Pharmacokinetic approach in therapeutic monitoring of antineoplastic drugs and the impact on pharmacoeconomics: A systematic review

Abstract

Objectives

Therapeutic drug monitoring aims to quantify the concentration of a drug in a biological matrix. In oncology, the therapeutic arsenal is vast and therapeutic drug monitoring optimizes treatment and reduces costs. This review will analyze the financial impact of therapeutic monitoring of anticancer drugs in healthcare institutions.

Methods

Keywords were selected using Decs (MeSH). Through the Pubmed, Scopus, and Virtual Health Library (VHL) databases, 74 articles were found, of which 4 meet the inclusion criteria. Methodological quality and risk of bias were assessed according to the Research Triangle Institute Item Bank (RTI-Item Bank) scale.

Key findings

Therapeutic drug monitoring is an important tool for dose reduction or dose increase due to toxicity and lack of response, respectively. The main barriers are associated costs and lack of cost–benefit data. An alternative is to use population pharmacokinetic models, measured plasma concentration(s) and relevant patient characteristics, estimated individual pharmacokinetic parameters, and predicted drug concentrations at any point in the dosing range.

Conclusions

Therapeutic drug monitoring is understood as a technology that adds costs to payers. Future studies should generate clinical evidence of population pharmacokinetics from therapeutic drug monitoring studies.

Keywords

Cancer individualizing therapy economics oncology and chemotherapy

Introduction

Therapeutic drug monitoring (TDM) is intended to quantify the concentration of a drug or substances in a biological matrix, usually, the blood plasma, to assess the safety and efficacy of drug treatment. The result of therapeutic monitoring reflects the concentration of the drug in blood/plasma or less usually the urine, thus enabling the results to be extrapolated to inferences of what is occurring in the tissues. This information reflects the dose–time–effect correlation, o that dose variations alter the plasma concentration and there may be underdosing, therapeutic, or toxic dosing.¹ TDM is a specialty of clinical pharmacokinetics to adjust doses and individualize therapy considering the genetic, demographic, and clinical characteristics of each patient.²

Advances in clinical management bring into discussion the individualization of dosage, considering the characteristics of each individual and their particularities. Drug monitoring is a practice that offers health professionals and users the improvement of health care such as the optimization of therapy and identification of cases of non-adherence to treatment. This process is patient-centered, involving, the quantification in biological fluids of the serum concentration through pharmacokinetic parameters, the evaluation of laboratory parameters, the collection of biological materials at specific times, and the interpretation of results for clinical decision making. TDM occurs through a multidisciplinary team, either in an inpatient or outpatient setting.²

In the clinical evaluation of patients, dose adjustments are generally based on the pharmacological response assessed clinically or through biomarkers, however, some drugs are complex to manage, such as those with a narrow therapeutic range, significant metabolic differences between individuals, and those of therapeutic and toxic effect of difficult distinction when compared, being the pharmacological classes anticonvulsants, antibiotics, antidepressants, immunosuppressants, antineoplastics, cardiotonic, antiarrhythmic, and antiasthmatic consolidated as candidates for therapeutic monitoring.³

In oncology, the therapeutic arsenal is vast with different cancer types, treatment protocols, and various routes of administration. The main lines of cancer treatment are immunotherapy or targeted therapy, chemotherapy, radiotherapy, and, in some types of cancer, halogen transplantation. These options can be used in combinations depending on each patient's background, being the goal of each treatment is to confer the patient with prolonged progression-free survival (PFS), increased overall survival (OS), and reduced relapse.⁴

In therapeutic choices, the primary goal is to provide the patient with a quality of life. Chemotherapeutic drugs have considerable cytotoxicity that causes a variety of adverse events and can affect the functional and social capacity of patients.⁵ Dose individualization with the use of TDM is an important tool for treatment optimization. Dose calculation considering body surface area does not meet the reality of most of these patients that in their majority already have other underlying diseases, develop other immune-mediated diseases, and are polypharmacy, thus increasing the risk of toxicity by the various possible interactions that directly impact on patient compliance and the development of adverse events that may result in ineffective responses or even treatment discontinuation, thus impacting the OS of each patient.⁶

