Dataset Information

Understanding the dose-response relationship of allopurinol: predicting the optimal dosage.

ABSTRACT:

Aims

The aim of the study was to identify and quantify factors that control the plasma concentrations of urate during allopurinol treatment and to predict optimal doses of allopurinol.

Methods

Plasma concentrations of urate and creatinine (112 samples, 46 patients) before and during treatment with various doses of allopurinol (50-600 mg daily) were monitored. Non-linear and multiple linear regression equations were used to examine the relationships between allopurinol dose (D), creatinine clearance (CLcr) and plasma concentrations of urate before (UP) and during treatment with allopurinol (UT).

Results

Plasma concentrations of urate achieved during allopurinol therapy were dependent on the daily dose of allopurinol and the plasma concentration of urate pre-treatment. The non-linear equation: UT = (1 - D/(ID50 + D)) × (UP - UR) + UR , fitted the data well (r(2) = 0.74, P < 0.0001). The parameters and their best fit values were: daily dose of allopurinol reducing the inhibitable plasma urate by 50% (ID50 = 226 mg, 95% CI 167, 303 mg), apparent resistant plasma urate (UR = 0.20 mmol l(-1), 95 % CI 0.14, 0.25 mmol l(-1)). Incorporation of CLcr did not significantly improve the fit (P = 0.09).

Conclusions

A high baseline plasma urate concentration requires a high dose of allopurinol to reduce plasma urate below recommended concentrations. This dose is dependent on only the pre-treatment plasma urate concentration and is not influenced by CLcr .

SUBMITTER: Graham GG

PROVIDER: S-EPMC3845316 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the dose-response relationship of allopurinol: predicting the optimal dosage.

Graham Garry G GG Kannangara Diluk R W DR Stocker Sophie L SL Portek Ian I Pile Kevin D KD Indraratna Praveen L PL Datta Indira I Williams Kenneth M KM Day Richard O RO

British journal of clinical pharmacology 20131201 6

<h4>Aims</h4>The aim of the study was to identify and quantify factors that control the plasma concentrations of urate during allopurinol treatment and to predict optimal doses of allopurinol.<h4>Methods</h4>Plasma concentrations of urate and creatinine (112 samples, 46 patients) before and during treatment with various doses of allopurinol (50-600 mg daily) were monitored. Non-linear and multiple linear regression equations were used to examine the relationships between allopurinol dose (D), cr ...[more]

PMID: 23590252

Similar Datasets

Project description:To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on the level of local gains, presence of gain-changing events, and degree of feedforward gene activation, this region can appear as superlinear, sublinear, or even J-shaped. The general dose response transition proposed here was further examined in a complex anti-electrophilic stress pathway, which involves multiple genes, enzymes, and metabolic reactions. This work would help biologists and especially toxicologists to better assess and predict the cellular impact brought about by biological stressors.

Dataset Information

Understanding the dose-response relationship of allopurinol: predicting the optimal dosage.

Aims

Methods

Results

Conclusions

Publications

Understanding the dose-response relationship of allopurinol: predicting the optimal dosage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure