A Hexokinase 2 Modulator for Field-Directed Treatment of Experimental Actinic Keratoses.

Overexpression of hexokinase 2, and its binding to VDAC1 on the outer mitochondrial membrane of cancer cells, is key to their metabolic reprogramming to aerobic glycolysis, which enables them to proliferate. We describe Comp-1, an allosteric small molecule that selectively detaches hexokinase 2 from the mitochondria. Detachment of hexokinase 2 reduces glycolysis and triggers apoptosis in cancer cells, without affecting hexokinase 1-expressing normal cells. The anti-cancer activity of Comp-1 was demonstrated in the UVB-damaged skin model in SKH-1 mice. Topical treatment with Comp-1 led to 70% reduction in lesion number and area. This in vivo efficacy was obtained without local skin reactions or other safety findings. Mechanism-related pharmacodynamic markers, including hexokinase 2 and cleaved caspase 3 levels, are affected by Comp-1 treatment in vivo. Good Laboratory Practice toxicology studies in minipigs for 28 days and 13 weeks established no systemic toxicities and minimal dermal reaction for once-daily application of up to 20% and 15% ointment strengths, respectively. Thus, Comp-1 may address a significant unmet medical need for a non-irritating efficacious topical actinic keratosis treatment.

1-(sulfonyl)-5-(arylsulfonyl)indoline as activators of the tumor cell specific M2 isoform of pyruvate kinase.

Cancer cells preferentially use glycolysis rather than oxidative phosphorylation for their rapid growth. They consume large amount of glucose to produce lactate even when oxygen is abundant, a phenomenon known as the Warburg effect. This metabolic change originates from a shift in the expression of alternative spliced isoforms of the glycolytic enzyme pyruvate kinase (PK), from PKM1 to PKM2. While PKM1 is constitutively active, PKM2 is switched from an inactive dimer form to an active tetramer form by small molecule activators. The prevalence of PKM2 in cancer cells relative to the prevalence of PKM1 in many normal cells, suggests a therapeutic strategy whereby activation of PKM2 may counter the abnormal cellular metabolism in cancer cells, and consequently decreased cellular proliferation. Herein we describe the discovery and optimization of a series of PKM2 activators derived from the 2-((2,3-dihydrobenzo[b][1,4] dioxin-6-yl)thio)-1-(2-methyl-1-(methylsulfonyl)indolin-5-yl) ethanone scaffold. The synthesis, SAR analysis, enzyme active site docking, enzymatic reaction kinetics, selectivity and pharmaceutical properties are discussed.

An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression.

We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized. In this paper we detail the entire discovery process, which started with modeling the 3D structure of 5-HT1A using the PREDICT methodology, and then performing in silico screening on that structure leading to the discovery of a 1 nM lead compound (8). The lead compound was optimized following a strategy devised based on in silico 3D models and realized through an in silico-driven optimization process, rapidly overcoming selectivity issues (affinity to 5-HT1A vs alpha1-adrenergic receptor) and potential cardiovascular issues (hERG binding), leading to a clinical compound. Finally we report key in vivo preclinical and Phase I clinical data for 20m tolerability, pharmacokinetics, and pharmacodynamics and show that these favorable results are a direct outcome of the properties that were ascribed to the compound during the rational structure-based discovery process. We believe that this is one of the first examples for a Phase III drug candidate that was discovered and optimized, from start to finish, using in silico model-based methods as the primary tool.

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Modeling the 3D structure of GPCRs: advances and application to drug discovery.

G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.

A method for correlations analysis of coordinates: applications for molecular conformations.

We describe a new method to analyze multiple correlations between subsets of coordinates that represent a sample. The correlation is established only between specific regions of interest at the coordinates. First, the region(s) of interest are selected at each molecular coordinate. Next, a correlation matrix is constructed for the selected regions. The matrix is subject to further analysis, illuminating the multidimensional structural characteristics that exist in the conformational space. The method's abilities are demonstrated in several examples: it is used to analyze the conformational space of complex molecules, it is successfully applied to compare related conformational spaces, and it is used to analyze a diverse set of protein folding trajectories.

NO rebinding to myoglobin: a reactive molecular dynamics study.

The rebinding of NO to myoglobin after photolysis is studied using the 'reactive molecular dynamics' method. In this approach the energy of the system is evaluated on two potential energy surfaces that include the heme-ligand interactions which change between liganded and unliganded myoglobin. This makes it possible to take into account in a simple way, the high dimensionality of the transition seam connecting the reactant and product states. The dynamics of the dissociated NO molecules are examined, and the geometrical and energetic properties of the transition seam are studied. Analysis of the frequency of recrossing shows that the height of the effective rebinding barrier is dependent on the time after photodissociation. This effect is due mainly to protein relaxation and may contribute to the experimentally observed non-exponential rebinding rate of NO, as has been suggested previously.

Conformational polymorphism of wild-type and mutant prion proteins: Energy landscape analysis.

Conformational transitions are thought to be the prime mechanism of prion diseases. In this study, the energy landscapes of a wild-type prion protein (PrP) and the D178N and E200K mutant proteins were mapped, enabling the characterization of the normal isoforms (PrP(C)) and partially unfolded isoforms (PrP(PU)) of the three prion protein analogs. It was found that the three energy landscapes differ in three respects: (i) the relative stability of the PrP(C) and the PrP(PU) states, (ii) the transition pathways from PrP(C) to PrP(PU), and (iii) the relative stability of the three helices in the PrP(C) state. In particular, it was found that although helix 1 (residues 144-156) is the most stable helix in wild-type PrP, its stability is dramatically reduced by both mutations. This destabilization is due to changes in the charge distribution that affects the internal salt bridges responsible for the greater stability of this helix in wild-type PrP. Although both mutations result in similar destabilization of helix 1, they a have different effect on the overall stability of PrP(C) and of PrP(PU) isoforms and on structural properties. The destabilization of helix 1 by mutations provides additional evidences to the role of this helix in the pathogenic transition from the PrP(C) to the pathogenic isoform PrP(SC).