Adverse events in oncologic patients occur considering several factors, such as drug pharmacodynamics (PD) and pharmacokinetics, dosage, routes of administration, and intervals between treatment cycles. The occurrence of limiting toxicities may also be related to individual pharmacokinetic variability in the processes of absorption, distribution, metabolism, and excretion of drugs.⁷

The TDM, in this context, can bring tangible results for health institutions (public and private) in reducing costs, considering that patients with individualized therapies will have fewer occurrences of toxicity, effective treatment with the correct dose for their clinical situation, reduced hospitalization to treat toxic effects, and reduced complications due to treatment failure.⁸

Using this interventional pharmacoeconomic approach, there is an incentive to find ways to minimize the failure rate of therapy to minimize costs and improve outcomes. Cancer treatments are expensive, combined with the increasing prevalence of cancer, which leads to high individual and societal costs of treatment.⁹

Based on this, the present work aims to analyze the impact of therapeutic monitoring of antineoplastic drugs on the pharmacoeconomics of health institutions focused on oncological treatment and discuss its importance in the integral and individualized care of cancer patients.

Methods

A systematic review was conducted on the basis of recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).¹⁰

Search strategies and information sources

This article is a temporal and analytical systematic review made by searching three databases Pubmed, Scopus, and Virtual Health Library (VHL) that concentrates on its collection the international databases such as Lilacs, Medline, as well as national information bases.

The keywords, which were previously selected using Decs (MeSH), were crossed with Boolean operators and generated the search strategies that, were adapted for each data source (Table 1). The search strategy was organized to include published articles evaluating the pharmacokinetics of antineoplastic drugs through therapeutic monitoring and their impact on pharmacoeconomics in the period from 2017 to 2021.

Table 1.

Database search strategies.

Database	Search strategy^a	Number of publications
Pubmed: https://pubmed.ncbi.nlm.nih.gov/	((“antineoplastic agents”[Pharmacological Action] OR “antineoplastic agents”[MeSH Terms] OR (“antineoplastic”[All Fields] AND “agents”[All Fields]) OR “antineoplastic agents”[All Fields] OR (“antitumor”[All Fields] AND “drug”[All Fields]) OR “antitumor drug”[All Fields]) AND “drug monitoring”[All Fields] AND (“economical”[All Fields] OR “economics”[MeSH Terms] OR “economics”[All Fields] OR “economic”[All Fields] OR “economically”[All Fields] OR “economics”[MeSH Subheading] OR “economization”[All Fields] OR “economize”[All Fields] OR “economized”[All Fields] OR “economizes”[All Fields] OR “economizing”[All Fields])) AND ((y_5[Filter]) AND (ffrft[Filter]))	14
BVS: https://bvsms.saude.gov.br/	antitumor drug AND therapeutic drug monitoring AND economics AND (fulltext:(“1”)) AND (year_cluster:[2016 TO 2021])	40
Scopus: https://www.scopus.com/	(ALL (“drug monitoring”) AND ALL (pharmacokinetics) AND ALL (antineoplastic) AND ALL (pharmacoeconomics)) AND (LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017) OR LIMIT-TO (PUBYEAR, 2016))	20

Keyword searches refer to the individual search terms (and strings of search terms) entered into the databases by the researchers.

The search strategy was performed in August 2021 by the third and fourth authors and updated and rectified in September 2021 by the first and second authors. The publications found were stored and organized to eliminate duplicates. Next, the decision of inclusion or non-inclusion occurred by the reviewers, independently, blinded.

Criteria for eligibility and non-inclusion

To analyze the suitability of the publications to the inclusion criteria, the articles were submitted to the following sequential steps: (1) reading of the titles; (2) reading of the abstracts; (3) full reading. The screening of the articles included in the journal was carried out independently and blinded by the first and second authors.

Disagreements and agreements regarding the choice of articles were discussed and a consensus was obtained among the reviewers, and when no agreement was established, a third reviewer was chosen for evaluation. The organization of the citations and reference list of this review were managed by exporting the articles found in the databases to the form in Office Excel version 2016.

The articles included in this review were organized in an Excel spreadsheet, to begin the data extraction phase, which was performed by filling out a data collection instrument by the first and second authors and comprising the following information: Title of the article; country; first author; drug studied; time of the study; the method used for TDM; statistical analysis used; pharmacoeconomic analysis; main results.

