A Comprehensive Review on the Predictive Performance of the Sheiner-Tozer and Derivative Equations for the Correction of Phenytoin Concentrations

Facing the Facts of Altered Plasma Protein Binding: Do Current Models Correctly Predict Changes in Fraction Unbound in Special Populations?

Accounting for variability in plasma protein binding of drugs is an essential input to physiologically-based pharmacokinetic (PBPK) models of special populations. Prediction of fraction unbound in plasma (fu) in such populations typically considers changes in plasma protein concentration while assuming that the binding affinity remains unchanged. A good correlation between predicted vs observed fu data reported for various drugs in a given special population is often used as a justification for such predictive methods. However, none of these analyses evaluated the prediction of the fold-change in fu in special populations relative to the reference population. This would be a more appropriate assessment of the predictivity, analogous to drug-drug interactions. In this study, predictive performance of the single protein binding model was assessed by predicting fu for alpha-1-acid glycoprotein and albumin bound drugs in hepatic impairment, renal impairment, paediatric, elderly, patients with inflammatory disease, and in different ethnic groups for a dataset of >200 drugs. For albumin models, the concordance correlation coefficients for predicted fu were >0.90 for 16 out of 17 populations with sub-groups, indicating strong agreement between predicted and observed values. In contrast, concordance correlation coefficients for predicted fold-change in fu for the same dataset were <0.38 for all populations and sub-groups. Trends were similar for alpha-1-acid glycoprotein models. Accordingly, the predictions of fu solely based on changes in protein concentrations in plasma cannot explain the observed values in some special populations. We recommend further consideration of the impact of changes in special populations to endogenous substances that competitively bind to plasma proteins, and changes in albumin structure due to posttranslational modifications. PBPK models of special populations for highly bound drugs should preferably use measured fu data to ensure reliable prediction of drug exposure or compare predicted unbound drug exposure between populations knowing that these will not be sensitive to changes in fu.

Liquid Chromatography-Mass Spectrometry Analytical Methods for the Quantitation of p-Cresol Sulfate and Indoxyl Sulfate in Human Matrices: Biological Applications and Diagnostic Potentials

Indoxyl sulfate (IxS) and p-cresyl sulfate (pCS) are toxic uremic compounds with documented pathological outcomes. This review critically and comprehensively analyzes the available liquid chromatography-mass spectrometry methods quantifying IxS and pCS in human matrices and the biological applications of these validated assays. Embase, Medline, PubMed, Scopus, and Web of Science were searched until December 2023 to identify assays with complete analytical and validation data (N = 23). Subsequently, citation analysis with PubMed and Scopus was utilized to identify the biological applications for these assays (N = 45). The extraction methods, mobile phase compositions, chromatography, and ionization methods were evaluated with respect to overall assay performance (e.g., sensitivity, separation, interference). Most of the assays focused on human serum/plasma, utilizing acetonitrile or methanol (with ammonium acetate/formate or formic/acetic acid), liquid-liquid extraction, reverse phase (e.g., C18) chromatography, and gradient elution for analyte separation. Mass spectrometry conditions were also consistent in the identified papers, with negative electrospray ionization, select multiple reaction monitoring transitions and deuterated internal standards being the most common approaches. The validated biological applications indicated IxS and/or pCS were correlated with renal disease progression and cardiovascular outcomes, with limited data on central nervous system disorders. Methods for reducing IxS and/or pCS concentrations were also identified (e.g., drugs, natural products, diet, dialysis, transplantation) where inconsistent findings have been reported. The clinical monitoring of IxS and pCS is gaining significant interest, and this review will serve as a useful compendium for scientists and clinicians.

Assessing risk factors associated with breakthrough early post-traumatic seizures in patients receiving phenytoin prophylaxis

Objective

Post-traumatic seizure (PTS) is a well-known complication of traumatic brain injury (TBI). The objective of this study was to identify risk factors associated with breakthrough early PTS in TBI patients receiving phenytoin prophylaxis.

Methods

This was a single-centered retrospective study including adult patients admitted to the intensive care unit (ICU), had a TBI, and started on phenytoin for seizure prophylaxis within 24 h of admission. The primary outcome was the incidence and factors associated with early PTS, defined as a confirmed seizure on a continuous electroencephalogram within 7 days of TBI. Secondary outcomes included the association between early post-traumatic seizures and ICU length of stay, hospital length of stay, and in-hospital mortality.

