Plasma Protein Binding Of Drugs Research Articles

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 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.

Introducing the Dynamic Well-Stirred Model for Predicting Hepatic Clearance and Extraction Ratio

The well-stirred model (WSM) incorporating the fraction of unbound drug (fu) to account for the effect of plasma binding on intrinsic clearance has been widely used for predicting hepatic clearance under the assumption that drug protein binding reaches equilibrium instantaneously. Our theoretical analysis reveals that the effect of protein binding on intrinsic clearance is better accounted for with the dynamic free fraction (fD), a measure of drug protein binding affinity, which leads to a putative dynamic well-stirred model (dWSM) without the instantaneous equilibrium assumption. Using recombinant CYP3A4 as the in vitro clearance system, we demonstrate that the binding effect of albumin on the intrinsic clearance of both highly bound midazolam and highly free verapamil is fully corrected by their corresponding fD values, respectively. On the other hand, fu only corrects the binding effect of albumin on the intrinsic clearance of verapamil, and yields severe over-correction of the intrinsic clearance of midazolam. The results suggest that the traditional WSM is suitable for highly free drugs like verapamil but not necessarily for highly bound drugs such as midazolam due to the violation of the instantaneous equilibrium assumption or under-estimating the true free drug concentration. In comparison, the dWSM incorporating fD holds true as long as drug elimination follows steady-state kinetics, and hence, it is more broadly applicable to drugs with different protein binding characteristics. Here we demonstrate with 36 diverse drugs, that the dWSM significantly improves the accuracy of predicting human hepatic clearance and liver extraction ratio from in vitro microsomal clearance data, highlighting the importance of drug plasma protein binding kinetics in addressing the under-prediction of hepatic clearance by the WSM.

Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs

Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain.

The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set's external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model's accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

The evaluation of drug-plasma protein binding interaction on immobilized human serum albumin stationary phase, aided by different computational approaches

The drug-human serum albumin binding interaction was evaluated on a stationary phase immobilized with human serum albumin using a mixture of phosphate buffer (pH 7.0) and acetonitrile modifier as mobile phase. The 33 compounds that have a wide structural and therapeutic diversity were analyzed by performing a large number of experiments. The interaction mechanism was interpreted based on: i) retention characteristics of structurally related compounds, ii) retention modeling, iii) quantitative structure retention relationship (QSRR), and iv) molecular docking. Small structural differences of related compounds (e.g., reflected in different lipophilicity and polarity) have been found to affect their different binding to human serum albumin. It was found that drug retention in HSA column can be successfully described by using the quadratic function. The isocratic (logk(14%)) and extrapolated (b0(LSS)) retention factors showed the highest correlation (r > 0.76) with the constant that defines the binding affinity for human serum albumin (ACD/I-Lab). Therefore, selected chromatographic parameters can demonstrate reliable applicability for rapid screening of drug-plasma protein binding in drug discovery. In QSRR study, the resulting SVM/logk(14%) and MLR/b0(LSS) models display high internal and external predictive power. The constitutional properties (double bonds, aromatic rings, benzyl, allyl, -amino and -sulfur containing functional groups) supported by the charged parts of surface area had a significant impact on human serum albumin-binding affinity, which was also confirmed with molecular docking study. The high structural diversity of the data set provides wide applicability of tested chromatographic conditions and constructed models for defining the pharmacokinetic profile and possible structural modifications that can increase plasma protein binding of newly synthesized, pharmaceutically important compounds.

Optimizing a High-Throughput Solid-Phase Microextraction System to Determine the Plasma Protein Binding of Drugs in Human Plasma.

Plasma protein binding refers to the binding of a drug to plasma proteins after entering the body. The measurement of plasma protein binding is essential during drug development and in clinical practice, as it provides a more detailed understanding of the available free concentration of a drug in the blood, which is in turn critical for pharmacokinetics and pharmacodynamics studies. In addition, the accurate determination of the free concentration of a drug in the blood is also highly important for therapeutic drug monitoring and in personalized medicine. The present study uses C18-coated solid-phase microextraction 96-pin devices to determine the free concentrations of a set of drugs in plasma, as well as the plasma protein binding of drugs with a wide range of physicochemical properties. It should be noted that the extracted amounts used to calculate the binding constants and plasma protein bindings should be measured at respective equilibrium for plasma and phosphate buffer. Therefore, special attention is placed on properly determining the equilibration times required to correctly estimate the free concentrations of drugs in the investigated systems. The plasma protein binding values obtained with the 96-pin devices are consistent with those reported in the literature. The 96-pin device used in this research can be easily coupled with a Concept96 or other automated robotic systems to create an automated plasma protein binding determination protocol that is both more time and labor efficient compared to conventional equilibrium dialysis and ultrafiltration methods.

