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.

ADMET in silico modelling: towards prediction paradise?

In the absence of effective vaccines to control herpesvirus infections, nucleosidic antiviral drugs have been the mainstay of clinical treatment since their development in the late 1970s. However, given the drawbacks of these drugs, including the increasing emergence of drug-resistant clinical isolates, new strategies for treating herpesvirus infections are warranted. A range of promising new drugs with novel molecular targets has been developed, but will they cure latent infections?

Antiherpesvirus drugs: a promising spectrum of new drugs and drug targets

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

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Recent advances in computer-aided drug design

A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topological polar surface area, the predicted distribution coefficient at pH ) 6.5, the number of violations of the Lipinski’s rule-of-five, and the square of the number of hydrogenbond donors. The model was able to predict the fractional absorption with an r ) 0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r ) 0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA e 30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http:// modem.ucsd.edu/adme.

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ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification.

A data set consisting of 712 compounds was used for classification into two classes with respect to membrane permeation in a cell-based assay:  (0) apparent permeability (Papp) below 4 × 10-6 cm/s ...

In Silico Prediction of Membrane Permeability from Calculated Molecular Parameters

Theoretically-derived molecular descriptors important in human intestinal absorption.

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

Recent evidence suggests an important role for the kallikrein-kinin system in the pathophysiology of hyperalgesia, arthritis, inflammatory bowel disease, pancreatitis, asthma and endotoxemia. In fact, hyperactivity of kinins can be considered as pro-inflammatory because of their ability to cause all the cardinal signs of inflammation directly activating their B1 and B2 receptors. Kinins are also able to release other potent inflammatory mediators. In endotoxemia, excessive kinin release may account for inducing severe life threatening hypotension. Several specific kinin receptor antagonists are being developed with the major interest in treating these pathological conditions to block the activation of kinin receptors. Furthermore, kallikrein inhibitors may have a role in the treatment of arthritis and inflammatory gut diseases to block the excessive release of kallikrein. On the other side, all the components of the kallikrein-kinin system are located in the vascular smooth muscle as well as in the heart. From numerous observations obtained from clinical and experimental models of diabetes, hypertension, cardiac failure, ischemia, myocardial infarction and left ventricular hypertrophy, it can be assumed that the reduced activity of the local kallikrein-kinin system may be instrumental for the induction of cardiovascular related diseases. The ability of kallikrein gene delivery to produce a wide spectrum of beneficial effects makes it an excellent candidate in treating hypertension, cardiovascular and renal diseases. In addition, stable kinin agonists may also be available in the future as therapeutic agents for cardiovascular and renal disorders.

A.Pauzi M. Yusof

Papers

Theoretically-derived molecular descriptors important in human intestinal absorption.

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

Pathophysiological activities of the kallikrein-kinin system with emphasis on the cardiovascular disorders

Evaluation of tissue kallikrein activity on survival time after acute coronary artery ligation in hypertensive rats.