Results

A total of 105 patients were included in the final analysis. Patients with early PTS were older (65 vs. 48 years old, p = 0.01), had a higher Marshall score (5 vs. 2, p = 0.01), were more likely to have a Marshall score > 2 (73 vs. 37%, p = 0.01), and had more neurosurgeries for hematoma evacuation (57 vs. 19%, p = 0.01). In patients with early PTS, 57% had a level at the time of seizure, and of those, 87.5% had a therapeutic level (>10 mcg/mL). Patients with early PTS had a longer ICU length of stay (14.7 vs. 5.9 days, p = 0.04) and a greater proportion of hospital mortality (21 vs. 2%, p = 0.02).

Conclusion

Patients with higher age, Marshall score, and neurosurgical procedures for hematoma evacuation had higher incidences of breakthrough early PTS despite the use of phenytoin prophylaxis. The majority of patients with early PTS had therapeutic phenytoin levels at the time of seizure when a level was available; however, approximately half (43%) did not have a level.

Evaluation of phenytoin loading doses in overweight patients using actual versus adjusted body weight

BACKGROUND AND OBJECTIVES

The appropriate loading dose strategy for phenytoin/fosphenytoin in overweight patients is unknown. A small pharmacokinetic study indicated that overweight patients have a higher volume of distribution and potentially would benefit from using adjusted body weight (AdjBW) instead of actual body weight (ABW) to calculate the loading dose. The purpose of this study was to determine the optimal loading dose strategy of phenytoin in patients whose ABW is greater than 120% of their ideal body weight (IBW) using either ABW or AdjBW for calculation of the loading dose.

METHODS

This was a single center, retrospective study which included patients who received a loading dose of phenytoin of at least 10 mg/kg based on ABW, had a phenytoin level drawn <6 h after the end of the dose, and weighed ≥120% of their IBW. Patients were excluded if they received intramuscular phenytoin or fosphenytoin or were prescribed phenytoin prior to the loading dose. Patients were divided into two groups, those who were dosed using their AdjBW versus those dosed using ABW. The primary outcome was achievement of therapeutic phenytoin level of 10-20 mcg/mL. Secondary outcomes included achievement of a subtherapeutic or supratherapeutic level.

RESULTS

A total of 195 patients (128 in AdjBW group and 67 in ABW group) met criteria for inclusion. Patients in the AdjBW group weighed more (96.2 kg vs. 91.2 kg, p = 0.04) and received a lower dose in milligrams (1364 vs. 1760, p < 0.0001) and in mg/kg of ABW (14.2 vs. 19.3, p < 0.0001). The primary outcome was achieved in 74% of patients in the AdjBW group and 57% of patients in the ABW group (p = 0.02). Patients in the ABW group were more likely to have a supratherapeutic level (43% vs. 22%, p = 0.003), although adverse reactions did not differ between the groups.

DISCUSSION

Patients weighing >120% of their IBW (average body mass index 33.5 kg/m²) who received a 20 mg/kg loading dose based on AdjBW were more likely to achieve a therapeutic phenytoin concentration compared to those dosed based on ABW. Further research is needed to correlate this finding with clinical outcomes, such as resolution of status epilepticus.

Clinical Pharmacokinetic Monitoring of Free Valproic Acid Levels: A Systematic Review

BACKGROUND

Current guidelines recommend therapeutic drug monitoring as a critical component of valproic acid (VPA) therapy. Due to high protein binding, the active unbound (free) portion of VPA can be misrepresented by total VPA serum levels in certain clinical scenarios. Monitoring free VPA serum levels may be warranted when assessing the clinical response to VPA therapy.

OBJECTIVES

The aims were to conduct a systematic review to identify a therapeutic range for free VPA serum levels; to explore the correlation of free VPA serum levels with clinical toxicity and therapeutic benefit; and to examine predictors of discordance between free and total VPA levels.

