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Record W2077165819 · doi:10.1177/0091270011402500

Optimizing Drug Development and Use in Patients With Kidney Disease

2011· article· en· W2077165819 on OpenAlexaff
Thomas D. Nolin, Vikram Arya, Daniel Sitar, Marc Pfister

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineKidney diseaseIntensive care medicinePopulationPublic healthDiseaseDosingDrug developmentHealth careClearanceDrugInternal medicineEnvironmental healthPharmacologyPathologyEconomic growth

Abstract

fetched live from OpenAlex

The number of patients with chronic kidney disease (CKD) has risen sharply in recent years, bringing worldwide attention to the disease as an important public health concern. Currently, an estimated 26 million adults may have CKD in the United States, representing >13% of the adult population, and similar CKD prevalence rates of 13% and 16% have been reported in China and Australia, respectively.1 The prevalence of end-stage renal disease (ESRD) alone in the United States increased 260% over 20 years, from 149 000 in 1988 to 539 000 in 2008,2 imposing an enormous socioeconomic burden on health care providers. In 2008, health-related expenses for ESRD patients, who represented <1% of Medicare beneficiaries, accounted for 5.9% ($26.8 billion) of the entire Medicare budget.2,3 In addition, the expanding CKD population poses extraordinary challenges to those in clinical, regulatory, and research and development arenas. To discuss these unprecedented challenges, we are excited to announce plans to publish a special supplementary issue of the Journal of Clinical Pharmacology in 2011 that will address critical contemporary issues facing clinicians, clinical pharmacologists, researchers, and regulatory scientists involved in the development and use of drugs in patients with CKD. Clinicians have long recognized that systemic clearance of drugs predominantly cleared by the kidney is decreased in patients with CKD, and clinical use of renal dosing adjustment guidelines and recommendations for these drugs is commonplace. Nevertheless, patients with CKD exhibit high variability in drug response and increased frequency of adverse drug events compared with those with normal kidney function.4,5 The reasons for this variability are unclear, but several superimposed factors likely contribute to changes in drug exposure and response in CKD patients, including the influence of sex on renal drug clearance,6 the poorly described dissociation between glomerular filtration and tubular secretion as kidney disease progresses,7 altered nonrenal drug clearance,8 differential effects of various etiologies of kidney disease (eg, diabetes mellitus, primary glomerulopathies, autoimmune disease),9 and common comorbidities such as obesity on individual pathways of renal and nonrenal clearance.10 In addition, variability between and within methods used to determine kidney function (ie, measured or estimated glomerular filtration rate or creatinine clearance) may translate into discrepancies in corresponding renal drug-dosing regimens that could affect response. For example, estimated creatinine clearance determined using the Cockcroft-Gault equation is particularly prone to high variability due to inconsistent use of weight variables (ie, actual body weight versus ideal or adjusted body weight) and rounded serum creatinine values during manual calculations.11,12 Accordingly, the special issue of the journal will include articles that review the effect of kidney disease on pharmacokinetics, the impact of glomerular kidney disease on nonrenal clearance, practical insights into the use of old and new kidney function estimating equations, and clinical challenges and opportunities associated with use of drugs in patients with kidney disease. The 1998 Food and Drug Administration (FDA) guidance document pertaining to the conduct of pharmacokinetic studies in patients with impaired kidney function has resulted in more renal pharmacokinetic studies performed during drug development,13,14 with a corresponding increase in renal dosing data available for clinical use. However, there remains a paucity of current and clinically relevant data for guiding drug selection and dosing in patients with kidney disease, particularly ESRD patients requiring renal replacement therapy (RRT). Most hemodialysis drug clearance and dosing data that are used presently are based on conventional modalities that in some cases are obsolete.15 There is an increasing number of intermittent (eg, 6 times per week vs 3 times per week), continuous, and hybrid RRT modalities used today to treat adult and pediatric patients with kidney disease.16,17 However, there is very limited information in the literature that clearly guides their use. Recent data show that drug-dosing studies have been conducted for fewer than 20% and 1% of currently marketed drugs for use during continuous and hybrid replacement therapies, respectively.15 Patients with advanced kidney disease can be difficult to recruit for and retain in research studies and are thus frequently underrepresented in clinical trials,18 which likely contributes to the continuing lack of data guiding drug use in the CKD population. When combined with numerous additional factors, including demographic extremes (eg, weight, age), comorbidities, and technological advances (ie, in RRT machines, high-flux filters, and achievable flow rates) that may independently affect drug exposure and response, application of evidence-based data from previous studies, if available, becomes extremely difficult if not impossible. The special issue of the journal will examine novel RRT modalities; examine innovative approaches used to quantify the effect of RRT on drug disposition and dosing, including in vitro models and pharmacometric applications such as clinical trial simulation; and review RRT and renal dosing of drugs in adults as well as in pediatric patients. While it is well known that kidney disease can affect drug exposure, its effect on drug response is less clear. However, recent clinical studies demonstrate that CKD patients may respond to drugs in a dramatically different fashion than individuals with normal kidney function, exhibiting either greater likelihood of toxicity or reduced efficacy. For instance, patients with advanced CKD have been reported to require significantly lower warfarin dosages, exhibit poorer anticoagulation control (ie, spend less time with their international normalization ratio within the target range), have a higher risk for bleeding, and have a 2-fold higher risk for hemorrhagic complications after adjustment for standard genotypic and clinical variables compared with patients with an estimated glomerular filtration rate ≥30 mL/min/1.73 m2.19 These data suggest that CKD alters the normal warfarin pharmacodynamic profile and that warfarin dosing based on standard genotypic and clinical information, excluding renal function, may be insufficient for optimal use in CKD patients. Similarly, CKD patients appear to have altered responses to statin therapy, manifesting as reduced efficacy. Several large, well-designed randomized clinical trials in patients with normal kidney function have clearly demonstrated the lipid-lowering benefit of statin therapy, namely, reduction in the incidence of cardiovascular events and mortality.20 The 4-D and AURORA studies were 2 large, well-designed, and highly publicized randomized clinical trials that evaluated the effect of statin therapy in ESRD patients on the composite end point of death from cardiovascular causes, nonfatal myocardial infarction, and stroke.21,22 Both studies showed that statin therapy provided no significant clinical benefit, despite significant reductions in mean serum low-density lipoprotein concentrations. A possible explanation for the discrepant findings in the warfarin and statin response in CKD patients compared with patients with normal kidney function is that kidney disease somehow alters relevant physiologic or pathologic processes involved in the comorbid condition. These are extremely difficult to characterize, but novel physiologically based modeling techniques may begin to advance our understanding of these altered pharmacodynamic profiles. Separate articles describing physiologically based modeling of metabolic bone disease and drug-drug interactions in patients with CKD will be included in the special issue. Finally, novel advancements in our understanding of the optimal use of drug therapy in patients with kidney disease have facilitated development of regulatory recommendations for the design and conduct of trials in patients with impaired kidney function.23 The special issue will include an article that broadly discusses how the current state of knowledge in this field is leading to critical updates to the 1998 FDA renal guidance document.24 We have assembled a team of highly regarded clinicians, clinical pharmacologists, researchers, and regulatory scientists to contribute to this important and relevant special issue of the journal. We look forward to its publication in the coming months and believe that it will be a vital resource to those involved in the development and use of drugs in patients with kidney disease. Financial disclosure: None declared. Disclaimer: The views expressed in this article are those of the author. No official support or endorsements by the United States Food and Drug Administration are provided or should be inferred.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.156
GPT teacher head0.357
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2011
Admission routes1
Has abstractyes

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