Cost Of New Drugs Research Articles

Spending on anticancer drugs among Medicare beneficiaries: Analyzing predictors of drug expenditures

ObjectiveTo evaluate the factors associated with Medicare spending on newly approved anticancer drugs in the US from 2012 through 2021. Patient and MethodsUsing a cross-sectional analysis, we searched US FDA new oncology drug approvals (2012-2021). We analyzed clinical attributes and institutional factors influencing the annual cost of new anticancer drugs in the US. Annual treatment cost was calculated based on average spending per beneficiary from the Centers for Medicare and Medicaid Services, with product factors sourced from the FDA’s annual New Drug Therapy Approval reports and drug package inserts at the time of approval. ResultsOver a ten-year period, 112 new anticancer drugs were approved, of which 97 met the study's criteria. A significant majority, 93%, received expedited development designations from the FDA. At the time of approval, 40% of these drugs had data on progression-free survival, and 19% on overall survival rates; 29% were first-in-class. The study found a significant relationship between the year of approval and factors associated with the size of the treatment population. No statistically significant relationship was found between the clinical value of a drug and its price. ConclusionsSpending on anticancer drugs by Medicare are predominantly determined by reference pricing and the size of the anticipated treatment population, without an association with therapeutic value. The study advocates for reforms in reimbursement mechanisms for drugs lacking comparator arms and greater transparency for patients treated with these drugs.

Using Interventional Pharmacoeconomic Clinical Trials and Outcomes-Based Contracts to Repurpose Generic Drugs with Cost-Savings

The inability to enforce a monopoly price over low-cost therapies, such as repurposed generic drugs by using patents for new medical uses, means that pharmaceutical companies are not interested to develop these potentially lifesaving therapies, even if clinical trials would be significantly cheaper. With the cost of new drugs increasing unsustainably, new financial models are needed that can incentivize the development of such low-cost therapies, by leveraging the cost-savings they generate for payors. For example, by conducting a trial comparing a low-cost generic drug to an expensive patented drug, the cost-savings from patients taking a low-cost therapy rather than an expensive drug during the trial itself can exceed the cost of running the clinical trial, which means it is “self-funding,” while also potentially improving patient outcomes due to better safety, efficacy, convenience, or accessibility. This is referred to as “interventional pharmacoeconomics” or a “revolving research fund” and allows the sponsorship of clinical trials that can be entirely funded by payors. “Prize-like” outcomes-based contracts or advance market commitments can also be combined with such self-funding trials to incentivize obtaining regulatory approval and solve the “last-mile” problem. Self-funding trials can provide significant cost-savings for payors without financial risk. This article illustrates a four-step process for conducting such self-funding trials and other ethical, commercial, political, and legal barriers that need to be overcome in order to scale this novel and practically unlimited source of funding for the development of low-cost therapies.

Innovation, affordability, access: Alzheimer disease drugs and the Inflation Reduction Act.

This analysis examines the implications of new Alzheimer disease drugs in the era of the Inflation Reduction Act (IRA). It focuses on balancing innovation in Alzheimer disease treatment with affordability and access, assessing the impact on Medicare's budget, patient cost, and health care system readiness. A comprehensive review was conducted, synthesizing information from recent FDA drug approvals, drug pricing models, Medicare coverage policies, and the updated regulations under the IRA. This analysis reflects on the broader clinical and economic consequences of introducing new Alzheimer disease treatments. The study employs a qualitative review of existing literature, policy documents, and economic data. It explores the implications of Alzheimer disease drugs on health care policy, analyzing the economic and clinical impacts within the current health care landscape in the US. The study highlights the economic challenges posed by the high costs of new Alzheimer disease drugs, contrasting with their moderate clinical benefits and potential risks. It discusses the limitations of the IRA in regulating drug prices and the resulting implications for Medicare's budget. Additionally, it examines disparities in health care access and system preparedness for these newtreatments. The study findings underscore the need for a comprehensive approach to ensure fair pricing and equitable access to Alzheimer disease treatments. It suggests the application of frameworks such as the ISPOR Value Flower, focusing on diversity, equity, and comprehensive economic evaluations, to navigate the evolving landscape of Alzheimer disease treatment in the context of the IRA.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications.