METHODS

Medline, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), PsycINFO, BIOSIS Previews, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched from the time of database inception to June 20, 2021. Randomized controlled trials and observational studies that evaluated any patient receiving VPA with free VPA level monitoring were included.

RESULTS

Of 189 citations, we identified 27 relevant studies, which included 14 observational studies, two case series, and 11 case reports. Three studies provided a therapeutic range for free VPA levels between 20 and 410 μmol/L. Two studies suggested the occurrence of hyperammonemia and thrombocytopenia at free VPA serum levels above 60 µmol/L and 103.3 µmol/L, respectively. Two studies suggested an upper limit for neurotoxicity at free VPA serum levels of 70 µmol/L and 207.9 µmol/L. Hypoalbuminemia was identified as a predictor of therapeutic discordance.

CONCLUSIONS

This review demonstrates a paucity of data informing the clinical utility of free VPA serum levels. Further high-quality trials are needed to validate an optimal therapeutic range for free VPA levels.

Therapeutic drug monitoring of phenytoin and valproic acid in critically ill patients at Windhoek Central Hospital, Namibia

Background

Phenytoin and valproic acid, anticonvulsants, have a low therapeutic index and are highly plasma protein bound, mainly to albumin. Hypoalbuminaemia is common in critically ill patients and increases the unbound drug concentration. Thus, monitoring unbound rather than total plasma drug concentrations is recommended to optimise the dosing of these drugs.

Objective

This retrospective study determined unbound plasma concentrations of phenytoin and valproic as a more accurate value of drug levels than total plasma drug concentrations.

Methods

Total plasma concentrations were retrieved for 56 Intensive Care Unit patients for phenytoin and 93 for valproic acid. Total drug concentrations were converted to unbound concentrations using a serum albumin-based normalising equation.

Results

Total phenytoin plasma concentration was below (41.1% of patients), within (46.4%) or above (12.5%) the therapeutic range (10 μg/mL – 20 μg/mL). However, the predicted unbound plasma concentration of phenytoin was above the therapeutic range (1 μg/mL – 2 μg/mL) in the majority of patients (57.1%). For valproic acid, the total plasma concentration of most patients (87.1%) was below the therapeutic range (50 μg/mL – 100 μg/mL); among remaining patients (12.9%), it was within the therapeutic range. In the majority of patients (91.4%), the predicted unbound plasma concentration of valproic acid was between 2.5 μg/mL and 20 μg/mL.

Conclusion

The usefulness of monitoring the total phenytoin or valproic acid levels for dose optimisation is limited as it is an inaccurate indicator of a patient’s drug therapeutic state. Thus, the unbound plasma drug concentrations should be quantified experimentally or predicted in resource-limited settings.

Estimation of Free Phenytoin Concentration in Critically Ill Patients with Hypoalbuminemia: Direct-measurement vs Traditional Equations

Background

In critically ill patients with low albumin, dose individualization of phenytoin is a challenge. The currently used Sheiner-Tozer equation does not accurately predict the free phenytoin concentration in serum and can result in incorrect dose modifications. The best measure to advocate in these patients is the direct-measurement of free phenytoin concentration.

Aims and objectives

Phenytoin exhibits complex pharmacokinetics, requiring careful therapeutic drug monitoring. This study aimed to compare the accuracy of the established Sheiner-Tozer calculation method against the direct-measurement of free phenytoin concentration in serum by high performance liquid chromatography in critically ill patients with low albumin.

Materials and methods

Blood specimens for direct-measurement of both total and free phenytoin concentration were obtained from 57 patients with hypoalbuminemia monitored in the intensive care unit.

Results

The median [inter-quartile range (IQR)] for Sheiner-Tozer equation calculated total phenytoin concentration and direct-measured total was 17.14 (10.63-24.53) and 9.82 (6.02-13.85) μg mL-1, respectively. Approximately 53 and 5% of patients were found to be subtherapeutic and supratherapeutic for direct-measured total phenytoin concentrations, respectively. In contrast, on applying the Sheiner-Tozer calculation, 23 and 40% had subtherapeutic and supratherapeutic concentrations, respectively, for total phenytoin concentration. The median (IQR) for direct-measured, routine and Sheiner-Tozer equation calculated free phenytoin concentration were 1.92 (1.06-2.76), 0.98 (0.60-1.39), and 1.71 (1.06-2.45) μg mL-1, respectively. Only 45.7% of patients were in agreement with respect to the therapeutic category when direct-measured free was compared against routine calculation free.

