Boundary Between FDA Approved and Non-Approved Anticancer Drugs in Survival Benefit: A Meta-analysis

doi:10.21203/rs.3.rs-4883359/v1

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Boundary Between FDA Approved and Non-Approved Anticancer Drugs in Survival Benefit: A Meta-analysis

https://doi.org/10.21203/rs.3.rs-4883359/v1

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Survival benefit is a fundamental property of anticancer drugs for cancer patients. However, the survival benefits observed in registrational trials broadly ranged from significant to marginal, even for US Food and Drug Administration (FDA)-approved drugs. This study explores the boundary of survival benefits between FDA-approved and non-approved drugs. We screened 3,463 phase 3 investigational studies initiated from January 1990 to January 2021 with survival measurement on ClinicalTrial.gov and identified 208 randomized controlled registrational trials for anticancer drugs with overall survival (OS) measurement. Among 208 studies, 79 (38%) were conducted for FDA-approved, and 129 (62%) were for FDA-non-approved drugs. The pooled mean risk reduction of death across FDA-approved drugs was 29% (Hazard ratio (HR) 0.71), whereas FDA-non-approved drugs exhibited only a 6% risk reduction in OS (HR 0.94). The probability of FDA approval showed a sharp sensitivity to the HR for OS in the logistic regression model. A boundary was observed ranging from 0.74 to 0.86 in the HR for OS, with a 50% probability of FDA approval at HR 0.80. Our findings delineate the required survival benefits for new anticancer drugs based on the clinical and regulatory outcomes over 20 years.

Health sciences/Health care/Drug regulation

Health sciences/Health care/Therapeutics

Drug development

Clinical Trials

Cancer

Regulatory approval

Clinical benefit

Effect size

Improving patient survival is a fundamental requirement for novel anticancer drugs.^1,2

American Society of Clinical Oncology (ASCO) articulated that relative improvements in median overall survival (OS) by at least 20% are necessary for clinically meaningful effect.³ Similarly, the European Society for Medical Oncology (ESMO) used OS to evaluate clinical benefits on a standardized scale for the magnitude of clinical benefit (ESMO-MCBS).^4,5 The World Health Organization (WHO) requires at least a 4–6 month increase in OS for essential medicine.⁶ Although these attempts clarified the expected effect size from medical societies or public health, there were critics that the approved anticancer drugs showed only marginal survival benefits.⁷

In the new drug approval process, the U.S. Food and Drug Administration (FDA) individually evaluates the clinical data for the investigational drug, considering the specific therapeutic context. Likely attributable to these individual-based reviews, the survival benefits of pivotal trials for FDA-approved drugs are broadly distributed. Some trials showed marginal survival benefits, whereas others showed significant improvement in OS.^8,9,10,11,12 Moreover, several drugs that received accelerated approval recently failed to demonstrate survival benefits in the post-marketing studies.^13,14 Although anticancer drug development is characterized by the urgency of the need for new drugs, the tradeoff of the quality of evidence has been questioned.^15,16,17 The bar for regulatory approval for anticancer drugs should be maintained high to ensure their positive impact on public health. Therefore, it is critical to understand the threshold of survival benefit sufficient to grant regulatory approval.

Several previous studies explored the performance of FDA-approved drugs through systematic reviews.^3,18,19,20 They consistently reported that a significant fraction of the FDA-approved anticancer drugs did not exceed the overall survival benefit expected by the ASCO or ESMO, and the regulatory approval relied on the surrogate endpoints, e.g., progression-free survival (PFS). However, these studies collected data from only FDA-approved drugs; thus, the perspective of comparing FDA-approved and non-approved drugs has remained intact. This study was designed to collect both drugs and explore the boundary between FDA-approved and non-approved drugs.

This study collected data from publicly available databases and journals; therefore, institutional review board approval was not required per 45 CFR §46. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.²¹ All study data were extracted by one author (K.I.) using pre-specified forms and verified by a second author (S.O.). Fig. 1 shows the data collection flow, and Table S1 in supplemental material details the data source for individual studies.