Precisely adjusting the hepatic clearance of highly extracted drugs using the modified well-stirred model

Hepatic clearance has been widely studied for over 50 yr. Many models have been developed using either theoretical or empirical tests to predict drug metabolism. The well-stirred, parallel-tube, and dispersion metabolic models have been extensively discussed. However, to our knowledge, these models cannot fully describe all relevant scenarios in hepatic clearance. We addressed this issue using the isolated perfused rat liver technique with minor modifications. Diazepam was selected to illustrate different levels of drug plasma-protein binding by changing the added concentration of human serum albumin. The free fractions of diazepam at different albumin concentrations were assayed by rapid equilibrium dialysis. The experimental data provide new insights concerning an accepted formula used to describe hepatic clearance. Regarding drug concentrations passing through the liver, the driving force concentration (CH,ss) in terms of Cin (influx in the liver) or Cout (efflux from the liver) needs to be carefully considered when determining drug hepatic and intrinsic clearances. The newly established model, termed the modified well-stirred model, which was derived from the original formula, successfully estimated hepatic drug metabolism. Using the modified well-stirred model, a theoretical driving force concentration of diazepam passing through the liver was evaluated. The model was further used to assess the predictability of in vitro to in vivo extrapolation. This study was not intended to refute the existing models, but rather to augment them using experimental data. The results stress the importance of proper calculation of dose when the drug clearance deviates from the prediction of the well-stirred model.

Focused ultrasound enhances the anesthetic effects of topical lidocaine in rats

BackgroundHigh-intensity ultrasound has been used to induce acoustic cavitation in the skin and subsequently enhances skin permeability to deliver hydrophobic topical medications including lidocaine. In contrast, instead of changing skin permeability, pulsed application of low-intensity focused ultrasound (FUS) has shown to non-invasively and temporarily disrupt drug-plasma protein binding, thus has potential to enhance the anesthetic effects of hydrophilic lidocaine hydrochloride through unbinding it from serum/interstitial α1-acid glycoprotein (AAG).MethodsFUS, operating at fundamental frequency of 500 kHz, was applied pulse-mode (55-ms pulse duration, 4-Hz pulse repetition frequency) at a spatial-peak pulse-average intensity of 5 W/cm2. In vitro equilibrium dialysis was performed to measure the unbound concentration of lidocaine (lidocaine hydrochloride) from dialysis cassettes, one located at the sonication focus and the other outside the sonication path, all immersed in phosphate-buffered saline solution containing both lidocaine (10 µg/mL) and human AAG (5 mg/mL). In subsequent animal experiments (Sprague-Dawley rats, n = 10), somatosensory evoked potential (SSEP), elicited by electrical stimulations to the unilateral hind leg, was measured under three experimental conditions—applications of FUS to the unilateral thigh area at the site of administered topical lidocaine, FUS only, and lidocaine only. Skin temperature was measured before and after sonication. Passive cavitation detection was also performed during sonication to evaluate the presence of FUS-induced cavitation.ResultsSonication increased the unbound lidocaine concentration (8.7 ± 3.3 %) from the dialysis cassette, compared to that measured outside the sonication path (P < 0.001). Application of FUS alone did not alter the SSEP while administration of lidocaine reduced its P23 component (i.e., a positive peak at 23 ms latency). The FUS combined with lidocaine resulted in a further reduction of the P23 component (in a range of 21.8 − 23.4 ms after the electrical stimulations; F(2,27) = 3.2 − 4.0, P < 0.05), indicative of the enhanced anesthetic effect of the lidocaine. Administration of FUS neither induced cavitation nor altered skin conductance or temperature, suggesting that skin permeability was unaffected.ConclusionsUnbinding lidocaine from the plasma proteins by exposure to non-thermal low-intensity ultrasound is attributed as the main mechanism behind the observation.

HIV-1 Sanctuary Sites-the Role of Membrane-Associated Drug Transporters and Drug Metabolic Enzymes.