The inclusion criteria used were: studies dealing with the topic of therapeutic monitoring of antineoplastic drugs with an evaluation of pharmacoeconomics, published in the last 05 years, with the availability of complete articles, and the study population of individuals aged 18 years or older.

Articles that had children as the population did not discuss therapeutic monitoring in antineoplastic therapy, and talked about other topics such as laboratory evaluations or oncological markers, and cancer drugs in the treatment of other diseases were not included. The selected articles were read in their entirety by the reviewers for data extraction using a collection instrument, with a review of the data among themselves.

Study quality and risk of bias

After data extraction, methodological quality and risk of bias were assessed according to the Research Triangle Institute Item Bank (RTI-Item Bank) scale.¹¹ The RTI-Item Bank is composed of 29 items that may be eligible for better suitability to the nature of the study analyzed. Therefore, six items were chosen that best fit the research on the pharmacoeconomic impact of TDM in cancer treatment: (i) clearly defined inclusion and non-inclusion criteria; (ii) use of valid and reliable measures to assess inclusion and non-inclusion criteria; (iii) standardized recruitment strategy for study participants in all groups; (iv) appropriate sample selection; (v) outcomes assessed using valid and reliable measures consistently implemented to all study participants; (vi) confounding variables and effect modifiers considered in the design and/or data analysis (Table 2).

Table 2.

Assessment of risk of bias and methodological quality of studies.^a

Inclusion/Exclusion Criteria

1. Are critical inclusion/exclusion criteria clearly stated (does not require the reader to infer)?

2. Are the inclusion/exclusion criteria measured using valid and reliable measures?

3. Was the strategy for recruiting participants into the study the same across study groups/arms
of the study?

Correct sample size

4. Is the sample size appropriate?

Soundness of Information

5. Are outcomes assessed using valid and reliable measures, implemented consistently across all study participants?

6. Were the important confounding and effect-modifying variables taken into account in the design and/or analysis (e.g., through matching, stratification, interaction terms, multivariate analysis, or another statistical adjustment)?

The questions were based Research Triangle Institute Item Bank (RTI-Item Bank) scale.

The risk of bias was assessed and ranked using the research response to the items described above and was classified as follows: high risk of bias—when the study had one or more negative responses to the items; moderate risk of bias—when one or more items were considered “partially” or “cannot be determined”; low risk of bias—when all items on the scale registered a positive response (Table 3).

Table 3.

The overall risk of bias (based on RTI-Item Bank).

Study	Q1	Q2	Q3	Q4	Q5	Q6	Risk of bias
Cost-effectiveness of therapeutic drug monitoring for imatinib administration in chronic myeloid leukemia.	+	+	+	+	+	+	Low risk
Cost-effectiveness assessment of monitoring abiraterone levels in metastatic castration-resistant prostate cancer patients.	+	?	+	?	?	+	Moderate risk
Cost-effectiveness of monitoring endoxifen levels in breast cancer patients adjuvantly treated with tamoxifen.	+	?	+	?	?	+	Moderate risk
Optimizing the dose in patients treated with imatinib as first-line treatment for gastrointestinal stromal tumors: a cost-effectiveness study.	+	+	?	+	+	+	Low risk

Positive (+): low risk of bias

Negative (−): high risk of bias

Interrogation (?): it is unclear

High risk of bias—when the study had one or more negative responses

Moderate risk of bias—when one or more items were considered “partially” or “cannot be determined”

Low risk of bias—when all scale items registered a positive response

Results

The initial search identified 74 articles from Pubmed (14), SCOPUS (20), and the Virtual Health Library (40). After removing the duplicates (08), 66 articles were submitted for analysis of titles and abstracts, being excluded 38 articles for not answering the research question, 02 for not addressing the population of interest, and 18 for not being primary studies, being classified as opinion, guidelines or recommendations on the subject. Finally, 08 articles were submitted for a full reading. Four articles met the established inclusion and non-inclusion criteria. The agreement between these reviewers showed a coefficient of 0.852 (p < 0.001), indicating almost perfect agreement. A flow figure illustrating the progress of the selection and the article number at each step is presented, according to the PRISMA methodology version 2020 (Figure 1).

