Phenytoin Binding Research Articles

Study on enteral nutrient components causing decreased gastric phenytoin absorption.

Previously, we revealed that coadministration of particular enteral nutrients (ENs) decreases plasma concentrations and gastric absorption of phenytoin (PHT), an antiepileptic drug, in rats; however, the mechanism has not been clarified. We measured the permeability rate of PHT using a Caco-2 cell monolayer as a human intestinal absorption model with casein, soy protein, simulated gastrointestinal digested casein protein (G-casein or P-casein) or simulated gastrointestinal digested soy protein (G-soy or P-soy), dextrin, sucrose, degraded guar gum, indigestible dextrin, calcium, and magnesium, which are abundant in the ENs, and measured the solution's properties. We demonstrated that casein (40 mg/ml), G-soy or P-soy (10 mg/ml), and dextrin (100 mg/ml) significantly decreased the permeability rate of PHT compared with the control. By contrast, G-casein or P-casein significantly increased the permeability rate of PHT. We also found that the PHT binding rate to casein 40 mg/ml was 90%. Furthermore, casein 40 mg/ml and dextrin 100 mg/ml have high viscosity. Moreover, G-casein and P-casein significantly decreased the transepithelial electrical resistance of Caco-2 cell monolayers compared with casein and the control. Casein, digested soy protein, and dextrin decreased the gastric absorption of PHT. However, digested casein decreased PHT absorption by reducing the strength of tight junctions. The composition of ENs may affect the absorption of PHT differently, and these findings would aid in the selection of ENs for orally administered PHT.

Interaction of Phenytoin with Enteral Formulas, Proteins, and Dietary Fiber in Vitro and in Vivo

Simultaneous administration of enteral formula and phenytoin in the clinical setting is known to reduce the plasma concentration of phenytoin. In this study, we examined the binding of phenytoin with enteral formulas and its components by quantifying the free phenytoin concentration. Furthermore, we investigated the effect of enteral formulas on gastrointestinal absorption of phenytoin in rats. The free phenytoin rate was reduced in vitro when phenytoin and enteral formula or pectin, a dietary fiber in enteral formulas, were co-administered. In vivo, when phenytoin and the enteral formula Mei Balance R® were co-administered, the time to maximum plasma concentration (Tmax) after oral administration was significantly increased. Moreover, the area under the phenytoin concentration-time curve from time zero to 6 h (AUC0-6 h) was significantly increased by co-administration of phenytoin with the enteral formula PG Soft EJ®. These results showed the gastrointestinal absorption of phenytoin differs according to the type of enteral formula. In addition, we found the first time that plasma phenytoin levels increase when combined with enteral formula. Among the components of enteral formulas, in particular, milk protein delayed the absorption of phenytoin. Moreover, milk protein, casein and carrageenan tended to increase AUC0-6 h. These results suggest the change in phenytoin concentration is due not only to the binding of enteral formula but also to the disintegration of components such as protein. Therefore, when co-administrated of phenytoin and enteral formula, phenytoin must be monitored frequently according to the enteral formula interaction.

Early Exposure of Fosphenytoin, Levetiracetam, and Valproic Acid After High‐Dose Intravenous Administration in Young Children With Benzodiazepine‐Refractory Status Epilepticus

Fosphenytoin (FOS) and its active form, phenytoin (PHT), levetiracetam (LEV), and valproic acid (VPA) are commonly used second-line treatments of status epilepticus. However, limited information is available regarding LEV and VPA concentrations following high intravenous doses, particularly in young children. The Established Status Epilepticus Treatment Trial, a blinded, comparative effectiveness study of FOS, LEV, and VPA for benzodiazepine-refractory status epilepticus provided an opportunity to investigate early drug concentrations. Patients aged ≥2 years who continued to seizure despite receiving adequate doses of benzodiazepines were randomly assigned to FOS, LEV, or VPA infused over 10 minutes. A sparse blood-sampling approach was used, with up to 2 samples collected per patient within 2 hours following drug administration. The objective of this work was to report early drug exposure of PHT, LEV, and VPA and plasma protein binding of PHT and VPA. Twenty-seven children with median (interquartile range) age of 4 (2.5-6.5) years were enrolled. The total plasma concentrations ranged from 69 to 151.3 μg/mL for LEV, 11.3 to 26.7 μg/mL for PHT and 126 to 223 μg/mL for VPA. Free fraction ranged from 4% to 19% for PHT and 17% to 51% for VPA. This is the first report in young children of LEV concentrations with convulsive status epilepticus as well as VPA concentrations after a 40 mg/kg dose. Several challenges limited patient enrollment and blood sampling. Additional studies with a larger sample size are required to evaluate the exposure-response relationships in this emergent condition.

