Partition Coefficient Kp Research Articles

Defining operating regimes for partition coefficient measurements in protein chromatography.

Partition coefficient (Kp) measurements are widely used in early- and late-stage downstream process development for biologics to screen binding, elution, and selectivity behaviors of chromatographic resins and conditions. Although the procedure for performing these experiments is straightforward, obtaining accurate Kp and selectivity measurements can be challenging with equilibrium concentrations often being below the limit of detection (LOD) of the analyzing device, and with no guarantee that the partition coefficient measurement will sample from the linear portion of the isotherm. This work develops a theoretical framework and corresponding software tool to inform the selection of phase ratio and protein loading such that partition coefficient experiments satisfy the following three criteria: i.) equilibrium concentrations are above the LOD, ii.) measurements are sampled from the linear portion of the isotherm, and iii.) partition coefficient values fall within a practically relevant range for utility in downstream process development. First, the concept of feasibility maps is developed which calculates equilibrium concentrations, separation factors, and Kp values on the KL vs. qm plane to define a feasibility regime based on a determined LOD, minimum-required separation factor, and maximum relevant Kp. These data are then superimposed onto a large database of historical isotherm data from the literature comprised of 6,310 Langmuir isotherm points over a broad range of chromatography resins and protein modalities, which are used to binarily determine whether these three criteria are met for a given phase ratio and resin loading. Feasibility maps are calculated as a function of phase ratio and resin loading to define an operating regime bounded by a top operating curve and bottom operating line on the resin loading vs. phase ratio plane. Further, closed-form expressions for these operating curves are derived, and the sensitivity of these curves to experimental parameters such as hold-up volume, intraparticle porosity, isotherm linearity criteria, and LOD is then evaluated and discussed in the context of relative error due to isotherm curvature. The impact of measuring Kp values inside vs. outside these operating regimes is assessed by experimentally measuring Kp vs. salt sampled from these two regions and predicting elution salt by mechanistic column modeling from these equilibrium data. These results indicate that Kp measurements sampled from outside the operating regime result in substantial error in model prediction compared to column experiments. Together, this work provides a theoretical foundation for obtaining accurate partition coefficient and selectivity measurements for informing robust experimental design. Further, it develops a software tool for calculating operating regimes, which can leverage in-house historical isotherm data to calculate custom operating regimes.

Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin

Affected by point and non-point source pollution, the Fenhe River Basin faces significant environmental challenges. This study aimed to analyze the distribution characteristics and influencing factors of antibiotics in the water and sediments of the Fenhe River Basin. Samples were collected from 23 sites within the basin, and 26 antibiotics from five different classes were detected and analyzed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The water–sediment partition coefficient (Kp) was calculated, and spatial analysis was conducted using geographic information system (GIS) technology. The results showed that 25 antibiotics were detected in the water, with concentrations ranging from 130 to 1615 ng/L, and 17 antibiotics were detected in the sediments, with concentrations ranging from 121 to 426 μg/kg. For quinolones (QNs), except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, with considerable point source pollution from industries and non-point source pollution from developed agriculture. The distribution of Kp is closely associated with risk. This study found that the Kp values of the antibiotics were influenced by various factors such as temperature, water flow, and the physicochemical properties of sediments. Correlation analysis revealed significant relationships between Kp and parameters such as river width, water depth, water quality (total nitrogen, total phosphorus, and chemical oxygen demand), and sediment pH and clay content.

Partition coefficient screening – An effective approach for finding the best conditions for byproduct removal as demonstrated by a bispecific antibody purification case

In recombinant protein purification, differences in isoelectric point (pI)/surface charge and hydrophobicity between the product and byproducts generally form the basis for separation. For bispecific antibodies (bsAbs), in many cases the physicochemical difference between product and byproducts is subtle, making byproduct removal considerably challenging. In a previous report, with a bsAb case study, we showed that partition coefficient (Kp) screening for the product and byproducts under various conditions facilitated finding conditions under which effective separation of two difficult-to-remove byproducts was achieved by anion exchange (AEX) chromatography. In the current work, as a follow-up study, we demonstrated that the same approach enabled identification of conditions allowing equally good byproduct removal by mixed-mode chromatography with remarkably improved yield. Results from the current and previous studies proved that separation factor determination based on Kp screening for product and byproduct is an effective approach for finding conditions enabling efficient and maximum byproduct removal, especially in challenging cases.

