Dabigatran Dosing Proposal for Adults With Atrial Fibrillation: Stress-Testing Renal Function Range in Real World Patients.

Dabigatran is the first of four direct-acting oral anticoagulants approved to prevent stroke in adult patients with atrial fibrillation using a fixed two-dose scheme compared with warfarin dosing adjusted to a prothrombin time range associated with optimal risk reduction in stroke and serious bleeding. The pivotal phase III trial found dabigatran, depending on dose, is superior to warfarin in stroke reduction and similar in bleeding risk while also showing dabigatran efficacy and safety correlate with steady-state plasma concentrations. Because the relationship between dabigatran dose and plasma concentration is highly variable, a previously developed population pharmacokinetic model of over 9,000 clinical trial patients was used as a basis for simulations comparing the performance of dosing via the drug label to other proposed doses and regimens. Assessment of dosing regimen performance was based on simulations of trough plasma levels within the therapeutic concentration range of 75-150 ng/mL over a renal function range of 15-250 mL/min creatinine clearance, representing extremes for real-world patients. An improved regimen that best achieves this therapeutic range was identified, requiring five different dosing schedules, corresponding to specified renal function ranges, compared with the two approved in the label. The discussion focuses on how this information could better inform patient outcomes and future dabigatran development.

Leveraging a Previously Published Population Pharmacokinetic Model to Predict Rivaroxaban Exposure in Real-World Patients.

Population pharmacokinetic (PK)/pharmacodynamic models are commonly used to inform drug dosing; however, often real-world patients are not well represented in the clinical trial population. We sought to determine how well dosing recommended in the rivaroxaban drug label results in exposure for real-world patients within a reference area under the concentration-time curve (AUC) range. To accomplish this, we assessed the utility of a prior published rivaroxaban population PK model to predict exposure in real-world patients. We used the model to predict rivaroxaban exposure for 230 real-world patients using 3 methods: (1) using patient phenotype information only, (2) using individual post hoc estimates of clearance from the prior model based on single PK samples of rivaroxaban collected at steady state without refitting of the prior model, and (3) using individual post hoc estimates of clearance from the prior model based on PK samples of rivaroxaban collected at steady state after refitting of the prior model. We compared the results across 3 software packages (NONMEM, Phoenix NLME, and Monolix). We found that while the average patient-assigned dosing per the drug label will likely result in the AUC falling within the reference range, AUC for most individual patients will be outside the reference range. When comparing post hoc estimates, the average pairwise percentage differences were all <10% when comparing the software packages, but individual pairwise estimates varied as much as 50%. This study demonstrates the use of a prior published rivaroxaban population PK model to predict exposure in real-world patients.

Drug Dosing Recommendations for All Patients: A Roadmap for Change.

Most drug labels do not contain dosing recommendations for a significant portion of real-world patients for whom the drug is prescribed. Current label recommendations predominately reflect the population studied in pivotal trials that typically exclude patients who are very young or old, emaciated or morbidly obese, pregnant, or have multiple characteristics likely to influence dosing. As a result, physicians may need to guess the correct dose and regimen for these patients. It is now feasible to provide dose and regimen recommendations for these patients by integrating available scientific knowledge and by utilizing or modifying current regulatory agency-industry practices. The purpose of this commentary is to explore several factors that should be considered in creating a process that will provide more effective, safe, and timely drug dosing recommendations for most, if not all, patients. These factors include the availability of real-world data, development of predictive models, experience with the US Food and Drug Administration (FDA)'s pediatric exclusivity program, development of clinical decision software, funding mechanisms like the Prescription Drug Users Fee Act (PDUFA), and harmonization of global regulatory policies. From an examination of these factors, we recommend a relatively simple, efficient expansion of current practices designed to predict, confirm, and continuously improve drug dosing for more patients. We believe implementing these recommendations will benefit patients, payers, industry, and regulatory agencies.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables.

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Rivaroxaban Precision Dosing Strategy for Real-World Atrial Fibrillation Patients.

