Applying a cost-based pricing model for innovative cancer treatments subject to indication expansion: A case study for pembrolizumab and daratumumab.

BACKGROUND: Expanding the indication of already approved immuno-oncology drugs presents treatment opportunities for patients but also strains healthcare systems. Cost-based pricing models are discussed as a possibility for cost containment. This study focuses on two drugs, pembrolizumab (Keytruda) and daratumumab (Darzalex), to explore the potential effect of indication broadening on the estimated price when using the cost-based pricing (CBP) model proposed by Uyl-de Groot and Löwenberg (2018). METHODS: The model was used to calculate cumulative yearly prices, cumulative prices per indication, and non-cumulative indication-based prices using inputs such as research and development (R&D) costs, manufacturing costs, eligible patient population, and a profit margin. A deterministic stepwise analysis and scenario analysis were conducted to examine how sensitive the estimated price is to the different input assumptions. RESULTS: The yearly cumulative cost-based prices (CBPs) ranged from €52 to €885 for pembrolizumab per vial and €823 to €31,941 for daratumumab per vial. Prices were higher in initial years or indications due to smaller patient populations, decreased over time or after additional indications. Sensitivity analysis showed that the number of eligible patients had the most significant impact on the estimated price. In the scenario analysis the profit margin contributed most to a higher CBPs for both drugs. Lower estimates resulted from assumed lower R&D costs. DISCUSSION: The estimated CBPs are consistently lower than Dutch list prices for pembrolizumab (€2,861), mainly resulting from larger patient populations in registered indications. However, daratumumab's list prices fall within the range of modeled CBPs depending on the year or indication (€4,766). Both CBPs decrease over time or with additional indications. The number of eligible patients and initial R&D costs have the most significant influence on the CBPs. These findings contribute to the ongoing discussions on pharmaceutical pricing, especially concerning cancer drugs with expanding indications.

Drug-drug interactions in pediatric oncology patients.

BACKGROUND: Drug-drug interactions (DDIs) can negatively affect pharmacotherapy. However, pediatric DDI studies are scarce. We undertook an exploratory study to investigate prevalence and clinical relevance of DDIs between cytostatic and noncytostatic drugs in outpatient pediatric oncology patients. PROCEDURE: After informed consent and inclusion, the following information was collected: currently prescribed noncytostatic and cytostatic drugs, comorbidities, and use of over-the-counter (OTC) drugs, complementary and alternative medicines (CAMs), and dietary supplements. All medication was screened for DDIs according to two databases: Micromedex® Solutions and the Dutch drug database G-Standard. The researcher presented DDIs with an associated potential for adverse outcome and a proposal for intervention to three independent experts. If the experts considered a DDI to be potentially clinically relevant and requiring intervention, the physician was notified. RESULTS: Seventy-three patients were included (median age 8.9 years). A total of 67 different DDIs were counted (66 in Micromedex® Solutions, 14 in G-Standard, and 13 DDIs in both databases). The medication reviews resulted in 35 interventions related to 11 different DDIs. The majority of DDIs concerned noncytostatic drugs (25/35) and one third occurred between cytostatic and noncytostatic drugs (10/35). The use of QTc-interval-prolonging drugs resulted in one intervention. The use of OTC drugs, CAM, or dietary supplements did not lead to DDIs. CONCLUSIONS: This study resulted in a selection of 11 potentially clinically relevant DDIs for 73 outpatients in our pediatric oncology department. Interventions were formulated in close collaboration between physicians and clinical pharmacists. Future research should focus on assessing DDIs concerning QTc-interval prolongation.

Drug-drug interactions in patients treated for cancer: a prospective study on clinical interventions.

