12: Case Study: QSP Identification of Key Mechanism of Action for FGFR1/KLB Agonists
Case Study: QSP Identification of Key Mechanism of Action Underpinning Efficacy for FGFR1/KLB Agonists • FGF21 analogs, acting as FGFR1/β-Klotho agonists, are in development to treat metabolic diseases The Liver Meeting 2022 – Promising in preclinical studies – Variable efficacy in clinical trials •
Slide 13: NAFLDsym Predicted Association of Clinical Efficacy with Increased Adiponectin
NAFLDsym Predicted Association of Clinical Efficacy with Increased Adiponectin • Head-to-head comparison of four FGF21 analogs using NAFLDsym – Integration of compound exposure and effects of FGF21 analogs in simulated patients matched to clinical trials • Reproduction of relationship between increa
Slide 14: All Agree QSP Needs Standardization
All Agree QSP Needs Standardization • Regulatory agencies see increasing number of submissions with QSP – Currently, these are bespoke built-for-purpose models using a diversity of languages, modeling platforms, techniques… – The ability to assess and understanding the models within the Briefing doc
Slide 15: SLP Introducing QSP Standardization
SLP Introducing QSP Standardization • SLP will have a QSP standard available for its customers – Model implementation and extension – Virtual populations simultaneously fit to all relevant phase 2/3 drug trials – Consistent approach to model fitting and validation • Within these standards, the broad
Slide 16: Overview of Multiple Myeloma QSP Model
Overview of Multiple Myeloma QSP Model • The model represents the critical biological elements of MM disease biology and drug mechanisms of action – Disease biology: MM cells, MM-produced biomarkers (M protein, sFLC, sBCMA), immune cells (CTL, Th, Tregs, Macs, NK cells), and cytokines (IL-2, IL-6, I
Slide 17: Standard for Assessing Model Fitness
Standard for Assessing Model Fitness • In addition to a model’s “score” (used during optimization), we employ an alternative measure of model quality termed “coverage” that is independent of the training process • Coverage is the percentage of data inside the model’s 90% confidence interval – The Co
Slide 18: Overall Quality of Fit
Overall Quality of Fit • The parity plot shown here highlights quality of fit across all response endpoints in the myeloma model – Fit indicates the data was used in VPop optimization (569 clinical trial mean values) – Validation indicates the data was held out (194 clinical trial mean values) 18 NA
Slide 19: Model Successfully Predicts Tal + Dara Efficacy
Model Successfully Predicts Tal + Dara Efficacy Validation Data • Training data includes daratumumab and talquetamab Training Data monotherapy efficacy from the COLUMBA and MonumenTAL-1 trials. (Dara 1800mg dose not shown.) • Model successfully validates against Tal + Dara combo therapy efficacy fro
Slide 20: Model Predicts Dara/Bort Combos
Model Predicts Dara/Bort Combos Validation Data • Training data includes BOR and ORR Training Data from (bold indicates the largest trial, whose data is shown left) – Daratumumab: COLUMBA – Bortezomib: SUMMIT, APEX • Model successfully validates against – Bort + Dex: CASTOR, ENDEAVOR, OPTIMISMM MM-0
Slide 21: Model Predicts DoR for Dara+Bort+Dex Combo
Model Predicts DoR for Dara+Bort+Dex Combo DoR Survival Curve • Model training data included DoR for Bort monotherapy from Percent of Responders SUMMIT (shown in green) • Validation data is from – Bort+Dex: CASTOR, ENDEAVOR, OPTIMISMM MM-007 (blue) – Dara+Bort+Dex: CASTOR (red) Time (Week) • Model’s
Slide 22: Entry to Simulations Plus QSP via Consulting and/or Licensing Options
Entry to Simulations Plus QSP via Consulting and/or Licensing Options • Consulting projects – Fixed price with milestone-based payments – Time and materials with monthly payments • Licensing – Available for in-house use – High performance grid licensing (HPGL) available – Some training included • FT
Slide 23: Interested in learning more? Contact
Interested in learning more? Contact: Steven Chang Lisl Shoda President, QSP Solutions Pittsburgh Assoc. Vice President & Director of Immunology +1-661-723-7723 +1-650-743-9992 steve.chang@simulations-plus.com lisl.shoda@simulations-plus.com Learn More! www.simulations-plus.com 23 NASDAQ: SLP
Slide 24: Typical Savings of Biosimulation Along the Value Chain (QSP Efficacy Modeling)
Typical Savings of Biosimulation Along the Value Chain (QSP Efficacy Modeling) Improve probability of success by optimizing the 6-12 months per project Prioritize targets and compounds treatment approach as early as possible; avoid $100K+ per study animal costs wasted time preclinical / clinical stu
Slide 25: Typical Savings of Biosimulation Along the Value Chain (QST Safety Modeling)
Typical Savings of Biosimulation Along the Value Chain (QST Safety Modeling) Improve your probability of success by optimizing 6-12 months per project Pick your safest lead candidate safety as early as possible; avoid wasted time and $100K+ per study animal costs wasted preclinical and clinical stud
Slide 26: Simulations Plus Has Expertise to Develop QSP Models to Predict Efficacy Across Therapeutic Areas
Simulations Plus Has Expertise and Experience to Develop QSP Models to Predict Efficacy Across Therapeutic Areas Liver fat Fibrosis score Plasma ALT NAS Treatment 1 -4% -12% -5% -9% Treatment 2 -6% -14% -7% -11% Treatment 3 -7% -13% -8% -12% Treatment 4 -59% -25% -17% -38% Treatment 5 -62% -22% -15%
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