Background

Prolonging Overall Survival (OS) has long been viewed as the gold standard for cancer drugs, but the long time to show OS benefit in certain cancers or early treatment lines and increased impact of cross-over, patients lost to follow-up and later lines can result in delayed patient access to effective therapies. As such, manufacturers are increasingly using non-OS endpoints to accelerate approvals. This work reviews attitudes to non-OS endpoints by former regulators and payers, oncologists, and patient groups.

Methods

The work consists of a literature review of non-OS endpoints, 70 interviews with above mentioned stakeholders in the EU, US, Japan and Canada and roundtable discussions.

Results

Findings highlight the variation in stakeholder value attribution to non-OS endpoints by stakeholder ‘archetype’ and geography. Even when OS benefit is not available, regulators show openness to non-OS endpoints in the presence of robust evidence for added patient benefit, developing frameworks to expedite approval. Payers show more resistance, driven by difficulties in assessing added benefit / cost effectiveness without OS data, absence of suitable comparators and concerns over the subjectiveness of supplementary endpoints such as patient reported outcomes (PROs). Oncologists and patients are generally open to the use of non-OS endpoints, depending on context, setting and disease.

Conclusions

Stakeholders across the value chain must reflect on their role in redefining value, acknowledge the ‘true value of innovation’ as a function of alternative outcomes, and characterise the evidence to support such outcomes, to enable a consensus of what value looks like. This work suggests 3 approaches to achieve this goal:

• Improved knowledge among all stakeholders of the benefits and limitations of non-OS endpoints.

• Payer discussions to reflect “quality of survival” in value assessments and give more weight to PROs to complement other non-OS endpoints until OS becomes available, making innovative therapies available earlier.

• Further evidence generation to reduce payer concerns about the value of non-OS endpoints (e.g., by granting conditional access while generating evidence to show that price reflects long term benefit).

Legal entity responsible for the study

GSK.

Funding

GSK.

Disclosure

A. Fameli: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK. M. Glover: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK. B. Gutierrez: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK. A. Cimen: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK; Financial Interests, Personal, Stocks/Shares: AstraZeneca. L. Nelson: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK. S. Altimari: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK. T. Paulsson: Financial Interests, Institutional, Full or part-time Employment: GSK; Financial Interests, Institutional, Stocks/Shares: GSK.

Background

As patient access to therapeutic innovations is critical in oncology, our study aims to assess the availability and time to access to innovative cancer drugs and their indications for the EU market.

Methods

To obtain actual patient access beyond sales data, hospital pharmacists were invited to participate in a survey and interview regarding the timing, context and first patient access to six cancer medicines and their indications in their hospital. The selected medicines (Olaparib, Niraparib, Ipilimumab, Osimeritinib, Nivolumab, and Ibritunib) are recently registered, high-priced, and clinically relevant (ESMO-Magnitude of Clinical Benefit Scale>3).

Results

22 hospitals (five university- and seven comprehensive cancer centers) from Hungary, Italy, the Netherlands, Belgium, Switzerland, and France participated in this study. In every country, all medicines were available in at least one of the hospitals. A total overview per drug, hospital and country was produced. Niraparib was least available (in only six of the 18 hospitals that treated ovarian cancer) and Nivolumab was most available (in 17 of the 18 hospitals that treated at least one of its indications). High variation in patient access was observed between medicines with shortest time to access for Nivolumab (Mean: 266 days; SD: 473), and the longest time for Ipilimumab (Mean:1012 days; SD: 848). In Hungary, selected medicines were mostly available after a national reimbursement decision (Mean: 1338 days; SD: 830). In Switzerland, they were often earlier available through early access schemes or off-label use (Mean: 512 days; SD: 525). Heterogeneity was also observed within hospitals per countries. For example in the Netherlands, first access to Olaparib was reported in one hospital in early access scheme 164 days after EMA approval, while it became in another hospital available in reimbursed context 2212 days after EMA approval.

Conclusions

Due to divergent marketing strategies, cancer center priorities and reimbursement processes, there are very large differences in patient access to new cancer drugs between and within EU countries. Reducing complexity and harmonizing processes of early access schemes and off-label use could ensure more timely and equitable access to innovative cancer drugs across EU.

Legal entity responsible for the study

The Netherlands Cancer Institute.

Funding

European Fair Pricing Network.

Disclosure

V. Retel: Financial Interests, Institutional, Research Grant, The authors declare that they have nothing to disclose for the work under consideration.: Intuitive , Agendia . W. van Harten: Financial Interests, Institutional, Research Grant, The authors declare that they have nothing to disclose for the work under consideration.: Novartis, Intuitive Surgical , Agendia. All other authors have declared no conflicts of interest.