Conclusion

In patients with low albumin, free phenytoin concentration based on the Sheiner-Tozer corrected equation accurately classified patients based on their therapeutic category of free phenytoin in 73.7% of patients. Hence, for individualization of phenytoin dosage in critically ill patients with low albumin, we recommend direct-measurement of free phenytoin concentration.

How to cite this article

Wilfred PM, Mathew S, Chacko B, Prabha R, Mathew BS. Estimation of Free Phenytoin Concentration in Critically Ill Patients with Hypoalbuminemia: Direct-measurement vs Traditional Equations. Indian J Crit Care Med 2022;26(6):682-687.

A Novel Correction Equation Avoids High-Magnitude Errors in Interpreting Therapeutic Drug Monitoring of Phenytoin Among Critically Ill Patients

BACKGROUND

Phenytoin has a narrow therapeutic index and the potential of under-treatment or toxicity. Available equations are used to correct for the impact of hypoalbuminemia on unbound (free) phenytoin levels. The authors aimed to determine the accuracy of equations used to estimate free phenytoin in hospitalized patients and assess the impact of using additional clinical data.

METHODS

Concurrently measured total and free phenytoin levels in hospitalized patients (2014-2018) were retrospectively evaluated, excluding those from patients on renal replacement therapy and valproic acid. Differences between actual and estimated free phenytoin levels by the original (Original WTZ), Anderson-modified, and Kane-modified Winter-Tozer equations were assessed using Pearson correlations and Bland-Altman analysis. Thereafter, a population-derived formula was developed and validated in a testing cohort.

RESULTS

In the 4-year training cohort (n = 81), the Original WTZ equation had the smallest mean difference of all equations. A higher mean difference [-0.362 mcg/mL (95% CI -0.585 to -0.138) vs. -0.054 mcg/mL (95% CI -0.186 to 0.078)] was observed in intensive care unit (ICU) patients compared with non-ICU patients. A cross-validated multivariable model improved the accuracy of free phenytoin estimation in ICU and non-ICU patients, even in the separate testing cohort (n = 52) with respective mean differences of -0.322 mcg/mL (95% CI -0.545 to -0.098) and -0.025 mcg/mL (95% CI -0.379 to 0.329) and was superior to the Original WTZ [mean difference -0.858 mcg/mL (95% CI -1.069 to -0.647) vs. -0.106 mcg/mL (95% CI -0.362 to 0.151), respectively].

CONCLUSIONS

Free phenytoin levels in hospitalized patients cannot be accurately determined using available estimation equations, particularly in critically ill patients. Combining ICU status and other available clinical data can improve therapeutic drug monitoring and prevent high-magnitude errors, particularly when free phenytoin assays are not readily available.

The Effect of Plasma Protein Binding on the Therapeutic Monitoring of Antiseizure Medications

Epilepsy is a widely diffused neurological disorder including a heterogeneous range of syndromes with different aetiology, severity and prognosis. Pharmacological treatments are based on the use, either in mono- or in polytherapy, of antiseizure medications (ASMs), which act at different synaptic levels, generally modifying the excitatory and/or inhibitory response through different action mechanisms. To reduce the risk of adverse effects and drug interactions, ASMs levels should be closely evaluated in biological fluids performing an appropriate Therapeutic Drug Monitoring (TDM). However, many decisions in TDM are based on the determination of the total drug concentration although measurement of the free fraction, which is not bound to plasma proteins, is becoming of ever-increasing importance since it correlates better with pharmacological and toxicological effects. Aim of this work has been to review methodological aspects concerning the evaluation of the free plasmatic fraction of some ASMs, focusing on the effect and the clinical significance that drug-protein binding has in the case of widely used drugs such as valproic acid, phenytoin, perampanel and carbamazepine. Although several validated methodologies are currently available which are effective in separating and quantifying the different forms of a drug, prospective validation studies are undoubtedly needed to better correlate, in real-world clinical contexts, pharmacokinetic monitoring to clinical outcomes.

