Modeling Drug Albumin Binding Affinity with E-State Topological Structure Representation

Unraveling the versatility of human serum albumin - A comprehensive review of its biological significance and therapeutic potential

Human serum albumin (HSA) is a multi-domain macromolecule with diverse ligand binding capability because of its ability to allow allosteric modulation despite being a monomeric protein. Physiologically, HSA act as the primary carrier for various exogenous and endogenous compounds and fatty acids, and alter the pharmacokinetic properties of several drugs. It has antioxidant properties and is utilized therapeutically to improve the drug delivery of pharmacological agents for the treatment of several disorders. The flexibility of albumin in holding various types of drugs coupled with a variety of modifications makes this protein a versatile drug carrier with incalculable potential in therapeutics. This review provides a brief outline of the different structural properties of HSA, and its various binding sites, moreover, an overview of the genetic, biomedical, and allosteric modulation of drugs and drug delivery aspects of HSA is also included, which may be helpful in guiding advanced clinical applications and further research on the therapeutic potential of this extraordinary protein.

Food flavonoid ligand structure/estrogen receptor-α affinity relationships - toxicity or food functionality?

In silico molecular modelling is used to study interactions between flavonoid phytoestrogens and estrogen receptor (ER)α. Twenty flavonoids from foods were studied; e.g., genistein from soy, naringenin from grapefruit, phloretin from pears, chrysin from oyster mushrooms. These potential ligands' molecular attributes and their spatial arrangements that favour binding to the ligand binding cleft (LBC) of ERα are identified, and Docking Scores calculated. The Docking Score order is the same as the estrogenicity order for 8 of the flavonoids studied in detail. The number and position of flavonoid ring hydroxyls influence the Docking Scores which might relate to ERα's bio-activity. Hydrophobic interactions between ligands and ERα are also important; the number of rotatable CC bonds in ligands likely affects the magnitude of hydrophobic interactions and ligand fit. Our findings suggest that flavonoids with diverse structural features could have different binding energies and binding affinities with ERα, which might confer different functionalities and toxicities.

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.

Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity

Quantitative structure-activity relationship (QSAR) modelling is a major tool employed in the prediction of various endpoints. However, current QSAR literature is missing a full understanding of the impact of quantum chemical calculation methods on the estimation of molecular descriptors and model performance. Here, we provide a comprehensive analysis of the quantitative effects of different geometry optimization methods (semi-empirical, ab initio Hartee-Fock and density functional theory) on the molecular descriptors. Using experimental binding affinity to human serum albumin (HSA) data, we comparatively investigated the influence of employing descriptors derived from three calculation methods on the QSAR models. We propose a 4-descriptor QSAR model in line with the OECD validation principles for the prediction of drug binding affinity to HSA (log K_HSA) as a potential tool for drug development. We also confirm the prediction capability of the proposed model on a heterogeneous external set of chemicals. Furthermore, we recommend an activity-independent rational approach for the selection of geometry optimization method for an improved QSAR model development.

