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The increasing use of immune checkpoint inhibitors, such as durvalumab, places a significant financial burden on healthcare systems, strains hospital capacities, and contributes to environmental concerns.
Objective
We aimed to develop alternative dosing strategies to optimize durvalumab administration, reduce unnecessary drug use, and ensure sustainable cancer care without sacrificing efficacy.
Methods
Using the population pharmacokinetic model developed by the licensing holder, we designed two alternative dosing strategies for non-small cell lung cancer based on therapeutic drug monitoring. Adjustments were made to the dose or administration interval, following regulatory standards for in silico dose optimization. A pharmacoeconomic evaluation was conducted to estimate potential cost savings from a medical perspective.
Results
Both alternative strategies achieved high exposure levels, with 98.1–99.0% of patients exceeding a predefined efficacy target, surpassing the 95.4% predicted by the license holder for the approved 10 mg/kg 2-weekly regimen. They also reduced overall drug exposure by 7–24% and eliminated drug wastage, resulting in an average annual cost reduction of €25,163 (22.9%) per patient.
Conclusion
Therapeutic drug monitoring-guided adjustments for durvalumab offer a potentially cost-saving way to optimize drug use, reduce healthcare burdens, and lessen environmental impact while ensuring adequate patient exposure. Our proposal's evidence provides a solid basis for a non-inferiority study.
We investigated whether therapeutic drug monitoring (TDM)-based dosing strategies could improve the efficiency of durvalumab use in non-small cell lung cancer patients, addressing the need to reduce excessive drug use, high treatment costs, and the environmental burden associated with fixed dosing.
This study evaluated two therapeutic drug monitoring-based approaches using the sponsor’s pharmacokinetic model, improving target exposure (98.1–99.0%) compared with the standard regimen (95.4%).
TDM-guided dosing of durvalumab showed significant resource savings, with up to 25.6% less durvalumab required and cost savings of up to €29,242 per patient compared with the standard regimens.
1 Introduction
The rising number of cancer patients has strained hospital capacities and increased the workload for clinicians and healthcare providers [1]. Furthermore, the costs associated with the growing use of immune checkpoint inhibitors exert considerable financial pressure on healthcare systems globally, potentially limiting patient access to treatments [2]. A prime example of such an immune checkpoint inhibitor is durvalumab, an anti-PD-L1 monoclonal antibody initially approved by the FDA in 2017 to treat locally advanced or metastatic bladder cancer, which has had significant clinical applications [3]. By 2023, it was approved for six different indications, with its most impactful use being for unresectable stage III non-small cell lung cancer (NSCLC) following chemoradiotherapy, approved in 87 countries [4].
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Current licensed dosing strategies of 10 mg/kg every 2 weeks (Q2W) or 1500 mg every 4 weeks (Q4W) result in over 95% of patients achieving trough concentrations above the reported efficacy threshold of 53.3 µg/mL, with 75% reaching nearly double that threshold [5, 6]. This suggests that many patients receive doses exceeding what is necessary for optimal therapeutic effect. Such excessive drug administration not only escalates treatment costs but also imposes an additional burden on hospital staff and resources. Moreover, it exacerbates the environmental impact of drug production, transportation, wastage, and disposal [7, 8].
Given the financial burden, pressure on healthcare capacity, and environmental impact associated with current durvalumab treatment, there is a critical need for innovative dosing strategies. Since the license holder has postulated an efficacy threshold plasma concentration [5], dose tailoring may be used to optimize treatment regimens. To address this, we aim to determine whether a personalized dosing strategy, based on systemic durvalumab exposure, can maintain effective exposure while reducing drug usage and healthcare resource consumption.
2 Materials and Methods
2.1 General Approach
We investigated the impact of pharmacokinetically guided dose optimization of durvalumab, assuming that a plasma concentration of 53.3 µg/mL is the threshold for efficacy. This concentration, defined by the license holder, represents a hundred-fold of the Michaelis–Menten (Km) estimate of 0.533 µg/mL (95% CI 0.072–1.58) derived from their population pharmacokinetic model. Their simulations indicated that with a treatment regimen of 10 mg/kg Q2W, 95.4% of patients are expected to maintain a trough concentration (Ctrough) above this efficacy threshold [5].
