Biology subjects

Aulin, L. B. S.

Publications and source records attributed to Aulin, L. B. S..

6 recordsLinked to original sources

bioRxiv2026

Segmented regression to streamline semi-mechanistic pharmacodynamic modelling of antibiotic effects

Purpose Semi-mechanistic pharmacodynamic (PD) models quantify bacterial dynamics from time-kill (TK) assays, often showing multi-phasic kill and regrowth dynamics. Regrowth is typically captured with two subpopulations with distinct concentration-effect relationships. Since only total bacterial counts are observed, subpopulation dynamics must be inferred, causing identifiability issues, sensitivity to initial estimates, and model-testing burden. We aimed to establish an efficient and robust approach for developing semi-mechanistic PD models, demonstrated with 24-hour TK data for colistin against Pseudomonas aeruginosa. Methods A three-step workflow was developed: (1) log-transformed TK data were segmented into kill and regrowth phases, with linear regression estimating phase-specific slopes per concentration and replicate; (2) empirical slopes were pooled to derive concentration-effect relationships; (3) selected relationships and parameter estimates were incorporated into a semi-mechanistic PD model. Results Segmented regression captured kill and regrowth slopes at all concentrations. Slope saturation with increasing colistin and consistently weaker drug effect during regrowth indicated a less susceptible subpopulation, motivating a two-subpopulation model. The semi-mechanistic model adequately described bacterial dynamics at all concentrations, with parameters comparable to Step 2. Conclusion This three-step workflow enables efficient and robust identification of concentration-effect relationships, reducing model testing and computational burden for streamlined semi-mechanistic antibiotic PD model development.

pharmacology and toxicology↗

bioRxiv2025

The influence of heteroresistance, growth and antibiotic selection in shaping the invasion dynamics of colistin resistant Pseudomonas aeruginosa

Heteroresistance, where a small subpopulation of phenotypically resistant cells coexists within an otherwise susceptible population, plays a critical role in bacterial survival during antibiotic exposure. Yet, its influence on the invasion success of genetically resistant strains remains poorly understood. In this study, we investigate the influence of bacterial heteroresistance, growth, and antibiotic selection on the outcome of an invasion experiment. We quantified the invasion dynamics of a bioluminescent colistin-resistant Pseudomonas aeruginosa strain across a colistin concentration gradient during co-culture with colistin-susceptible clinical P. aeruginosa isolates with varying levels of heteroresistance. The observed variation in heteroresistance and fitness of the clinical isolates allowed investigation of the impact of these factors on invasion dynamics. We hypothesized that fitter isolates would limit invasion through competitive exclusion, while heteroresistant isolates, despite their growth costs, may serve as reservoirs for resistance evolution under antibiotic selection. Our results show that in antibiotic-free conditions, faster-growing isolates competitively excluded the colistin resistant invader while isolates with high levels of heteroresistance failed to do so. This competitive landscape shifted with increasing colistin concentration, giving the invader a fitness advantage that peaked around 2-4xMIC of the clinical isolate. The shift in the landscape over the colistin gradient might further have been influenced by the presence of heteroresistance in the clinical isolates. These findings reveal how both heteroresistance and competitive exclusion shapes invasion dynamics. Understanding these interactions is critical for redesigning treatment strategies that minimize ecological opportunities for resistant strains to establish and expand.

microbiology↗

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↗

bioRxiv2024

Pharmacodynamic modeling of colistin and imipenem against in vitro Pseudomonas aeruginosa biofilms

IntroductionAntibiotic treatment of chronic biofilm-associated infections can be challenging. Characterization of pharmacokinetic-pharmacodynamic (PK-PD) relationships for biofilm-associated infections may be relevant to inform the design of antibiotic treatment regimens for biofilm-associated infections. To this end, we aim to develop a mathematical PK-PD model for planktonic and biofilm bacterial infections and demonstrate how PK-PD simulations can be used to design optimized dosing schedules, using imipenem and colistin as proof-of-concept examples. MethodsPharmacodynamic models were developed using time-kill assay data from planktonic and alginate-bead biofilm cultures of Pseudomonas aeruginosa exposed to imipenem or colistin. The PD models were coupled to population PK models for plasma and epithelial lining fluid (ELF) to translate PD relationships for clinical dosing schedules and PK-PD indices. ResultsThe developed models incorporated sensitive and resistant bacterial subpopulations and were able to adequately capture the observed time-kill data. Simulation studies identified differences in suppression of bacterial growth dynamics for multiple clinical intravenous and inhalation-based treatment regimens and were used to infer biofilm-specific PK-PD indices associated with ELF target site concentrations. ConclusionIn conclusion, we demonstrate the utility of mathematical modeling for the characterization of PK-PD relationships underlying time-kill kinetic profiles in biofilm-associated infections and their utility in translating experimental findings to inform the optimization of clinical dosing schedules.

pharmacology and toxicology↗

bioRxiv2021

Design principles of collateral sensitivity-based dosing strategies

Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance.

pharmacology and toxicology↗

bioRxiv2020

Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone-resistance evolution

Collateral sensitivity (CS), which arises when resistance to one antibiotic increases sensitivity towards other antibiotics, offers novel treatment opportunities to constrain or reverse the evolution of antibiotic-resistance. The applicability of CS-informed treatments remains uncertain, in part because we lack an understanding of the generality of CS effects for different resistance mutations, singly or in combination. Here we address this issue in the Gram-positive pathogen Streptococcus pneumoniae by measuring collateral and fitness effects of clinically relevant gyrA and parC alleles, and their combinations, that confer resistance to fluoroquinolones. We integrated these results in a mathematical model which allowed us to evaluate how different in silico combination treatments impact the dynamics of resistance evolution. We identified common and conserved CS effects of different gyrA and parC alleles; however, the spectrum of collateral effects was unique for each allele or allelic pair. This indicated that allelic identity can impact the evolutionary dynamics of resistance evolution during monotreatment and combination treatment. Our model simulations, which included the experimentally derived antibiotic susceptibilities and fitness effects, and antibiotic specific pharmacodynamics, revealed that both collateral and fitness effects impact the population dynamics of resistance evolution. Overall, we provide evidence that allelic identity and interactions can have a pronounced impact on collateral effects to different antibiotics and suggest that these need to be considered in models examining CS-based therapies. SignificanceA promising strategy to overcome the evolution of antibiotic resistant bacteria is to use collateral sensitivity-informed antibiotic treatments that rely on cycling or mixing of antibiotics, such that that resistance towards one antibiotic confers increased sensitivity to the other. Here, focusing on multi-step fluoroquinolone resistance in Streptococcus pneumoniae, we show that antibiotic-resistance induces diverse collateral responses whose magnitude and direction are determined by allelic identity. Using mathematical simulations, we show that these effects can be exploited via combination treatment regimens to suppress the de novo emergence of resistance during treatment.

microbiology↗