In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review

Plasma protein binding (PPB) strongly affects drug distribution and pharmacokinetic behavior with consequences in overall pharmacological action. Extended plasma protein binding may be associated with drug safety issues and several adverse effects, like low clearance, low brain penetration, drug-drug interactions, loss of efficacy, while influencing the fate of enantiomers and diastereoisomers by stereoselective binding within the body. Therefore in holistic drug design approaches, where ADME(T) properties are considered in parallel with target affinity, considerable efforts are focused in early estimation of PPB mainly in regard to human serum albumin (HSA), which is the most abundant and most important plasma protein. The second critical serum protein α1-acid glycoprotein (AGP), although often underscored, plays also an important and complicated role in clinical therapy and thus the last years it has been studied thoroughly too. In the present review, after an overview of the principles of HSA and AGP binding as well as the structure topology of the proteins, the current trends and perspectives in the field of PPB predictions are presented and discussed considering both HSA and AGP binding. Since however for the latter protein systematic studies have started only the last years, the review focuses mainly to HSA. One part of the review highlights the challenge to develop rapid techniques for HSA and AGP binding simulation and their performance in assessment of PPB. The second part focuses on in silico approaches to predict HSA and AGP binding, analyzing and evaluating structure-based and ligand-based methods, as well as combination of both methods in the aim to exploit the different information and overcome the limitations of each individual approach. Ligand-based methods use the Quantitative Structure-Activity Relationships (QSAR) methodology to establish quantitate models for the prediction of binding constants from molecular descriptors, while they provide only indirect information on binding mechanism. Efforts for the establishment of global models, automated workflows and web-based platforms for PPB predictions are presented and discussed. Structure-based methods relying on the crystal structures of drug-protein complexes provide detailed information on the underlying mechanism but are usually restricted to specific compounds. They are useful to identify the specific binding site while they may be important in investigating drug-drug interactions, related to PPB. Moreover, chemometrics or structure-based modeling may be supported by experimental data a promising integrated alternative strategy for ADME(T) properties optimization. In the case of PPB the use of molecular modeling combined with bioanalytical techniques is frequently used for the investigation of AGP binding.

Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking

MOTIVATION

Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.

Topological, hydrophobicity, and other descriptors on α-glucosidase inhibition: a QSAR study on xanthone derivatives

Quantitative structure activity relationship analysis was performed on a series of xanthone derivatives to establish the structural features required for α-glucosidase inhibitory activity. The computational and statistical analysis was performed with V life MDS (Molecular Design Suite) and Statistica software. The selected models show significant predictive power, stability, and reliability in terms of cross-validated correlation coefficient (Q(2)(cv) > 0.74 and Q(2)(test) > 0.5) and other validation parameters. The results show that the SaaaC count, MMFF_6 and dipole moment are mainly contributed for the activity along with the hydrophobicity descriptors. It describes that heteroatoms (oxygen atom connected with carbon atom) in the molecules are favourable for α-glucosidase inhibitory activity. The E-state count descriptor suggests that when carbon atoms connected with three aromatic bonds and hydrogen or other atoms are favourable for the activity. The SAHA and SAMH descriptors show that the hydrophilic area in the molecule is important for the activity while high hydrophilicity is unfavourable for the activity. This study concluded that hydrophilic, polar and/or electron negative groups, which are responsible for hydrogen bonding and interaction with the enzyme for favourable activity.

CE50: quantifying collision induced dissociation energy for small molecule characterization and identification

Survival yield analysis is routinely used in mass spectroscopy as a tool for assessing precursor ion stability and internal energy. Because ion internal energy and decomposition reaction rates are dependent on chemical structure, we reasoned that survival yield curves should be compound-specific and therefore useful for chemical identification. In this study, a quantitative approach for analyzing the correlation between survival yield and collision energy was developed and validated. This method is based on determining the collision energy (CE) at which the survival yield is 50% (CE(50)) and, further, provides slope and intercept values for each survival yield curve. In initial experiments using a defined set of homologous compounds, we found that CE(50) values were easily determined, quantitative, highly reproducible, and could discriminate between structural and even positional isomers. Further analysis demonstrated that CE(50) values were independent of cone potential and orthogonal to compound mass. Experimentally determined CE(50) values for a diverse set of 54 compounds were correlated to Molconn molecular structure descriptors. The resulting model yielded a statistically significant linear correlation between experimental and calculated CE(50) values and identified several structural characteristics related to precursor ion stability and fragmentation mechanism. Thus, the CE(50) is a promising method for compound identification and discrimination.