We evaluated four strategies in a representative population (Table S1, see electronic supplementary material [ESM]). The first strategy, hereafter referred to as TDM-based dose, involved tailoring the individual dose based on therapeutic drug monitoring (TDM) while maintaining a fixed interval. The second strategy, hereafter referred to as TDM-based interval, was focused on individualizing the dosing interval based on TDM. These TDM-based strategies aimed to reduce the cumulative dose while maintaining the individual exposure above the proposed efficacy target throughout the dosing interval. The two currently approved durvalumab dosing regimens of 10 mg/kg Q2W and 1500 mg Q4W were also simulated as reference strategies. For all four strategies, we calculated the fraction of patients above the efficacy target just before administering a subsequent dose during the maintenance phase, as well as the associated costs. These analyses are detailed comprehensively in the following sections.
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2.2 Population Pharmacokinetic Modeling and Simulation
All population pharmacokinetic simulations were performed using the software package NONMEM® version 7.5.1 (ICON, Ireland) assisted by the modeling toolkit Perl speaks NONMEM (PsN) version 5.3.0, and with Pirana version 2.9.9 (Certara, NJ, USA) as an interface and modeling environment. The pharmacokinetic model used was the semi-mechanistic time-varying clearance model described by Baverel et al. (2018) [9] (Supplemental Model Code, see ESM). Statistical data analysis and summarization were executed with R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria) via the RStudio version 2022.02.1 interface (PBC, Boston, MA, USA).
For the TDM-based dose and interval strategies, the starting dose was 1500 mg on the first day, followed by dose or interval adjustments based on the Ctrough measured, 4 weeks after the first dose (Table 1A). Subsequent doses and intervals were tailored based on individual exposure, whereby we aimed to reach a steady-state Ctrough above the target of 53.3 µg/mL, ensuring adequate drug exposure while minimizing unnecessary durvalumab use. The cut-off values for dose adjustments (40, 90, and 130 µg/mL) were selected accordingly, with the 40-µg/mL threshold intended to align with this 53.3-µg/mL steady-state target, considering accumulation of durvalumab between the initial dose and steady-state conditions. These adjustments were re-evaluated on the Ctrough following the fifth dose. Thereby, for both TDM-based strategies, only pre-defined dose adjustments were implemented, as this approach allowed for more precise tailoring of the treatment while minimizing the risk of sub-target trough levels. Due to susceptibility differences to dosing adjustments, a distinction was made between patients with personalized dosing, based on Ctrough after the first dose, and those still treated by the initial strategy of 1500 mg every 4 weeks (Table 1B). The treatment timeline of the TDM strategies is depicted in Fig. S1 (see ESM).
Table 1
Dosing strategies over time
A. Adjustments based on the durvalumab level after the first dose
B. Re-evaluation based on the durvalumab level after the fifth dose
Strategy
Dosing strategy at 1st dose
Ctrough (µg/mL)
Dose adjustment
Interval adjustment
Strategy
Dosing strategy at 5th dose
Ctrough (µg/mL)
Dose adjustment
Interval adjustment
Approved dose (weight-based)
10 mg/kg per 2 weeks
No adjustment
No adjustment
Approved dose (weight-based)
10 mg/kg per 2 weeks
No adjustment
No adjustment
Approved dose (fixed-dose)
1500 mg per 4 weeks
No adjustment
No adjustment
Approved dose (fixed-dose)
1500 mg per 4 weeks
No adjustment
No adjustment
TDM-based dose
1500 mg per 4 weeks
< 40
+ 240 mg
TDM-based dose or TDM-based interval
Not adjusted, 1500 mg per 4 weeks
< 53.3
+ 240 mg
40–90
No adjustment
53.3–90
No adjustment
> 90
− 500 mg
≥ 90
− 500 mg
TDM-based interval
1500 mg per 4 weeks
< 40
+ 240 mg
Adjusted dose or interval
<53.3
+ 500 mg
40–90
No adjustment
53.3–90
No adjustment
91–130
+ 2 weeks
> 90
− 240 mg
> 130
+ 4 weeks
Ctrough trough concentration, TDM therapeutic drug monitoring
Doses were rounded to the nearest achievable dose using 120-mg or 500-mg vials to minimize waste. For the 10-mg/kg dosing strategy, the per-cycle dose was calculated as ten times the patient's weight, with the used amount of drug rounded up to the nearest amount that could be prepared from whole vials. The difference between these rounded and calculated doses was considered wastage.
