Based on Lyapunov stabilization theory, this paper proposes a proportional plus integral time-delayed controller to stabilize unstable equilibrium points (UPOs) embedded in chaotic attractors. The criterion is successfully applied to the classic Chua’s circuit. Theoretical analysis and numerical simulation show the effectiveness of this controller.

Optimizing the solubility of small molecules is important in a wide variety of contexts, including in drug discovery where the optimization of aqueous solubility is often crucial to achieve oral bioavailability. In such a context, solubility optimization cannot be successfully pursued by indiscriminate increases in polarity, which would likely reduce permeability and potency. Moreover, increasing polarity may not even improve solubility itself in many cases, if it stabilizes the solid-state form. Here we present a novel physics-based approach to predict the solubility of small molecules, that takes into account three-dimensional solid-state characteristics in addition to polarity. The calculated solubilities are in good agreement with experimental solubilities taken both from the literature as well as from several active pharmaceutical discovery projects. This computational approach enables strategies to optimize solubility by disrupting the three-dimensional solid-state packing of novel chemical matter, illustrated here for an active medicinal chemistry campaign.

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.

Activating PKG-1α induces a long-term hyperexcitability (LTH) in nociceptive neurons. Since the LTH correlates directly with chronic pain in many animal models, we tested the hypothesis that inhibiting PKG-1α would attenuate LTH-mediated pain. We first synthesized and characterized compound N46 (N-((3R,4R)-4-(4-(2-fluoro-3-methoxy-6-propoxybenzoyl)benzamido)pyrrolidin-3-yl)-1H-indazole-5-carboxamide). N46 inhibits PKG-1α with an IC50 of 7.5 nmol, was highly selective when tested against a panel of 274 kinases, and tissue distribution studies indicate that it does not enter the CNS. To evaluate its antinociceptive potential, we used 2 animal models in which the pain involves both activated PKG-1α and LTH. Injecting complete Freund's adjuvant (CFA) into the rat hind paw causes a thermal hyperalgesia that was significantly attenuated 24 hours after a single intravenous injection of N46. Next, we used a rat model of osteoarthritic knee joint pain and found that a single intra-articular injection of N46 alleviated the pain 14 days after the pain was established and the relief lasted for 7 days. Thermal hyperalgesia and osteoarthritic pain are also associated with the activation of the capsaicin-activated transient receptor protein vanilloid-1 (TRPV1) channel. We show that capsaicin activates PKG-1α in nerves and that a subcutaneous delivery of N46 attenuated the mechanical and thermal hypersensitivity elicited by exposure to capsaicin. Thus, PKG-1α appears to be downstream of the transient receptor protein vanilloid-1. Our studies provide proof of concept in animal models that a PKG-1α antagonist has a powerful antinociceptive effect on persistent, already existing inflammatory pain. They further suggest that N46 is a valid chemotype for the further development of such antagonists.

Blockade of the hERG potassium channel prolongs the ventricular action potential (AP) and QT interval, and triggers early after depolarizations (EADs) and torsade de pointes (TdP) arrhythmia. Opinions differ as to the causal relationship between hERG blockade and TdP, the relative weighting of other contributing factors, definitive metrics of preclinical proarrhythmicity, and the true safety margin in humans. Here, we have used in silico techniques to characterize the effects of channel gating and binding kinetics on hERG occupancy, and of blockade on the human ventricular AP. Gating effects differ for compounds that are sterically compatible with closed channels (becoming trapped in deactivated channels) versus those that are incompatible with the closed/closing state, and expelled during deactivation. Occupancies of trappable blockers build to equilibrium levels, whereas those of non-trappable blockers build and decay during each AP cycle. Occupancies of ~83% (non-trappable) versus ~63% (trappable) of open/inactive channels caused EADs in our AP simulations. Overall, we conclude that hERG occupancy at therapeutic exposure levels may be tolerated for nontrappable, but not trappable blockers capable of building to the proarrhythmic occupancy level. Furthermore, the widely used Redfern safety index may be biased toward trappable blockers, overestimating the exposure-IC50 separation in nontrappable cases.