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

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. 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. 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. 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.

Potential protein-binding displacement interactions with perampanel: An in vitro analysis

Plasma protein binding and effects on volume of distribution and pharmacologically active, circulating-drug concentrations are complex issues. Protein-binding displacement often underlies drug–drug interactions. Perampanel is a once-daily oral anti-seizure drug for focal seizures and primary generalized tonic-clonic seizures. Perampanel is also indicated for monotherapy use for focal seizures in the United States. Perampanel is extensively but slowly metabolized via CYP3A4. Its elimination t½ is about 100 h, and it displays substantial plasma protein binding (>95%). Here, we examine perampanel’s potential to displace highly bound anti-seizure drugs and the ability of warfarin, a standard highly protein-bound drug, to displace perampanel. Protein binding of perampanel, phenytoin, valproate, and warfarin was assessed using equilibrium dialysis. Plasma samples containing each compound were dialyzed against phosphate buffered saline. For phenytoin, valproate, and warfarin, plasma samples were also dialyzed in the presence of perampanel. After 24 h equilibrium dialysis, amounts of test compounds were analyzed to calculate plasma protein binding. At clinically relevant concentrations, perampanel did not displace other highly bound drugs or vice versa. Protein-binding displacement may confound therapeutic drug monitoring of extensively protein-bound medications. Without empirical data, clinicians might be concerned that addition of perampanel could alter unbound concentrations of other medications, resulting in adverse effects. Our data indicate perampanel has low potential for drug interactions resulting from protein-binding displacement.

Phenytoin-Bovine Serum Albumin interactions - modeling plasma protein – drug binding: A multi-spectroscopy and in silico-based correlation

The study focused on the analysis of the nature and site of binding of Phenytoin (PHT) -(a model hydrophobic drug) with Bovine Serum Albumin (BSA) (a model protein used as a surrogate for HSA). Interactions with defined amounts of Phenytoin and BSA demonstrated a blue shift (hypsochromic -change in the microenvironment of the tryptophan residue with decrease in the polar environment and more of hydrophobicity) with respect to the albumin protein and a red shift (bathochromic -hydrophobicity and polarity related changes) in the case of the model hydrophobic drug. This shift, albeit lower in magnitude, has been substantiated by a fairly convincing, Phenytoin-mediated quenching of the endogenous fluorophore in BSA. Spectral shifts studied at varying pH, temperatures and incubation periods (at varying concentrations of PHT with a defined/constant BSA concentration) showed no significant differences (data not shown). FTIR analysis provided evidence of the interaction of PHT with BSA with a stretching vibration of 1737.86cm−1, apart from the vibrations characteristically associated with the amine and carboxyl groups respectively. Our in vitro findings were extended to molecular docking of BSA with PHT (with the different ionized forms of the drug) and the subsequent LIGPLOT-based analysis. In general, a preponderance of hydrophobic interactions was observed. These hydrophobic interactions corroborate the tryptophan-based spectral shifts and the fluorescence quenching data. These results substantiates our hitherto unreported in vitro/in silico experimental flow and provides a basis for screening other hydrophobic drugs in its class.

Omeprazole inhibits oxidative drug metabolism. Studies with diazepam and phenytoin in vivo and 7-ethoxycoumarin in vitro.

