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Objectives: To expand a translational pharmacokinetic-pharmacodynamic (PKPD) modelling approach for assessing the combined effect of polymyxin B and minocycline against Klebsiella pneumoniae., Methods: A PKPD model developed based on in vitro static time-kill experiments of one strain (ARU613) was first translated to characterize that of a more susceptible strain (ARU705), and thereafter to dynamic time-kill experiments (both strains) and to a murine thigh infection model (ARU705 only). The PKPD model was updated stepwise using accumulated data., Results: The same model structure could be used in each translational step, with parameters being re-estimated. Dynamic data were well predicted by static-data-based models. The in vitro - in vivo differences were primarily quantified as a change in polymyxin B effect: a lower killing rate constant in vivo compared to in vitro (concentration of 3 mg/L corresponds to 0.05 /h and 57 /h, respectively), and a slower adaptive resistance rate (the constant in vivo was 2.5% of that in vitro). There was no significant difference in polymyxin B - minocycline interaction functions. Predictions based on both in vitro and in vivo parameters indicated that the combination has a greater-than-monotherapy antibacterial effect in humans, forecasting a reduction of approximately 5 and 2 log 10 CFU/mL at 24 hours, respectively, under combined therapy, while in monotherapy the maximum bacterial load was reached., Conclusions: The study demonstrated the utility of the PKPD modelling approach to understand translation of antibiotic effects across experimental systems and showed a promising antibacterial effect of polymyxin B and minocycline in combination against K. pneumoniae., (Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In vitro time-kill curve (TKC) experiments are an important part of the pharmacokinetic- pharmacodynamic (PKPD) characterisation of antibiotics. Traditional TKCs use Mueller-Hinton broth (MHB), which lacks specific plasma components that could potentially influence the bacterial growth and killing dynamics, and affect translation to in vivo. This study aimed to evaluate the impact of plasma on the PKPD characterisation of two antibiotics; cefazolin and clindamycin. TKC experiments were conducted in pure MHB, and MHB spiked with 20% and 70% human plasma. Plasma protein binding (PPB) data were available, and a linear model described cefazolin's PPB, while clindamycin's PPB was best described by a second-order polynomial model. PKPD models were developed based on pure MHB and described drug effects using an E max model, with consideration of adaptive resistance for cefazolin. The observed bacterial growth and killing in the plasma-spiked MHB TKC data was insufficiently described when applying the developed PPB and PKPD models. In plasma spiked MHB, a growth delay was observed, estimated to 0.25 h (20% plasma), or 2.90 h (70% plasma) for cefazolin, and 0.64 h (20% plasma), or 1.40 h (70% plasma) for clindamycin. Furthermore, the drug effect was higher than expected in plasma-spiked MHB, with bacterial stasis and/or killing at unbound concentrations below MIC, necessitating drug effect parameter scaling (C 50 for cefazolin, Hill coefficient for clindamycin). The findings highlight significant differences in bacterial growth and killing dynamics between pure MHB and plasma-spiked MHB and exemplify how PKPD modelling may be used to improve the translation of in vitro results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The emergence of β-lactamase-producing bacteria has led to the use of β-lactam (BL) antibiotic and β-lactamase inhibitor (BLI) drug combinations. Despite therapeutic drug monitoring (TDM) being endorsed for BLs, the impact of TDM on BLIs remains unclear., Objectives: Evaluate whether BLIs are available in effective exposures at the site of infection and assess if TDM of BLIs could be of interest., Methods: Population pharmacokinetic models for 9 BL and BLI compounds were used to simulate drug concentrations at infection sites following EMA-approved dose regimens, considering plasma protein binding and tissue penetration. Predicted target site concentrations were used for probability of target attainment (PTA) analysis., Results: Using EUCAST targets, satisfactory (≥90%) PTA was observed for BLs in patients with typical renal clearance (CrCL of 80 mL/min) across various sites of infection. However, results varied for BLIs. Avibactam achieved satisfactory PTA only in plasma, with reduced PTAs in abdomen (78%), lung (73%) and prostate (23%). Similarly, tazobactam resulted in unsatisfactory PTAs in intra-abdominal infections (79%), urinary tract infections (64%) and prostatitis (34%). Imipenem-relebactam and meropenem-vaborbactam achieved overall satisfactory PTAs, except in prostatitis and high-MIC infections for the latter combination., Conclusions: This study highlights the risk of solely relying on TDM of BLs, as this can indicate acceptable exposures of the BL while the BLI concentration, and consequently the combination, can result in suboptimal performance in terms of bacterial killing. Thus, dose adjustments also based on plasma concentration measurements of BLIs, in particular for avibactam and tazobactam, can be valuable in clinical practice to obtain effective exposures at the target site., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Objectives: The use of mouse models of complicated urinary tract infection (cUTI) has usually been limited to a single timepoint assessment of bacterial burden. Based on longitudinal in vitro and in vivo data, we developed a pharmacokinetic-pharmacodynamic (PKPD) model to assess the efficacy of apramycin, a broad-spectrum aminoglycoside antibiotic, in mouse models of cUTI., Methods: Two Escherichia coli strains were studied (EN591 and ATCC 700336). Apramycin exposure-effect relationships were established with in vitro time-kill data at pH 6 and pH 7.4 and in mice with cUTI. Immunocompetent mice were treated with apramycin (1.5-30 mg/kg) starting 24 h post-infection. Kidney and bladder tissue were collected 6-96 h post-infection for cfu determination. A PKPD model integrating all data was developed and simulations were performed to predict bacterial burden in humans., Results: Treatment with apramycin reduced the bacterial load in kidneys and bladder tissue up to 4.3-log compared with vehicle control. In vitro and in vivo tissue time-course efficacy data were integrated into the PKPD model, showing 76%-98% reduction of bacterial net growth and 3- to 145-fold increase in apramycin potency in vivo compared with in vitro. Simulations suggested that an 11 mg/kg daily dose would be sufficient to achieve bacterial stasis in kidneys and bladder in humans., Conclusions: PKPD modelling with in vitro and in vivo PK and PD data enabled simultaneous evaluation of the different components that influence drug effect, an approach that had not yet been evaluated for antibiotics in the cUTI model and that has potential to enhance model-informed drug development of antibiotics., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST)., Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario., Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16)., Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address., Competing Interests: Declarations. Funding: Open access funding provided by Uppsala University. This research was funded by the Swedish Cancer Society (CAN 20 1226 PjF, 23 2921 Pj). The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) and National Academic Infrastructure for Super­computing in Sweden (NAISS) at Uppsala University partially funded by the Swedish Research Council through grant agreement no. 2022-06725 and no. 2018-05973. Conflict of interest: The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this manuscript. Availability of data and material: The data that support the findings of this study are not openly accessible, but can be made available from the authors upon reasonable request from the corresponding author L.E.F. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Code availability: The code that support the findings of this study are not openly accessible, but can be made available from the authors upon reasonable request from the corresponding author L.E.F. Author contributions: M.C, J.N. L.E.F., and M.O.K. wrote the manuscript; M.C., L.E.F., and M.O.K. designed the research and analyzed the data; M.C. and J.N. performed the research., (© 2024. The Author(s).)