We have developed a new methodology for protein ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Blockade of the human Ether-à-go-go-related gene potassium channel (hERG) alters the time-dependent properties of the ventricular action potential (AP), leading to prolongation of the QT interval in the ECG (LQT), and in some cases, early after depolarizations (EADs), torsade de pointes arrhythmia (TdP), and ventricular fibrillation (VF). Opinions differ as to the specific cause–effect relationship between hERG blockade and proarrhythmia, the role and weighting of non-hERG contributions, definitive clinical and preclinical metrics of proarrhythmic potential (serving to avoid both false negatives and positives), and safe upper limit of blockade. Definitive risk assessment criteria await improved understanding of hERG blockade mechanisms pertinent to the in vivo setting. Here, we have used in silico methods to characterize the effects of channel gating and binding kinetics on blocker occupancy and the effects of the blockade on the time-dependent properties of the human cardiac AP. The lifetimes of bound blockers are capped at the gating cycle length and open channel time for trappable blockers (unexpelled during channel closing) and nontrappable blockers, respectively. Trappable blockers accumulate to equilibrium occupancy levels, whereas nontrappable blocker occupancy is necessarily refreshed during every AP cycle. Occupancy levels associated with EADs were reached abruptly in our simulations at ∼83% (∼5Kd) versus ∼63% (∼1.6Kd) for nontrappable and trappable blockers, respectively. A significantly wider exposure proarrhythmic occupancy margin may therefore exist for nontrappable blockers relative to the current safety standard (which we show is likely weighted toward the trappable phenotype).

Chaos control here means to design a controller that is able to mitigating or eliminating the chaos behavior of nonlinear systems that experiencing such phenomenon. In this paper, an Adaptive Sliding Mode Controller (ASMC) is presented based on Lyapunov stability theory. The well known Chua's circuit is chosen to be our case study in this paper. The study shows the effectiveness of the proposed adaptive sliding mode controller.

Designing tight binding ligands is a primary objective of small molecule drug discovery. Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Coarse graining usually results in significantly faster simulations at the cost of reducing accuracy relative to atomistic representations. One must choose what to coarse grain carefully. Atomistic simulations of proteins, particularly to study conformational changes of proteins, are of great interest but hard to propagate long enough to obtain sufficient sampling. Protein and drug-molecule interactions and conformational behavior are quite intricate, significantly limiting applications from simplified models such as coarse grain approaches. However, water and membrane lipids are readily amenable to coarse graining suggesting that an approach in which the protein and ligand retain atomistic representations while their environment is simplified. As an experiment, we have developed such a mixed resolution model and have applied it to a GPCR, globular proteins and antimicrobial peptides. We present those results to highlight what is working well and what challenges remain for this approach.

Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein-ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 Å, and 76% of test set inhibitors have an RMSD of less than 2.0 Å. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization of the SAR properties of two different series of congeneric compounds with satisfactory success.

Poor permeability of the lipopolysaccharide-based outer membrane of Gram-negative bacteria is compensated by the existence of protein channels (porins) that selectively admit low molecular weight substrates, including many antibiotics. Improved understanding of the translocation mechanisms of porin substrates could help guide the design of antibiotics capable of achieving high intracellular exposure. Energy barriers to channel entry and exit govern antibiotic fluxes through porins. We have previously reported a hypothesis that the costs of transferring protein solvation to and from bulk medium underlie the barriers to protein-ligand association and dissociation, respectively, concomitant with the gain and loss of protein-ligand interactions during those processes. We have now applied this hypothesis to explain the published rates of entry (association) and exit (dissociation) of six antibiotics to/from reconstituted E. coli porin OmpC. WaterMap was used to estimate the total water transfer energies resulting from transient occupation by each antibiotic. Our results suggest that solvation within the porin cavity is highly energetically favorable, and the observed moderately fast entry rates of the antibiotics are consistent with replacement of protein-water H-bonds. The observed ultrafast exit kinetics is consistent with the lack of intrachannel solvation sites that convey unfavorable resolvation during antibiotic dissociation. These results are aligned with known general relationships between antibiotic efficacy and physicochemical properties, namely unusually low logP, reflecting an abundance of H-bond partners. We conclude that antibiotics figuratively "melt" their way through porin solvation at a rate determined by the cost of exchanging protein-solvent for protein-antibiotic H-bonds.

2664-Pos Board B434 Molecular Determinants of the Human Zinc Transporter, Hzip4 Robert Dempski, Sagar Antala, Tuong-Vi Nguyen. Worcester Polytechnic Institute, Worcester, MA, USA. Zinc is an essential micronutrient which is required for the function of hundreds of cellular enzymes. In addition, zinc is the second most abundant transition metal found in biological systems (iron is most abundant). However, the concentration of free zinc is nano to picomolar since most zinc is bound to proteins. This makes investigating the mechanism of zinc transport across the plasma membrane a challenge. Our interest has been to elucidate the mechanism of zinc transport mediated by one member of the ZIP family of proteins. To this end, we have developed a radiometric uptake assay to study the mechanism of zinc transport. In addition, we have expressed and purified specific domains of the protein to examine the role of these domains in zinc coordination and transport. Thus, using a mixture of biochemical and biophysical techniques, we have begun to describe the mechanism of zinc transport to gain insight into the function of this protein.