Monitoring for valproate and phenytoin toxicity in hypoalbuminaemia: A retrospective cohort study

AIMS

Equations to calculate albumin-adjusted total concentrations have been validated to correlate with measured free concentrations for both phenytoin and valproate, but there is a lack of data to assess correlation with clinical outcomes. We aimed to assess the association of hypoalbuminaemia and albumin-adjusted total concentrations with concentration-dependent toxicity for phenytoin and valproate and review the impact on management decisions following concentration monitoring in hypoalbuminaemia.

METHODS

Patients undergoing concentration monitoring for phenytoin or valproate between January and December 2018 were included. Patients were identified using a centralised laboratory database with data extracted from medical records.

RESULTS

Total phenytoin concentrations were measured for 144 patients, with hypoalbuminaemia (≤30 g L^-1 ) recorded in 59 (41%) patients. Albumin-adjusted phenytoin concentration >20 mg L^-1 was associated with increased neurological adverse effects (77% vs. 43%, P < .001). On logistic regression, higher albumin-adjusted phenytoin concentration was an independent risk factor for neurotoxicity (OR 1.06, 95% CI 1.01-1.12, P = .011). Total valproate concentrations were measured for 383 patients, with hypoalbuminaemia (≤30 g L^-1 ) noted in 53 (14%) patients. For the valproate cohort, hypoalbuminaemia (42% vs. 28%, P = .039) and albumin-adjusted valproate concentration >100 mg L^-1 (49% vs. 23%, P < .001) were both associated with increased neurotoxicity. On multiple logistic-regression, valproate daily dose (aOR = 1.01, 95% CI 1.00-1.02, P = .006) and albumin-adjusted valproate concentration (aOR 1.01, 95% CI 1.00-1.02, P = .033) were independent risk factors for neurotoxicity after accounting for confounders.

CONCLUSION

While measuring free drug concentrations in hypoalbuminaemia would be ideal, the adjustment equations can help identify vulnerable patients needing further assessment of potential concentration-dependent toxicity.

Serum Albumin Levels: Who Needs Them?

OBJECTIVES

The purpose of this critical narrative review is to discuss common indications for ordering serum albumin levels in adult critically ill patients, evaluate the literature supporting these indications, and provide recommendations for the appropriate ordering of serum albumin levels.

DATA SOURCES

PubMed (1966 to August 2020), Cochrane Library, and current clinical practice guidelines were used, and bibliographies of retrieved articles were searched for additional articles.

STUDY SELECTION AND DATA EXTRACTION

Current clinical practice guidelines were the preferred source of recommendations regarding serum albumin levels for guiding albumin administration and for nutritional monitoring. When current comprehensive reviews were available, they served as a baseline information with supplementation by subsequent studies.

DATA SYNTHESIS

Serum albumin is a general marker of severity of illness, and hypoalbuminemia is associated with poor patient outcome, but albumin is an acute phase protein, so levels vacillate in critically ill patients in conjunction with illness fluctuations. The most common reasons for ordering serum albumin levels in intensive care unit (ICU) settings are to guide albumin administration, to estimate free phenytoin or calcium levels, for nutritional monitoring, and for severity-of-illness assessment.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

Because hypoalbuminemia is common in the ICU setting, inappropriate ordering of serum albumin levels may lead to unnecessary albumin administration or excessive macronutrient administration in nutritional regimens, leading to possible adverse effects and added costs.

CONCLUSIONS

With the exception of the need to order serum albumin levels as a component of selected severity-of-illness scoring systems, there is little evidence or justification for routinely ordering levels in critically ill patients.

Phenytoin Serum Concentrations in Patients With Left Ventricular Support Devices: A Case Series

PURPOSE

Achieving therapeutic levels of phenytoin is critical to its efficacy and safety. Free serum levels represent pharmacologically active phenytoin due to the high protein binding of the drug. Predicting free serum levels in patients with left ventricular support devices can be challenging, as the pharmacokinetics (PK) can be significantly altered, and equations to correct total levels have not been validated in this population. The aim of this case series was to describe serum phenytoin concentrations in critically ill patients requiring left ventricular support devices.

