Eco Friendly and Budget Smart: An Economic and Environmental Evaluation of Alternative PD-1 and PD-L1 Inhibitor Dosing Regimens

Abstract

Background

Immune checkpoint inhibitors (ICIs) are clinically beneficial but associated with high costs that represent a growing challenge for healthcare budgets and may affect affordability, especially in resource-limited settings. Moreover, the healthcare sector is a significant source of greenhouse gas emissions, and medication-related waste—such as that from vial-based therapies—has been identified as a contributing factor. Alternative dosing strategies could reduce the environmental and financial impact of ICI therapy while maintaining clinical safety and efficacy.

Methods

Population pharmacokinetic simulations were performed using virtual cohorts representative of the original cancer populations treated with ICIs. The analysis was conducted from a Western European hospital perspective, using Dutch public data to estimate costs (based on volume-dependent pricing) and carbon emissions from drug production, travel, and medical waste.

Results

Under the US Food and Drug Administration exposure-matching criteria, optimized dosing regimens reduced drug costs by up to €23,311 (− 28%) and carbon emissions by up to 255 kgCO₂e (− 30%) per patient, depending on the drug and dosing strategy. Using a broader therapeutic window approach, cost savings reached up to €40,135 (− 69%) and carbon reductions up to 501 kgCO₂e (− 63%) per patient. Incorporating vial sharing further increased potential cost savings to €5,721 per patient (− 31%). All estimates reflect European pricing and emissions factors, modeled over an 8-month treatment period.

Conclusions

These findings suggest that optimizing dosing strategies can yield meaningful economic and environmental benefits in ICI therapy while maintaining drug exposure within levels defined by US Food and Drug Administration criteria or broader therapeutic windows. A user-friendly application developed in this study allows users to generate virtual populations and evaluate tailored dosing strategies, facilitating practical implementation in diverse healthcare settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40273-025-01535-7.

Key Points for Decision Makers

Alternative dosing regimens—including fixed or weight-based dosing, extended intervals, and vial sharing—were evaluated to assess the ability to reduce the economic and environmental burden of immune checkpoint inhibitor therapy.

Under US Food and Drug Administration exposure-matching criteria, optimized regimens reduced drug costs by up to €23,311 (− 28%) and carbon emissions by up to 255 kgCO₂e (− 30%) per patient.

Regimens aligned with broader therapeutic windows achieved cost reductions of up to €40,135 (− 69%) and carbon savings of up to 501 kgCO₂e (− 63%) per patient.

Vial sharing offered additional per-patient savings when implemented alongside optimized dosing.

Introduction

Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that restore the immune system’s ability to recognize and remove cancer cells. These therapies—such as pembrolizumab, nivolumab, avelumab, atezolizumab, and durvalumab—have demonstrated significant clinical benefits across a range of cancers. However, the high costs of ICIs may limit access, especially in settings with constrained healthcare resources [1]. Even in high-income countries, rising anticancer drug prices present growing challenges for healthcare budget sustainability [1]. For example, despite being used in only ~12% of patients with cancer in 2021, ICIs accounted for 36% of total oncology drug spending in the USA [2]. A further increase in costs is anticipated because of recent advances that have shifted ICIs from second-line to first-line treatment, allowing their use earlier in the disease course [3]. As a result, global spending on ICIs is projected to rise at an annual rate of 16.4%, reaching $US168.8 billion (approximately €154 billion) by 2032 [4].

In parallel, concern about the environmental footprint of cancer therapies is growing. Combined emissions from hospitals, medical services, and the healthcare supply chain in Organisation for Economic Co-operation and Development countries, China, and India contribute to 5% of their total carbon emissions [5]. Importantly, 70–80% of these healthcare-related emissions are embedded in purchased pharmaceuticals, medical equipment, and related services [6]. Academic research on the environmental impact of ICIs is limited, but one study involving 7,813 individuals in the USA estimated that alternative dosing of pembrolizumab could reduce travel-related carbon emissions by 200,000 kg CO₂e annually (24% reduction) [7]. A Dutch study using a process-based lifecycle assessment at Erasmus University Medical Center estimated that alternative dosing strategies could reduce carbon emissions from pembrolizumab and nivolumab treatments by 21–26% and 9–11%, respectively, from a total annual emissions of 445,000 kg CO₂e and an average of 94 kg CO₂e per dose [8].

In response to these challenges, international efforts—such as the European Society for Medical Oncology Climate Change Task Force and the International Consortium Study on antineoplastic medicine access—reflect an increasing focus on the economic and environmental sustainability of oncology care [9, 10]. These initiatives reflect a shift in medical practice toward considering costs and the environmental burden alongside clinical outcomes. Although patients may consider environmental factors in the context of minor ailments, treatment efficacy and safety remain paramount in serious illnesses; however, many still support environmentally sustainable policies when treatment efficacy and safety remain uncompromised [11].

A growing number of articles have discussed strategies to reduce the wastage of ICIs, focusing primarily on monetary benefits rather than environmental impact, although the two often align [7]. Within these efforts, several strategies have been proposed to reduce the costs of ICIs, primarily centered on the volume-based pricing model, where reductions in drug volumes directly correlate with cost savings (Fig. 1). These key strategies include the following:

• Method I: dose reduction—administering lower doses of the drug [12]

• Method II: less frequent dosing—extending the interval between doses to reduce the total number of treatments required [12–15]

• Method III: weight-based to fixed dosing—transitioning from weight-based to fixed doses to reduce drug wastage by using entire vials [16, 17].

• Method IV: vial sharing between patients has been proposed to reduce drug wastage effectively [18–20].

Fig. 1.