An Evaluation of Sex-Specific Pharmacokinetics and Bioavailability of Kokusaginine: An In Vitro and In Vivo Investigation.

Kokusaginine is a bioactive ingredient extracted from Ruta graveolens L., which has a range of biological activities. Its pharmacokinetic (PK) properties are particularly important for clinical applications; however, they have not been fully elucidated. In addition, the effect of sex differences on drug metabolism is increasingly being recognized, but most studies have ignored this important factor. This study aims to fill this knowledge gap by taking an in-depth look at the PK properties of kokusaginine and how gender affects its metabolism and distribution in the body. It also lays the foundation for clinical drug development. In this study, a sensitive ultra-high-performance liquid chromatography (UPLC) method was developed and validated for quantifying kokusaginine in Sprague Dawley (SD) rat plasma and tissue homogenates. Metabolic stability was evaluated in vitro using gender-specific liver microsomes. Innovatively, we incorporated sex as a variable into both in vitro and in vivo PK studies in SD rats, analyzing key parameters with Phoenix 8.3.5 software. The developed UPLC method demonstrated high sensitivity and precision, essential for PK analysis. Notably, in vitro studies revealed a pronounced sex-dependent metabolic variability (p < 0.05). In vivo, gender significantly affected the Area Under the Moment Curve (AUMC)(0-∞) of the plasma PK parameter (p < 0.05) and the AUMC(0-t) of brain tissue (p < 0.0001), underscoring the necessity of sex-specific PK assessments. The calculated absolute bioavailability of 71.13 ± 12.75% confirmed the favorable oral absorption of kokusaginine. Additionally, our innovative tissue-plasma partition coefficient (Kp) analysis highlighted a rapid and uniform tissue distribution pattern. This study presents a sex-inclusive PK evaluation of kokusaginine, offering novel insights into its metabolic profile and distribution. These findings are instrumental for informing clinical medication practices, dosage optimization, and a nuanced understanding of drug efficacy and safety in a sex-specific context.

Evaluation of Drug Blood-Brain-Barrier Permeability Using a Microfluidic Chip.

The blood-brain-barrier (BBB) is made up of blood vessels whose permeability enables the passage of some compounds. A predictive model of BBB permeability is important in the early stages of drug development. The predicted BBB permeabilities of drugs have been confirmed using a variety of in vitro methods to reduce the quantities of drug candidates needed in preclinical and clinical trials. Most prior studies have relied on animal or cell-culture models, which do not fully recapitulate the human BBB. The development of microfluidic models of human-derived BBB cells could address this issue. We analyzed a model for predicting BBB permeability using the Emulate BBB-on-a-chip machine. Ten compounds were evaluated, and their permeabilities were estimated. Our study demonstrated that the permeability trends of ten compounds in our microfluidic-based system resembled those observed in previous animal and cell-based experiments. Furthermore, we established a general correlation between the partition coefficient (Kp) and the apparent permeability (Papp). In conclusion, we introduced a new paradigm for predicting BBB permeability using microfluidic-based systems.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Development of extended pharmacokinetic models for propofol based on measured blood and brain concentrations

