Biology subjects

van Hasselt, J. G. C.

Publications and source records attributed to van Hasselt, J. G. C..

14 recordsLinked to original sources

bioRxiv2026

Collateral effects of bacteriophage resistance on antibiotic sensitivity in Pseudomonas aeruginosa

Collateral effect (CEs) of phage resistance can alter antibiotic sensitivity, leading to collateral sensitivity (CS). CS of phage resistance can potentially be used to design more effective phage--antibiotic combination treatments, yet insufficient knowledge on the occurrence of such phage resistance associated CS is currently available. Here, we characterized CEs across diverse Pseudomonas aeruginosa strains, antibiotics, and phages. For each of eight strains (seven clinical isolates and PAO1), we generated mutants resistant to phage PAO1-1 and, separately, mutants resistant to each of six antibiotics. Phage-resistant mutants were also generated using four additional phages (M32, NP1, NP3, and PNM), and double phage- and antibiotic-resistant mutants from one strain. We measured growth curves across antibiotic concentration ranges after which the area-under-the-curve (AUC) was quantified as a measure of bacterial burden. Dose-response curves for AUC against antibiotic concentrations were constructed to estimate a fold change in EC50 of mutants relative to their parental strains. PAO1-1-resistant mutants showed significant CS to colistin and polymyxin B. For other antibiotics, CEs varied by phage and parental strain: PAO1-1-resistant mutants showed CS to tobramycin, whereas mutants resistant to the other four phages tested were associated with CR. For double phage- and antibiotic resistant mutants we found CS to colistin and tobramycin relative to the parental strain. Resistance mutations occurred predominantly in genes involved in metabolism, virulence, and DNA replication. Antibiotic-resistant mutants also showed increased phage sensitivity, with the strongest effect in colistin-resistant mutants. In conclusion this study demonstrates collateral effects of phage resistance are most specific to both strain- and antibiotic-specific.

pharmacology and toxicology↗

bioRxiv2026

From exact gradients to exact standard errors: third-order sensitivity equations for the FOCE and FOCEI population likelihood

Asymptotic standard errors for the parameters of a nonlinear mixed-effects model fitted by first-order conditional estimation (FOCE) or FOCE with interaction (FOCEI) require the observed (Fisher) information -- the negative second derivative of the population objective at the optimum. The gradient of this objective can be computed exactly from sensitivity equations, but the observed information is conventionally still formed by finite differencing, which is less accurate and step-size dependent. Our objectives are to (i) derive the FOCE and FOCEI observed information in closed form within the same sensitivity-equation framework, and (ii) quantify the precision this recovers. Writing the objective as a data term plus the log-determinant of the first-order inner Hessian, the population Hessian splits so that the data term reuses the second-order sensitivities already needed for the gradient, whereas the log-determinant term requires third-order sensitivity equations -- confining the third-order dependence to a single term, where it enters in exactly two places. A finite-difference error analysis shows the differenced Hessian attains an accuracy no better than the square root of the objectives evaluation accuracy, whereas the analytic form is limited only by the sensitivity and differential-equation solutions, with no step size to tune. We illustrate this on a one-compartment oral model with first-order absorption fitted to warfarin data, where the differenced standard error is usable only over a narrow band of step sizes while the analytic value carries none. Implemented in the open-source R package nlmixr2, the method makes exact, reproducible standard errors routine, supporting more dependable confidence intervals, identifiability assessment, and uncertainty propagation.

pharmacology and toxicology↗

bioRxiv2026

Accelerating drug development in infectious diseases using zebrafish disease models supported by pharmacokinetic pharmacodynamic modeling as new approach methodology (NAM)

The development of novel therapeutics for infectious diseases remains a global health priority. To accelerate the treatment development, innovative strategies through new approach methodology (NAM) are needed to bridge speed of in vitro with predictive power of in vivo studies, while reducing mammalian experiments. The zebrafish (Danio rerio), particularly the embryo/larva, has been established as a valuable non-mammalian in vivo model in biomedical research. We developed a standardized and streamlined workflow for the zebrafish as NAM, which consisted of 3 steps: drug selection and efficacy evaluation, internal exposure assessment, and PKPD modelling. Compounds with higher tolerated doses than minimum inhibitory concentration were selected. Drug efficacy was quantified through longitudinal individual fluorescence microscopy at baseline and 24 and 48h on treatment. Drug exposure was quantified in larval homogenates and exposure medium from 0-48h on treatment. The PKPD relationship was quantified by non-linear mixed effects modelling. For case study bedaquiline, PKPD was quantified using a one-compartment model with age-depending elimination, and an Emax concentration-response relationship on the delayed logistic bacterial growth function, with an EC50 of 26.6 {micro}g/mL and an Emax of 1.07-1.37. In the case of clarithromycin, in contrast, negligible internal exposure after waterborne treatment were observed, illustrating the risk of false negatives without internal exposure assessments. Bactericidal efficacy was confirmed by intravenous drug injections, showing a clear dose dependent antimycobacterial effect. The standardized zebrafish NAM workflow presented here facilitates the translation of drug efficacy to higher vertebrates, reducing rodent studies to confirmatory or replacing them completely, thus accelerating drug development.

pharmacology and toxicology↗

bioRxiv2026

A Novel Hollow Fiber Infection Model (HFIM) for Antiviral PK/PD studies of CMV infection.