The effect of treatment with omeprazole, a substituted benzimidazole, on the elimination of diazepam and phenytoin was studied in two groups of 8 subjects. Omeprazole given orally in a daily dose of 40 mg over 7 days decreased diazepam plasma clearance from 22.4 +/- 2.8 to 10.1 +/- 1.5 ml/kg X h and prolonged the half-life of diazepam from 36.9 +/- 4.1 to 85.0 +/- 14.7 h. Plasma concentrations of desmethyldiazepam, a diazepam metabolite, were reduced after omeprazole treatment. Omeprazole also reduced the plasma clearance of phenytoin from 25.1 +/- 2.0 to 21.4 +/- 1.8 ml/kg X h and prolonged its half-life from 20.7 +/- 1.9 to 26.3 +/- 2.7 h. Renal excretion of the major metabolite of phenytoin (p-hydroxyphenyl-phenyl-hydantoin) was not changed. Omeprazole did not affect the volume of distribution and the plasma protein binding of either diazepam or phenytoin. In vitro studies with human liver microsomes showed that omeprazole in equimolar concentrations (0.5 mM) was a stronger inhibitor than cimetidine of 7-ethoxycoumarin deethylase activity. These data confirm that omeprazole interferes with the elimination of other drugs by an inhibition of the drug-metabolizing monooxygenase system of the human liver.

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

In settings where free phenytoin concentrations are not available, the Sheiner-Tozer equation-Corrected total phenytoin concentration = Observed total phenytoin concentration/[(0.2 × Albumin) + 0.1]; phenytoin in µg/mL, albumin in g/dL-and its derivative equations are commonly used to correct for altered phenytoin binding to albumin. The objective of this article was to provide a comprehensive and updated review on the predictive performance of these equations in various patient populations. A literature search of PubMed, EMBASE, and Google Scholar was conducted using combinations of the following terms: Sheiner-Tozer, Winter-Tozer, phenytoin, predictive equation, precision, bias, free fraction. All English-language articles up to November 2015 (excluding abstracts) were evaluated. This review shows the Sheiner-Tozer equation to be biased and imprecise in various critical care, head trauma, and general neurology patient populations. Factors contributing to bias and imprecision include the following: albumin concentration, free phenytoin assay temperature, experimental conditions (eg, timing of concentration sampling, steady-state dosing conditions), renal function, age, concomitant medications, and patient type. Although derivative equations using varying albumin coefficients have improved accuracy (without much improvement in precision) in intensive care and elderly patients, these equations still require further validation. Further experiments are also needed to yield derivative equations with good predictive performance in all populations as well as to validate the equations' impact on actual patient efficacy and toxicity outcomes. More complex, multivariate predictive equations may be required to capture all variables that can potentially affect phenytoin pharmacokinetics and clinical therapeutic outcomes.

Population pharmacokinetics of phenytoin in critically ill children

The objective was to study the population pharmacokinetics of bound and unbound phenytoin in critically ill children, including influences on the protein binding profile. A population pharmacokinetic approach was used to analyze paired protein-unbound and total phenytoin plasma concentrations (n = 146 each) from 32 critically ill children (0.08-17 years of age) who were admitted to a pediatric hospital, primarily intensive care unit. The pharmacokinetics of unbound and bound phenytoin and the influence of possible influential covariates were modeled and evaluated using visual predictive checks and bootstrapping. The pharmacokinetics of protein-unbound phenytoin was described satisfactorily by a 1-compartment model with first-order absorption in conjunction with a linear partition coefficient parameter to describe the binding of phenytoin to albumin. The partitioning coefficient describing protein binding and distribution to bound phenytoin was estimated to be 8.22. Nonlinear elimination of unbound phenytoin was not supported in this patient group. Weight, allometrically scaled for clearance and volume of distribution for the unbound and bound compartments, and albumin concentration significantly influenced the partition coefficient for protein binding of phenytoin. The population model can be applied to estimate the fraction of unbound phenytoin in critically ill children given an individual's albumin concentration.