Background: High-dose methotrexate (HD-MTX)-based polychemotherapy is widely used for patients with central nervous system (CNS) lymphoma. The pharmacokinetic (PK) variability and unpredictable occurrence of toxicity remain major concerns in HD-MTX treatment., Objectives: This study aimed to characterize the population PK of HD-MTX in patients with CNS lymphoma and to identify baseline predictors and exposure thresholds that predict a high risk of nephro- and hepatotoxicity., Methods: Routinely monitored serum MTX concentrations after intravenous infusion of HD-MTX and MTX dosing information were collected retrospectively. Acute event of toxicity (≥ grade 1) was defined according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 on the basis of serum creatinine and alanine aminotransferase. A population PK model was developed in NONMEM. Toxicity data were analyzed using a logistic regression model, and potential baseline and exposure-related predictors were investigated., Results: In total, 1584 MTX concentrations from 110 patients were available for analysis. A two-compartment population PK model adequately described the data. Estimated glomerular filtration rate (eGFR), treatment regimen, albumin, alkaline phosphatase, and body weight were identified as significant covariates that explain the PK variability of HD-MTX. Baseline eGFR and sex were identified as significant predictors for renal toxicity, and MTX dose (mg/m 2 ) was the strongest predictor for hepatotoxicity. The MTX area under the concentration-time curve (AUC 24-∞ ) and concentration at 24 h (C 24h ) were shown to correlate with renal toxicity only, and 49,800 μg/L × h (109.6 μmol/L × h) and C 24h > 3930 μg/L (8.65 μmol/L) were potential exposure thresholds predicting high risk (proportion > 60%)., Conclusions: A population PK model was developed for HD-MTX in patients with CNS lymphoma. The toxicity analysis showed that lower baseline eGFR and male sex, and higher MTX dose are associated with increased risk of acute nephro- and hepatotoxicity, respectively. The proposed exposure thresholds that predict high risk of renal toxicity and the developed models hold the potential to guide HD-MTX dosage individualization and better prevent acute toxicity., Competing Interests: Declarations. Funding: This study is partially supported by a Madeleine Fellowship and LUMC MD/PhD stimulation (F.A.d.G and J.S.P.V.) Conflict of interest: The authors have no competing interests to declare that are relevant to the content of this article. Daan Touw is an Editorial Board member of Clinical Pharmacokinetics. Daan Touw was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. Availability of data: The datasets generated during and/or analyzed during the current study are not openly available owing to reasons of privacy but are available from the corresponding author on reasonable request. Ethical approval: This study is approved by the local Ethical Committee of each institute (number G20.126) and did not fall within the scope of the WMO (Medical Scientific Research Act). A waiver for informed consent was granted. All performed procedures were in accordance with the ethical standards of the institutional medical ethical committee and the 1964 Declaration of Helsinki and its later amendments. Consent to participate: This study is approved by the local Ethical Committee of each institute (number G20.126), and did not fall within the scope of the WMO (Medical Scientific Research Act). A waiver for informed consent was granted. Consent for publication: Not applicable. Code availability: The population modeling analysis was performed with NONMEM (version 7.4.4, ICON Development Solutions, Ellicott City, MD, USA) aided with Perl-speaks-NONMEM (PsN) (version 4.9, Uppsala University, Uppsala, Sweden). Data management and plots generation were performed with R statistics software (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria). Author contributions: Dirk Jan A.R. Moes and Joost S.P. Vermaat contributed to the study conception and design. Fleur A. de Groot, Marcel Nijland, Jeanette K. Doorduijn, Daan J. Touw, Thijs Oude Munnink, Brenda C.M. de Winter, and Anyue Yin contributed to the collection of data. Anyue Yin and Dirk Jan A.R. Moes performed the analysis. Fleur A. de Groot, Lena E. Friberg, Henk-Jan Guchelaar, and Joost S.P. Vermaat contributed to the data analysis and result interpretation. The initial draft of the manuscript was written by Anyue Yin and Dirk Jan A.R. Moes, and all authors commented on the manuscript. All authors reviewed and approved the final manuscript., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: To investigate the pharmacokinetics (PK) of ceftazidime-avibactam (CAZ-AVI) in critically ill patients undergoing continuous venovenous hemodiafiltration (CVVHDF), and compare with a general phase III trial population., Methods: A prospective PK study was conducted in critically ill patients who received CVVHDF for acute kidney injury, treated with CAZ-AVI (1000/250 mg or 2000/500 mg q8h). Plasma and CVVHDF-circuit samples were collected to determine CAZ-AVI concentrations. Individual PK parameters at steady-state were estimated using non-compartmental analysis. For visual comparison, plasma concentrations from CVVHDF patients were overlaid with simulated data from patients not receiving CVVHDF based on previously developed population PK models., Results: A total of 35 plasma samples and 16 CVVHDF-circuit samples were obtained from four patients, with two patients sampled on two separate occasions. Median total clearance and volume of distribution were 4.54 L/h and 73.2 L for CAZ and 10.5 L/h and 102 L for AVI, respectively. Median contribution of CVVHDF to total clearance was 19.8% for CAZ and 5.3% for AVI. Observed CAZ-AVI PK profiles were generally within the 90% confidence interval of model predictions, but the observed concentrations were notably lower early (0-2 h) and higher later (4-8 h) in the dosing interval, suggesting a higher volume of distribution., Conclusions: These results suggest that the CAZ-AVI dose regimens used in this study can be applicable in critically ill patients undergoing CVVHDF, despite the different shape of the PK profiles observed in this population. Further research with a larger patient cohort is warranted to validate and refine these findings., Competing Interests: Declaration of competing interest H.G. has received speaker honoraria from Pfizer and MSD. I.K. has received speaker honoraria from Pfizer. Menarini and bioMérieux. All other authors declare no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative., Objectives: To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome., Methods: A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters., Results: A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range., Conclusions: The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Objectives: Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling., Methods: A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted., Results: The model-predicted PK metrics (AUC, C max , CL, V ss ) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, C max , and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For V ss , the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas V ss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume., Conclusions: These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure., Competing Interests: Declaration of competing interests None declared., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Carbapenem-resistant bacteria pose a threat to public health. Characterising the pharmacokinetics-pharmacodynamics (PKPD) of meropenem longitudinally in vivo against resistant bacteria could provide valuable information for development and translation of carbapenem-based therapies., Objectives: To assess the time course of meropenem effects in vivo against strains with high MIC to predict PK/PD indices and expected efficacy in patients using a modelling approach., Methods: A PKPD model was built on longitudinal bacterial count data to describe meropenem effects against six Escherichia coli and Klebsiella pneumoniae strains (MIC values 32-128 mg/L) in a 24 h mouse thigh infection model. The model was used to derive PK/PD indices from simulated studies in mice and to predict the efficacy of different infusion durations with high-dose meropenem (2 g q8 h/q12 h for normal/reduced kidney function) in patients., Results: Data from 592 mice were available for model development. The estimated meropenem concentration-dependent killing rate was not associated with differences in MIC. The fraction of time that unbound concentrations exceeded EC 50 (fT >EC50 , EC 50 = 1.01 mg/L) showed higher correlations than fT >MIC . For all investigated strains, bacteriostasis at 24 h was predicted for prolonged infusions of high-dose meropenem monotherapy in >90% of patients., Conclusions: The developed PKPD model successfully described bacterial growth and meropenem killing over time in the thigh infection model. For the investigated strains, the MIC, determined in vitro, or MIC-based PK/PD indices, did not predict in vivo response. Simulations suggested prolonged infusions of high-dose meropenem to be efficacious in patients infected by the studied strains., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The majority of bispecific costimulatory antibodies in cancer immunotherapy are capable of exerting tumor-specific T-cell activation by simultaneously engaging both tumor-associated targets and costimulatory receptors expressed by T cells. The amount of trimeric complex formed when the bispecific antibody is bound simultaneously to the T cell receptor and the tumor-associated target follows a bell-shaped curve with increasing bispecific antibody exposure/dose. The shape of the curve is determined by the binding affinities of the bispecific antibody to its two targets and target expression. Here, using the case example of FAP-4-1BBL, a fibroblast activation protein alpha (FAP)-directed 4-1BB (CD137) costimulator, the impact of FAP-binding affinity on trimeric complex formation and pharmacology was explored using mathematical modeling and simulation. We quantified (1) the minimum number of target receptors per cell required to achieve pharmacological effect, (2) the expected coverage of the patient population for 19 different solid tumor indications, and (3) the range of pharmacologically active exposures as a function of FAP-binding affinity. A 10-fold increase in FAP-binding affinity (from a dissociation constant [K D ] of 0.7 nM-0.07 nM) was predicted to reduce the number of FAP receptors needed to achieve 90% of the maximum pharmacological effect from 13,400 to 4,000. Also, the number of patients with colon cancer that would achieve 90% of the maximum effect would increase from 6% to 39%. In this work, a workflow to select binding affinities for bispecific antibodies that integrates preclinical in vitro data, mathematical modeling and simulation, and knowledge on target expression in the patient population, is provided. The early implementation of this approach can increase the probability of success with cancer immunotherapy in clinical development., Competing Interests: JS, CC, CM, TT, AB and NF are employed by F. Hoffmann - La Roche Ltd. and own Roche stock or stock options. L.E.F. works as and advisor for Pharmetheus AB, a company for pharmacometric services. SJ is an employee at Pharmetheus AB, a company for pharmacometric services., (Copyright © 2024 Sanchez, Claus, McIntyre, Tanos, Boehnke, Friberg, Jönsson and Frances.)

