A general approach for the prediction of the intestinal absorption of drugs: regression analysis using the physicochemical properties and drug-membrane electrostatic interaction

The Whole Is Bigger than the Sum of Its Parts: Drug Transport in the Context of Two Membranes with Active Efflux

Cell envelope plays a dual role in the life of bacteria by simultaneously protecting it from a hostile environment and facilitating access to beneficial molecules. At the heart of this ability lie the restrictive properties of the cellular membrane augmented by efflux transporters, which preclude intracellular penetration of most molecules except with the help of specialized uptake mediators. Recently, kinetic properties of the cell envelope came into focus driven on one hand by the urgent need in new antibiotics and, on the other hand, by experimental and theoretical advances in studies of transmembrane transport. A notable result from these studies is the development of a kinetic formalism that integrates the Michaelis-Menten behavior of individual transporters with transmembrane diffusion and offers a quantitative basis for the analysis of intracellular penetration of bioactive compounds. This review surveys key experimental and computational approaches to the investigation of transport by individual translocators and in whole cells, summarizes key findings from these studies and outlines implications for antibiotic discovery. Special emphasis is placed on Gram-negative bacteria, whose envelope contains two separate membranes. This feature sets these organisms apart from Gram-positive bacteria and eukaryotic cells by providing them with full benefits of the synergy between slow transmembrane diffusion and active efflux.

Fluorescence studies on the interaction between chlorpromazine and model cell membranes

The fluorescence quenching of membrane fluorophores and the fluorescence enhancement of chlorpromazine were simultaneously observed during chlorpromazine–lipid membrane interaction.

Characterization of inclusion complexes of organic ions with hydrophilic hosts by ion transfer voltammetry with solvent polymeric membranes

The quantitative characterization of inclusion complexes formed in aqueous phase between organic ions and hydrophilic hosts by ion-transfer voltammetry with solvent polymeric membrane ion sensors is studied, both in a theoretical and experimental way. Simple analytical solutions are presented for the determination of the binding constant of the complex from the variation with the host concentration of the electrochemical signal. These solutions are valid for any voltammetric technique and for solvent polymeric membrane ion sensors comprising one polarisable interface (1PI) and also, for the first time, two polarisable interfaces (2PIs). Suitable experimental conditions and data analysis procedures are discussed and applied to the study of the interactions of a common ionic liquid cation (1-octyl-3-metyl-imidazolium) and an ionisable drug (clomipramine) with two hydrophilic cyclodextrins: α-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin. The experimental study is performed via square wave voltammetry with 2PIs and 1PI solvent polymeric membranes and in both cases the electrochemical experiments enable the detection of inclusion complexes and the determination of the corresponding binding constant.

Physicochemical properties of polymers: An important system to overcome the cell barriers in gene transfection

Delivery of the macromolecules including DNA, miRNA, and antisense oligonucleotides is typically mediated by carriers due to the large size and negative charge. Different physical (e.g., gene gun or electroporation), and chemical (e.g., cationic polymer or lipid) vectors have been already used to improve the efficiency of gene transfer. Polymer-based DNA delivery systems have attracted special interest, in particular via intravenous injection with many intra- and extracellular barriers. The recent progress has shown that stimuli-responsive polymers entitled as multifunctional nucleic acid vehicles can act to target specific cells. These nonviral carriers are classified by the type of stimulus including reduction potential, pH, and temperature. Generally, the physicochemical characterization of DNA-polymer complexes is critical to enhance the transfection potency via protection of DNA from nuclease digestion, endosomal escape, and nuclear localization. The successful clinical applications will depend on an exact insight of barriers in gene delivery and development of carriers overcoming these barriers. Consequently, improvement of novel cationic polymers with low toxicity and effective for biomedical use has attracted a great attention in gene therapy. This article summarizes the main physicochemical and biological properties of polyplexes describing their gene transfection behavior, in vitro and in vivo. In this line, the relative efficiencies of various cationic polymers are compared.

