Benefit‐Risk Analysis for Decision‐Making: An Approach

Raju, G.; Gurumurthi, Karthik; Domike, Reuben

doi:10.1002/cpt.507

Cited by 11 publications

(35 citation statements)

References 48 publications

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“…In this study, the CUI analysis did allow more transparent and efficient decision making for the phase III dose selection. Beyond dose selection, the CUI can also be applied to support other key decision making in drug development and regulatory evaluation …”

Section: Discussionmentioning

confidence: 99%

“…built on the US Food and Drug Administration qualitative benefit‐risk framework by combining benefits and risks in terms of a common metric (e.g., gain in adjusted years of remaining life vs. placebo), which is equivalent to a CUI. These articles reviewed the Food and Drug Administration decisions on drugs for multiple myeloma, non‐small cell lung cancer, multiple sclerosis, and tuberculosis …”

mentioning

confidence: 99%

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Exposure–Response‐Based Product Profile–Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

Zhu¹,

Poland

Wada

et al. 2019

CPT Pharmacom & Syst Pharma

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The aims of this work were to characterize ipatasertib exposure–response (E‐R) relationships in a phase II study and to quantitatively assess benefit‐risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration‐resistant prostate cancer. Logistic regression and Cox proportional‐hazards models characterized E‐R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E‐R models. Despite a steeper E‐R relationship when accounting for dose modifications, similar dose‐response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit‐risk balance than other doses (200–500 mg daily), was selected for further development in metastatic castration‐resistant prostate cancer .

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Exposure–Response‐Based Product Profile–Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

Zhu¹,

Poland

Wada

et al. 2019

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

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“…A benefit is a desirable (or positive) effect of a therapy (e.g., increase in duration of life and/or in quality of life) . This work, similar to prior work, uses median OS (directly measured or estimated as described below) as the measure of benefit.…”

Section: Approachmentioning

confidence: 97%

“…Uncertainty can be an important factor in review of clinical trial data and could be statistical (e.g., related to number of patients treated in an arm of a clinical trial) or epistemic (e.g., related to heterogeneity of subpopulations participating in the clinical trial, quality of evidence, etc.) in nature, as described and illustrated in prior work . The level of uncertainty and understanding can be specific to patient subpopulations, trials conducted, variability and susceptibility to bias of the trial measures, the totality of available evidence, etc.…”

Section: Approachmentioning

confidence: 99%

“…The statistical uncertainty associated with the estimation of OS from ORR or PFS, and the estimation of PFS from ORR, is particularly relevant here. In cases where confidence intervals were not reported in the clinical trial reports for ORR, the normal approximation method for 95% confidence interval was used, as in prior work, and as shown below:

Upper Confidence Limit = p + 1.96 \times \sqrt (p (1 - p) / n)

Lower Confidence Limit = p - 1.96 \times \sqrt (p (1 - p) / n)

where n is the sample size and p is the proportion of interest.…”

Section: Approachmentioning

confidence: 99%

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Using a Benefit–Risk Analysis Approach to Capture Regulatory Decision Making: Renal Cell Carcinoma

Raju

Gurumurthi

Domike

et al. 2019

Clin Pharma and Therapeutics

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Drug regulators such as the US Food and Drug Administration (FDA) make decisions about drug approvals based on benefit–risk analysis. In this work, a quantitative benefit–risk analysis approach captures regulatory decision making about new drugs to treat renal cell carcinoma (RCC). Fifteen FDA decisions on RCC drugs based on clinical trials whose results were published from 2005 to 2018 were identified and analyzed. The benefits and risks of the new drug in each clinical trial were quantified relative to comparators (typically the control arm of the same clinical trial) to estimate whether the benefit–risk was positive or negative. A sensitivity analysis was demonstrated using pazopanib to explore the magnitude of uncertainty. FDA approval decision outcomes for the clinical trials assessed were consistent and logical using this benefit–risk framework.

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Using a Benefit–Risk Analysis Approach to Capture Regulatory Decision Making: Melanoma

Raju

Gurumurthi

Domike

et al. 2019

Clin Pharma and Therapeutics

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Drug regulators seek to make decisions regarding drug approvals based on analysis of the relevant benefits and risks. In this work, 25 US Food and Drug Administration (FDA) decisions on melanoma drugs were identified and analyzed based on clinical trial results published between 1999 and 2017. In each case, the benefits and risks of the new drug in each clinical trial relative to a comparator (typically the control arm of the same clinical trial) were quantified. The benefits and risks were analyzed using a common scale to allow for direct comparison. A sensitivity analysis was conducted using vemurafenib to explore the magnitude of uncertainty in the quantitative assessments. The associated FDA decision outcomes of the new drugs were consistent with the benefits and risks quantified in this work.Drug regulatory agencies base drug approval decisions on the benefits and risks of a drug (see representative quotations in Table S1). Benefit-risk assessment approaches 1,2 are valuable in providing a clear set of expectations and also have the potential to support the accessibility of safe and effective medicines to patients over the product lifecycle as new information becomes available. [3][4][5] As part of the Prescription Drug User Fee Act and the US Food and Drug Administration's (FDA) Safety and Innovation Act, the FDA committed to structuring their assessments of new drug applications and biological license applications as benefit-risk assessments. In prior work, a framework for such assessments was proposed 6 and used for analysis of FDA decision-making in new drug applications and biological license applications for the disease areas of non-small cell lung cancer (NSCLC) 7 and multiple myeloma. 8 In this work, that same approach is applied to the disease area of melanoma of the skin. In prior work on NSCLC 7 overall survival (OS) was used as the measure of benefit. In the prior work on multiple myeloma, 8 because FDA drug decisions were based primarily on other, non-OS metrics, 9 progression-free survival (PFS) was used as the common measure of benefit. In the present work, similar to prior work on NSCLC, 7 OS was used as the common measure of benefit.There were an estimated 1,169,351 people living with melanoma of the skin in the United States in 2014. 10 An estimated 91,270 new cases of and 9, 320 deaths related to melanoma of the skin were projected to occur in the United States in 2018. 11 Melanoma of the skin represented an estimated 5.2% of all new cases of cancer in the United States in 2017.Among patients newly diagnosed with melanoma, 84% are in the local (confined to the primary site) stage of the disease, 9% are in the regional (spread to regional lymph nodes) stage, 4% are in the distant metastatic stage, and the stage of 3% is unknown. The prognosis for melanoma patients varies by the stage of the disease at diagnosis. The 5-year relative survival of patients in the local, regional, and distant stages is 98.5%, 62.9%, and 19.9%, respectively. 10 With the advent of small molecule-targeted therapies...

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Benefit‐Risk Analysis for Decision‐Making: An Approach

Cited by 11 publications

References 48 publications

Exposure–Response‐Based Product Profile–Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

Exposure–Response‐Based Product Profile–Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

Using a Benefit–Risk Analysis Approach to Capture Regulatory Decision Making: Renal Cell Carcinoma

Using a Benefit–Risk Analysis Approach to Capture Regulatory Decision Making: Melanoma

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