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism-driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti-OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology-based model, to capture the long-term effects and variability of therapeutic OSM neutralization on human hematopoiesis. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (I max ) and OSM concentration for 50% suppression (IC 50 ) for platelets (89.1% [95% confidence interval: 83.4-93.0], 6.03 pg/mL [4.41-8.26]) than RBCs (57.0% [49.7-64.0], 2.93 pg/mL [2.55-3.36]). Reduction in hemoglobin and platelets increased erythro- and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM-restricted hematopoiesis. The physiology-based model was substantiated by preclinical data and utilized in exploration of once-weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58-76], 29% [20-38]) and grade 3 (17% [10-25], 3% [0-7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia., (© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Continuous infusion of meropenem has been proposed to increase target attainment in critically ill patients, although stability might limit its practical use. This study investigated the impact of meropenem degradation and infusion bag changes on the concentration-time profiles and bacterial growth and killing of P. aeruginosa given different continuous-infusion solutions., Methods: A semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model quantifying meropenem concentrations (C MEM ) and bacterial counts of a resistant P. aeruginosa strain (ARU552, MIC = 16 mg/L) over 24 h was used to translate in vitro antibiotic effects to patients with severe infections. Concentration-dependent drug degradation of saline infusion solutions was considered using an additional compartment in the population PK model. C MEM , fT >MIC (time that concentrations exceed the MIC) and total bacterial load (B TOT ) after 24 h were simulated for different scenarios (n = 144), considering low- and high-dose regimens (3000/6000 mg/day±loading dose), clinically relevant infusion solutions (20/40/50 mg/mL), different intervals of infusion bag changes (every 8/24 h, q8/24 h), and varied renal function (creatinine clearance 40/80/120 mL/min) and MIC values (8/16 mg/L)., Results: Highest deviations between changing infusion bags q8h and q24h were observed for 50 mg/mL solutions and scenarios with C MEM_24 h close to the MIC, with differences (Δ) in C MEM_24 h up to 4.9 mg/L, ΔfT >MIC ≤65.7%, and ΔB TOT_24h ≤1.1 log10 CFU/mL, thus affecting conclusions on whether bacteriostasis was reached., Conclusions: In summary, this study indicated that for continuous infusion of meropenem, eight-hourly infusion bag changes improved PK/PD target attainment and might be beneficial particularly for high meropenem concentrations of saline infusion solutions and for plasma concentrations in close proximity to the MIC., Competing Interests: Declaration of competing interest None associated with the present work., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption., Methods: Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values., Results: Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (C trough ) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations., Conclusion: Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection., (© 2024. The Author(s).)