Workflow of evaluated dosing methods and outcomes. Visual representation of the workflow used to identify alternative dosing regimens. The assessed dosing methods include lower doses (method I), less frequent dosing (method II), weight-based dosing versus fixed dosing (method III), and vial sharing between patients (method IV). Each dosing method was evaluated according to two targets: Target I, which pertains to the US Food and Drug Administration (FDA) criteria for supporting alternative dosing regimens of programmed cell death receptor-1 (PD-1) or programmed cell death-ligand 1 (PD-L1)-blocking antibodies and Target II, which refers to the percentage of individuals achieving levels within the specified therapeutic window for each drug. Following the initial exposure evaluation, the outcomes of each dosing regimen were compared in terms of financial and environmental impacts. AUC area under the plasma concentration–time curve, C_max maximum trough concentration, C_min minimum trough concentration

To evaluate the feasibility of alternative dosing strategies, one must consider whether lower or less frequent doses still maintain drug levels within a therapeutic range that ensures clinical benefit. This is typically assessed using pharmacokinetic metrics that determine whether alternative regimens keep drug levels within a safe and effective range (Fig. 1), assuming consistent efficacy and safety within the defined pharmacokinetic exposure boundaries (Target I). One regulatory approach, used by the US Food and Drug Administration (FDA), permits alternative dosing strategies if the resulting drug exposure (i.e., the concentration of drug in the body over time) is similar to that of the originally approved regimen [1, 21]. This method—often referred to as "exposure matching"—relies on pharmacokinetic simulations to compare new dosing schedules against those tested in pivotal clinical trials. It has been used to support modified dosing regimens for ICIs, particularly for those targeting programmed cell death receptor-1 (PD-1) and programmed cell death-ligand 1 (PD-L1). In this context, the FDA accepts that clinical outcomes can be assumed comparable if the new regimen produces exposure levels within a predefined range of the original dose (Target II). A second, more flexible approach uses trough concentration targets (minimum trough concentration [C_min]) established during earlier drug development, under the assumption that efficacy is maintained above a minimum concentration [12, 22–25]. Unlike FDA criteria, C_min-based strategies may allow lower drug exposures, based on the assumption that the therapeutic effect of the drug plateaus at lower doses than currently approved [22]. However, this saturation point can vary by cancer type and may not reflect how much drug actually reaches the tumor. Moreover, saturation is usually measured in blood samples, which may not represent drug levels at the tumor site, especially for large, water-soluble molecules such as monoclonal antibodies [26]. As such, whereas alternative doses based on the FDA criteria allow for direct clinical application, those based on the current C_min exposure targets would require additional clinical assessments.

Overall, much effort has been put into evaluating the potential for alternative dosing strategies, with modeling and simulations playing a central role [1]. However, most original research focuses on comparing two methodologies within a specific patient population and is often limited to one or two drugs. This narrow focus makes it difficult to compare the economic benefits of methods with each other. Additionally, it is challenging to understand how results may translate to other patient populations or how they might differ between various ICIs. This variability could be the reason for contradictory findings, such as differing conclusions about the relative cost implications of weight-based versus fixed-dosing methods [16–18, 27]. Lastly, little emphasis has been placed on the potential environmental impact of alternative dosing practices.

Although individual studies have explored cost or exposure impacts of specific ICIs, a comprehensive comparison across multiple agents, dosing methods, and environmental parameters is still lacking. The aim of this study was to provide a practical and comparative evaluation of cost-saving strategies across multiple ICIs. The results are designed to inform hospital-level purchasing and policy decisions and offer clinicians tools to assess cost- and emission-reducing alternatives without compromising clinical care. To address this, we adopted a systematic approach by comparing different alternative dosing methods (Methods I–IV) for PD-1 and PD-L1 compounds. We used both the (Target I) FDA criteria and (Target II) a therapeutic window to assess exposure target attainment. We conducted a cost-minimization analysis from the hospital perspective using exposure as the primary endpoint, assuming similar efficacy and safety outcomes within the exposure targets. Through model-based simulations, we evaluated the impact of alternative doses, dosing frequencies, and vial-sharing strategies on the percentage of patients within the target range, total costs, and the estimated carbon dioxide equivalent footprint. Additionally, we developed a user-friendly tool to facilitate the assessment of alternative patient populations and treatment conditions, allowing simulations of the above scenarios based on specified inputs.

Methods

Generation of Virtual Patients

This study focused on five commonly used ICIs (pembrolizumab, nivolumab, avelumab, atezolizumab, and durvalumab) representing both PD-1 and PD-L1 inhibitors approved for a broad range of cancer indications. The evaluation was drug specific but not limited to a single cancer type; models were based on pooled population pharmacokinetic data across multiple tumor types, as used in regulatory submissions.

Population pharmacokinetic models [28–32] for each drug were sourced from peer-reviewed studies based on data from phase I–III clinical trials, developed by the license holders. Further details regarding model assumptions, covariate distributions, and exposure targets are available in electronic supplementary material (ESM)-1. Simulations and analyses were conducted in R (version 4.3.3), with models implemented using the mrgsolve package (version 1.1.1). Each model was validated by visually comparing simulated outcomes with published concentration–time profiles to ensure accuracy.

A theoretical virtual population of 1000 adults with cancer was generated to reflect real-world heterogeneity in key covariates such as body weight, age, and sex. Body weight was modeled with a log-normal distribution (median 76.7 ± standard deviation 23.5 kg), approximating values reported in pooled ICI clinical trials. Other covariates were simulated based on distributions reported in original model publications. The same population was used across all dosing simulations to allow consistent cross-comparison of exposure, cost, and carbon impact.

Impact of Alternative Dosing Strategies

Testing Alternative Doses and Intervals

To evaluate the impact of alternative dosing strategies on cost and emission, we systematically tested different doses and administration intervals for each medication. Doses were varied from zero up to two to three times the originally approved dose, using increments aligned with commercially available vial sizes (Method I: dose reduction). This upper boundary reflects current clinical practice, where several PD-1/PD-L1 inhibitors have been reapproved at fixed doses that are substantially higher than their original weight-based counterparts (e.g., nivolumab 480 mg every 4 weeks [Q4W] vs 3 mg/kg every 2 weeks [Q2W] [33]). Administration intervals were assessed from 1 to 12 weeks, in weekly increments (Method II: less frequent dosing), reflecting ranges that are either approved or currently under clinical investigation for PD-1/PD-L1 inhibitors [14, 15]. Each medication was evaluated using both fixed and weight-based dosing regimens across various dose combinations and intervals (Method III: weight-based to fixed dosing), to reflect both traditional and increasingly common fixed-dose approvals in clinical practice. An optimization algorithm developed in R was used to identify the vial combination that minimized drug wastage while meeting the required dose per patient. The algorithm searched all exact combinations of commercially available vial sizes, rounding up when necessary, and accounted for vial-related CO₂e emissions based on estimated drug volume per vial. The methodological approach was structured in accordance with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [33].

