Combination of exposure-response and case-control analyses in regulatory decision making.

Yang, Jeremy J.; Wang, Y.; Zhao, Hai-Zhen; Garnett, Christine; Gobburu, Jogarao; Pierce, William D.; Schechter, Genevieve; Summers, John C.; Keegan, Patricia; Booth, Brian; Rahman, Norzaura Abd

doi:10.1200/jco.2011.29.15_suppl.4087

Cited by 29 publications

(52 citation statements)

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“…Exposure-response models may be confounded by observed and unobserved prognostic and disease risk factors (18,19). Prognostic factors may impact both OS and PK exposure ( Fig.…”

Section: Discussionmentioning

confidence: 99%

“…However, exposure-response models may be confounded by disease severity and prognostic factors that may be observed (e.g., albumin, ECOG score, number of metastatic sites) or unobserved (e.g., inflammatory status). Confounding is related to the fact that disease severity and health status may impact both OS and PK (18,19), especially for monoclonal antibodies. One way to address the confounding issue is to incorporate an explanatory variable, such as TGI metrics as illustrated in the causal pathway in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Exposure–Response and Tumor Growth Inhibition Analyses of the Monovalent Anti-c-MET Antibody Onartuzumab (MetMAb) in the Second- and Third-Line Non-Small Cell Lung Cancer

Han

Chanu

Jönsson³

et al. 2016

AAPS J

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Abstract.The phase III trial comparing onartuzumab + erlotinib vs. erlotinib in the second-and third-line non-small cell lung cancer (NSCLC) did not meet its primary endpoint of overall survival (OS). The objective was to assess whether doses higher than the phase III dose (15 mg/kg) might yield better efficacy without compromising the safety profile. Data were from 636 patients from the phase II and III NSCLC studies. Tumor growth inhibition (TGI) models were fit to longitudinal tumor size data to estimate individual TGI metrics including time to tumor re-growth (TTG). Cox regression models were developed for timeto-event endpoints (progression-free survival (PFS), OS, and TTG) to investigate relationships with baseline prognostic factors and onartuzumab exposure. Incidence of adverse events was modeled by logistic regression. In the final models, higher onartuzumab exposure was associated with longer PFS, but not with longer OS. Longer OS was associated with higher baseline albumin, longer TTG, smaller number of metastatic sites, female gender, lower ECOG score, and younger age. TTG was the only TGI metric retained in the final OS model. Onartuzumab exposure was not significantly associated with TTG after adjusting for prognostic factors. Higher Cmin was associated with increased incidence of infusion reactions and peripheral edema. Higher onartuzumab exposure was not significantly associated with improved OS after adjusting for prognostic factors and TTG, and there was a trend of unknown clinical significance toward increased incidence of infusion reactions and peripheral edema. These results did not support testing higher onartuzumab doses.

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“…Exposure-response models may be confounded by observed and unobserved prognostic and disease risk factors (18,19). Prognostic factors may impact both OS and PK exposure ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exposure–Response and Tumor Growth Inhibition Analyses of the Monovalent Anti-c-MET Antibody Onartuzumab (MetMAb) in the Second- and Third-Line Non-Small Cell Lung Cancer

Han

Chanu

Jönsson³

et al. 2016

AAPS J

View full text Add to dashboard Cite

show abstract

“…Yang et al took an approach of incorporating a case-control comparison in the ER analysis to reduce the bias introduced by confounding risk factors when evaluating the recommended dosing regimen for trastuzumab in a registration trial (Yang et al 2012). Their analysis suggested that patients with the lowest quartile of trastuzumab exposure did not benefit from addition of trastuzumab treatment to chemotherapy.…”

Section: Application Of Population Pk Modelmentioning

confidence: 99%

Introduction to Pharmacometrics and Quantitative Pharmacology with an Emphasis on Physiologically Based Pharmacokinetics

Derendorf

2014

Applied Pharmacometrics

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“…Under‐dosing is commonly caused by factors leading to higher drug clearance (eg, body size, renal function, younger age, genotype, drug enzyme induction). More recently, large tumor burden has been associated with poor response when a fixed dose of a monoclonal antibody is used that results in low drug plasma concentrations (eg, trastuzumab in gastric cancer, ado‐trastuzumab in breast cancer) . These examples suggest that fixed doses may not be appropriate for all patients, indicate a greater need for pharmacists and prescribers to focus on drug dosing, and highlight the usefulness of new tools that will help to identify patients in need of PDD …”

Section: The Need For Precision Dosingmentioning

confidence: 99%

Precision drug dosing: A major opportunity for patients and pharmacists

Pearce

Giblin

Buckthal

et al. 2018

J Am Coll Clin Pharm

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Precision medicine customizes drug treatment to an individual's disease and genetics in order to optimize therapeutic benefit and minimize risk. Surprisingly, drug dosing is generally not discussed as a component of precision medicine. Because dosing regimens are an independent factor modulating efficacy and toxicity, consideration should be given to the clinical significance of precision drug dosing (PDD) in patient care. Pharmacists routinely face dilemmas when they are asked for dosing recommendations for patients with characteristics that fall outside of those represented in the drug label (eg, morbid obesity, pregnancy, frailty, neonatal patients, or elderly patients). The necessary data and technology now exist to create and implement PDD software for high priority drug‐disease targets in order to assist pharmacists and prescribers in choosing the safest and most effective dose for every patient. Within 5 to 10 years, we believe this software will be available. This study discusses how pharmacists can participate in leading the implementation of these PDD tools to achieve effective and routine clinical use.

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Combination of exposure-response and case-control analyses in regulatory decision making.

Cited by 29 publications

References 0 publications

Exposure–Response and Tumor Growth Inhibition Analyses of the Monovalent Anti-c-MET Antibody Onartuzumab (MetMAb) in the Second- and Third-Line Non-Small Cell Lung Cancer

Exposure–Response and Tumor Growth Inhibition Analyses of the Monovalent Anti-c-MET Antibody Onartuzumab (MetMAb) in the Second- and Third-Line Non-Small Cell Lung Cancer

Introduction to Pharmacometrics and Quantitative Pharmacology with an Emphasis on Physiologically Based Pharmacokinetics

Precision drug dosing: A major opportunity for patients and pharmacists

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