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

Exploring the Multi-level Interaction Mechanism between Drugs and Targets Based on Artificial Intelligence

<p>With the improvement of people's living standards and the increasing emphasis on health, new and higher demands are also placed on the medical industry. The diagnosis and treatment methods combining artificial intelligence and medicine are bound to receive more and more attention and promotion. At present, drug design is stepping into the era of artificial intelligence, and academia and industry are actively applying artificial intelligence technology to all aspects of drug research and development, including drug screening, molecular characterization, drug activity and druggability optimization, chemical reaction design, etc. Relying on the big data information of millions of patients, the artificial intelligence system can quickly and accurately mine and screen out suitable drugs. Through computer simulations, artificial intelligence can make predictions about drug activity, safety, and side effects, and find the best drug that matches the disease. This technology will shorten the drug development cycle, reduce the cost of new drugs and improve the success rate of new drug development.</p>

Innovation Options and Profitability of Pharmaceutical Brand Manufacturers

Much research exists covering clinical development success rates, development costs of new drugs, and market launch impact on stock market valuation of companies, but little systematic work has been done to establish the impact of research input on new product launches and, in turn, their impact on profitability of drug manufacturers. This article investigates these relations using data from the world’s largest pharmaceutical brand manufacturers and their product launches in the US over a period of more than 25 years. The objective is to determine the impact of innovation intensity on innovation output intensity and of innovation output intensity on profitability. It is shown that there is a complete lack of evidence that launches of New Molecular Entities (NMEs) necessarily lead to higher profitability, suggesting that many launches of NMEs are not particularly successful from an economic point of view. Furthermore, it was found that intangible knowledge assets acquired by company mergers and acquisitions do often not live up to their valuation. This leads to the conclusion that such intangible assets seem to be overpriced on average. The more and more frequently used strategy of launching new drugs without NMEs like combination drugs or extension of indications increased short-term profitability making this a valid approach to avoid setbacks when patent protection of blockbuster drugs expires.

Association of Research and Development Investments With Treatment Costs for New Drugs Approved From 2009 to 2018

Drug companies frequently claim that high prices are needed to recoup spending on research and development. If high research and development costs justified high drug prices, then an association between these 2 measures would be expected. To examine the association between treatment costs and research and development investments for new therapeutic agents approved by the US Food and Drug Administration (FDA) from 2009 to 2018. This cross-sectional study analyzed 60 drugs approved by the FDA between January 1, 2009, and December 31, 2018, for which data on research and development investments and list or net prices were available. Data sources included the FDA and SSR Health databases. The primary independent variable was estimated research and development investment. The outcome was standardized treatment costs (ie, annual treatment costs for both chronic and cycle drugs, and treatment costs for the maximum length of treatment recommended for acute drugs). Standardized treatment costs were estimated separately using list and net prices obtained from SSR Health at the time of launch and in 2021. To test the association between research and development investments and treatment costs, correlation coefficients were estimated and linear regression models were fitted that controlled for other factors that were associated with treatment costs, such as orphan status. Two models were used: a fully adjusted model that was adjusted for all variables in the data set associated with treatment costs and a parsimonious model in which highly correlated variables were excluded. No correlation was observed between estimated research and development investments and log-adjusted treatment costs based on list prices at launch (R = -0.02 and R2 = 0.0005; P = .87) or net prices 1 year after launch (R = 0.08 and R2 = 0.007; P = .73). This result held when 2021 prices were used to estimate treatment costs. The linear regression models showed no association between estimated research and development investments and log-adjusted treatment costs at launch (β = 0.002 [95% CI, -0.02 to 0.02; P = .84] in the fully adjusted model; β = 0.01 [95% CI, -0.01 to 0.03; P = .46] in the parsimonious model) or from 2021 (β = -0.01 [95% CI, -0.03 to 0.01; P = .30] in the fully adjusted model; β = -0.004 [95% CI, -0.02 to 0.02; P = .66] in the parsimonious model). Results of this study indicated that research and development investments did not explain the variation in list prices for the 60 drugs in this sample. Drug companies should make further data available to support their claims that high drug prices are needed to recover research and development investments, if they are to continue to use this argument to justify high prices.

