Plasma Protein Binding Data Research Articles

Modeling adult COX-2 cerebrospinal fluid pharmacokinetics to inform pediatric investigation.

Hysteresis is reported between plasma concentration and analgesic effect from nonsteroidal anti-inflammatory drugs. It is possible that the temporal delay between plasma and CSF nonsteroidal anti-inflammatory drugs mirrors this hysteresis. The temporal relationship between plasma and CSF concentrations of COX-inhibitors (celecoxib, rofecoxib, valdecoxib) has been described. The purpose of this secondary data analysis was to develop a compartmental model for plasma and CSF disposition of these COX-2 inhibitors. Plasma and CSF concentration-time profiles and protein binding data in 10 adult volunteers given oral celecoxib 200 mg, valdecoxib 40 mg and rofecoxib 50 mg were available for study. Nonlinear mixed effects models with a single plasma compartment were used to link a single CSF compartment with a transfer factor and an equilibration rate constant (Keq). To enable predictive modeling in pediatrics, celecoxib pharmacokinetics were standardized using allometry. Movement of all three unbound plasma COX-2 drugs into CSF was characterized by a common equilibration half-time (T1/2 keq) of 0.84 h. Influx was faster than efflux and a transfer scaling factor of 2.01 was required to describe conditions at steady-state. Estimated celecoxib clearance was 49 (95% CI 34-80) L/h/70 kg and the volume of distribution was 346 (95% CI 237-468) L/70 kg. The celecoxib absorption half-time was 0.35 h with a lag time of 0.62 h. Simulations predicted a 70-kg adult given oral celecoxib 200 mg with maintenance 100 mg twice daily would have a mean steady-state total (bound and unbound) plasma concentration of 174 μg L-1 and CSF concentration of 1.1μg L-1 . A child (e.g., 25 kg, typically 7 years) given oral celecoxib 6mg kg-1 with maintenance of 3mg kg-1 twice daily would have 282 and 1.7μg L-1 mean plasma and CSF concentrations, respectively. Transfer of unbound COX-2 inhibitors from plasma to CSF compartment can be described with a delayed effect model using an equilibration rate constant to collapse observed hysteresis. An additional transfer factor was required to account for passage across the blood-brain barrier. Use of a target concentration strategy for dose and consequent plasma (total and unbound) and CSF concentration prediction could be used to inform pediatric clinical studies.

Early Drug-Induced Liver Injury Risk Screening: “Free,” as Good as It Gets

Abstract For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been thoroughly evaluated. Therefore, we utilized a systematic approach to evaluate the predictive value of in vitro safety assays including bile salt export pump transporter inhibition and cytotoxicity in HepG2 and transformed human liver epithelial along with physicochemical properties. We also evaluated the predictive value of in vitro ADME assays including hepatic partition coefficient (Kp) and its unbound counterpart because they provide insight on hepatic accumulation potential. The datasets comprised of 569 marketed drugs with FDA DILIrank annotation (most vs less/none), dose and physicochemical information, 384 drugs with Kp and plasma protein binding data, and 279 drugs with safety assay data. For each dataset and combination of input parameters, we developed random forest machine learning models and measured model performance using the receiver operator characteristic area under the curve (ROC AUC). The median ROC AUC across the various data and parameters sets ranged from 0.67 to 0.77 with little evidence of additive predictivity when including safety or ADME assay data. Subsequent machine learning models consistently demonstrated daily dose, fraction sp3 or ionization, and cLogP/D inputs produced the best, simplest model for predicting clinical DILI risk with an ROC AUC of 0.75. This systematic framework should be used for future assay predictive value assessments and highlights the need for continued improvements to clinical DILI risk annotation.

Evaluation of Tissue Binding in Three Tissues across Five Species and Prediction of Volume of Distribution from Plasma Protein and Tissue Binding with an Existing Model.

