Can the Internet help to meet the challenges in ADME and e-ADME?

Biophysical Techniques for Target Validation and Drug Discovery in Transcription-Targeted Therapy

In the post-genome era, pathologies become associated with specific gene expression profiles and defined molecular lesions can be identified. The traditional therapeutic strategy is to block the identified aberrant biochemical activity. However, an attractive alternative could aim at antagonizing key transcriptional events underlying the pathogenesis, thereby blocking the consequences of a disorder, irrespective of the original biochemical nature. This approach, called transcription therapy, is now rendered possible by major advances in biophysical technologies. In the last two decades, techniques have evolved to become key components of drug discovery platforms, within pharmaceutical companies as well as academic laboratories. This review outlines the current biophysical strategies for transcription manipulation and provides examples of successful applications. It also provides insights into the future development of biophysical methods in drug discovery and personalized medicine.

Introduction to cheminformatics

Cheminformatics is a relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. The chemical data of interest typically includes information on small molecule formulas, structures, properties, spectra, and activities (biological or industrial). Cheminformatics originally emerged as a vehicle to help the drug discovery and development process, however cheminformatics now plays an increasingly important role in many areas of biology, chemistry, and biochemistry. The intent of this unit is to give readers some introduction into the field of cheminformatics and to show how cheminformatics not only shares many similarities with the field of bioinformatics, but that it can also enhance much of what is currently done in bioinformatics.

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r2 = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r2 = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r2 = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.

In silico prediction of ADMET properties: how far have we come?

There have been considerable advances in the last few years in both the quantity and the quality of in silico ADMET property predictions. Most ADMET properties are now computable, and the accuracy of some of the software predictions for physicochemical properties in particular is close to that of measured data. There is, however, universal agreement that more good experimental ADMET data are needed for use in in silico model development, for models are only as good as the data on which they are based. Many data remain confidential but it is to be hoped that, with projects such as the Vitic toxicity database, being developed by Lhasa Limited, pharmaceutical companies will be prepared to release data to an 'honest broker' on a confidential basis, so that better in silico models can be developed. Incorporation of calculated ADMET properties into drug discovery and development is a multi-factorial problem and really needs a multi-factorial solution. Some progress is being made in this direction and it is hoped that within the foreseeable future software will be available for this purpose.

Recent advances in pharmacokinetic extrapolation from preclinical data to humans

The early characterisation of drug metabolism and pharmacokinetic (DMPK) properties of new chemical entities plays a key role in the pharmaceutical industry's effort to reduce attrition. Specifically, a major goal of early DMPK studies is to accurately predict the behaviour of new chemical entities in humans, thus allowing likely failures to be terminated rapidly and resource to be placed on molecules most likely to succeed. The present review summarises progress over the past several years in the key technologies used in the pharmaceutical industry to achieve these goals: namely, in vivo, in vitro and in silico/computational tools. The limitations of the various assays are discussed, with attention also given to likely future directions in this field.

Bioinformatics in drug development and assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.

ADMET in silico modelling: towards prediction paradise?

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.