Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.     

A model validation and consensus building environment

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a 'consensus' model that may be a much better model than any of the individual predictive model. The KnowItAll system can handle two types of predictions: real number and categorical classification.     

Theoretical development of a Gaussian potential function in the description of the radial portion of isotropic bending vibrations

Analysis of a Gaussian potential function suitable for modeling degenerate bending vibrations in weakly bound molecular complexes is presented. Approximate eigenvalues and eigenvectors are obtained by application of perturbation theory. Comparison to the “exact” eigenvalues obtained via a numerical solution shows that the first- and higher-order perturbation corrections are consistent with variational principles.     

A model validation and consensus building environment

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll®. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a ‘consensus’ model that may be a much better model than any of the individual predictive model. The KnowItAll® system can handle two types of predictions: real number and categorical classification.     

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.     

In silico prediction of cytochrome P450-mediated drug metabolism

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.     

Generic method for on-line extraction of drug substances in the presence of biological matrices using turbulent flow chromatography

The use of liquid chromatography/mass spectrometry (LC/MS) to quantify drugs in biological matrices has been well established over the last decade. Extremely fast LC/MS methods are commonplace in the pharmaceutical industry for high-throughput Absorption, Distribution, Metabolism and Excretion (ADME) screening. However, to truly take full advantage of high-throughput ADME screening, a generic method is needed that eliminates the need to develop a new method for each new compound being screened. New developments in the stationary phase of turbulent flow columns has allowed us to develop an on-line biological sample cleanup method that is suitable for over 99% of the compounds in the Cephalon database.     

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Quantitative structure–activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug–drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.     

Molecular docking studies and structural&electronic analysis of gefarnate

This study focused on the structural/electronic features of an anti-ulcer agent, gefarnate. The molecular geometry of the compound was calculated using Gaussian 09 W software and the structure was optimized using the DFT/B3LYP method with the 6–31++G(d,p) basis set ground state. Also, in silico studies like molecular docking studies and ADME/T estimation were carried out using web-based tools and software. The protein used in these calculations is the crystal structure of the 3U6J, VEGFR2 kinase domain in complex with a pyrazolone inhibitor. The binding energy for the gefarnate molecule-VEGFR2 kinase complex has been computed as −8.6 kcal/mol. The compound showed no toxicity properties including cytotoxic, mutagenic, carcinogenic, or immunogenic.     

Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA

We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) Wold:1977 to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation Clark:1998. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.     

The role of in vitro ADME assays in antimalarial drug discovery and development

The high level of attrition of drugs in clinical development has led pharmaceutical companies to increase the efficiency of their lead identification and development through techniques such as combinatorial chemistry and high-throughput (HTP) screening. Since the major reasons for clinical drug candidate failure other than efficacy are pharmacokinetics and toxicity, attention has been focused on assessing properties such as metabolic stability, drug-drug interactions (DDI), and absorption earlier in the drug discovery process. Animal studies are simply too labor-intensive and expensive to use for evaluating every hit, so it has been necessary to develop and implement higher throughput in vitro ADME screens to manage the large number of compounds of interest. The antimalarial drug development program at the Walter Reed Army Institute of Research, Division of Experimental Therapeutics (WRAIR/ET) has adopted this paradigm in its search for a long-term prophylactic for the prevention of malaria. The overarching goal of this program is to develop new, long half-life, orally bioavailable compounds with potent intrinsic activity against liver- and blood-stage parasites. From the WRAIR HTP antimalarial screen, numerous compounds are regularly identified with potent activity. These hits are now immediately evaluated using a panel of in vitro ADME screens to identify and predict compounds that will meet our specific treatment criteria. In this review, the WRAIR ADME screening program for antimalarial drugs is described as well as how we have implemented it to predict the ADME properties of small molecule for the identification of promising drug candidates.     

An Alternate Solution of the Fluorescence Recovery Kinetics after Spot-Bleaching for Measuring Diffusion Coefficients. 1. Theory and Numerical Analysis

The classical three-point method for obtaining a diffusion coefficient of probes from the fluorescence recovery function in the Fluorescence Recovery After Photobleaching (FRAP) technique, when Gaussian or uniform spot-photobleaching profiles are used, leads to an expression that is not suitable for linear fittings. Therefore, determination of the diffusion coefficient is complex and very dependent on the recovery time. In this work we propose a new solution for the fluorescence recovery function after spot-bleaching using a Gaussian beam, which is a better alternative method than the three-point method for the data analysis of the FRAP kinetics. The new method can be applied for shorter recovery times thereby minimizing errors due to convective effects. It leads to a linear relationship between the recovery function and the diffusion coefficient and, as shown in the analysis of a model case, allows a more accurate determination of the diffusion coefficient of fluorescent probes.     