Background

Progress in cancer treatment happen, partly, as a result of the creation of new anticancer drugs (ACD). New ACDs with novel mechanisms of action were developed by the discovery of new targets or different ways to block them, yet access to these drugs is uneven across different countries. Our objective is to compare the new ACDs approved since 2010 by the United States Food and Drug Administration (FDA), the European Medicines Agency (EMA) and the Brazilian Health Regulatory Agency (ANVISA).

Methods

Data were collected on the FDA, EMA and ANVISA online databases regarding new ACDs approved between January 2010 and May 2022 (date of submission/approval, mechanism of action). Normal distribution was tested by the Shapiro-Wilk test, comparisons were made with the Mann-Whitney test and the data are reported using median days and interquartile range (IQR1-IQR3).

Results

82 new ACDs were approved by FDA from Jan/2010 to May/2022 and 22 (26,8%) were designated as first in class. Checkpoint inhibitors (n=9), anti-HER2/anti-EGFR (n=6 each) and anti-ALK/antiandrogens (n=5 each) were the most approved. EMA approved 74/82 (90,2%) and ANVISA approved 49/82 (59,7%). No new ACD approved by EMA or ANVISA, but not by the FDA was found. Time to approval after submission was 200 days (155-273) at FDA, 359 days (292-416) at EMA and 362 days (264-673) at ANVISA (p<0.00001 for FDA to EMA and FDA to ANVISA). The difference between the submission dates for FDA and EMA was 20 days ([-1]-83). Between FDA and ANVISA the difference was 306 days (134-685). Nineteen (23%) were first submitted at EMA and none at ANVISA. Regarding approval dates for FDA and EMA, the difference was 184 days (60-323) and 9 were approved at EMA before FDA. EMA took 143 days more (84-207) to review the same ADCs. Comparing ANVISA and FDA, approval happened 654 days (402-1098) later and took 174 days (74-411) more. Six were approved at ANVISA before EMA, but none before FDA.

Conclusions

New ACDs are submitted for approval at FDA and EMA at similar dates, but the longer appraisal period by EMA pushes the approval date at Europe to approximately 6 months later. The same steps at ANVISA delay the approval by almost 2 years. Such procedures cause a significant difference in available medications between these regions.

Legal entity responsible for the study

Rafael Barreto.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

Background

In order to prepare a comprehensive healthcare system for cancer patients in the region, it is necessary to first prepare data on how cancer patients in the region use medical institutions. The data on cancer patients’ use of regional medical institutions will be useful in establishing plans to strengthen facilities and manpower for cancer patient care at medical institutions in the region. In this study, relevance index, which is a type of analysis indicator for medical service area, was analyzed for cancer diseases.

Methods

Claim data from the Korean National Health Insurance Service were used for the analysis. Specifically, from 2016 to 2020, the inpatient and outpatient medical service use data of clinics, medical institutions, and public health institutions for medical or dental field were analyzed. The relevance index, which is the value obtained by dividing the amount of services used by medical institutions located in a specific area by the total medical services used by people living in a specific area, was measured by medical service type (outpatient and inpatient) and indicator type (number of patients and co-payments).

Results

Korea’s relevance index for cancer in inpatient treatment has hardly improved. In particular, the relevance index for cancer was highly variable by region. Seoul had the highest relevance index for cancer (5-year average 89.98%), while Gyeongsangbuk-do (5-year average 30.76%) had the lowest relevance index for cancer. “Bone and connective tissue cancer” (5-year average of 40.2%) had the lowest relevance index, while “Other neoplasms and unspecified cancers” (5-year average of 70.3%) had the highest relevance index. The relevance index for cancer in outpatient treatment tended to be higher than that in inpatient treatment. Furthermore, the relevance index for cancer based on co-payment tended to be lower than that based on the number of patients.

Conclusions

In order to provide timely, effective, and patient-centered cancer treatment, it is necessary to have a comprehensive cancer treatment system in the region. The relevance index for each cancer type and year in Korea calculated in this study will be an important indicator to monitor in order to establish a comprehensive cancer treatment system in the region.

Legal entity responsible for the study

The authors.

Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HA21C0107).

Disclosure

All authors have declared no conflicts of interest.

Background

QIs assessing the care pathway in onco-hematology are very limited and often linked to hospital concerns. They do not take into account the entire pathway, and in particular the coordination links between non-hospital professionals (GPs, pharmacists and private nurses) and the hospital. Our objective is to select a set of QIs on the care pathway in onco-hematology by involving all relevant stakeholders: hospital professionals, patient associations and non-hospital professionals.