The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin-meropenem combination therapy in carbapenem-resistant Gram-negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic-pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The in vivo fitness was the most important factor for change in bacterial count. A model-predicted growth at 24 h of ≥2-log 10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti-infective PKPD data can be integrated through pharmacodynamic modeling and identify patient- and pathogen-specific factors related to clinical outcomes - an approach that may improve understanding of study outcomes., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV])., Methods: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV., Results: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred., Conclusions: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects., (© 2024. The Author(s).)

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MIC CIP 0.023-1 mg/L and MIC CST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC 50 ) in combination was 160% of the EC 50 in monodrug experiments, while for colistin, the change in EC 50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations., (© 2024. The Author(s).)

Asparaginase is an essential component of acute lymphoblastic leukemia (ALL) therapy, yet its associated toxicities often lead to treatment discontinuation, increasing the risk of relapse. Hypersensitivity reactions include clinical allergies, silent inactivation, or allergy-like responses. We hypothesized that even moderate increases in asparaginase clearance are related to later inactivation. We therefore explored mandatory monitoring of asparaginase enzyme activity (AEA) in patients with ALL aged 1-45 years treated according to the ALLTogether pilot protocol in the Nordic and Baltic countries to relate mean AEA to inactivation, to build a pharmacokinetic model to better characterize the pharmacokinetics of peg-asparaginase and assess whether an increased clearance relates to subsequent inactivation. The study analyzed 1631 real-time AEA samples from 253 patients, identifying inactivation in 18.2% of the patients. This inactivation presented as mild allergy (28.3%), severe allergy (50.0%), or silent inactivation (21.7%). A pharmacokinetic transit compartment model was used to describe AEA-time profiles, revealing that 93% of patients with inactivation exhibited prior increased clearance, whereas 86% of patients without hypersensitivity maintained stable clearance throughout asparaginase treatment. These findings enable prediction of inactivation and options for either dose increments or a shift to alternative asparaginase formulations to optimize ALL treatment strategies., (© 2024. The Author(s).)

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance., (© 2024 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: Vincristine-induced peripheral neuropathy (VIPN) is a common adverse effect of vincristine, a drug often used in pediatric oncology. Previous studies demonstrated large inter- and intrapatient variability in vincristine pharmacokinetics (PK). Model-informed precision dosing (MIPD) can be applied to calculate patient exposure and individualize dosing using therapeutic drug monitoring (TDM) measurements. This study set out to investigate the PK/pharmacodynamic (PKPD) relationship of VIPN and determine the utility of MIPD to support clinical decisions regarding dose selection and individualization., Methods: Data from 35 pediatric patients were utilized to quantify the relationship between vincristine dose, exposure and the development of VIPN. Measurements of vincristine exposure and VIPN (Common Terminology Criteria for Adverse Events [CTCAE]) were available at baseline and for each subsequent dosing occasions (1-5). A PK and PKPD analysis was performed to assess the inter- and intraindividual variability in vincristine exposure and VIPN over time. In silico trials were performed to portray the utility of vincristine MIPD in pediatric subpopulations with a certain age, weight and cytochrome P450 (CYP) 3A5 genotype distribution., Results: A two-compartmental model with linear PK provided a good description of the vincristine exposure data. Clearance and distribution parameters were related to bodyweight through allometric scaling. A proportional odds model with Markovian elements described the incidence of Grades 0, 1 and ≥ 2 VIPN overdosing occasions. Vincristine area under the curve (AUC) was the most significant exposure metric related to the development of VIPN, where an AUC of 50 ng⋅h/mL was estimated to be related to an average VIPN probability of 40% over five dosing occasions. The incidence of Grade ≥ 2 VIPN reduced from 62.1 to 53.9% for MIPD-based dosing compared with body surface area (BSA)-based dosing in patients. Dose decreases occurred in 81.4% of patients with MIPD (vs. 86.4% for standard dosing) and dose increments were performed in 33.4% of patients (no dose increments allowed for standard dosing)., Conclusions: The PK and PKPD analysis supports the use of MIPD to guide clinical dose decisions and reduce the incidence of VIPN. The current work can be used to support decisions with respect to dose selection and dose individualization in children receiving vincristine., (© 2023. The Author(s).)

Objectives: Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time-kill experiments and developed an in silico pharmacokinetic/pharmacodynamic (PK/PD) model. The present study assessed the synergistic potential of this antibiotic combination in dynamic experiments., Methods: Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 and 8 mg/L, and minocycline MICs 1 and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72 h dynamic time-kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4× and 8× MIC. WGS was applied to reveal resistance genes and mutations., Results: The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed overall agreement with the in silico predictions., Conclusions: Polymyxin B and minocycline in combination showed synergistic effects, mainly against the KPC-3-producing K. pneumoniae. The agreement between the experimental results and in silico predictions supports the use of PK/PD models based on static time-kill data to predict the activity of antibiotic combinations at dynamic drug concentrations., (© The Author(s) 2023. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Appropriate antibiotic dosing to ensure early and sufficient target attainment is crucial for improving clinical outcome in critically ill patients. Parametric survival analysis is a preferred modeling method to quantify time-varying antibiotic exposure - response effects, whereas bias may be introduced in hazard functions and survival functions when competing events occur. This study investigated predictors of in-hospital mortality in critically ill patients treated with meropenem by pharmacometric multistate modeling. A multistate model comprising five states (ongoing meropenem treatment, other antibiotic treatment, antibiotic treatment termination, discharge, and death) was developed to capture the transitions in a cohort of 577 critically ill patients treated with meropenem. Various factors were investigated as potential predictors of the transitions, including patient demographics, creatinine clearance calculated by Cockcroft-Gault equation (CLCR CG ), time that unbound concentrations exceed the minimum inhibitory concentration (fT >MIC ), and microbiology-related measures. The probabilities to transit to other states from ongoing meropenem treatment increased over time. A 10 mL/min decrease in CLCR CG was found to elevate the hazard of transitioning from states of ongoing meropenem treatment and antibiotic treatment termination to the death state by 18%. The attainment of 100% fT >MIC significantly increased the transition rate from ongoing meropenem treatment to antibiotic treatment termination (by 9.7%), and was associated with improved survival outcome. The multistate model prospectively assessed predictors of death and can serve as a useful tool for survival analysis in different infection scenarios, particularly when competing risks are present., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage., Competing Interests: Declaration of Competing Interest EIKI and LEF declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EBE and SMM are employees of Ultimovacs ASA or Ultimovacs AB. SMM controls shares in Ultimovacs ASA via a privately owned company., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 10 6 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time-course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil-mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil-mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune-competent and immune-compromised mice. Neutrophil-mediated bacterial killing was described by two first-order processes-phagocytosis and digestion-scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 10 6 cells/mL (phagocytosis) and 6.55 × 10 6 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 10 6 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 10 9  colony forming units/g lung). The model-based characterization of neutrophil-mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host-response at the individual level., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