Physicochemical Characterization and Cytotoxicity Screening of a Novel Colloidal Nanogold-Based Phenytoin Conjugate.

A novel, colloidal nanogold-based drug delivery system for phenytoin, a well-known anti-epileptic drug with an enhanced efflux via P-glycoprotein, has been proposed in this study. The vital physical properties that would aid in predicting the biological interaction of this system were profiled using various techniques such as UV-Vis, DLS, and TEM in corroboration with theoretical calculations. It was significant to note that the binding of phenytoin to colloidal nanogold was strongly pH-dependent with the optimum at pH 5.5 and a consistently reproducible spectral shift. Analysis of the conjugate by FTIR revealed that the imide functional group of phenytoin mediated a dative coordinate bond to colloidal nanogold at the optimum pH. The amount of the drug bound to the gold was quantified to be 85.8±2.5% (w/v) by HPLC. Hypothetically, the surface charge of the conjugate could possibly imply charge-mediated uptake across the cell membrane. Further, the novel conjugate was screened for its cytotoxicity in two cell lines and the dosage range was identified. Subsequent development, thorough evaluations in suitable model systems, and the potential for bioimaging to track the payload would validate our hypothesis on the conjugate for better intracellular retention at the site of action, and thereby achieve the targeted delivery.

Network‐specific mechanisms may explain the paradoxical effects of carbamazepine and phenytoin

A common notion of the mechanism by which the antiepileptic drugs (AEDs) carbamazepine and phenytoin act is that they block sodium channels by binding preferentially to the inactivated state, thereby allowing normal neuronal firing while blocking ictal activity. However, these drugs have unpredictable efficacy and, in some cases, may exacerbate seizures. Previous studies have suggested that reducing sodium channel availability in the dentate gyrus (DG) paradoxically increases excitability. We used a biophysically detailed computer model of the DG to test the hypothesis that AEDs increase excitability by disproportionately reducing negative feedback mechanisms. We built a Markov model of sodium channel gating that reproduces responses to voltage clamp experiments in the presence of carbamazepine and phenytoin. We incorporated this validated Markov model into a biophysically realistic computer model of DG neurons and networks. Simulated drug concentrations were similar to those measured in cerebral spinal fluid in medicated patients. Single neuron models were stimulated with current injections, and networks were stimulated with perforant path synaptic input. In the network model, environmental effects were studied by introducing mossy fiber sprouting. As expected, drugs reduced sodium channel availability, which in turn reduced action potential amplitude. This had only a small effect on action potential (AP) firing rate during brief (100 msec) current injections. Paradoxically, long current injections (2,500 msec) increased AP firing rates. This was caused by reduced calcium entry and consequently reduced activation of calcium activated potassium channels. It is important to note that the main determinant of drug effect was resting membrane potential (RMP) and not action potential firing rate. Binding of phenytoin and carbamazepine is slow and, thus drug effects are largely determined by the long term state of the RMP. This paradoxical AP firing increase was dependent on the unusually large calcium-activated potassium conductances expressed by DG granule cells. This predicts that drug efficacy in a given network will depend on the precise makeup of conductances in the network. RMP is expected to vary with the level of activity in the network. We simulated the effects of drugs on single shot stimulus responses in networks with mossy fiber sprouting and varied the RMP in all neurons as a model for network activity. For an RMP of -50 mV, representing an active network, drugs had no effect, or in some cases, increased excitability. Drugs had an increasingly larger inhibitory effect on network responses as RMP decreased. An important prediction is that drugs will be unable to block ictal activity invading an active network. Our key findings are that drug effects depend on both intrinsic properties of the network and its behavioral state. This may explain the paradoxical and unpredictable effects of some AEDs on seizure control in some patients.

Binding of chlorpromazine, phenytoin and aspirin to the erythrocytes and lipoproteins in whole human blood.

