Ceftazidime Plasma Concentrations and Neurotoxicity: The Importance of Therapeutic Drug Monitoring in Patients Undergoing Different Modalities of Renal Replacement Therapy. A Grand Round.

ABSTRACT: Ceftazidime-avibactam (CTZ-AVM) is a novel cephalosporin/beta-lactamase inhibitor with broad-spectrum activity against multidrug-resistant Pseudomonas aeruginosa . Ceftazidime-induced neurotoxicity is a well-described adverse effect, particularly in patients with renal insufficiency. However, appropriate dosing of ceftazidime-avibactam in patients undergoing renal replacement therapy (RRT) is sparsely investigated, and therapeutic drug monitoring to guide dosing remains lacking. Furthermore, when dose adjustment for impaired renal function is based on CTZ-AVM product information, inferior cure rates have been obtained compared with those with the standard therapy for intra-abdominal infections. Maintaining an effective dose while avoiding toxicity in these patients is challenging. Here, the authors describe the case of a critically ill patient, undergoing 2 modalities of RRT, who developed ceftazidime-induced neurotoxicity as confirmed using ceftazidime therapeutic drug monitoring. This case illustrates a therapeutic drug monitoring-based approach for guiding ceftazidime-avibactam dosing in this context and in diagnosing the cause of neurological symptoms and signs.

It cuts both ways: A single-center retrospective review describing a three-way interaction between flucloxacillin, voriconazole and tacrolimus.

AIM: Tacrolimus is a CYP3A4 substrate with a narrow therapeutic index that requires dose adjustment when used with voriconazole, a recognized CYP3A4 inhibitor. Interactions involving flucloxacillin and tacrolimus or voriconazole individually have been shown to result in decreased concentrations of the latter two drugs. Tacrolimus concentrations have been reported to be unaffected by flucloxacillin when voriconazole is administered; however, this has not been extensively investigated. METHODS: Retrospective review of voriconazole and tacrolimus concentrations and subsequent dose adjustment following flucloxacillin administration. RESULTS: Eight transplant recipients (five lung, two re-do lung, one heart) received concurrent flucloxacillin, voriconazole and tacrolimus. Voriconazole trough concentrations were measured before flucloxacillin initiation in three of eight patients and all trough concentrations were therapeutic. Following flucloxacillin initiation, all eight patients exhibited subtherapeutic concentrations of voriconazole (median concentration 0.15 mg/L [interquartile range (IQR) 0.10-0.28]). In five patients, voriconazole concentrations remained subtherapeutic despite dose increases, and treatment for two patients was changed to alternative antifungal agents. All eight patients required tacrolimus dose increases to maintain therapeutic concentrations after flucloxacillin initiation. Median total daily dose prior to flucloxacillin treatment was 3.5 mg [IQR 2.0-4.3] and this increased to 13.5 mg [IQR 9.5-20] (P=0.0026) during flucloxacillin treatment. When flucloxacillin was ceased, the median tacrolimus total daily dose reduced to 2.2 mg [IQR 1.9-4.7]. Supra-therapeutic tacrolimus concentrations were observed in seven patients after flucloxacillin discontinuation (median concentration 19.7 µg/L [IQR 17.9-28.0]). CONCLUSION: A significant three-way interaction was shown between flucloxacillin, voriconazole and tacrolimus, resulting in subtherapeutic voriconazole concentrations, and requiring substantial tacrolimus dose increases. Administration of flucloxacillin to patients receiving voriconazole should be avoided. Tacrolimus concentrations should be closely monitored, and dosing adjusted during and after flucloxacillin administration.

Effect of Therapeutic Plasma Exchange on Itraconazole Pharmacokinetics: A Case Study.