FAP-4-1BBL is a bispecific antibody exerting 4-1BB-associated T-cell activation only while simultaneously bound to the fibroblast activation protein (FAP) receptor, expressed on the surface of cancer-associated fibroblasts. The trimeric complex formed when FAP-4-1BBL is simultaneously bound to FAP and 4-1BB represents a promising mechanism to achieve tumor-specific 4-1BB stimulation. We integrated in vitro data with mathematical modeling to characterize the pharmacology of FAP-4-1BBL as a function of trimeric complex formation when combined with the T-cell engager cibisatamab. This relationship was used to prospectively predict a range of clinical doses where trimeric complex formation is expected to be at its maximum. Depending on the dosing schedule and FAP-4-1BBL plasma: tumor distribution, doses between 2 and 145 mg could lead to maximum trimeric complex formation in the clinic. Due to the expected variability in both pharmacokinetic and FAP expression in the patient population, we predict that detecting a clear dose-response relationship would remain difficult without a large number of patients per dose level, highlighting that mathematical modeling techniques based on in vitro data could aid dose selection., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression-free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: Describing the kinetics of cytokines involved as biomarkers of sepsis progression could help to optimise interventions in septic patients. This work aimed to quantitively characterise the cytokine kinetics upon exposure to live E. coli by developing an in silico model, and to explore predicted cytokine kinetics at different bacterial exposure scenarios., Methods: Data from published in vivo studies using a porcine sepsis model were analysed. A model describing the time courses of bacterial dynamics, endotoxin (ETX) release, and the kinetics of TNF and IL-6 was developed. The model structure was extended from a published model that quantifies the ETX-cytokines relationship. An external model evaluation was conducted by applying the model to literature data. Model simulations were performed to explore the sensitivity of the host response towards differences in the input rate of bacteria, while keeping the total bacterial burden constant., Results: The analysis included 645 observations from 30 animals. The blood bacterial count was well described by a one-compartment model with linear elimination. A scaling factor was estimated to quantify the ETX release by bacteria. The model successfully described the profiles of TNF, and IL-6 without a need to modify the ETX-cytokines model structure. The kinetics of TNF, and IL-6 in the external datasets were well predicted. According to the simulations, the ETX tolerance development results in that low initial input rates of bacteria trigger the lowest cytokine release., Conclusion: The model quantitively described and predicted the cytokine kinetics triggered by E. coli exposure. The host response was found to be sensitive to the bacterial exposure rate given the same total bacterial burden., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Salma M. Bahnasawy, Lena E. Friberg, Elisabet I. Nielsen reports financial support was provided by European Union. Salma M. Bahnasawy, Lena E. Friberg, Elisabet I. Nielsen reports financial support was provided by Swedish Research Council]., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Colistin heteroresistance (HR) refers to a bacterial population comprised of several subpopulations with different levels of resistance to colistin. In this study, we discuss the classic form of HR, in which a resistant subpopulation exists within a predominantly susceptible population. We investigated the prevalence of colistin HR and its evolution into full resistance among 173 clinical carbapenem-resistant Acinetobacter baumannii isolates and examined the effect of HR on clinical outcomes. To determine HR, we performed population analysis profiling. Our results showed a high prevalence of HR (67.1%). To examine evolution of HR strains into full resistance, the HR strains were grown in colistin-containing broth, transferred onto colistin-containing plates, and colonies on these plates were transferred into colistin-free broth. Many of the HR strains (80.2%) evolved into full resistance, 17.2% reverted to HR, and 2.6% were borderline. We used logistic regression to compare 14-day clinical failure and 14-day mortality between patients infected by HR versus susceptible non-HR carbapenem-resistant A. baumannii. In the subgroup of patients with bacteremia, HR was significantly associated with 14-day mortality. IMPORTANCE To our knowledge, this is the first large-scale study to report on HR in Gram-negative bacteria. We described the prevalence of colistin HR in a large sample of carbapenem-resistant A. baumannii isolates, the evolution of many colistin HR isolates to a resistant phenotype following colistin exposure and withdrawal, and the clinical consequences of colistin HR. We found a high prevalence of HR among clinical carbapenem-resistant A. baumannii isolates; most evolved into a resistant phenotype following colistin exposure and withdrawal. In patients treated with colistin, evolution of HR A. baumannii into full resistance could lead to higher rates of treatment failure and contribute to the reservoir of colistin-resistant pathogens in health care settings., Competing Interests: The authors declare a conflict of interest. E.D.M. reports grants and/or personal fees from Pfizer, M.S.D., Rosh, Anglini, Nordic Pharma, and Sanofi-Aventis. G.L.D. reports grants and/or personal fees from Pfizer, Menarini, and M.S.D. K.S.K. reports grants and/or personal fees from Spero Therapeutics, Q.P.E.X., and MicuRx. M.P. reports grants and/or personal fees from Shionogi and Pfizer. Y.C. reports grants and/or personal fees from Allecra Therapeutics, Genentech, Nabriva Therapeutics, Pfizer, PPD, Q.P.E.X. Biopharma, Roche Pharmaceuticals, Spero Therapeutics, VenatoRX Pharmaceuticals. All other authors have no interests to declare.

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Dr. Reddy's Laboratories rituximab (DRL_RI; Dr. Reddy's Laboratories SA, Basel, Switzerland) is under development as a rituximab biosimilar. Study RI-01-002 (Clinical Trials Registry - India/2012/11/003129), comparing DRL_RI to the reference medicinal product (RMP) MabThera® (Roche, Grenzach-Wyhlen, Germany), demonstrated pharmacokinetic (PK) equivalence and showed comparable pharmacodynamic, efficacy, safety, and immunogenicity profiles. We used data from the same study to perform population PK and PK-pharmacodynamic analyses: first exploring possible factors influencing the PK similarity assessment between products and then performing simulations to investigate the impact of tumor size on rituximab PK. Nonlinear mixed-effects models for PK, tumor size, tumor size-PK, and tumor response were developed independently. The final PK model included drug product as a dose-scaling parameter and predicted a 6.75% higher dose reaching the system in RMP-treated patients. However, when tumor size was included in the tumor size-PK model, the drug product effect was no longer observed. The model rather indicated that patients with larger tumor size have higher clearance. Further simulations confirmed that higher baseline tumor size is associated to slightly lower rituximab exposure. Tumor response, described by a continuous-time Markov model, did not differ between drug products. Both had higher effects during the first 20 weeks of treatment. Also, the model described a subpopulation of nonresponders to treatment (42%) with faster transitions to a worse state. The different rituximab exposure initially detected between drug products (6.75%) was shown using PK/PK-pharmacodynamic analysis to be attributed to a tumor size imbalance between treatment groups. PK/PK-pharmacodynamic analyses may contribute to PK similarity assessments., (© 2022 Dr. Reddy's Laboratories SA. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual- and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter- and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80-125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method., (© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