The Ctrough was predicted for each dosing strategy for each virtual patient throughout 1 year of treatment and at steady-state conditions (≥ 4 doses after the latest dose adjustment). The percentage of patients achieving a steady-state Ctrough > 53.3 µg/mL was calculated and compared with the 95.4% reference percentage. Furthermore, the median total durvalumab dose per patient per year and over a mean treatment duration of 8 months [10], along with the predicted amount of drug wastage in the same period, was calculated under the assumption of wastage of partially used vials. Additionally, the median number of administrations and the median dosing interval were calculated for the 1-year and the 8-month treatment duration.
2.3 Pharmacoeconomic Evaluation
The pharmacoeconomic analysis was conducted from a Dutch medical perspective, focusing solely on direct costs associated with durvalumab treatment. Cost components included the expenses for the drug itself, which were calculated based on reimbursement rates set by Dutch insurers at €5.075 per milligram, with consideration for potential drug wastage [11]. The analysis incorporated all relevant cost components to evaluate the pharmacoeconomic impact of dosing interval adjustments, with drug administration costs estimated at €379.45 per infusion, according to assessments provided by the Dutch National Health Care Institute (Zorginstituut Nederland, ZIN) [12]. Routine check-ups with a clinician incurred costs of €128.30 per visit, also based on ZIN evaluations. Routine laboratory testing expenses were estimated at €42.15 per test, based on the mean cost derived from prices at three Dutch hospital laboratories (OLVG Lab BV in Amsterdam, Reinier Haga Medisch Diagnostisch Centrum in The Hague, and Radboudumc Laboratorium voor Diagnostiek in Nijmegen) [12‐14]. The costs of TDM testing were determined based on the average expense of an ELISA kit and the related labor costs [15‐22]. These labor costs included the salaries of laboratory personnel conducting the tests and the clinicians interpreting the results, which were calculated using the average wages specified in the Dutch Collective Labor Agreement (CLA) and included the social security contributions [20, 22, 23]. In total, the cost of TDM testing per patient amounted to €57.50 per test. All cost data were adjusted to June 2024 using the consumer price index (CPI) published by Statistics Netherlands (Centraal Bureau voor de Statistiek, CBS) [24]. Additional methodological details are available in Method S1 and Table S2 in the ESM.
To assess the potential cost savings of TDM-based strategies compared with the standard dosing regimens, the mean cost differences and percentage reductions for overall per-patient costs and each cost component, including drug acquisition, administration, monitoring, and TDM testing, were calculated.
3 Results
3.1 Population Pharmacokinetic Simulations
Overall, TDM-based strategies led to a predicted 36–44% reduction in durvalumab trough levels and a predicted 7–24% reduction in total drug exposure, as expressed in the geometric mean of the average concentration in steady-state conditions (Cavg,ss), compared with licensed dosing approaches (Table 2 and Fig. 1).
