Computer-Aided Drug Design 2003-2005

BY BERNARD COUPEZ, HENRIK MOBITZ AND RICHARD A. LEWIS

Novartis Institutes for Biomedical Research, Basel CH-4002, Switzerland

1 Introduction

The themes for this review again have been driven strongly by the need of the Pharmaceutical industry to make the discovery process quicker and more reliable. Virtual screening in all its forms is at the heart of most research, from bioavailability niters through to rigorous estimations of the free energy of binding. Two areas of relative heat have been docking/scoring, and ADME/ Tox. On the other hand, 3D-QSAR and pharmacophores have become quiet. Part of the reason for this may arise from the successes in high-throughput crystallography, delivering more targets and complexes, the relative failure of HTS, and the increase in the amount of high quality data coming from late-phase research/early-phase development concerning the fate of clinical candidates. These trends look set to continue in the future, and the next two years should yield many new breakthroughs.

2 ADME/Tox and Druggability

There has been a fresh impetus to the modelling of ADME, Toxicity and druggability phenomena, partly driven by a desire to understand why such complex phenomena can, apparently, be described so simply, and partly to see if better models can be built, to improve the attrition rate in medicinal chemistry still further.

2.1 Druggability and Bioavailability. – In the continuing debate over what physicochemical properties are required for bioavailability, Vieth et al. have surveyed 1729 marketed drugs with respect to their route of administration, h-bonding capability, lipophilicity and flexibility. One conclusion they draw is that these properties have not varied substantially over time, implying that oral bioavailability is independent of target or molecular complexity. Compounds with lower molecular weight, balanced lipophilicity and less flexibility tend to be favoured. Leeson and Davis claim that molecular weight, flexibility, the number of O and N atoms and hydrogen-bond acceptors have risen, by up to 29%. This may be partly due to the choice of 1983 as the reference year, or the advent of more complex targets with greater selectivity needs (e.g. kinases). In the same vein, a study re-examined the correlation of flexibility and polar surface area (PSA) with bioavailability proposed by Veber et al. One conclusion is that there are significant differences in the ways of denning flexibility and PSA, and the correlations depend markedly on the method used (this is not surprising, as neither quantity is precisely definable). A second conclusion was that the limits denned (Number of rotatable bond< 10, PSA< 140 Å2 excluded a significant number of compounds with acceptable rat bioavailability. In the authors' words, "This observation underscores the potential danger of attempting to generalise a very complicated endpoint and of using that generalisation in a prospective selection application". Despite this, another bioavail-ability score has been devised, to predict the probability that a compound has > 10% bioavailability in the rat. Compounds are grouped by ionisation class (anions, cations, neutral). It was found that the standard rule-of-5 does well for cations and neutrals (88% of the compounds predicted to have low bioavailability are observed as such). Anionic compounds were better described by PSA limits. Some simple rules are given to compute the bioavailability score. In Abbott laboratories, this score is now routinely computed for all compounds and is used for hit-list triaging. It will be interesting to see if the results can be repeated on other data sets; the paper has certainly sparked much interest in the modelling community. Wegner provides support for the idea that human intestinal absorption correlates with PSA, by generating a classification model. The justification is that the error in the experimental data is 25%, and 80% of the observations occur in the top and bottom quartiles, that is, the data is more binary than evenly spread. In addition to PSA, other descriptors that reflect the electronic character of atoms and their environment also came to the fore.

2.2 Metabolism, Inhibitors and Substrates. – The field of cytochrome modelling is becoming more mature as we begin to understand the limitations of the experimental data and the subtleties of the mechanisms (the whole field of cytochrome P450 modelling, including homology, pharmacophore and 3D-QSAR models has been reviewed in detail recently). Empirical models are still preferred, especially for rapid evaluation of large libraries. In one case, use of a jury system improved prediction accuracy to over 90%. Chohan et al. have developed 4 models for Cytochrome P450 (Cyp) 1A2 inhibition, and identified the expected descriptors as being important to the QSAR (lipophilicity, aromaticity, HOMO/LUMO energies). Perhaps a more interesting result in this paper was the use of the k index to assess predictive powers of the models using test data.

k = observed agreement-chance agreement/total observed-chance agreement

This index should prove useful for data sets that are diverse and noisy. The validity of QSAR model predictions has also been studied by Guha and Jurs. The protocol is quite straightforward. The initial QSAR models were built, and the residuals of the compounds in the training set were used to classify the trains set predictions into good and bad. The threshold for the classification is arbitrary. Test compounds were predicted, and the predictions were grouped by substructural similarity to the nearest neighbour in the training set. It was seen that test compounds that had neighbours with low/good residuals were themselves well-predicted, with the reverse being the case for neighbours with high residuals. The success rate for classifying the strength of the prediction was 73% to 94%. The Merck group performed a retrospective study of in-house data sets, and concluded that the distance to the nearest neighbour, and the number of nearest neighbours (local density) were the two most useful measures for predicting prediction quality. They also concluded that distance does not have to be measured in the same descriptor space as was used to build the QSAR model. Topological descriptors combined with a Dice coefficient worked equally well.

A number of groups have been active in the prediction of the most likely sites of metabolism of molecules that are...