Serotonin (5-HT) receptors are neuromodulator neurotransmitter receptors which when activated generate a signal transduction pathway within cells resulting in cell-cell communication. 5-hydroxytryptamine (serotonin) receptor 2B (5-HT2B) is a subtype of the seven members of 5-hydroxytrytamine (5-HT) family of receptors which is the largest member of the super family of 7-transmembrane G-protein coupled receptors (GPCRs). Not only do 5-HT receptors play physiological roles in the cardiovascular system, gastrointestinal and endocrine function and the central nervous, but they also play a role in behavioral functions. In particular 5-HT2B receptor is wide spread with regards to its distribution throughout bodily tissues and is expressed at high levels in the lungs, peripheral tissues, liver, kidney and prostate just to name a few. Hence 5-HT2B participates in multiple biological functions including CNS regulation, regulation of gastrointestinal motality, cardiovascular regulation and 5-HT transport system regulation. While 5-HT2B is a viable drug target and has therapeutic indications for treating obesity, psychotherapy, Parkinson's disease etc. there is a growing concern regarding adverse drug reactions, specifically valvulopathy associated with 5-HT2B agonists. Due to the sequence homology experienced by 5-HT2 subtypes there is also a concern regarding the off target effects of 5-HT2A and 5-HT2C agonists. The concept of subtype selectivity is of paramount importance and can be tackled with the aid of in silico studies, specifically cheminformatics, to develop models to predict valvulopathy associated toxicity of drug candidates prior to clinical trials. This review has highlighted three in silico approaches thus far that have been successful in either predicting 5-HT2B toxicity of molecules or identifying important interactions between 5-HT2B and drug molecules that bring about valvulopathy related toxicities.

Glucuronidation has been recognized as an important clearance mechanism in humans. Therefore, knowledge about the contribution of glucuronidation to clearance of drug candidates is of great value for early drug development. In this article, we discuss the recent progress made to predict in vivo glucuronidation parameters (e.g., hepatic clearance, and intestinal availability) using in vitro data, which are readily obtained using microsomes and hepatocytes, so called "in vitro-in vivo extrapolation" (IVIVE). Of note the intrinsic clearances obtained from microsomal incubations in the presence of bovine serum albumin (BSA) provide accurate predictions of the in vivo clearances in addition to those from hepatocytes. Further, we describe the lack of correlation between cellular and microsomal production of glucuronide and provide possible reasons. Due to the high prediction accuracy, those who study in vitro glucuronidation are encouraged to map their data to in vivo using IVIVE strategy for more informative data interpretation.

One of the major reasons for late-stage failure of drug candidates is due to problems uncovered in pharmacokinetics during clinical trials. There is now a general consensus for earlier consideration of these effects in the drug discovery process. Computer-aided design technology provides us with tools to develop predictive models for such pharmacokinetic properties. Among these tools, we focus on pharmacophore modeling techniques in this article. Pharmacophore models that are reported for various cytochrome P450 (CYP) enzymes are reviewed for the isoenzymes CYP1A2, 2B6, 2C9, 2C19, 2D6, 2E1, and 3A4. In addition pharmacophore models for related metabolic processes through CYP19 (aromatase), CYP51 (14α-lanosterol demethylase), PXR (pregnane X-receptor), and finally for human intrinsic clearance are also reviewed. The models reported by various scientists are schematically represented in the figures in order to visually demonstrate their similarities and differences. The different models developed by different researchers or sometimes even by the same research group for different sets of ligands, provide a clear picture of the challenges in coming up with a single model with good predictive values. The main reason for this challenge is due to the relatively large size of the active sites and flexibility of the CYP isoenzymes, resulting with multiple binding sites. We propose development of multiple-diverse pharmacophore models for each binding mode (as opposed to a single predictive model for each CYP isoenzyme). After scoring and prioritization of the models, we propose the use of a battery of pharmacophore models for each CYP isoenzyme binding mode to computationally obtain a P450 interaction profile for drug candidates early in the drug development cycle, when decisions on their fate can be made before incurring cost of synthesis and testing.

The blockage of the voltage dependent ion channel encoded by human ether-a-go-go related gene (hERG) may lead to drug-induced QT interval prolongation, which is a critical side-effect of non-cardiovasular therapeutic agents. Therefore, identification of potential hERG channel blockers at the early stage of drug discovery process will decrease the risk of cardiotoxicity-related attritions in the later and more expensive development stage. Computational approaches provide economic and efficient ways to evaluate the hERG liability for large-scale compound libraries. In this review, the structure of the hERG channel is briefly outlined first. Then, the latest developments in the computational predictions of hERG channel blockers and the theoretical studies on modeling hERG-blocker interactions are summarized. Finally, the challenges of developing reliable prediction models for hERG blockers, as well as the strategies for surmounting these challenges, are discussed.