Carrier Mediated Distribution System (CAMDIS): a new approach for the measurement of octanol/water distribution coefficients

Here we present a miniaturized assay, referred to as Carrier-Mediated Distribution System (CAMDIS) for fast and reliable measurement of octanol/water distribution coefficients, log D(oct). By introducing a filter support for octanol, phase separation from water is facilitated and the tendency of emulsion formation (emulsification) at the interface is reduced. A guideline for the best practice of CAMDIS is given, describing a strategy to manage drug adsorption at the filter-supported octanol/buffer interface. We validated the assay on a set of 52 structurally diverse drugs with known shake flask log D(oct) values. Excellent agreement with literature data (r(2) = 0.996, standard error of estimate, SEE = 0.111), high reproducibility (standard deviation, SD < 0.1 log D(oct) units), minimal sample consumption (10 μL of 100 μM DMSO stock solution) and a broad analytical range (log D(oct) range = -0.5 to 4.2) make CAMDIS a valuable tool for the high-throughput assessment of log D(oc)t.

Polyethylenimine-capped Ag nanoparticle film as a platform for detecting charged dye molecules by surface-enhanced Raman scattering and metal-enhanced fluorescence

Many drugs are charged molecules and are weak bases or acids having counterions. Their binding to biological surfaces is generally difficult to assess by vibrational spectroscopy. In this work, we demonstrated the potential of surface-enhanced Raman scattering (SERS) conducted using a polyethylenimine (PEI)-capped Ag nanoparticle film for the quantification of an electrostatic adsorption process of charged drug molecules, by using charged dye molecules such as sulforhodamine B (SRB) and rhodamine-123 (R123) as model drugs. It was possible to detect small-sized anions such as SCN(-) at 1 × 10(-9) M by SERS because of the cationic property of PEI. We were subsequently able to detect a prototype anionic dye molecule, SRB, by SERS at a subnanomolar concentration. On the other hand, it was difficult to detect cationic dyes such as R123 because of the electrostatically repulsive interaction with PEI. Nonetheless, we found that even R123 could be detected at subnanomolar concentrations by SERS by depositing an anionic polyelectrolyte such as poly(sodium 4-styrenesulfonate) (PSS) and poly(acrylic acid) (PAA) onto the PEI-capped Ag nanoparticles. Another noteworthy point is that a subnanomolar detection limit can also be achieved by carefully monitoring the fluorescence background in the measured SERS spectra. This was possible because charged dyes were not in contact with Ag but formed ion pairs with either PEI or PSS (PAA), allowing metal-enhanced fluorescence (MEF). The PEI-capped Ag nanoparticle film can thus serve as a useful indicator to detect charged drug molecules by SERS and MEF.

Effects of bile salts on propranolol distribution into liposomes studied by capillary electrophoresis

The objective of this study was to study the effect of four different bile salts, cholate (C), deoxycholate (DC), taurocholate (TC), monoketocholate (MKC), on the membrane binding of a cationic model drug, propranolol, using capillary electrophoresis. The apparent distribution coefficient of propranolol in a buffer/liposome system, in the absence and presence of various concentrations of the bile salts, was measured using capillary electrophoresis frontal analysis. At bile salt concentrations which did not disrupt the liposomes, the bile salts increased the apparent distribution coefficient of propranolol in a concentration-dependent manner, to various extents (DC>C>TC>MKC). The mechanisms for these increases were inferred from studies of ion pairing between bile salts and propranolol using mobility shift affinity capillary electrophoresis and from zeta potential measurements. The bile salts ion-paired with propranolol to different extents as indicated by the estimated complexation constants (K range: 30-58 M(-1)). This was found to have a minor effect on the membrane distribution of propranolol only. The major effect is proposed to be due to the insertion of bile salt into the liposomal membranes leading to a more negatively charged membrane surface thereby providing stronger electrostatic interactions with the positively charged propranolol.

Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices

The in vitro determination of the permeability through cultured Caco-2 cells is the most often-used in vitro model for drug absorption. In this report, we use the largest data set of measured P(Caco-2), consisting of 157 structurally diverse compounds. Linear discriminant analysis (LDA) was used to obtain quantitative models that discriminate higher absorption compounds from those with moderate-poorer absorption. The best LDA model has an accuracy of 90.58% and 84.21% for training and test set. The percentage of good correlation, in the virtual screening of 241 drugs with the reported values of the percentage of human intestinal absorption (HIA), was greater than 81%. In addition, multiple linear regression models were developed to predict Caco-2 permeability with determination coefficients of 0.71 and 0.72. Our method compares favorably with other approaches implemented in the Dragon software, as well as other methods from the international literature. These results suggest that the proposed method is a good tool for studying the oral absorption of drug candidates.

