Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug-target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug-target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand-target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods' utility in discovering novel drug candidates.

The endocannabinoid anandamide is removed from the synaptic space by a selective transport system, expressed in neurons and astrocytes, which remains molecularly uncharacterized. Here we describe a partly cytosolic variant of the intracellular anandamide-degrading enzyme, fatty acid amide hydrolase-1 (FAAH-1), termed FAAH-like anandamide transporter (FLAT), which lacks amidase activity but binds anandamide with low micromolar affinity and facilitates its translocation into cells. Known anandamide transport inhibitors, such as AM404 and OMDM-1, block these effects. Additionally, we identify a competitive antagonist of the interaction of anandamide with FLAT, the phthalazine derivative ARN272, which prevents anandamide internalization in vitro, interrupts anandamide deactivation in vivo, and exerts profound analgesic effects in rodent models of nociceptive and inflammatory pain, which are mediated by CB1 cannabinoid receptors. The results identify FLAT as a critical molecular component of anandamide transport in neural cells and a potential target for therapeutic drugs.

RECANATINI*, M.; POLUZZI, E.; MASETTI, M.; CAVALLI, A.; DE PONTI, F.; Med. Res. Rev. 25 (2005) 2, 133-166; Dip. Sci. Farm., Univ. Bologna, I-40126 Bologna, Italy; Eng.) -Lindner 24-263

Prolongation of the QT interval of the electrocardiogram is a typical effect of Class III antiarrhythmic drugs, achieved through blockade of potassium channels. In the past decade, evidence has accrued that several classes of drugs used for non-cardiovascular indications may prolong the QT interval with the same mechanism (namely, human ether-a-go-go-related gene (hERG) K(+) channel blockade). The great interest in QT prolongation is because of several reasons. First, drug-induced QT prolongation increases the likelihood of a polymorphous ventricular arrhythmia (namely, torsades de pointes, TdP), which may cause syncope and degenerate into ventricular fibrillation and sudden death. Second, the fact that several classes of drugs, such as antihistamines, fluoroquinolones, macrolides, and neuroleptics may cause the long QT syndrome (LQTS) raises the question whether this is a class effect (e.g., shared by all agents of a given pharmacological class) or a specific effect of single agents within a class. There is now consensus that, in most cases, only a few agents within a therapeutic class share the ability to significantly affect hERG K(+) channels. These compounds should be identified as early as possible during drug development. Third, QT prolongation and interaction with hERG K(+) channels have become surrogate markers of cardiotoxicity and have received increasing regulatory attention. This review briefly outlines the mechanisms leading to QT prolongation and the different strategies that can be followed to predict this unwanted effect. In particular, it will focus on the approaches recently proposed for the in silico screening of new compounds.

The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.