Vial Sharing

An additional analysis investigated the potential benefits of vial sharing (Method IV). The virtual population was randomly divided into groups of five patients (ranging from two to eight, based on a study in which pembrolizumab or nivolumab were administered across multiple cancer types [18]) to represent those receiving treatment at the same hospital and time [12]. The optimization algorithm calculated the required vial sizes and quantities for each group rather than for individual patients. Further details regarding the vial sharing assumptions and methodology are available in ESM-1.

Exposure Targets for the Alternative Dosing Strategies of ICIs (PD-1/PD-L1 Inhibitors)

Two exposure targets were evaluated under the assumption of therapeutic equivalence, that is, that comparable systemic exposures yield similar clinical efficacy and safety outcomes. This is consistent with how exposure–response relationships are interpreted in regulatory science and early phase drug development.

FDA Criteria: Exposure Alignment

The first approach used FDA exposure-matching criteria (Fig. 1: Target I), which recommend comparing new dosing strategies against exposures observed in pivotal trials for each PD-1/PD-L1 inhibitor, two major classes of ICIs [21]. To meet regulatory standards for alternative dosing regimens, average drug exposures under the new regimen must remain within an acceptable range compared with the original, trial-based dosing regimen. Specifically, the geometric mean of the area under the plasma concentration–time curve (AUC)—or the average concentration—as well as the C_min at steady state or after the first dose, should not fall more than 20% below those observed with the reference regimen [21]. Additionally, the peak plasma concentration (C_max) at steady state should not exceed that of the reference by more than 25%, unless prior clinical data demonstrate that higher exposures are not linked to increased safety risks [21]. The list of reference regimens used for comparison is provided in Table 1 under the column “Originally approved regimen.”

Table 1.

Overview of characteristics and pharmacokinetic models for the simulation of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) compounds

Drug	Originally approved regimena	Approved alternative flat dose regimen(s)	PK models used	Target exposures (therapeutic index)	Available vial sizes (mg)
Atezolizumab	1200 mg Q3W	840 mg Q2W 1680 mg Q4W	Stroh et al. [29]	Cmin: 6 mg/L [12] Cmax based on 1680 mg Q3W regimen: 1323 mg/L (201)	840, 1200
Avelumab	10 mg/kg Q2W	800 mg Q2W	Wilkins et al. [28]	Cmin: 6 mg/L (61) Cmax based on 20 mg/kg Q2W regimen: 821 mg/L (137)	200
Durvalumab	10 mg/kg Q2W	1500 mg Q4W	Baverel et al. [32]	Cmin: 50 mg/L (12) Cmax based on 1500 mg Q2W regimen: 1291 mg/L (26)	120, 500
Nivolumab	3 mg/kg Q2W	240 mg Q2W 480 mg Q4W	Bajaj et al. [30]	Cmin: 10 mg/L (62) Cmax based on 10 mg/kg Q2W regimen: 637 mg/L (64)	40, 100, 120, 240
Pembrolizumab	2 mg/kg Q3W	200 mg Q3W 400 mg Q6W	Ahamadi et al. [31]	Cmin: 10 mg/L (54) Cmax based on 10 mg/kg Q3W regimen: 556 mg/L (56)	100

C_max maximum concentration, C_min minimum concentration, PK pharmacokinetic, QXW every X weeks

^aReference regimen in simulations

Steady state, the point at which drug concentrations plateau with repeated dosing, was conservatively defined as > 657 days (94 weeks) to ensure stability across all dosing frequencies. The reference regimen for each drug was defined as the dose and schedule used in pivotal clinical trials that led to regulatory approval. Each alternative regimen was simulated with the corresponding virtual cohort of 1000 patients. If the average exposure met all FDA thresholds, the regimen was classified as compliant; otherwise, it was deemed non-compliant.

Therapeutic Window: Exposure Window

The second exposure target utilized a “therapeutic window” approach (Fig. 1: Target II), setting a target C_min based on drug development benchmarks. This method defines acceptable exposure based on thresholds observed during drug development rather than requiring close alignment with trial-based reference regimens. To prevent the selection of excessively high doses by the search algorithm, the maximum allowable exposure was capped using the 95th percentile of simulated C_max,ss values across all approved or trial-tested regimens for a virtual cohort of 10,000 patients. This approach ensured the dosing regimen stayed within clinically evaluated safety limits, while still allowing flexibility in dose optimization. By using the 95th percentile of observed exposures, the dose was confined to a safe range, aligned with previously tested doses, thus avoiding extrapolation beyond established clinical data and mitigating potential safety risks. Unlike the FDA criteria, this approach evaluated whether each individual patient’s C_min and C_max remained within the therapeutic window, summarizing results as the percentage of patients meeting these targets.

Determining Costs and Carbon Emission

As therapeutic equivalence was assumed, no health outcomes or patient preference utilities were modeled; the analysis instead focused on cost and environmental impact per treated patient cohort. A detailed breakdown of all costing and carbon emission assumptions, sources, and unit values is provided in Table S1 in the ESM. The methodological approach was structured in accordance with the CHEERS statement [34], with detailed item mapping provided in Table S2 in ESM-1. Although the analysis does not adopt a formal country-specific perspective, the unit cost and carbon estimates were selected to broadly reflect a Western European hospital context. Cost and emission data were primarily sourced from publicly available Dutch health studies and hospital data, and costs are expressed in €, year 2023 values. Costs were calculated by aggregating direct drug costs and associated administration expenses, including healthcare personnel and facility usage per administration event. The analysis assumed an 8-month treatment period, reflecting the typical duration of clinical trials and real-world studies, though this may vary by indication and therapy [35]. Given the short time horizon, no discounting of costs or emissions was applied.

Environmental impact was assessed by quantifying carbon emissions from three sources. First, emissions from drug production and distribution, and incineration of vials, were calculated, assuming 0.021 kg CO₂e per mg of drug produced and 0.066 kg CO₂e per vial (Table S1 in ESM-1) [36, 37]. Second, the carbon footprint associated with patient travel to the hospital was included, assuming private car travel averaging 60 km per visit based on estimates from a Dutch healthcare setting, resulting in 39.7 kg CO₂e per administration [38, 39]. Third, emissions from single-use items generated during ICI administration were estimated at 0.14 kg CO₂e per administration [38].