The quantitative binding of 14C-labelled chlorpromazine, phenytoin or aspirin (at 10 microM) to blood cells and plasma lipoproteins in whole human blood or to the washed erythrocytes in an isotonic protein-free medium has been studied. The fractions of chlorpromazine, phenytoin and aspirin bound to the blood cells in whole blood amounted to about 40, 14 and 2% of the total amount added, and those to the lipoproteins amounted to 7, 2 and 1%, respectively. Their binding to the washed erythrocytes in protein-free medium was 95, 76 and 40%, respectively. Their octanol:water partition coefficients were 214, 170 and less than 0.1, respectively. These results suggest that the amphiphilic drugs with relatively high hydrophobicity may be bound to the blood cells, mainly to erythrocytes, to considerable extents when administered clinically, and also that their binding to plasma lipoproteins may not be negligible.

Clozapine recognition via molecularly imprinted polymers; bulk polymerization versus precipitation method

AbstractTwo clozapine (CLZ) imprinted polymers were prepared by bulk and precipitation methods. Methacrylic acid and ethylene glycol dimethacrylate (EDMA) were used as functional and crosslinker monomers, respectively. The mean diameter and particle size distribution of the imprinted (P‐MIP) and nonimprinted (P‐NIP) particles obtained in precipitation method were examined. A conventional batch‐adsorption test was applied for characterization of CLZ–polymer interaction. Dissociation constant (KD) and maximum binding sites (Bmax) were calculated using Scatchard analysis. To evaluate the recognition properties of polymers, phenytoin (PTN) binding to each polymer was also studied and compared to CLZ. The imprinting factor (IF) and selectivity factor (α) were also determined for each polymer. Average diameter and polydispersity of P‐MIP were 925 nm and 0.17, respectively. The data for P‐NIP were 1.05 μm and 0.18. The KD, IF, and α values calculated for P‐MIP were 0.45 μM, 3.26, and 17.43, respectively. The data for imprinted polymer, prepared by bulk polymerization (B‐MIP), were 14.5 μM, 1.95, and 3.67. These results demonstrated that precipitation polymerization is a more convenient, more effective, and more reproducible method than bulk polymerization for the synthesis of uniformly sized micron and submicron‐imprinted polymer particles. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

Therapeutic Drug Monitoring of Phenytoin in Critically Ill Patients

Therapeutic drug monitoring of phenytoin is necessary to ensure therapeutic and nontoxic levels. Hypoalbuminemia, renal failure, and interactions with other highly protein-bound drugs (e.g., valproic acid) alter protein binding of phenytoin. When these conditions are present, free serum concentrations, which represent the pharmacologically active entity, cannot be predicted from total serum concentrations. Besides general alterations in drug distribution and elimination, protein binding is often altered in critically ill patients. Case reports describe phenytoin toxicity secondary to inappropriate dosage adjustments based solely on total serum concentrations in patients with hypoalbuminemia. Free drug measurements and theoretical equations to facilitate the interpretation of total phenytoin serum levels have been introduced. However, they are not widely implemented in clinical practice because evidence of improvements in patient outcomes is limited. Knowledge of the pharmacokinetic properties of phenytoin is indispensable for correct interpretation of total serum concentrations when protein binding is altered. Free serum concentrations should be measured, or theoretically calculated if measurements are unavailable, to avoid misinterpretation of total serum levels and consequent inappropriate adjustments in the dosage of phenytoin in critically ill patients.

Possible Mechanisms for the Pharmacokinetic Interaction Between Phenytoin and Folinate in Rats