A Cheminformatic Toolkit for Mining Biomedical Knowledge

PURPOSE

Cheminformatics can be broadly defined to encompass any activity related to the application of information technology to the study of properties, effects and uses of chemical agents. One of the most important current challenges in cheminformatics is to allow researchers to search databases of biomedical knowledge, using chemical structures as input.

MATERIALS AND METHODS

An important step towards this goal was the establishment of PubChem, an open, centralized database of small molecules accessible through the World Wide Web. While PubChem is primarily intended to serve as a repository for high throughput screening data from federally-funded screening centers and academic research laboratories, the major impact of PubChem could also reside in its ability to serve as a chemical gateway to biomedical databases such as PubMed.

CONCLUSION

This article will review cheminformatic tools that can be applied to facilitate annotation of PubChem through links to the scientific literature; to integrate PubChem with transcriptomic, proteomic, and metabolomic datasets; to incorporate results of numerical simulations of physiological systems into PubChem annotation; and ultimately, to translate data of chemical genomics screening efforts into information that will benefit biomedical researchers and physician scientists across all therapeutic areas.

Equation chapter 1 section 1A new method for predicting human hepatic clearance fromin Vitro experimental data using molecular descriptors

The present study demonstrated that the information of molecular descriptors of drugs increases the accuracy of predicting human in vivo hepatic clearance from in vitro experimental data in humans and rats. A new method uses not only the experimental data but also the information of molecular descriptors. Predictions for the datasets from hepatocyte experiments and microsome experiments were made by the present method, and the prediction accuracy was compared with those of the previous methods, such as methods using in vitro-in vivo scaling factor and multiple linear regression analysis, that use only the experimental data. Results showed that the present method was the most accurate prediction model with the lowest prediction errors and the strongest correlations. These results suggest that the information of molecular descriptors is significant for predicting the human in vivo pharmacokinetic parameters from in vitro experimental data. This study also demonstrated that in vitro experimental data in humans and rats were important information for predicting human in vivo hepatic clearance, and the additional rat in vivo data were not significant for prediction with the information of molecular descriptors. These results imply that the present method can be useful for high-throughput drug candidate screening by reducing the time and cost in the early stage of the drug discovery process.

Application of data mining approaches to drug delivery

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Computational approaches for modeling human intestinal absorption and permeability

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.

Quantifying hydrogen bonding in QSAR and molecular modeling

Quantitative descriptions of hydrogen bonding for use in QSAR and molecular modeling by means of H-bond descriptors have been analyzed in detail in this paper. Ten new H-bond surface and enthalpy integral descriptors were proposed. The usefulness of these new descriptors, as well as previously developed descriptors was verified using a set of 154 drugs for which data for intestinal absorption in humans were available. The results showed that descriptors such as the number of H-bond acceptor and donor atoms and polar surface area (PSA) did not sufficiently describe the actual H-bonding ability of atoms in molecules. Thus, to enable successful modeling it was necessary to introduce descriptors directly related to the experimental thermodynamics of hydrogen bonding.

In silico approaches for predicting ADME properties of drugs

Combinatorial chemistry and high-throughput screening have increased the possibility of finding new lead compounds at much shorter time periods than conventional medicinal chemistry. However, too much promising drug candidates often fail because of unsatisfactory ADME properties. In silico ADME studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, many in silico approaches for predicting ADME properties of compounds from their chemical structure have been developed, ranging from data-based approaches such as quantitative structure-activity relationship (QSAR), similarity searches, and 3-dimensional QSAR, to structure-based methods such as ligand-protein docking and pharmacophore modelling. In addition, several methods of integrating ADME properties to predict pharmacokinetics at the organ or body level have been studied. In this article, we briefly summarize in silico ADME approaches.

Immobilized artificial membrane chromatography: quantitative structure-retention relationships of structurally diverse drugs

