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Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety. [ABSTRACT FROM AUTHOR]

CP-690,550, a selective inhibitor of the Janus kinase family, is being developed as an oral disease-modifying antirheumatic drug for the treatment of rheumatoid arthritis (RA). A semi-mechanistic model was developed to characterize the time course of drug-induced absolute neutrophil count (ANC) reduction in a phase 2a study. Data from 264 RA patients receiving 6-week treatment (placebo, 5, 15, 30 mg bid) followed by a 6-week off-treatment period were analyzed. The model included a progenitor cell pool, a maturation chain comprising transit compartments, a circulation pool, and a feedback mechanism. The model was adequately described by system parameters (BASE(h), ktr(h), [gamma], and k(circ)), disease effect parameters (DIS), and drug effect parameters (k(off) and k(D)). The disease manifested as an increase in baseline ANC and reduced maturation time due to increased demand from the inflammation site. The drug restored the perturbed system parameters to their normal values via an indirect mechanism. ANC reduction due to a direct myelosuppressive drug effect was not supported. The final model successfully described the dose- and time-dependent changes in ANC and predicted the incidence of neutropenia at different doses reasonably well. [ABSTRACT FROM AUTHOR]

Background and Objective: Preterm and term newborn infants show wide interindividual variability (IIV) in pharmacokinetic parameters of gentamicin. More extensive knowledge and use of predictive covariates could lead to faster attainment of therapeutic concentrations and a reduced need for concentration monitoring. This study was performed to characterize the population pharmacokinetics of gentamicin in preterm and term neonates and to identify and quantify relationships between patient characteristics and IIV. A secondary aim was to evaluate cystatin C as a marker for gentamicin clearance in this patient population.Methods: Data were collected in a prospective study performed in the Neonatal Intensive Care Unit at the University Children's Hospital, Uppsala, Sweden. Population pharmacokinetic modelling was performed using nonlinear mixed-effects modelling (NONMEM) software. Bodyweight was included as the primary covariate according to an allometric power model. Other evaluated covariates were age (postmenstrual age, gestational age [GA], postnatal age [PNA]), markers for renal function (serum creatinine, serum cystatin C) and concomitant medication with cefuroxime, vancomycin or indometacin. Covariate-parameter relationships were explored using a stepwise covariate model building procedure. The predictive performance of the developed model was evaluated using an independent external dataset for a similar patient population.Results: Sixty-one newborn infants (GA range 23.3-42.1 weeks, PNA range 0-45 days) were enrolled in the study. In total, 894 serum gentamicin samples were included in the analysis. The concentration-time profile was described using a three-compartment model. Gentamicin clearance increased with the GA and PNA (included in a nonlinear fashion). The GA was also identified as having a significant influence on the central volume of distribution, with a preterm neonate having a larger central volume of distribution per kilogram of bodyweight than a term neonate. Cystatin C and creatinine were not correlated with gentamicin clearance in this study population. The external dataset was well predicted by the developed model.Conclusion: Bodyweight and age (GA and PNA) were found to be major factors contributing to IIV in gentamicin clearance in neonates. Based on these data, cystatin C and serum creatinine were not correlated with gentamicin clearance and therefore not likely to be predictive markers of renal function in this patient population. Based on predictions from the developed model, preterm neonates do not reach targeted peak and trough gentamicin concentrations after a standard dosage regimen of 4 mg/kg given once daily, suggesting a need for higher loading doses and prolonged dosing intervals in this patient population. [ABSTRACT FROM AUTHOR]

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: Declartion of competing interest Nothing to declare, (Copyright © 2025. Published by Elsevier Ltd.)

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: 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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)