van Hasselt, C. J. G. | bioRxiv Science

bioRxiv2025

Aggregate data modelling: a fast implementation for fitting pharmacometrics models to summary-level data in R

Pharmacometric modelling is traditionally performed using individual level data. Recently a new method was developed to fit pharmacometric models to summary level - or aggregate - data. This methodology allows for jointly modelling different data sources, once transformed into aggregate data. As such, the method can be applied to a combination of individual data, pharmacometric models, and aggregate data. In this study we aimed to (1) implement this methodological framework into an accessible R package (admr) and (2) develop a novel algorithm with enhanced computational efficiency. The developed R-package allows calculating aggregate data from different data sources, jointly fitting one or multiple data sources and assessing model performance. The implementation of the newly developed algorithm improves computational efficiency by iteratively reweighting internal Monte Carlo predictions. Three simulation scenarios using different data generating models demonstrated an improvement of 3 to 100-fold speed-up when using the novel Iterative Reweighting Monte Carlo (IR-MC) algorithm, while maintaining the convergence properties of the original MC algorithm. These analyses demonstrated that estimation with the IR-MC algorithm is increasingly more efficient as model complexity rises as compared to the standard MC algorithm, indicating the utility for more complex pharmacometric models. In conclusion, the aggregate data modelling implementation in the admr R package allows for a fast and user-friendly application of the aggregate data modelling framework.

pharmacology and toxicology↗

bioRxiv2025

Divergent spontaneous antibiotic-resistance evolution confers reciprocal and exploitable collateral sensitivity effects

The global rise of antibiotic-resistant pathogens has outpaced the development of new antibiotics, prompting the urgent need for alternative treatment strategies. One such approach is to leverage collateral sensitivity (CS), where resistance to one antibiotic increases susceptibility to another. However, the clinical implementation of CS-based therapies depends on the consistency of these responses, which is challenged by variable resistance mutations and the dynamics of resistant strains during infection. Here, we combined experiments and mathematical models to assess the consistency and consequences of CS responses in the Gram-positive pathogen Streptococcus pneumoniae following the de novo acquisition of resistance to five commonly used antibiotics. We found that many collateral responses were unpredictable and inconsistent between different resistance mutations. However, for two antibiotic pairs, we identified consistent unidirectional (RIF [->] FUS) and bidirectional (LNZ {leftrightarrow} FUS) CS interactions, despite the divergent evolutionary trajectories of resistant strains, as revealed by whole-genome sequencing. To evaluate if CS for these combinations can be exploited to design dosing strategies to eradicate S. pneumoniae infections while suppressing resistance, we developed a mathematical stochastic pharmacokinetic-pharmacodynamic (PK-PD) model, which integrated our experimentally derived PD parameters with existing clinical PK models. Our model-based analyses confirmed the superiority of these antibiotic combinations over monotherapy and showed that their efficacy depends on the presence of CS interactions between the administered antibiotics. In summary, our study demonstrates how consistent CS interactions can be leveraged to inform treatment strategies, laying the groundwork for CS-guided therapies to preserve antibiotic efficacy.

microbiology↗