Combinatorial Chemistry & High Throughput Screening - Volume 14, Issue 5, 2011

The importance of optimizing Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME/T) properties for potential drug candidates has been widely recognized. Although great progress has been made on high throughput (HT) ADME experimental assays, compared with high throughput screening (HTS) activity assays or combinatorial synthesis, the traditional ADME/T experiments still have low throughput capacity. Consequently, there is increasing interest in the development of in silico approaches for predicting the important ADME/T properties. In silico models have great potentials to predict in vitro behavior and in vivo ADME properties quickly to assist in prioritizing the large numbers of compounds, while reducing the need for experiments. In this special issue, we provide an overview of in silico approaches and important published models for predicting ADME/T properties from chemical structures. This special issue has been arranged in 8 contributions. The first two reviews by Rupp et al. and Wang et al. survey the literature on computational methods to predict the pKa and solubility of small molecules, two important molecular features related to many ADME properties. Geerts and Vander Heyden provide a summary of the in silico modeling of both Caco-2 permeability and human intestinal absorption. Hou and collaborators review present knowledge and recent progress related to the in silico prediction of an essential ADME property, oral bioavailability. Taboureau and Jorgensen review the theoretical approaches to predict hERG blockers and discuss potential integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia. Two important contributions from Zhang et al. and DeLisle et al. present the in silico approaches for the prediction of CYP-mediated drug metabolism. Finally, Mohan provides an insight into the impact of toxicoinformatics that allow for the prediction of toxic effects triggered by pharmaceuticals. I would like to thank to all of the authors for their excellent contributions, and the Editor-in-Chief of Combinatorial Chemistry & High Throughput Screening for his kind invitation to act as guest editor for this special issue.

The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups, commonly expressed as the negative decadic logarithm pKa of the acid dissociation constant (Ka). We survey the literature on computational methods to predict the pKa of small molecules. In this, we address data availability (used data sets, data quality, proprietary versus public data), molecular representations (quantum mechanics, descriptors, structured representations), prediction methods (approaches, implementations), as well as pKa-specific issues such as mono- and multiprotic compounds. We discuss advantages, problems, recent progress, and challenges in the field.

Aqueous solubility is one of the major physiochemical properties to be optimized in drug discovery. It is related to absorption and distribution in the ADME-Tox (Absorption, Distribution, Metabolism, Excretion, and Toxicity). Aqueous solubility and membrane permeability are the two key factors that affect a drug's oral bioavailability. Because of the importance of aqueous solubility, a lot of efforts have been spent on developing reliable models to predict this physiochemical property. Although some progress has been made and a lot of models have been constructed, it is concluded that accurate and reliable aqueous models targeted to predict solubility of drug-like molecules, have not emerged based on the outcome of an aqueous solubility prediction campaign sponsored by Goodman et al. In this review paper, we provide a snapshot of the latest development in the field. The challenges of developing high quality aqueous solubility models as well as the strategies of surmounting those challenges have been discussed. We conclude that the biggest challenge of modeling aqueous solubility is to collect more high quality, unskewed and drug-relevant solubility data which are sufficient diverse to cover most the chemical space of drugs. The second challenge is to develop good descriptors to account for the lattice energy of solvation. In order to develop accurate and predictable in silico solubility models, the key is to collect a sufficient number of high quality experimental data and the suspicious data must be verified. In addition, the molecular descriptors must be relevant to the energies in the solvation process (the lattice energy for crystal packing, the energy of forming cavity in solvent, and the solvation energy), and the models must be carefully cross-validated and evaluated using the external data sets.

The accurate prediction of the in vivo pharmacokinetics of a new potential drug compound based on only its virtual structure is the ultimate goal of in silico ADME-Tox property modeling. A comprehensive review is made on recent studies concerning the A (absorption) in ADME-Tox, i.e. the in silico modeling of both Caco-2 permeability and human intestinal absorption. The data sets used, the descriptors selected to build the models, the variable selection methods, the modeling techniques and the performed model validation are critically discussed. It was concluded that reliable models which improve the success rate of compound selection and drug development are still lacking. Limiting the quality of the models are, for instance, inappropriate compilation of data sets, lack of an appropriate outlier detection and unrepresentativeness of training and test sets for the data population. The definition of some best practices or guidelines for the different steps of the modeling procedure might improve the predictions and make the procedure uniform, i.e. “standard tools” in drug development would become available.

Unfavorable oral bioavailability is an important reason accounting for the failure of the drug candidates. Considering the lack of in vitro high-throughput screening assay for oral bioavailability, it is critical to develop in silico models for early predictions of oral bioavailability. In this review, we summarize present knowledge and recent progress related to the in silico prediction of oral bioavailability, including the current available datasets of oral bioavailability in human, the roles of physiochemical properties contributing to oral bioavailability, and the available theoretical models to predict oral bioavailability. Particularly, the regression model recently developed by us was demonstrated, which is based on the largest dataset of oral bioavailability in human. Although promising progress has been made recently, it is still indispensable to improve the accuracy of the models to predict oral bioavailability.

The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.

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.

Cytochrome P450 enzymes are the predominant mediators of phase I metabolism of exogenous small molecules. As a result of their extensive role in metabolism of xenobiotics, drug compounds, and endogenous compounds, as well as their wide tissue distribution, significant drug discovery resources are spent to avoid interacting with this class of enzymes. Here we review historical and recent in silico modeling of 7 cytochrome P450 enzymes of particular interest, specifically CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. For each we provide a brief biological background including known inhibitors, substrates, and inducers, as well as details of computational modeling efforts and advances in structural biology. We also provide similar details for 3 nuclear receptors known to regulate gene expression of these enzyme families.

Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computerassisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple R's- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.

Tingjun Hou is currently the Professor for the Institute of Functional Nano & Soft Materials (FUNSOM) at Soochow University in Suzhou, T.J. Hou's areas of scientific interest include: developing methods for computer-aided drug design (CADD) and designing potential lead compounds for important drug targets using the CADD techniques; inferring the proteinprotein interaction networks mediated by modular domains using molecular modeling/bioinformatic techniques and biological experiments; investigating the molecular mechanisms of drug resistance and developing computational models for drug resistance. Tingjun holds a B.Sc in Chemistry from Peking University (1997) and Ph.D in Computational Chemistry from Peking University (2002). Tingjun worked as a Postdoctoral Fellow and a Research Scientist, in the College of Chemistry and Molecular Engineering at Peking University (2002∼2004) and the Department of Chemistry and Biochemistry at UCSD (2004∼2009). In 2009, Tingjun joined Soochow University as a Full Professor. Tingjun has co-authored more than 130 peer reviewed manuscripts, 4 book chapters and 1 multimedia courseware. He is the Associate Editor of Theoretical Biology & Medical Modelling and a member of the Editorial Advisory Board for the journals: Current Pharmaceutical Design, Mini- Reviews in Medicinal Chemistry, Current Computer-Aided Drug Design and Medicinal Chemistry.....