Despite significant advances in the treatment of human immunodeficiency virus-1 (HIV) infection with highly active antiretroviral drug therapy, the persistence of the virus in cellular and anatomic reservoirs is a major obstacle preventing total HIV eradication. Viral persistence could result from a variety of contributing factors including, but not limited to, non-adherence to treatment and adverse drug reactions, latently infected cells carrying replication-competent virus, drug-drug interactions, and inadequate antiretroviral drug (ARV) concentrations reached in several anatomic sites such as the brain, testis, and gut-associated lymphoid tissues. The distribution of ARVs at specific sites of infection is primarily dependent on drug physicochemical properties and drug plasma protein binding, as well as drug efflux, influx, and metabolic processes. A thorough understanding of the functional roles of drug transporters and metabolic enzymes in the disposition of ARVs in immune cell types and tissues that are characterized as HIV reservoirs and sanctuaries is critical to overcome the challenge of suboptimal drug distribution at sites of persistent HIV infection. This review summarizes the current knowledge related to the expression and function of drug transporters and metabolic enzymes in HIV cellular and anatomic reservoirs, and their potential contribution to drug-drug interactions and insufficient drug concentration at these sites.

Canine orosomucoid (alpha-1 acid glycoprotein) variants and their influence on drug plasma protein binding.

Orosomucoid polymorphisms influence plasma drug binding in humans; however, canine variants and their effect on drug plasma protein binding have not yet been reported. In this study, the orosomucoid gene (ORM1) was sequenced in 100 dogs to identify the most common variant and its allele frequency determined in 1,464 dogs (from 64 breeds and mixed-breed dogs). Plasma protein binding extent of amitriptyline, indinavir, verapamil, and lidocaine were evaluated by equilibrium dialysis using plasma from ORM1 genotyped dogs (n=12). Free and total drug plasma concentrations were quantified by liquid chromatography-mass spectrometry. From the five polymorphisms identified in canine ORM1, two were nonsynonymous. The most common was c.70G>A (p.Ala24Thr) with an allele frequency of 11.2% (n=1464). Variant allele frequencies varied by breed, reaching 74% in Shetland Sheepdogs (n=21). Free drug fractions did not differ significantly (p>.05; Mann-Whitney U) between plasma collected from dogs with c.70AA (n=4) and those with c.70GG (n=8) genotypes. While c.70G>A did not affect the extent of plasma protein binding in our study, the potential biological and pharmacological implication of this newly discovered ORM1 variant in dogs should be further investigated.

Evaluation of competitive binding interaction of neratinib and tamoxifen to serum albumin in multidrug therapy.

Co-administration of two drugs to obtain a therapeutic goal is a common practice clinically and for effective use of drug therapy. However, the co-administration can sometimes cause adverse effects due to pharmacokinetic drug interactions. Breast Cancer treatment regimen include tyrosine kinase inhibitor neratinib (NRB) and/or tamoxifen (TMX). In this study neratinib and tamoxifen interaction with bovine serum albumin (BSA) and human serum albumin (HSA) individually and in combination using fluorescence spectroscopy was studied. The aim of this study was to find out whether there is a possibility of either of the two drugs interfering in the plasma protein binding of the other drug. Subdomain IIA of both the BSA and HSA was found to bind tamoxifen and neratinib. The λex = 280 nm and 295 nm were used for the analysis of neratinib-SA, tamoxifen-SA, neratinib: SA in presence of constant concentration of tamoxifen and similarly tamoxifen-SA in presence of constant concentration of neratinib. The interaction study of the binary and the ternary systems suggest that neratinib doesn't affect the interaction between SA and tamoxifen. In contrast, the interaction between neratinib and SA was affected by tamoxifen. The binding constant and quenching constant values suggest that tamoxifen dislodges neratinib from its serum albumin complex whereas neratinib doesn't affect the interaction between SA and tamoxifen. Thus, it was concluded from the results the study that during simultaneous administration of neratinib and tamoxifen, their competition for the SA binding sites should be taken into account.

Novel SPME fibers based on a plastic support for determination of plasma protein binding of thiosemicarbazone metal chelators: a case example of DpC, an anti-cancer drug that entered clinical trials.

Solid-phase microextraction (SPME) is an alternative method to dialysis and ultrafiltration for the determination of plasma protein binding (PPB) of drugs. It is particularly advantageous for complicated analytes where standard methods are not applicable. Di-2-pyridylketone 4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC) is a lead compound of novel thiosemicarbazone anti-cancer drugs, which entered clinical trials in 2016. However, this agent exhibited non-specific binding on filtration membranes and had intrinsic chelation activity, which precluded standard PPB methods. In this study, using a simple and fast procedure, we prepared novel SPME fibers for extraction of DpC based on a metal-free, silicon string support, covered with C18 sorbent. Reproducibility of the preparation process was demonstrated by the percent relative standard deviation (RSD) of ≤ 9.2% of the amount of DpC extracted from PBS by several independently prepared fibers. The SPME procedure was optimized by evaluating extraction and desorption time profiles. Suitability of the optimized protocol was verified by examining reproducibility, linearity, and recovery of DpC extracted from PBS or plasma. All samples extracted by SPME were analyzed using an optimized and validated UHPLC-MS/MS method. The developed procedure was applied to the in vitro determination of PPB of DpC at two clinically relevant concentrations (500 and 1000ng/mL). These studies showed that DpC is highly bound to plasma proteins (PPB ≥ 88%) and this did not differ significantly between both concentrations tested. This investigation provides novel data in the applicability of SPME for the determination of PPB of chelators, as well as useful information for the clinical development of DpC. Graphical abstract.

