Ionization-specific analysis of human intestinal absorption

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

A corneal-PAMPA-based in silico model for predicting corneal permeability

The capability to predict corneal permeability based on physicochemical parameters has always been a desirable objective of ophthalmic drug development. However, previous work has been limited to cases where either the diversity of compounds used was lacking or the performance of the models was poor. Our study provides extensive quantitative structure-property relationship (QSPR) models for corneal permeability predictions. The models involved in vitro corneal permeability measurements of 189 diverse compounds. Preliminary analysis of data showed that there is no significant correlation between corneal-PAMPA (Parallel Artificial Membrane Permeability Assay) permeability values and other pharmacokinetically relevant in silico drug transport parameters like Caco-2, jejunal permeability and blood-brain partition coefficient (logBB). Two different QSPR models were developed: one for corneal permeability and one for corneal membrane retention, based on experimental corneal-PAMPA permeability data. Partial least squares regression was applied for producing the models, which contained classical molecular descriptors and ECFP fingerprints in combination. A complex validation protocol (including internal and external validation) was carried out to provide robust and appropriate predictions for the permeability and membrane retention values. Both models had an overall fit of R² > 0.90, including R²-values not lower than 0.85 for validation runs, and provide quick and accurate predictions of corneal permeability values for a diverse set of compounds.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Biopartitioning micellar chromatography under different conditions: Insight into the retention mechanism and the potential to model biological processes

In the present study, 88 structurally- diverse drugs were investigated by biopartitioning micellar chromatography (BMC) using Brij-35 as surfactant under different chromatographic conditions. It was found that temperature and presence of NaCl have only a minor effect in BMC retention. Correlation of BMC retention factors with octanol-water partitioning required the inclusion of fractions of ionized species as additional parameters, showing that there is a weaker effect of ionization in BMC environment. Compared to Immobilized Artificial Membrane (IAM) Chromatography, BMC retention factors cover a relatively narrow span, two-fold smaller than retention factors on IAM stationary phases as a result of the presence of micelles facilitating elution of lipophilic compounds and the absence of secondary attractive electrostatic interactions in the BMC environment. Similarities/dissimilarities between BMC, octanol-water partitioning and IAM Chromatography were investigated by Linear Free Energy Relationships (LSER). BMC retention factors were used to construct relationships with cell permeability,% Human Oral Absorption (%HOA) and Plasma Protein Binding (%PPB). Linear BMC models were obtained with Caco-2 cell lines and Parallel Artificial Membrane Permeability Assay (PAMPA). For %HOA, a hyperbolic model was established upon incorporation of topological polar surface area (tPSA) as additional parameter. A sigmoidal model was constructed for %PPB and a linear one for the corresponding thermodynamic binding constant logK. In both cases inclusion of the fraction of anionic species with a positive sign was required reflecting the preference of human albumin for acidic drugs.

Physicochemical QSAR Analysis of Passive Permeability Across Caco-2 Monolayers

Caco-2 cell line is frequently used as a simplified in vitro model of intestinal absorption. In this study, a database of 1366 Caco-2 permeability coefficients (P_e) for 768 diverse drugs and drug-like compounds was compiled from public sources. The collected data represent permeation rates measured at varying experimental conditions (pH from 4.0 to 8.0, and stirring rates from 0 to >1000 rpm) that presumably account for passive diffusion across mucosal epithelium. These data were subjected to multistep nonlinear regression analysis using a minimal set of physicochemical descriptors (octanol-water log D, pKa, hydrogen bonding potential, and molecular size). The model was constructed in a mechanistic manner incorporating the following components: (i) a hydrodynamic equation of size- and charge-specific along with nonspecific diffusion across the paracellular pathway; (ii) transcellular diffusion represented by thermodynamic membrane/water partitioning ratio; (iii) stirring-dependent limit of maximum achievable permeability due to the presence of unstirred water layer. The obtained model demonstrates good accuracy of log P_e predictions with a residual mean square error <0.5 log units for all training and validation sets. Given its robust performance and straightforward interpretation in terms of simple physicochemical properties, the proposed model may serve as a valuable tool to guide drug discovery efforts toward readily absorbable compounds.