Permeability is important in governing the ability of drug substances to transport across gastrointestinal membrane and also crucial for proper drug distribution to pharmacological target organs and cells, is therefore frequently utilized in drug discovery and development. In this report, we have performed a systematic analysis, using principal component analysis on the historically measured permeability data from in-house Caco-2 and parallel artificial membrane permeability assays on discovery new chemical entities from multiple projects. This work allows for establishment of a permeability diagnosis model by purposefully identifying most influencing physicochemical properties of the permeability issues, including polarity-lipophilicity line contributed primarily by polar surface area and logP, number of rotation bond, fractional ionization at neutral pH and efflux ratio. A number of cases were also shown to demonstrate the applicability of the current model. The analysis of the model over internal drug discovery compounds exhibited promising diagnostic and predictive power of the model. The advantages and limitation of the model as well as the integral strategy to apply it in drug discovery to guide projects for permeability-related optimization were also presented.

Molecular properties such as the molecular weight, hydrophobicity parameter logP, and the total polar surface area (TPSA) have been used extensively in modern drug discovery. We investigated these properties and ADMET scores of the top 200 therapeutic drugs by the U.S. retail sales (2010) and classified them according to the clinical indications and/or route of administration. This list of drugs provides ample information of these molecular descriptors for successfully approved drugs. The mean logP for oral drugs is 2.51 while the logP for injectable drugs seems to be smaller. Among different types of clinical indications, drugs used for anti-HIV, and antibiotics tend to have lower logP. The molecular weights of anti-HIV drugs, antihypertensives and antibiotics appear to be larger. The ADMET scores, derived from a combination of molecular weights and logP, are consistent for oral drugs, with a mean score of 1.52 and a standard deviation of 0.99. Many clinical drugs that violate Lipinski's rule of five criteria can still exhibit ADMET scores that are very close to the mean value for oral drugs (1.52) and lie within the acceptable standard deviation. The molecular properties of MW, logP, and TPSA appear to vary according to their clinical indications. Many drugs form salts or cocrystals with acids or solvents that increase their solubility. Our data show that formation of hydrochloride is the most common method to increase solubility of drug ingredients. Cytochrome P450 isozymes 3A4, 2D6, 2C9, 2C8 and 3C5 are the top five proteins that catalyze the 200 most prescribed drugs. Drugs metabolized by 3A4 appear to have larger molecular weights and those metabolized by 2D6 have lower molecular weights. CYP2C8-metabolized drugs appear to be most hydrophilic, with the smallest logP and the largest polar surface areas.

There are numerous small molecular compounds around us to affect our health, such as drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over decades, properties related to absorption, distribution, metabolism, excretion, and toxicity (ADMET) have become one of the most important issues to assess the effects or risks of these compounds on human body. Recent high-rate drug withdrawals increase the pressure on regulators and pharmaceutical industry to improve preclinical safety testing. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques have been widely used to estimate these properties. In this review, we would briefly describe the recent advances of in silico ADMET prediction, with emphasis on substructure pattern recognition method that we developed recently. Challenges and limitions in the area of in silico ADMET prediction were further discussed, such as application domain of model, model validation techniques, global versus local models. At last, several new promising research directions were provided, such as computational systems toxicology (toxicogenomics), data-integration and meta-decision making systems, which could be used for systemic in silico ADMET prediction in drug discovery and hazard risk assessment.

Understanding the molecular basis of drug action has been an important objective for pharmaceutical scientists. With the increasing speed of computers and the implementation of quantum chemistry methodologies, pharmacodynamics and pharmacokinetic problems have become more computationally tractable. Historically the former has been the focus of drug design, but within the last two decades the efforts to understand the latter have increased. It takes about fifteen years and over $1 billion dollars for a drug to go from laboratory hit, through lead optimization, to final approval by the U.S. Food and Drug Administration. While the costs have increased substantially, the overall clinical success rate for a compound to emerge from clinical trials is approximately 10%. Most of the attrition rate can be traced to ADMET (absorption, distribution, metabolism, excretion, and toxicity) problems, which is a powerful impetus to study these issues at an earlier stage in drug discovery. Quantum mechanics offers pharmaceutical scientists the opportunity to investigate pharmacokinetic problems at the molecular level prior to laboratory preparation and testing. This review will provide a perspective on the use of quantum mechanics or a combination of quantum mechanics coupled with other classical methods in the pharmacokinetic phase of drug discovery. A brief overview of the essential features of theory will be discussed, and a few carefully selected examples will be given to highlight the computational methods.

Pharmacophore approaches have evolved to be one of the most successful tools in drug discovery, especially since the past two decades. 3D pharmacophore methods are now commonly used as part of more complex workflows in drug discovery campaigns, and have been successfully and extensively applied in virtual screening (VS) approaches. This review provides a perspective of how to assess the performance of 3D pharmacophore models to be used in VS. Since 3D VS protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of training and test sets on the outcome of evaluations. Moreover, we highlight the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D pharmacophore-based virtual screening methods.