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

Recent advances in synthesis of sugar and nucleoside coumarin conjugates and their biological impact

Naturally occurring coumarin and sugar molecules have a diverse range of applications along with superior biocompatibility. Coumarin, a member of the benzopyrone family, exhibits a wide spectrum of medicinal properties, such as anti-coagulant, anti-bacterial, anti-tumor, anti-oxidant, anti-cancer, anti-inflammatory and anti-viral activities. The sugar moiety functions as the central scaffold for the synthesis of complex molecules, attributing to their excellent biocompatibility, well-defined stereochemistry, benign nature and outstanding aqueous solubility. When the coumarin moiety is conjugated with the sugar or nucleoside molecule, the resulting conjugates exhibit significant biological properties. Due to the remarkable growth of such bioconjugates in the field of science over the last decade, owing to their future prospect as a potential bioactive core, an update to this area is very much needed. The present review focusses on the synthesis, characterization and the various therapeutic applications of coumarin conjugates, i.e., sugar and nucleoside coumarin conjugates along with their perspective for future research.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

BACKGROUND

Enhancing a compound's biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process.

METHODS

Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold.

RESULTS

Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563).

CONCLUSION

An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Efficient algorithm for expanding theoretical electron densities in canterakis-zernike functions

An algorithm for the efficient computation of Canterakis-Zernike moments of theoretically computed molecular electron densities and rotationally invariant Fingerprint indices derived from them is reported. The algorithm is suitable for any density expressed in terms of Gaussian- or Slater-type functions within the Linear Combination of Atomic Orbitals framework at any level of computation. Electron density is expressed as a one-center expansion of real regular spherical harmonics times radial factors by means of translation techniques, which facilitates the efficient computation of the moments in terms of a single one-dimension numerical integration. The performance of the algorithm is analyzed showing that the computation of radial factors in the quadrature points is responsible for almost all computational time. The procedure is applicable to any density obtained with standard packages for molecular structure calculations. © 2018 Wiley Periodicals, Inc.

Synthesis and antimycobacterial activity of 1-(β-d-Ribofuranosyl)-4-coumarinyloxymethyl- / -coumarinyl-1,2,3-triazole

A series of β-d-ribofuranosyl coumarinyl-1,2,3-triazoles have been synthesized by Cu-catalyzed cycloaddition reaction between azidosugar and 7-O-/7-alkynylated coumarins in 62-70% overall yields. The in vitro antimycobacterial activity evaluation of the synthesized triazolo-conjugates against Mycobacterium tuberculosis revealed that compounds were bactericidal in nature and some of them were found to be more active than one of the first line antimycobacterial drug ethambutol against sensitive reference strain H37Rv, and 7 to 420 times more active than all four first line antimycobacterial drugs (isoniazid, rifampicin, ethambutol and streptomycin) against multidrug resistant clinical isolate 591. Study of in silico pharmacokinetic profile indicated the drug like characters for the test molecules. Further, transmission electron microscopic experiments revealed that these compounds interfere with the constitution of bacterial cell wall possibly by targeting mycobacterial InhA and DNA gyrase enzymes. Study conducted on the activities of the test compounds on bacterial InhA and DNA gyrase revealed that the most bactericidal test compound, N¹-(β-d-ribofuranosyl)-C⁴-(4-methylcoumarin-7-oxymethyl)-1,2,3-triazole (6b) and its corresponding directly linked conjugate N¹-(β-d-ribofuranosyl)-C⁴-(4-methylcoumarin-7-yl)-1,2,3-triazole (11b) significantly inhibited the activity of both the enzymes. The results were further supported by molecular docking studies of the compound 6b and 11b with bacterial InhA and DNA gyrase B enzymes. Further, the cytotoxicity study of some of the better active compounds on THP-1 macrophage cell line using MTT assay showed that the synthesized compounds were non-cytotoxic.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine

BACKGROUND

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life.

METHODS

In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold.

RESULTS

Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed.

CONCLUSIONS

An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698).

GENERAL SIGNIFICANCE

Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Estimation of acute oral toxicity in rat using local lazy learning

Background

Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD₅₀, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of LD₅₀. Unfortunately, it is difficult to accurately predict compound LD₅₀ using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.

Results

In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD₅₀ prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R² of 0.712 on a test set containing 2,896 compounds.

Conclusion

Encouraged by the promising results, we expect that our consensus LLL model of LD₅₀ would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

The importance of plasma protein binding in drug discovery

Plasma protein binding of drugs is a well-recognised phenomena, but it is only recently that the implications for drug action in vivo have been fully appreciated. Plasma proteins, by virtue of their high concentration, control the free drug concentration in plasma and in compartments in equilibrium with plasma, thereby, effectively attenuating drug potency in vivo. The historical background and thermodynamic basis for the 'Free Drug Principle' is presented, along with special considerations for intracellular targets, deep compartments and α1-acid glycoprotein binding. Real and apparent exceptions to the principle are discussed along with a survey of citations from the recent medicinal chemistry literature.

3D pharmacophore mapping using 4D QSAR analysis for the cytotoxicity of lamellarins against human hormone-dependent T47D breast cancer cells

4D quantitative structure-activity relationship (QSAR) and 3D pharmacophore models were built and investigated for cytotoxicity using a training set of 25 lamellarins against human hormone dependent T47D breast cancer cells. Receptor-independent (RI) 4D QSAR models were first constructed from the exploration of eight possible receptor-binding alignments for the entire training set. Since the training set is small (25 compounds), the generality of the 4D QSAR paradigm was then exploited to devise a strategy to maximize the extraction of binding information from the training set and to also permit virtual screening of diverse lamellarin chemistry. 4D QSAR models were sought for only six of the most potent lamellarins of the training set as well as another subset composed of lamellarins with constrained ranges in molecular weight and lipophilicity. This overall modeling strategy has permitted maximizing 3D pharmacophore information from this small set of structurally complex lamellarins that can be used to drive future analog synthesis and the selection of alternate scaffolds. Overall, it was found that the formation of an intermolecular hydrogen bond and the hydrophobic interactions for substituents on the E ring most modulate the cytotoxicity against T47D breast cancer cells. Hydrophobic substitutions on the F-ring can also enhance cytotoxic potency. A complementary high-throughput virtual screen to the 3D pharmacophore models, a 4D fingerprint QSAR model, was constructed using absolute molecular similarity. This 4D fingerprint virtual high-throughput screen permits a larger range of chemistry diversity to be assayed than with the 4D QSAR models. The optimized 4D QSAR 3D pharmacophore model has a leave-one-out cross-correlation value of xv-r2 = 0.947, while the optimized 4D fingerprint virtual screening model has a value of xv-r2 = 0.719. This work reveals that it is possible to develop significant QSAR, 3D pharmacophore, and virtual screening models for a small set of lamellarins showing cytotoxic behavior in breast cancer screens that can guide future drug development based upon lamellarin chemistry.