Microbubbles as ultrasound triggered drug carriers

Originally developed as contrast agents for ultrasound imaging and diagnostics, in the past years, microbubbles have made their way back from the patients' bedside to the researcher's laboratory. Microbubbles are currently believed to have great potential as carriers for drugs, small molecules, nucleic acids, and proteins. This review provides insight into this intriguing new frontier from the perspective of the pharmaceutical scientist. First, basic aspects on the application of ultrasound-targeted microbubble destruction for drug delivery will be presented. Next, we will review the recently applied approaches for manufacturing and drug-loading microbubbles. Important quality issues and characterization techniques for advanced microbubble formulation will be discussed. Finally, we will provide an assessment of the prospects for microbubbles in drug and gene therapy, illustrating the problems and requirements for their future development.

Application of structure-activity relationships to investigate the molecular mechanisms of hepatocyte toxicity and electrophilic reactivity of alpha,beta-unsaturated aldehydes

Covalent binding of reactive electrophiles to cellular targets is a molecular interaction that has the potential to initiate severe adverse biological effects. Therefore, a measure for electrophilic reactivity with biological nucleophiles could serve as an important correlate to toxic effects such as hepatocyte death. To determine if electrophile reactivity correlates with rat hepatocyte cytotoxicity, the inherently electrophilic alpha,beta-unsaturated aldehydes were chosen for investigation. Reactivity was measured with simple assays that used glutathione, a soft nucleophile, and butylamine, a harder nucleophile, as models for protein thiol and amine nucleophilic sites, respectively. Despite their higher reactivity with thiols, a linear relationship was only observed between hepatocyte cytotoxicity and amine reactivity. Structure-activity relationships were also investigated for hepatocyte toxicity, and results showed toxicity was well modelled by log P and electronic parameters E(LUMO) and partial charge of the carbonyl carbon (C'(carb)). Hydrophobicity and electronic descriptors were only significant in separate distinct models, suggesting that there were simultaneously occurring mechanisms that affected toxicity. Log P was linked to the ease of oxidation by a microsomal aldehyde dehydrogenase enzyme, while the electronic descriptors and amine reactivity were linked to direct alkylation. Even with the presence of electrophile characteristics, alpha,beta-unsaturated aldehyde hepatocyte toxicity could not be predicted exclusively by electrophilic reactivity as oxidative metabolism was also a factor for toxicity.

ANN-QSAR Model of Drug-binding to Human Serum Albumin

Quantitative structure-activity relationship study was performed to understand drug binding to human serum albumin. This study was performed on 94 different human serum albumin drug and drug-like compounds by using the principal component-artificial neural network modeling method, with application of eigenvalue ranking factor selection procedure. The results obtained by principal component-artificial neural network gives better regression models with good prediction ability using a relatively low number of principal components in comparison to other quantitative structure-activity relationship studies on the same data set of compounds. A 0.8497 coefficient of determination was obtained using principal component-artificial neural network with six extracted principal components.

Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation

Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial neural network model. These models were based on 3343 aromatic and 1674 non-aromatic compounds for training sets; 938 compounds were used in external validation testing. The range in -log S(o) is -1.6 to 10. Topological structure descriptors were used with all models. A genetic algorithm was used for descriptor selection for regression models. For the artificial neural network (ANN) model, descriptor selection was done with a backward elimination process. All models performed well with r2 values ranging 0.72 to 0.84 in external validation testing. The mean absolute errors in validation ranged from 0.44 to 0.80 for the classes of compounds for all the models. These statistical results indicate a sound ANN model. Furthermore, in a comparison with eight other available models, based on predictions using a validation test set (442 compounds), the artificial neural network model presented in this work (CSLogWS) was clearly superior based on both the mean absolute error and the percentage of residuals less than one log unit. In the ANN model both E-State and hydrogen E-State descriptors were found to be important.