The hollow fiber infection model (HFIM) is a translational in vitro model that links time-varying human pharmacokinetic profiles to the associated viral dynamic responses, from which pharmacokinetic/pharmacodynamic (PK/PD) targets can be derived. Establishing such targets is essential for antiviral dose selection and optimization. This is particularly important for cytomegalovirus (CMV) infection treatment, which primarily affects vulnerable patient populations. PK/PD targets for ganciclovir, the first-line drug for treatment, are not yet defined. The lack of an undefined PK/PD target makes dose optimization challenging and may result in suboptimal exposure, prolonged toxicity, and the emergence of resistance. For the first time, we have demonstrated the use of a low-cost hemodialyzer hollow fiber cartridge with application for CMV infection using ganciclovir. We have established a system that 1) supports CMV culture for PD analysis, 2) reproduces a clinically relevant ganciclovir PK profile, and 3) maintains consistent drug exposure in the infected cells, allowing reliable PK/PD analysis. Quantitative methods such as tissue culture infectious dose 50% (TCID50) and quantitative PCR were used to assess both active virus replication and genome copies production. Ganciclovir PK was measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This validation study serves as a fundamental step that can allow further PK/PD studies for ganciclovir and other antiviral agents that is still largely understudied. Consequently, this model could provide an affordable and practical platform for establishing clinically relevant PK/PD targets and guide treatment optimization.

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

Late treatment initiation leads to reduced antiviral potency

The timing of initiation is critical in antiviral treatment and viral dynamic (VD) modeling is a powerful tool to study the within-host viral load changes and evaluate antiviral treatment effects using mathematical equations. Previous simulation studies have shown that early treatment initiation is critical to maximize the therapeutic response in antiviral treatment in an acute viral infection such as influenza and SARS-CoV-2. A recent experimental study demonstrated that late therapy initiation can lead to diminished antiviral potency. However, most VD model simulations with varying treatment initiation time accounted only for the effect of initiation condition (i.e., state of different cell populations when the therapy started), the loss of drug potency has been under-investigated. This may overestimate the antiviral effect, potentially resulting in suboptimal dose selection. To this end, we aimed to characterize relationship between the drug potency (EC50) and the timing of drug addition, using nirmatrelvir and GS-441524 against SARS-CoV-2 as an example. Viral load data were obtained from in vitro experiments with various drug concentrations and treatment initiated between 0 to 3 days post infection. EC50 values were fitted for each treatment initiation group and were found to vary with the timing of treatment initiation in both drugs. Also, a VD model with time-varying EC50 provided better fits than a constant EC50 model (BIC = 1667.90 vs. 1677.84). Further simulations also indicated that a constant EC50 model overestimated the antiviral efficacy when treatment started late. These findings highlighted the importance of considering EC50 shift when optimizing dosage regimens for patients presenting late.

pharmacology and toxicology↗

bioRxiv2024

The endocannabinoid 2-arachidonoylglycerol is released and transported on demand via extracellular microvesicles

While it is known that endocannabinoids (eCB) modulate multiple neuronal functions, the molecular mechanism governing their release and transport remains elusive. Here, we propose an "on-demand release" model, wherein the formation of microvesicles, a specific group of extracellular vesicles (EVs) containing the eCB, 2-arachidonoylglycerol (2-AG), is the rate-limiting step. A co-culture model system that combines a reporter cell line expressing the fluorescent eCB sensor, GRABeCB2.0, and neuronal cells revealed that neurons release EVs containing 2-AG, but not anandamide, in a stimulus-dependent process regulated by PKC, DAGL, Arf6, and which was sensitive to inhibitors of eCB facilitated diffusion. A vesicle contained approximately 2000 2-AG molecules. Accordingly, hippocampal eCB-mediated synaptic plasticity was modulated by Arf6 and transport inhibitors. This "on demand release" model, supported by mathematical analysis, offers a cohesive framework for understanding eCB signaling at the molecular level and suggests that microvesicles carrying signaling lipids regulate neuronal functions in parallel to canonical synaptic vesicles.