Table 2
Pharmacokinetic parameters per dosing strategy at the maximum treatment duration
Strategy
Geometric mean Cavg,ss (µg/mL) [CV%]
Geometric mean Ctrough,ss (µg/mL) [CV%]
Average per patient per cycle dose (mg)
Weight-based
217 [35]
170 [49]
1275a
Fixed-dose
263 [32]
161 [57]
1500a
TDM-based dose
202 [22]
103 [42]
1167a
TDM-based interval
199 [21]
96 [42]
1115a
Ratios
TDM-based dose
Weight-based
0.93
0.61
0.92
Fixed-dose
0.77
0.64
0.78
TDM-based interval
Weight-based
0.92
0.56
0.87
Fixed-dose
0.76
0.60
0.74
Cavg,ss average steady-state concentration, Ctrough,ss trough concentration in steady-state conditions
aAfter normalization to a 4-weekly dosing interval
Fig. 1
Levels of durvalumab per dosing strategy. (A) The evolution of durvalumab trough levels over a full year of treatment for each dosing strategy. (B) The distribution of steady-state trough levels for each dosing strategy. The solid line represents the median level, while the shaded areas indicate the interquartile range (A). The horizontal line at 53.3 µg/mL indicates the threshold level for efficacy (A and B). TDM therapeutic drug monitoring
The predicted percentage of patients attaining the pharmacokinetic efficacy target Ctrough ≥ 53.3 µg/mL was above the threshold of 95.4% for all strategies, with 99.2% and 97.8% for the approved 10 mg/kg Q2W and 1500 mg Q4W strategies, respectively, and 99.0% and 98.1% for the TDM-based dose, and TDM-based interval strategies, respectively.
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TDM, following the first dose, was predicted to lead to treatment adjustments in most patients, with the pattern of adjustments being consistent across both TDM-based strategies. Specifically, 42.3% of patients had either a dose reduction or interval extension, 5.6% had an increase in dose, and 52.1% maintained their original 1500-mg Q4W dose. Following both TDM interventions, 81.1–82.2% of patients received a lower per-cycle dose (normalized to a 4-weekly dosing interval), and only 10.7–11.6% of patients had no change in their dosing strategy since the therapy started (Fig. S2, see ESM).
At the 8-month treatment duration, the TDM-based dose strategy maintained a consistent median dose of 1500 mg (IQR: 1000–1500), while the TDM-based interval strategy sustained a median interval of 4 weeks (IQR: 4–6 weeks). Over the 12-month treatment period, the TDM-based median dose regimen was reduced to 1000 mg (IQR: 740–1000), whereas the TDM-based interval strategy continued with a median 1500-mg dose (IQR: 1000–1500) and an unchanged median 4-week interval (IQR: 4–6 weeks). The treatment characteristics of the approved dosing strategies remained unchanged over time. The weight-based strategy administered a median dose of 640 mg (IQR: 570.4–705) with a median wastage of 53 mg (IQR: 20.6–87.6) at a 2-week interval, while the fixed-dose strategy maintained a 1500-mg dose every 4 weeks.
3.2 Pharmacoeconomic Evaluation
The predicted overall per-patient expenses associated with TDM-based strategies for dose and interval adjustments were comparable, with mean costs per patient of €62,025 and €62,899 for 8 months and €85,030 and €83,740 for 12 months, respectively. In contrast, the expenses for currently licensed dosing strategies of 10 mg/kg Q2W and 1500 mg Q4W were higher, with mean costs of €78,048 and €73,293 for 8 months and €112,982 and €106,114 for 12 months, respectively (Fig. 2). Overall, it is predicted that TDM-based strategies reduce the mean treatment costs by an average of 17.4% at the mean treatment duration and by 22.9% at the maximum treatment duration compared with the currently licensed strategies. The expenses per treatment component are presented in Table S3 (see ESM).
Fig. 2
Cost trajectory per dosing strategy. The solid lines represent the mean treatment costs over time, the shaded area represents the 95% confidence interval, and the vertical dashed lines represent the mean and maximum treatment duration. TDM therapeutic drug monitoring
We predicted that the TDM-based dosing and interval strategies result in substantial cost reductions compared with the licensed regimens (Fig. 3). Across both strategies, drug wastage costs were eliminated entirely. Furthermore, costs related to the administered durvalumab are considerably reduced, with mean reductions of €9785 (14.3%) to €21,433 (21.7%) per patient, depending on the treatment duration and the specific TDM approach. For the TDM-based interval strategy, these savings are complemented by an annual mean cost decrease from €528 (10.7%) to €8208 (57.4%) in routine laboratory and clinician checks and outpatient daycare, attributed to less frequent dosing and monitoring requirements. Similarly, the TDM-based dosing strategy yields yearly mean savings of €7152 (50%) compared with the weight-based 2-weekly regimen, but no such cost reduction is observed when compared with the fixed-dose regimen, as both strategies follow the same 4-week dosing interval. Moreover, the introduction of TDM itself incurs an additional cost of €115, though the substantial reductions in other areas outweigh this.
