Elucidation of the Structure of Solanoeclepin A, a Natural Hatching Factor of Potato and Tomato Cyst Nematodes, by Single-crystal X-ray Diffraction

Potato crops can be severely damaged by potato cyst nematodes Globodera rostochiensis and Globodera pallida, nematodes highly specific to potatoes and some other Solanaceae. Hatching of juveniles is controlled by agents excreted by the roots of some Solanaceae species. Over seventy years much effort has been expended by many groups to isolate these agents and to determine their structures. However, all attempts have failed. We report here the structure determination of a hatching factor excreted from potato and tomato roots. The hatching factor bears some resemblance to Glycinoeclepins as found by Masamune et al.2-5 and is hence designated as Solanoeclepin A.1 C27H30O9.3H2O, Mr = 498.5, monoclinic, P21, a = 11.289(2), b = 20.644(4), c = 11.632(12) Å, β = 90.81(4), V = 2711(3) Å3, Z = 4, Dx = 1.35 g cm–3, λ(Cu-K&alpha ) = 1.5418 Å, μ(Cu-Kα ) = 9.0 cm–1, F(000) = 1176, –60 °C. Final R = 0.117 for 3721 observed reflections

Screening approach for identifying cocrystal types and resolution opportunities in complex chiral multicomponent systems

Cocrystallization of racemic-compound-forming chiral molecules can result in conglomerate cocrystals or diastereomerically related cocrystals, which enable the application of chiral separation techniques such as preferential crystallization and classic resolution. Here, a systematic method to identify the types and phase diagrams of cocrystals formed by chiral target compounds and candidate coformers in a particular solvent system is presented, which allows the design of suitable chiral resolution processes. The method is based on saturation temperature measurements of specific solution compositions containing both enantiomers of chiral molecules and a coformer. This method is applied to analyze three different systems. For racemic phenylalanine (Phe) in water/ethanol mixtures one of the enantiomers selectively cocrystallizes with the opposite enantiomer of valine (Val), forming the more stable diastereomerically related cocrystal. The racemic compound ibuprofen crystallizes with the nonchiral coformer 1,2-bis(4-pyridyl)ethane (BPN) as racemic compound cocrystals. More interestingly, when it is combined with trans-1-(2-pyridyl)-2-(4-pyridyl)ethylene (BPE), the racemic compound ibuprofen cocrystallizes as a conglomerate, which in principle enables the application of preferential crystallization of this racemic compound. The systematic method shows the benefit of using pseudo-binary phase diagrams. Such pseudo-binary phase diagrams depict the saturation temperature on a very specific route through the quaternary phase diagram, allowing the identification of various cocrystal types as well as the corresponding cocrystallization conditions. The systematic method can be used to identify a suitable solid phase for chiral separation, and the obtained phase diagram information enables the performance of a crystallization-mediated chiral resolution process design. Such a guideline for a chiral resolution process design has never been reported for conglomerate cocrystal systems such as IBU:BPE, presented in this study

Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447

The conformation of the idopyranose ring revisited: How subtle O-substituent induced changes can be deduced from vicinal 1H-NMR coupling constants

The idopyranose ring plays a pivotal role in the conformational, dynamical, and intermolecular binding aspects of glycosaminoglycans like heparin and dermatan sulfate and it was early on assigned a role in the Sugar Code governing biological recognition processes. There is consensus that next to the two canonical 1C4 and 4C1 chair conformations, the conformational space accessible to the idopyranose ring entails a 2SO skew-boat conformation, but the equilibrium between these three ring puckers has evaded satisfactory quantification. In this study a meta-analysis of X-ray solid-state data and vicinal NMR coupling constants is presented, based on the Truncated Fourier Puckering (TFP) formalism and the generalized Karplus (CAGPLUS) equation. This approach yields a model-free, granular and consistent reckoning of 159 idopyranose solution puckering equilibria studied by NMR and allows us to reproduce the involved 636 NMR vicinal couplings with an overall residual RMS(Jobs-Jcalc) of 0.184 Hz. Our analyses show that for all ring systems examined, the idopyranosyl chair conformations take up the same ring pucker irrespective of the ring substituent pattern or a vast variety in experimental conditions. Instead, it is the (skew-)boat conformation that adapts to the substitution pattern of the idopyranose ring or a specific sulfation pattern of neighboring saccharides. All idopyranose rings are involved in conformational equilibria that subsume the aforementioned conformers which turn out to differ only a few kJ/mole in conformational energy. Thus, the plasticity and flexibility of idopyranose remains intact under practically all circumstances and, as the glycosidic linkages in heparin are considered to be relatively stiff, the iduronic moiety functions as the linchpin of heparin flexibility thereby being rather a “space(r)” than a “letter” in the alleged Sugar Code alphabet

Formal Synthesis of Solanoeclepin A: Enantioselective Allene Diboration and Intramolecular 2+2 Photocycloaddition for the Construction of the Tricyclic Core

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Enantioselective Approach to the Right-Hand Substructure of Solanoeclepin A

Item does not contain fulltextAn enantioselective synthesis of the right-hand substructure of solanoeclepin A has been developed. The key step was an intramolecular [2+2] photocycloaddition between an allene and a butenolide providing a methylenecyclobutane with three quaternary carbon atoms in a complex tetracyclic framework. Other crucial steps included an enantioselective Noyori transfer hydrogenation of a ketone, a diastereoselective silver-mediated silyl dienolate allylation, and a diastereoselective cyclopropanation of an allylic alcohol. The installation of the bridgehead methyl group by reduction of the lactone moiety proved to be troublesome

Time-dependent clearance of mycophenolic acid in renal transplant recipients

Pharmacokinetic studies of the immunosuppressive compound mycophenolic acid (MPA) have shown a structural decrease in clearance (CL) over time after renal transplantation. The aim of this study was to characterize the time-dependent CL of MPA by means of a population pharmacokinetic meta-analysis, and to test whether it can be described by covariate effects. One thousand eight hundred and ninety-four MPA concentration-time profiles from 468 renal transplant patients (range 1-9 profiles per patient) were analyzed retrospectively by nonlinear mixed effect modelling. Sampling occasions ranged from day 1-10 years after transplantation. The pharmacokinetics of MPA were described by a two-compartment model with time-lagged first order absorption, and a first-order term for time-dependent CL. The model predicted the mean CL to decrease from 35 l h(-1) (CV = 44%) in the first week after transplantation to 17 l h(-1) (CV = 38%) after 6 months. In a covariate model without a term for time-dependent CL, changes during the first 6 months after transplantation in creatinine clearance from 19 to 71 ml min(-1), in albumin concentration from 35 to 40 g l(-1), in haemoglobin from 9.7 to 12 g dl(-1) and in cyclosporin predose concentration from 225 to 100 ng ml(-1) corresponded with a decrease of CL from 32 to 19 l h(-1). Creatinine clearance, albumin concentration, haemoglobin and cyclosporin predose concentration explained, respectively, 19%, 12%, 4% and 3% of the within-patient variability in MPA CL. By monitoring creatinine clearance, albumin concentration, haemoglobin and cyclosporin predose concentration, changes in MPA exposure over time can be predicted. Such information can be used to optimize therapy with mycophenolate mofeti