Chromatographic Estimation of Drug Disposition Properties by Means of Immobilized Artificial Membranes (IAM) and C18 Columns

Predicting biophysical properties of small molecules from chromatographic measurements and the solvation parameter model

Biopartitioning processes are challenging to study and often require the sacrifice of multiple animals. Therefore, it is more practical and cost-effective to correlate these processes with easily determined properties, such as chromatographic retention data, or to make predictions based on structural descriptors such as quantitative structure-property relationships or linear free energy relationships. Abraham's solvation parameter model uses six solute properties to characterize the interactions responsible for the transfer of neutral compounds between immiscible phases in chromatographic or biological systems. This review discusses the prediction of biological properties of small molecules from chromatographic measurements and the solvation parameter model. It covers the characteristics of solute descriptors in the solvation parameter model, as well as experimental approaches for their determination. Additionally, it explores recent applications of the solvation parameter model in characterizing biological systems and its use in identifying surrogate chromatographic models for predicting biological properties.

Ability of biomimetic chromatography and physicochemical systems to predict the skin permeation of neutral compounds. A comparison study

Five in vitro physicochemical systems have been evaluated in terms of its ability to emulate the skin permeation of neutral compounds: the permeation in two different PAMPA membranes, the classical octanol-water partition coefficient, and two biomimetic chromatography systems, one based in cerasome electrokinetic chromatography and another based in reversed-phase liquid chromatography measurements. The coefficients of the solvation parameter model equation of the mentioned systems have been compared to the ones of the skin permeation process through different comparison parameters. Moreover, a method to predict whether a physicochemical system is able to emulate satisfactorily a biological one, just by the analysis of the equation coefficients has been developed. Results reveal that the two PAMPA systems are a good choice to emulate directly the skin permeation of neutral compounds. Instead, the other three systems need a volume correction term to provide a satisfactory emulation. However, after the correction, all the evaluated systems show a similar ability to emulate well skin permeation, as predicted.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log Kp) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log Kp model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log Kp data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

Comparison of supercritical fluid chromatographic methods to predict the skin permeability of pharmaceutical and cosmetic compounds

Supercritical fluid chromatography (SFC) was explored as an alternative for liquid chromatography to predict the skin permeability of pharmaceutical and cosmetic compounds. Nine dissimilar stationary phases were applied to screen a test set of 58 compounds. The experimental retention factors (log k), in addition to two sets of theoretical molecular descriptors, were applied to model the skin permeability coefficient. Different modelling approaches, i.e. multiple linear regression (MLR) and partial least squares (PLS) regression, were used. In general, the MLR models performed better than the PLS models for a given descriptor set. The results obtained on a cyanopropyl (CN) column provided the best correlation with the skin permeability data. The retention factors obtained on this column were included in a simple MLR model, together with the octanol-water partition coefficient and the number of atoms (r² = 0.81, RMSEC = 0.537 or 20.5% and RMSECV = 0.580 or 22.1%). The overall best MLR model included the chromatographic descriptor from a phenyl column and 18 descriptors (r² = 0.98, RMSEC = 0.167 or 6.2% and RMSECV = 0.238 or 8.9%). This model showed a good fit, on top of very good predictive features. However, stepwise MLR models with a reduced complexity could also be determined, with the best performance parameters obtained with the CN-column based retention and eight descriptors (r² = 0.95, RMSEC = 0.282 or 10.7% and RMSECV = 0.353 or 13.4%). SFC thus provides a suitable alternative to the liquid chromatographic techniques previously applied to model the skin permeability.