Propofol’s pharmacokinetics have been extensively studied using human blood samples and applied to target-controlled infusion systems; however, information on its concentration in the brain remains scarce. Therefore, this study aimed to simultaneously measure propofol plasma and brain concentrations in patients who underwent awake craniotomy and establish new pharmacokinetic model. Fifty-seven patients with brain tumors or brain lesions who underwent awake craniotomy were sequentially assigned to model-building and validating groups. Plasma and brain (lobectomy or uncapping margins) samples were collected at five time-points. The concentration of propofol was measured using high-performance liquid chromatography. Population pharmacokinetic analysis was conducted through a nonlinear mixed-effects modeling program using a first-order conditional estimation method with interactions. Propofol’s brain concentrations were higher than its plasma concentrations. The measured brain concentrations were higher than the effect site concentrations using the previous models. Extended models were constructed based on measured concentrations by incorporating the brain/plasma partition coefficient (Kp value). Extended models showed good predictive accuracy for brain concentrations in the validating group. The Kp value functioned as a factor explaining retention in the brain. Our new pharmacokinetic models and Kp value can predict propofol’s brain and plasma concentrations, contributing to safer and more stable anesthesia.

Impact of variability of in silico and in vitro octanol/water partition coefficients of compounds on the input parameters and results of simplified human physiologically based pharmacokinetic models after virtual oral administrations

The octanol/water partition coefficient, P (logP), is a hydrophobicity index and is one of the determining factors of the pharmacokinetics of chemical compounds. LogP values obtained from in silico software, open chemistry databases, and in vitro liquid chromatography retention factors may vary. Some chemicals (boscalid, etoxazole, and permethrin) have up to four-order-magnitude differences in in silico/in vitro P values. This study aimed to evaluate the effects of logP values of these three compounds, along with bisphenol A, 1,2-dibromobenzene, tetrabromobisphenol A, trazodone, and triazolam, on the input parameters and output plasma/hepatic concentration-time profiles of simple physiologically based pharmacokinetic (PBPK) models. Although the blood-to-plasma concentration ratios (~0.9-0.6) were slightly affected by variations in logP values, logarithmic plasma unbound fraction values and liver-to-plasma partition coefficients (Kp,h) were, respectively, inversely and linearly correlated with logP values (Kp,h was stable at ~6.7 for logP > 4). LogP was among the input parameters for previously established machine learning systems; consequently, the resulting logarithmic intrinsic clearance values were correlated with logP values in the range 2-8. However, the bioavailability, absorption rate constants, and volumes of distribution were not affected. PBPK-modeled plasma and hepatic maximum concentrations and areas under the concentration-time curves after virtual oral doses were mostly within ~0.5- to ~2-fold ranges, except for substances with low in vitro logP values, e.g., etoxazole and permethrin. These results suggest that in silico logP values are generally suitable for pharmacokinetic modeling; nevertheless, caution is needed for compounds with low in vitro logP values of ~2.

Research on partition of phosphorus in the Three Gorges Reservoir on the Yangtze River

Partition of phosphorus (P) plays an essential role in its ecological effect in surface waters. Yet limited river sampling hinders our understanding for it. P partition between suspended sediments (SS) and aqueous phase in the mainstem of the Three Gorges Reservoir (TGR) on the Yangtze River were studied based on data during 2004–2019. The results reveal that the percentage of DP (dissolved phosphorus) in TP (total phosphorus) (i.e, λ (DP/TP)) decreased remarkably with increasing concentrations of SS, and the empirical equation by nonlinear fitting is λ (DP/TP) = (SS/50 + 80)/(SS + 98) (SS: mg/L, Model I). When SS increased from several mg/L to 180 mg/L, λ (DP/TP) decreased sharply from averagely 0.80 to 0.25. In the range of SS﹥ ~ 400 mg/L, λ (DP/TP) tended to be relatively steady remaining between 0.05 and 0.20 with an average of 0.12. The partition coefficient (Kp) of P between SS and aqueous phase was found to decrease with rising SS and Ce (aqueous concentration of P, i.e., DP).The empirical equation based on SS is Kp (L/g) = 1000 × (49 × SS + 900)/(SS2 + 4000 × SS) (SS: mg/L, Model II). When SS increased from <3 mg/L to ~50 mg/L, Kp decreased rapidly from averagely 88 to 23 L/g, and when SS exceeded 50 mg/L, the pace of decreasing of Kp slowed down. The equation based on Ce is Kp (L/g) = 45.88–194.44 × Ce (mg/L) (Model III). When Ce increased from 0.025 to 0.25 mg/L, the average Kp decreased from 50 to 7.0 L/g. Compared with the influence of variation in SS and Ce, the influence of temperature change on Kp can be ignored. New models are advantageous over previously reported ones, and they can be used to better predict P partition and determine whether SS is a sink or a source.