Screening and sample collection

We searched ClinicalTrial.gov in May 2021 to identify all registered Phase 3 studies measuring survival outcomes initiated from Jan 1, 1990, to Jan 1, 2021. Among 3,463 screened studies, we identified 949 randomized controlled trials for anticancer therapies with results posted on ClinicalTrial.gov. We collected the relevant literature from PubMed using the NCT numbers and identified 663 study reports. We excluded trials that did not have a statistical hypothesis for superiority in OS and the local studies that did not involve study sites in the US. We defined a pair of arms in the dataset to test a statistical hypothesis for OS as a single data sample. Throughout the data collection process, 208 samples were identified as registrational trials in the US for anticancer drugs with measurement of OS as primary or co-primary endpoints. We searched the FDA website (drugs@FDA) to assign the approval status to the drugs tested in the 208 trials.

Collection of study characteristics

The literature in Supplementary Table S1 in supplemental material was used to extract study characteristics, i.e., sample size, study duration, type of study sponsorship, year of study start, cancer type, treatment line, modality of the study drug, age of control drug in the market, type of primary endpoint, study blind setting, and arm design. We estimated the age of the control drug/regimen in the market by the duration of the first year of reporting the control regimen/drug and the year when the cited literature was published. In addition, we collected primary study results, including hazard ratio (HR) for OS and PFS, absolute extension of median OS and median PFS, the p-value for the statistical tests, and median OS of the control arm. Finally, we flagged breakthrough therapy, fast track, and orphan drug designations based on the FDA orphan drug website and the relevant pharmaceutical companies' websites.

Statistical analysis

All statistical analyses were performed using Stata statistical software (StataCorp, 2022; Stata Statistical Software Release 17. College Station, TX: StataCorp LP). All tests were two-sided, and a p-value of < 0.05 was considered statistically significant. Data analysis was performed from Jan 2023 to Dec 2023 by the first author, K.I., and then verified by the second author, SO. Descriptive statistics were used to characterize the FDA-approved and non-approved groups. Pearson's chi-square test was used for categorical variables, and the t-test was employed for continuous variables. In addition, we performed a funnel plot analysis to assess the publication bias.

Using a random-effects restricted maximum likelihood model, we estimated the pooled mean effect size in HR for OS by the FDA approval status, regulatory designation, and study characteristics. The heterogeneity was described using the I² statistics.

The distribution of the observed effect sizes in OS and PFS was visualized in the histogram. The probability of FDA approval was derived by the number of FDA-approved drugs divided by the total number of drugs in the specific effect size range. Logistic regression analysis was performed to fit the probability of FDA approval over the observed effect size in OS. The boundary of FDA-approved and non-approved drugs was defined as a range of HR for OS that varied the probability of FDA approval from 20% to 80%. To further characterize the boundary, we reviewed the Oncologic Drug Advisory Committee (ODAC) incidence rate for the drugs in and outside the boundary and calculated the odds ratio. Finally, the summary receiver-operating characteristics (ROC) curve and the area under the curve (AUC) were utilized to compare the regression models with HR for OS and other survival endpoints: absolute extension of median OS, HR for PFS, and absolute extension of median PFS.

Characteristics of the included studies

We screened 3,463 phase 3 studies, which assessed survival outcomes as an endpoint. Two hundred-eight studies with 158,250 participants were identified as eligible for the analysis (Fig. 1). 79 (38.0%) trials resulted in FDA approval, and 129 (62.0%) trials did not lead to approval. Table 1 and 2 show the study characteristics and cancer types by FDA approval status. Pharmaceutical companies sponsored most included studies (197 trials, 94.7%). The included studies mainly started after 2005 (195 trials, 93.7%). The mean study duration was 5.6 years, and the average sample size was 761 patients. The dataset included 27 cancer types (19 carcinomas, one sarcoma, seven hematologic malignancies). Approximately half of the trials were conducted in second-line treatment or later (99 trials, 47.6%), and the remaining half were in the first line (99 trials, 47.6%). The most common type of cancer was non-small cell cancer; 41 trials (19.7%), followed by prostate cancer; 27 trials (13.0%), gastric cancer, melanoma, and pancreatic cancer; 14 trials (7%) respectively.