Volume of distribution (Vd) is a primary pharmacokinetic parameter used to calculate the half-life and plasma concentration-time profile of drugs. Numerous models have been relatively successful in predicting Vd, but the model developed by Korzekwa and Nagar is of particular interest because it utilizes plasma protein binding and microsomal binding data, both of which are readily available in vitro parameters. Here, Korzekwa and Nagar's model was validated and expanded upon using external and internal data sets. Tissue binding, plasma protein binding, Vd, physiochemical, and physiologic data sets were procured from literature and Genentech's internal data base. First, we investigated the hypothesis that tissue binding is primarily governed by passive processes that depend on the lipid composition of the tissue type. The fraction unbound in tissues (futissue) was very similar across human, rat, and mouse. In addition, we showed that dilution factors could be generated from nonlinear regression so that one futissue value could be used to estimate another one regardless of species. More importantly, results suggested that microsomes could serve as a surrogate for tissue binding. We applied the parameters from Korzekwa and Nagar's Vd model to two distinct liver microsomal data sets and found remarkably close statistical results. Brain and lung data sets also accurately predicted Vd, further validating the model. Vd prediction accuracy for compounds with log D7.4 > 1 significantly outperformed that of more hydrophilic compounds. Finally, human Vd predictions from Korzekwa and Nagar's model appear to be as accurate as rat allometry and slightly less accurate than dog and cynomolgus allometry. SIGNIFICANCE STATEMENT: This study shows that tissue binding is comparable across five species and can be interconverted with a dilution factor. In addition, we applied internal and external data sets to the volume of distribution model developed by Korzekwa and Nagar and found comparable Vd prediction accuracy between the Vd model and single-species allometry. These findings could potentially accelerate the drug research and development process by reducing the amount of resources associated with in vitro binding and animal experiments.

The correlation of plasma protein binding and molecular properties of selected antifungal drugs

Antifungal agents are the group of drugs commonly prescribed in the treatment of fungal infections, which are widely spread among the global population. Their properties, such as absorption, distribution, metabolism, route of elimination or plasma protein binding (PPB), considerably influence their therapeutic success, while a number of the molecular physicochemical properties of the drug notably influence all these processes. Lipophilicity (log P), molecular weight (Mw), volume (Vol), polar surface area (PSA) and solubility (log S) play important roles in drug absorption, penetration into tissues, distribution and route of elimination or the degree of plasma protein binding. In this study, the relationships between these five molecular properties of eight antifungal drugs and their plasma protein binding data obtained from relevant literature were investigated. The Selected physicochemical molecular descriptors of the drug were calculated using software packages. The established relationships between PPB and PSA; Mw; Vol and log S were showed relatively poor correlation (r < 0.35). The best correlation was obtained for the relationship between PPB data and the lipophilicity descriptor X log P3 (correlation coefficient r = 0.55). In further investigation, multiple linear regression analysis was applied. The best correlation was obtained with application of lipophilicity with polar surface area (r = 0.918) and volume (r = 0.916) or molecular weight (r = 0.896) as independent variables.

Quantitative PK/PD Prediction of the Efficacious and Safe Dose Ranges of the LMP7 Inhibitor M3258 for Phase I Application in Relapsed/Refractory Multiple Myeloma Patients