Formation of a Bile Salt-Drug Hydrogel to Predict Human Intestinal Absorption

The unique character of bile salts to self-assemble into hydrogels in the presence of halide salts was exploited in this work to facilitate the prediction of human intestinal absorption (%HIA) for a set of 25 compounds. This was achieved by firstly incorporating each compound separately within the process of gel formation to create a series of gel-drug membranes. Scanning electron microscopy analysis of the freeze-dried samples of the blank bile salt hydrogels and drug-loaded bile salt hydrogels indicated a unique microstructure made of a network of intertwined fibrils. Drug-loaded sodium deoxycholate hydrogels were then utilized as the donor phase to study permeability using flow-through and static diffusion cells. The resulting values of the release-permeability coefficient (K_p) were then analyzed, along with other molecular descriptors, for the %HIA using multiple linear regression. Overall, when comparing predicted values (using the systems presented in this study) with known literature values, it can be seen that both methods (i.e., using static and flow-through cells) had good predictability with R²_PRED values of 79.8% and 79.7%, respectively. This study therefore proposes a novel, accurate, and precise way to predict HIA for compounds of pharmaceutical interest using a simple in vitro permeation system. It is important to develop alternatives to the current methods used in prediction of HIA, which are expensive and time-consuming or include the use of animals. Therefore, the proposed method in this study being economic and time-saving provides superiority over these current methods and suggests the possibility of its use as an alternate to such methods for prediction of HIA.

Daily uptake of mycotoxins - TDI might not be protective for nursed infants

Exclusive breast feeding is recommended by international bodies for the first six months of life. Because of the presence of contaminants, breast feeding might lead to toxicologically relevant exposure of the nursed child. Exposure towards mycotoxins is of specific interest because of their widespread occurrence in food and of their toxicological profile. We calculated the relationship between maternal intake at the level of the existing TDIs and the exposure in the nursed infants of several mycotoxins to evaluate whether maternal exposure at the TDI is also safe for the nursed infant. If published information was not available we used in silico methods for estimating toxicokinetic parameters and the lactational transfer. A single dose and a continuous daily intake scenario were considered. Maternal intake at the TDI exceeds the age-adjusted TDI (TDI/3) values for infants in case of deoxynivalenol and patulin in the single dose scenario. Exceedance is particularly pronounced for ochratoxin A in the continuous daily intake scenario (29.2 fold above the child adjusted TDI). According to published data in infants impaired kidney function may result from this exceedance. When setting a TDI, the safety of the exclusively nursed infant should be considered in the continuous daily intake scenario.

Synthesis, Biological Activity and Preliminary in Silico ADMET Screening of Polyamine Conjugates with Bicyclic Systems

Polyamine conjugates with bicyclic terminal groups including quinazoline, naphthalene, quinoline, coumarine and indole have been obtained and their cytotoxic activity against PC-3, DU-145 and MCF-7 cell lines was evaluated in vitro. Their antiproliferative potential differed markedly and depended on both their chemical structure and the type of cancer cell line. Noncovalent DNA-binding properties of the most active compounds have been examined using ds-DNA thermal melting studies and topo I activity assay. The promising biological activity, DNA intercalative binding mode and favorable drug-like properties of bis(naphthalene-2-carboxamides) make them a good lead for further development of potential anticancer drugs.