Shiny Application

To facilitate the evaluation of alternative dosing approaches, we developed a specialized application using the R package Shiny (version 1.8.1.1). This application allows users to compare two different dosing regimens for each investigated drug, adhering to the methodology and code framework of the primary simulations. It offers the flexibility to modify virtual population demographics, incorporate alternative cost structures, and adjust dosing intervals to fit specific scenarios (e.g., hospital, country). Users can also customize exposure targets and input banded dosing regimens. Additionally, the application provides tools for calculating cost efficiencies under vial-sharing scenarios.

Results

Virtual Patients and Simulation Settings

The selected pharmacokinetic models, reference dosing regimens (based on FDA criteria) and target exposures (for therapeutic windows) are outlined in Table 1. Table 2 presents a summary of both approved dosing regimens and the five lowest-cost alternative dosing approaches adhering to the FDA exposure criteria (Target I), including associated cost and carbon savings, for each drug. Table 3 provides similar information for the five lowest-cost alternative dosing regimens following the therapeutic window criteria (Target II). Detailed results for each individual drug, including all potential alternative regimens for each drug are listed in Table S3 in ESM-2. An overview of the population covariate distributions and simulation assumptions used, including the vial sharing methodology, is available in Table S1 in ESM-1. Reporting of the economic analysis was guided by CHEERS 2022, to ensure transparent and comprehensive documentation of methods, assumptions, and limitations (see Table S2 in ESM-1).

Table 2.

Approved dosing regimens and alternative doses following US Food and Drug Administration (FDA) criteria (Target I)

Dosing regimena	Adhering to FDA criteria (first common interval/steady state)	Pts in therapeutic window (first dose/steady state), %	Costs (% reduction), €b	Costs after vial sharing, €b	Carbon in kgCO2e (% reduction)b
Atezolizumab
Approved regimens
1200 mg Q3W	yes/yes	100/99.9	58,435 [reference]	58,435	791 [reference]
840 mg Q2W	yes/yes	100/100	65,880 (13)	65,880	1046 (32)
1680 mg Q4W	yes/no	100/95.4	58,435 (0)	58,435	687 (− 13)
Alternative regimen
1440 mg Q4W	yes/yes	99.4/100	58,342 (0)	51,189	687 (− 13)
1560 mg Q4W	yes/yes	99.9/99.4	58,342 (0)	54,925	687 (− 13)
1080 mg Q3W	yes/yes	100/99.8	58,435 (0)	54,031	790 (0)
13 mg/kg Q3W	yes/yes	100/100	59,524 (2)	52,429	798 (1)
20 mg/kg Q4W	yes/yes	100/99.8	61,510 (5)	55,997	707 (− 11)
Avelumab
Approved regimens
10 mg/kg Q2W	yes/yes	98.3/98.5	71,851 [reference]	66,840	1100 [reference]
800 mg Q2W	yes/yes	98.8/99.0	66,053 (− 8)	66,053	1061 (− 4)
Alternative regimen
700 mg Q2W	yes/yes	98.1/98.3	66,053 (− 8)	60,243	1061 (− 4)
800 mg Q2W	yes/yes	98.8/99.0	66,053 (− 8)	66,053	1061 (− 4)
9 mg/kg Q2W	yes/yes	97.6/98.2	66,626 (− 7)	61,819	1065 (− 3)
11 mg/kg Q2W	yes/yes	98.6/99.0	76,949 (7)	71,884	1135 (3)
12 mg/kg Q2W	yes/yes	98.6/99.0	81,957 (14)	76,992	1169 (6)
Durvalumab
Approved regimens
10 mg/kg Q2W	yes/yes	71.2/99.2	91,212 [reference]	87,778	1046 [reference]
1500 mg Q4W	no/no	83.8/93.9	76,118 (− 17)	76,118	655 (− 37)
Alternative regimen
1080 mg Q3W	yes/yes	81.1/98.1	75,888 (− 17)	75,888	762 (− 27)
1100 mg Q3W	yes/yes	83.3/99.2	77,107 (− 15)	77,107	768 (− 27)
1120 mg Q3W	yes/yes	85.3/98.6	78,326 (− 14)	78,326	773 (− 26)
640 mg Q2W	yes/yes	51.1/98.8	80,914 (− 11)	73,700	1002 (− 4)
660 mg Q2W	yes/yes	60.3/98.9	80,914 (− 11)	75,428	1002 (− 4)
Nivolumab
Approved regimens
3 mg/kg Q2W	yes/yes	94.0/99.8	64,073 [reference]	62,647	838 [reference]
480 mg Q4W	no/no	94.0/96.4	55,231 (− 14)	55,231	476 (− 43)
360 mg Q3W	yes/no	97.2/98.8	57,744 (− 10)	57,744	596 (− 29)
240 mg Q2W	yes/yes	96.8/99.8	62,770 (− 2)	62,770	835 (0)
Alternative regimen
320 mg Q3W	yes/yes	93.9/98.5	52,445 (− 18)	52,445	583 (− 30)
200 mg Q2W	yes/yes	90.3/99.5	54,821 (− 14)	54,821	815 (− 3)
340 mg Q3W	yes/yes	95.3/98.7	55,094 (− 14)	55,094	589 (− 30)
220 mg Q2W	yes/yes	93.7/99.6	58,795 (− 8)	58,795	825 (− 3)
240 mg Q2W	yes/yes	96.8/99.8	62,770 (− 2)	62,770	835 (0)
Pembrolizumab
Approved regimens
2 mg/kg Q3W	yes/yes	44.4/92.7	82,329 [reference]	67,668	537 [reference]
200 mg Q3W	yes/no	76.9/96.1	78,691 (− 4)	78,691	534 (− 1)
400 mg Q6W	yes/no	82.6/75.5	73,666 (− 11)	73,666	295 (− 45)
Alternative regimen
200 mg Q4W	yes/yes	57.9/86.7	59,018 (− 28)	59,018	401 (− 25)
100 mg Q2W	yes/yes	17.8/94.9	66,557 (− 19)	66,557	759 (41)
2 mg/kg Q3W	yes/yes	44.4/92.7	82,329 (0)	67,668	537 (0)
2 mg/kg Q2W	yes/yes	72/99.3	123,494 (50)	101,656	806 (50)
100 mg Q1W	yes/yes	63.9/100	129,416 (57)	129,416	1477 (175)

kCO₂e metric tons of carbon dioxide equivalent, pts patients, QXW every X weeks

^aThe approved (reference) regimens are shown in bold and are based on dosing schedules from pivotal phase III clinical trials that supported regulatory approval, as listed in Table 1 under “Originally approved regimen” and cited in the main text