In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Binding of anti-inflammatory drug cromolyn sodium to bovine serum albumin

Fluorescence spectroscopy in combination with circular dichroism (CD) and UV-vis absorption spectroscopy were employed to investigate the binding of anti-inflammatory drug cromolyn sodium (Intal) to bovine serum albumin (BSA) under the physiological conditions with Intal concentrations of 0-6.4 x 10(-5)mol L(-1). In the mechanism discussion, it was proved that the fluorescence quenching of BSA by Intal is a result of the formation of Intal-BSA complex. Quenching constants were determined using the Stern-Volmer equation to provide a measure of the binding affinity between Intal and BSA. The thermodynamic parameters Delta G, Delta H, Delta S at different temperatures (298, 304, and 310 K) were calculated and the results indicate the electrostatic interactions play a major role in Intal-BSA association. Binding studies concerning the number of binding sites (n=1) and apparent binding constant K(b) were performed by fluorescence quenching method. Utilizing fluorescence resonant energy transfer (FRET) the distance R between the donor (BSA) and acceptor (Intal) has been obtained. Furthermore, CD and synchronous fluorescence spectrum were used to investigate the structural change of BSA molecules with addition of Intal, the results indicate that the secondary structure of BSA molecules was changed in the presence of Intal.

Effect of cholesterol on DMPC phospholipid membranes and QSAR model construction in membrane-interaction QSAR study through molecular dynamics simulation

In this study, both pure DMPC and DMPC/cholesterol mixed membrane monolayer were built to compare the physical-chemical properties and dynamics properties through molecular dynamics simulation and normal-mode analysis. The results show that the addition of cholesterol decreases the area of per molecule of membrane, increases the lipid amplitude motion, and changes the solute diffusion coefficient. It is also found that the addition of cholesterol greatly changes the solute-membrane 1,4-nonbonded interaction energy (deltaE14). MI-QSAR models were constructed based on solute-membrane interaction energy descriptors and other intramolecular descriptors. The results show that deltaE14 substitutes deltaE(HB) as the second important descriptor compared with the previous study. Final results suggest that short range solute-membrane interaction energy changes due to the uptake of the solute may play an important decision on permeability in DMPC/cholesterol membrane. A test set was applied to evaluate the predictivity of MI-QSAR models. The result suggests that the combination of F(H2O) and deltaE14 not only improves r2 and q2, but also greatly improves the model predictivity. Based on the combination of q2 and r(pre)2 values, a two-term model is better used to predict the solute permeability in this study (r2 = 0.859, q2 = 0.803, and r(pre)2 = 0.540). Due to the small sample both in training set and test set, more datasets are necessary to make a final decision about the model construction and prediction.

Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses

Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.

Prediction of plasma protein binding of drugs using Kier–Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses

A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier-Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound's binding affinity. This scheme has relatively poor predictivity based both on Kier-Hall valence connectivity indices similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier-Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.

The Prediction of ADMET Properties Using Structure Information Representations

The electrotopological state and molecular connectivity indices are defined as a system for molecular-structure description, using the term Structure-Information Representation. This system is built on the depiction of a molecule as a network composed of atoms of varying valence electron counts that constitute the valence state, bonded in discrete patterns constituting an electrotopological state. The system is employed in the structure-activity analysis of two sets of ADMET data. Models are created relating hepatotoxicity and human metabolic stability. The validity of these models makes them useful for activity prediction.

Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones

The relationship between the topochemical indices and cyclin-dependent kinase 2 (CDK2) inhibitory activity of indole-2-ones has been investigated. The relationship of topochemical versions of well known topological indices of Wiener's index--a distance-based topological descriptor, molecular connectivity index, an adjacency-based topological descriptor and eccentric connectivity index--an adjacency-cum-distance based topological descriptor with CDK2 inhibitory activity of indole-2-ones has been investigated. A data set comprising 67 analogues of substituted indole-2-ones was selected for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index for each of 67 analogues comprising the data set were computed. The resulting data was analyzed and suitable models developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue in the data set using these models, which was then compared with the reported CDK2 inhibitory activity. Accuracy of prediction was found to vary from a minimum of 88% for a model based upon molecular connectivity topochemical index to a maximum of approximately 90% for model based upon eccentric connectivity topochemical index.