The chromatographic capacity factors (log k') for 32 structurally diverse drugs were determined by high performance liquid chromatography (HPLC) on a stationary phase composed of phospholipids, the so-called immobilized artificial membrane (IAM). In addition, quantitative structure-retention relationships (QSRR) were developed in order to explain the dependence of retention on the chemical structure of the neutral, acidic, and basic drugs considered in this study. The obtained retention data were modeled by means of multiple regression analysis (MLR) and partial least squares (PLS) techniques. The structures of the compounds under study were characterized by means of calculated physicochemical properties and several nonempirical descriptors. For the carboxylic compounds included in the analysis, the obtained results suggest that the IAM-retention is governed by hydrophobicity factors followed by electronic effects due to polarizability in second place. Further, from the analysis of the results obtained of two developed quantitative structure-permeability studies for 20 miscellaneous carboxylic compounds, it may be concluded that the balance between polarizability and hydrophobic effects is not the same toward IAM phases and biological membranes. These results suggest that the IAM phases could not be a suitable model in assessing the acid-membrane interactions. However, it is not possible to generalize this observation, and further work in this area needs to be done to obtain a full understanding of the partitioning of carboxylic compounds in biological membranes. For the non-carboxylic compounds included in the analysis, this work shows that the hydrophobic factors are of prime importance for the IAM-retention of these compounds, while the specific polar interactions, such as electron pair donor-acceptor interactions and electrostatic interactions, are also involved, but they are not dominant.

Drug permeation in biomembranes

In the past decades, it has become increasingly apparent that in addition to therapeutic effect, drugs need to exhibit favourable absorption, distribution, metabolism and excretion (ADME) characteristics to produce a desirable response in vivo. As the recent progress in drug discovery technology enables rapid synthesis of vast numbers of potential drug candidates, robust methods are required for the effective screening of compounds synthesized within such programs, so that compounds with poor pharmacokinetic properties can be rejected at an early stage of drug development. Furthermore, a viable in silico method would save resources by enabling virtual screening of drug candidates already prior to synthesis. This review gives a general overview of the approaches aimed at predicting biological permeation, one of the cornerstones behind the ADME behaviour of drugs. The most important experimental and computational models are reviewed. Physicochemical factors underlying the permeation process are discussed.

Biopartitioning micellar separation methods: modelling drug absorption

The search for new pharmacologically active compounds in drug discovery programmes often neglects biopharmaceutical properties as drug absorption. As a result, poor biopharmaceutical characteristics constitute a major reason for the low success rate for candidates in clinical development. Since the cost of drug development is many times larger than the cost of drug discovery, predictive methodologies aiding the selection of bioavailable drug candidates are of profound significance. This paper has been focussed on recent developments and applications of chromatographic systems, particularly those systems based on amphiphilic structures, in the frame of alternative approaches for estimating the transport properties of new drugs. The aim of this review is to take a critical look at the separations methods proposed for describing and predicting drug passive permeability across gastrointestinal tract and the skin.

Physicochemical and physiological properties of 5alpha-cyprinol sulfate, the toxic bile salt of cyprinid fish

5alpha-Cyprinol sulfate was isolated from bile of the Asiatic carp, Cyprinus carpio. 5alpha-Cyprinol sulfate was surface active and formed micelles; its critical micellization concentration (CMC) in 0.15 M Na+ using the maximum bubble pressure device was 1.5 mM; by dye solubilization, its CMC was approximately 4 mM. At concentrations >1 mM, 5alpha-cyprinol sulfate solubilized monooleylglycerol efficiently (2.1 molecules per mol micellar bile salt). When infused intravenously into the anesthetized rat, 5alpha-cyprinol sulfate was hemolytic, cholestatic, and toxic. In the isolated rat liver, it underwent little biotransformation and was poorly transported (Tmax congruent with 0.5 micromol/min/kg) as compared with taurocholate. 5alpha-Cyprinol, its bile alcohol moiety, was oxidized to its corresponding C27 bile acid and to allocholic acid (the latter was then conjugated with taurine); these metabolites were efficiently transported. 5alpha-Cyprinol sulfate inhibited taurocholate uptake in COS-7 cells transfected with rat asbt, the apical bile salt transporter of the ileal enterocyte. 5alpha-Cyprinol had limited aqueous solubility (0.3 mM) and was poorly absorbed from the perfused rat jejunum or ileum. Sampling of carp intestinal content indicated that 5alpha-cyprinol sulfate was present at micellar concentrations, and that it did not undergo hydrolysis during intestinal transit. These studies indicate that 5alpha-cyprinol sulfate is an excellent digestive detergent and suggest that a micellar phase is present during digestion in cyprinid fish.

