Current Topics in Medicinal Chemistry - Volume 3, Issue 11, 2003

The development of computational tools for the prediction of passive transport is reviewed with particular reference to four diverse approaches: the rule-of-5, polar surface area, Volsurf and Abraham's General Solvation Equation. To illustrate the current state of the art, several examples of the application of in silico methods in drug design projects drawn from the recent medicinal chemistry literature are presented. In conclusion, the current challenges facing practitioners of this discipline are outlined and possible directions towards their resolution are suggested.

Drug discovery is a long, arduous process broadly grouped into disease target identification, target validation, high-throughput identification of “hits” and “leads”, lead optimization, and pre-clinical and clinical evaluation. Each area is a vast discipline in itself. However, all but the first two stages involve, to varying degrees, the characterization of absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of the molecules being pursued as potential drug candidates. Clinical failures of about 50% of the Investigational New Drug (IND) filings are attributed to their inadequate ADMET attributes. It is, therefore, no surprise that, in the current climate of social and regulatory pressure on healthcare costs, the pharmaceutical industry is searching for any means to minimize this attrition. Building mathematical models, called in silico screens, to reliably predict ADMET attributes solely from molecular structure is at the heart of this effort in reducing costs as well as development cycle times. This article reviews the emerging field of in silico evaluation of ADME characteristics. For different approaches that have been employed in this area, a critique of the scope and limitations of their descriptors, statistical methods, and reliability are presented. For instance, are geometry-based descriptors absolutely essential or is lower-level structure quantification equally good? What advantages, if any, do we have for methods like artificial neural networks over the least squares optimization methods with rigorous statistical diagnostics? Is any in silico screen worth application, let alone interpretation, if it is not adequately validated? Once deemed acceptable, what good is an in silico screen if it cannot be made available at the workbench of drug discovery teams distributed across the globe throughout multi-national pharmaceutical companies? These are not mere discussion points, rather this article embarks on the stepwise mechanics of developing a successful in silico screen. The process is exemplified by our efforts in developing one such screen for predicting metabolic stability of chemicals in a human S9 liver homogenate assay. A real-life use of this in silico screen in a variety of discovery projects at GlaxoSmithKline is presented, highlighting successes and limitations of such applications. Finally, we project some capabilities of in silico ADME tools for greater impact and contribution to successful, efficient drug discovery.

Cytochromes P450 (Cyt P450s) constitute the most important biotransformation enzymes involved in the biotransformation of drugs and other xenobiotics. Because drug metabolism by Cyt P450s plays such an important role in the disposition and in the pharmacological and toxicological effects of drugs, early consideration of ADME-properties is increasingly seen as essential for the discovery and the development of new drugs and drug candidates.The primary aim of this paper is to present various computational approaches used to rationalize and predict the activity and substrate selectivity of Cyt P450s, as well as the possibilities and limitations of these approaches, now and in the future. Attention is also paid to the experimental validation of these approaches by using highthroughput screening (HTS) of affinities to drug-drug interactions at the level of Cyt P450-isoenzymes. Since human Cyt P450 2D6 is one of the most important drug metabolizing enzymes and since in this regard much pioneering work has been done with this Cyt P450, Cyt P450 2D6 is chosen as a model for this discussion.Apart from early mechanism-based ab initio calculations on substrates of Cyt P450 2D6, pharmacophore modeling of ligands (i.e. both substrates and inhibitors) of Cyt P450 2D6 and protein homology modeling have been used succesfully for the rationalisation and prediction of metabolite formation by this Cyt P450 isoenzyme. Significant protein structurerelated species differences have been reported recently.It is concluded that not one computational approach is capable of rationalizing and reliably predicting metabolite formation by Cyt P450 2D6, but that it is rather the combination of the various complimentary approaches. It is moreover concluded, that experimental validation of the computational models and predictions is often still lacking. With the advent of novel, easily and well applicable in vitro based high throughput assays for ligand binding and turnover this limitation could be overcome soon, however. When effective links with other new and recent developments, such as bioinformatics, neural network computing, genomics and proteomics can be created, in silico rationalisation and prediction of drug metabolism by Cyt P450s is likely to become one of the key technologies in early drug discovery and development processes.