Potency and plasma protein binding of drugs in vitro-a potentially misleading pair for predicting in vivo efficacious concentrations in humans.

In drug discovery or preclinical stages of development, potency parameters such as IC50, Ki, or Kd in vitro have been routinely used to predict the parameters of efficacious exposure (AUC, Cmin, etc.) in humans. However, to our knowledge, the fundamental assumption that the potency in vitro is correlated with the efficacious concentration in vivo in humans has not been investigated extensively. Thus, the present review examined this assumption by comparing a wide range of published pharmacokinetic (PK) and potency data. If the drug potency in vitro and its in vivo effectiveness in humans are well correlated, the steady-state average unbound concentrations in humans [Cu_ss.avg = fu·F·Dose/(CL·τ) = fu·AUCss/τ] after treatment with approved dosage regimens should be higher than, or at least comparable to, the potency parameters assessed in vitro. We reviewed the ratios of Cu_ss.avg/potency in vitro for a total of 54 drug entities (13 major therapeutic classes) using the dosage, PK, and in vitro potency reported in the published literature. For 54 drugs, the Cu_ss.avg/in vitro potency ratios were < 1 for 38 (69%) and < 0.1 for 22 (34%) drugs. When the ratios were plotted against fu (unbound fraction), “ratio < 1” was predominant for drugs with high protein binding (90% of drugs with fu ≤ 5%; i.e., 28 of 31 drugs). Thus, predicting the in vivo efficacious unbound concentrations in humans using only in vitro potency data and fu should be avoided, especially for molecules with high protein binding.

Effect of age on plasma protein binding of several veterinary drugs in dairy calves 2

The intent of this study was to determine what influence, if any, increasing age has on the binding of drugs to plasma proteins in cattle. Plasma from three different cohorts of calves were used. The first group (n = 20) had plasma samples taken at 1, 7 and 21 days of age. These were compared to results from a second group of calves at 8 weeks and third group sampled at 6 months of age. The plasma protein binding of danofloxacin, florfenicol, flunixin meglumine and tulathromycin was determined in vitro via microcentrifugation using three different drug concentrations spiked into the individual plasma samples derived from each calf. Albumin concentrations were lowest at 1 day of age as compared to plasma samples taken from 2 month old and 6 month old calves. There were significant decreases in alpha1-acid glycoprotein in calves until 21 days of age. However, statistically significant age-effects on plasma protein binding were not observed for any of the drugs evaluated in this study. Findings from these calves suggest that age is not an important factor in the binding of these drugs to plasma proteins.

QUANTITATIVE STRUCTURE–PHARMACOKINETICS RELATIONSHIP FOR PLASMA PROTEIN BINDING OF NEUTRAL DRUGS

Objective: Plasma protein binding (PPB) of drugs is important pharmacokinetic (PK) phenomena controlling the free drug concentration in plasma and the overall PK and pharmacodynamic profile. Prediction of PPB at the very early stages of drug development process is of paramount importance for the success of new drug candidates. The study presents a quantitative structure–pharmacokinetics relationship (QSPkR) modelling of PPB for neutral drugs.Methods: The dataset consists of 117 compounds, described by 138 molecular descriptors. Genetic algorithm and stepwise multiple linear regression are used for variable selection and QSPkR models development. The QSPkRs are evaluated by internal and external validation procedures.Results: A robust, significant and predictive QSPkR with explained variance r2 0.768, cross-validated q2LOO-CV 0.731,and geometric mean fold error of prediction (GMFEP) 1.79 is generated, which is able to predict the extent of PPB for 67.6% of the drugs in the dataset within the 2-fold error of experimental values. A simple empiric rule is proposed for distinguishing between drugs with different binding affinity, which allowed correct classification of 78% of the high binders and 87.5% of the low binders.Conclusions: PPB of neutral drugs is favored by lipophilicity, dipole moment, the presence of substituted aromatic and fused rings and a nine-member ring system, and is disfavored by the presence of aromatic N-atoms. Keywords: Plasma protein binding (PPB), Quantitative structure–pharmacokinetics relationship (QSPkR), In silico prediction, Human serum albumin (HSA), Alpha-1-acid glycoprotein (AGP).