Synergistic antibacterial effects of herbal extracts and antibiotics on methicillin-resistant Staphylococcus aureus: A computational and experimental study

Antibiotic resistance has become a serious global concern, and the discovery of antimicrobial herbal constituents may provide valuable solutions to overcome the problem. In this study, the effects of therapies combining antibiotics and four medicinal herbs on methicillin-resistant Staphylococcus aureus (MRSA) were investigated. Specifically, the synergistic effects of Magnolia officinalis, Verbena officinalis, Momordica charantia, and Daphne genkwa in combination with oxacillin or gentamicin against methicillin-resistant (ATCC43300) and methicillin-susceptible (ATCC25923) S. aureus were examined. In vitro susceptibility and synergistic testing were performed to measure the minimum inhibitory concentration and fractional inhibitory concentration (FIC) index of the antibiotics and medicinal herbs against MRSA and methicillin-susceptible S. aureus. To identify the active constituents in producing these synergistic effects, in silico molecular docking was used to investigate the binding affinities of 139 constituents of the four herbs to the two common MRSA inhibitory targets, penicillin binding proteins 2a (PBP2a) and 4 (PBP4). The physicochemical and absorption, distribution, metabolism, and excretion properties and drug safety profiles of these compounds were also analyzed. D. genkwa extract potentiated the antibacterial effects of oxacillin against MRSA, as indicated by an FIC index value of 0.375. M. officinalis and V. officinalis produced partial synergistic effects when combined with oxacillin, whereas M. charantia was found to have no beneficial effects in inhibiting MRSA. Overall, tiliroside, pinoresinol, magnatriol B, and momorcharaside B were predicted to be PBP2a or PBP4 inhibitors with good drug-like properties. This study identifies compounds that deserve further investigation with the aim of developing therapeutic agents to modulate the effect of antibiotics on MRSA. Impact statement Antibiotic resistant is a well-known threat to global health and methicillin-resistant Staphylococcus aureus is one of the most significant ones. These resistant bacteria kill thousands of people every year and therefore a new effective antimicrobial treatment is necessary. This study identified the herbs and their associated bioactive ingredients that can potential the effects of current antibiotics. These herbs have long history of human usage in China and have well-defined monograph in the Chinese Pharmacopeia. These indicate their relatively high clinical safety and may have a quicker drug development process than that of a new novel antibiotic. Based on the results of this study, the authors will perform further in vitro and animal studies, aiming to accumulate significant data for the application of clinical trial.

Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches

Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R(2)) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.

Identification of key active constituents of Buchang Naoxintong capsules with therapeutic effects against ischemic stroke by using an integrative pharmacology-based approach

Integrative pharmacology has been used to identify the key active constituents (KACs) of Buchang Naoxintong capsules (BNCs), a traditional Chinese medical preparation.

Aqueous acidities of primary benzenesulfonamides: Quantum chemical predictions based on density functional theory and SMD

Aqueous pK(a) of selected primary benzenesulfonamides are predicted in a systematic manner using density functional theory methods and the SMD solvent model together with direct and proton exchange thermodynamic cycles. Some test calculations were also performed using high-level composite CBS-QB3 approach. The direct scheme generally does not yield a satisfactory agreement between calculated and measured acidities due to a severe overestimation of the Gibbs free energy changes of the gas-phase deprotonation reaction by the used exchange-correlation functionals. The relative pK(a) values calculated using proton exchange method compare to experimental data very well in both qualitative and quantitative terms, with a mean absolute error of about 0.4 pK(a) units. To achieve this accuracy, we find it mandatory to perform geometry optimization of the neutral and anionic species in the gas and solution phases separately, because different conformations are stabilized in these two cases. We have attempted to evaluate the effect of the conformer-averaged free energies in the pK(a) predictions, and the general conclusion is that this procedure is highly too costly as compared with the very small improvement we have gained.

Improving the prediction of drug disposition in the brain

INTRODUCTION

Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task.

AREAS COVERED

This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals.

EXPERT OPINION

Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.

Coexistence of passive and carrier-mediated processes in drug transport

The permeability of biological membranes is one of the most important determinants of the pharmacokinetic processes of a drug. Although it is often accepted that many drug substances are transported across biological membranes by passive transcellular diffusion, a recent hypothesis speculated that carrier-mediated mechanisms might account for the majority of membrane drug transport processes in biological systems. Based on evidence of the physicochemical characteristics and of in vitro and in vivo findings for marketed drugs, as well as results from real-life discovery and development projects, we present the view that both passive transcellular processes and carrier-mediated processes coexist and contribute to drug transport activities across biological membranes.