Introduction/Objectives Combination therapies are commonly for treatment of various cancers. Methods to predict expected therapeutic effects of combination therapies are…
Read morePoster: Methodology - New Modelling Approaches
Parameter Significance Test Using Mixture Models (PaSTUM) allowing Type-1 Error Control for Exposure Response Modelling
Introduction: Exposure-response (ER) modelling is a crucial step in clinical drug development. Model misspecifications can inflate type I error (T1)…
Read moreLeveraging Machine Learning to Decode Polymeric Long-Acting Injectables and Predict Their Performance
Introduction Polymeric long-acting injectables (LAIs) offer significant benefits for the treatment of many chronic diseases, which extend therapeutic durations, reduce…
Read moreAI/ML algorithm for clinical data reconstruction: Turning AD into IPD.
Introduction: PK/PD data analysis is a cornerstone of drug development and efficacy and safety studies. However, individual-level PK/PD data is…
Read moreModel-based reproduction of receptor-targeting antibody pharmacodynamics allows to identify ligand turnover parameters
Introduction: In early drug discovery, clinical data on monoclonal antibodies targeting a specific protein ligand may be unavailable before the…
Read morePBPK modeling and simulation of the pharmacokinetics of an antibody drug conjugate in a patient population of phase I clinical trials
Introduction: Antibody-drug conjugates (ADCs) represent a promising anticancer approach [1]. Although physiologically based pharmacokinetics (PBPK) modeling became important in the…
Read moreAggregate data modeling in R: a new method for fitting pharmacometrics models to summary-level data
Introduction/Objectives: Population pharmacokinetic and pharmacodynamic (PK/PD) models from different studies sometimes conflict with each other [1]. When faced with conflicting…
Read moreOne model to rule them all? A meta-analysis of therapeutic antibody population PK models.
Introduction: Antibodies and their derivatives are widely used in treating various diseases, including the fields of oncology, hematology, infectious and…
Read moreQuantitative systems pharmacology model of B-cell immune response
Objectives: B-cell immunity plays a crucial role in adaptive immune response. Dysregulation of autoreactive B-cell selection, excessive activation, and B…
Read moreA Minimal Physiologically based pharmacokinetic Framework Incorporating Antigen Shedding Dynamics for Optimizing Immuno-Oncology Therapies
Introduction: Immuno-oncology leverages monoclonal antibodies (mAbs) and related modalities (e.g., antibody-drug conjugates, bispecific antibodies) to target tumor-associated antigens. Physiologically based…
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