The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling.

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

High throughput screening for rapid and reliable prediction of monovalent antibody binding behavior in flowthrough mode.

Flowthrough (FT) anion exchange (AEX) chromatography is a widely used polishing step for the purification of monoclonal antibody (mAb) formats. To accelerate downstream process development, high throughput screening (HTS) tools have proven useful. In this study, the binding behavior of six monovalent mAbs (mvAbs) was investigated by HTS in batch binding mode on different AEX and mixed-mode resins at process-relevant pH and NaCl concentrations. The HTS entailed the evaluation of mvAb partition coefficients (Kp) and visualization of results in surface-response models. Interestingly, the HTS data grouped the mvAbs into either a strong-binding group or a weak-binding/FT group independent of theoretical Isoelectric point. Mapping the charged and hydrophobic patches by in silico protein surface property analyses revealed that the distribution of patches play a major role in predicting FT behavior. Importantly, the conditions identified by HTS were successfully verified by 1 mL on-column experiments. Finally, employing the optimal FT conditions (7-9 mS/cm and pH 7.0) at a mini-pilot scale (CV = 259 mL) resulted in 99% yield and a 21-23-fold reduction of host cell protein to <100 ppm, depending on the varying host cell protein (HCP) levels in the load. This work opens the possibility of using HTS in FT mode to accelerate downstream process development for mvAb candidates in early research.

Central Nervous System Distribution of Panobinostat in Preclinical Models to Guide Dosing for Pediatric Brain Tumors.

Achieving adequate exposure of the free therapeutic agent at the target is a critical determinant of efficacious chemotherapy. With this in mind, a major challenge in developing therapies for central nervous system (CNS) tumors is to overcome barriers to delivery, including the blood-brain barrier (BBB). Panobinostat is a nonselective pan-histone deacetylase inhibitor that is being tested in preclinical and clinical studies, including for the treatment of pediatric medulloblastoma, which has a propensity for leptomeningeal spread and diffuse midline glioma, which can infiltrate into supratentorial brain regions. In this study, we examined the rate, extent, and spatial heterogeneity of panobinostat CNS distribution in mice. Transporter-deficient mouse studies show that panobinostat is a dual substrate of P-glycoprotein (P-gp) and breast cancer resistant protein (Bcrp), which are major efflux transporters expressed at the BBB. The CNS delivery of panobinostat was moderately limited by P-gp and Bcrp, and the unbound tissue-to-plasma partition coefficient of panobinostat was 0.32 and 0.21 in the brain and spinal cord in wild-type mice. In addition, following intravenous administration, panobinostat demonstrated heterogeneous distribution among brain regions, indicating that its efficacy would be influenced by tumor location or the presence and extent of leptomeningeal spread. Simulation using a compartmental BBB model suggests inadequate exposure of free panobinostat in the brain following a recommended oral dosing regimen in patients. Therefore, alternative approaches to CNS delivery may be necessary to have adequate exposure of free panobinostat for the treatment of a broad range of pediatric brain tumors. SIGNIFICANCE STATEMENT: This study shows that the central nervous system (CNS) penetration of panobinostat is limited by P-gp and Bcrp, and its efficacy may be limited by inadequate distribution to the tumor. Panobinostat has heterogeneous distribution into various brain regions, indicating that its efficacy might depend on the anatomical location of the tumors. These distributional parameters in the mouse CNS can inform both preclinical and clinical trial study design and may guide treatment for these devastating brain tumors in children.