There was no significant difference in the characteristics between the FDA-approved group and non-approved group for sample size, study duration, study sponsorship, cancer types, treatment line, modality of study drug, age of control drug in market, median OS of control drug, primary endpoint, study arm design, fast track designation, and orphan drug designation. On the other hand, the year of study start (p = 0.017), study blind setting (p = 0.029), and breakthrough therapy designation (p < 0.001) showed significant differences between FDA-approved and non-approved drugs.

The funnel plot showed symmetric distribution and no significant publication bias for the FDA-approved drugs (Egger test, p = 0.727; Begg-Mazumdar test, p = 0.403, see Supplementary Fig. S1 online). However, the FDA non-approved drugs exhibited a slightly asymmetrical distribution and showed significance (Egger test, p < 0.001; Begg-Mazumdar test, p < 0.001).

Pooled effect size by subgroup meta-analysis

To explore the difference in average survival benefit by the regulatory status and the study characteristics, we used meta-subgroup analysis based on a random-effects restricted maximum likelihood model (Fig. 2). Overall pooled mean HR for OS in total 208 trials was 0.85 (95% CI: 0.83, 0.87) with 70.12% I², indicating large amounts of heterogeneity in the dataset. The most clear separation was observed between FDA-approved and non-approved subgroups (p < 0.001). The combined risk reduction of death associated with any condition across all FDA-approved drugs was 29% compared with the control (HR 0.71 [95% CI: 0.69, 0.73]). In contrast, the FDA non-approved drug showed only a 6% risk reduction in OS (HR 0.94 [95% CI: 0.92, 0.96]). Followed by FDA approval status, breakthrough therapy designation(BTD) showed a significant separation (HR 0.70 [95% CI: 0.66, 0.74] with BTD, HR 0.87 [95% CI: 0.85, 0.90] without BTD, p < 0.001). In contrast, fast-track designation and orphan drug designation did not significantly differ in the combined risk reduction of death (p = 0.83 and p = 0.13, respectively). The pooled effects size by study characteristics are detailed in Supplementary Fig. S2 and Fig. S3 online.

Boundary between FDA-approved drugs and non-approved drugs

The histogram in Fig. 3-a shows the distribution of hazard ratio for OS by FDA approval status, which indicates that each group has a distinct peak of the distribution. The FDA-approved group peaked at around HR 0.65 to 0.70 for OS, whereas the FDA-non-approved group peaked at around HR 0.95 to 1.0 for OS. The two groups’ distributions overlap from HR 0.70 to HR 0.90 for OS. The most remarkable overlap was observed in HR 0.80 to 0.85 for OS.

Fig. 3-b shows an overlay of a scatter plot for the actual probability of FDA approval over the HR for OS and a logistic regression curve using HR for OS to predict the probability of FDA approval. The logistic regression model showed a reverse sigmoidal curve with sharp sensitivity of FDA approval to the HR for OS (coefficient: 19.8, p < 0.001).

This model projected the probability of FDA approval at 20% at HR 0.86 and 80% at HR 0.74. Therefore, the range of HR for OS at 0.74 to 0.86 was identified as a boundary between FDA-approved and non-approved drugs based on the predefined criteria. This boundary was consistent with the overlap area observed in the histogram. Drugs in the boundary area showed a significantly higher odds ratio for the incidence that the FDA consults with the ODAC, compared with those outside the boundary area (Odds ratio: 22.3, p = 0.0001, see Supplementary Table S2 and Table S3 online).

The same histogram was depicted for other survival endpoints: extension of median OS, hazard ratio in PFS, and extension of median PFS. However, contrary to HR for OS, no clear separation was observed between FDA-approved and non-approved groups (see Supplementary Fig. S4 online). Similarly, the ROC curve showed the most extensive AUC in the predictive model using HR for OS (AUC: 0.97 [95% CI 0.94 – 0.99]) among the survival endpoints. The extension of median OS (AUC: 0.92 [95%CI 0.89 – 0.96]), hazard ratio for PFS (AUC: 0.81 [95%CI 0.75 – 0.87]), and the extension of median PFS (AUC: 0.72 [95%CI 0.64 – 0.80]) followed respectively in Fig. 4.

