Identifying the association between tacrolimus exposure and toxicity in heart and lung transplant recipients: A systematic review.

AIMS: Tacrolimus is the cornerstone of immunosuppression management in heart and lung transplant recipients, improving overall survival. However, tacrolimus-associated toxicities, including nephrotoxicity, neurotoxicity, new-onset diabetes mellitus after transplant (NODAT), and gastrointestinal toxicity, are known contributors to increased post-transplant morbidity outcomes and reduced graft and recipient survival rates. The aim of this systematic review was to identify correlations between pharmacokinetic measures of tacrolimus exposure in heart and lung recipients and tacrolimus toxicities. METHODS: MEDLINE, Embase, the Cochrane Library, CENTRAL and WHO Clinical Trial Registries were searched for published studies evaluating tacrolimus toxicities and their correlation to pharmacokinetic monitoring parameters in thoracic transplant recipients. Studies were reviewed by two authors, with data extracted for evaluation. Risk of bias was assessed using the PEDro scale for randomised control trials and the Newcastle Ottawa Scale for non-randomised cohort studies. RESULTS: Eighteen studies were eligible; a randomised control trial, 11 observational cohort studies, and 6 case series or studies. Of these, 9 studies were in heart transplant recipients alone and 5 in lung transplant recipients alone, 2 studies were in heart and lung transplant recipients and 2 were heart, lung, liver or renal transplant recipients. Studies used variable criteria to define toxicities. Tacrolimus trough concentration (C0) was the marker of tacrolimus exposure most commonly used. Ten studies reported on nephrotoxicity. Elevated tacrolimus C0 was associated with acute kidney injury occurrence and severity in three observational studies. Increasing C0 was a predictor of renal impairment in 6 studies. One study found that for each 5 ng/mL per year of tacrolimus exposure, defined by consecutive AUC, eGFR declined by 1.3 mL/min/1.73m2 (p < 0.001). Comparatively, 2 studies failed to find a significant association between nephrotoxicity and tacrolimus exposure. Seven studies reported on neurotoxicity, including neuro-encephalopathies, polyneuropathies and symptomatic change in neurological status. Neurotoxicity occurred both with tacrolimus C0 within therapeutic range and with supratherapeutic C0. No significant association was found between NODAT and tacrolimus C0 in two studies. One study reported on gastrointestinal toxicity, with supratherapeutic C0 and elevated peak concentration in one lung transplant recipient three days prior to symptom development. CONCLUSION: No clearly defined relationship between tacrolimus exposure and toxicities is described in the literature. Studies with clear toxicity criteria and pharmacokinetic markers of tacrolimus exposure are required to provide valuable information that may optimise tacrolimus therapy, helping to reduce toxicities in heart and lung transplant recipients.

Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients.

OBJECTIVES: Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance. METHODS: Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression. RESULTS: Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p = 0.007) significantly influenced the performance of the Goti model. CONCLUSIONS: The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting.

OBJECTIVES: Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated. METHOD: Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting. RESULTS: A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions. CONCLUSION: There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.

A hydrophobic area of the GABA ρ1 receptor containing phenylalanine 124 influences both receptor activation and deactivation.

Experimental evidence suggests that GABA ρ1 receptors are potential therapeutic targets for the treatment of a range of neurological conditions, including anxiety and sleep disorders. Homology modelling of the GABA ρ1 extracellular N-terminal domain has revealed a novel hydrophobic area that extends beyond, but not including the GABA-binding site. Phenylalanine 124 (F124) is predicted to be involved in maintaining the structural integrity of the orthosteric-binding site. We have assessed the activity of a series of GABA ρ1 receptors that incorporate a mutation at F124. Wild-type and mutant human GABA ρ1 subunits were expressed in Xenopus laevis oocytes and AD293 cells, and the pharmacology and kinetic properties of the receptors were measured using electrophysiological analysis. Mutation of F124 had minimal effect on receptor pharmacology. However, the rate of deactivation was significantly increased compared to wild type. This study provides further information about the role of residues within a novel hydrophobic area of the GABA ρ1 receptor. This knowledge can help future studies into the design of potent and subtype-selective ligands with therapeutic value.

Oleoyl-L-carnitine inhibits glycine transport by GlyT2.

BACKGROUND AND PURPOSE: Concentrations of extracellular glycine in the CNS are regulated by two Na(+)/Cl(-) -dependent glycine transporters, GlyT1 and GlyT2. Selective inhibitors of GlyT1 have been developed for the treatment of schizophrenia, whilst selective inhibitors of GlyT2 are analgesic in animal models of pain. We have assessed a series of endogenous lipids as inhibitors of GlyT1 and GlyT2. EXPERIMENTAL APPROACH: Human GlyT1 and GlyT2 were expressed in Xenopus laevis oocytes, and the inhibitory actions of a series of acylcarnitines on glycine transport were measured using electrophysiological techniques. KEY RESULTS: Oleoyl-L-carnitine inhibited glycine transport by GlyT2, with an IC(50) of 340 nM, which is 15-fold more potent than the previously identified lipid inhibitor N-arachidonyl-glycine. Oleoyl-L-carnitine had a slow onset of inhibition and a slow washout. Using a series of chimeric GlyT1/2 transporters and point mutant transporters, we have identified an isoleucine residue in extracellular loop 4 of GlyT2 that conferred differences in sensitivity to oleoyl-L-carnitine between GlyT2 and GlyT1. CONCLUSIONS AND IMPLICATIONS: Oleoyl-L-carnitine is a potent non-competitive inhibitor of GlyT2. Previously identified GlyT2 inhibitors show potential as analgesics and the identification of oleoyl-L-carnitine as a novel GlyT2 inhibitor may lead to new ways of treating pain.

Novel structural determinants of single-channel conductance in nicotinic acetylcholine and 5-hydroxytryptamine type-3 receptors.

Nicotinic ACh (acetylcholine) and 5-HT3 (5-hydroxytryptamine type-3) receptors are cation-selective ion channels of the Cys-loop transmitter-gated ion channel superfamily. Numerous lines of evidence indicate that the channel lining domain of such receptors is formed by the alpha-helical M2 domain (second transmembrane domain) contributed by each of five subunits present within the receptor complex. Specific amino acid residues within the M2 domain have accordingly been demonstrated to influence both single-channel conductance (gamma) and ion selectivity. However, it is now clear from work performed on the homomeric 5-HT3A receptor, heteromeric 5-HT3A/5-HT3B receptor and 5-HT3A/5-HT3B receptor subunit chimaeric constructs that an additional major determinant of gamma resides within a cytoplasmic domain of the receptor termed the MA-stretch (membrane-associated stretch). The MA-stretch, within the M3-M4 loop, is not traditionally thought to be implicated in ion permeation and selection. Here, we describe how such observations extend to a representative neuronal nicotinic ACh receptor composed of alpha4 and beta2 subunits and, by inference, probably other members of the Cys-loop family. In addition, we will attempt to interpret our results within the context of a recently developed atomic scale model of the nicotinic ACh receptor of Torpedo marmorata (marbled electric ray).