^bTotal average treatment cost and carbon emission over the entire treatment period. % calculated as $\frac{\text{alternative cost}/\text{carbon}-\text{reference cost}/\text{carbon}}{\text{reference cost}/\text{carbon}}\times 100$. Positive percentages indicate an increase in cost or emissions compared with the reference regimen, whereas negative percentages indicate a reduction

Table 3.

Approved dosing regimens and alternative doses following the therapeutic window (Target II)

Dosing regimena	Adhering to FDA criteria (first common interval/steady state)	Pts in therapeutic window (first dose/steady state), %	Costs (% reduction), €b	Costs after vial sharing, €b	Carbon kgCO2e (% reduction)b
Atezolizumab
Approved regimens
1200 mg Q3W	yes/yes	100/99.9	58,435 [reference]	58,435	791 [reference]
840 mg Q2W	yes/yes	100/100	65,880 (13)	65,880	1046 (32)
1680 mg Q4W	yes/no	100/95.4	58,435 (0)	58,435	687 (− 13)
Alternative regimen
600 mg Q7W	no/no	90.8/91.7	18,300 (− 69)	14,499	290 (− 63)
720 mg Q7W	no/no	92.6/93.7	18,300 (− 69)	16,566	290 (− 63)
840 mg Q7W	no/no	94.7/94.9	18,300 (− 69)	18,300	290 (− 63)
840 mg Q8W	no/no	90.1/90.8	18,300 (− 69)	18,300	290 (− 63)
6 mg/kg Q7W	no/no	87.9/90.1	18,445 (− 68)	12,724	291 (− 63)
Avelumab
Approved regimens
10 mg/kg Q2W	yes/yes	98.3/98.5	71,851 [reference]	66,840	1100 [reference]
800 mg Q2W	yes/yes	98.8/99.0	66,053 (− 8)	66,053	1061 (− 4)
Alternative regimen
400 mg Q2W	no/no	90.8/93	40,565 (− 44)	40,565	890 (19)
5 mg/kg Q2W	no/no	86.8/90.8	46,720 (− 35)	41,573	931 (− 15)
6 mg/kg Q2W	no/no	91.3/93.6	51,499 (− 28)	46,682	963 (− 12)
500 mg Q2W	no/no	92.5/94.3	53,309 (− 26)	47,477	976 (− 11)
600 mg Q2W	no/no	96.9/98	53,309 (− 26)	53,309	976 (− 11)
Durvalumab
Approved regimens
10 mg/kg Q2W	yes/yes	71.2/99.2	91,212 [reference]	87,778	1046 [reference]
1500 mg Q4W	no/no	83.8/93.9	76,118 (− 17)	76,118 ()	655 (− 37)
Alternative regimen
720 mg Q3W	no/no	36.7/91.8	53,942 (− 41)	53,942	668 (− 36)
740 mg Q3W	no/no	41.6/92.7	55,162 (− 40)	55,162	673 (− 36)
9 mg/kg Q3W	no/no	32.5/90.5	56,143 (− 38)	53,696	677 (− 35)
10 mg/kg Q3W	no/no	44.2/92.5	60,808 (− 33)	58,514	697 (− 33)
6 mg/kg Q2W	no/no	19.8/94.5	63,092 (− 31)	58,883	925 (− 13)
Nivolumab
Approved regimens
3 mg/kg Q2W	yes/yes	94.0/99.8	64,073 [reference]	62,647	838 [reference]
480 mg Q4W	no/no	94.0/96.4	55,231 (− 14)	55,231	476 (− 43)
360 mg Q3W	yes/no	97.2/98.8	57,744 (− 10)	57,744	596 (− 29)
240 mg Q2W	yes/yes	96.8/99.8	62,770 (− 2)	62,770	835 (0)
Alternative regimen
100 mg Q2W	no/no	23.0/91.8	34,949 (− 45)	34,949	767 (− 9)
200 mg Q3W	no/no	70.6/93.5	36,547 (− 43)	36,547	544 (− 35)
300 mg Q4W	no/no	77.4/90.6	37,346 (− 42)	37,346	432 (− 48)
120 mg Q2W	no/no	42.6/96.3	38,923 (− 39)	38,923	776 (− 7)
220 mg Q3W	no/no	75.9/95.0	39,197 (− 39)	39,197	550 (− 34)
Pembrolizumab
Approved regimens
2 mg/kg Q3W	yes/yes	44.4/92.7	82,329 [reference]	67,668	537 [reference]
200 mg Q3W	yes/no	76.9/96.1	78,691 (− 4)	78,691	534 (− 1)
400 mg Q6W	yes/no	82.6/75.5	73,666 (− 11)	73,666	295 (− 45)
Alternative regimen
100 mg Q2W	yes/yes	17.8/94.9	66,557 (− 19)	66,557	759 (41)
200 mg Q3W	yes/no	76.9/96.1	78,691 (− 4)	78,691	534 (− 1)
3 mg/kg Q4W	yes/yes	69.7/90.0	80,949 (− 2)	71,214	418 (− 22)
2 mg/kg Q3W	yes/yes	44.4/92.7	82,329 (0)	67,668	537 (0)
300 mg Q4W	yes/no	86.7/93.4	84,758 (3)	84,758	422 (− 22)

FDA US Food and Drug Administration, pts patients, QXW every X weeks

^aThe approved (reference) regimens are shown in bold and are based on dosing schedules from pivotal phase III clinical trials that supported regulatory approval, as listed in Table 1 under “Originally approved regimen” and cited in the main text

^bTotal average treatment cost and carbon emission over the entire treatment period. % calculated as $\frac{\text{alternative cost}/\text{carbon}-\text{reference cost}/\text{carbon}}{\text{reference cost}/\text{carbon}}\times 100$. Positive percentages indicate an increase in cost or emissions compared with the reference regimen, whereas negative percentages indicate a reduction

Cost-reduction Analysis Across Alternative Dosing Strategies

Decreased Dose Versus Increased Interval

For atezolizumab, combining extended dosing intervals with reduced doses yielded the greatest cost savings (FDA criteria [Target I] atezolizumab 1560 mg Q4W, Table 2; therapeutic window [Target II] atezolizumab 600 mg every 7 weeks, Table 3). Avelumab showed cost reductions primarily through dose reduction alone. For durvalumab, cost savings primarily came from dosage reductions, with some dosing regimens including extended intervals. Nivolumab demonstrated cost savings through dose reduction while maintaining approved dosing intervals. Pembrolizumab benefited from both extended intervals and dose reduction but not from a combination of these strategies.