ABSTRACT: The authors present the case of a 34-year-old male patient who underwent therapeutic plasma exchange (TPE) for amyopathic dermatomyositis. Immunosuppression resulted in Aspergillus lentulus pulmonary infection , requiring treatment with super bioavailable-itraconazole. Therapeutic itraconazole concentrations were attained after 2 weeks of treatment after dose adjustments. Interestingly, a substantial reduction in plasma itraconazole concentration was observed during TPE, which was attributed to an insufficient delay between the dosing of itraconazole and TPE initiation. Furthermore, there was an increase in plasma concentration post-TPE, which presumably reflects the redistribution of itraconazole from peripheral compartments back into plasma. This was confirmed by sampling of the TPE plasmapheresate, which revealed that changes in plasma concentration overestimated itraconazole clearance. These findings highlight that the pharmacokinetics of itraconazole are altered during TPE, which should be considered when timing drug administration and obtaining plasma concentrations.

Therapeutic Drug Monitoring of the Echinocandin Antifungal Agents: Is There a Role in Clinical Practice? A Position Statement of the Anti-Infective Drugs Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology.

PURPOSE: Reduced exposure to echinocandins has been reported in specific patient populations, such as critically ill patients; however, fixed dosing strategies are still used. The present review examines the accumulated evidence supporting echinocandin therapeutic drug monitoring (TDM) and summarizes available assays and sampling strategies. METHODS: A literature search was conducted using PubMed in December 2020, with search terms such as echinocandins, anidulafungin, caspofungin, micafungin, or rezafungin with pharmacology, pharmacokinetics (PKs), pharmacodynamics (PDs), drug-drug interactions, TDM, resistance, drug susceptibility testing, toxicity, adverse drug reactions, bioanalysis, chromatography, and mass spectrometry. Data on PD/PD (PK/PD) outcome markers, drug resistance, PK variability, drug-drug interactions, assays, and TDM sampling strategies were summarized. RESULTS: Echinocandins demonstrate drug exposure-efficacy relationships, and maximum concentration/minimal inhibitory concentration ratio (Cmax/MIC) and area under the concentration-time curve/MIC ratio (AUC/MIC) are proposed PK/PD markers for clinical response. The relationship between drug exposure and toxicity remains poorly clarified. TDM could be valuable in patients at risk of low drug exposure, such as those with critical illness and/or obesity. TDM of echinocandins may also be useful in patients with moderate liver impairment, drug-drug interactions, hypoalbuminemia, and those undergoing extracorporeal membrane oxygenation, as these conditions are associated with altered exposure to caspofungin and/or micafungin. Assays are available to measure anidulafungin, micafungin, and caspofungin concentrations. A limited-sampling strategy for anidulafungin has been reported. CONCLUSIONS: Echinocandin TDM should be considered in patients at known risk of suboptimal drug exposure. However, for implementing TDM, clinical validation of PK/PD targets is needed.

Therapeutic Drug Monitoring of Antibiotics in the Elderly: A Narrative Review.

BACKGROUND: Antibiotic dosing adaptation in elderly patients is frequently complicated by age-related changes affecting the processes of drug absorption, distribution, metabolism, and/or elimination. These events eventually result in treatment failure and/or development of drug-related toxicity. Therapeutic drug monitoring (TDM) can prevent suboptimal antibiotic exposure in adult patients regardless of age. However, little data are available concerning the specific role of TDM in the elderly patients. METHODS: This review is based on a PubMed search of the literature published in the English language. The search involved TDM studies of antibiotics in the elderly patients performed between 1990 and 2021. Additional studies were identified from the reference lists of the retrieved articles. Studies dealing with population pharmacokinetic modeling were not considered. RESULTS: Only a few studies, mainly retrospective and with observational design, have specifically dealt with appropriate antibiotic dosing in the elderly patients based on TDM. Nevertheless, some clinical situations in which the selection of optimal antibiotic dosing in the elderly patients was successfully guided by TDM were identified. CONCLUSIONS: Elderly patients are at an increased risk of bacterial infections and inadequate drug dosing compared with younger patients. Therefore, the availability of TDM services can improve the appropriateness of antibiotic prescriptions in this population.

Emerging therapeutic drug monitoring of anti-infective agents in Australian hospitals: Availability, performance and barriers to implementation.