The plasma concentration of phenytoin (PHT) is decreased by coadministration of folinate (leucovorin; LV), a folate (FA) analogue. The aim of this study was to examine the effect of LV on the pharmacokinetics of PHT in rats in vivo and to investigate the mechanism of the interaction. LV (50 mg/kg) was administered orally to rats concomitantly given intravenous PHT (50 mg/kg) to evaluate the effect of LV on the pharmacokinetics of PHT. The effect of LV on the plasma protein binding of PHT was investigated by using plasma from rats that had received oral LV. We also examined the effects of LV on the uptake of PHT into isolated rat hepatocytes and on the metabolism of PHT in isolated rat hepatocytes and rat hepatic microsomes. LV significantly increased the systemic clearance (2-fold) and liver-to-blood partition coefficient (1.24-fold) of PHT. However, it did not affect the plasma protein binding or hepatic uptake of PHT. LV increased the metabolism of PHT in isolated rat hepatocytes, with a significant 1.41-fold increase in the maximum rate of metabolism and a decrease in the Michaelis-Menten constant. On the other hand, 5-methyltetrahydrofolate (5-MTHF), a primary metabolite of LV and FA, significantly increased p-hydroxylation of PHT in rat hepatic microsomes, whereas LV and FA themselves had no effect. In conclusion, these results suggest that, in rats, LV, an FA analogue, decreases the plasma concentration of PHT by increasing the hepatic metabolism of PHT, and the increase in the PHT metabolism is, at least in part, attributable to 5-MTHF.

Total phenytoin concentrations do not accurately predict free phenytoin concentrations in critically ill children*

To determine the relationship between estimated free, measured free, and measured total phenytoin levels in critically ill pediatric patients, assess the utility of the Sheiner-Tozer equation in predicting free phenytoin levels, and identify comedications that may influence phenytoin binding or confound attempts to maintain therapeutic concentrations. Retrospective chart review. Twenty-four-bed medical-surgical pediatric intensive care unit. Sixty critically ill pediatric patients receiving phenytoin for treatment of seizures in a large multidisciplinary intensive care unit. The linear correlation between free and total phenytoin concentrations was moderate (r = .795), but the mean difference between actual free concentrations and those estimated from total concentrations using the Sheiner-Tozer equation was -0.31 +/- 0.5 microg/mL (95% confidence interval, -1.3 to 0.7). This difference was of concern, as 10% of patients had toxic free levels (>2 microg/mL) when simultaneously measured total levels were therapeutic (<20 microg/mL). The mean free/total phenytoin ratio was 0.13 +/- 0.07 (range, 0.06-0.42) and varied considerably among patients. Free fractions were particularly elevated in children whose serum albumin concentrations were <2.5 g/dL (0.22, p < .001). However, the relationship between free phenytoin and serum albumin concentration appeared to be nonlinear. Coadministration of valproic acid and cefazolin also increased free fraction (p < .001). Measured total phenytoin concentrations are unreliable for directing therapy in critically ill children. In part, this is because phenytoin binding shows greater variability in this population than has been reported in adults. This phenomenon is exacerbated by coadministration of other highly protein-bound drugs. Instead, free phenytoin concentrations should be routinely measured in critically ill children to prevent possible intoxications and ensure therapeutic dosing. Corrections using the Sheiner-Tozer equation were unreliable.

Studies by biointeraction chromatography of binding by phenytoin metabolites to human serum albumin

Biointeraction studies based on high performance affinity chromatography were used to investigate the binding of human serum albumin (HSA) to two major phenytoin metabolites: 5-(3-hydroxyphenyl)-5-phenylhydantoin (m-HPPH) and 5-(4-hydroxyphenyl)-5-phenylhydantoin (p-HPPH). This was initially examined by conducting self-competition zonal elution experiments in which m-HPPH or p-HPPH were placed in both the mobile phase and injected sample. It was found that each metabolite had a single major binding site on HSA. Competitive zonal elution experiments using l-tryptophan, warfarin, digitoxin, and cis-clomiphene as site-selective probes indicated that m-HPPH and p-HPPH were interacting with the indole-benzodiazepine site of HSA. The estimated association equilibrium constants for m-HPPH and p-HPPH at this site were 3.2 (+/-1.2)x10(3) and 5.7 (+/-0.7)x10(3)M(-1), respectively, at pH 7.4 and 37 degrees C. Use of these metabolites as competing agents for injections of phenytoin demonstrated that m-HPPH and p-HPPH had direct competition with this drug at the indole-benzodiazepine site. However, the use of phenytoin as a competing agent indicated that this drug had additional negative allosteric interactions on the binding of these metabolites to HSA. These results agreed with previous studies on the binding of phenytoin to HSA and its effects on the interactions of HSA with site-selective probes for the indole-benzodiazepine site.