Evaluation of rat intestinal absorption data and correlation with human intestinal absorption

The absorption of 111 drug and drug-like compounds was evaluated from 111 references based on the ratio of urinary excretion of drugs following oral and intravenous administration to intact rats and biliary excretion of bile duct-cannulated rats. Ninety-eight drug compounds for which both human and rat absorption data were available were selected for correlation analysis between the human and rat absorption. The result shows that the extent of absorption in these two species is similar. For 94% of the drugs the absorption difference between humans and rats is less than 20% and for 98% of drugs the difference is less than 30%. There is only one drug for which human absorption is significantly different from rat absorption. The standard deviation is 11% between human and rat absorption. The linear relationship between human and rat absorption forced through the origin, as determined by least squares regression, is %Absorption (human)=0.997%Absorption (rat) (n=98, SD=11). It is suggested that the absorption in rats could be used as an alternative method to human absorption in pre-clinical oral absorption studies.

Inhibitory Effects of Basic Drugs on the Sodium-Dependent Transport of L-Alanine via System B0 in the Small Intestine

To elucidate the mechanism of the interaction of basic drugs with Na(+)-dependent L-alanine absorption from the small intestine, we investigated the effect of imipramine on the transport of L-alanine via system B(0), which is thought to be one of the main Na(+)-dependent systems for intestinal absorption of short-chain neutral amino acids, including L-alanine. The uptake of L-alanine by cells that express hATB(0) (human amino acid transporter B(0)) was inhibited in the presence of imipramine. The Eadie-Hofstee plot showed that the inhibition was not competitive with the substrate (L-alanine) but competitive with Na(+). When rat intestinal brush border membrane vesicles were used, several basic drugs had inhibitory effects. Inhibition was also observed in intestinal absorption evaluated by an in situ single-pass perfusion technique. However, the potencies of inhibition were different. The potency of inhibition was dependent on the lipophilicity of the drugs.

Interaction of ochratoxin A with human serum albumin. Binding sites localized by competitive interactions with the native protein and its recombinant fragments

Competitive interactions of ochratoxin A (OTA) and several other acidic compounds were utilized to gain insight into the localization of binding sites and the nature of binding interactions between anionic species and human serum albumin (HSA). Depolarization of OTA fluorescence in the presence of a competing anion was used to quantify ligand-protein interactions. The results obtained were rationalized in terms of OTA displacement from its major binding site. Based on their ability to displace OTA, two distinct groups of the anionic ligands were revealed. The first group contained structurally diverse compounds that shared a common binding site in subdomain IIA (Sudlow Site I). The second group consisted of three non-steroidal anti-inflammatory drugs, which showed much lower affinity to Site I than the OTA dianion. The major site for these drugs was located in domain III. Fluorescence spectroscopy measurements of OTA, warfarin (WAR) and naproxen (NAP) complexes with recombinant proteins corresponding to the domains of HSA (D1-D3) revealed binding to all domains but with different affinities. The binding constants for OTA and WAR decreased in the series D2z.Gt;D3>D1. In contrast, NAP showed the most favorable interaction with D3 and comparable affinities to the two remaining domains. The OTA binding constant for D2, 7.9 x 10(5) M(-1), was smaller than the largest constant for HSA by a factor of approximately 7. The binding constant for OTA with D3, 1.1 x 10(5) M(-1), was very close to that of the secondary binding site for HSA.

Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method

Caco-2 cell monolayers are widely used systems for predicting human intestinal absorption. This study was carried out to develop a quantitative structure-property relationship (QSPR) model of Caco-2 permeability using a novel genetic algorithm-based partial least squares (GA-PLS) method. The Caco-2 permeability data for 73 compounds were taken from the literature. Molconn-Z descriptors of these compounds were calculated as molecular descriptors, and the optimal subset of the descriptors was explored by GA-PLS analysis. A fitness function considering both goodness-of-fit to the training data and predictability of the testing data was adopted throughout the genetic algorithm-driven optimization procedure. The final PLS model consisting of 24 descriptors gave a correlation coefficient (r) of 0.886 for the entire dataset and a predictive correlation coefficient (r(pred)) of 0.825 that was evaluated by a leave-some-out cross-validation procedure. Thus, the GA-PLS analysis proved to be a reasonable QSPR modeling approach for predicting Caco-2 permeability.

On the mechanism of human intestinal absorption

In order to investigate whether the main step in intestinal absorption in humans is dominated by partition or by diffusion, we have transformed % human intestinal absorption into a first-order rate constant, and have regressed the latter, as logk, against our solvation parameters. The obtained regression coefficients are compared with those for diffusion and partition processes. The coefficients in the logk equation are completely different to those for water/solvent partitions, but are very similar to those for processes (not involving transport through membranes) in which diffusion is the major step. It is suggested that the main step in the absorption process is diffusion through a stagnant mucus layer, together with transfer across the mucusmid R:membrane interface. It is further shown that for strong Bronsted acids and bases, the rate constant for absorption of ionic species is close to that for absorption of the corresponding neutral species, so that to a first approximation the % intestinal absorption can be calculated from properties of the neutral species.

