MS06 - MFBM-01

Emerging trends in quantitative pharmacometric modelling

Thursday, July 17 at 10:20am

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Stuart Johnston (The University of Melbourne), Matthew Faria

Description:

Mathematical models that describe how therapeutic agents interact with biological systems are playing an increasingly vital role in the development of novel drugs. Regulatory agencies, such as the FDA, now recognise the predictive power of accurately developed, calibrated, and validated models in the drug approval pipeline. By combining models and experimental data of pharmacokinetics and pharmacodynamics, we can generate predictions of drug efficacy and safety. However, the majority of research in pharmacometrics focuses on traditional small molecule drugs. Novel therapies are moving beyond this paradigm towards targeted and personalised approaches. Accordingly, we require models capable of describing the more complex and detailed interactions between the therapy and biological system of interest. Moreover, we must ensure that the relevant biological parameters and metrics can be identified and estimated from experiments. In this session, we will hear about recent developments in quantitative pharmacometrics that combine approaches from pharmacokinetic/pharmacodynamic modelling, quantitative systems pharmacology, computational statistics and machine learning to develop quantitative pipelines for establishing the efficacy and safety of next-generation therapeutics.

Irina Kareva

Northeastern University

"From pre-clinical data to first in human dose projections: a different puzzle every time"

Identification and pre-clinical testing to determine dose-response relationships for new compounds is only one step in the drug discovery and development process. Translation of pre-clinical data into efficacious and safe human doses is a separate process, which may rely on assessing target occupancy, biomarker modulation, benchmarking against competitor compounds, and other detective work, to identify compound-specific criteria to predict safety and efficacy criteria in humans. In this talk, I will review the general approaches that help us make these predictions and then go through a few examples to describe how we solved the dose prediction puzzle for each individual case.

Yun Min Song

Institute for Basic Science

"Beyond FDA Guidance: Enhancing Accuracy in Predicting Drug-Drug Interactions"

The U.S. Food and Drug Administration (FDA) guidance recommends several model-based approaches to assess potential drug-drug interactions (DDIs) mediated by enzyme induction or inhibition. However, the FDA-recommended equation for predicting DDIs mediated by induction is known to have low accuracy. Moreover, a systematic and reliable method for accurately estimating inhibition constants—key parameters for predicting DDIs mediated by inhibition—remains lacking, with considerable inconsistencies reported across studies. In this talk, we demonstrate that the inaccuracy of the FDA-recommended equation stems from a fundamental limitation of the Michaelis-Menten equation, and we propose an improved model that considerably enhances predictive accuracy (~2-fold). Furthermore, we introduce a novel, optimized method for estimating inhibition constants, termed 50-BOA, which substantially reduces the number of required experiments (>75%)% while maintaining high precision and accuracy.

Stuart Johnston

The University of Melbourne

"Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments"

Nano-engineered particles are a promising tool for medical diagnostics, biomedical imaging, and targeted drug delivery. Fundamental to the assessment of particle performance are in vitro particle-cell interaction experiments. These experiments can be summarised with key parameters that facilitate objective comparisons across various cell and particle pairs, such as the particle-cell association rate. Previous studies often focus on point estimates of such parameters and neglect heterogeneity in routine measurements. In this study, we develop an ordinary differential equation-based mechanistic mathematical model that incorporates and exploits the heterogeneity in routine measurements. Connecting this model to data using approximate Bayesian computation parameter inference and prediction tools, we reveal the significant role of heterogeneity in parameters that characterise particle-cell interactions. We then generate predictions for key quantities, such as the time evolution of the number of particles per cell. Finally, by systematically exploring how the choice of experimental time points influences estimates of key quantities, we identify optimal experimental time points that maximise the information that is gained from particle-cell interaction experiments.

Thibault Delobel

Institut Curie

"Integrating glioblastoma plasticity into combination treatment design: a quantitative systems pharmacology and machine learning approach"

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median survival below 18 months and no curative treatment currently available. To investigate resistance mechanisms to temozolomide (TMZ), the standard-of-care chemotherapy, we generated perturbed proteomic data (with and without TMZ) for 12 patient-derived cell lines (PDCLs). Pathways enrichment and independent component analysis revealed a high inter-patient heterogeneity. Commonly dysregulated proteins across PDCLs were matched to pharmacological compounds targeting them using a new pipeline. These molecules are currently being evaluated in a dedicated drug screening. The next step is to build a digital twin for each PDCL, enabling personalized prediction of combination therapies. A previously developed quantitative systems pharmacology (QSP) model of TMZ pharmacokinetics-pharmacodynamics serves as a foundation. To initiate model individualization, we developed a method to personalize model parameters using publicly available multi-omics and TMZ cytotoxicity data. Current work focuses on integrating proteomics-derived key species into the core model, via network reconstruction methods, to obtain PDCL-specific digital twins and infer personalized treatment by combining QSP and machine learning. This integrative approach, combining data analysis, network reconstruction, and mechanistic modeling, opens the path for efficient patient specific therapies in GBM.

#SMB2025 Follow

Annual Meeting for the Society for Mathematical Biology, 2025.