Of the four selected articles, presented in an overview in Supplemental Appendix S1 and further discussed in the results section below, three were conducted in the Netherlands and one in the United States, being two associated with imatinib mesylate, one with abiraterone, and one with tamoxifen. About the type of cancer, the researchers approached different types, being approached leukemia, prostate cancer, breast, and gastrointestinal tract. The study time varied according to the treatment time of each pathology, following the pre-established protocols.

In the selected articles, the methodology of the studies used for the monitoring approach was based on the comparison between a group of patients in which therapeutic monitoring was carried out with a group in which it was not. Pharmacoeconomic analysis was performed on quality-adjusted life years (QALYs) as a measure of efficacy, disease PFS, disease progression, and death.

Van Nuland et al.¹² developed a Markov model with disease-free survival (DFS), recurrent disease (RD), and death states, associated with tamoxifen drug monitoring through a difference in the percentage of patients who reached adequate serum concentrations. The research concluded that there was an increase of 0.0115 QALYs and a savings of €1564 per patient. Deterministic sensitivity analysis demonstrated a large effect on the incremental cost-effectiveness ratio (ICER) of differences in costs and utilities between DFS and RD states. Probabilistic sensitivity analysis showed that the probability of cost-effectiveness at a willingness-to-pay (WTP) of €0 per QALY was 89.8%, showing that TDM of tamoxifen-treated ERα+ adjuvant breast cancer patients is likely to add QALYs and save costs from the healthcare payer's point of view.

In another approach Kim et al.⁹ measured the cost of living and QALY in imatinib treatment with TDM, resulting in US$2137 K and 12.37, respectively. The cost of living and QALY for patients without TDM was US$2132 K and 12.23, respectively. The incremental cost and QALY for TDM versus NTDM were US$4417 and 0.15. In addition, a probabilistic sensitivity analysis was conducted, which showed that TDM was cost-effective relative to the no TDM group in 90% of the scenarios tested at a WTP threshold of US$100,000/QALY. The research concludes that the cost-effectiveness over a lifetime horizon is within the acceptable range.

In the same year and also evaluating the drug imatinib, Zuidema et al.¹³ created a survival model to simulate progression, mortality, and treatment costs over a 5-year time horizon, comparing fixed dosing with TDM-guided dosing. The costs over these years were estimated to be US$106994.85 and US$150477.08 for fixed dosing versus TDM-guided dosing, respectively. A quality-adjusted life-year gain of 0.74 was estimated with TDM-guided dosing compared with fixed dosing. A mean ICER of €58785.70/QALY gain was found, mainly caused by the longer use and higher dosages of imatinib, suggesting that TDM-guided dosing may be a cost-effective intervention for patients with metastatic/resectable GIST treated with imatinib.

In 2021, Ten Ham et al.¹⁴ concluded that abiraterone monitoring resulted in 0.149 QALY with cost increments of €22145 and incremental cost-effectiveness of €177821/QALY. The research also analyzed the impact of the dietary intervention, which assumed equal effects and estimated incremental costs of US$7599, resulting in incremental cost-effectiveness of US$61019/QALY. The odds of TDM with a dose increase or dietary intervention being cost-effective were 8.04% and 81.9%, respectively. Thereby, the study concludes that abiraterone monitoring followed by a dose increase in this study is not cost-effective while monitoring in combination with a dietary intervention is likely to be cost-effective.

Regarding the methodological analysis of study quality and risk of bias, applying the criteria of the RTI-Item Bank methodology, 02 studies were considered with a low risk of bias and 02 studies with low risk. In two studies, the inclusion/exclusion criteria were not clearly described (Q2), the sample size was considered questionable (Q4), and the outcome evaluation measures were not clearly described as to implementation consistently across all study participants (Q5). In addition, one study did not clearly describe whether the strategy for recruiting study participants was the same across study/study groups (Q4) (Table 3).

Discussion

TDM can be defined as a measurement of the concentrations of a given drug in biological samples, to improve its efficacy and reduce related toxicities by individualizing the dosage of the drug.¹⁵

Cytotoxic agents can exhibit a very narrow therapeutic index, associated with significant variability in pharmacokinetic exposure, which makes the inclusion of therapeutic monitoring substantially important in oncology treatment.¹⁶ In addition, other factors favor the implementation of TDM for antineoplastic drugs: long-term therapy, lack of an easily measurable biomarker for drug effect, and feasible and recurrent dose adaptation strategies.¹⁷ Thus, a significant correlation is achieved between the dose of a drug administered, systemic exposure, and PD, avoiding that some patients receive sub-therapeutic doses, while others receive supra-therapeutic doses.¹⁸

TDM has shown to be an important tool to assist the medical team in situations where treatments require dose reduction for toxicity or dose increase due to lack of response. The main barrier to its implementation and use in oncologic treatments is the cost associated with the method and the lack of cost-benefit data from oncologic institutions.

