Approved drugs are rarely initially studied in """"""""real-world"""""""" patients in a manner sufficient to adequately detail their toxicity profile. Thus, an opportunity exists to refine drug dosing schemes even after they are approved by the Food and Drug Administration. Advances in mathematical modeling techniques now allow design of dosing schemes that minimize toxicity in """"""""real-world"""""""" patients after the drug exposure-toxicity relationship and the variability of drug exposure in the target population is known. Vancomycin is a prototype drug that is a cornerstone in the treatment of Gram positive infections and represents a preventable cause of Acute Kidney Injury (AKI). Owing to over 50 years of clinical experience, much is known about vancomycin pharmacokinetic (PK) exposure. However, the relationship with the pharmacodynamic (PD) outcome of AKI remains poorly defined. This project seeks as a long term goal to integrate data from validated PK/PD models (in vitro, animal, and human) and human PK studies to construct clinical drug dosing strategies that minimize the probability of antibiotic-exposure related adverse events while maximizing efficacy. The overall objective of this application is to employ vancomycin as a prototype drug that causes AKI to elucidate the PK/PD relationship and identify optimal dosing schemes. The central hypothesis of this research is that the intensity and shape of the vancomycin exposure profile accounts for the onset and the extent of AKI. Our hypothesis has been formulated from observations that AKI occurs with contemporary vancomycin dosing schemes in humans Recent animal studies confirm causality when humanized vancomycin exposures are used. This work expands upon previous clinical studies, in silico studies, and laboratory efforts, and employs well validated techniques to focus on the prevention of drug-induced AKI. Specifically, use of an animal toxicity model will allow for carefully planned permutations of vancomycin exposures and bypass the shortcomings of prior clinical analyses where PK/PD endpoints have not been discerned because of homogenous human dosing schemes. The rationale that underlies the proposed research is that the drug exposure-toxicity link must be clearly defined before optimal human regimens can be designed. This application will address two specific aims.
In Aim #1, the vancomycin exposure profile that causes acute kidney injury will be determined by 1) employing carefully controlled dose-range and dose-fractionation studies in rats and 2) measuring AKI with novel biomarkers and traditional histopathology.
In Aim #2, mathematical probability modeling will be conducted with Monte Carlo Simulations that incorporate 1) known vancomycin exposure variability in critical care patients and 2) identified thresholds for vancomycin induced AKI and 3) targets for vancomycin efficacy. We expect that the proposed work will lead to the outcome of vancomycin dosing schemes that minimize AKI while maximizing efficacy for """"""""real-world"""""""" patients. This contribution is expected to be significan since optimizing drug therapies to avoid preventable adverse events is the first step to improving the safety of drugs already available in the market.
The proposed research is relevant to public health because it is estimated that each year 342,000 critically ill Americans will experience acute kidney injury directly due to a drug insult. Many of these events are preventable, and the cost is formidable at $4 billion dol- lars annually. Improving drug dosing schemes to mitigate vancomycin induced kidney injury will advance the NIH mission to protect and improve patient health and further advance the FDA Center for Drug Evaluation and Research's (CDER) Safety First/Safe Use initiative to address significant post-market safety issues as the highest priority.
|Joshi, M D; O'Donnell, J N; Venkatesan, N et al. (2017) High-Performance Liquid Chromatography Method for Rich Pharmacokinetic Sampling Schemes in Translational Rat Toxicity Models With Vancomycin. Clin Transl Sci 10:496-502|
|Watson, W A; Rhodes, N J; Echenique, I A et al. (2017) Resolution of acyclovir-associated neurotoxicity with the aid of improved clearance estimates using a Bayesian approach: A case report and review of the literature. J Clin Pharm Ther 42:350-355|
|O'Donnell, J Nicholas; Rhodes, Nathaniel J; Miglis, Cristina M et al. (2017) Doses, durations, and gender predict vancomycin-induced kidney injury in pre-clinical studies. Int J Antimicrob Agents :|
|O'Donnell, J Nicholas; Ghossein, Cybele; Rhodes, Nathaniel J et al. (2017) Eight unexpected cases of vancomycin associated acute kidney injury with contemporary dosing. J Infect Chemother 23:326-332|
|Martirosov, Dmitriy M; Bidell, Monique R; Pai, Manjunath P et al. (2017) Relationship between day 1 and day 2 Vancomycin area under the curve values and emergence of heterogeneous Vancomycin-intermediate Staphylococcus aureus (hVISA) by Etest® macromethod among patients with MRSA bloodstream infections: a pilot study. BMC Infect Dis 17:534|
|Martirosov, D M; Bidell, M R; Pai, M P et al. (2017) Relationship between vancomycin exposure and outcomes among patients with MRSA bloodstream infections with vancomycin Etest® MIC values of 1.5mg/L: A pilot study. Diagn Microbiol Infect Dis 88:259-263|
|Rhodes, Nathaniel J; Prozialeck, Walter C; Lodise, Thomas P et al. (2017) Correction for Rhodes et al., Evaluation of Vancomycin Exposures Associated with Elevations in Novel Urinary Biomarkers of Acute Kidney Injury in Vancomycin-Treated Rats. Antimicrob Agents Chemother 61:|
|O'Donnell, J Nicholas; Rhodes, Nathaniel J; Lodise, Thomas P et al. (2017) 24-Hour Pharmacokinetic Relationships for Vancomycin and Novel Urinary Biomarkers of Acute Kidney Injury. Antimicrob Agents Chemother 61:|
|Rhodes, Nathaniel J; Prozialeck, Walter C; Lodise, Thomas P et al. (2016) Evaluation of Vancomycin Exposures Associated with Elevations in Novel Urinary Biomarkers of Acute Kidney Injury in Vancomycin-Treated Rats. Antimicrob Agents Chemother 60:5742-51|
|Rhodes, Nathaniel J; O'Donnell, J Nicholas; Lizza, Bryan D et al. (2016) Tree-Based Models for Predicting Mortality in Gram-Negative Bacteremia: Avoid Putting the CART before the Horse. Antimicrob Agents Chemother 60:838-44|
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