Non-destructive measurement of cavity pressure during injection molding process based on ultrasonic technology and Gaussian process

Non-destructive measurement of the cavity pressure is of great importance for monitoring, optimizing and controlling the injection molding process. However, to date, almost all researches have relied on embedded pressure probes, and holes have to be drilled in the molds. In this paper, a non-destructive cavity pressure measurement method is proposed based on ultrasonic technology and a Gaussian process. According to the pressure-volume-temperature profile, the cavity pressure of a given polymer can be treated as a function of the density and the temperature. Moreover, the cavity pressure is significantly affected by injection hydro-cylinder pressure. Ultrasonic technology is employed to detect the variation of polymer density during injection molding. The Gaussian process is adopted to model the functional relationships between the cavity pressure, the ultrasonic signal, the mold temperature and the injection hydro-cylinder pressure. Experimental results show that the proposed Gaussian process regression model has a better modeling performance than that of the neural network regression model, and the proposed measurement method is capable of measuring the cavity pressure at different processing conditions and measurement locations during injection molding. In general, the proposed method offers several advantages: (1) non-destructive, (2) flexible, (3) no wires, (4) low-cost, and (5) health and safety, so it has great application prospects in injection molding.     

Induction of decision trees via evolutionary programming

Decision trees have been used extensively in cheminformatics for modeling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimize a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints. Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less complex classifiers with average increases in predictive accuracy of 5-10% over the traditional method.     

Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

The OPLS-AA all-atom force field and the Analytical Generalized Born plus Non-Polar (AGBNP) implicit solvent model, in conjunction with torsion angle conformational search protocols based on the Protein Local Optimization Program (PLOP), are shown to be effective in predicting the native conformations of 57 9-residue and 35 13-residue loops of a diverse series of proteins with low sequence identity. The novel nonpolar solvation free energy estimator implemented in AGBNP augmented by correction terms aimed at reducing the occurrence of ion pairing are important to achieve the best prediction accuracy. Extended versions of the previously developed PLOP-based conformational search schemes based on calculations in the crystal environment are reported that are suitable for application to loop homology modeling without the crystal environment. Our results suggest that in general the loop backbone conformation is not strongly influenced by crystal packing. The application of the temperature Replica Exchange Molecular Dynamics (T-REMD) sampling method for a few examples where PLOP sampling is insufficient are also reported. The results reported indicate that the OPLS-AA/AGBNP effective potential is suitable for high-resolution modeling of proteins in the final stages of homology modeling and/or protein crystallographic refinement.     

Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.     

A generic fast solid-phase extraction high-performance liquid chromatography/mass spectrometry method for high-throughput drug discovery

High-performance liquid chromatography/mass spectrometry (HPLC/MS) is increasingly perceived to be an essential tool in drug discovery at many key steps, like drug screening, lead identification, ADME profiling, and drug metabolism and pharmacology studies. High-throughput screenings in the early phase for metabolic stability, protein binding, permeability (ADME) and bioavailability are widely used to weed out compounds that do not exhibit the necessary characteristics. For such high-throughput LC/MS studies, a generic LC/MS method that can be used for a variety of compounds is desired. In this study, we used a small set of compounds with a wide range of properties to guide method development, and achieved a sample throughput of 1.7 min/sample. Here, we present a generic fast method that achieves good peak separation and peak shape on conventional HPLC systems using a column-switching mechanism for on-line solid-phase extraction (SPE)-HPLC/MS analysis. The method has a linear response range from 1 to 500 nM for the tested compounds. When a larger set of 658 randomly picked small molecules were analyzed using this method, 612 were observed with good signal intensity and HPLC peak shapes. This generic fast SPE-LC/MS method has been used to screen more than 1.5 million compounds repetitively against over 200 protein targets for hit confirmation and semi-quantitation of binding constants from biological assays. Over 7000 different compounds for a variety of protein-binding assays have been studied using this method for quantitative analysis as well.