Weight-Based Dosing Versus Fixed Dosing

Across all drugs, fixed dosing regimens were generally less costly than weight-based dosing, though the differences were modest (Tables 2 and 3, Fig. 2). As Tables 2 and 3 emphasize, for atezolizumab and avelumab, weight-based dosing yielded cost and emission reductions comparable to the most efficient fixed-dose regimens. In contrast, for durvalumab and nivolumab, fixed dosing was superior in reducing both costs and emissions. However, pembrolizumab was an exception: weight-based dosing resulted in lower overall costs than fixed dosing.

Fig. 2.

Density of cost distribution for alternative dosing strategies. Cost distributions for each alternative dosing strategy, as grouped by dosing regimens meeting the US Food and Drug Administration (FDA) exposure criteria and those meeting the therapeutic window requirements. Dashed vertical lines indicate the costs associated with the reference dosing regimen

Vial Sharing

Vial sharing across patients led to reduced costs and carbon emissions for all weight-based dosing regimens, with varying degrees of impact (Tables 2 and 3, Fig. 3). As highlighted in the tables and the figure, the most notable savings were observed with atezolizumab, avelumab, and pembrolizumab. For atezolizumab, vial sharing made weight-based dosing regimens more financially and environmentally advantageous than fixed-dose regimens (Table S1 in ESM-1).

Fig. 3.

Histogram of cost decrease due to vial sharing. Distribution in cost decrease resulting from vial sharing for each alternative dosing strategy, grouped by dosing regimens meeting US Food and Drug Administration (FDA) criteria and those meeting the therapeutic window requirements. Cost decrease = ((cost before vial sharing − cost after vial sharing) / cost before vial sharing) × 100%

FDA Exposure Criteria

Dosing regimens based on FDA criteria were generally associated with higher costs than those derived from the therapeutic window approach (Tables 2 and 3, Fig. 2). This was particularly evident with atezolizumab, where FDA-based regimens showed almost no cost reduction, whereas the therapeutic window approach identified regimens with up to a 69% reduction in costs. However, pembrolizumab was an exception, where the most cost-efficient regimen based on FDA criteria outperformed the best regimen identified using the therapeutic window approach.

Therapeutic Window Exposure Criteria

The percentage of patients remaining within the therapeutic window varied across drugs. For avelumab, durvalumab, nivolumab, and pembrolizumab, <90% of patients reached the therapeutic window after the first dosing interval both for approved doses and for suggested alternative doses (Table 3). These percentages improved at steady state. Specifically, pembrolizumab showed low percentages within the therapeutic window after the initial dose, with only 44.5% of patients falling within the therapeutic window at the approved regimen of 2 mg/kg every 3 weeks (Q3W).

Interactive Application

The Shiny application is available for public access at: https://leokarlsson4558.shinyapps.io/PD1_and_PDL1_inhibitor_dosing_comparison/. Detailed instructions and a description of the application’s outputs are provided directly on the website.

Discussion

This study investigated alternative dosing regimens for ICIs, including weight-based, fixed, and varied dosing frequencies and vial sharing, to reduce economic and environmental impact while maintaining efficacy and safety. Although the analysis did not adopt a country-specific healthcare payer perspective, the cost and emission estimates were primarily derived from Dutch sources and reflected a Western European hospital context. A user-friendly application was developed to allow tailoring of dosing strategies based on virtual populations and location-specific variables. Our results demonstrate that alternative dosing based on FDA criteria can reduce costs by up to 28% and carbon emissions by up to 30%. In comparison, dosing strategies guided by the therapeutic window yielded cost reductions of up to 69% and emission reductions of up to 63%. Furthermore, vial sharing provided additional cost savings of up to 31% when used in conjunction with the lowest-cost dosing regimens, as summarized in Tables 2 and 3. These findings suggest that implementing alternative dosing strategies can reduce costs and environmental impact without compromising therapeutic efficacy.

Numerous studies have investigated alternative dosing regimens for atezolizumab, nivolumab, and pembrolizumab [1, 13–15, 18, 19, 40]. Our findings indicate that alternative dosing regimens for atezolizumab and nivolumab can substantially reduce costs, which may explain the extensive research focus on these agents. For instance, a study of nivolumab dosing reported a 66% cost reduction with C_min,ss levels above the threshold in over 90% of patients, which aligns with our results showing a 47% cost reduction and over 90% of patients achieving target concentrations [15]. Similarly, a recent study in real-world patients suggested that annual drug costs with atezolizumab could be reduced from €68,153 to €16,200 or €14,007 per patient, depending on the de-escalation strategy (i.e. shifting from 1200 mg Q3W to 1200 mg every 12 weeks, or alternatively 92 mg Q3W), while maintaining trough levels above the target of 6 mg/L [41, 42]. In contrast, avelumab and durvalumab have been less thoroughly examined. The high target C_min of durvalumab of 50 mg/L, potentially due to differences in binding affinity [43] or exposure target determination, and shorter half-life (21 vs 27 days for atezolizumab) limit opportunities for cost savings [12, 16]. Similarly, the shorter half-life of 6 days for avelumab restricts the extension of dosing intervals, making dose reductions necessary for cost savings (Tables 2 and 3). Additionally, avelumab exposure–response studies have suggested that dose adjustments could affect efficacy [12], although this effect may be confounded by disease status, as a matched analysis found no significant difference between exposure groups [44].