In order to reduce costs and ensure better therapy, Phase 1 clinical trials in cancer patients have been added to population pharmacokinetic (PopPK) models. On the one hand, these computational models allow for reducing the number of collections of biological samples, for example.¹⁹ On the other hand, these models seek to understand the disease, population, and treatment variations (covariates) by determining the best doses—as reported by Royer et al.²⁰ in a PopPK study of palbociclib in women with metastatic breast cancer.

It is known that protein kinase inhibitors (PKIs) have drastically changed the treatment and prognosis of certain types of cancer since most protein kinases promote cell proliferation, survival, and migration, when constitutively overexpressed, or active, they are also associated with oncogenesis. However, the therapeutic response to PKIs varies greatly between patients, which may result in ineffectiveness in some cases and unacceptable adverse reactions in others. In this context, TDM is ideal for optimizing the therapeutic activity of PKIs through an adaptation of the dosing regimen. However, the characterization of PKI concentrations over time based on a PopPK approach is being increasingly used as it can analyze data collected from a large number of patients. In addition, blood samples can be collected at various times after drug administration (sparse sampling) from patients taking different doses in routine clinical care (observational analysis). The absence of a restrictive design facilitates the participation of outpatients with cancer, in addition to reducing the cost of the method using liquid chromatography coupled with tandem mass spectrometry.²¹

Given this motivation, the articles selected in this review explored the effect of TDM on quality-of-life gains, adverse event occurrences, therapy failures, and costs associated with the treatment of three different antineoplastic drugs (imatinib, tamoxifen, and abiraterone), relating these factors to the costs associated with the process.

To this end, Kim et al.⁹ performed a microsimulation model with patients with chronic myeloid leukemia (CML) taking the drug imatinib, comparing a treatment arm with TDM (TDM arm) and one without TDM (NTDM arm), with primary outcomes based on costs and QALYs, as per the costs and protocols practiced in the United States. The target population for this study was 55-year-old patients newly diagnosed with CML, which was determined based on the previous population effectiveness study.

The motivation to study imatinib was because this drug has great inter-patient pharmacokinetic variability, which can lead to changes in doses (sub-therapeutic and supra-therapeutic) with a consequent impact on treatment efficacy. In addition, other studies show that plasma concentrations between 1100 and 3200 μg/L obtained better efficacy and fewer adverse events, leading to the understanding of the importance of maintaining the treatment of patients within the therapeutic range.

Thus, in the medium to long term, the study demonstrated an ICER of < US$100,000/QALY with a small gain in QALY compared to NTDM. This increased cost was associated, according to the authors, with the TDM patients’ arms having more years to live, consequently increased health care costs, inflation estimated at 9%, and increased drug prices (31%–58%).

Also, according to Kim et al.,⁹ TDM although cost-effective, had a modest impact on the cost-effectiveness assessment, being associated with a gain of 0.15 (95% CI: −0.13; 0.28) QALYs and an increase in the cost of US$4417 (95% CI: −52,582; 32,097). In addition, there is considerable uncertainty in the estimates: the confidence intervals for QALYs and cost are large relative to the average.

Thus, according to the study data, the TDM of imatinib for patients with CML should be preferentially used in patients with unsatisfactory responses to treatment, which was ratified by the two-way sensitivity analysis conducted. Another possibility of using the method would be to define the initial dose, preventing the patient from starting treatment with an underdose or overdose.

It is worth noting that the analyses performed in this study were based on the US reality and that there are different costs in other contexts and places, which brings an important limitation to the data. However, the study brought a mechanistic model that relates TDM to dosage, tolerance, and adherence, which leads to the possibility of it being cost-effective, reducing the cost of CML treatment in the short term (5–10 years) and increasing it in the long term, but with an acceptable cost of 86.2862% of the WTP, limit of US$100,000/QALY gained when evaluated in the long term.