Containment of phenol-impacted groundwater by vertical cutoff wall with backfill consisting of sand and bentonite modified with hydrophobic and hydrophilic polymers

A novel soil-bentonite backfill is proposed for use in vertical cutoff walls to contain phenol in groundwater at contaminated sites. The backfill consists of sand and bentonite modified with tetramethylammonium and carboxymethylcellulose, labeled as STCMB backfill. Flexible-wall permeability and double-reservoir diffusion tests were conducted to investigate the impact of phenol solution on hydraulic conductivity (k), effective diffusion coefficient (D*) and partition coefficient (Kp) of the backfill, respectively. The permeability results showed k of the STCMB backfill decreased by 0.91 times when the permeating liquid was changed from tap water to phenol solution. The diffusion testing results showed that D* values for the STCMB and conventional backfill (labeled as SCB backfill) were 4.0 × 10−10 m2/s and 3.0 × 10−10 m2/s, respectively, whereas Kp values for the STCMB and SCB backfills were 2.0 mL/g and 0.75 mL/g, respectively. The octanol-water partition coefficient model is suitable for estimating Kp for nonpolar organics. Furthermore, a series of solute transport simulations using Pollute V7 program was performed to evaluate the performance of vertical cutoff walls comprising STCMB and SCB backfills in containing phenol in lateral flowing groundwater. Overall, the STCMB backfill has demonstrated superior effectiveness in containing phenol in groundwater.

Atmospheric mercury in a developed region of eastern China: Interannual variation and gas-particle partitioning

Atmospheric mercury plays a crucial role in the biogeochemical cycle of mercury. This study conducted an intensive measurement of atmospheric mercury from 2015 to 2018 at a regional site in eastern China. During this period, the concentration of particle-bound mercury (PBM) decreased by 13%, which was much lower than those of gaseous elemenral mercury (GEM, 30%) and reactive gaseous mercury (GOM, 62%). The gradual decrease in the correlation between PBM and CO, K, and Pb indicates that the influence of primary emissions on PBM concentration was weakening. Moreover, the value of the partitioning coefficient (Kp) increased gradually from 0.05 ± 0.076 m3/μg in 2015 to 0.16 ± 0.37 m3/μg in 2018, indicating that GOM was increasingly inclined to adsorb onto particulate matter. Excluding the influence of meteorological conditions and the primary emissions, the change in aerosol composition is designated as the main trigger factor for the increasing gas-particle partitioning of reactive mercury (RM). The increasing ratio of Cl−, NO3−, and organics (Org) in the chemical composition of particle matters (PM2.5), as well as the decrease in the proportion of SO42−, NH4+, and K+, are conducive to the adsorption of GOM onto particles, forming PBM, which led to an increase of Kp and a lag of PBM reduction compared to GEM and GOM under the continuous control measures of anthropogenic mercury emissions. The evolution of aerosol compositions in recent years affects the migration and transformation of atmospheric mercury, which in turn can affect the biogeochemical cycle of mercury.

Thermodynamic modelling of the partitioning of lysozyme in aqueous two-phase systems composed of polyethylene glycol and salt at different temperatures

Knowledge about the interactions during the protein transfer is necessary to optimize the application of aqueous two-phase systems (ATPS) in purification processes. Theoretical knowledge combined with modelling and simulation can help to select the systems with the best partition values. Therefore, this study aimed to determine new interaction parameters through the contribution groups of lysozyme, using UNIFAC (Universal Functional Activity Coefficient) modelling. First, the partition of lysozyme was carried out, and the polymer molar mass (4000 or 6000), and the effects of the type of salt (Na2SO4 or Na3C6H5O7), the overall composition of the system (3 mooring lines), and temperature T = (293.15, 298.15, 303.15 and 308.15) K were evaluated. The results showed that lysozyme had a preference for the bottom phase of the ATPS system, the increase in the overall composition led to a tendency of increasing the partition coefficient (Kp), and the systems made up of higher molar mass polymers exhibited a bottom Kp value. The results of the thermodynamic parameters showed that the transfer of lysozyme in the phases was governed by entropy contribution. New interaction parameters of the contribution groups of lysozyme in the system phases were estimated. The UNIFAC model showed low deviation values. Therefore, the present results allow estimating new partitioning data for proteins presenting the same contribution groups, which can reduce the number of experiments and optimize the partitioning process.