AIMS: The purpose of the study was to assess the status of emerging therapeutic drug monitoring (TDM) of anti-infective agents in Australian hospitals. METHODS: A nationwide cross-sectional survey of all Australian hospitals operating in the public and private health sector was conducted between August and September 2019. The survey consisted of questions regarding institutional TDM practice for anti-infective agents and clinical vignettes specific to ß-lactam antibiotics. RESULTS: Responses were received from 82 unique institutions, representing all Australian states and territories. All 29 (100%) of principal referral (major) hospitals in Australia participated. Five surveys were partially complete. Only 25% (20/80) of hospitals had TDM testing available on-site for any of the eight emerging TDM candidates considered: ß-lactam antibiotics, anti-tuberculous agents, flucytosine, fluoroquinolones, ganciclovir, human immunodeficiency virus (HIV) drugs, linezolid and teicoplanin. A considerable time lag was noted between TDM sampling and reporting of results. With respect to ß-lactam antibiotic TDM, variable indications, pharmacodynamic targets and sampling times were identified. The three greatest barriers to local TDM performance were found to be (1) lack of timely assays/results, (2) lack of institutional-wide expertise and/or training and (3) lack of guidelines to inform ordering of TDM and interpretation of results. The majority of respondents favoured establishing national TDM guidelines and increasing access to dose prediction software, at rates of 89% and 96%, respectively. CONCLUSION: Translating emerging TDM evidence into daily clinical practice is slow. Concerted efforts are required to address the barriers identified and facilitate the implementation of standardised practice.

Optimal Practice for Vancomycin Therapeutic Drug Monitoring: Position Statement From the Anti-infectives Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology.

ABSTRACT: Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM.

Consensus guidelines for optimising antifungal drug delivery and monitoring to avoid toxicity and improve outcomes in patients with haematological malignancy and haemopoietic stem cell transplant recipients, 2021.

Antifungal agents can have complex dosing and the potential for drug interaction, both of which can lead to subtherapeutic antifungal drug concentrations and poorer clinical outcomes for patients with haematological malignancy and haemopoietic stem cell transplant recipients. Antifungal agents can also be associated with significant toxicities when drug concentrations are too high. Suboptimal dosing can be minimised by clinical assessment, laboratory monitoring, avoidance of interacting drugs, and dose modification. Therapeutic drug monitoring (TDM) plays an increasingly important role in antifungal therapy, particularly for antifungal agents that have an established exposure-response relationship with either a narrow therapeutic window, large dose-exposure variability, cytochrome P450 gene polymorphism affecting drug metabolism, the presence of antifungal drug interactions or unexpected toxicity, and/or concerns for non-compliance or inadequate absorption of oral antifungals. These guidelines provide recommendations on antifungal drug monitoring and TDM-guided dosing adjustment for selected antifungal agents, and include suggested resources for identifying and analysing antifungal drug interactions. Recommended competencies for optimal interpretation of antifungal TDM and dose recommendations are also provided.

Are vancomycin dosing guidelines followed? A mixed methods study of vancomycin prescribing practices.

AIMS: Despite the availability of international consensus guidelines, vancomycin dosing and therapeutic drug monitoring (TDM) remain suboptimal. This study aimed to assess concordance of vancomycin dosing and TDM with institutional guidelines and to identify factors taken into consideration by clinicians when prescribing vancomycin. METHODS: A retrospective audit of 163 patients receiving vancomycin therapy (≥48 hours) was undertaken. Data collected included patient characteristics, dosing history and plasma vancomycin and creatinine concentrations. Concordance of dosing and TDM with institutional guidelines was evaluated. Semi-structured interviews, including simulated prescribing scenarios, were undertaken with prescribers (n = 17) and transcripts analysed. RESULTS: Plasma vancomycin concentrations (n = 1043) were collected during 179 courses of therapy. Only 24% of courses commenced with a loading dose with 72% lower than recommended. The initial maintenance dose was concordant in 42% of courses with 34% lower than recommended. Only 14% of TDM samples were trough vancomycin concentrations. Dose was not adjusted for 60% (21/35) of subtherapeutic and 43% (18/42) of supratherapeutic trough vancomycin concentrations, respectively. Interview participants reported that patient characteristics (including renal function), vancomycin concentrations, guidelines and expert advice influenced vancomycin prescribing decisions. Despite referring to guidelines when completing simulated prescribing scenarios, only 37% of prescribing decisions aligned with guideline recommendations. CONCLUSION: Poor compliance with institutional vancomycin guidelines was observed, despite prescriber awareness of available guidelines. Multifaceted strategies to support prescriber decision-making are required to improve vancomycin dosing and monitoring.

