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

A new workflow for the effective curation of membrane permeability data from open ADME information

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

One of the most challenging goals in modern pharmaceutical research is to develop models that can predict drugs’ behavior, particularly permeability in human tissues. Since the permeability is closely related to the molecular properties, numerous characteristics are necessary in order to develop a reliable predictive tool. The present study attempts to decode the permeability by correlating the apparent permeability coefficient (P_app) of 33 steroids with their properties (physicochemical and structural). The P_app of the molecules was determined by in vitro experiments and the results were plotted as Y variable on a Partial Least Squares (PLS) model, while 37 pharmacokinetic and structural properties were used as X descriptors. The developed model was subjected to internal validation and it tends to be robust with good predictive potential (R²Y = 0.902, RMSEE = 0.00265379, Q²Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD_ss) were proved to be more important than others in terms of drugs P_app. The models can be utilized to predict the permeability of a new candidate drug avoiding needless animal experiments, as well as time and material consuming experiments.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network

The aim of this study is to establish a promising QSPR model for the Caco-2 permeability prediction.

Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties

Identification of chemical compounds having desirable properties is a central goal of screening campaigns. Iterative screening is a means of surveying a set of compounds, during which their property values are determined and used as feedback for regression models. Quantitative models that assess the relationships between chemical structures and property/activity are repeatedly updated through this type of cycle, and the efficient sampling of compounds for the subsequent test is a key factor in the early identification of target compounds. Nevertheless, methodological approaches to comparisons and to establishing the degree of extrapolation of sampled compounds, including the effects of applicability domains, are still required. In the present study, we conducted a series of virtual experiments to assess the characteristics of different iterative screening methods. Genetic algorithm-based partial least-squares regression, support vector regression, Bayesian optimization with Gaussian Process (GP), and batch-based Bayesian optimization with GP (GP_batch) were all compared, based on the analysis of one million compounds extracted from the ZINC database. Our results show that, irrespective of the diversity of the initial set of compounds, it was possible to identify a compound having the desired property value using the appropriate screening method. However, overall, the GP_batch method was found to be preferable when evaluating properties either which are difficult to predict or for which a key factor is present in the set of molecular descriptors.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.

Technologies for Measuring Pharmacokinetic Profiles

The creation of a pharmacokinetic (PK) curve, which follows the plasma concentration of an administered drug as a function of time, is a critical aspect of the drug development process and includes such information as the drug's bioavailability, clearance, and elimination half-life. Prior to a drug of interest gaining clearance for use in human clinical trials, research is performed during the preclinical stages to establish drug safety and dosing metrics from data obtained from the PK studies. Both in vivo animal models and in vitro platforms have limitations in predicting human reaction to a drug due to differences in species and associated simplifications, respectively. As a result, in silico experiments using computer simulation have been implemented to accurately predict PK parameters in human studies. This review assesses these three approaches (in vitro, in vivo, and in silico) when establishing PK parameters and evaluates the potential for in silico studies to be the future gold standard of PK preclinical studies.

Quantitative structure-activity relationship model for the fetal-maternal blood concentration ratio of chemicals in humans

A quantitative structure-activity relationship (QSAR) model of the fetal-maternal blood concentration ratio (F/M ratio) of chemicals was developed to predict the placental transfer in humans. Data on F/M ratio of 55 compounds found in the literature were separated into training (75%, 41 compounds) and testing sets (25%, 14 compounds). The training sets were then subjected to multiple linear regression analysis using the descriptors of molecular weight (MW), topological polar surface area (TopoPSA), and maximum E-state of hydrogen atom (Hmax). Multiple linear regression analysis and a cross-validation showed a relatively high adjusted coefficient of determination (Ra(2)) (0.73) and cross-validated coefficient of determination (Q(2)) (0.71), after removing three outliers. In the external validation, R(2) for external validation (R(2)pred) was calculated to be 0.51. These results suggested that the QSAR model developed in this study can be considered reliable in terms of its robustness and predictive performance. Since it is difficult to examine the F/M ratio in humans experimentally, this QSAR model for prediction of the placental transfer of chemicals in humans could be useful in risk assessment of chemicals in humans.