Novel ways to fund cost-efficiency studies of expensive drugs: Efforts to maintain sustainable healthcare expenditure in the Netherlands.

e18831 Background: The rising costs of new cancer drugs is a major burden for healthcare system. Healthcare providers and payers constantly look for solutions to reduce inefficient use of these drugs. Methods: These solutions may involve dose-rounding, dose-banding or vial-sharing to minimise the quantity of discarded pharmaceuticals. Alternative dosing strategies, such as tapering and/or increasing the dose-interval, shortening treatment duration, or boosting potency of the drugs may also be applied. Another approach is the development of predictive/monitoring biomarkers that can prevent mis-prescribing and overuse of therapies to patients who will not benefit from such a therapy. Provided that the therapeutic benefit is retained, a lower drug exposure evidently not only results in savings for the national healthcare budget but may also limit (off-target) toxicity, reduce hospitalisation and improve quality-of-life for patients. Results: Once compelling evidence of non-inferiority is available, the roll-out one of the strategies mentioned above is justified. Parties providing financial support for cost-efficiency initiatives are then often eager to support implementation and generate potential shared savings. However, when (additional) research is required to generate adequate evidence, fund raising and finding support can be quite challenging. In the Netherlands a dedicated national fund was recently established. Treatmeds, a foundation under Dutch law, is tasked to support cost-efficiency studies and to secure and manage (part of) the funding needed for those initiatives. Treatmeds is backed by Dutch healthcare insurers. Its governance provides for decisions based on the advice of an independent Scientific Advisory Board, an Intervention Board and a Financial Board. The Scientific Advisory Board aides with selecting studies for support on a pure scientific basis, the Intervention Board considers aspects as efficiency, the possibility of reimbursement under the Dutch compulsory healthcare insurance, the cost-benefit ratio of a study and the Financial Board ultimately decides on the financial aspects of supporting a study. Although Treatmeds only has just started its activities, it is expected that it can have a pivotal role in acceleration of efficiency tools in clinical practice. Conclusions: From our own experience we can state that a dedicated national fund supporting research on improved efficiency and quality of care can be of great value to accelerate study start-up and roll-out.

Emerging drugs for the treatment of hereditary angioedema due to C1-inhibitor deficiency

ABSTRACT Introduction Hereditary angioedema due to C1-inhibitor (C1-INH-HAE) is a rare disease characterized by unpredictable swelling attacks that may be life-threatening when affecting the upper airways. Understanding the pathophysiology of HAE and the mechanism of bradykinin-mediated angioedema allowed the development of new therapies for the treatment of HAE: clinical trials are ongoing to expand the number of drugs available for on-demand treatment and prophylaxis. Areas covered Authors discuss the products that have been used to treat this disease for many years and present the most recently marketed products and those which are under development. Expert opinion Significant therapeutic progress has been made in HAE. In particular, drugs targeting specific molecules involved in the angioedema formation were developed and studies with new drugs are ongoing. In the coming years, more effective therapies with easier administration route options for on-demand treatment and long-term prophylaxis will be available to treat this disease and the variety of patients. Gene therapy strategies may offer a definitive treatment. High costs of current and new drugs may be a limiting factor for their availability, especially in developing countries.

WORLD PHARMACEUTICAL MARKET: TRENDWATCHING

At present, the importance of the pharmaceutical sector of the economy has increased significantly in public perception. This review provides up-to-date information at the beginning of 2022 on the main trends in the global pharmaceutical market. Among the current trends are: the key role of pharmaceuticals in the fight against the SARS-Covid-19 pandemic, the problems of a latent pandemic of drug-resistant infections, the high cost of new drugs and an increase in the market share of cheap biosimilars, the expansion of cooperation between developers of new drugs and vaccines, features in changes in the structure pharmaceutical companies, expanding opportunities in the retail pharmaceutical market, analysts՚ opinions on market prospects for the next 5 years. Materials from open sources were used: industry and business media, websites of pharmaceutical companies and non-profit organizations, WHO publications and scientific publications.

Using RBOs and Megafunds to Hedge Longevity Risk and Specialty Drug Costs

Longevity risk, the risk that a member of a pension plan or an annuity holder lives much longer than anticipated, can cause shortfalls in funding for writers of insurance policies, annuities, and pension benefits. Reimbursement risk, the risk faced by health benefit providers in paying for expensive new drugs, can similarly cause shortfalls for health insurance firms and other benefit providers, particularly as the costs of specialty drugs increase. These risks can be acute when scientific breakthroughs increase new drug development rates. An emerging asset class, research-backed obligations (RBOs) (Fernandez et al. 2012; Hull et al. 2019), has the potential to provide a natural hedge for a broad class of such risks. This article demonstrates how to calculate simplified, first-order portfolio-specific hedge ratios. The author presents simulation results that demonstrate the benefits of such hedges for a hypothetical health insurance portfolio facing rapid increases in reimbursement risk due to the high cost of new specialty drugs.