Current fluconazole treatment regimens result in under-dosing of critically ill adults during early therapy.

PURPOSE: To evaluate current fluconazole treatment regimens in critically ill adults over the typical treatment course. METHODS: Data from critically ill adults treated with fluconazole (n=30) were used to develop a population pharmacokinetic model. Probability of target attainment (PTA) (fAUC24/MIC >100) was determined from simulations for four previously proposed treatment regimens: (i) 400 mg once daily, (ii) an 800 mg loading dose followed by 400 mg once daily, (iii) 400 mg twice daily, and (iv) a 12 mg/kg loading dose followed by 6 mg/kg once daily. The effect of body weight (40, 70, 120 kg) and renal function (continuous renal replacement therapy (CRRT); 20, 60, 120, 180 mL/min creatinine clearance) on PTA was assessed. RESULTS: Early (0-48 h) fluconazole target attainment for infections with a minimum inhibitory concentration (MIC) of 2 mg/L was highly variable. PTA was highest with an 800 mg loading dose for underweight (40 kg) patients and with a 12 mg/kg loading dose for the remainder. End-of-treatment PTA was highest with the 400 mg twice daily maintenance dosing for patients who were under- or normal weight and 6 mg/kg maintenance dosing for overweight (120 kg) patients. None of the fluconazole regimens reliably attained early targets for MICs of ≥4 mg/L. CONCLUSION: Current fluconazole dosing regimens do not achieve adequate early target attainment in critically ill adults, particularly in those who are overweight, have higher creatinine clearance, or are undergoing CRRT. Current fluconazole dosing strategies are generally inadequate to treat organisms with an MIC of ≥4 mg/L.

Model-Optimized Fluconazole Dose Selection for Critically Ill Patients Improves Early Pharmacodynamic Target Attainment without the Need for Therapeutic Drug Monitoring.

Fluconazole has been associated with higher mortality compared with the echinocandins in patients treated for invasive candida infections. Underexposure from current fluconazole dosing regimens may contribute to these worse outcomes, so alternative dosing strategies require study. The objective of this study was to evaluate fluconazole drug exposure in critically ill patients comparing a novel model-optimized dose selection method with established approaches over a standard 14-day (336-h) treatment course. Target attainment was evaluated in a representative population of 1,000 critically ill adult patients for (i) guideline dosing (800-mg loading and 400-mg maintenance dosing adjusted to renal function), (ii) guideline dosing followed by therapeutic drug monitoring (TDM)-guided dose adjustment, and (iii) model-optimized dose selection based on patient factors (without TDM). Assuming a MIC of 2 mg/liter, free fluconazole 24-h area under the curve (fAUC24) targets of ≥200 mg · h/liter and <800 mg · h/liter were used for assessment of target attainment. Guideline dosing resulted in underexposure in 21% of patients at 48 h and in 23% of patients at 336 h. The TDM-guided strategy did not influence 0- to 48-h target attainment due to inherent procedural delays but resulted in 37% of patients being underexposed at 336 h. Model-optimized dosing resulted in ≥98% of patients meeting efficacy targets throughout the treatment course, while resulting in less overexposure compared with guideline dosing (7% versus 14%) at 336 h. Model-optimized dose selection enables fluconazole dose individualization in critical illness from the outset of therapy and should enable reevaluation of the comparative effectiveness of this drug in patients with severe fungal infections.

Risk factors for candidaemia: A prospective multi-centre case-control study.