This cross-sectional study characterized the registrational phase 3 studies for anticancer drugs with measurements of overall survival over the past 20 years and reported a boundary between FDA-approved and non-approved drugs in survival benefit. Our findings underscore the importance of HR for OS in the regulatory decision and introduce a quantitative perspective for the regulatory bar for the new anticancer drugs in the US. In recent years, it has been criticized that the survival benefits of several FDA-approved drugs were lower than expected, resulting in concerns that the regulatory bar might have been lowered. However, the threshold identified by this study revealed that at least an HR 0.80 for OS was required to maintain a 50% probability of FDA approval. Remarkably, this level was in line with the expectations of the ASCO. The finding could be a valuable reference for drug development in oncology.

The strengths of this study are the inclusion of both FDA-approved and non-approved drugs without selection by clinical or regulatory outcomes. This approach contributed to the comprehensiveness of this study. Our study showed a 38.0% (79 of 208 trials) success rate of FDA approval for the drugs in which the pivotal phase 3 trials were initiated from 1999 to 2018. The success rate is consistent with several previous reports. Wong C.H. et al. (2019) reported a 35.5% success rate from phase 3 to approval in oncology based on the 1,236 phase transitions from 2000 to 2015.²² Similarly, Hay M et al. (2014) reported a 37.0% success rate for 383 phase transitions from 2003 to 2011.²³ Biotechnology innovation organization reported a consistent success rate in oncology from 2006 to 2015 (33.0%) and a slightly higher rate from 2011 to 2020 (43.9%).^24,25 These previous studies comprehensively collected the clinical trials without selection. Therefore, the consistency of our report supports the comprehensiveness of the dataset. Consequently, the dataset enabled us to compare the FDA-approved and non-approved drugs for the study characteristics and calculate the probability of success in the FDA approval.

Boundary between FDA-approved and non-approved drugs

Our dataset suggested that the probability of FDA approval is sharply sensitive to the HR for OS. Survival benefit is primarily expected for anticancer drugs; thus, it seems reasonable that such a sensitivity is observed. The range of HR at 0.74 to 0.86 for OS was identified as the boundary to separate the FDA-approved and non-approved drugs, exhibiting a 20% to 80% probability of FDA approval. From the logistic regression model, a 50% likelihood of FDA approval is derived at HR 0.80 for OS, consistent with the ASCO's recommended criteria: at least 20% risk reduction of death compared to the available standard care (i.e., HR = 0.80). Although the ASCO working groups did not intend to set the standards for regulatory approval, the identified boundary showed remarkable consistency.

In the ROC analysis, the regression model with HR for OS showed greater predictability to the FDA approval over the other models using an extension of median OS, HR for PFS, and an extension of median PFS. Two aspects of study design may partly explain this tendency—first, all studies in our dataset employed OS as a primary or co-primary endpoint. Therefore, the regulatory outcome may have shown higher sensitivity with OS than PFS. Second, the hypothesized hazard ratio generally determines the sample size rather than the expected absolute extension of median OS or PFS. Therefore, the HR has more influence than the absolute median extension of survival endpoints. These two aspects may determine the order of sensitivity, as shown in ROC. Further interpretation of this trend implies that the FDA may make decisions based on OS over PFS and the result of the hazard ratio over median extension.