Along this same line, Van Nuland et al.¹² also evaluated the cost-effectiveness of TDM in patients treated with imatinib, but in patients with metastatic gastrointestinal stromal tumors (GIST) as first-line treatment. The motivation for this study was the fact that the fixed-dose regimen achieves adequate plasma levels in only approximately 40% of patients.

The research data suggest that the use of TDM provides additional clinical benefit and may be cost-effective for patients with metastatic/resectable GIST starting with imatinib as first-line treatment, with an average incremental cost-utility ratio of US$58758.70/QALY.

In the Netherlands, where the study was conducted, the WTP threshold in patients with a higher burden of disease is €80,000/QALY gained. Thus, TDM proved to be cost-effective in 100% of cases (€72,000/QALY gained). Conducting this analysis in other countries may have a different cost-effectiveness ratio.

As reported by Kim et al.,⁹ the cost of the drug imatinib is the main cost driver in this analysis, making the reduction in drug costs in the long term, influencing the positive impact of the analysis. This was also ratified by the sensitivity analysis. In addition, due to the limited data available, not all differences in the imatinib health status subgroups could be taken into account and may have led to an underestimation of the effect of TDM in the imatinib group due to a lower occurrence of adverse events in the imatinib supra-therapeutic health status. In addition, OS data for the sub-therapeutic imatinib group were not available and were extrapolated based on the PFS of the imatinib subgroups, which may have led to a bias in estimating the dose-based effect by TDM.

The model design is based on a real-world situation, with patient compliance throughout all phases of treatment, which in practice may not be entirely true, and there may have been a bias in estimating the cost-effectiveness of TDM-guided dosing. The effect of GIST and its treatment on work absence and associated costs were explored, but ultimately not taken into account in this model due to a lack of available data.

Hence, it is described that in TDM-guided dosing, there was an additional clinical benefit and may be cost-effective compared to fixed dosing in patients with metastatic/unresectable GIST using imatinib as the first line of treatment. Unlike Kim et al.,⁹ Zuidema et al.¹³ believe that cost-effectiveness will improve in the long term due to reduced drug costs due to patent loss.

Ten Ham et al.¹⁴ in turn evaluated in their study the cost-effectiveness of abiraterone TDM in patients with metastatic castration-resistant prostate cancer (mCRPC), as the response to this therapy is associated with minimal plasma concentrations (C_min).

The authors, corroborating Kim et al.,⁹ associate the increased costs with the prolonged use of abiraterone, which is quite favorable from a clinical point of view. Furthermore, it is important to stress that the ICER is calculated based on the association of TDM with a low-fat meal. The costs and effects reported from this association, are higher than the effect, cost, and ICER of abiraterone alone.

To show the correlation of the probability of the intervention being cost-effective for different WTP thresholds, a cost-effectiveness acceptability curve was constructed. Because of the short survival time for mCRPC patients, the threshold related to the highest disease burden (€80,000/QALY) was applied. At last, the incremental net monetary benefit (INMB) was calculated. A positive INMB implies that an intervention is cost-effective, and guidelines advise the adoption of such an intervention.

Thereby, the Markov model was used, which included three health states: PFS, progressed disease (PD), and death. TDM of abiraterone was not cost-effective for patients with mCRPC followed by a dose increase from the health care provider's point of view, given a WTP of €80,000. The scenario in which patients with low plasma concentrations were advised to combine taking the drug with a low-fat meal resulted in an ICER of €60,717.

This cost-effectiveness assessment may assist in decision-making in the future integration of abiraterone TDM followed by a dietary intervention into the standard abiraterone treatment practices of patients with mCRPC. As well as the findings of Zuidema et al.,¹³ showed the impact of abiraterone cost decrease in the long term, which would reduce the ICER in both scenarios.

Finally, the findings were confirmed in the probabilistic analysis and cost-effectiveness analyses, which showed a very small probability of cost-effectiveness at the WTP threshold of €80,000/QALY when combining TDM with a dose escalation. Based on this model, TDM followed by a dietary intervention to increase the clinical efficacy of abiraterone in patients with mCRPC may be a cost-effective option for clinical practice.