METHODS

A retrospective chart review was performed including patients who received phenytoin therapy and had at least 1 set of simultaneously measured free and total serum phenytoin levels during left ventricular support with a mechanical device. Corrected total phenytoin levels were calculated using Sheiner-Tozer equations.

RESULTS

Three patients were included in this case series. Patients 1 and 2 required venoarterial extracorporeal membrane oxygenation (ECMO) during phenytoin therapy, and patient 3 had a durable left ventricular assist device (LVAD). Measured phenytoin levels ranged from 4.1 to 11.4 µg/mL, and calculated corrected levels were 6.8 to 18.4. Measured free phenytoin levels ranged from 1.2 to 3.6 µg/mL, which correlated with free fractions of 15.8% to 37.9%.

CONCLUSION

This case series demonstrates a higher percentage of free phenytoin compared to the total serum level than would be predicted and an inability to rely on corrected total phenytoin level to predict whether it is within therapeutic range. Monitoring of free serum phenytoin concentrations should be strongly considered in critically ill patients requiring LVAD or ECMO support.

Nonlinear protein binding of phenytoin in clinical practice: Development and validation of a mechanistic prediction model

AIMS

To individualize treatment, phenytoin doses are adjusted based on free concentrations, either measured or calculated from total concentrations. As a mechanistic protein binding model may more accurately reflect the protein binding of phenytoin than the empirical Winter-Tozer equation that is routinely used for calculation of free concentrations, we aimed to develop and validate a mechanistic phenytoin protein binding model.

METHODS

Data were extracted from routine clinical practice. A mechanistic drug protein binding model was developed using nonlinear mixed effects modelling in a development dataset. The predictive performance of the mechanistic model was then compared with the performance of the Winter-Tozer equation in 5 external datasets.

RESULTS

We found that in the clinically relevant concentration range, phenytoin protein binding is not only affected by serum albumin concentrations and presence of severe renal dysfunction, but is also concentration dependent. Furthermore, the developed mechanistic model outperformed the Winter-Tozer equation in 4 out of 5 datasets in predicting free concentrations in various populations.

CONCLUSIONS

Clinicians should be aware that the free fraction changes when phenytoin exposure changes. A mechanistic binding model may facilitate prediction of free phenytoin concentrations from total concentrations, for example for dose individualization in the clinic.

Predicting Drug Binding to Human Serum Albumin and Alpha One Acid Glycoprotein in Diseased and Age Patient Populations

Plasma protein binding, namely the fraction unbound (f_u), can be an important determinant of the disposition and response of drugs. The primary objective of this study was to predict f_u values of 183 drugs utilizing either a single binding protein model, where the predominant binding protein had been established, or a multiple binding protein model (MBPM), where the relative binding contribution of human serum albumin (HSA) or alpha 1 acid glycoprotein (AAG) is known. Mean protein concentrations, dependent on disease or age, were used to account for changes in f_u. A simple scaling approach for binding protein concentration was employed to account for quantitative changes in molar concentrations of either HSA or AAG in their respective conditions. The MBPM predictive model works best if the relative binding contribution of HSA and AAG is known, and a scaler for the change in protein concentration can be adjusted accordingly. The value of MBPM was most evident when considering reported changes in lidocaine binding because of increasing AAG concentration in response to trauma. The present approach enhances the ability to predict f_u in diseased and age populations because of quantitative changes in major binding proteins.

Correlation between measured and calculated free phenytoin serum concentration in neurointensive care patients with hypoalbuminemia

Purpose

In critically ill patients, monitoring free phenytoin concentration is a valuable method for phenytoin-dosage adjustment. However, due to technical difficulties and the high cost of these methods, the Sheiner–Tozer equation is routinely used for estimating free phenytoin concentration in clinical practice. There have been conflicting results concerning accuracy and precision of the Sheiner–Tozer equation for prediction of free phenytoin concentration in various patient populations. Therefore, this study was conducted to evaluate the accuracy and correlation of measured and calculated free phenytoin concentrations in neurointensive care patients with hypoalbuminemia.

Methods

A total of 65 adult neurointensive care patients with hypoalbuminemia who were receiving phenytoin for prevention or treatment of seizures were recruited in this study. In addition to measuring free phenytoin concentration by HPLC, free phenytoin concentration was calculated using both conventional and revised Sheiner–Tozer equations. Eventually, the correlation and level of agreement between measured and calculated free phenytoin concentrations were evaluated.