M3258 is an orally bioavailable, potent, selective, reversible inhibitor of the large multifunctional peptidase 7 (LMP7, β5i, PSMB8) proteolytic subunit of the immunoproteasome; a crucial component of the cellular protein degradation machinery, which is highly expressed in malignant hematopoietic cells including multiple myeloma. M3258 was previously shown to deliver strong in vivo preclinical efficacy in multiple myeloma xenograft models, as well as a more benign non-clinical safety profile compared to approved pan-proteasome inhibitors, exemplified by a lack of effects on the central and peripheral nervous systems and cardiac and respiratory organs. Here we describe preclinical PK/PD and PK/efficacy modelling which led to a prediction of the PK profile, and the efficacious and safe dose ranges of M3258 in human which were used to guide the design of the phase I dose-escalation trial of M3258 in >3 line relapsed/refractory multiple myeloma (RRMM) patients. Mouse, rat, dog and monkey PK, plasma protein binding and intrinsic clearance data were used to estimate a half-life of approximately 6 hours for M3258 in human. The human total clearance and volume of distribution for M3258 were predicted to be 0.033 L/h/kg and 0.28 L/kg, respectively, whilst oral bioavailability was estimated to be above 80%. LMP7 proteolytic activity was assessed as a PD readout in human multiple myeloma tumor cells xenografted to mice as well as in dog peripheral blood mononuclear cells (PBMCs). A strong PK/PD relationship was observed for M3258 across both species. LMP7 inhibition by M3258 also correlated strongly with anti-tumor efficacy in multiple myeloma xenografts, with maximal efficacy observed at M3258 exposure delivering sustained inhibition of tumor LMP7 activity. Quantitative PK/PD/efficacy modeling predicted the biologically efficacious dose (BED) of M3258 upon oral application to be between 10 - 90 mg daily in human. By incorporating data from nonclinical safety studies, these data suggest an attractive human PK profile of M3258, enabling oral application, as well as an improved human therapeutic index compared to approved pan-proteasome inhibitors. M3258 is being investigated in a phase I, first-in-man, 2-part, open label clinical study designed to determine the safety, tolerability, PK, PD and early signs of efficacy of M3258 as a single agent (dose-escalation) and co-administered with dexamethasone (dose-expansion) in participants with RRMM whose disease has progressed following > 3 prior lines of therapy and for whom no effective standard therapy exists. Integration of these data will guide the selection of the BED for potential further clinical development of M3258. Disclosures Lignet: Merck Healthcare KGaA: Employment. Esdar:Merck Healthcare KGaA: Employment. Friese-Hamim:Merck Healthcare KGaA: Employment. Becker:Merck Healthcare KGaA: Employment, Other: Holding shares with a value below 1000-USD. Drouin:EMD Serono Research and Development Institute: Employment. El Bawab:Merck Healthcare KGaA: Employment. Goodstal:EMD Serono Research and Development Institute: Employment. Gimmi:Merck Healthcare KGaA: Employment. Haselmayer:Merck Healthcare KGaA: Employment. Jährling:Merck Healthcare KGaA: Employment. Sanderson:Merck Healthcare KGaA: Employment. Sloot:Merck Healthcare KGaA: Employment. Stinchi:Merck Healthcare KGaA: Employment. Victor:Merck Healthcare KGaA: Employment. Walter:Merck Healthcare KGaA: Employment. Rohdich:Merck Healthcare KGaA: Employment.

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds.

We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.

Assessment of the relationship between the molecular properties of calcium channel blockers and plasma protein binding data

In this study we investigated the relationship between the calcium channel blockers (CCBs), amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem, and their calculated molecular descriptors: polar surface area (PSA), molecular weight (Mw), volume value (Vol), aqueous solubility data (logS), lipophilicity (logP), acidity (pKa values) and plasma protein binding (PPB) data, obtained from relevant literature. The relationships between the computed molecular properties of selected CCBs and their PPB data were investigated by simple linear regression analysis that revealed very low correlations (R2<0.35). When multiple linear regression (MLR) analysis was applied to investigate reliable correlations between the CCBs? calculated molecular descriptors and PPB data, the best correlations were found for the relationships between CCBs, and PPB data and lipophilicity, and with application of the molecular descriptor (Mw, Vol, or pKa) data as additional independent variables (R2=0.623; R2=0.741; R2=0.657, respectively), with an acceptable probability value (P<0.05), confirming that lipophilicity, together with other molecular properties, are essential for the drugs? PPB. We conclude that this could be considered as an additional in vitro approach for modeling CCBs.

Estimation of plasma protein binding of selected antipsychotics using computed molecular properties

The plasma protein binding (PPB) data of twelve antipsychotics (aripiprazole, clozapine, olanzapine, quetiapine, risperidone, sertindole, ziprasidone, chlorpromazine, flupentixol, fluphenazine, haloperidol, zuclopenthixol) were estimated using computed molecular descriptors, which included the electronic descriptor ? polar surface area (PSA), the constitutional parameter ? molecular weight (Mw), the geometric descriptor ? volume value (Vol), the lipophilicity descriptor (logP) and aqueous solubility data (logS), and the acidity descriptor (pKa). The relationships between computed molecular properties of the selected antipsychotics and their PPB data were investigated by simple linear regression analysis. Low correlations were obtained between the PPB data of the antipsychotics and PSA, Mw, Vol, pKa, logS (R <0.30) values, while relatively higher correlations (0.35 < R2 < 0.70) were obtained for the majority of logP values. Multiple linear regression (MLR) analysis was applied to access reliable correlations of the PPB data of the antipsychotics and the computed molecular descriptors. Relationships with acceptable probability values (P<0.05) were established for five lipophilicity descriptors (logP values) with application of the acidity descriptor (pKa) as independent variables: AlogP (R2=0.705), XlogP3 (R2=0.679), ClogP (R2=0.590), XlogP2 (R2=0.567), as well as for the experimental lipophilicity parameter, logPexp (R2=0.635). The best correlations obtained in MLR using AlogP and pKa as independent variables were checked using three additional antipsychotics: loxapine, sulpiride and amisulpride, with the PPB values of 97%, ?less than? 40% and 17%, respectively. Their predicted PPB values were relatively close to the literature data. The proposed technique confirmed that lipophilicity, together with acidity significantly influences the PPB of antipsychotics. The described procedure can be regarded as an additional in vitro approach to the modeling of the investigated group of drugs.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for Fub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.