The same Markov model used in the study by Ten Ham et al.¹⁴ was also used in the study by Van Nuland et al.¹² It evaluated the time spent by patients at a given stage of the disease by comparing a group of HER2 + breast cancer patients on tamoxifen treatment with TDM with a group where TDM was not used. In addition, incremental costs and effects were attributed to the intervention, leading to an ICER, which represented the added costs divided by the QALYs gained due to the intervention. Therefore, the ICER would indicate how much must be invested to gain one QALY.

The model included parameters such as disease states, DFS, RD, and death, and showed that TDM of tamoxifen after 3 months with dose escalation, with serum concentrations less than 5.97 ng/mL, is likely to be cost-effective. Although the individual QALY benefit is relatively small, the affected population is large, which can lead to significant QALY gains on a macro level. This was ratified by the deterministic sensitivity analysis which showed that the costs associated with DFS and RD states had the greatest impact on the ICER. To study the impact of varying the risk ratio and target threshold, sensitivity analyses were carried out with the confidence interval of the described risk ratio and the percentage of patients below or above the threshold.¹²

However, the number of patients who acquired a serum concentration > 5.97 ng/mL after 3 months of treatment without dose escalation was taken from a study with only 122 participants retrospectively evaluated in records from the Antoni van Leeuwenhoek hospital, where the study was conducted. It would be better if the relation between serum tamoxifen concentrations and recurrence rates in the adjuvant setting were studied prospectively.¹²

Although the relation between tamoxifen efficacy and serum tamoxifen concentrations was demonstrated in a retrospective study, the study directly showed the cost-effectiveness of monitoring during therapy optimization, thus guiding best clinical practice. The probabilistic sensitivity analysis, moreover, shows a high probability of cost-effectiveness. Based on these results, it is expected that tamoxifen monitoring would be cost-effective even when the differences in recurrence rates were smaller.¹²

The studies have demonstrated that therapeutic monitoring in the short term adds costs to treatment due to the cost of the technology or the dose adjustment required for some patients. However, in the medium–long term, it contributed to increased DFS rate, increased quality of life, and delayed costs in patients who need to migrate to other lines of treatment, hospitalizations for complications of disease progression, and impacts on patient quality of life.^9,12,14

To overcome this problem, a patient population-specific PK model with Bayesian simulations can be used to allow a more accurate dose adjustment. This approach uses information about typical pharmacokinetics in a population, factors that may influence pharmacokinetic parameters (e.g. renal function and drug clearance), and a mathematical (Bayesian) approach to assess a patient's drug exposure based on their characteristics and the measured drug concentrations. Using this approach increases the efficiency of the TDM procedure, resulting in a greater proportion of patients achieving a drug concentration or exposure in the target range, reducing the cost of multiple measurements of drug concentrations to achieve a drug concentration in the target range.²²

Conclusion

The monitoring of therapeutic drugs is still perceived as an additional cost for funders, whether in the public or private health system. There are still many gaps to be answered about pharmacoeconomics and cost–benefit studies on the use of this methodology in clinical practice. Future MT-based studies will serve as a basis for accurate popPK; ensuring more accurate drug exposure and dose recommendation for a given population, with consequent cost savings.

Supplemental Material

sj-xlsx-1-opp-10.1177_10781552231171827 - Supplemental material for Pharmacokinetic approach in therapeutic monitoring of antineoplastic drugs and the impact on pharmacoeconomics: A systematic review

Supplemental material, sj-xlsx-1-opp-10.1177_10781552231171827 for Pharmacokinetic approach in therapeutic monitoring of antineoplastic drugs and the impact on pharmacoeconomics: A systematic review by Islania Almeida Brandão Barbosa, Taiane Candeias da Silva, Monielly Vasconcelos Pereira de Souza, Lorena Argolo Pedreira and Ana Leonor Pardo Campos Godoy in Journal of Oncology Pharmacy Practice

Footnotes

Author's contribution

All authors have read and approved the final manuscript. IABB, ALPCG, and TCS designed the study. MVPS and LAP performed the search strategy. IABB and TCS updated and rectified the search strategy. IABB, TCS, MVPS, and LAP decided on the inclusion or non-inclusion of the studies, independently, and blinded. IABB, TCS, MVPS, and LAP wrote the manuscript. ALPCG, IABB, and TCS revised the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Islania Almeida Brandão Barbosa

Supplemental material

Supplemental material for this article is available online.

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