Drugs in the boundary area

The drugs outside of the boundary area were considered relatively easy to predict the FDA approval status based on the OS results because the drugs with less than HR 0.74 showed clear separation on the survival curve; conversely, the drugs with HR 0.87 or more for OS showed marginal or no separation to the control drugs. On the other hand, the drugs in the boundary area from HR 0.74 to 0.86 resulted in variable regulatory outcomes. The FDA may have considered therapeutic context, e.g., cancer type, stage of disease, available treatment in the target population, and made diverse decisions. If the FDA needs to be more evident in its regulatory decision, it can convene the ODAC to hear the experts' opinions. Our dataset includes six drugs for which the FDA consulted with ODAC. Among them, five drugs are in the boundary area in HR for OS: quizartinib for acute myeloid leukemia (HR 0.76 [95% CI: 058, 0.98]), sipuleucel-T for prostate cancer (HR 0.78 [95% CI: 0.61, 0.98]), pembrolizumab for gastric cancer (HR 0.82 [95% CI: 0.66, 1.03]), atezolizumab plus platinum chemotherapy for urothelial carcinoma (HR 0.83 [95% CI: 0.69, 1.00]), and atezolizumab plus nab-paclitaxel for triple-negative breast cancer (HR 0.86 [95% CI: 0.72, 1.02]) (see Supplementary Table S2 online). The odds ratio for ODAC consultation was 22.3 with the drugs in the boundary area compared with the drugs outside the boundary area (see Supplementary Table S3 online). Suppose a drug with the effect size in the boundary is filed for a new drug application. In that case, the investigators and the sponsor should anticipate the possibility of the FDA’s consultation with ODAC. In that situation, further supportive data from other studies may be required to reinforce the uncompelling survival benefit in OS.

Limitation

This study has several limitations. First, this trial included only OS-powered registrational trials. However, endpoints other than OS, e.g., PFS, ORR, and DOR, have been vital for regulatory approval of anticancer drugs.²⁶ Our findings may not apply to the drugs approved by other endpoints. Second, our study included clinical studies initiated from 1999 to 2018. Over the last 20 years, various new modalities have been introduced to cancer treatment, e.g., molecularly targeted therapies, immune checkpoint inhibitors, or antibody-drug conjugates. These modalities are known to show different survival curves.²⁷ A traditional approach using the hazard ratio of OS may not capture such different characteristics of survival benefit. Therefore, our analysis based on such a high-level efficacy index did not capture such detailed differences in survival benefits. Third, the dataset has significant heterogeneity attributable to the various anticancer drugs and target diseases. Some of the study characteristics were imbalanced among FDA-approved and non-approved groups, which may limit the interpretation of the data.

The probability of FDA approval showed sharp sensitivity to the HR for OS, which suggested the range of HR at 0.74 to 0.86 for OS as a boundary to separate FDA-approved and non-approved drugs. HR 0.80 for OS is the point to achieve a 50% likelihood of FDA approval. Our findings suggest prioritizing the investigational drugs that have reasonable potential to clear this bar. This study primarily focused on survival outcomes; however, the regulator reviews broader perspectives, including safety profile, disease severity, and unmet medical needs in the indication. Further investigation from such multiple facets will be warranted to understand the boundary between FDA-approved and non-approved drugs.

Author Contributions

K.I. and S.O. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: K.I.

Administrative, technical, or material support: K.I.

Supervision: S.O.

Disclosure of Potential Conflicts of Interest

K.I. is an employee of Daiichi Sankyo, Inc. S.O. declares no potential conflicts of interest.

Funding from Ministry of Education, Culture, Sports, Science and Technology (MEXT): Shunsuke Ono KAKEN-HI:19K07215

Data availability

Data available from the corresponding author ([email protected]) upon request.