Drug discovery and regulatory considerations for improving in silico and in vitro predictions that use Caco-2 as a surrogate for human intestinal permeability measurements

There is a growing need for highly accurate in silico and in vitro predictive models to facilitate drug discovery and development. Results from in vitro permeation studies across the Caco-2 cell monolayer are commonly used for drug permeability screening in industry and are also accepted as a surrogate for human intestinal permeability measurements by the US FDA to support new drug applications. Countless studies carried out in this cell line with published permeability measurements have enabled the development of many in silico prediction models. We identify several common cases that illustrate how using Caco-2 permeability measurements in these in silico and in vitro predictive models will not correlate with human intestinal permeability and will further lead to inaccuracies in these models. We provide guidelines and recommendations for improving these models to more accurately predict clinically relevant information, thereby enhancing the drug discovery, development, and regulatory approval processes.

Testing physical models of passive membrane permeation

The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

Quantitative structure/activity relationship modelling of pharmacokinetic properties using genetic algorithm-combined partial least squares method

Quantitative structure/activity relationship (QSAR) approaches have widely been applied to gain deeper understandings of the relationships between ADME parameters and molecular structure and properties. QSAR models for predicting ADME properties are required to cover structurally diverse compounds. In the present investigation, we describe application of genetic algorithm-combined partial least squares (GA-PLS) method to QSAR modelling of various ADME properties. By selecting an appropriate set of molecular descriptors automatically by the use of genetic algorithm, many ADME properties could be well-explained by simple molecular descriptors derived from 2-dimensional chemical structure.

An atomistic model of passive membrane permeability: application to a series of FDA approved drugs

We apply an atomistic model of passive membrane permeability to a series of weakly basic drugs. The computational model uses conformational sampling in combination with an all-atom force field and implicit solvent model to estimate relative passive membrane permeabilities. The model does not require the use of training data for rank-ordering compounds, and as such represents a different approach from the more commonly employed QSPR models. We compare the computational results to previously published experimental PAMPA and Caco-2 permeabilities.

Theoretical assessment of a new experimental protocol for determining kinetic values describing mechanism (time)-based enzyme inhibition

We have shown previously that the conventional experimental protocol (CEP) used to characterise mechanism-based enzyme inhibition (MBI) of drug metabolism in vitro may introduce substantial bias in estimates of the relevant kinetic parameters. The aim of this study was to develop and assess, by computer simulation, an alternative, mechanistically-based experimental protocol (MEP). This protocol comprises three parts viz. assessment of the metabolism of the mechanism-based enzyme inactivator (MBEI), of its ability to participate in competitive inhibition and its ability to cause time-dependent inhibition. Thus, values of the maximum inactivation rate constant (k(inact)), the inactivator concentration associated with half-maximal rate of inactivation (K(I)), the partition ration (r), and the reversible inhibition constant (K(i)) of the MBEI are determined by nonlinear optimization of the experimental data using a model that allows for metabolism of both probe substrate and MBEI, the time-course of inactivation of the enzyme, and reversible inhibition of the metabolism of both probe substrate and MBEI. Sensitivity analysis is used to estimate the degree of confidence in the final parameter values. Virtual experiments using the MEP and the CEP were simulated, applying starting kinetic parameters reported for 16 known MBEIs. In the presence of simulated experimental error (5% CV), the MEP recovered accurate estimates of the kinetic values for all compounds, while estimates using the CEP were less accurate and less precise. The MEP promises to improve consistency in the determination of in vitro measures of MBI and, thereby, the quantitative assessment of its in vivo consequences.

Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach

The ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN). Our implementation of neural network models for the prediction of intestinal absorption is based on the correlation of Caco-2 cell apparent permeability (P (app)) values, as a measure of intestinal absorption, to the structures of two different data sets of drug candidates. Several molecular descriptors of the compounds were calculated and the optimal subsets were selected using a genetic algorithm; therefore, the method was indicated as Genetic Algorithm-Neural Network (GA-NN). A methodology combining a genetic algorithm search with neural network analysis applied to the modelling of Caco-2 P (app) has never been presented before, although the two procedures have been already employed separately. Moreover, we provide new Caco-2 cell permeability measurements for more than two hundred compounds. Interestingly, the selected descriptors show to possess physico-chemical connotations which are in excellent accordance with the well known relevant molecular properties involved in the cellular membrane permeation phenomenon: hydrophilicity, hydrogen bonding propensity, hydrophobicity and molecular size. The predictive ability of the models, although rather good for a preliminary study, is somewhat affected by the poor precision of the experimental Caco-2 measurements. Finally, the generalization ability of one model was checked on an external test set not derived from the data sets used to build the models. The result obtained is of interesting practical application and underlines that the successful model construction is strictly dependent on the structural space representation of the data set used for model development.

