hERG-related drug toxicity and models for predicting hERG liability and QT prolongation

Predicting in vivo safety characteristics using physiochemical properties and in vitro assays

There is increasing pressure on the pharmaceutical industry to deliver safer and more effective medicines while constraining research and development costs. In order to meet these demands, the industry is looking for basic design principles in terms of physicochemical properties as well as the use of higher throughput in vitro assays to select and evaluate new molecular entities for further development. Recent advances in understanding the relationships between a chemical’s properties and its propensity for adverse events, as well as the development of new in vitro screening technologies, have enhanced our ability to potentially select molecules more likely to succeed in becoming drugs. However, these approaches are still limited by the availability of data and our lack of understanding of the mechanisms by which compounds can cause toxicity.

Strategies to discover unexpected targets for drugs active at G protein-coupled receptors

G protein-coupled receptors (GPCRs) are an evolutionarily conserved family of signaling molecules comprising approximately 2% of the human genome; this receptor family remains a central focus in basic pharmacology studies and drug discovery efforts. Detailed studies of drug action at GPCRs over the past decade have revealed existing and novel ligands that exhibit polypharmacology-that is, drugs with activity at more than one receptor target for which they were designed. These "off-target" drug actions can be a liability that causes adverse side effects; however, in several cases, drugs with less selectivity demonstrate better clinical efficacy. Here we review physical screening and cheminformatic approaches that define drug activity at the GPCR receptorome. In many cases, such profiling has revealed unexpected targets that explain therapeutic actions as well as off-targets underlying drug side effects. Such drug-receptor profiling has also provided new insights into mechanisms of action of existing drugs and has suggested directions for future drug development.

Chemical-protein interactome and its application in off-target identification

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

Challenges of antibacterial discovery revisited

The discovery of novel antibiotic classes has not kept pace with the growing threat of bacterial resistance. Antibiotic candidates that act at new targets or via distinct mechanisms have the greatest potential to overcome resistance; however, novel approaches are also associated with higher attrition and longer timelines. This uncertainty has contributed to the withdrawal from antibiotic programs by many pharmaceutical companies. Genomic approaches have not yielded satisfactory results, in part due to nascent knowledge about unprecedented molecular targets, the challenge of achieving antibacterial activity by lead optimization of enzyme inhibitors, and the limitations of compound screening libraries for antibacterial discovery. Enhanced diversity of compound screening banks, entry into new chemical space, and new screening technologies are currently being exploited to improve hit rates for antibacterial discovery. Antibacterial compound lead optimization faces hurdles associated with the high plasma exposures required for efficacy. Lead optimization would be enhanced by the identification of new antibiotic classes with improved tractability and by expanding the predictability of in vitro safety assays. Implementing multiple screening and target identification strategies is recommended for improving the likelihood of discovering new antibacterial compounds that address unmet needs.

Discovery of (2,4-dihydroxy-5-isopropylphenyl)-[5-(4-methylpiperazin-1-ylmethyl)-1,3-dihydroisoindol-2-yl]methanone (AT13387), a novel inhibitor of the molecular chaperone Hsp90 by fragment based drug design

Inhibitors of the molecular chaperone heat shock protein 90 (Hsp90) are currently generating significant interest in clinical development as potential treatments for cancer. In a preceding publication (DOI: 10.1021/jm100059d ) we describe Astex's approach to screening fragments against Hsp90 and the subsequent optimization of two hits into leads with inhibitory activities in the low nanomolar range. This paper describes the structure guided optimization of the 2,4-dihydroxybenzamide lead molecule 1 and details some of the drug discovery strategies employed in the identification of AT13387 (35), which has progressed through preclinical development and is currently being tested in man.

Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System

BACKGROUND

Drug-induced torsades de pointes (TdP) is a complex regulatory and clinical problem due to the rarity of this sometimes fatal adverse event. In this context, the US FDA Adverse Event Reporting System (AERS) is an important source of information, which can be applied to the analysis of TdP liability of marketed drugs.

OBJECTIVE

To critically evaluate the risk of antimicrobial-induced TdP by detecting alert signals in the AERS, on the basis of both quantitative and qualitative analyses.

METHODS

Reports of TdP from January 2004 through December 2008 were retrieved from the public version of the AERS. The absolute number of cases and reporting odds ratio as a measure of disproportionality were evaluated for each antimicrobial drug (quantitative approach). A list of drugs with suspected TdP liability (provided by the Arizona Centre of Education and Research on Therapeutics [CERT]) was used as a reference to define signals. In a further analysis, to refine signal detection, we identified TdP cases without co-medications listed by Arizona CERT (qualitative approach).

RESULTS

Over the 5-year period, 374 reports of TdP were retrieved: 28 antibacterials, 8 antifungals, 1 antileprosy and 26 antivirals were involved. Antimicrobials more frequently reported were levofloxacin (55) and moxifloxacin (37) among the antibacterials, fluconazole (47) and voriconazole (17) among the antifungals, and lamivudine (8) and nelfinavir (6) among the antivirals. A significant disproportionality was observed for 17 compounds, including several macrolides, fluoroquinolones, linezolid, triazole antifungals, caspofungin, indinavir and nelfinavir. With the qualitative approach, we identified the following additional drugs or fixed dose combinations, characterized by at least two TdP cases without co-medications listed by Arizona CERT: ceftriaxone, piperacillin/tazobactam, cotrimoxazole, metronidazole, ribavirin, lamivudine and lopinavir/ritonavir.

DISCUSSION

Disproportionality for macrolides, fluoroquinolones and most of the azole antifungals should be viewed as 'expected' according to Arizona CERT list. By contrast, signals were generated by linezolid, caspofungin, posaconazole, indinavir and nelfinavir. Drugs detected only by the qualitative approach should be further investigated by increasing the sensitivity of the method, e.g. by searching also for the TdP surrogate marker, prolongation of the QT interval.

CONCLUSIONS

The freely available version of the FDA AERS database represents an important source to detect signals of TdP. In particular, our analysis generated five signals among antimicrobials for which further investigations and active surveillance are warranted. These signals should be considered in evaluating the benefit-risk profile of these drugs.

Bayesian quantitative electrophysiology and its multiple applications in bioengineering

Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bioengineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.