Results

The mean albumin concentration of studied patients was 2.63±0.57 g/dL. There was a significant but weak–moderate correlation between measured and calculated free phenytoin concentration using conventional and revised Sheiner–Tozer equations (r=0.45 and r=0.43, respectively). Conventional and revised Sheiner–Tozer equations were not able to predict free phenytoin concentrations accurately in 33.85% and 35.4% of patients, respectively. Although the sex of patients did not have a significant impact on the level of agreement, younger patients had a higher level of agreement.

Conclusion

Although there was a moderate correlation between calculated and measured free phenytoin concentration, the Sheiner–Tozer equation was not able to predict free phenytoin concentration accurately in all patients, especially in older patients. Therefore, monitoring free phenytoin serum concentration besides clinical outcomes should be considered for phenytoin-dose adjustment in critically ill patients.

The predictive performances of equations used to estimate unbound phenytoin concentrations in a medical ICU population and the impact of exogenous albumin administration

PURPOSE

This study evaluated the predictive performances of four equations (Sheiner-Tozer [ST], Kane-modified ST, Anderson-modified ST, and Cheng-modified ST) used to estimate free phenytoin concentrations in a medical intensive care unit (MICU) and assessed the impact of exogenously administered albumin.

METHODS

Thirty MICU subjects receiving phenytoin were retrospectively evaluated. Predictive performances were assessed by mean absolute error (MAE), mean prediction error (MPE), and root mean squared error (RMSE); linear regression of predicted vs. actual unbound concentrations; and Bland-Altman plots. Parameters were further delineated by recent exogenous albumin administration.

RESULTS

The measured unbound phenytoin concentration was 2.14±0.84μg/mL for all 90 levels, 1.89±0.92μg/mL for the 58 levels without albumin, and 2.58±0.83μg/mL (p<0.0001 vs. without albumin) for the 32 levels after exogenous albumin. R² values were below 0.4 for all equations. The ST equation over-predicted unbound concentrations whereas all other equations under-estimated unbound concentrations. All equations possessed bias and lacked precision. Bland-Altman plots demonstrated greatest bias with the ST equation. Albumin administration introduced additional bias, limited precision, reduced R² values, and completely negated the performances of the ST and Kane-modified ST equations.

CONCLUSION

Estimating unbound concentrations with equations in the MICU population is discouraged.

Revolutionizing Therapeutic Drug Monitoring with the Use of Interstitial Fluid and Microneedles Technology

While therapeutic drug monitoring (TDM) that uses blood as the biological matrix is the traditional gold standard, this practice may be impossible, impractical, or unethical for some patient populations (e.g., elderly, pediatric, anemic) and those with fragile veins. In the context of finding an alternative biological matrix for TDM, this manuscript will provide a qualitative review on: (1) the principles of TDM; (2) alternative matrices for TDM; (3) current evidence supporting the use of interstitial fluid (ISF) for TDM in clinical models; (4) the use of microneedle technologies, which is potentially minimally invasive and pain-free, for the collection of ISF; and (5) future directions. The current state of knowledge on the use of ISF for TDM in humans is still limited. A thorough literature review indicates that only a few drug classes have been investigated (i.e., anti-infectives, anticonvulsants, and miscellaneous other agents). Studies have successfully demonstrated techniques for ISF extraction from the skin but have failed to demonstrate commercial feasibility of ISF extraction followed by analysis of its content outside the ISF-collecting microneedle device. In contrast, microneedle-integrated biosensors built to extract ISF and perform the biomolecule analysis on-device, with a key feature of not needing to transfer ISF to a separate instrument, have yielded promising results that need to be validated in pre-clinical and clinical studies. The most promising applications for microneedle-integrated biosensors is continuous monitoring of biomolecules from the skin's ISF. Conducting TDM using ISF is at the stage where its clinical utility should be investigated. Based on the advancements described in the current review, the immediate future direction for this area of research is to establish the suitability of using ISF for TDM in human models for drugs that have been found suitable in pre-clinical experiments.