Fig. 3
Cost differences per treatment component per patient between TDM-based dosing strategies and licensed dosing regimens. Comparison of costs between TDM-based strategies and the 10-mg/kg every 2 weeks regimen at (A) 8 months and (B) 12 months, as well as between TDM-based strategies and the 1500-mg every 4 weeks regimen at (C) 8 months and (D) 12 months. TDM therapeutic drug monitoring
We demonstrate the potential of TDM-based strategies to potentially improve the cost effectiveness of treatment with durvalumab. The TDM-based strategies resulted in 7–24% lower Cavg,ss and 36–44% lower Ctrough,ss than the approved dosing strategies. Over both 8-month and 12-month periods, TDM-based interval and dose adjustment strategies resulted in substantial cost reductions, ranging from €10,394 (14.2%) to €29,242 (25.9%) per patient, compared with currently licensed regimens.
Some authors of this publication previously developed a dose-banding strategy to optimize durvalumab dosing using the same base pharmacokinetic model by Baverel et al. [9, 25]. Their approach resulted in a geometric mean Ctrough,ss of 123 µg/mL after a 2-weekly dose of 588 mg and 97 µg/mL after a 4-weekly dose of 1176 mg, reflecting a 13% reduction compared with reference levels (142 µg/mL at 10 mg/kg Q2W or 123 µg/mL at 20 mg/kg Q4W) [25]. In comparison, the TDM-based strategies achieved a slightly greater reduction in geometric mean Ctrough,ss (96–103 µg/mL, 36–44% reduction) while using fewer drugs (1167 mg for TDM-based dose and 1115 mg for TDM-based interval). Regarding exposure, the dose-banding strategy reduced the geometric mean Cavg,ss by 13% compared with the reference levels [25]. TDM-based dose adjustment lowered the geometric mean Cavg,ss by 7% versus weight-based and 23% versus fixed-dose, while the TDM-based interval strategy reduced it by 8% and 24%, respectively.
Both dose-banding and TDM-based strategies are predicted to effectively reduce drug exposure and, thereby, help dispel the common misconception among patients that "more is always better" in immunotherapy. While dose-banding is more straightforward to implement in clinical practice—following predefined dosing categories without requiring TDM, thereby reducing additional costs and labor—it does not account for interindividual variability caused by other factors than body weight, which may reduce therapeutic efficacy due to undertreatment, or increased durvalumab use and costs due to overtreatment. In contrast, TDM-based strategies provide a more precise, patient-specific approach by continuously adjusting dosing based on individual pharmacokinetics. This approach leads to improved drug utilization and more significant exposure reduction without compromising effective exposure.
Because the Cavg,ss were 7–24% lower and the Ctrough,ss were 36–44% lower, these strategies do not fully comply with the FDA's criteria of maintaining less than a 20% deviation from the reference dosing regimen [26]. Nonetheless, since 98.1–99.0% of patients remained above the 53.5-µg/mL pharmacokinetic efficacy target set by the license holder, which exceeds the 95.4% the license holder simulated for the approved 10-mg/kg Q2W strategy, this provides a solid basis for a non-inferiority study. However, it is important to interpret the 53.3-µg/mL threshold with caution. Although this value, published by the license holder, is currently the only available reference for durvalumab's clinical efficacy, its validity as the true minimum effective concentration remains uncertain. Research suggests potentially lower thresholds for immune checkpoint inhibitors, indicating there may be further room for reducing dose or extending dosing intervals in immunotherapy [27‐29]. However, since the real-world efficacy of durvalumab at a lower threshold has not been studied, we adopted a regulatory-aligned benchmark of 53.3 µg/mL. Notably, even at this threshold, the TDM-dosing strategies still allowed for a lower per-cycle dose (normalized to a 4-week dosing interval) in 81.1–81.2% of patients. The projected savings associated with durvalumab dose tailoring are primarily due to a significant reduction in drug use, directly contributing to a lower carbon footprint and endorsing sustainable healthcare. Moreover, the TDM-based dosing interval has an added benefit, as 42.3% of patients can extend their intervals by 2 or 4 weeks, which enhances patient convenience and lowers hospital visit costs.