Modeled Rat Hepatic and Plasma Concentrations of Chemicals after Virtual Administrations Using Two Sets of in Silico Liver-to-Plasma Partition Coefficients

The hepatic elimination of chemical substances in pharmacokinetic models requires hepatic intrinsic clearance (CLh,int) parameters for unbound drug in the liver, and these are regulated by the liver-to-plasma partition coefficients (Kp,h). Both Poulin and Theil and Rodgers and Rowland have proposed in silico expressions for Kp,h for a variety of chemicals. In this study, two sets of in silico Kp,h values for 14 model substances were assessed using experimentally reported in vivo steady-state Kp,h data and time-dependent virtual internal exposures in the liver and plasma modeled by forward dosimetry in rats. The Kp,h values for 14 chemicals independently calculated using the primary Poulin and Theil method in this study were significantly correlated with those obtained using the updated Rodgers and Rowland method and with reported in vivo steady-state Kp,h data in rats. When pharmacokinetic parameters were derived based on individual in vivo time-dependent data for diazepam, phenytoin, and nicotine in rats, the modeled liver and plasma concentrations after intravenous administration of the selected substrates in rats using two sets of in silico Kp,h values were mostly similar to the reported time-dependent in vivo internal exposures. Similar results for modeled liver and plasma concentrations were observed with input parameters estimated by machine-learning systems for hexobarbital, fingolimod, and pentazocine, with no reference to experimental pharmacokinetic data. These results suggest that the output values from rat pharmacokinetic models based on in silico Kp,h values derived from the primary Poulin and Theil model would be applicable for estimating toxicokinetics or internal exposure to substances.

Active Uptake of Oxycodone at Both the Blood-Cerebrospinal Fluid Barrier and The Blood-Brain Barrier without Sex Differences: A Rat Microdialysis Study

BackgroundOxycodone active uptake across the blood-brain barrier (BBB) is associated with the putative proton-coupled organic cation (H+/OC) antiporter system. Yet, the activity of this system at the blood-cerebrospinal fluid barrier (BCSFB) is not fully understood. Additionally, sex differences in systemic pharmacokinetics and pharmacodynamics of oxycodone has been reported, but whether the previous observations involve sex differences in the function of the H+/OC antiporter system remain unknown. The objective of this study was, therefore, to investigate the extent of oxycodone transport across the BBB and the BCSFB in female and male Sprague-Dawley rats using microdialysis.MethodsMicrodialysis probes were implanted in the blood and two of the following brain locations: striatum and lateral ventricle or cisterna magna. Oxycodone was administered as an intravenous infusion, and dialysate, blood and brain were collected. Unbound partition coefficients (Kp,uu) were calculated to understand the extent of oxycodone transport across the blood-brain barriers. Non-compartmental analysis was conducted using Phoenix 64 WinNonlin. GraphPad Prism version 9.0.0 was used to perform t-tests, one-way and two-way analysis of variance followed by Tukey’s or Šídák’s multiple comparison tests. Differences were considered significant at p < 0.05.ResultsThe extent of transport at the BBB measured in striatum was 4.44 ± 1.02 (Kp,uu,STR), in the lateral ventricle 3.41 ± 0.74 (Kp,uu,LV) and in cisterna magna 2.68 ± 1.01 (Kp,uu,CM). These Kp,uu values indicate that the extent of oxycodone transport is significantly lower at the BCSFB compared with that at the BBB, but still confirm the presence of active uptake at both blood-brain interfaces. No significant sex differences were observed in neither the extent of oxycodone delivery to the brain, nor in the systemic pharmacokinetics of oxycodone.ConclusionsThe findings clearly show that active uptake is present at both the BCSFB and the BBB. Despite some underestimation of the extent of oxycodone delivery to the brain, CSF may be an acceptable surrogate of brain ISF for oxycodone, and potentially also other drugs actively transported into the brain via the H+/OC antiporter system.