Rivaroxaban is a direct-acting oral anticoagulant approved to prevent strokes in patients with atrial fibrillation. Dosage recommendations are approved for all adult patients to receive either 15 mg or 20 mg once daily depending upon renal function. There are a number of reasons to believe rivaroxaban dosing could be more effective and/or safer for more patients if increased dosing precision is available. Because real-world patients are more diverse than those studied in phase III clinical trials, we evaluated the extremes of creatinine clearance (CrCl) on rivaroxaban clearance using a published population pharmacokinetic model and applying exposure variation limits (±20%) based on published literature. The proposed dosing recommendations are 10 mg once daily (CrCl 15-29 ml/min), 15 mg once daily (CrCl 30-69 ml/min), 10 mg twice daily (CrCl 70-159 ml/min), and 15 mg twice daily (CrCl 160-250 ml/min). These new dosing recommendations should be prospectively tested for predictive accuracy and to assess the impact on AF patient efficacy and safety.

A proposal for scientific framework enabling specific population drug dosing recommendations.

Over the last 3 decades, there has been little change in the paradigm to derive dosing recommendations for specific populations (e.g., renal failure, elderly, or obese patients) despite better understanding of clearance pathways in these groups and availability of modeling and simulation tools. Dosing recommendations for specific populations are often incomplete or unavailable at the time of drug approval. Currently, there is no regulatory framework to incorporate model-based dosing recommendations for specific populations. This paper proposes a scientific framework for using modeling and simulation to support specific population dosing recommendations. This framework creates a knowledgebase of drug and population attributes where model-based approaches can be developed to inform dosing recommendations. The framework may benefit patients by having reliable dosing information at the time of drug approval. Patients with conditions where studies are difficult to perform would benefit from dosing based on state-of-the-art knowledge. Industry and regulators would benefit from a scientific and efficient approach to improve specific population prediction. A research approach to determine specific population dose prediction is discussed along with challenges and risks. We hope to initiate a dialogue to explore the role of modeling based on data for drugs with similar clearance mechanisms to predict drug dosing.

Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004-2006.

The End-of-Phase 2A meetings are proposed to identify opportunities to make innovative medical products available sooner and to increase the quality of drug applications through early meetings between sponsors and the FDA. This article summarizes the overall experience across 11 pilot End-of-Phase 2A meetings since 2004. Four case studies are presented in more detail to demonstrate the various issues and methods encountered at these meetings. Overall, industry and FDA scientists ranked these meetings to be "very helpful" (average score of 4 on a scale of 1 to 5). In almost all the instances the sponsors changed their drug development plans subsequent to these extensive quantitative analyses-based meetings. A draft Guidance is being developed to be issued in 2008, and we hope this initiative will be resourced by then.

Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications.

The value of quantitative thinking in drug development and regulatory review is increasingly being appreciated. Modeling and simulation of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression is often referred to as the pharmacometrics analyses. The objective of the current report is to assess the role of pharmacometrics at the US Food and Drug Administration (FDA) in making drug approval and labeling decisions. The New Drug Applications (NDAs) submitted between 2000 and 2004 to the Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed. For those NDA reviews that included a pharmacometrics consultation, the clinical pharmacology scientists ranked the impact on the regulatory decision(s). Of about a total of 244 NDAs, 42 included a pharmacometrics component. Review of NDAs involved independent, quantitative evaluation by FDA pharmacometricians, even when such analysis was not conducted by the sponsor. Pharmacometric analyses were pivotal in regulatory decision making in more than half of the 42 NDAs. Of the 14 reviews that were pivotal to approval related decisions, 5 identified the need for additional trials, whereas 6 reduced the burden of conducting additional trials. Collaboration among the FDA clinical pharmacology, medical, and statistical reviewers and effective communication with the sponsors was critical for the impact to occur. The survey and the case studies emphasize the need for early interaction between the FDA and sponsors to plan the development more efficiently by appreciating the regulatory expectations better.