ACoP14 Posters

SxP reviewed the ACoP program and abstracts to select content of interest for SxP

Link to the ACoP14 program and abstracts

Monday Nov 6

• M-004 - PMX668
  • Ahmed Elmokadem: Hierarchical Deep Compartment Modeling: A Workflow to Leverage Machine Learning for Hierarchical Pharmacometric Modeling [AI/ML]
• M-007 - QSP662
  • Alexander Kulesza: Cross-disease and administration route computational approach to support the development of an immunomodulatory drug [Statistical Methodology]
• M-011 - MCS891
  • Alexis Hoerter: Identifying Strategies For Effective Treatment Of M. Tuberculosis Infection Using An Agent Based Model [AI/ML]
• M-016 - STPM781
  • Anna Largajolli: Evaluation of the Boruta Machine Learning Algorithm for Covariate Selection [AI/ML]
• M-052 - SFTL693
  • Corey J. Bishop: A Target-Mediated Drug Disposition-(TMDD) based Shiny Application for Streamlining Simulations and Facilitating Drug Development Decisions [Tools]
• M-070 - STPM842
  • Elisabeth Rouits: Benefit of Bayesian dynamic borrowing methods in evaluating the impact of newly developed oncology drugs on the standard-of-care (SOC) [Bayesian Statistics]
• M-094 - PMX559
  • Hamim Zahir: A retrospective Pooled Concentration-QTc Analysis for Omaveloxolone Using Plasma Concentration and Electrocardiogram Data from Clinical Pharmacology Studies [MBMA]
• M-117 - STPM819
  • Jafar Sadik Shaik: Statistical and Pharmacometric Analysis using Estimand Framework: A Case Study [Estimands]
• M-122L - PMX1016
  • E. Niclas Jonsson: Checklists and best practices to support the informed use of Forest plots to illustrate the impact of covariates in pharmacometric models [Visualization, Tools]

Tuesday Nov 7

• T-001 - STPM608
  • James Ousey: Application of landmark and longitudinal model-based meta-analysis (MBMA) of efficacy endpoints across systemic melanoma therapies to inform clinical trial design [MBMA]
• T-013 - STPM761
  • Jie Liu: Application of Machine Learning Methods to Identify Predictors of Placebo Response in Pediatric Major Depression Disease Studies [AI/ML]
• T-026 - SFTL840
  • Jose Storopoli: Bayesian Pharmacometric Software Benchmarks [Bayesian Statistics, Tools]
• T-035 - PMX565
  • Karen Schneck: Uncertain about Credible Prediction Intervals? : A Review and Exploration of the Concepts of Confidence and Prediction Intervals for Pharmacometric Models [Statistical Methodology]
• T-056 - STPM744
  • Leila Kheibarshekan Asl: A Comparative Analysis of Mixed Effects Modeling and Machine Learning Techniques in R for Identifying Covariate Effects in Initial Population Pharmacokinetic Modeling [AI/ML]
• T-067 - STPM650
  • Madison Snyder (SxP award winner!): Meta-analysis of Change in Lung Capacity and Skin Thickening for Interstitial Lung Disease (ILD) with Mixed Connective Tissue Disorders [Meta Analysis]
• T-078 - STPM824
  • Masato Fukae: Machine learning analyses of clinical efficacy and safety of busulfan conditioning treatment for hematopoietic stem cell transplantation in pediatric patients with or without malignancy [AI/ML]
• T-082 - STPM837
  • Matthew Wiens: Illustrating Integration and Interpretation of the Deep Compartment Model Approach using Keras and R in a Population PK Modeling Analysis [AI/ML]
• T-086 - STPM766
  • Meng Hu: Comparing Three Bayesian Approaches with the Two One-Sided t-Test (TOST) for Bioequivalence Studies in Real-world Dataset [Bayesian Statistics]
• T-115 - PMX822
  • Parsshava Mehta: Link of T-Cell Modulation and Disability Progression with High Dose Corticosteroids in Relapsing-Remitting Multiple Sclerosis using an integrative PK-semi-mechanistic PD/ MBMA approach [MBMA]
• T-123 - STPM867
  • Tim Waterhouse: Connecting ISOP with Statisticians: An Introduction to the Biopharmaceutical Section of the American Statistical Association

Wednesdsay Nov 8

• W-005 - SFTL678
  • Po-Wei Chen: An Integrated Workflow of Item Response Theory Modeling in Monolix [Tools, Statistical Methodology]
• W-016 - STPM555
  • Rong Chen: NPSA: Nonparametric Simulated Annealing for Global Optimization [Statistical Methodology]
• W-023 - MCS546
  • Samira Jamalian: Modeling Alzheimer’s Disease Progression using Neural-ODEs: At the Intersection of Pharmacometrics and Deep Learning [AI/ML, Disease Progression]
• W-049 - PMX866
  • Sooyoung Lee: Bayesian Population Pharmacokinetic Modeling for Veliparib Using Sparse Data from a Phase II Clinical Trial in Patients with Hematologic Malignancies [Bayesian Statistics]
• W-056 - SFTL606
  • Stephanie Kong: Comparison of Parameter Identifiability: NONMEM and NLMIXR2 in Population Models with Nonlinear Pharmacokinetics [Tools, Statistical Methodology]
• W-065 - QSP572
  • Tao Peng: Improving categorical endpoint longitudinal exposure-response modeling through virtual populations [Statistical Methodology]
• W-068 - STPM527
  • Thanh Vo: Virtual Trial Comparisions and Bioequivalence Assessment: From Data-Based to Probabilistic Assessment [Bayesian Statistics, Statistical Methodology]
• W-075 - STPM539
  • Varun Aggarwal: Leveraging Disease Progression Models for Feasibility Assessment of Response Adaptive Randomization in Clinical Trials [Disease Progression]
• W-106 - SFTL578
  • Yuchen Wang: ERMod Poisson: A Semi-Automated Exposure-Response (E-R) Analysis and Reporting Tool with Prediction Feature [Tools, Statistical Methodology]