A drug repositioning algorithm based on a deep autoencoder and adaptive fusion

BackgroundDrug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning.ResultsIn this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset.ConclusionThe proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

Refunds If a Drug Doesn’t Work

No abstract available. Article truncated after 150 words. One aspect of the high cost of healthcare is the cost of new drugs. Cancer drugs have received much of the attention because of their extremely high price (1). For example, crizotinib, used to treat non-small cell lung cancer (NSCLC), costs $19,144 for each month's supply. Pfizer, the manufacturer of crizotinib, has just announced that they are offering a refund if its drug "doesn't work" (2). If crizotinib use is discontinued and documentation of ineffectiveness is provided, Pfizer will refund the out-of-pocket amount that was paid for up to the first three bottles (30-day supply) of crizotinib, up to a maximum of $19,144 for each month's supply, or a total of $57,432. Of course, the cost of care includes more than just a single drug and can be much higher and Pfizer is reimbursing only the drug cost. Although Pfizer claims that its pilot program is a first in the …

Recent Advances in Repurposing Disulfiram and Disulfiram Derivatives as Copper-Dependent Anticancer Agents.

Copper (Cu) plays a pivotal role in cancer progression by acting as a co-factor that regulates the activity of many enzymes and structural proteins in cancer cells. Therefore, Cu-based complexes have been investigated as novel anticancer metallodrugs and are considered as a complementary strategy for currently used platinum agents with undesirable general toxicity. Due to the high failure rate and increased cost of new drugs, there is a global drive towards the repositioning of known drugs for cancer treatment in recent years. Disulfiram (DSF) is a first-line antialcoholism drug used in clinics for more than 65 yr. In combination with Cu, it has shown great potential as an anticancer drug by targeting a wide range of cancers. The reaction between DSF and Cu ions forms a copper diethyldithiocarbamate complex (Cu(DDC)2 also known as CuET) which is the active, potent anticancer ingredient through inhibition of NF-κB and ubiquitin-proteasome system as well as alteration of the intracellular reactive oxygen species (ROS). Importantly, DSF/Cu inhibits several molecular targets related to drug resistance, stemness, angiogenesis and metastasis and is thus considered as a novel strategy for overcoming tumour recurrence and relapse in patients. Despite its excellent anticancer efficacy, DSF has proven unsuccessful in several cancer clinical trials. This is likely due to the poor stability, rapid metabolism and/or short plasma half-life of the currently used oral version of DSF and the inability to form Cu(DDC)2 at relevant concentrations in tumour tissues. Here, we summarize the scientific rationale, molecular targets, and mechanisms of action of DSF/Cu in cancer cells and the outcomes of oral DSF ± Cu in cancer clinical trials. We will focus on the novel insights on harnessing the immune system and hypoxic microenvironment using DSF/Cu complex and discuss the emerging delivery strategies that can overcome the shortcomings of DSF-based anticancer therapies and provide opportunities for translation of DSF/Cu or its Cu(DDC)2 complex into cancer therapeutics.

Innovation and Health Disparities During an Epidemic: The Case of HIV

We examine whether medical innovation can reinforce existing health disparities by disproportionately benefiting socioeconomically advantaged patients. The reason is that less advantaged patients often do not use new medications. This may be due to high costs of new drugs, but could also reflect differences in how side effects of new treatments interact with labor supply. To investigate, we develop a dynamic lifecycle model in which the effect of medical treatment on labor supply varies across sociodemographic groups. We estimate the model using rich data on treatment choices and employment decisions of men infected with HIV. In the model, treatments can improve long-run health, but can also cause immediate side effects that interact with the utility cost of work. Estimates indicate that HIV-infected men often forego medication to avoid side effects, in part to remain employed. This effect is stronger for people with fewer years of education, leading to lower use of treatment and worse health outcomes. As a result, while a breakthrough HIV treatment - known as HAART - improved lifetime utility for all patients, it disproportionately benefitted those with higher levels of completed education, thereby reinforcing existing inequality. A counterfactual subsidy that increases non-labor income reduces employment for all education groups, but only increases adoption of HAART and improves health among lower-education individuals, who face a starker health-work tradeoff.

Pharmaceutical drug development: high drug prices and the hidden role of public funding.

In 2019, the record for the most expensive drug was broken at US$2.1 million per patient. The high costs of new drugs are justified by the pharmaceutical industry as the expense required for maintaining research and development (R&D) pipelines. However, this does not take into account that globally the public pays for between one to two-thirds of upfront R&D costs through taxpayers or charitable donations. Governments are effectively paying twice for medicines; first through R&D, and then paying the high prices upon approval. High drug prices distort research priorities, emphasising financial gains and not health gains. In this manuscript, issues surrounding the current patent-based drug development model, public funding of research and pharmaceutical lobbying will be addressed. Finally, innovations in drug development to improve public health needs and guaranteeing medication access to patients will be explored.

