Passive lipoidal diffusion and carrier-mediated cell uptake are both important mechanisms of membrane permeation in drug disposition.

Recently, it has been proposed that drug permeation is essentially carrier-mediated only and that passive lipoidal diffusion is negligible. This opposes the prevailing hypothesis of drug permeation through biological membranes, which integrates the contribution of multiple permeation mechanisms, including both carrier-mediated and passive lipoidal diffusion, depending on the compound's properties, membrane properties, and solution properties. The prevailing hypothesis of drug permeation continues to be successful for application and prediction in drug development.

Predictivity of Simulated ADME AutoQSAR Models over Time.

The automation of model building and model updating (autoQSAR) is an important step forward towards real-time small molecule drug discovery project support using the latest experimental data. We present here a simulation study using real company data of the behaviour of QSAR models over time. Three different global QSAR models, namely, human plasma protein binding, aqueous solubility and log D7.

Improving compound quality through in vitro and in silico physicochemical profiling.

Many compounds entering clinical studies do not survive the numerous hurdles for a good pharmacological lead to a drug on the market. The reasons for attrition have been widely studied which resulted in more early attention to compound quality related to physical chemistry, drug metabolism and pharmacokinetics (DMPK), and toxicology/safety. This paper will briefly review current physicochemical in vitro assays and in silico predictions to support compound and library design through to lead optimization.

QSAR modeling using automatically updating correction libraries: application to a human plasma protein binding model.

It is assumed that compounds occupying the same region of model space will be subject to similar errors in prediction, and hence, where these errors are known, they can be applied to predictions. Thus, any available measured data can be used to refine predictions of query compounds. This study describes the application of a correction library to a human plasma protein binding model.

From in vivo to in vitro/in silico ADME: progress and challenges.

High-throughput screening technologies in biological sciences of large libraries of compounds obtained via combinatorial or parallel chemistry approaches, as well as the application of design rules for drug-likeness, have resulted in more hits to be evaluated with respect to their ADME or drug metabolism and pharmacokinetic properties. The traditional in vivo methods using preclinical species, such as rat, dog or monkey, are no longer sufficient to cope with this demand. This editorial discusses the changes towards medium- to high-throughput in vitro and in silico ADME screening.

Utility of human/human-derived reagents in drug discovery and development: An industrial perspective.

The shift to combinatorial chemistry and parallel synthesis in drug discovery has resulted in large numbers of compounds entering the lead seeking and lead development phases of the process. To support this, higher throughput computational (in silico) and in vitro approaches have become the forefront of the drug metabolism and pharmacokinetic (DMPK) input into drug discovery. This has been accompanied by a shift in focus from animal-derived data to human based studies, reflecting the realisation that extrapolation from animals to human has its limitations.

Biosensor analysis of the interaction between drug compounds and liposomes of different properties; a two-dimensional characterization tool for estimation of membrane absorption.

The interactions between 78 drug compounds and immobilised liposomes were investigated using an assay based on surface plasmon resonance technology. The drugs were screened at a single concentration and allowed to interact simultaneously with two different types of liposomes. When the drug-liposome responses are plotted against one another they generally fall into three distinct bands: low response-low percent fraction absorbed in humans (Fa), medium response-medium Fa, and high response-high Fa.

Which in vitro screens guide the prediction of oral absorption and volume of distribution?

The development of medium to high-throughput in vitro screening of ADME (Absorption, Distribution, Metabolism, Excretion) properties has been the reply to higher demands on drug metabolism scientists to cope with progress in chemistry and biology. Two areas will be discussed here, namely screens for oral absorption and for volume of distribution. The prediction of these human pharmacokinetic parameters can be based on proper combination of simple physicochemical measurements.

Prediction of oral bioavailability by adaptive fuzzy partitioning.

An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds.

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.

High-throughput and in silico techniques in drug metabolism and pharmacokinetics.

The high-throughput screening (HTS) of large proprietary compound collections and combinatorial libraries has increased the pressure on gathering pharmacokinetic and drug metabolism data as early as possible. Properties related to absorption, distribution, metabolism and excretion (ADME) can be estimated by a range of in vivo and in vitro methods, most of which are now available or under development in high(er)-throughput modus. In addition, progress has been made in in silico methods using various quantitaTive structure-activity relationship (QSAR) and molecular modeling techniques that employ a range of recently introduced descriptors tailored to e-ADME.