OBJECTIVES: Candidaemia carries a mortality of up to 40% and may be related to increasing complexity of medical care. Here, we determined risk factors for the development of candidaemia. METHODS: We conducted a prospective, multi-centre, case-control study over 12 months. Cases were aged ≥18 years with at least one blood culture positive for Candida spp. Each case was matched with two controls, by age within 10 years, admission within 6 months, admitting unit, and admission duration at least as long as the time between admission and onset of candidaemia. RESULTS: A total of 118 incident cases and 236 matched controls were compared. By multivariate analysis, risk factors for candidaemia included neutropenia, solid organ transplant, significant liver, respiratory or cardiovascular disease, recent gastrointestinal, biliary or urological surgery, central venous access device, intravenous drug use, urinary catheter and carbapenem receipt. CONCLUSIONS: Risk factors for candidaemia derive from the infection source, carbapenem use, host immune function and organ-based co-morbidities. Preventive strategies should target iatrogenic disruption of mucocutaneous barriers and intravenous drug use.

Evaluation of amikacin use and comparison of the models implemented in two Bayesian forecasting software packages to guide dosing.

AIMS: Bayesian forecasting software can assist in guiding therapeutic drug monitoring (TDM)-based dose adjustments for amikacin to achieve therapeutic targets. This study aimed to evaluate amikacin prescribing and TDM practices, and to determine the suitability of the amikacin model incorporated into the DoseMeRx® software as a replacement for the previously available software (Abbottbase®). METHODS: Patient demographics, pathology, amikacin dosing history, amikacin concentrations and Abbottbase® predicted TDM targets (area under the curve up to 24 hours, maximum concentration and trough concentration) were collected for adults receiving intravenous amikacin (2012-2017). Concordance with the Australian Therapeutic Guidelines was assessed. Observed and predicted amikacin concentrations were compared to determine the predictive performance (bias and precision) of DoseMeRx®. Amikacin TDM targets were predicted by DoseMeRx® and compared to those predicted by Abbottbase®. RESULTS: Overall, guideline compliance for 63 courses of amikacin in 47 patients was suboptimal. Doses were often lower than recommended. For therapy >48 h, TDM sample collection timing was commonly discordant with recommendations, therapeutic target attainment low and 34% of dose adjustments inappropriate. DoseMeRx® under-predicted amikacin concentrations by 0.9 mg/L (95% confidence interval [CI] -1.4 to -0.5) compared with observed concentrations. However, maximum concentration values (n = 19) were unbiased (-1.7 mg/L 95%CI -5.8 to 0.8) and precise (8.6% 95%CI 5.4-18.1). Predicted trough concentration values (n = 7) were, at most, 1 mg/L higher than observed. Amikacin area under the curve values estimated using Abbottbase® (181 mg h/L 95%CI 161-202) and DoseMeRx® (176 mg h/L 95%CI 152-199) were similar (P = .59). CONCLUSION: Amikacin dosing and TDM practice was suboptimal compared with guidelines. The model implemented by DoseMeRx® is satisfactory to guide amikacin dosing.

A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study.

Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose-recommendation software.

AIMS: Dose-prediction software can optimise vancomycin therapy, improving therapeutic drug monitoring processes and reducing drug toxicity. Success of software in hospitals may be dependent on prescriber uptake of software recommendations. This study aimed to identify the perceived psychosocial and environmental barriers and facilitators to prescriber acceptance of dose-prediction software. METHODS: Semi-structured interviews, incorporating prescribing scenarios, were undertaken with 17 prescribers. Participants were asked to prescribe the next maintenance dose of vancomycin for a scenario(s) and then asked if they would accept a recommendation provided by a dose-prediction software. Interviews further explored opinions of dose-prediction software. Interview transcripts were analysed using an inductive approach to identify themes and the Theoretical Domains Framework was used to synthesise barriers and facilitators to software acceptance. RESULTS: When presented with software recommendations, half of the participants were comfortable with accepting the recommendation. Key barriers to acceptance of software recommendations aligned with 2 Theoretical Domains Framework domains: Knowledge (uncertainty of software capability) and Beliefs about Consequences (perceived impact of software on clinical outcomes and workload). Key facilitators aligned with 2 domains: Beliefs about Consequences (improved efficiency) and Social Influences (influence of peers). A novel domain, Trust, was identified as influential. CONCLUSION: Prescribers reported barriers to acceptance of dose-prediction software aligned with limited understanding of, and scepticism about, software capabilities, as well as concerns about clinical outcomes. Identification of key barriers and facilitators to acceptance provides essential information to design of implementation strategies to support the introduction of this intervention into the workplace.