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However, implementing TDM introduces additional costs and requires specific expertise in, for example, pharmacokinetic modeling. Besides the availability of a bioanalytical assay, the use of clinically friendly software to tailor the individual dose based on measured drug levels and pharmacokinetic data is a prerequisite for successful clinical implementation. The high cost efficiency of TDM could offset these expenses, including assay development and integration expenses such as personnel training. Therefore, a centralized approach may be a viable solution for institutions that prefer to avoid the challenges of testing with partially filled assay plates or face other barriers, such as insufficient availability of qualified personnel or difficulties in implementing and conducting assays. In a centralized model, hospitals collaborate by sending patient samples to a designated testing facility, where the assays are conducted, the results are interpreted, and individualized treatment recommendations are formulated. The recommendations are then communicated by the centralized pharmacist to the treating physician. This approach alleviates the burden of assay implementation and personnel training, ensuring that hospitals lacking the necessary resources can still benefit from TDM.
Our pharmacoeconomic analysis acknowledged inherent uncertainties stemming from variations in healthcare systems, reimbursement policies, and regional practices. For example, the cost of TDM testing can vary. Our analysis used a cost estimate of €57.50 per TDM test, whereas the nationwide reimbursement set by the Dutch Healthcare Authority (Nederlandse Zorgautoriteit, NZa) is €115. However, variations in costs and reimbursements are likely to have a minimal impact on overall savings, as using NZa’s reimbursement rate would reduce total savings by only 0.1–0.2%. Furthermore, the cost of durvalumab itself presents a complex variable. Although we estimated potential mean cost decreases of durvalumab per patient ranging from €9785 (14.3%) to €21,433 (21.7%) based on treatment duration and TDM approach, the actual price is often confidential. This lack of transparency underscores the limitations of relying solely on reimbursed prices for cost projections. Nevertheless, since the reduction in drug use remains consistent across strategies, the proportional cost savings will persist regardless of the actual vial price. In addition, the potential cost savings from TDM-based strategies are subject to significant variability across healthcare systems and regions. Factors such as local median and maximum treatment durations, healthcare practices, resource availability, and economic conditions contribute to this variability.
5 Conclusion
TDM-based dosing offers a key advantage over weight-based, fixed, and dose-banding strategies by tailoring treatment to individual pharmacokinetics, thereby improving drug utilization, minimizing unnecessary exposure, and reducing the risks of undertreatment or overtreatment. As our approach allows for dose adjustments based on durvalumab concentrations in both steady-state and non–steady-state conditions, it enables individualized dosing throughout the entire treatment course—from initiation to maintenance. Although this study was based on modeling and simulation, the results lay a solid foundation for studying the non-inferiority of our proposed dosing strategy, potentially in combination with additional cost-saving practices such as vial sharing, in lung cancer patients.
Declarations
Conflict of interest
Financial interest: A.v.d.W. has relationships with AstraZeneca, Boehringer Ingelheim GmbH, Pfizer Inc, F. Hoffmann-La Roche Ltd, Takeda Oncology, Bristol Myers Squibb Co., and Eli Lilly and Company, involving consulting, advisory roles, funding grants, and speaking fees. D.D. has consulting and speaking engagements with Bristol Myers Squibb Co., Merck & Co. Inc., AstraZeneca, Roche, and Pfizer. M.v.d.H. received financial support from the Radboudumc Department of Respiratory Diseases. The parties of interest had no role in the study’s design, data collection, analysis, interpretation, manuscript writing, or the decision to publish. Non-financial interest: A.v.d.W. holds a fiduciary role on the board of the ROS1ders advocacy group. R.t.H. is affiliated with Radboud University Medical Center. The parties of interest had no role in the study’s design, data collection, analysis, interpretation, manuscript writing, or the decision to publish. Editorial Board membership: D.M. is an editorial board member of Clinical Pharmacokinetics. D.M. was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. No interest: The remaining authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Code availability
The population pharmacokinetic model code is included as Supplementary Information.