TYRP1-TCB is a CD3 T-cell bispecific (CD3-TCB) antibody for the treatment of advanced melanoma. A tumor growth inhibition (TGI) model was developed using mouse xenograft data with TYRP1-TCB monotherapy or TYRP1-TCB plus anti-PD-L1 combination. The model was translated to humans to inform a refined clinical strategy. From xenograft mouse data, we estimated an EC 50 of 0.345 mg/L for TYRP1-TCB, close to what was observed in vitro using the same tumor cell line. The model showed that, though increasing the dose of TYRP1-TCB in monotherapy delays the time to tumor regrowth and promotes higher tumor cell killing, it also induces a faster rate of tumor regrowth. Combination with anti-PD-L1 extended the time to tumor regrowth by 25% while also decreasing the tumor regrowth rate by 69% compared to the same dose of TYRP1-TCB alone. The model translation to humans predicts that if patients' tumors were scanned every 6 weeks, only 46% of the monotherapy responders would be detected even at a TYRP1-TCB dose resulting in exposures above the EC 90 . However, combination of TYRP1-TCB and anti-PD-L1 in the clinic is predicted to more than double the overall response rate (ORR), duration of response (DoR) and progression-free survival (PFS) compared to TYRP1-TCB monotherapy. As a result, it is highly recommended to consider development of CD3-TCBs as part of a combination therapy from the outset, without the need to escalate the CD3-TCB up to the Maximum Tolerated Dose (MTD) in monotherapy and without gating the combination only on RECIST-derived efficacy metrics., (© 2022. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)

Background: Children with febrile neutropenia commonly exhibit alterations of pharmacokinetic (PK) parameters, leading to decreased β-lactam concentrations., Aims: This study evaluated piperacillin PK and probability of target attainment (PTA) with continuous infusion of piperacillin-tazobactam, in order to optimize the dosing regimen., Methods: This prospective PK study included children with cancer, aged 1-17 years, who were treated with piperacillin-tazobactam for suspected or verified infection. A piperacillin-tazobactam loading dose (100 mg/kg) was administered followed by continuous infusion (300 mg/kg/day). The unbound fraction of piperacillin was quantified by high-performance liquid chromatography and PK were described using population PK modeling. PK data was used to update and extend a previous PK model built on data following intermittent administration. Monte Carlo simulations were performed to assess PTA for targets of 100% time above the minimum inhibitory concentration (100% fT > MIC) and 50% fT > 4xMIC., Results: We included 68 fever episodes among 38 children with a median (IQR) age of 6.5 years and body weight of 27.4 kg (15.1-54.0). A three-compartment model adequately described the concentration-time data. Median (95% confidence interval) estimates for clearance and piperacillin concentration at steady state were 14.2 L/h/70 kg (13.0; 15.3) and 47.6 mg/L (17.2; 129.5), respectively. Body weight or lean body weight was significantly associated with the PK parameters, and body weight was integrated in the final PK model. Based on piperacillin exposure, continuous infusion was the only dosing regimen to achieve optimal PTA for the P. aeruginosa breakpoint (16 mg/L) with the target of 100% fT > MIC, and a daily dose of 300 mg/kg reached optimal PTA. The strict target of 50% fT > 4xMIC (64 mg/L) was not feasibly attained by any dosing regimen at recommended doses., Conclusion: Unlike conventional piperacillin intermittent administration and extended infusion regimens, continuous infusion allows the target of 100% fT > MIC to be reached for children with febrile neutropenia., (© 2021 The Authors. Cancer Reports published by Wiley Periodicals LLC.)