Quantitative Systems Toxicology Identifies Independent Mechanisms for Hepatotoxicity and Bilirubin Elevations Due to AKR1C3 Inhibitor BAY1128688.

BAY1128688 is a selective inhibitor of AKR1C3, investigated recently in a trial that was prematurely terminated due to drug-induced liver injury. These unexpected observations prompted use of the quantitative systems toxicology model, DILIsym, to determine possible mechanisms of hepatotoxicity. Using mechanistic in vitro toxicity data as well as clinical exposure data, DILIsym predicted the potential for BAY1128688 to cause liver toxicity (elevations in serum alanine aminotransferase (ALT)) and elevations in serum bilirubin. Initial simulations overpredicted hepatotoxicity and bilirubin elevations, so the BAY1128688 representation within DILIsym underwent optimization. The liver partition coefficient Kp was altered to align simulated bilirubin elevations with those observed clinically. Altering the mode of bile acid canalicular and basolateral efflux inhibition was necessary to accurately predict ALT elevations. Optimization results support that bilirubin elevations observed early during treatment are due to altered bilirubin metabolism and transporter inhibition, which is independent of liver injury. The modeling further supports that on-treatment ALT elevations result from inhibition of bile acid transporters, particularly the bile salt excretory pump, leading to accumulation of toxic bile acids. The predicted dose-dependent intrinsic hepatotoxicity may increase patient susceptibility to an adaptive immune response, accounting for ALT elevations observed after completion of treatment. These BAY1128688 simulations provide insight into the mechanisms behind hepatotoxicity and bilirubin elevations and may inform the potential risk posed by future compounds.

Introduction of constrained Trp analogs in RW9 modulates structure and partition in membrane models

Over the past decades, many cell-penetrating peptides (CPP) have been studied for their capacity to cross cellular membranes, mostly in order to improve cellular uptake of therapeutic agents. Even though hydrophobic and anionic CPPs have been described, many of them are polycationic, due to the presence of several arginine (Arg) residues. Noteworthy, however, the presence of aromatic amino acids such as tryptophan (Trp) within CPPs seems to play an important role to reach high membranotropic activity. RW9 (RRWWRRWRR) is a designed CPP derived from the polyarginine R9 presenting both features. In general, when interacting with membranes, CPPs adopt an optimal conformation for membrane interactions – an amphipathic helical secondary structure in the case of RW9. Herein, we assumed that the incorporation of a locally constrained amino acid in the peptide sequence could improve the membranotropic activity of RW9, by facilitating its structuration upon contact with a membrane, while leaving a certain plasticity. Therefore, two cyclized Trp derivatives (Tcc and Aia) were synthesized to be incorporated in RW9 as surrogates of Trp residues. Thus, a series of peptides containing these building blocks has been synthesized by varying the type, position, and number of modifications. The membranotropic activity of the RW9 analogs was studied by spectrofluorescence titration of the peptides in presence of liposomes (DMPG), allowing to calculate partition coefficients (Kp). Our results indicate that the partitioning of the modified peptides depends on the type, the number and the position of the modification, with the best sequence being [Aia4]RW9. Interestingly, both NMR analysis and molecular dynamic (MD) simulations indicate that this analog presents an extended conformation similar to the native RW9, but with a much-reduced structural flexibility. Finally, cell internalization properties were also confirmed by confocal microscopy.

Development of a 2D-QSAR Model forTissue-to-Plasma PartitionCoefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (Kp) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the Kp value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma. Instead of experiments, many researchers have sought in silico methods. Today, most of the models for Kp prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for Kp using physicochemical descriptors instead of in vivo experimental data as explanatory variables. We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1-14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for Kp value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model. Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)]. We could develop a 2D-QSAR model for Kp value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.