PCN85 COST-EFFECTIVENESS OF OLAPARIB AS A MAINTENANCE TREATMENT OPTION FOR NEWLY DIAGNOSED BRCA-MUTATED OVARIAN CANCER WHO ARE IN RESPONSE AFTER FIRST-LINE PLATINUM-BASED CHEMOTHERAPY IN PANAMA

SOLO1 was an international, Phase III, randomised, double-blind, placebo-controlled trial that assessed the efficacy and safety of olaparib (OLA) versus placebo in patients with newly diagnosed advanced BRCA-mutated ovarian cancer who were in response (complete or partial) following first-line platinum-based chemotherapy. The acquisition cost of new oncology drugs is a concern for payors in emerging markets. This study aimed to evaluate the cost-effectiveness of OLA therapy in this new indication versus routine surveillance (RS) in the Panamian population, using national estimates of costs and treatment patterns. A three-state partitioned survival model was developed to simulate the lifetime (50 years) incremental cost-effectiveness ratio (ICER) of OLA versus RS from a payer perspective. Progression-free survival (PFS) and overall survival (OS) curves were estimated using data from SOLO1 and extrapolated using parametric survival models for Panama. Mortality and morbidity rates for the groups were modelled based on assumptions validated with local clinicians. Health state utilities and adverse event frequencies were obtained from SOLO1 study. Drug costs were provided by the Panamian Institute of Oncology (ION). Healthcare resource usage and costs were based on local clinician input and local publications. A 1.5% discount rate was applied to costs and outcomes. Maintenance treatment with OLA in first line setting after platinum based chemotherapy compared to routine surveillance as currently practiced was associated with an ICER of $ $35,512 per QALY. This ICER is lower than the WHO-WTP threshold of three times GDP per capita of $$48,736 and the willingness-to-pay (WTP) analysis showed an estimated 100% probability to be cost-effective. The results were robust to univariate sensitivity analysis, with all variables having a non-significant impact. OLA is a cost-effective treatment option for patients with newly diagnosed BRCA-mutated ovarian cancer, who are in response (complete or partial) after first-line platinum-based chemotherapy in Panama.

PCN96 OLAPARIB COST-EFFECTIVENESS AS A MAINTENANCE TREATMENT OPTION FOR NEWLY DIAGNOSED BRCA-MUTATED OVARIAN CANCER WHO ARE IN RESPONSE AFTER FIRST-LINE PLATINUM-BASED CHEMOTHERAPY IN COSTA RICA

SOLO1 was an international, Phase III, randomised, double-blind, placebo-controlled trial that assessed the efficacy and safety of Olaparib (OLA) versus placebo in patients with newly diagnosed advanced BRCA-mutated ovarian cancer who were in response (complete or partial) following first-line platinum-based chemotherapy. The acquisition cost of new oncology drugs is a concern for payors in emerging markets. This study aimed to evaluate the cost-effectiveness of OLA therapy in this new indication versus routine surveillance (RS) in the Costa Rican population, using national estimates of costs and treatment patterns. A three-state partitioned survival model was developed to simulate the lifetime (50 years) incremental cost-effectiveness ratio (ICER) of OLA versus RS from a payer perspective. Progression-free survival (PFS) and overall survival (OS) curves were estimated using data from SOLO1 and extrapolated using parametric survival models for Costa Rica. Mortality and morbidity rates for the groups were modelled based on assumptions validated with local clinicians. Health state utilities and adverse event frequencies were obtained from SOLO1 study. Drug costs were provided by the Caja Costarricense de Seguro Social (CCSS) Healthcare resource usage and costs were based on local clinician input and local publications. A 1.5% discount rate was applied to costs and outcomes. Maintenance treatment with OLA in first line setting after platinum based chemotherapy compared to routine surveillance as currently practiced was associated with an ICER of $37,004. per QALY. This ICER is lower than the WHO-WTP threshold of three times GDP per capita of $37,026 and the willingness-to-pay (WTP) analysis showed an estimated 99% probability to be cost-effective. OLA is a cost-effective treatment option for patients with newly diagnosed BRCA-mutated ovarian cancer, who are in response (complete or partial) after first-line platinum-based chemotherapy in Costa Rica

The High Cost of Insulin in the United States: An Urgent Call to Action

The High Cost of Insulin in the United States: An Urgent Call to Action