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

Solute–Solvent Interactions in Hydrophilic Interaction Liquid Chromatography: Characterization of the Retention in a Silica Column by the Abraham Linear Free Energy Relationship Model

The Abraham linear free energy relationship model has been used to characterize a hydrophilic interaction liquid chromatography (HILIC) silica column with acetonitrile/water and methanol/water mobile phases. Analysis by the model for acetonitrile/water mobile phases points to solute volume and hydrogen bond basicity as the main properties affecting retention, whereas solute hydrogen bond acidity, dipolarity and polarizability practically do not affect it. Formation of a cavity is easier in acetonitrile-rich mobile phases than in the aqueous stationary phase, and hence increase of solute volume decreases retention. Conversely, hydrogen bond acidity is stronger in the aqueous stationary phase than in the acetonitrile-rich mobile phase and thus an increase of solute hydrogen bond basicity increases retention. Results are similar for methanol/water mobile phases with the difference that solute hydrogen bond acidity is significant too. Increase in hydrogen bond acidity of the solute decreases retention showing that methanol mobile phases must be better hydrogen bond acceptors than acetonitrile ones, and even than water-rich stationary phases. The results are like the ones obtained in zwitterionic HILIC columns bonded to silica or polymer supports for acetonitrile/water mobile phases, but different for solute hydrogen bond acidity for a polymer bonded zwitterionic column with methanol/water mobile phases, indicating that bonding support plays an important role in HILIC retention. Comparison to RPLC characterized systems confirms the complementarity of HILIC systems to RPLC ones because the main properties affecting retention are the same but with reversed coefficients. The least retained solutes in RPLC are the most retained in HILIC.

IAM Chromatographic Models of Skin Permeation

Chromatographic retention factor log k_IAM obtained from IAM HPLC chromatography with buffered aqueous mobile phases and calculated molecular descriptors (surface area—S_a; molar volume—V_M; polar surface area—PSA; count of freely rotable bonds—FRB; H-bond acceptor count—HA; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; and polarizability—α) obtained for a group of 160 structurally unrelated compounds were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that log k_IAM obtained in the conditions described in this study is not sufficient as a sole predictor of the skin permeability coefficient. Simple put, potentially useful models based on log k_IAM and readily available calculated descriptors, accounting for 85 to 91% of the total variability, were generated using Multiple Linear Regression (MLR).The models proposed in the study were tested on a group of 20 compounds with known experimental log K_p values.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log K_p, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

RP-18 TLC Chromatographic and Computational Study of Skin Permeability of Steroids

The skin permeability of steroids, as investigated in this study, is important because some of these compounds are, or could, be used in preparations applied topically. Several models of skin permeability, involving thin layer chromatographic and calculated descriptors, were generated and validated using K_p reference values obtained in silico and then tested on a group of solutes whose experimental K_p values could be found (log K_p^exp). The study established that the most applicable log K_p model is based on RP-18 thin layer chromatographic data (R_M) and the calculated descriptors V_M (molar volume) and PSA (polar surface area). Two less efficient, yet simple, equations based on PSA or V_M combined with HD (H-donor count) can be used with caution for rapid, rough estimations of compounds’ skin permeability prior to their chemical synthesis.

Comparison of in-silico modelling and reversed-phase liquid chromatographic retention on an octadecyl silica column to predict skin permeability of pharmaceutical and cosmetic compounds

This study focuses on the in-silico modelling of the skin permeability using a test set of pharmaceutical and cosmetic compounds. Two sets of theoretical molecular descriptors, obtained from the E-Dragon and Vega ZZ software programs, were used in the models. Different linear regression methods, i.e. Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression, were applied for modelling and estimating the skin permeability. The best model was obtained using a stepwise MLR approach on the E-Dragon descriptor set. In a second step, the retention of the test set compounds was measured on a C18 column at two pH levels: pH 5.5 and pH 7. Different organic-modifier fractions were applied in the mobile phase to be able to extrapolate the retention factors to a log k_w value, with k_w the estimated retention factor in an aqueous mobile phase without organic modifier. Thereafter it was examined whether combining this chromatographic descriptor with the theoretical descriptors could improve the modelling of the skin permeability. The chromatographic descriptor often did not show an added value compared to the models containing only theoretical descriptors. Therefore, the in-silico models were preferred, and these models could be useful to predict the skin permeability of pharmaceutical and cosmetic compounds.

Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs

Several chromatographic parameters (R_M⁰ and S obtained from RP-18 TLC with methanol—pH 7.4 phosphate buffer mobile phases by extrapolation to zero concentration of methanol; R_f and R_M obtained from RP-18 TLC with acetonitrile—pH 7.4 phosphate buffer 70:30 v/v as a mobile phase) and calculated molecular descriptors (molecular weight—M_W; molar volume—V_M; polar surface area—PSA; total count of nitrogen and oxygen atoms—(N+O); H-bond donor count—HD; H-bond acceptor count—HA; distribution coefficient—log D; total energy—E_T; binding energy—E_b; hydration energy—E_h; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; electronic energy—E_e; surface area—S_a; octanol-water partition coefficient—log P; dipole moment—DM; refractivity—R, polarizability—α) and their combinations (R_f/PSA, R_M/M_W, R_M/V_M) were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that neither R_M⁰ nor S obtained in the conditions used in this study is a good predictor of the skin permeability coefficient. The chromatographic parameters R_f and R_f/PSA were also unsuitable for this purpose. A simple and potentially useful, purely computational model based on (N+O), log D and HD as independent variables and accounting for ca. 83% of total variability was obtained. The evaluation of parameters derived from R_M (R_M, R_M/M_W, R_M/V_M) as independent variables in log K_p models proved that R_M/V_M is the most suitable descriptor belonging to this group. In a search for a reliable log K_p model based on this descriptor two possibilities were considered: a relatively simple model based on 5 independent variables: (N+O), log D, R_M/V_M, E_T and E_h and a more complex one, involving also E_b, M_W and PSA.

Estimation of the octanol-water distribution coefficient of basic compounds by a cationic microemulsion electrokinetic chromatography system

The octanol-water partition coefficient (Po/w), or the octanol-water distribution coefficient (Do/w) for ionized compounds, is a key parameter in the drug development process. In a previous work, this parameter was estimated through the retention factor measurements in a sodium dodecyl sulfate (SDS) - microemulsion electrokinetic chromatography (MEEKC) system for acidic compounds. Nonetheless, when ionized basic compounds were analyzed, undesirable ion pairs were formed with the anionic surfactant and avoided a good estimation of log Do/w. For this reason, an alternative MEEKC system based on a cationic surfactant has been evaluated to estimate Po/w or Do/w of neutral compounds and ionized bases. To this end, it has been characterized through the solvation parameter model (SPM) and compared to the octanol-water partition system. Results pointed out that both systems show a similar partition behavior. Hence, the log Po/w of a set of neutral compounds has been successfully correlated against the logarithm of the retention factor (log k) determined in this MEEKC system. Then, the log Do/w of 6 model bases have been estimated at different pH values and they have been compared to data from the literature, determined by the reference shake-flask and potentiometric methods. Good agreement has been observed between the literature and the estimated values when the base is neutral or partially ionized (up to 99% of ionization).

Learning how to use IAM chromatography for predicting permeability

The interest for IAM (Immobilized Artificial Membranes) chromatography in the prediction of drug permeability is increasing. Here we firstly set-up a dataset of 253 molecules including neutral and ionized drugs and few organic compounds for which we either measured or retrieved from the literature IAM.PC.DD2 log K_w^IAM data. Then we applied block relevance (BR) analysis to extract from PLS models the relative contribution of intermolecular forces governing log K_w^IAM and Δlog K_w^IAM (a combined descriptor calculated from log K_w^IAM). Finally, the relationship between log K_w^IAM, Δlog K_w^IAM and passive permeability determined in both PAMPA and MDCK-LE systems was looked for. Models provided the basis for a rational application of IAM chromatography in permeability prediction.

Lipophilicity and biomimetic properties to support drug discovery

INTRODUCTION

Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental - based estimation of ADME properties in early drug discovery stages. Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated. Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.