neuroscience↗

bioRxiv2024

Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa

Polymicrobial infections are infections that are caused by multiple pathogens, and are common in patients with cystic fibrosis (CF). Although polymicrobial infections are associated with poor treatment responses in CF, the effects of the ecological interactions between co-infecting pathogens on antibiotic sensitivity and treatment outcome are poorly characterized. To this end, we systematically quantified the impact of these effects on the antibiotic sensitivity of Pseudomonas aeruginosa for nine antibiotics in the presence of thirteen secondary cystic fibrosis-associated bacterial and fungal pathogens through time-kill assays. We fitted pharmacodynamic models to these kill curves for each antibiotic-species combination and found that interspecies interactions changing the antibiotic sensitivity of P. aeruginosa are abundant. Interactions that lower antibiotic sensitivity are more common than those that increase it, with generally more substantial reductions than increases in sensitivity. For a selection of co-infecting species, we performed pharmacokinetic-pharmacodynamic modelling of P. aeruginosa treatment. We predicted that interspecies interactions can either improve or reduce treatment response to the extent that treatment is rendered ineffective from a previously effective antibiotic dosing schedule and vice versa. In summary, we show that quantifying the ecological interaction effects as pharmacodynamic parameters is necessary to determine the abundance and the extent to which these interactions affect antibiotic sensitivity in polymicrobial infections. ImportanceIn cystic fibrosis (CF) patients, chronic respiratory tract infections are often polymicrobial, involving multiple pathogens simultaneously. Polymicrobial infections are difficult to treat as they often respond unexpectedly to antibiotic treatment, which might possibly be explained because co-infecting pathogens can influence each others antibiotic sensitivity, but it is unknown to what extent such effects occur. To investigate this, we systematically quantified the impact of co-infecting species on antibiotic sensitivity, focusing on P. aeruginosa, a common CF pathogen. We studied for a large set co-infecting species and antibiotics whether changes in antibiotic response occur. Based on these experiments, we used mathematical modeling to simulate P. aeruginosas response to colistin and tobramycin treatment in the presence of multiple pathogens. This study offers comprehensive data on altered antibiotic sensitivity of P. aeruginosa in polymicrobial infections, serves as a foundation for optimizing treatment of such infections, and consolidates the importance of considering co-infecting pathogens.

pharmacology and toxicology↗

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↗

bioRxiv2023

Does nonlinear blood-brain barrier transport matter for morphine dosing strategies?

Morphine blood-brain barrier (BBB) transport is governed by passive diffusion, active efflux and saturable active influx. These processes may be associated with nonlinear concentration-dependencies which impact plasma and brain extracellular fluid (brainECF) pharmacokinetics of morphine. In this study, we aim to evaluate the impact of nonlinear BBB transport on brainECF pharmacokinetics of morphine and its metabolites for different dosing strategies using a physiologically based pharmacokinetic simulation study. We extended the human physiologically based pharmacokinetic, LeiCNS-PK3.0, model with equations for nonlinear BBB transport of morphine. Simulations for brainECF pharmacokinetics were performed for various dosing strategies: intravenous (IV), oral immediate (IR) and extended release (ER) with dose range of 0.25-150mg and dosing frequencies of 1-6 times daily. The impact of nonlinear BBB transport on morphine CNS pharmacokinetics was evaluated by quantifying (i) the relative brainECF to plasma exposure (AUCu,brainECF/AUCu,Plasma) and (ii) the impact on the peak-to-trough ratio (PTR) of concentration-time profiles in brainECF and plasma. We found that the relative morphine exposure and PTRs are dose dependent for the evaluated dose range. The highest relative morphine exposure value of 1.4 was found for once daily 0.25mg ER and lowest of 0.1 for 6-daily 150mg IV dosing. At lower doses the PTRs were smaller and increased with increasing dose and stabilized at higher doses independent of dosing frequency. Relative peak concentrations of morphine in relation to its metabolites changed with increasing dose. We conclude that nonlinearity of morphine BBB transport affect the relative brainECF exposure and the fluctuation of morphine and its metabolites. HighlightsNonlinear transport affects relative morphine exposure in brainECF. Nonlinear transport affects PK fluctuations of morphine in brainECF. Nonlinear transport affects brainECF PK relationship of morphine and its metabolites. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

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↗

bioRxiv2020

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

bioinformatics↗

bioRxiv2017

A Comparison of mRNA Sequencing with Random Primed and 3’-Directed Libraries

Deep mRNA sequencing (mRNAseq) is the state-of-the-art for whole transcriptome measurements. A key step is creating a library of cDNA sequencing fragments from RNA. This is generally done by random priming, creating multiple sequencing fragments along the length of each transcript. A 3 end-focused library approach cannot detect differential splicing, but has potentially higher throughput at lower cost (~10-fold lower), along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) to correct for PCR bias. Here, we compare implementation of such a 3-digital gene expression (3-DGE) approach with \"conventional\" random primed mRNAseq, which has not yet been done. We find that while conventional mRNAseq detects ~15% more genes, the resulting lists of differentially expressed genes and therefore biological conclusions and gene signatures are highly concordant between the two techniques. We also find good quantitative agreement on the level of individual genes between the two techniques in terms of both read counts and fold change between two conditions. We conclude that for high-throughput applications, the potential cost savings associated with the 3-DGE approach are a very reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable much larger scale studies focused on not only differential expression analysis, but also quantitative transcriptome profiling. The computational scripts and programs, along with experimental standard operating procedures used in our pipeline presented here, are freely available on our website (www.dtoxs.org).

genomics↗