Application of Reverse Dosimetry to Compare In Vitro and In Vivo Estrogen Receptor Activity

High-throughput screening (HTS) assays may provide an efficient way to identify endocrine-active chemicals. However, nominal in vitro assay concentrations of a chemical may not accurately reflect the blood or tissue levels that cause in vivo effects because in vitro assays do not fully account for chemical pharmacokinetics. In this in vitro-to-in vivo extrapolation (IVIVE) study, we used metabolic clearance and plasma protein binding data with population-based pharmacokinetic (PK) models to quantitatively compare in vitro and in vivo dosimetry for reference chemicals estradiol and bisphenol A, and 230 environmental chemicals that potentially interact with the estrogen receptor (ER) pathway. We first determined the point-of-departure (POD) values from an HTS ER transactivation assay, BG1Luc HTS, and then estimated the daily oral equivalent doses (OEDs) in rats and humans that would result in a steady-state in vivo blood concentration equivalent to the POD values. Where available, we compared the OEDs to the lowest effective doses (LEDs) in rat uterotrophic assays and human exposure values. For all the chemicals with in vivo data, OEDs estimated from POD values were lower than the LEDs in rat uterotrophic assays, suggesting that BG1Luc HTS assay may provide a more conservative hazard estimate for use in risk assessment. Further, human exposure values were generally lower than OEDs estimated from the BG1Luc HTS assay across all environmental chemicals with reported exposure information. In cases where the human OEDs are orders of magnitude lower than the estimated exposures, chemical-induced toxicity appears unlikely. Our modeling approach highlights the importance of PK considerations in ranking endocrine-active chemicals based on in vitro HTS assays.

Estimation of angiotensin-converting enzyme inhibitors protein binding degree using chromatographic hydrophobicity data.

Angiotensin-converting enzyme (ACE) inhibitors represent a significant group of drugs primarily used in the treatment of hypertension and congestive heart failure. Selected ACE inhibitors (enalapril, quinapril, fosinopril, lisinopril, cilazapril) were studied in order to establish a fast and easy estimation method of their plasma protein binding degree based on their lipophilicity data. Chromatographic hydrophobicity data (parameter C0) were obtained on cellulose layers under conditions of normal-phase thin-layer chromatography (NPTLC), using different binary solvent systems. The ACE inhibitors lipophilicity descriptors (logP) values were calculated using the software package Virtual Computational Chemistry Laboratory.The ACE inhibitors plasma protein binding data were collected from relevant literature. ACE inhibitors protein binding data varied from negligible (lisinopril) to 99% (fosinopril). The calculated lipophilicity descriptors, logP(KOWWIN) values ranged from -0.94 (lisinopril) to 6.61 (fosinopril). Good correlations were established between plasma protein binding values and calculated logP(KOWWIN) values (R2 = 0.8026) as well as chromatographic hydrophobicity data, C0 parameters (R2 = 0.7662). Even though good correlation coefficients (R2) were obtained in both relations, unacceptable probability value with p > 0.05 was found in relation between protein binding data and calculated logP(KOWWIN) values. Subsequently, taking into consideration the request for probability value lower than 0.05, a better relationship was observed between protein binding data and chromatographically obtained hydrophobicity parameters C0 values. Cellulose layers are easily available and cost effective sorbent to assess hydrophobicity. Experimentally obtained data on ACE inhibitors hydrophobicity and plasma protein binding estimation are important parameters in evaluating bioavailability of these drugs.

Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing.

