Characterizing the kinetics of presepsin and associated inflammatory biomarkers in human endotoxemia

In this study, we describe the kinetics of a new potential inflammatory biomarker, presepsin, together with a panel of well-established biomarkers in a human endotoxemia study. We evaluated biomarker correlations and identified combinations that could hold valuable insights regarding the state of infection.Pharmacolog

Predictions of bedaquiline and pretomanid target attainment in lung lesions of tuberculosis patients using translational minimal physiologically based pharmacokinetic modeling

BACKGROUND\nMETHODS\nRESULTS\nCONCLUSIONS\nSite-of-action concentrations for bedaquiline and pretomanid from tuberculosis patients are unavailable. The objective of this work was to predict bedaquiline and pretomanid site-of-action exposures using a translational minimal physiologically based pharmacokinetic (mPBPK) approach to understand the probability of target attainment (PTA).\nA general translational mPBPK framework for the prediction of lung and lung lesion exposure was developed and validated using pyrazinamide site-of-action data from mice and humans. We then implemented the framework for bedaquiline and pretomanid. Simulations were conducted to predict site-of-action exposures following standard bedaquiline and pretomanid, and bedaquiline once-daily dosing. Probabilities of average concentrations within lesions and lungs greater than the minimum bactericidal concentration for non-replicating (MBCNR) and replicating (MBCR) bacteria were calculated. Effects of patient-specific differences on target attainment were evaluated.\nThe translational modeling approach was successful in predicting pyrazinamide lung concentrations from mice to patients. We predicted that 94% and 53% of patients would attain bedaquiline average daily PK exposure within lesions (Cavg-lesion) > MBCNR during the extensive phase of bedaquiline standard (2 weeks) and once-daily (8 weeks) dosing, respectively. Less than 5% of patients were predicted to achieve Cavg-lesion > MBCNR during the continuation phase of bedaquiline or pretomanid treatment, and more than 80% of patients were predicted to achieve Cavg-lung >MBCR for all simulated dosing regimens of bedaquiline and pretomanid.\nThe translational mPBPK model predicted that the standard bedaquiline continuation phase and standard pretomanid dosing may not achieve optimal exposures to eradicate non-replicating bacteria in most patients.Pharmacolog

Model‐based dose optimization framework for bedaquiline, pretomanid, and linezolid for the treatment of drug‐resistant tuberculosis

AIM\nMETHODS\nRESULTS\nCONCLUSION\nBedaquiline, pretomanid, and linezolid combination (BPaL) treatment against Mycobacterium tuberculosis is promising yet safety and adherence concerns exist that motivates exploration of alternative dosing regimens. We developed a mechanistic modeling framework to compare the efficacy of the current and alternative BPaL treatment strategies.\nPharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time-kill data. These models were combined with pharmacokinetic models, incorporating bodyweight, lesion volume, site-of-action distribution, bacterial susceptibility, and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400mg once daily (QD) 14-days followed by 200mg three times a week, linezolid 1200mg QD) and alternative dosing regimens (bedaquiline 200mg QD, linezolid 600mg QD).\nThe framework adequately described the observed anti-bacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming units (CFU)-clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non-replicating-clearance state was predicted to be 15-days from the CFU-clearance state.\nThe model-based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug-resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating- and non-replicating bacteria from lung and lesions.Pharmacolog

Publisher correction: anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance

Pharmacolog

Unraveling the effects of acute inflammation on pharmacokinetics: a model-based analysis focusing on renal glomerular filtration rate and cytochrome P450 3A4-mediated metabolism

UNLABELLED\nMETHODS\nRESULTS\nCONCLUSION\nBACKGROUND AND OBJECTIVES: Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the effect of acute inflammation as represented by interleukin-6 (IL-6) levels on drug clearance, focusing on renal glomerular filtration rate (GFR) and cytochrome P450 3A4 (CYP3A4)-mediated metabolism.\nA physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-time curve (AUC) was computed for these scenarios.\nInflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction.\nThis analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes.Pharmacolog

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

Morphine blood-brain barrier (BBB) transport is governed by passive diffusion, active efflux and saturable active influx. This may result in nonlinear plasma concentration-dependent 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 0.25-150 mg 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 0.25 mg ER and lowest of 0.1 for 6-daily 150 mg 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 affects the relative brainECF exposure and the fluctuation of morphine and its metabolites mainly at lower dosing regimens.Pharmacolog

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.Microbial Biotechnolog

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 toward other antibiotics, offers 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 that 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.Microbial BiotechnologyPharmacolog

Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction

Background and PurposeDevelopment of combination therapies has received significant interest in recent years. Previously, a two‐receptor one‐transducer (2R‐1T) model was proposed to characterize drug interactions with two receptors that lead to the same phenotypic response through a common transducer pathway. We applied, for the first time, the 2R‐1T model to characterize the interaction of noradrenaline and arginine‐vasopressin on vasoconstriction and performed inter‐species scaling to humans using this mechanism‐based model. Experimental ApproachContractile data were obtained from in vitro rat small mesenteric arteries after exposure to single or combined challenges of noradrenaline and arginine‐vasopressin with or without pretreatment with the irreversible α‐adrenoceptor antagonist, phenoxybenzamine. Data were analysed using the 2R‐1T model to characterize the observed exposure–response relationships and drug–drug interaction. The model was then scaled to humans by accounting for differences in receptor density. Key ResultsWith receptor affinities set to published values, the 2R‐1T model satisfactorily characterized the interaction between noradrenaline and arginine‐vasopressin in rat small mesenteric arteries (relative standard error ≤20%), as well as the effect of phenoxybenzamine. Furthermore, after scaling the model to human vascular tissue, the model also adequately predicted the interaction between both agents on human renal arteries. Conclusions and ImplicationsThe 2R‐1T model can be of relevance to quantitatively characterize the interaction between two drugs that interact via different receptors and a common transducer pathway. Its mechanistic properties are valuable for scaling the model across species. This approach is therefore of significant value to rationally optimize novel combination treatments. Pharmacolog