Ladanie, A., et al. Clinical Trial Evidence Supporting US Food and Drug Administration Approval of Novel Cancer Therapies Between 2000 and 2016. JAMA Netw Open. 3(11): e2024406. doi: 10.1001/jamanetworkopen.2020.24406 (2020).
Royle, K. L., Meads, D., Visser-Rogers, J. K., White, I. R., & Cairns, D. A. How is overall survival assessed in randomized clinical trials in cancer, and are subsequent treatment lines considered? A systematic review. Trials. 24(1): 708. doi: 10.1186/s13063-023-07730-1 (2023).
Ellis, L. M., et al. American Society of Clinical Oncology perspective: Raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol. 32(12):1277–1280 (2014).
Cherny, N.I., et al. A standardised, generic, validated approach to stratify the magnitude of clinical benefit that can be anticipated from anticancer therapies: the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). Ann Oncol. 26(8):1547–1573 (2015).
Cherny, N.I., et al. ESMO-Magnitude of Clinical Benefit Scale version 1.1. Ann Oncol. 28(10):2340–2366 (2017).
World Health Organization Executive Summary, The Selection and Use of Essential Medicines 2019: Report of the 22nd WHO Expert Committee on the Selection and Use of Essential Medicines (2019).
Fojo, T., Mailankody, S., & Lo, A. Unintended consequences of expensive cancer therapeutics—the pursuit of marginal indications and a me-too mentality that stifles innovation and creativity: the John Conley Lecture. JAMA Otolaryngol Head Neck Surg. 140(12):1225–1236 (2014).
Senan, S., et al. PROCLAIM: Randomized Phase III Trial of Pemetrexed-Cisplatin or Etoposide-Cisplatin Plus Thoracic Radiation Therapy Followed by Consolidation Chemotherapy in Locally Advanced Nonsquamous Non-Small-Cell Lung Cancer. J Clin Oncol. 34(9):953–962 (2016).
Garon, E.B., et al. Ramucirumab plus docetaxel versus placebo plus docetaxel for second-line treatment of stage IV non-small-cell lung cancer after disease progression on platinum-based therapy (REVEL): a multicentre, double-blind, randomized phase 3 trial. Lancet. 384(9944):665 − 73 (2014).
Saura, C., et al. Neratinib Plus Capecitabine Versus Lapatinib Plus Capecitabine in HER2-Positive Metastatic Breast Cancer Previously Treated With ≥ 2 HER2-Directed Regimens: Phase III NALA Trial. J Clin Oncol. 38(27):3138–3149 (2020).
Thatcher, N., et al. Necitumumab plus gemcitabine and cisplatin versus gemcitabine and cisplatin alone as first-line therapy in patients with stage IV squamous non-small-cell lung cancer (SQUIRE): an open-label, randomised, controlled phase 3 trial. Lancet Oncol. 16(7):763–774 (2015).
Tabernero, J., et al. Ramucirumab versus placebo in combination with second-line FOLFIRI in patients with metastatic colorectal carcinoma that progressed during or after first-line therapy with bevacizumab, oxaliplatin, and a fluoropyrimidine (RAISE): a randomised, double-blind, multicentre, phase 3 study. Lancet Oncol. 16(5):499–508 (2015).
Beaver, J.A. & Pazdur, R. "Dangling" Accelerated Approvals in Oncology. N Engl J Med. 384(18):e68. doi: 10.1056/NEJMp2104846 (2021).
Lemery, S. & Pazdur, R. Approvals in 2021: dangling Accelerated Approvals, drug dosing, new approvals and beyond. Nat Rev Clin Oncol. 19(4):217–218 (2022).
Schnog, J.B., Samson, M.J., Gans, R.O.B., & Duits, A.J. An urgent call to raise the bar in oncology. Br J Cancer. 125(11):1477–1485 (2021).
Niraula, S. & Nugent, Z. New Cancer Drug Approvals From the Perspective of a Universal Healthcare System: Analyses of the Pan-Canadian Oncology Drug Review Recommendations. J Natl Compr Canc Netw. 16(12):1460–1466 (2018).
Gyawali, B., Hey, S.P., & Kesselheim, A.S. Assessment of the Clinical Benefit of Cancer Drugs Receiving Accelerated Approval. JAMA Intern Med. 179(7):906–913 (2019).
Molto Valiente, C., et al. Comparison of the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) in clinical trials supporting US Food and Drug Administration (FDA) approval of orphan vs. non-orphan drugs. ESMO abstract 28(5), V642 (2017).
Tibau, A., et al. Magnitude of Clinical Benefit of Cancer Drugs Approved by the US Food and Drug Administration Based on Single-Arm Trials. JAMA Oncol. 4(11):1610–1611 (2018).
Kumar, H., Fojo, T., & Mailankody, S. An Appraisal of Clinically Meaningful Outcomes Guidelines for Oncology Clinical Trials. JAMA Oncol. 2(9):1238–1240 (2016).
von Elm, E., et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 370(9596):1453–1457 (2007).
Wong, C.H., Siah, K.W., & Lo, A.W. Estimation of clinical trial success rates and related parameters. Biostatistics. 20(2):273–286 (2019).
Hay, M., Thomas, D.W., Craighead, J.L., Economides, C., & Rosenthal, J. Clinical development success rates for investigational drugs. Nat Biotechnol. 32(1):40–51 (2014).
Thomas, D.W., et al. Clinical Development Success Rates 2006–2015. BIO Industry Analysis (2016).
Thomas, D.W., et al. Clinical Development Success Rates and Contributing Factors 2011–2020. BIO/QLS Advisors/Informa UK Ltd. White paper (2021).
Saad, E.D. & Buyse, M. Statistical controversies in clinical research: endpoints other than overall survival are vital for regulatory approval of anticancer agents. Ann Oncol. 27(3):373–378 (2016).
Ferrara, R., et al. Do immune checkpoint inhibitors need new studies methodology? J Thorac Dis. 10(Suppl 13): S1564-S1580 (2018).