Evolutionary computation and multimodal search: a good combination to tackle molecular diversity in the field of peptide design

The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.

Chemical substituent effect on pyridine permeability and mechanistic insight from computational molecular descriptors

The objective was (1) to evaluate the chemical substituent effect on Caco-2 permeability, using a congeneric series of pyridines, and (2) compare molecular descriptors from a computational chemistry approach against molecular descriptors from the Hansch approach for their abilities to explain the chemical substituent effect on pyridine permeability. The passive permeability of parent pyridine and 14 monosubstituted pyridines were measured across Caco-2 monolayers. Computational chemistry analysis was used to obtain the following molecular descriptions: solvation free energies, solvent accessible surface area, polar surface area, and cavitation energy. Results indicate that the parent pyridine was highly permeable and that chemical substitution was able to reduce pyridine permeability almost 20-fold. The substituent effect on permeability provided the following rank order: 3-COO- < 4-NH2 < 3-CONH2 < 3-Cl < 3-CHO < 3-OH < 3-CH2OH < 3-C6H5 < 3-NH2 < 3-CH2C6H5 < 3-C2H5 < 3-H < 3-CH3 < 3-F < 4-C6H5. This substituent effect was better explained via molecule descriptors from the computational chemistry approach than explained by classic descriptors from Hansch. Computational descriptors indicate that aqueous desolvation, but not membrane partitioning per se, dictated substituent effect on permeability.

Computational models to predict aqueous drug solubility, permeability and intestinal absorption

In the last decade, poor intestinal absorption of candidate drugs intended for oral administration has been identified as a major bottleneck in drug development. Poor intestinal absorption can often be related to poor aqueous solubility and/or poor permeability across the intestinal wall. Other factors, such as poor stability and the metabolism of the compounds, can also decrease the amount of compound absorbed. In an effort to design compounds with enhanced absorption profile, theoretical predictions of solubility and permeability, among other factors, have gained increased interest, and a large number of papers have been published. In this review, the databases and techniques used for the development of in silico absorption models will be discussed. The focus is on aqueous drug solubility, which has become a major problem in drug development.

Recent progress in the computational prediction of aqueous solubility and absorption

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties.

ENPDA: an evolutionary structure-based de novo peptide design algorithm

One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

In Silico Prediction of Pharmacokinetic Properties

In silico methods for predicting pharmacokinetic properties range from data-based approaches such as quantitative structure-activity relationships (QSARs), similarity searches, and 3-dimensional QSAR, to structure-based methods such as ligand-protein docking and pharmacophore modelling. Data-based modelling approaches are effective for many drug absorption, distribution, metabolism, and excretion (ADME) processes such as passive membrane permeation, where their molecular mechanism is barely delineated. Therefore QSAR approaches have been applied to simulate the relationships between ADME parameters and molecular structure and properties. In the present investigation, we describe the application of the genetic algorithm-combined partial least-squares (GA-PLS) method to QSAR modelling of various ADME properties. By selecting an appropriate set of molecular descriptors automatically using the genetic algorithm, many ADME properties could be well explained by simple molecular descriptors derived from the 2-dimensional chemical structure.

Prediction of oral drug absorption in humans by theoretical passive absorption model

The purpose of the present study was to examine the oral drug absorption predictability of the theoretical passive absorption model (TPAM). As chemical descriptors of drugs, the octanol/buffer distribution coefficient at pH 6.0 (D(ow)), intrinsic octanol-water partition coefficient (P(ow)), pK(a), and molecular weight (MW) were calculated from the chemical structure. Total passive intestinal membrane permeation consists of transcellular, paracellular and unstirred water layer (UWL) permeation. Transcellular permeation was modeled based on the pH-partition hypothesis with correction for cationic species permeation, and the independent variables were D(ow), P(ow), and pK(a). Paracellular permeation was modeled as a size-restricted diffusion within a negative electrostatic field-of-force, and the independent variables were MW and pK(a). UWL permeation was modeled as diffusion across a water layer, and the independent variable was MW. Cationic species permeation in the transcellular permeation model and the effect of a negative electric field-of-force in the paracellular permeation model were the extensions to the previous TPAM. The coefficients of the paracellular and UWL permeation models were taken from the literature. A data set of 258 compounds with observed values of Fa% (the fraction of a dose absorbed in humans) taken from the literature was employed to optimize four fitting coefficients in the transcellular permeation model. The TPAM predicted Fa%, with root mean square errors of 15-21% and a correlation coefficient (CC) of 0.78-0.88. In addition, the TPAM predicted the effective human intestinal membrane permeability with a CC of 0.67-0.77, as well as the contribution of paracellular permeation. The TPAM was found to predict oral absorption from the chemical structure of drugs with adequate predictability for usage in drug discovery.

