Christel A. S. Bergström

Identification of Novel Specific and General Inhibitors of the Three Major Human ATP-Binding Cassette Transporters P-gp, BCRP and MRP2 Among Registered Drugs

PurposeTo study the inhibition patterns of the three major human ABC transporters P-gp (ABCB1), BCRP (ABCG2) and MRP2 (ABCC2), using a dataset of 122 structurally diverse drugs.MethodsInhibition was investigated in cellular and vesicular systems over-expressing single transporters. Computational models discriminating either single or general inhibitors from non-inhibitors were developed using multivariate statistics.ResultsSpecific (n = 23) and overlapping (n = 19) inhibitors of the three ABC transporters were identified. GF120918 and Ko143 were verified to specifically inhibit P-gp/BCRP and BCRP in defined concentration intervals, whereas the MRP inhibitor MK571 was revealed to inhibit all three transporters within one log unit of concentration. Virtual docking experiments showed that MK571 binds to the ATP catalytic site, which could contribute to its multi-specific inhibition profile. A computational model predicting general ABC inhibition correctly classified 80% of both ABC transporter inhibitors and non-inhibitors in an external test set.ConclusionsThe inhibitor specificities of P-gp, BCRP and MRP2 were shown to be highly overlapping. General ABC inhibitors were more lipophilic and aromatic than specific inhibitors and non-inhibitors. The identified specific inhibitors can be used to delineate transport processes in complex experimental systems, whereas the multi-specific inhibitors are useful in primary ABC transporter screening in drug discovery settings.

Early pharmaceutical profiling to predict oral drug absorption: current status and unmet needs.

Preformulation measurements are used to estimate the fraction absorbed in vivo for orally administered compounds and thereby allow an early evaluation of the need for enabling formulations. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the pharmaceutical profiling methods available, with focus on in silico and in vitro models typically used to forecast active pharmaceutical ingredients (APIs) in vivo performance after oral administration. An overview of the composition of human, animal and simulated gastrointestinal (GI) fluids is provided and state-of-the art methodologies to study API properties impacting on oral absorption are reviewed. Assays performed during early development, i.e. physicochemical characterization, dissolution profiles under physiological conditions, permeability assays and the impact of excipients on these properties are discussed in detail and future demands on pharmaceutical profiling are identified. It is expected that innovative computational and experimental methods that better describe molecular processes involved in vivo during dissolution and absorption of APIs will be developed in the OrBiTo. These methods will provide early insights into successful pathways (medicinal chemistry or formulation strategy) and are anticipated to increase the number of new APIs with good oral absorption being discovered.

Structural requirements for drug inhibition of the liver specific human organic cation transport protein 1.

The liver-specific organic cation transport protein (OCT1; SLC22A1) transports several cationic drugs including the antidiabetic drug metformin and the anticancer agents oxaliplatin and imatinib. In this study, we explored the chemical space of registered oral drugs with the aim of studying the inhibition pattern of OCT1 and of developing predictive computational models of OCT1 inhibition. In total, 191 structurally diverse compounds were examined in HEK293-OCT1 cells. The assay identified 47 novel inhibitors and confirmed 15 previously known inhibitors. The enrichment of OCT1 inhibitors was seen in several drug classes including antidepressants. High lipophilicity and a positive net charge were found to be the key physicochemical properties for OCT1 inhibition, whereas a high molecular dipole moment and many hydrogen bonds were negatively correlated to OCT1 inhibition. The data were used to generate OPLS-DA models for OCT1 inhibitors; the final model correctly predicted 82% of the inhibitors and 88% of the noninhibitors of the test set.

Experimental and Computational Screening Models for Prediction of Aqueous Drug Solubility

AbstractPurpose. To devise experimental and computational models to predict aqueous drug solubility. Methods. A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. Results. The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/mL, covering a range of almost seven log10 units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R2of 0.91 and RMSEtr of 0.61) was devised. Conclusions. A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.

Prediction and identification of drug interactions with the human ATP-binding cassette transporter multidrug-resistance associated protein 2 (MRP2; ABCC2)

The chemical space of registered oral drugs was explored for inhibitors of the human multidrug-resistance associated protein 2 (MRP2; ABCC2), using a data set of 191 structurally diverse drugs and drug-like compounds. The data set included a new reference set of 75 compounds, for studies of hepatic drug interactions with transport proteins, CYP enzymes, and compounds associated with liver toxicity. The inhibition of MRP2-mediated transport of estradiol-17beta-D-glucuronide was studied in inverted membrane vesicles from Sf9 cells overexpressing human MRP2. A total of 27 previously unknown MRP2 inhibitors were identified, and the results indicate an overlapping but narrower inhibitor space for MRP2 compared with the two other major ABC efflux transporters P-gp (ABCB1) and BCRP (ABCG2). In addition, 13 compounds were shown to stimulate the transport of estradiol-17beta-D-glucuronide. The experimental results were used to develop a computational model able to discriminate inhibitors from noninhibitors according to their molecular structure, resulting in a predictive power of 86% for the training set and 72% for the test set. The inhibitors were in general larger and more lipophilic and presented a higher aromaticity than the noninhibitors. The developed computational model is applicable in an early stage of the drug discovery process and is proposed as a tool for prediction of MRP2-mediated hepatic drug interactions and toxicity.