Table 1

Study characteristics by FDA approval status
Categorical variable		All	FDA approved group	FDA non-approved group	P value
		(number of studies)	(number of studies, %)	(number of studies, %)
Number of studies		208	79	129
Number of total patients		158,250	62,721	95,529
Sample size per study	(patients, mean (95% CI))	761 (713–808)	794 (712–876)	741 (682–799)	p = 0.284 ^a
Study duration	(year, mean (95% CI))	5.6 (5.3–5.9)	6.0 (5.6–6.4)	5.4 (4.9–5.8)	p = 0.059 ^a
Study sponsorship	Pharmaceuticals	197 (94.7)	77 (97.5)	120 (93.0)	p = 0.164 ^b
	Clinical Trial Group	11 (5.3)	2 (2.5)	9 (7.0)
Year of study start	1999–2004	13 (6.3)	8 (10.1)	5 (3.9)	p = 0.017 ^b
	2005–2009	53 (25.5)	13 (16.5)	40 (31.0)	(Base: 1995–2004)
	2010–2014	87 (41.8)	31 (39.2)	56 (43.4)
	2015–2018	55 (26.4)	27 (34.2)	28 (21.7)
Major category of cancer	Carcinoma	187 (89.9)	70 (88.6)	117 (90.7)	p = 0.636 ^b
	Sarcoma	4 (2.0)	1 (1.3)	3 (2.3)	(Base: carcinoma)
	Hematological malignancy	17 (8.2)	8 (10.1)	9 (7.0)
Treatment line	Second line or later	99 (47.6)	40 (50.6)	59 (45.7)	p = 0.442 ^b
	First line	99 (47.6)	37 (46.8)	62 (48.1)	(Base: second-line or later)
	Neoadjuvant/adjuvant/chemoradiation therapy	10 (4.8)	2 (2.5)	8 (6.2)
Modality of study drug	Small molecule	97 (46.6)	31 (39.2)	66 (51.2)	p = 0.068 ^b
	Naked antibody	96 (46.2)	44 (55.7)	52 (40.3)	(Base: small molecule)
	ADC	6 (2.9)	3 (3.8)	3 (2.3)
	Cell, peptide, others	9 (4.3)	1 (1.3)	8 (6.2)
Age of control drug in the market	≤ 16 years	106 (51.0)	47 (59.5)	59 (45.7)	p = 0.054 ^b
	> 16 years	102 (49.0)	32 (40.5)	70 (54.3)
OS of control drug	< 9 months	68 (32.7)	30 (38.0)	38 (29.5)	p = 0.188 ^b
	9 ≦ months	124 (59.6)	41 (51.9)	83 (64.3)	(Base: < 9 months)
	NA	16 (7.7)	8 (10.1)	8 (6.2)
Primary endpoint	OS (single primary)	150 (72.1)	51 (64.6)	99 (76.7)	p = 0.057 ^b
	OS and other endpoints (co-primary)	58 (27.9)	28 (35.4)	30 (23.3)
Study blind setting	Open-label study	117 (56.3)	52 (65.8)	65 (50.4)	p = 0.029 ^b
	Blinded study	91 (43.7)	27 (34.2)	64 (49.6)
Study arm design	Addition to SOC	91 (43.8)	27 (34.2)	64 (49.6)	p = 0.102 ^b
	Replacement of SOC	72 (34.6)	35 (44.3)	37 (28.7)	(Base: addition to SOC)
	New treatment line/addition to BSC	38 (18.3)	14 (17.7)	24 (18.6)
	Partial replacement of SOC regimen	7 (3.4)	3 (3.8)	4 (3.1)
Fast Track Designation	Yes	44 (21.1)	16 (20.2)	28 (21.7)	p = 0.803 ^b
	No	164 (78.9)	63 (79.8)	101 (78.3)
Breakthrough Therapy Designation	Yes	29 (13.9)	23 (29.1)	6 (4.7)	p < 0.001 ^b
	No	179 (86.1)	56 (70.9)	123 (95.4)
Orphan Drug Designation	Yes	80 (38.5)	32 (40.5)	48 (37.2)	p = 0.635 ^b
	No	128 (61.5)	47 (59.5)	81 (62.8)
^a t-test
^b Pearson’s chi-squared test