Funding
No funds, grants, or other support was received.
Data availability
Data is included as Supplementary Information.
Author contributions
F. de Vries: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Validation, Writing—original draft. E.J.F. Franssen: Conceptualization, Supervision, Resources, Writing—review and editing. A.A.J. Smit: Conceptualization, Writing—review and editing. D.J.A.R. Moes: Writing—review and editing. A.J. van der Wekken: Writing—review and editing. T. Oude Munnink: Writing—review and editing. J.J.M.A. Hendrix: Writing—review and editing. D.W. Dumoulin: Writing—review and editing. S.L.W. Koolen: Writing—review and editing. W. Kievit: Methodology, Writing—review and editing. M.M. van den Heuvel: Conceptualization, Writing—review and editing. R. ter Heine: Conceptualization, Methodology, Supervision, Resources, Writing—original draft. All authors have provided their formal approval for this submission.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
TDM-Based Tailored Dosing of Durvalumab in Lung Cancer Patients: A Comprehensive Population Pharmacokinetic–Pharmacoeconomic Evaluation
Verfasst von
Fenna de Vries
Eric J. F. Franssen
Arthur A. J. Smit
Dirk Jan A. R. Moes
Anthonie J. van der Wekken
Thijs Oude Munnink
Jeroen J. M. A. Hendrikx
Daphne W. Dumoulin
Stijn L. W. Koolen
Wietske Kievit
Michel M. van den Heuvel
Rob ter Heine
Cinausero M, Garattini SK, Minisini AM, Valent F, Riosa C, Iacono D, et al. Incremental oncology workload generated by immunotherapy in the first-year of treatment. J Clin Oncol. 2020;38(15_suppl):e14143. https://doi.org/10.1200/JCO.2020.38.15_suppl.e14143.CrossRef
Rodriguez-Jimenez L, Romero-Martin M, Spruell T, Steley Z, Gomez-Salgado J. The carbon footprint of healthcare settings: a systematic review. J Adv Nurs. 2023;79(8):2830–44. https://doi.org/10.1111/jan.15671.CrossRefPubMed
9.
Baverel PG, Dubois VFS, Jin CY, Zheng Y, Song X, Jin X, et al. Population pharmacokinetics of durvalumab in cancer patients and association with longitudinal biomarkers of disease status. Clin Pharmacol Ther. 2018;103(4):631–42. https://doi.org/10.1002/cpt.982.CrossRefPubMedPubMedCentral
Nederlandse Federatie van Universitair Medische Centra. Collectieve Arbeidsovereenkomst Universitair Medische Centra 2024 t/m 2025. Utrecht: NFU; 2024. p. 92–102.
Ter Heine R, van den Heuvel MM, Piet B, Deenen MJ, van der Wekken AJ, Hendriks LEL, et al. A systematic evaluation of cost-saving dosing regimens for therapeutic antibodies and antibody-drug conjugates for the treatment of lung cancer. Target Oncol. 2023;18(3):441–50. https://doi.org/10.1007/s11523-023-00958-6.CrossRefPubMedPubMedCentral
26.
U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Oncology Center of Excellence. Pharmacokinetic-based criteria for supporting alternative dosing regimens of programmed cell death receptor-1 (PD-1) or programmed cell death-ligand 1 (PD-L1) blocking antibodies for treatment of patients with cancer: guidance for industry. 2022. https://www.fda.gov/media/151745/download. Accessed 3 Sep 2024.
27.
Jimenez-Labaig P, Mohamed F, Tan NJI, Sanna I, El Bairi K, Khan SZ, et al. Expanding access to cancer immunotherapy: a systematic review of low-dose PD-(L)1 inhibitor strategies. Eur J Cancer. 2025;225: 115564. https://doi.org/10.1016/j.ejca.2025.115564.CrossRefPubMed