Molecular properties that influence the oral bioavailability of drug candidates

Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two criteria of (1) 10 or fewer rotatable bonds and (2) polar surface area equal to or less than 140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a high probability of good oral bioavailability in the rat. Data sets for the artificial membrane permeation rate and for clearance in the rat were also examined. Reduced polar surface area correlates better with increased permeation rate than does lipophilicity (C log P), and increased rotatable bond count has a negative effect on the permeation rate. A threshold permeation rate is a prerequisite of oral bioavailability. The rotatable bond count does not correlate with the data examined here for the in vivo clearance rate in the rat.

A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential

Artificial membrane permeability measurement is a potentially high throughput and low cost alternative for in vitro assessment of drug absorption potential. It will be an ideal screening/profiling tool in the lead generation program of drug discovery research if it is proven to be generally applicable for classifying drug absorption potential and is advantageous over other in vitro or in silico methods. This study provides an in-depth evaluation of the method in close comparison to Caco-2, LogD, LogP, polar surface area (PSA), and quantitative structure-property relationship (QSPR) predictions using a large and diverse compound set. It showed that the accuracy of using artificial membrane permeability in assessing drug absorption is comparable to Caco-2, but significantly better than LogP, LogD, PSA, and QSPR predictions. This study also explored the artificial membrane composition by adopting a hydrophilic filter membrane for artificial membrane (lecithin-dodecane) support. The use of hydrophilic filter membrane increased the rate of permeation significantly and reduced the transport time to 2 h or less as compared with over 10 h when a hydrophobic filter membrane is used.

Three-dimensional quantitative structure-permeability relationship analysis for a series of inhibitors of rhinovirus replication

Multiple three-dimensional quantitative structure-activity relationship (3D-QSAR) approaches were applied to predicting passive Caco-2 permeability for a series of 28 inhibitors of rhinovirus replication. Catalyst, genetic function approximation (GFA) with MS-WHIM descriptors, CoMFA, and VolSurf were all used for generating 3D-quantitative structure permeability relationships utilizing a training set of 19 molecules. Each of these approaches was then compared using a test set of nine molecules not present in the training set. Statistical parameters for the test set predictions (r(2) and leave-one-out q(2)) were used to compare the models. It was found that the Catalyst pharmacophore model was the most predictive (test set of predicted versus observed permeability, r(2) = 0.94). This model consisted of a hydrogen bond acceptor, hydrogen bond donor, and ring aromatic feature with a training set correlation of r(2) = 0.83. The CoMFA model consisted of three components with an r(2) value of 0.96 and produced good predictions for the test set (r(2) = 0.84). VolSurf resulted in an r(2) value of 0.76 and good predictions for the test set (r(2) = 0.83). Test set predictions with GFA/WHIM descriptors (r(2) = 0.46) were inferior when compared with the Catalyst, CoMFA, and VolSurf model predictions in this evaluation. In summary it would appear that the 3D techniques have considerable value in predicting passive permeability for a congeneric series of molecules, representing a valuable asset for drug discovery.

Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure–activity relationship (QSAR) with the Abraham descriptors

The human intestinal absorption of 241 drugs was evaluated. Three main methods were used to determine the human intestinal absorption: bioavailability, percentage of urinary excretion of drug-related material following oral administration, and the ratio of cumulative urinary excretion of drug-related material following oral and intravenous administration. The general solvation equation developed by Abraham's group was used to model the human intestinal absorption data of 169 drugs we considered to have reliable data. The model contains five Abraham descriptors calculated by the ABSOLV program. The results show that Abraham descriptors can successfully predict human intestinal absorption if the human absorption data is carefully classified based on solubility and administration dose to humans.