Weight-Based Dosing Versus Fixed Dosing

The results of our study reinforce previous findings that highlight cost reduction as the primary driver of the transition from weight-based to flat dosing for ICIs [17]. Flat dosing regimens are particularly effective in minimizing drug waste when vial sizes align with the prescribed doses (Tables 1, 2, and 3). Notably, some studies report significant savings with weight-based dosing, with reductions of 16–24% for pembrolizumab and 14% for nivolumab [27, 45, 46]. However, these studies frequently overlooked the cost implications of unused drug inherent in weight-based dosing. Our vial sharing strategies, which minimize waste, demonstrate that weight-based dosing can be more cost efficient under such conditions. Given that hospitals bear the cost of entire vials rather than merely that of the used drug, our findings offer a more realistic cost representation without vial sharing. Consequently, fixed doses result in lower overall costs when accounting for vial-based drug wastage [27].

Decreased Dose Versus Increased Interval

For atezolizumab, the lowest-cost strategies involved extending dosing intervals and reducing doses, with maximum cost reduction achieved through a combination of these approaches, owing to its wide therapeutic window (therapeutic index 201 vs 26–137 for other drugs). Pembrolizumab also shows benefits from extended dosing intervals and dose reductions, though not in combination, likely due to its fixed 100 mg vial size, which restricts dose adjustments at higher intervals. For avelumab, 2-week dosing intervals were recommended because of its short half-life, with cost savings primarily achieved through dose reductions. In the case of durvalumab and nivolumab, recommended regimens aligned with approved intervals, with cost reductions driven mainly by substantial dose reductions, probably driven by the availability of multiple vial sizes and low C_max thresholds, which limit the potential for high doses and extended intervals. In general, extended dosing intervals can reduce the logistical burden on healthcare centers and contribute to improved patient workforce participation by reducing the frequency of hospital visits, while also potentially improving efficacy by optimizing administration timing [47]. However, schedules of concurrent medications should be taken into consideration.

Vial Sharing

Vial sharing yielded modest to moderate cost savings (0–31%) across the most economical dosing regimens in Tables 2 and 3 (calculated as ((cost after vial sharing − cost before vial sharing) / cost before vial sharing) × 100%), with the most significant reduction for atezolizumab, where vial sharing made a previously more expensive regimen comparatively less costly (e.g., €58,435 for reference regimen vs €25,848 for 120 mg Q2W; €65,880 without vial sharing, Table S3 in ESM-2). Fixed-dosing regimens generally produced minimal benefit, as vial sizes often matched the required dose, whereas all weight-based regimens resulted in cost reductions. Consistent with previous findings [18, 27], savings from vial sharing were influenced by vial size differences and drug cost per milligram. Atezolizumab (available in 840 mg and 1200 mg vials) showed notable savings, whereas reductions were smaller for nivolumab (40 and 100 mg vials) and durvalumab (120 mg vials), as also clearly visible in Fig. 3. Pembrolizumab, despite its smaller 100 mg vial size, also had savings due to its high cost per milligram. Although most savings were <5%, they still led to significant absolute reductions [19]. Automated systems could further optimize vial sharing for patients with doses scheduled within a 7-day window of each other, reducing waste [20]. In practice, organizing clinics to implement vial sharing requires additional planning, infrastructure, and staff coordination. This could incur additional operational costs depending on the local setup. Simultaneously, the rise of fixed dosing may limit the effectiveness of vial sharing. Increasing the availability of vial sizes or introducing smaller vials, as suggested by prior studies [18, 19], could enhance cost efficiency, as shown by the impact of the now-discontinued pembrolizumab 50 mg vials [19].

Reduction in Carbon Emissions

The carbon emissions of alternative dosing strategies closely mirror cost patterns, with estimated reductions ranging from 0 to 63% (as can be seen in Tables 2 and 3 and in Table S3 in ESM-2). Although the potential environmental benefits are notable, they are only considered because they align with the overarching goal of maintaining effective treatment outcomes. Our predicted 29% reduction in CO₂e emissions for pembrolizumab aligns with a previously reported 24% reduction [7]. The maximum total CO₂e savings across all five drugs range from approximately 39 to 501 kg CO₂e per patient cohort, which is roughly equivalent to the annual carbon footprint of 0.004–0.047 for average EU citizens (based on 10,700 kg CO₂e per capita in 2022) [48]. The importance of accounting for drug packaging is emphasized by the 1560 mg Q4W dosing regimen for atezolizumab, which, despite a small cost reduction, showed higher predicted emissions. Avelumab showed higher emissions than durvalumab and atezolizumab, whereas nivolumab resulted in higher emissions than pembrolizumab (Table 2). This is largely due to differences in drug volume and solution density: durvalumab (50 mg/ml) and atezolizumab (60 mg/ml) are denser than avelumab (20 mg/ml), and pembrolizumab (25 mg/ml) is denser than nivolumab (10 mg/ml).

Although we assumed production emissions to be constant, they may also influence outcomes. Additionally, our analysis did not account for emissions associated with infrastructure—such as facility construction, heating, or fill-and-finish processes—which may contribute modestly to total emissions. Furthermore, our carbon footprint estimates did not include upstream corporate emissions such as sales, general, and administrative costs, which may contribute substantially to pharmaceutical emissions according to recent studies [55]. Lastly, our analysis did not account for the indirect carbon rebound effect—that is, the emissions associated with reinvesting saved healthcare funds elsewhere in the system. In European countries, healthcare spending has been estimated to generate approximately 0.5 kg CO₂e per $US spent (∼€0.87) [49], although this figure is higher in countries with more carbon-intensive healthcare systems. As such, reinvested cost savings could partially offset the environmental benefits of dosing optimizations, depending on how and where the savings are utilized.

FDA Exposure Criteria

The results of this study reveal that some approved alternative dosing regimens do not meet FDA criteria (Table 2), a discrepancy that can be explored using the developed Shiny application. This may be due to differences in virtual populations and covariate distributions compared with previous simulations. More likely, pre-guideline simulations did not adhere strictly to FDA criteria, instead prioritizing efficacy with high C_min or AUC values. Strategies that extend dosing intervals by doubling the dose and interval length maintain AUC values but result in higher C_max values. These findings suggest that FDA guidelines may be excessively stringent, particularly concerning C_max thresholds, which restricts the adoption of alternative dosing regimens, although the guidelines do specify that exceptions to the 125% rule for bioequivalence may be justified if the safety profile is adequately explained [21]. Additionally, finding alternative doses that adhere to FDA exposure guidelines offers the significant advantage of requiring no further clinical studies, allowing for direct clinical implementation.