An overview of the combined quantum mechanical / molecular mechanical (QM / MM) approach and its application to studies of biotransformation enzymes and drug metabolism is given. Theoretical methods to simulate enzymatic reactions have rapidly developed during the last decade. In particular, QM / MM methods provide detailed insights into enzyme catalyzed reactions, which can be extremely valuable in complementing experimental research. QM / MM methods allow the reacting groups in the active site of an enzyme to be studied at a quantum mechanical level, while the surrounding protein and solvent is included at a classical (and computationally less expensive) molecular mechanical level. Existing QM / MM implementations vary in the level of interaction between the QM and MM regions and in the way the partitioning into QM and MM regions is setup. Some general considerations concerning reaction modeling are discussed and a number of QM / MM studies related to drug metabolism are described. These studies illustrate that theoretical modeling of important metabolic reactions provides detailed insights into mechanisms of reaction and specific catalytic effects of enzyme residues as well as explaining variation in rates of conversion of different metabolites. Such information is essential in the development of methods to predict metabolism of drugs and to understand metabolic effects of genetic polymorphism in biotransformation enzymes.

Simulation models for the prediction of pharmacokinetics in humans and other mammalian species, which are based on the physiology and mechanistic models of absorption, distribution, metabolism and elimination are reviewed. The structure of such models is explained with reference to papers describing the mathematical details and alternative representations of organ flow and distribution. Approaches to the modelling of more complex tissues such as tumours and the liver are also reviewed as well as some specific transport processes such as biliary secretion and methods of ADME property estimation by experimental and in silico models. Specific approaches to the modelling of gastro-intestinal transit are explained as is the extension of the approach to simulating drug-drug interactions following co-administration of more than one drug.

A “snapshot” of current medicinal chemistry work on bioavailability is drawn from issues of J. Med. Chem. covering the time period between September 2001 and September 2002. An exhaustive compilation of reported absolute oral bioavailability (F) values for this period is included, covering 34 structural series and 107 distinct compounds, with data for multiple species in many cases. This is supplemented with a discussion of more qualitative oral bioavailability results, and with illustrative examples addressing clearance, prodrug design, and blood / brain barrier penetration problems. Papers discussing predictions pertaining to one or another aspect of bioavailability are also discussed, and some thoughts on future directions of work on in silico prediction in this area are presented.

Mutagenicity and carcinogenicity are chronic effects of primary concern for human health. A unifying approach to their mechanistic understanding is the recognition that many chemicals provoke both effects by electrophilic attack to the biological macromolecules, as such or after metabolism (genotoxic carcinogenicity). QSARs of individual classes of genotoxic carcinogens have contributed to the elucidation of the chemical determinants of this activity. Little work has been done on the epigenetic carcinogens, acting through non-genotoxic, very specific mechanisms. However, the existing QSARs for individual chemical classes are too few to be of real usefulness in the screening of masses of candidate drugs. Models for predicting the carcinogenicity of “any type” of chemicals have been proposed: prospective prediction exercises pointed to the serious limitations of most of these approaches. The best alternative is provided by panels of human experts. The above prediction exercises considered samples of general chemicals, thus we specifically addressed in this paper the issue of pharmaceutical drugs. We applied our expert knowledge to a database of drugs whose carcinogenicity / noncarcinogenicity status was known. Whereas most of the noncarcinogens were correctly identified, our prediction of carcinogens was less successful than with the general chemicals. Several carcinogenic drugs did not show recognized structural alerts, and supposedly acted by epigenetic mechanisms. Whereas the contribution of human experts is highly valuable in this phase (e.g. priority setting), more work is necessary on: a) epigenetic carcinogens; b) efficient computerized models.