Momentum is growing worldwide to use in vitro high-throughput screening (HTS) to evaluate human health effects of chemicals. However, the integration of dosimetry into HTS assays and incorporation of population variability will be essential before its application in a risk assessment context. Previously, we employed in vitro hepatic metabolic clearance and plasma protein binding data with in vitro in vivo extrapolation (IVIVE) modeling to estimate oral equivalent doses, or daily oral chemical doses required to achieve steady-state blood concentrations (Css) equivalent to media concentrations having a defined effect in an in vitro HTS assay. In this study, hepatic clearance rates of selected ToxCast chemicals were measured in vitro for 13 cytochrome P450 and five uridine 5'-diphospho-glucuronysyltransferase isozymes using recombinantly expressed enzymes. The isozyme-specific clearance rates were then incorporated into an IVIVE model that captures known differences in isozyme expression across several life stages and ethnic populations. Comparison of the median Css for a healthy population against the median or the upper 95th percentile for more sensitive populations revealed differences of 1.3- to 4.3-fold or 3.1- to 13.1-fold, respectively. Such values may be used to derive chemical-specific human toxicokinetic adjustment factors. The IVIVE model was also used to estimate subpopulation-specific oral equivalent doses that were directly compared with subpopulation-specific exposure estimates. This study successfully combines isozyme and physiologic differences to quantitate subpopulation pharmacokinetic variability. Incorporation of these values with dosimetry and in vitro bioactivities provides a viable approach that could be employed within a high-throughput risk assessment framework.

Rational use of plasma protein and tissue binding data in drug design.

It is a commonly accepted assumption that only unbound drug molecules are available to interact with their targets. Therefore, one of the objectives in drug design is to optimize the compound structure to increase in vivo unbound drug concentration. In this review, theoretical analyses and experimental observations are presented to illustrate that low plasma protein binding does not necessarily lead to high in vivo unbound plasma concentration. Similarly, low brain tissue binding does not lead to high in vivo unbound brain tissue concentration. Instead, low intrinsic clearance leads to high in vivo unbound plasma concentration, and low efflux transport activity at the blood-brain barrier leads to high unbound brain concentration. Plasma protein and brain tissue binding are very important parameters in understanding pharmacokinetics, pharmacodynamics, and toxicities of drugs, but these parameters should not be targeted for optimization in drug design.

Minocycline pharmacokinetics and pharmacodynamics in dogs: dosage recommendations for treatment of meticillin-resistant Staphylococcus pseudintermedius infections.

Although minocycline is not licensed for use in dogs, this tetracycline has therapeutic potential against meticillin-resistant Staphylococcus pseudintermedius. The aim of this study was to establish rational dosage recommendations for minocycline use in dogs. Specific objectives were to generate and analyse minocycline pharmacokinetic (PK) data on plasma and interstitial fluid (ISF) concentrations, plasma protein binding and pharmacodynamic (PD) data on antimicrobial activity against S.pseudintermedius. Six healthy dogs from a research colony were used in this study. Dogs were administered 5mg/kg intravenously and 10mg/kg orally (p.o.) of minocycline hydrochloride in separate crossover experiments. In vivo drug concentrations in plasma and in ISF collected by ultrafiltration were measured by high-performance liquid chromatography. Pharmacokinetic analysis was performed on plasma and ISF concentrations. PK/PD analysis was completed using in vitro data on plasma protein binding and minocycline susceptibility in 168 S.pseudintermedius isolates. Minocycline distributed to the ISF to a higher degree than predicted by the protein-unbound fraction in plasma. A large volume of distribution after oral administration, with plasma and ISF elimination half-lives of 4.1 and 7.4h, respectively, demonstrated that the ISF serves as a drug reservoir for sustained tissue concentrations. Monte Carlo simulation, used to assess target attainment at different drug dosages, indicated that p.o. administration of 5mg/kg twice daily is sufficient to inhibit S.pseudintermedius strains with minimal inhibitory concentrations ≤0.25μg/mL. Besides dosage recommendations for therapy of meticillin-resistant Staphylococcus pseudintermedius infections in dogs, the study also provides PK/PD data necessary to consider species-specific clinical breakpoints for minocycline susceptibility testing.

Species differences in drug plasma protein binding

Comparison of the human plasma protein binding data for a variety of drug discovery compounds indicates that compounds tend to be slightly more bound to human plasma proteins, than compared to plasma proteins from rats, dogs or mice.