Therapeutic Window Exposure Criteria

Conversely, using therapeutic windows as the exposure target allows for more flexible dosing regimens, offering greater potential for reducing both financial costs and environmental impact compared with FDA-based exposure criteria, as demonstrated in Fig. 2. However, the use of therapeutic windows is the subject of ongoing debate regarding the adequacy of C_min targets. Several studies have used this target for alternative dosing strategies [12, 22–25], whereas others have argued that they are based on inadequate clinical endpoints and may be set too low [26]. Additionally, concerns arise about increased clearance due to target-mediated drug disposition and potential non-linear clearance at lower exposures [50]. Indeed, although two unrandomized studies demonstrated that nivolumab doses substantially lower than the labeled dose were equally as effective as the approved dose or more effective than no nivolumab [51, 52], a randomized study evaluating low-dose nivolumab at 0.3 mg/kg Q3W (approximately 20 mg for a patient weighing 70 kg) found lower survival than with more standard dosing [53]. Our simulations suggest that intermediate doses (e.g., nivolumab 180 mg Q2W or 340 mg Q3W) may maintain therapeutic exposure; however, given the uncertainty surrounding C_min thresholds, widespread implementation of such regimens would require additional trials to demonstrate therapeutic equivalence. Notably, our cost-minimization analysis assumed equivalent efficacy and safety across compared regimens. However, our findings indicate that fewer patients meet C_min thresholds after the first dose compared with steady state, as concentrations often fall below the C_min threshold, even for approved doses. For drugs such as durvalumab and nivolumab, initiating treatment with a higher loading dose could enhance efficacy early on. Therapeutic drug monitoring and model-informed precision dosing are recommended to ensure exposures remain above C_min or to individualize dosing intervals [12, 13, 54].

Study Limitations

The study's limitations arise largely from assumptions in the simulations. Treatment duration differences [35] and regional, financial, and logistical disparities could also influence outcomes. The analysis was based on currently approved branded products and did not account for the future entry of biosimilars, which could further reduce costs. Conversely, manufacturers may respond to reduced sales volumes from more efficient dosing by raising unit prices, potentially offsetting projected savings. Variations in virtual populations, including patient demographics such as weight, may affect the differences observed between flat and weight-based dosing strategies, potentially influencing the generalizability of results across regions. Simulations were based on current vial sizes and a group size of two to eight for vial sharing. To address all these issues and enhance adaptability to diverse settings, we created a Shiny app that enables users to customize simulations for specific regional, financial, and logistical contexts and change the underlying assumptions. Additionally, we did not account for change in clearance over time (reported average decrease of 22–30% [12, 28, 56–58]), potentially allowing for further dose reductions or increased intervals over treatment duration. This was because the magnitude and direction of time-varying clearance vary significantly between individual patients, as it is influenced by factors such as disease status, treatment response, and cancer type. Consequently, optimized dosing over time would likely require a more individualized approach. Moreover, concerns about dose reductions or interval extensions may arise due to faster clearance at lower doses, likely linked to target-mediated drug disposition. In such cases, the linear population pharmacokinetic model may overestimate low exposures. However, FDA-based dosing strategies are designed to match exposures, reducing this risk. For therapeutic window regimens, proposed dose adjustments should still maintain median steady-state trough concentrations above critical levels [12, 50]. Finally, our analysis assumed an 8-month treatment period. Although this aligns with current clinical practice, there is an opportunity to significantly reduce costs by shortening treatment durations. Such reductions are currently being evaluated in several clinical trials [59]. These studies will need to confirm whether a significant reduction in treatment duration can be achieved without compromising treatment efficacy.

Conclusion

Debate on the optimal dosing of ICIs has been thorough, and evidence that lower doses of ICIs retain efficacy is accumulating. We demonstrate that alternative dosing strategies can help reduce costs and carbon emissions while maintaining treatment efficacy and safety. Dosing strategies based on current C_min exposure targets would require additional clinical assessments before being suitable for clinical implementation, whereas those based on FDA criteria allow for direct clinical application. Using therapeutic window criteria, we identified alternative regimens for each ICI: atezolizumab 600–840 mg Q7–8W, avelumab 400 mg Q2W, durvalumab 680–720 mg Q3W, nivolumab 180 mg Q3W, and pembrolizumab 100 mg Q2W. These strategies reduced costs by up to 69% and carbon emissions by up to 63% while maintaining target exposure levels in over 90% of patients at steady state. However, the assumption of therapeutic equivalence remains unconfirmed for some regimens, particularly those based on C_min thresholds. Further clinical studies are needed to formally establish equivalence in terms of efficacy, safety, and quality of life [12]. Such trials could explore long-term outcomes, healthcare resource use, and environmental benefits. Given the potential for substantial drug savings, these trials may be financially self-sustaining. The Shiny tool to evaluate tailored dosing strategies facilitates practical implementation across diverse healthcare systems and target populations. Therapeutic drug monitoring and model-informed precision dosing could further refine dose adjustments to maintain target thresholds and account for time-varying clearance [12]. Lastly, although many studies, including ours, focused on volume-based pricing, pay-for-performance models could also be considered [60]. These outcome-based models offer additional opportunities for cost savings and incentivize optimal dosing strategies.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

Open access funding provided by Uppsala University. This research was funded by the Swedish Cancer Society (CAN 20 1226 PjF, 23 2921 Pj).

Declarations

Author Contributions

MC, LK, JC, and RH wrote the manuscript. MC and LK designed and performed the research. MC, LK, and RH analyzed the data.

Data Availability

Data will be made available on reasonable request.

Conflicts of Interest

Rob ter Heine has received research funding from Stichting Treatmeds to investigate alternative dosing regimens for pembrolizumab. All other authors have no potential conflicts of interest with respect to the research, authorship, and/or publication of this manuscript.