Lipophilicity and biomimetic properties measured by HPLC to support drug discovery

HPLC methods that use chromatographic retention times for gaining information about the properties of compounds for the purpose of designing drug molecules are reviewed. Properties, such as lipophilicity, protein binding, phospholipid binding, and acid/base character can be incorporated in the design of molecules with the right biological distribution and pharmacokinetic profile to become an effective drug. Standardization of various methodologies is suggested in order to obtain data suitable for inter-laboratory comparison. The published HPLC methods for lipophilicity, acid/base character, protein and phospholipid binding are critically reviewed and compared with each other using the solvation equation approach. One of the most important discussion points is how these data can be used in models and how they can influence the drug discovery process. Therefore, the published models for volume of distribution, unbound volume of distribution and drug efficiency are also discussed. The general relationships between the chemical structure and biomimetic HPLC properties are described in view of ranking and selecting putative drug molecules.

High-throughput logPo/w determination from UHPLC measurements: Revisiting the chromatographic hydrophobicity index

A fast and accurate lipophilicity determination is fundamental in the drug discovery process, as long as it is a relevant property in the absorption, distribution, metabolism, excretion and toxicity (ADMET) of a potential drug substance. In the present work, different models based on chromatographic retention values for a large set of compounds and some of their molecular descriptors (calculated by ACD/Labs or CODESSA programs) have been examined in order to establish reliable equations for logPo/w determination from fast chromatographic hydrophobicity index (CHI) measurements. This appears to be a very interesting high-throughput methodology for screening purposes, since CHI values can be measured by UHPLC in very short runs (<4min) and molecular descriptors can be easily computed from the structure of any compound. The selected final descriptors were Abraham's hydrogen-bond acidity (A) and excess molar refraction (E) from ACD/Labs, and hydrogen-bond acidity HDCA-1/TMSA and HOMO-LUMO polarizability descriptors from CODESSA software. The proposed equations allow an accurate determination of logPo/w with standard errors in the range of 0.4 units.

Drug penetration across the blood-brain barrier: an overview

The brain is one of the most protected organs in the body. There are two key barriers that control the access of endogenous substances and xenobiotics (drugs or toxins) to the CNS. These physiological structures are the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier. The BBB represents the main determinant of the effective delivery of drugs to the CNS. Good access through the BBB is essential if the target site is located within the CNS or, in contrast, can be a disadvantage if adverse reactions occur at central level. The development of new drugs targeted to the CNS requires a better knowledge of the factors affecting BBB permeation as well as in vitro and in silico predictive tools to optimize screening, and to reduce the attrition rate at later stages of drug development. This review discusses the particular characteristics of the biology and physiology of the BBB with respect to the permeation and distribution of drugs into the brain. The factors affecting rate, extent and distribution into the brain are discussed and a brief description of the in silico, in vitro, in situ and in vivo methods used to measure BBB transport are presented. Finally, the lastest proposals and strategies to enhance transport across the BBB of new CNS drugs are summarized.

Database of published retention factors for immobilized artificial membrane HPLC and an assessment of the effect of experimental variability

A database was collated of published experimental logarithmic values for the relative retention factors (log k(IAM)) measured using an immobilized artificial membrane column and high-performance liquid chromatography (IAM HPLC). Log k(IAM) is an alternative measure of hydrophobicity to the octanol/water partition coefficient (log K(OW)). While there are several accepted methods to measure log K(OW), no standardized method exists to determine log k(IAM). The database of collated log k(IAM) values includes 13 key experimental parameters and contains 1,686 values for 555 compounds, which are predominantly polar organic compounds and include drug molecules and surfactants. These compounds are acidic, basic, and neutral and both ionized and un-ionized under the conditions of analysis. The data compiled demonstrated experimental variability for each experimental parameter considered, including column stationary phase, pH, temperature, and mobile phase. Reducing the experimental variability allowed for greater consistency in the datasets.