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.

Application of a pharmacokinetics-pharmacodynamics approach to the free propofol plasma levels during coronary artery bypass grafting surgery with hypothermic cardiopulmonary bypass.

OBJECTIVES:The objective of this study was to apply a pharmacokinetics-pharmacodynamics approach to investigate the free propofol plasma levels in patients undergoing coronary artery bypass grafting under hypothermic conditions compared with the off-pump procedure.METHODS:Nineteen patients scheduled for on-pump coronary artery bypass grafting under hypothermic conditions (n=10) or the equivalent off-pump surgery (n=9) were anesthetized with sufentanil and propofol target-controlled infusion (2 μg/mL) during surgery. The propofol concentration was then reduced to 1 μg/mL, and a pharmacokinetics-pharmacodynamics analysis using the maximum-effect-sigmoid model obtained by plotting the bispectral index values against the free propofol plasma levels was performed.RESULTS:Significant increases (two- to five-fold) in the free propofol plasma levels were observed in the patients subjected to coronary artery bypass grafting under hypothermic conditions. The pharmacokinetics of propofol varied according to the free drug levels in the hypothermic on-pump group versus the off-pump group. After hypothermic coronary artery bypass was initiated, the distribution volume increased, and the distribution half-life was prolonged. Propofol target-controlled infusion was discontinued when orotracheal extubation was indicated, and the time to patient extubation was significantly higher in the hypothermic on-pump group than in the off-pump group (459 versus 273 min, p=0.0048).CONCLUSIONS:The orotracheal intubation time was significantly longer in the hypothermic on-pump group than in the off-pump group. Additionally, residual hypnosis was identified through the pharmacokinetics-pharmacodynamics approach based on decreases in drug plasma protein binding in the hypothermic on-pump group, which could explain the increased hypnosis observed with this drug in this group of patients.

Structural properties governing drug-plasma protein binding determined by high-performance liquid chromatography method

The high-performance liquid chromatography (HPLC) method employing stationary phases immobilized with plasma proteins was used for this study to investigate the structural properties governing drug-plasma protein binding. A set of 65 compounds with a broad range of structural diversity (in terms of volume, hydrogen-bonding, polarity and electrostatic force) were selected for this purpose. The Abraham linear free energy relationship (LFER) analyses of the retention factors on the immobilized HSA (human serum albumin) and AGP (α1-acid glycoprotein) stationary phases showed that McGowan’s characteristic molecular volume (V), dipolarity/polarizability (S) and hydrogen bond basicity (B) are the three significant molecular descriptors of solutes determining the interaction with immobilized plasma proteins, whereas excess molar refraction (E) is less important and hydrogen bond acidity (A) is not of statistical significance in both systems, for electrically neutral compounds. It was shown that ionised acids, as carboxylate anions, bind very strongly to the immobilized HSA stationary phase and that ionised bases, as cations bind strongly to the AGP stationary phase. This is the first time that the effect of ionised species on plasma protein binding has been determined quantitatively; the increased binding of acids to HSA is due almost entirely to acids in their ionised form.

Quantitative Structure - Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs.

Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure - pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pfu) of basic drugs. A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors. Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation. Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pfu of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume. The fraction ionized as a base fB and the presence of quaternary C-atoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

The Plasma Protein Binding Proteome of Ertapenem: A Novel Compound-Centric Proteomic Approach for Elucidating Drug–Plasma Protein Binding Interactions

Ertapenem is an important first-line carbapenem antibiotic used for the treatment of aerobic Gram-negative bacterial infections. It is the only marketed carbapenem that is highly bound to plasma proteins and displays a concentration-dependent and saturable plasma protein binding profile. To date, the plasma components responsible for sequestering ertapenems antibacterial activity remain uncharacterized. In the present study, we have employed an orthogonal, multiplatform approach, including novel compound-centric displacement proteomics and surface plasmon resonance to characterize the plasma protein binding proteome of ertapenem. In proof-of-concept, the capacity of physiological cocktails of the identified plasma proteins to inhibit the antibacterial activity of ertapenem was assessed with in vitro microbiological assays. We show that fibrinogen, complement C4, haptoglobulin, α-1-antitrypsin, fibronectin, transferrin, immunoglobulin G, hemopexin, and humans serum albumin are responsible for the majority of the sequestering activity in plasma. No binding was observed to α-1-acid-glycoprotein. The findings of this study have broad reaching implications for antibiotic drug design and for dose tailoring to suit the plasma protein levels of individual patients in order to maximize the clinical efficacy of important first-line antibiotics such as ertapenem.