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. Proponents of the carrier-mediated only concept argue against passive lipoidal diffusion. However, the arguments are not supported by broad pharmaceutics literature. The carrier-mediated only concept lacks substantial supporting evidence and successful applications in drug development.

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. The most important physicochemical properties include lipophilicity (log P/D), pKa, solubility, and permeability. These drive key ADMET properties such as absorption, cell penetration, access to the brain, volume of distribution, plasma protein binding, metabolism, and toxicity, as well as biopharmaceutical behavior. Much data are now available from medium- to high-throughput physchem and ADMET in vitro assays, either in the public domain (see, e.g., PubChem, PubMed) or in drug companies' in-house databases. Such data are increasingly being computer-modelled and used in predictive chemistry. New pipelining technology makes it easier to build and update QSAR models so that such models can use the latest available data to produce robust local and global predictive in silico ADMET models.

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.

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. In silico approaches may be regarded as human derived tools for DMPK, since models (template/pharmacophore and protein homology modelling), for example, for the human CYP enzymes, are widely used for identifying qualitatively enzyme/substrate interactions. Quantitative assessment of drug metabolism using human hepatocytes or sub-cellular fractions provide a valuable tool both for the screening out of high metabolic lability and in estimations of human intrinsic clearance. In terms of drug absorption, the human colon adenocarcinoma cell line, Caco-2, offers a versatile human derived system for measuring drug permeability, despite over expression of the efflux transporter P-glycoprotein (P-gp). The importance of P-gp can then be further assessed in recombinant systems expressing the human P-gp, where substrate affinity and inhibition potency can be measured, important factors when considering transporter mediated drug-drug interactions. The primary cause of pharmacokinetic-based drug-drug interactions (DDIs) is through enzyme inhibition or induction, with the CYP enzymes being of major importance. Human liver microsomes and hepatocytes are invaluable tools in assessment of DDI vulnerability of new chemical entities, having the capacity to identify enzymes responsible for specific routes of metabolism, and hence areas of vulnerability for a DDI. In addition, human-based screening tools can be used to identify the perpetrator of a DDI through enzyme inhibition/induction. Large differences in the nature of enzymes induced and the extent of induction when comparing animals to man are known. Thus, in vitro models allowing assessment of induction potential in human tissue, establishes some relevance to the clinical situation.

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. For drugs with medium to high Fa values, basic compounds could be resolved from acidic and neutral compounds to a large extent. This technique has the potential to be utilized as a screening tool for binning novel compounds into low, medium, or high Fa based on a simple experimental measurement. The assay was applied to 11 kinase inhibitors, 9 thrombin inhibitors, and 11 carbonic anhydrase inhibitors highlighting a subset that may have incomplete intestinal absorption (low to medium Fa). Assay conditions were optimized making the assay suitable for routine analysis and for compound characterization early in drug discovery where solubility may be an issue.

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. In the future in vitro screens most likely will be combined with in silico assessments of various ADME properties leading to the concept of in combo screening in drug discovery.

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. In addition, much more attention is now put on early safety and risk assessment of promising lead series and potential clinical candidates.

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. These in silico approaches are promising filters for virtual libraries to aid synthesis as well as the selection of compounds for acquisition and screening in the early stages of drug discovery.

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. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.

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 D7.4 , are updated on a monthly basis over a period of three years. The effect of updating the models on their predictivity is studied using a series of monthly temporal test sets in addition to a final terminal temporal test set. Partial Least Squares (PLS), Random Forest (RF) and Bayesian Neural Networks (BNN) models are examined, covering three distinctly different approaches to QSAR modelling. It is demonstrated that the models are able to predict forward in time, but that updating models on a regular basis increases their ability to make predictions for current compounds. The degree of the improvement depends on the property studied and the model building technique used. These results demonstrate the importance of updating models on a regular basis. For both static models predicting forward in time, and regularly updating models it is shown that RF models are the most predictive for these data sets.

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. Compounds that have been measured since the model was built are entered into the library to improve predictions of current compounds. Time-series simulations were conducted to measure the time dependence of the correction library. This study demonstrates significant improvements in predictions where a library is applied, compared with both a static model and an updating model that includes recently measured data.