A Global Drug Inhibition Pattern for the Human ATP-Binding Cassette Transporter Breast Cancer Resistance Protein (ABCG2)

In this article, we explore the entire structural space of registered drugs to obtain a global model for the inhibition of the drug efflux transporter breast cancer resistance protein (BCRP; ABCG2). For this purpose, the inhibitory effect of 123 structurally diverse drugs and drug-like compounds on mitoxantrone efflux was studied in Saos-2 cells transfected with human wild-type (Arg482) BCRP. The search for BCRP inhibitors throughout the drug-like chemical space resulted in the identification of 29 previously unknown inhibitors. The frequency of BCRP inhibition was 3 times higher for compounds reported to interact with other ATP-binding cassette (ABC) transporters than for compounds without reported ABC transporter affinity. An easily interpreted computational model capable of discriminating inhibitors from noninhibitors using only two molecular descriptors, octanol-water partition coefficient at pH 7.4 and molecular polarizability, was constructed. The discriminating power of this two-descriptor model was 93% for the training set and 79% for the test set, respectively. The results were supported by a global pharmacophore model and are in agreement with a two-step mechanism for the inhibition of BCRP, where both the drugs capacity to insert into the cell membrane and to interact with the inhibitory binding site of the transporter are important.

Early Identification of Clinically Relevant Drug Interactions With the Human Bile Salt Export Pump (BSEP/ABCB11)

A comprehensive analysis was performed to investigate how inhibition of the human bile salt export pump (BSEP/ABCB11) relates to clinically observed drug-induced liver injury (DILI). Inhibition of taurocholate (TA) transport was investigated in BSEP membrane vesicles for a data set of 250 compounds, and 86 BSEP inhibitors were identified. Structure-activity modeling identified BSEP inhibition to correlate strongly with compound lipophilicity, whereas positive molecular charge was associated with a lack of inhibition. All approved drugs in the data set (n = 182) were categorized according to DILI warnings in drug labels issued by the Food and Drug Administration, and a strong correlation between BSEP inhibition and DILI was identified. As many as 38 of the 61 identified BSEP inhibitors were associated with severe DILI, including 9 drugs not previously linked to BSEP inhibition. Further, among the tested compounds, every second drug associated with severe DILI was a BSEP inhibitor. Finally, sandwich-cultured human hepatocytes (SCHH) were used to investigate the relationship between BSEP inhibition, TA transport, and clinically observed DILI in detail. BSEP inhibitors associated with severe DILI greatly reduced the TA canalicular efflux, whereas BSEP inhibitors with less severe or no DILI resulted in weak or no reduction of TA efflux in SCHH. This distinction illustrates the usefulness of SCHH in refined analysis of BSEP inhibition. In conclusion, BSEP inhibition in membrane vesicles was found to correlate to DILI severity, and altered disposition of TA in SCHH was shown to separate BSEP inhibitors associated with severe DILI from those with no or mild DILI.

Prediction of ADMET Properties

This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

Molecular Descriptors Influencing Melting Point and Their Role in Classification of Solid Drugs.

The aim of the study was to investigate whether easily and rapidly calculated 2D and 3D molecular descriptors could predict the melting point of drug-like compounds, to allow a melting point classification of solid drugs. The melting points for 277 structurally diverse model drugs were extracted from the 12th edition of the Merck Index. 2D descriptors mainly representing electrotopology and electron accessibilities were calculated by Molconn-Z and the AstraZeneca in-house program Selma. 3D descriptors for molecular surface areas were generated using the programs MacroModel and Marea. Correlations between the calculated descriptors and the melting point values were established with partial least squares projection to latent structures (PLS) using training and test sets. Three different descriptor matrixes were studied, and the models obtained were used for consensus modeling. The calculated properties were shown to explain 63% of the melting point. Descriptors for hydrophilicity, polarity, partial atom charge, and molecular rigidity were found to be positively correlated with melting point, whereas nonpolar atoms and high flexibility within the molecule were negatively correlated to this solid-state characteristic. Moreover, the studied descriptors were successful in providing a qualitative ranking of compounds into classes displaying a low, intermediate, or high melting point. Finally, a mechanism for the relation between the molecular descriptors and their effect on the melting point and the aqueous solubility was proposed.

Global and Local Computational Models for Aqueous Solubility Prediction of Drug-Like Molecules

The aim of this study was to develop in silico protocols for the prediction of aqueous drug solubility. For this purpose, high quality solubility data of 85 drug-like compounds covering the total drug-like space as identified with the ChemGPS methodology were used. Two-dimensional molecular descriptors describing electron distribution, lipophilicity, flexibility, and size were calculated by Molconn-Z and Selma. Global minimum energy conformers were obtained by Monte Carlo simulations in MacroModel and three-dimensional descriptors of molecular surface area properties were calculated by Marea. PLS models were obtained by use of training and test sets. Both a global drug solubility model (R(2) = 0.80, RMSE(te) = 0.83) and subset specific models (after dividing the 85 compounds into acids, bases, ampholytes, and nonproteolytes) were generated. Furthermore, the final models were successful in predicting the solubility values of external test sets taken from the literature. The results showed that homologous series and subsets can be predicted with high accuracy from easily comprehensible models, whereas consensus modeling might be needed to predict the aqueous drug solubility of datasets with large structural diversity.