Objectives: New drugs and methods to efficiently fight carbapenem-resistant gram-negative pathogens are sorely needed. In this study, we characterized the preclinical pharmacokinetics (PK) and pharmacodynamics of the clinical stage drug candidate apramycin in time kill and mouse lung infection models. Based on in vitro and in vivo data, we developed a mathematical model to predict human efficacy., Methods: Three pneumonia-inducing gram-negative species Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae were studied. Bactericidal kinetics were evaluated with time-kill curves; in vivo PK were studied in healthy and infected mice, with sampling in plasma and epithelial lining fluid after subcutaneous administration; in vivo efficacy was measured in a neutropenic mouse pneumonia model. A pharmacokinetic-pharmacodynamic model, integrating all the data, was developed and simulations were performed., Results: Good lung penetration of apramycin in epithelial lining fluid (ELF) was shown (area under the curve (AUC) ELF /AUC plasma  = 88%). Plasma clearance was 48% lower in lung infected mice compared to healthy mice. For two out of five strains studied, a delay in growth (∼5 h) was observed in vivo but not in vitro. The mathematical model enabled integration of lung PK to drive mouse PK and pharmacodynamics. Simulations predicted that 30 mg/kg of apramycin once daily would result in bacteriostasis in patients., Discussion: Apramycin is a candidate for treatment of carbapenem-resistant gram-negative pneumonia as demonstrated in an integrated modeling framework for three bacterial species. We show that mathematical modelling is a useful tool for simultaneous inclusion of multiple data sources, notably plasma and lung in vivo PK and simulation of expected scenarios in a clinical setting, notably lung infections., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Antibiotic resistance presents an incessant threat to our drug armamentarium that necessitates novel approaches to therapy. Over the past several decades, investigation of pharmacokinetic and pharmacodynamic (PKPD) principles has substantially improved our understanding of the relationships between the antibiotic, pathogen, and infected patient. However, crucial gaps in our understanding of the pharmacology of antibacterials and their optimal use in the care of patients continue to exist; simply attaining antibiotic exposures that are considered adequate based on traditional targets can still result in treatment being unsuccessful and resistance proliferation for some infections. It is this salient paradox that points to key future directions for research in antibiotic therapeutics. This Personal View discusses six priority areas for antibiotic pharmacology research: (1) antibiotic-pathogen interactions, (2) antibiotic targets for combination therapy, (3) mechanistic models that describe the time-course of treatment response, (4) understanding and modelling of host response to infection, (5) personalised medicine through therapeutic drug management, and (6) application of these principles to support development of novel therapies. Innovative approaches that enhance our understanding of antibiotic pharmacology and facilitate more accurate predictions of treatment success, coupled with traditional pharmacology research, can be applied at the population level and to individual patients to improve outcomes., Competing Interests: Declaration of interests There was no funding to support this Personal View. JL and RLN licensed their new-generation polymyxin molecules to Qpex Biopharma. JL received grant support, a seminar honorarium, and consultation fees from Northern Antibiotics, Qpex Biopharma, Healthcare Pharmaceuticals, Genentech, and DanDi Bioscience. JBB received research consulting fees from MicuRx Pharmaceuticals and Lupin Pharmaceuticals. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure., Objectives: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial., Methods: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis., Results: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min., Conclusions: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily., (© The Author(s) 2022. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Antimicrobial resistance has become one of the greatest threats to human health, and new antibacterial treatments are urgently needed. As a tool to develop novel therapies, animal models are essential to bridge the gap between preclinical and clinical research. However, despite common usage of in vivo models that mimic clinical infection, translational challenges remain high. Standardization of in vivo models is deemed necessary to improve the robustness and reproducibility of preclinical studies and thus translational research. The European Innovative Medicines Initiative (IMI)-funded "Collaboration for prevention and treatment of MDR bacterial infections" (COMBINE) consortium, aims to develop a standardized, quality-controlled murine pneumonia model for preclinical efficacy testing of novel anti-infective candidates and to improve tools for the translation of preclinical data to the clinic. In this review of murine pneumonia model data published in the last 10 years, we present our findings of considerable variability in the protocols employed for testing the efficacy of antimicrobial compounds using this in vivo model. Based on specific inclusion criteria, fifty-three studies focusing on antimicrobial assessment against Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii were reviewed in detail. The data revealed marked differences in the experimental design of the murine pneumonia models employed in the literature. Notably, several differences were observed in variables that are expected to impact the obtained results, such as the immune status of the animals, the age, infection route and sample processing, highlighting the necessity of a standardized model., Competing Interests: JLH was employed by GlaxoSmithKline Pharmaceuticals. SS and SR were employed by Evotec. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Arrazuria, Kerscher, Huber, Hoover, Lundberg, Hansen, Sordello, Renard, Aranzana-Climent, Hughes, Gribbon, Friberg and Bekeredjian-Ding.)

The rise in antimicrobial resistance (AMR), and increase in treatment-refractory AMR infections, generates an urgent need to accelerate the discovery and development of novel anti-infectives. Preclinical animal models play a crucial role in assessing the efficacy of novel drugs, informing human dosing regimens and progressing drug candidates into the clinic. The Innovative Medicines Initiative-funded "Collaboration for prevention and treatment of MDR bacterial infections" (COMBINE) consortium is establishing a validated and globally harmonized preclinical model to increase reproducibility and more reliably translate results from animals to humans. Toward this goal, in April 2021, COMBINE organized the expert workshop "Advancing toward a standardized murine model to evaluate treatments for AMR lung infections". This workshop explored the conduct and interpretation of mouse infection models, with presentations on PK/PD and efficacy studies of small molecule antibiotics, combination treatments (β-lactam/β-lactamase inhibitor), bacteriophage therapy, monoclonal antibodies and iron sequestering molecules, with a focus on the major Gram-negative AMR respiratory pathogens Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Here we summarize the factors of variability that we identified in murine lung infection models used for antimicrobial efficacy testing, as well as the workshop presentations, panel discussions and the survey results for the harmonization of key experimental parameters. The resulting recommendations for standard design parameters are presented in this document and will provide the basis for the development of a harmonized and bench-marked efficacy studies in preclinical murine pneumonia model., Competing Interests: JLH was employed by GlaxoSmithKline Pharmaceuticals. SS and SR were employed by Evotec. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Arrazuria, Kerscher, Huber, Hoover, Lundberg, Hansen, Sordello, Renard, Aranzana-Climent, Hughes, Gribbon, Friberg and Bekeredjian-Ding.)

Pharmacokinetic/pharmacodynamic (PKPD) models have emerged as valuable tools for the characterization and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics such as minimum inhibitory concentration and PKPD indices, PKPD models enable description of the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. A literature review was conducted to identify PKPD models based on: (i) antibiotic compounds; (ii) Gram-positive or Gram-negative pathogens; and (iii) in-vitro or in-vivo longitudinal colony-forming unit data. In total, 132 publications were identified that were released between 1963 and 2021, including models based on exposure to single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g. static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarizes the details of each model, namely variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects and interactions. Furthermore, the review highlights the main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings, and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models will assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time, and ultimately facilitate model-informed antibiotic translation, dosing and drug development., Competing Interests: Declaration of competing interests None declared., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Pharmacokinetic-pharmacodynamic (PKPD) models have met increasing interest as tools to identify potential efficacious antibiotic dosing regimens in vitro and in vivo. We sought to investigate the impact of diversely shaped clinical pharmacokinetic profiles of meropenem on the growth/killing patterns of Pseudomonas aeruginosa (ARU552, MIC = 16 mg/L) over time using a semi-mechanistic PKPD model and a PK/PD index-based approach. Bacterial growth/killing were driven by the PK profiles of six patient populations (infected adults, burns, critically ill, neurosurgery, obese patients) given varied pathogen features (e.g., EC50, growth rate, inoculum), patient characteristics (e.g., creatinine clearance), and ten dosing regimens (including two dose levels and 0.5-h, 3-h and continuous-infusion regimens). Conclusions regarding the most favourable dosing regimen depended on the assessment of (i) the total bacterial load or fT>MIC (time that unbound concentrations exceed the minimum inhibitory concentration); (ii) the median or P0.95 profile of the population; and (iii) 8 h or 24 h time points. Continuous infusion plus loading dose as well as 3-h infusions (3-h infusions: e.g., for scenarios associated with low meropenem concentrations, P0.95 profiles, and MIC ≥ 16 mg/L) appeared superior to standard 0.5-h infusions at 24 h. The developed platform can serve to identify promising strategies of efficacious dosing for clinical trials.