Table 2

Cancer types by FDA approval status
Categorical variable		All	FDA approved group	FDA non-approved group	P value
		(number of studies)	(number of studies, %)	(number of studies, %)
Cancer type	Non-small cell lung cancer	41 (19.7)	14 (17.7)	27 (20.9)	p = 0.213 ^b
	Prostate cancer	27 (13.0)	9 (11.4)	18 (14.0)	(Base: acute lymphocytic leukemia)
	Gastric cancer	14 (6.7)	4 (5.1)	10 (7.8)
	Melanoma	14 (6.7)	8 (10.1)	6 (4.7)
	Pancreatic cancer	14 (6.7)	2 (2.5)	12 (9.3)
	Hepatocellular carcinoma	12 (5.8)	4 (5.1)	8 (6.2)
	Acute myeloid leukemia	10 (4.8)	5 (6.3)	5 (3.9)
	Renal cell carcinoma	10 (4.8)	6 (7.6)	4 (3.1)
	Breast cancer	9 (4.3)	4 (5.1)	5 (3.9)
	Colorectal cancer	9 (4.3)	5 (6.3)	4 (3.1)
	Head and neck cancer	8 (3.9)	5 (6.3)	3 (2.3)
	Urothelial carcinoma	8 (3.9)	3 (3.8)	5 (3.9)
	Glioblastoma	5 (2.4)	0	5 (3.9)
	Small cell lung cancer	5 (2.4)	2 (2.5)	3 (2.3)
	Soft tissue sarcoma	4 (1.9)	1 (1.3)	3 (2.3)
	Mesothelioma	3 (1.4)	1 (1.3)	2 (1.6)
	Ovarian cancer	3 (1.4)	0	3 (2.3)
	Acute lymphocytic leukemia	2 (1.0)	2 (2.5)	0
	Esophageal cancer	2 (1.0)	2 (2.5)	0
	Adrenocortical carcinoma	1 (0.5)	0	1 (0.8)
	Cervical cancer	1 (0.5)	1 (1.3)	0
	Chronic lymphocytic leukemia	1 (0.5)	0	1 (0.8)
	Chronic myelogenous leukemia	1 (0.5)	1 (1.3)	0
	Endometrial cancer	1 (0.5)	0	1 (0.8)
	Multiple myeloma	1 (0.5)	0	1 (0.8)
	Myelodysplastic syndromes	1 (0.5)	0	1 (0.8)
	Non-Hodgkin's lymphoma	1 (0.5)	0	1 (0.8)
^a t-test
^b Pearson’s chi-squared test

Competing interest reported. K.I. is an employee of Daiichi Sankyo, Inc. S.O. declares no potential conflicts of interest.

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Boundary Between FDA Approved and Non-Approved Anticancer Drugs in Survival Benefit: A Meta-analysis

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