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... Show moreMorphine 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. Show less
Bock, M.; Theut, A.M.; Hasselt, J.G.C. van; Wang, H.; Fuursted, K.; Høiby, N.; ... ; Moser, C. 2023
BACKGROUND\nMETHODS\nRESULTS\nCONCLUSION\nIn the POET (Partial Oral Endocarditis Treatment) trial, oral step-down therapy was noninferior to full-length intravenous antibiotic administration. The... Show moreBACKGROUND\nMETHODS\nRESULTS\nCONCLUSION\nIn the POET (Partial Oral Endocarditis Treatment) trial, oral step-down therapy was noninferior to full-length intravenous antibiotic administration. The aim of the present study was to perform pharmacokinetic/pharmacodynamic analyses for oral treatments of infective endocarditis to assess the probabilities of target attainment (PTAs).\nPlasma concentrations of oral antibiotics were measured at day 1 and 5. Minimal inhibitory concentrations (MICs) were determined for the bacteria causing infective endocarditis (streptococci, staphylococci, or enterococci). Pharmacokinetic/pharmacodynamic targets were predefined according to literature using time above MIC or the ratio of area under the curve to MIC. Population pharmacokinetic modeling and pharmacokinetic/pharmacodynamic analyses were done for amoxicillin, dicloxacillin, linezolid, moxifloxacin, and rifampicin, and PTAs were calculated.\nA total of 236 patients participated in this POET substudy. For amoxicillin and linezolid, the PTAs were 88%-100%. For moxifloxacin and rifampicin, the PTAs were 71%-100%. Using a clinical breakpoint for staphylococci, the PTAs for dicloxacillin were 9%-17%.Seventy-four patients at day 1 and 65 patients at day 5 had available pharmacokinetic and MIC data for two oral antibiotics. Of those, 13 patients at day 1 and 14 patients at day 5 did only reach the target for one antibiotic. One patient did not reach target for any of the two antibiotics.\nFor the individual orally administered antibiotic, the majority of patients reached the target level. Patients with sub-target levels were compensated by the administration of two different antibiotics. The findings support the efficacy of oral step-down antibiotic treatment in patients with infective endocarditis. Show less
As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus... Show moreAs a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data. Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients. The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes. By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population. Show less
Fu, Y.; Snelder, N.; Guo, T.; Graaf, P.H. van der; Hasselt, J.G.C. van 2023
Early prediction, quantification and translation of cardiovascular hemodynamic drug effects is essential in pre-clinical drug development. In this study, a novel hemodynamic cardiovascular systems ... Show moreEarly prediction, quantification and translation of cardiovascular hemodynamic drug effects is essential in pre-clinical drug development. In this study, a novel hemodynamic cardiovascular systems (CVS) model was developed to support these goals. The model consisted of distinct system- and drug-specific parameter, and uses data for heart rate (HR), cardiac output (CO), and mean atrial pressure (MAP) to infer drug mode-of-action (MoA). To support further application of this model in drug development, we conducted a systematic analysis of the estimation performance of the CVS model to infer drug- and system-specific parameters. Specifically, we focused on the impact on model estimation performance when considering differences in available readouts and the impact of study design choices. To this end, a practical identifiability analysis was performed, evaluating model estimation performance for different combinations of hemodynamic endpoints, drug effect sizes, and study design characteristics. The practical identifiability analysis showed that MoA of drug effect could be identified for different drug effect magnitudes and both system- and drug-specific parameters can be estimated precisely with minimal bias. Study designs which exclude measurement of CO or use a reduced measurement duration still allow the identification and quantification of MoA with acceptable performance. In conclusion, the CVS model can be used to support the design and inference of MoA in pre-clinical CVS experiments, with a future potential for applying the uniquely identifiable systems parameters to support inter-species scaling. Show less
Mehta, K.M.; Guo, T.; Graaf, P.H. van der; Hasselt, J.G.C. van 2023
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... Show moreBACKGROUND\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. Show less
Aulin, L.B.S.; Tandar, S.T.; Zijp, T. van; Ballegooie, E. van; Graaf, P.H. van der; Saleh, M.A.A.E.W.; ... ; Hasselt, J.G.C. van 2022
Prediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was... Show morePrediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).\nThe modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.\nIn the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.\nThe developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials. Show less
Liu, F.; Aulin, L.B.S.; Kossen, S.S.A.; Cathalina, J.E.J.; Bremmer, M.; Foks, A.C.; ... ; Hasselt, J.G.C. van 2022
Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood.... Show moreSepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis. Show less
Mehta, K.M.; Guo, T.; Wallis, R.S.; Graaf, P.H. van der; Hasselt, J.G.C. van 2022
Quantitative systems pharmacology (QSP) modeling of the host immune response against Mycobacterium tuberculosis can inform the rational design of host-directed therapies (HDTs). We aimed to develop... Show moreQuantitative systems pharmacology (QSP) modeling of the host immune response against Mycobacterium tuberculosis can inform the rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy induction in combination with antibiotics. A QSP framework for autophagy was developed by extending a model for host immune response to include adenosine monophosphate-activated protein kinase (AMPK)-mTOR-autophagy signaling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against M. tuberculosis. We compared the model predictions to mice infection experiments and derived predictions for the pathogen- and host-associated dynamics in humans treated with metformin in combination with antibiotics. The model adequately captured the observed bacterial load dynamics in mice M. tuberculosis infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reduction of the intracellular bacterial load when the overall bacterial load is low, late during antibiotic treatment. We present the first QSP framework for HDTs against M. tuberculosis, linking cellular-level autophagy effects to disease progression and adjunctive HDT treatment response. This framework may be extended to guide the design of HDTs against M. tuberculosis. Show less