Astegolimab is a fully human immunoglobulin G2 monoclonal antibody that binds to the ST2 receptor and blocks the interleukin-33 signaling. It was evaluated in patients with uncontrolled severe asthma in the phase 2b study (Zenyatta) at doses of 70, 210, and 490 mg subcutaneously every 4 weeks for 52 weeks. This work aimed to characterize astegolimab pharmacokinetics, identify influential covariates contributing to its interindividual variability, and make a descriptive assessment of the exposure-response relationships. A population pharmacokinetic model was developed using data from 368 patients in the Zenyatta study. Predicted average steady-state concentration was used in the subsequent exposure-response analyses, which evaluated efficacy (asthma exacerbation rate) and biomarker end points including forced expiratory volume in 1 second, fraction exhaled nitric oxide, blood eosinophils, and soluble ST2. A 2-compartment disposition model with first-order elimination and first-order absorption best described the astegolimab pharmacokinetics. The relative bioavailability for the 70-mg dose was 15.3% lower. Baseline body weight, estimated glomerular filtration rate, and eosinophils were statistically correlated with pharmacokinetic parameters, but only body weight had a clinically meaningful influence on the steady-state exposure (ratios exceeding 0.8-1.25). The exposure-response of efficacy and biomarkers were generally flat with a weak trend in favor of the highest dose/exposure. This study characterized astegolimab pharmacokinetics in patients with asthma and showed typical pharmacokinetic behavior as a monoclonal antibody-based drug. The exposure-response analyses suggested the highest dose tested in the Zenyatta study (490 mg every 4 weeks) performed close to the maximum effect, and no additional response may be expected above it., (© 2021 Genentech Inc. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

Purpose: Flucloxacillin is a β-lactam penicillin commonly used in the treatment of bone and soft tissue infections. In a recent porcine study, we found surprisingly low time for which the free concentration was maintained above the minimal inhibitory concentration (fT>MIC) in bone and soft tissue, following flucloxacillin oral (PO) and intravenous (IV) administration at 1g every 6h (q6h). In addition to plasma, sampling was obtained from subcutaneous tissue, knee joint, cancellous bone and cortical bone, using microdialysis. To identify flucloxacillin dosing regimens that result in theoretically therapeutic concentrations, we developed a population pharmacokinetic (PK) model for the porcine data, and combined it with a human flucloxacillin population PK model for simulations., Methods: A four-compartment model was developed, and various dosing regimens and modes of administration were simulated. Predicted concentrations were compared to %fT>MIC (0.5 mg/L and 2 mg/L)., Results: Continuous infusion (CI) resulted in higher %fT>MIC compared to intermittent administration. For intermittent IV dosing (4, 8 and 12g/24h), fT>MIC (0.5 mg/L) was ≥70% in plasma, and ranged between 42-96% in the sampled tissue in a typical individual. By applying CI, 4g/day was sufficient to achieve ≥98% fT>MIC (0.5 mg/L) in all sampled tissues. For MIC 2 mg/L, ≥50% fT>MIC was only achieved in plasma at CI 8 and 12g/24h and IV 3g q6h., Conclusions: To reach efficacious flucloxacillin bone and tissue concentrations, dose increment or continuous infusion needs to be considered., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

There has been accumulating interest in nebulised colistin methanesulfonate (CMS) for the treatment of ventilator-associated pneumonia (VAP). In this study, pulmonary and systemic pharmacokinetics following nebulisation of CMS at a dose of 3 MIU and 5 MIU, using a vibrating mesh nebuliser, for VAP caused by extensively drug-resistant Gram-negative pathogens was assessed. Blood samples and mini-bronchoalveolar lavage (mini-BAL) was performed post-dose at 1, 4 and 8 h. Concentrations of CMS and formed colistin in mini-BAL and plasma were determined by liquid chromatography-tandem mass spectrometry, and pharmacokinetic analysis was conducted using a population approach. The study population included three groups (n = 10 per group): (A) intravenous CMS and concomitantly nebulised CMS at a dose of 3 MIU (30 min duration); (B) nebulised CMS at a dose of 3 MIU (30 min duration) as monotherapy; and (C) nebulised CMS 5 MIU (45 min duration) as monotherapy. Mean plasma formed colistin concentrations were <1 mg/L following CMS nebulisation as monotherapy (groups B and C). Predicted trough concentrations of formed colistin in the epithelial lining fluid (ELF) following 24-h dosing of 3 MIU and 5 MIU nebulised CMS were 120.4 mg/L and 200.7 mg/L, respectively. The model predicted that concomitant intravenous CMS (group A) had minimal impact on the formed colistin concentration in ELF. This study demonstrated high ELF formed colistin concentrations following nebulised CMS (constantly above colistin MICs), while plasma concentrations were lower than those associated with nephrotoxicity. Our results provide important information for optimisation of nebulised colistin therapy., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Purpose: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti-programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)-tumor growth dynamics (TGD) models., Patients and Methods: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models., Results: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition., Conclusions: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination., (©2021 The Authors; Published by the American Association for Cancer Research.)

Objectives: To describe the population genetics and antibiotic resistance gene distribution of carbapenem-resistant Acinetobacter baumannii (CRAB) isolates causing infections in three Mediterranean countries., Methods: Isolates were collected during the 2013-17 AIDA clinical trial in six hospitals in Israel, Greece and Italy. WGS, bioinformatic characterization and antibiotic resistance profiling were performed., Results: In the 247 CRAB isolates characterized in this study, ST distribution varied by country: 29/31 (93.5%) Greek isolates, 34/41 (82.9%) Italian isolates and 70/175 (40.0%) Israeli isolates belonged to ST2. The identified ST2 isolates included eight distinct clades: 2C, 2D and 2H were significantly more common in Italy, while 2F was unique to Greece. The uncommon ST3 was not present among Greek isolates and constituted only 5/41 (12%) Italian isolates. On the other hand, it was much more common among Israeli isolates: 78/175 (44.6%) belonged to ST3. The vast majority of isolates, 240/247 (97.2%), were found to harbour acquired carbapenemases, primarily blaOXA-23. The chromosomal oxaAb (blaOXA-51-like) and ampC genes characteristic of this organism were also ubiquitous. Most (96.4%) ST3 isolates carried a broad-host-range plasmid IncP1α., Conclusions: The geographical differences in CRAB populations support the theory that clonal spread of CRAB leads to endemicity in hospitals and regions. The close association between antibiotic resistance genes and clades, and between plasmids and STs, suggest that de novo creation of MDR A. baumannii is rare. The clustering of antibiotic resistance genes and plasmids that is unique to each clade/ST, and nearly uniform within clades/STs, suggests that horizontal transmission is rare but crucial to the clade's/ST's success., (© The Author(s) 2022. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)