Acyl substitution at the ortho position of anilides enhances oral bioavailability of thiophene sulfonamides: TBC3214, an ETA selective endothelin antagonist

Sitaxsentan (3, TBC11251) (Wu et al. J. Med. Chem. 1997, 40, 1690) is an orally active ET(A) selective endothelin antagonist that attenuates pulmonary vascular hypertension and cardiac hypertrophy in rats (Tilton et al. Pulm. Pharmacol. Ther. 2000, 13, 87). It has demonstrated efficacy in a phase II clinical trial for congestive heart failure (Givertz et al. Circulation 2000, 101, 2922). During the discovery of 3, we observed several structure-oral bioavailability relationships. To investigate whether there is any generality in these trends, we synthesized some similar pairs of compounds in the latest series (Wu et al. J. Med. Chem. 1999, 42, 4485) and evaluated their oral properties. In both series, an acyl group at the 2-position of the anilide of these thiophene sulfonamides improved oral bioavailability. As a result of this exercise, TBC3214 (17) was identified as a sitaxsentan follow-on candidate. It is very potent (IC(50) for ET(A) = 40 pM) and highly selective for ET(A) vs ET(B) receptors (400 000-fold), with a half-life of >4 h and oral bioavailability of 25% in rats, 42% in cats, and 70% in dogs.

Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

Caco-2 cell permeability vs human gastro-intestinal absorption: QSPR analysis

The aim of this study is to elucidate quantitative structure-permeability relationship (QSPR) of various organic molecules through Caco-2 cells, and to ascertain the relationship between gastrointestinal (GI) absorption in humans and Caco-2 cell permeability. Caco-2 cell permeability and human GI absorption data were obtained from the literature. The maximum hydrogen bond-forming capacity corrected for intra-molecular H-bonding (Hbc) and Lien's QSAR model were used in this study. The latest CQSAR software was utilized in calculating the logarithm of partition coefficient in octanol/water (Clog P) and in deriving all regression equations. For 51 compounds, a significant correlation was obtained between Caco-2 cell permeability (log Pcaco-2) and Hbc, octanol/PBS (phosphate buffered saline, pH 7.4) distribution coefficient (log Doct), log MW and an indicator variable (I) for the charge, with a correlation coefficient of 0.797. When these compounds were divided into three subgroups, namely neutral, cationic and anionic compounds, much better correlations (r = 0.968, 0.915 and 0.931, respectively) were obtained using different combinations of various physico-chemical parameters. A plot of human GI absorption vs. Caco-2 cell permeability obtained from different laboratories reveals that Caco-2 cell permeability cannot be used to precisely predict human GI absorption for compounds with Pcaco-2 below 5 x 10(-6) cm/s, due to interlaboratory and experimental variabilities, and the lack of a simple correlation between human GI absorption and Caco-2 cell permeability. Caco-2 cell permeability may be estimated from the structures of drug molecules using the above-mentioned physicochemical parameters. In general, for compounds with Pcaco-2 above 5 x 10(-6) cm/s, human GI absorption ranges from 50 to 100%. This is generally acceptable for development into oral dosage form. For the compounds with Pcaco-2 below 5 x 10(-6) cm/s, careful interpretation of caco-2 cell permeability and use of internal standard for comparison are recommended. Otherwise, good drug candidates may be excluded due to incorrectly predicted poor absorption.

Absorption prediction from physicochemical parameters

Modern drug design not only focuses on the pharmacological activity of a compound but also considers its ability to be absorbed and to reach its site of action. The prediction of in vivo barrier permeabilities, particularly intestinal absorption and blood-brain barrier passage, are substantial concerns in the development of new drug compounds. For several decades, n-octanol-water partition coefficients dominated absorption prediction. In recent years, the basic physicochemical parameters describing both membrane permeability and lipophilicity have been established. This review provides an outline of some selected absorption-prediction models with emphasis on intestinal absorption.

Recognizing molecules with drug-like properties

A variety of successful approaches to the problem of recognizing 'drug-like' molecules have been employed. These range from simple counting schemes such as the Lipinski 'rule of five' to the analysis of the multidimensional 'chemistry space' occupied by drugs, to neural network learning systems. With this variety of tools, it now appears possible to design libraries that are enriched in compounds which have desirable or 'drug-like' properties. Verifying the robustness of these methods, and extending them, will form the basis of research in this field during the next few years.

Pharmacokinetics and metabolism in early drug discovery

The need for high-throughput approaches in absorption, distribution, metabolism and excretion studies is driven by the impact of high-speed chemistry and pharmacological screening. Perhaps an even greater impact is that these studies will, in the future, provide large data sets that can be used to predict biological events related to absorption, bioavailability and metabolism of drugs. Through linking of in silico and in vitro methods, considerable progress has recently been made towards this future perspective. Despite this progress, these approaches do not yet replace in vivo methods.