BACKGROUND: Drug-drug interactions (DDIs) are of major concern in oncology, since cancer patients typically take many concomitant medications. Retrospective studies have been conducted to determine the prevalence of DDIs. However, prospective studies on DDIs needing interventions in cancer patients have not yet been carried out. Therefore, a prospective study was designed to identify DDIs leading to interventions among ambulatory cancer patients receiving anticancer treatment. PATIENTS AND METHODS: Patients starting with a new treatment regimen with i.v. or oral anticancer medication were asked to participate. The patients' medication was checked for DDIs by using drug interaction software. An expert team of clinical pharmacologists evaluated the relevance of these identified DDIs. If a DDI was qualified as potentially clinically relevant, an intervention was proposed to the treating (hemato)oncologist. Several variables were studied as determinants for performing an intervention. Descriptive statistics and uni- and multivariate logistic regression analyses were carried out. RESULTS: In this study, 302 patients were included. A total of 603 DDIs were identified by the drug interaction software and judged by the expert team. Of all 603 DDIs, 120 DDIs were considered potentially clinically relevant. These 120 DDIs, present in a total of 81 patients, resulted in a clinical intervention already executed by the (hemato)oncologist in 39 patients (13%), while an additional intervention was proposed by a clinical pharmacologist in 42 patients (14%). The number of comorbidities and the number of 'over-the-counter' drugs were identified as determinants. CONCLUSIONS: Clinical interventions on DDIs are frequently required among patients starting with anticancer therapy. Structured screening for these potentially clinically relevant DDIs, by (hemato)oncologists in close collaborations with clinical pharmacologists, should take place before the start and during anticancer treatment. CLINICAL TRIALS NUMBER: This study was registered at the Dutch Trial Registry under number NTR3760.

Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs.

BACKGROUND: Potential drug-drug interactions (PDDIs) in patients with cancer are common, but have not previously been quantified for oral anticancer treatment. We assessed the prevalence and seriousness of potential PDDIs among ambulatory cancer patients on oral anticancer treatment. METHODS: A search was conducted in a computer-based medication prescription system for dispensing oral anticancer drugs to outpatients in three Dutch centres. Potential drug-drug interactions were identified using electronic (Drug Interaction Fact software) and manual screening methods (peer-reviewed reports). RESULTS: In the 898 patients included in the study, 1359 PDDIs were identified in 426 patients (46%, 95% confidence interval (CI)=42-50%). In 143 patients (16%), a major PDDI was identified. The drug classes most frequently involved in a major PDDI were coumarins and opioids. The majority of cases concerned central nervous system interactions, PDDIs that can cause gastrointestinal toxicity and prolongation of QT intervals. In multivariate analysis, concomitant use of more drugs (odds ratio (OR)=1.66, 95% CI=1.54-1.78, P<0001) and genito-urinary cancer (OR=0.25, 95% CI=0.12-0.52, P<0001) were risk factors. CONCLUSION: Potential drug-drug interactions are very common among cancer patients on oral cancer therapy. Physicians and pharmacists should be more aware of these potential interactions.

Potential drug interactions in cancer therapy: a prevalence study using an advanced screening method.

BACKGROUND: In cancer patients, drug interactions may intensify adverse events or reduce antitumour effects. We assessed the prevalence of potential drug interactions (PDIs) among ambulatory cancer patients on i.v. treatment using an advanced screening method. PATIENTS AND METHODS: Data on drugs used for comorbidities, anticancer agents, over-the-counter (OTC) drugs, and comorbidities were collected by means of a structured interview among the patients and review of medical charts. PDIs were identified using electronic (Drug Interaction Facts software, version 4.0) and manual screening methods (peer-reviewed reports). RESULTS: In this study, 278 patients were enrolled. We identified 348 PDIs. Of all patients, 161 (58%) had at least one PDI. Of all PDIs, 34% was classified as major and 60% as moderate. Coumarins, quinolones, antiepileptics, and hydrochlorothiazide were frequently part of a PDI. Interactions that potentially cause QT interval prolongation, gastrointestinal toxicity, and central nervous system depression were also common. In multivariate analysis, an increasing number of drugs [odds ratio (OR) = 1.4, confidence interval (CI) 1.23-1.52; P < 0.001] and the use of an OTC drug (OR = 0.56, CI 0.32-0.97; P = 0.045) were risk factors. CONCLUSIONS: PDIs are common in patients treated for an (haemato-) oncological disease. Screening for potential interactions should take place routinely before administering chemotherapy.