Clinical evaluation of plasma free phenytoin measurement and factors influencing its protein binding

The relationship between free phenytoin concentrations and clinical responses, and the factors influencing protein binding of phenytoin were investigated. A total of 119 plasma samples from 70 patients treated orally with phenytoin were analysed. The mean free phenytoin concentration was significantly higher in the patients who received phenytoin monotherapy and were classified as having a complete response (1.25 +/- 1.09 microg/ml) than that in the partial response group (0.59 +/- 0.07 microg/ml), whereas the mean total concentrations were not significantly different between the two groups. Samples were divided into three groups based on the free fraction of phenytoin, i.e. low, <5%; medium, 5%-10%; high, > 10%. The mean age (55.3 +/- 10.9 years) was significantly higher in the high group than in the low (42.7 +/- 21.2 years) and medium (42.8 +/- 16.0 years) groups. The mean creatinine clearance (CLcr) (55.3 +/- 10.9 ml/min) and serum albumin concentration (3.30 +/- 1.25 g/dl) were significantly lower in the high group than the low (88.3 +/- 29.0 ml/min and 4.08 +/- 0.50 g/dl, respectively) and medium (95.0 +/- 32.8 ml/min and 3.95 +/- 0.92 g/dl, respectively) groups. These results suggest that the free phenytoin concentration, rather than the total concentration, is more useful for monitoring antiepileptic effects in patients receiving phenytoin monotherapy. It was also found that the free phenytoin fraction was significantly influenced by aging, CLcr and serum albumin levels.

Studies of phenytoin binding to human serum albumin by high-performance affinity chromatography

High-performance affinity chromatography was used to study the binding of phenytoin to an immobilized human serum albumin (HSA) column. This was accomplished through frontal analysis and competitive binding zonal elution experiments, the latter of which used four probe compounds for the major and minor binding sites of HSA injected into the presence of mobile phases containing known concentrations of phenytoin. It was found that phenytoin can interact with HSA at the warfarin-azapropazone, indole-benzodiazepine, tamoxifen, and digitoxin sites of this protein. The association constants for phenytoin at the indole-benzodiazepine and digitoxin sites were determined to be 1.04 (±0.05) × 10 4 M −1 and 6.5 (±0.6) × 10 3 M −1, respectively, at pH 7.4 and 37 °C. Both allosteric interactions and direct binding for phenytoin appear to take place at the warfarin-azapropazone and tamoxifen sites. This rather complex binding system indicates the importance of identifying the binding regions on HSA for specific drugs as a means for understanding the transport of such substances in blood and in characterizing their potential for drug–drug interactions.

The Binding of Selected Therapeutic Drugs to Human Serum α-1 Acid Glycoprotein and to Human Serum Albumin In Vitro

The binding of acetaminophen, lidocaine, phenobarbital, phenytoin, theophylline, and valproic acid to human serum alpha-1 acid glycoprotein (orosomucoid) and to human serum albumin separately in vitro was investigated using equilibrium dialysis of the unlabeled drugs. Each drug was studied at a therapeutic concentration. Alpha-1 acid glycoprotein was studied at one elevated and two physiological concentrations, whereas albumin was studied at one physiological and two low concentrations. The nonphysiological concentrations were consistent with those that might be seen in a variety of clinical conditions. Acetaminophen, phenobarbital, theophylline, and valproic acid showed negligible binding to alpha-1 acid glycoprotein. However, lidocaine and phenytoin demonstrated binding to this protein, and increases in the alpha-1 acid glycoprotein concentration produced decreases in the unbound (free) or "active" concentration of these two drugs. All drugs but acetaminophen bound to albumin, and decreases in the albumin concentration yielded increases in the unbound (free) or "active" concentration of the remaining 5 drugs. These findings are significant when lidocaine, phenytoin, phenobarbital, theophylline, or valproic acid are used in patients with clinical conditions that may affect the concentration of these two binding proteins.