Use of Population Pharmacokinetic Analyses Among FDA-Approved Biologics

Biologics, especially monoclonal antibodies, are increasingly important in the pharmaceutical marketplace. Population pharmacokinetic (PK) analyses could be useful to guide the need for dose adjustments among special populations, yet it is unknown how commonly such analyses are performed during biologics development. We summarized the characteristics of population PK models of biologics and examined their role in informing the drug labels. To do so, we extracted relevant characteristics of 86 biologics approved by the U.S. Food and Drug Administration's Center for Drug Evaluation and Research between 2003 and 2017. Ninety-four percent of monoclonal antibodies (51 of 54 biologics), 75% of fusion proteins with Fc receptor (6 of 8 biologics), and 33% of other proteins (8 of 24 biologics) included population PK analyses. Of these analyses, approximately half (45%) used a 2-compartment model with linear clearance as the base model structure. Body size was the most frequently included covariate in the final models (included in 94% of the 64 biologics in which covariate analysis was performed), although age (11%), sex (35%), race (26%), and renal function (27%) were also included in some models. In 70% to 90% of cases in which the effect of these covariates was examined, information regarding the effect of these on PK was included in the label. These results suggest that population PK analyses provide important information about the impact of intrinsic factors on the PK in the label of biologics by the U.S. Food and Drug Administration.

Anatomy of Risk Evaluation and Mitigation Strategies (REMS)

This Chapter provides an introduction and overview of the U.S. FDA REMS program and applicable regulatory aspects. Topics covered include the 2015 Draft Guidance, organization structure and functions, a discussion on pharmacovigilance and adverse event reports, and a discussion of the applicability of REMS in oncology.

An Informatics Approach to Facilitate Clinical Management of Patients With Retrievable Inferior Vena Cava Filters

OBJECTIVE

Long indwelling times for inferior vena cava (IVC) filters that are used to prevent venous thromboembolism can result in complications. To improve care for patients receiving retrievable IVC filters, we developed and evaluated an informatics-based initiative to facilitate patient tracking, clinical decision-making, and care coordination.

MATERIALS AND METHODS

A semiautomated filter-tracking application was custom-built to query our radiology information system to extract and transfer key data elements related to IVC filter insertion procedures into a database. A web-based interface displayed key information and facilitated communication between the interventional radiology clinical team and referring physicians. A set of filter management options was provided depending on each patient's clinical condition. The system was launched in April 2016. Using retrospective observational cohort methods, we compared filter retrieval rates during a test period from July through December 2016 with a control period of the same 6 months in 2015.

RESULTS

System development required approximately 100 hours of development time. Two hundred ninety-three IVC filter placements and 83 filter retrievals were tracked during the study periods. The overall filter retrieval rate was 23% in the control period and 34% in the test period. Mean times from filter placement to retrieval in the control and test periods were not significantly different (88.9 and 102.7 days, respectively; p = 0.32).

CONCLUSION

A semiautomated approach to tracking patients with IVC filters can facilitate care coordination and clinical decision-making for a device with known potential complications. Similar applications designed to improve provider communication and documentation of filter management plans, including appropriateness for retrieval, can be replicated.

Comparison of treatment effect sizes from pivotal and postapproval trials of novel therapeutics approved by the FDA based on surrogate markers of disease: a meta-epidemiological study

BACKGROUND

The U.S. Food and Drug Administration (FDA) often approves new drugs based on trials that use surrogate markers for endpoints, which involve certain trade-offs and may risk making erroneous inferences about the medical product's actual clinical effect. This study aims to compare the treatment effects among pivotal trials supporting FDA approval of novel therapeutics based on surrogate markers of disease with those observed among postapproval trials for the same indication.

METHODS

We searched Drugs@FDA and PubMed to identify published randomized superiority design pivotal trials for all novel drugs initially approved by the FDA between 2005 and 2012 based on surrogate markers as primary endpoints and published postapproval trials using the same surrogate markers or patient-relevant outcomes as endpoints. Summary ratio of odds ratios (RORs) and difference between standardized mean differences (dSMDs) were used to quantify the average difference in treatment effects between pivotal and matched postapproval trials.

RESULTS

Between 2005 and 2012, the FDA approved 88 novel drugs for 90 indications based on one or multiple pivotal trials using surrogate markers of disease. Of these, 27 novel drugs for 27 indications were approved based on pivotal trials using surrogate markers as primary endpoints that could be matched to at least one postapproval trial, for a total of 43 matches. For nine (75.0%) of the 12 matches using the same non-continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than postapproval trials. On average, treatment effects were 50% higher (more beneficial) in the pivotal than the postapproval trials (ROR 1.5; 95% confidence interval CI 1.01-2.23). For 17 (54.8%) of the 31 matches using the same continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than the postapproval trials. On average, there was no difference in treatment effects between pivotal and postapproval trials (dSMDs 0.01; 95% CI -0.15-0.16).

CONCLUSIONS

Many postapproval drug trials are not directly comparable to previously published pivotal trials, particularly with respect to endpoint selection. Although treatment effects from pivotal trials supporting FDA approval of novel therapeutics based on non-continuous surrogate markers of disease are often larger than those observed among postapproval trials using surrogate markers as trial endpoints, there is no evidence of difference between pivotal and postapproval trials using continuous surrogate markers.