Methods

The study was conducted in 3 main stages. First, a review of available QIs assessing the clinical pathway was conducted from the gray literature and literature reviews. Selection criteria were applied to extract oncological clinical pathway QIs. Secondly, the list of pre-selected QIs was submitted separately to 3 working groups for a consensus selection: hospital professionals (oncologists, pharmacists and SRNs of various hospitals in France), patient associations and non-hospital professionals. The final stage consists of meeting all the experts to pool the selected QIs: a rating of each QI on two dimensions (relevance and feasibility) (1^st meeting) and a vote for a final consensus (2^nd meeting).

Results

5731 QIs were identified and 131 selected after application of selection criteria to extract onco-hematology care pathway QIs. 36 QIs were selected by the 3 working groups, including 1 by all 3 groups (13 QIs by hospital professionals, 10 by patient associations and 13 by non-hospital professionals). Experts met by videoconference and established a score for the 34 QIs, the weighted averages range from 12.9 to 16.4.

Conclusions

Final consensus for a set of QIs will be achieved in July 2022 during a plenary meeting. The project will provide a consensus for set of QIs by involving all relevant stakeholders: hospital professionals, patient associations and non-hospital professional involved in the care pathway in onco-haematology. These QIs could then be tested in the institutions of the project participants in order to validate their metrological qualities.

Legal entity responsible for the study

The authors.

Funding

Novartis Oncology.

Disclosure

M. Ferrua: Financial Interests, Personal, Advisory Role: Novatis Oncology. All other authors have declared no conflicts of interest.

Background

Cancer patients may present to a healthcare provider with signs and symptoms without a clear diagnosis pathway. This often leads to a lengthy diagnosis process, treatment delay, poor outcomes, and high costs. Limited evidence on cancer diagnosis durations in the United States (US) exists. This study quantified time to diagnosis (TTD) among patients diagnosed with cancer in the US.

Methods

A retrospective claims analysis of patients newly diagnosed with cancer in 2018-2019 was conducted using Optum’s de-identified Clinformatics® Data Mart Database, which includes Medicare Advantage and commercially insured members. Patients were identified using ICD-10 codes requiring ≥2 outpatient visits ≥30 days apart or 1 inpatient visit without a prior cancer claim. The first diagnostic test was selected by reviewing the records for diagnostic tests prior to the cancer diagnosis until a gap of >60 days was observed. The index date was defined as the first diagnostic test date or, if available, the office visit date closest to and <4 weeks prior to the first diagnostic test. The TTD (time from index to cancer diagnosis date) was summarized descriptively and by tumor type.

Results

A total of 458,818 patients were identified across 20 cancer types with a mean age of 71 years, a mean Charlson comorbidity index of 2.1, and 50% were male. Breast (26%), prostate (19%), lung (13%), urothelial and bladder (9%), and kidney (8%) cancers were most prevalent. Mean TTD was 5.2 months (median=3.9 months, SD=5.5 months) and varied across cancer types and among patients with the same cancer type; 15.4% of patients had a TTD of >6 months. Multiple myeloma (52.8%), stomach (48.8%), esophagus (46.1%), colorectal (44.6%), lymphoma (31.9%), kidney (28.7%), and gallbladder (26.5%) cancers had more than a quarter of patients with >6 months TTD.

Conclusions

A non-trivial proportion of patients newly diagnosed with cancer experienced a lengthy diagnosis process of >6 months with large heterogeneity across patients and cancer types, highlighting cancer diagnosis inefficiency in the US. Policies, guidelines, and interventions that streamline cancer diagnosis pathways are critical to optimize patient outcomes.

Legal entity responsible for the study

*GRAIL LLC, a subsidiary of Illumina, Inc., *Currently held separate from Illumina Inc. under the terms of the Interim Measures Order of the European Commission dated 29 October 2021.

Funding

*GRAIL, LLC, a subsidiary of Illumina, Inc., *Currently held separate from Illumina Inc. under the terms of the Interim Measures Order of the European Commission dated 29 October 2021.

Disclosure

M.D. Gitlin: Non-Financial Interests, Institutional, Funding: GRAIL. N. McGarvey: Non-Financial Interests, Institutional, Funding: GRAIL . N. Shivaprakash: Non-Financial Interests, Institutional, Funding: GRAIL . Z. Cong: Financial Interests, Personal, Stocks/Shares: GRAIL .