Comprehensive Assessment of Human Pharmacokinetic Prediction Based on In Vivo Animal Pharmacokinetic Data, Part 1: Volume of Distribution at Steady State

The authors present a comprehensive analysis on the estimation of volume of distribution at steady state (VD(ss) ) in human based on rat, dog, and monkey data on nearly 400 compounds for which there are also associated human data. This data set, to the authors- knowledge, is the largest publicly available, has been carefully compiled from literature reports, and was expanded with some in-house determinations such as plasma protein binding data. This work offers a good statistical basis for the evaluation of applicable prediction methods, their accuracy, and some methods-dependent diagnostic tools. The authors also grouped the compounds according to their charge classes and show the applicability of each method considered to each class, offering further insight into the probability of a successful prediction. Furthermore, they found that the use of fraction unbound in plasma, to obtain unbound volume of distribution, is generally detrimental to accuracy of several methods, and they discuss possible reasons. Overall, the approach using dog and monkey data in the íie-Tozer equation offers the highest probability of success, with an intrinsic diagnostic tool based on aberrant values (<0 or >1) for the calculated fraction unbound in tissue. Alternatively, methods based on dog data (single-species scaling) and rat and dog data (íie-Tozer equation with 2 species or multiple regression methods) may be considered reasonable approaches while not requiring data in nonhuman primates.

The influence of protein binding on the excretion of some sulphanilamidopyrimidines in man.

Abstract The metabolism and excretion of sulphadimethoxine, sulphorthodimethoxine, sulphamethomidine, sulphasomidine and sulphadimethoxypyrimidine have been investigated in man. The plasma protein binding data including the percentage bound at various concentrations of the drugs, the number of binding sites (n), the binding capacity of plasma albumin (rmax) and the dissociation constant of the sulphonamide-albumin complex (K) for these compounds have been measured by ultrafiltration. The short-acting sulphasomidine is bound to a much lesser extent at the concentrations obtained clinically in the plasma than the long-acting drugs. Sulphasomidine occupies only one binding site on plasma albumin compared with the other sulphonamides investigated which occupy two sites. This may partly account for the rapid excretion of this compound in man. Sulphadimethoxine has two major metabolites, the N4-acetyl derivative and sulphadimethoxine-N1-glucuronide. N4-Acetyl sulphadimethoxine has similar binding constants to the parent compound. However the N1-glucuronide forms a weak complex with plasma albumin which has a lower binding capacity for this metabolite (rmax = 0.58) compared with sulphadimethoxine itself (rmax = 1.77). The results are discussed in relation to the excretion of these compounds.

Retention of structurally diverse drugs in human serum albumin chromatography and its potential to simulate plasma protein binding

The retention behavior of 39 structurally diverse neutral, basic and acidic drugs was investigated on an HSA stationary phase using PBS buffer (pH 7.0) and acetonitrile or 2-propanol as organic modifiers. Extrapolated or directly measured log k w values as well as isocratic retention factors were correlated with plasma protein binding data taken from the literature. Retention factors determined in the presence of 10% acetonitrile led to high quality 1:1 correlation with apparent log K HSA values. The derived reference equation was successfully validated using a secondary set of 24 drugs. Further analysis of HSA retention into more fundamental properties revealed the involvement of anionic species in solute-stationary phase interactions, expressed by the negatively charged fraction, besides the partitioning mechanism which was reflected by lipophilicity. Protonation of basic drugs, although less important, may also influence retention, leading to reduced partitioning into the HSA surface as a net effect, while it seems to have no effect on HSA binding. The above results were further confirmed by linear solvation energy relationships (LSER).

Prediction of volume of distribution values in human using immobilized artificial membrane partitioning coefficients, the fraction of compound ionized and plasma protein binding data

We developed an improved Lombardeo's method (J. Med. Chem., 2004) for prediction of VD ss in human. With Elog D substituted by log k IAM, together with f i (7.4) (the fraction of compound ionized at pH 7.4) and log f u (logarithmic fraction of compound unbound in plasma), the predictive equation of f ut (the fraction of the compound unbound in tissues) for the 121 compounds was built, predictive VD ss was further obtained from the Øie–Tozer equation. Our model could be applicable to structurally diverse compounds, including acids, bases, neutrals, and ampholytes. Interior and exterior validation results indicated the model had a robust predictive ability. Compared to the Lombardeo's and Hollósy's (J. Med. Chem., 2006) methods, our model can be generally applicable with better predictive accuracy.