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.

[High throughput screening of pharmacokinetics and metabolism in drug discovery (III)--investigation on in- silico model for membrane permeability and CYP1A2 inhibition]

Pharmacokinetic and metabolic screening plays an important role in the optimization of a lead compound in drug discovery. Since those screening methods are time-consuming and labor intensive, in silico models would be effective to select compounds and guide derivatization prior to the screening. We investigated in silico models for permeability in Caco-2 cells, brain distribution and cytochrome P450 (CYP) inhibition using molecular weight, lipophilicity (clog D(7.4)), polar surface area (PSA), and number of rotatable bonds (RB). A variety of test compounds was selected from different Caco-2 assay projects. The permeability determined exhibited a good correlation with a combination of PSA and clog D(7.4) rather than with PSA alone. In the brain distribution, PSA, in addition to lipophilicity, was one of the determinant parameters, and compounds were significantly distributed to the brain in rats with the decrease in the PSA value. When this approach was adapted to CYP1A2 inhibition in the fluorometric assay, the inhibitory potential for two plane core structures was successfully predicted by utilizing number of RB, PSA, and clog D(7.4). In particular, an increase in the number of RB weakened the inhibitory potential due to a loss of the plane structures. These results suggest that the PSA and RB are key parameters to design chemical structures in terms of the improvement of both membrane permeability in the brain and gastrointestine and CYP1A2 inhibition, respectively.

Two‐ and three‐dimensional QSAR of carrier‐mediated transport of β‐lactam antibiotics in Caco‐2 cells

In this study, we investigated whether such a topological descriptor-based approach is suitable for predicting the carrier-mediated transport of 20 beta-lactam antibiotics that are substrates of peptide transporters. To select the molecular descriptors that can effectively predict a targeted property in QSAR analysis, the genetic algorithm-combined partial least squares approach was used. The feasibility of the two-dimensional (2D)-QSAR approach was compared with that of comparative molecular field analysis (CoMFA). The logarithm of the uptake values of 20 beta-lactam antibiotics in Caco-2 cells obtained from the literature ranged from -1.15 to 1.09 (nmol/cm2/2 h). When preliminary leave-one-out cross-validated partial least squares analyses implemented in the SYBYL/CoMFA program were conducted, the r2pred was 0.759 and the standard error of prediction (s) was 0.373. However, the 2D-QSAR approach based on Molconn-Z descriptors gave a better predictability (r2pred = 0.923, s = 0.211), where 14 descriptors were selected and the optimal number of principal components was 4. Considering that the 2D-topological descriptors are less computationally intensive and practically completely automated, the simple 2D-QSAR model is also of great importance in drug discovery settings.

QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method

PURPOSE

To develop a quantitative structure/activity relationship (QSAR) model for predicting drug-CYP 3A4 interactions.

METHOD

The inhibitory effect of 53 structurally diverse drugs on the metabolism of 7-benzyloxy-4-trifluoromethyl coumarin (BFC) by recombinant CYP 3A4 was evaluated using a rapid microtiter plate assay. For each drug, a total of 220 two-dimensional topological indices were calculated using Molconn-Z software. Using a genetic algorithm-based partial least squares (GA-PLS) method, the desired descriptors were automatically selected to maximize the predictability of the IC50 values.

RESULTS

The IC50 values of the drugs tested ranged from 9 nM to 2 mM. Based on the GA-PLS method, five principal components derived from 20 Molconn-Z descriptors were found to be effective for QSAR modeling. Interestingly, these descriptors suggested that the molecular size would be an important factor in determining drug-CYP 3A4 interactions. In the leave-one-out prediction, the rpred and the standard error of prediction (s) were 0.754 and 0.787, respectively. Even in an external validation, the predictions were in good agreement with experimental values (rpred = 0.744, s = 0.769, n = 9).

CONCLUSIONS

The proposed model, in which two-dimensional topological descriptors were used as molecular descriptors, was able to predict drug-CYP 3A4 interactions with reasonable accuracy.