Liu, F.; Aulin, L.B.S.; Guo, T.; Krekels, E.H.J.; Moerland, M.; Graaf, P.H. van der; Hasselt, J.G.C. van 2022
Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like... Show moreClinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor alpha (TNF-alpha), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h(-1) and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-alpha, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans. Show less
Kok, M.; Maton, L.; Peet, M.P. van der; Hankemeier, T.; Hasselt, J.G.C. van 2022
The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic... Show moreThe emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. Show less
Aulin, L.B.S.; Kleijburg, A.; Moerland, M.; Hasselt, J.G.C. van 2022
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... Show moreIn 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. Show less
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... Show moreCollateral 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. Show less
Maas, P.; Hartog, I. den; Kindt, A.S.D.; Boman, S.; Hankemeier, T.; Hasselt, J.G.C. van 2022
Immunometabolism, which concerns the interplay between metabolism and the immune system, is increasingly recognized as a potential source of novel drug targets and biomarkers. In this context, the... Show moreImmunometabolism, which concerns the interplay between metabolism and the immune system, is increasingly recognized as a potential source of novel drug targets and biomarkers. In this context, the use of metabolomics to identify metabolic characteristics associated with specific functional immune response processes is of value. Currently, there is a lack of tools to determine known associations between metabolites and immune processes. Consequently, interpretation of metabolites in metabolomics studies in terms of their role in the immune system, or selection of the most relevant metabolite classes to include in metabolomics studies, is challenging. Here, we describe the Immunometabolic Atlas (IMA), a public web application and library of R functions to infer immune processes associated with specific metabolites and vice versa. The IMA derives metabolite-immune process associations utilizing a protein-metabolite network analysis algorithm that associates immune system-associated annotated proteins in Gene Ontology to metabolites. We evaluated IMA inferred metabolite-immune system associations using a text mining strategy, identifying substantial overlap, but also demonstrating a significant chemical space of immune system-associated metabolites that should be confirmed experimentally. Overall, the IMA facilitates the interpretation and design of immunometabolomics studies by the association of metabolites to specific immune processes. Show less
Mehta, K.; Spaink, H.P.; Ottenhoff, T.H.M.; Graaf, P.H. van der; Hasselt, J.G.C. van 2022
Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB)... Show moreHost-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies. Show less
The use of systems-based pharmacological modeling approaches to characterize mode-of-action and concentration-effect relationships for drugs on specific hemodynamic variables has been demonstrated.... Show moreThe use of systems-based pharmacological modeling approaches to characterize mode-of-action and concentration-effect relationships for drugs on specific hemodynamic variables has been demonstrated. Here, we (i) expand a previously developed hemodynamic system model through integration of cardiac output (CO) with contractility (CTR) using pressure-volume loop theory, and (ii) evaluate the contribution of CO data for identification of system-specific parameters, using atenolol as proof-of-concept drug. Previously collected experimental data was used to develop the systems model, and included measurements for heart rate (HR), CO, mean arterial pressure (MAP), and CTR after administration of atenolol (0.3-30 mg/kg) from three in vivo telemetry studies in conscious Beagle dogs. The developed cardiovascular (CVS)-contractility systems model adequately described the effect of atenolol on HR, CO, dP/dtmax, and MAP dynamics and allowed identification of both system- and drug-specific parameters with good precision. Model parameters were structurally identifiable, and the true mode of action can be identified properly. Omission of CO data did not lead to a significant change in parameter estimates compared to a model that included CO data. The newly developed CVS-contractility systems model characterizes short-term drug effects on CTR, CO, and other hemodynamic variables in an integrated and quantitative manner. When the baseline value of total peripheral resistance is predefined, CO data was not required to identify drug- and system-specific parameters. Confirmation of the consistency of system-specific parameters via inclusion of data for additional drugs and species is warranted. Ultimately, the developed model has the potential to be of relevance to support translational CVS safety studies. Show less
Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this... Show moreQuantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (TTS) was prolonged from 52 to 60 weeks. Extending the treatment holiday resulted in inferior outcomes. The simulated adaptive regimens showed to further prolong median PFS to 56-64 weeks and TTS to 114-132 weeks under different treatment designs. A prospective clinical study is required to validate the results and to confirm the added value of the suggested schedules. Show less
Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this... Show moreQuantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (T-TS) was prolonged from 52 to 60 weeks. Extending the treatment holiday resulted in inferior outcomes. The simulated adaptive regimens showed to further prolong median PFS to 56–64 weeks and TTS to 114–132 weeks under different treatment designs. A prospective clinical study is required to validate the results and to confirm the added value of the suggested schedules. Show less
Background\Objectives\Methods\Results\Conclusions\Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to... Show moreBackground\Objectives\Methods\Results\Conclusions\Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking.\nTo develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data. We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database. We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions. Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future. Show less
