Should Basket Trials Be Pathways to Drug Registration for Biomarker-Defined Subgroups of Advanced Cancers?

The prognosis of patients with solid tumours has remarkably improved with the development of molecular-targeted drugs and immune checkpoint inhibitors. However, the improvements in the prognosis of pancreatic cancer and biliary tract cancer is delayed compared to other carcinomas, and the 5-year survival rates of distal-stage disease are approximately 10 and 20%, respectively. However, a comprehensive analysis of tumour cells using The Cancer Genome Atlas (TCGA) project has led to the identification of various driver mutations. Evidently, few mutations exist across organs, and basket trials targeting driver mutations regardless of the primary organ are being actively conducted. Such basket trials not only focus on the gate keeper-type oncogene mutations, such as HER2 and BRAF, but also focus on the caretaker-type tumour suppressor genes, such as BRCA1/2, mismatch repair-related genes, which cause hereditary cancer syndrome. As oncogene panel testing is a vital approach in routine practice, clinicians should devise a strategy for improved understanding of the cancer genome. Here, the gene mutation profiles of pancreatic cancer and biliary tract cancer have been outlined and the current status of tumour-agnostic therapy in these cancers has been reported.

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Variance prior specification for a basket trial design using Bayesian hierarchical modeling

Clinical Trials ◽

10.1177/1740774518812779 ◽

2018 ◽

Vol 16 (2) ◽

pp. 142-153 ◽

Cited By ~ 6

Author(s):

Kristen M Cunanan ◽

Alexia Iasonos ◽

Ronglai Shen ◽

Mithat Gönen

Keyword(s):

Standard Deviation ◽

Prior Distribution ◽

Hierarchical Modeling ◽

Bayesian Hierarchical Modeling ◽

Bayesian Hierarchical ◽

Inverse Gamma ◽

Wide Range ◽

Basket Trial ◽

Basket Trials ◽

Variance Parameter

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

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International Drug Registration Efforts

The Journal of Clinical Pharmacology ◽

10.1002/j.1552-4604.1987.tb03008.x ◽

1987 ◽

Vol 27 (4) ◽

pp. 253-259 ◽

Cited By ~ 2

Author(s):

Viviane Schuermans ◽

Alain Raoult ◽

Marcel Moens ◽

Jos Heykants ◽

André Reyntjens ◽

...

Keyword(s):

Drug Registration

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Federal Drug Registration Rules and Regulations

Journal of the American Pharmaceutical Association (1961) ◽

10.1016/s0003-0465(16)34025-3 ◽

1963 ◽

Vol 3 (5) ◽

pp. 255-258

Keyword(s):

Drug Registration

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Open-label trials and drug registration: a European regulator’s view

Journal of Thrombosis and Haemostasis ◽

10.1111/j.1538-7836.2008.02835.x ◽

2008 ◽

Vol 6 (2) ◽

pp. 232-234 ◽

Cited By ~ 12

Author(s):

M. CASTEELS ◽

B. FLAMION

Keyword(s):

Open Label ◽

Drug Registration

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REGULATORY REQUIREMENTS FOR DRUG PRODUCTS REGISTRATION IN SOUTH AFRICA

International Journal of Drug Regulatory Affairs ◽

10.22270/ijdra.v3i4.171 ◽

2018 ◽

Vol 3 (4) ◽

pp. 1-8

Author(s):

Senthil V. ◽

L. Srianitha ◽

R. Baviyapriyadharshini

Keyword(s):

South Africa ◽

South African ◽

Pharmaceutical Market ◽

Regulatory Body ◽

Regulatory Requirements ◽

Drug Products ◽

Drug Registration ◽

African Health ◽

Regulatory Submissions ◽

South African Health

The South African Pharmaceutical market is one of the emerging markets in the world and it is important to study on how to register a drug in the promising pharmaceutical market in Africa. The MCC is the regulatory body which deals with the quality, safety and efficacy of the medicines in South African market which regulates by approving the medicines by very specific process which is unique to South African health system. They have a specific type of CTD for Regulatory submissions which is generally well known as ZA CTD. This article provides the insight on the Drug Registration process in South Africa, the details of data to be submitted to the agency and the pathways of registration an applicant can avail, categories a drug can be registered by MCC, Application fees to be paid to the agency on various types of applications are also dealt.

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Efficiency of a Randomized Confirmatory Basket Trial Design Constrained to Control the False Positive Rate by Indication

10.1101/2020.07.22.216127 ◽

2020 ◽

Author(s):

Linchen He ◽

Yuru Ren ◽

Han Chen ◽

Daphne Guinn ◽

Deepak Parashar ◽

...

Keyword(s):

False Positive ◽

Power Efficiency ◽

Statistical Significance ◽

False Positive Rate ◽

Pooled Analysis ◽

Positive Control ◽

Design Parameters ◽

Molecular Oncology ◽

Basket Trial ◽

Basket Trials

PURPOSEMolecular oncology determines biomarker-defined niche indications. Basket trials pool histologic indications sharing molecular pathophysiology, potentially improving development efficiency. Currently basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since false positive control by indications (FWER) may sometimes be required, we now simulate a variant of this basket design controlling FWER at 0.025k, the total FWER of k separate randomized trials.METHODSThe previous design eliminated indications at an interim analysis, conducting a final pooled analysis of remaining indications. To control FWER, we rechecked individual indications at a prospectively defined level of statistical significance after any positive pooled result. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling FWER and minimizing estimation bias were allowable.RESULTSSequential analyses (interim, pooled, and post-individual tests)) result in cumulative power losses. Optimal performance results when k = 3,4. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power (“uncorrected”) or at the power achieved in the basket trial (“corrected”, because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible, with power ≈ 60%.CONCLUSIONSEven under FWER control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical. The design is particularly attractive when enrollment challenges preclude full powering of individual indications.

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