An Olefin Metathesis-Based Strategy for the Synthesis of New Antitumoral Compounds Inspired by Natural Products

In particular, one precursor showed potent antiangiogenic effect in vitro and in vivo, suggesting the potential therapeutic application of solomonamide derivatives as inhibitors of the persistent and deregulated angiogenesis that characterizes cancer. For the last target, the celebesides, [26]-membered cyclodepsipeptides with anti-HIV activity, we envisioned a RCM reaction to access their macrocyclic core. We established a solid-phase synthesis strategy for the preparation of the peptidic chain, while the synthesis of the polyketide fragment was achieved in a stereoselective manner. Fecha de lectura de Tesis Doctoral: 13 de diciembre 2019.his PhD Thesis proposes the discovery and development of new drugs inspired by bioactive natural products, which represent valid platforms for the generation of new chemical entities of pharmaceutical interest. In particular, the selected natural products are (-)-depudecin, solomonamides and celebesides. We have established synthetic strategies oriented to the target, but with the possibility of the extension of such strategies to the construction of a molecular diversity through the generation of analogues. For the synthesis of (-)-depudecin, a unique antitumoral detransforming agent, we have established a new convergent total synthesis utilizing an olefin cross-metathesis (CM) reaction as the key step. In addition, the synthetic route was amenable to stereochemical and functional modifications, allowing the preparation of several analogues, which were used for a structure-activity relationship (SAR) study in order to identify new leads based on depudecin. In the case of the solomonamides, we have developed a synthetic strategy that comprises two phases: a) a cyclisation phase for the construction of the [15]-membered ring contained in these cyclopeptides utilizing an olefin ring-closing metathesis (RCM) as the key reaction; and b) an oxidation phase to give access to the natural products that would allow for the generation of a variety of analogues via oxidative transformations. In addition, we identified several structurally related solomonamide precursors possessing significant cytotoxicities against various tumor cell lines

Multiple scattering effects on heavy meson production in p+A collisions at backward rapidity

We study the incoherent multiple scattering effects on heavy meson production in the backward rapidity region of p+A collisions within the generalized high-twist factorization formalism. We calculate explicitly the double scattering contributions to the heavy meson differential cross sections by taking into account both initial-state and final-state interactions, and find that these corrections are positive. We further evaluate the nuclear modification factor for muons that come form the semi-leptonic decays of heavy flavor mesons. Phenomenological applications in d+Au collisions at a center-of-mass energy $\sqrt{s}=200$ GeV at RHIC and in p+Pb collisions at $\sqrt{s}=5.02$ TeV at the LHC are presented. We find that incoherent multiple scattering can describe rather well the observed nuclear enhancement in the intermediate $p_T$ region for such reactions.Comment: 10 pages, 6 figures, published version in PL

B-quark mediated neutrinoless μ−−e−\mu^--e^- conversion in presence of R-parity violation

We found that in supersymmetric models with R-parity non-conservation the b-quarks may appreciably contribute to exotic neutrinoless muon-electron conversion in nuclei via the triangle diagram with two external gluons. This allowed us to extract previously overlooked constraints on the third generation trilinear R-parity violating parameters significantly more stringent than those existing in the literature.Comment: 6 pages, 1 figur

A Dynamical Graph Prior for Relational Inference

Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit a graph neural network (GNN) on a learnable graph to the dynamics. They use one-step message-passing GNNs -- intuitively the right choice since non-locality of multi-step or spectral GNNs may confuse direct and indirect interactions. But the \textit{effective} interaction graph depends on the sampling rate and it is rarely localized to direct neighbors, leading to local minima for the one-step model. In this work, we propose a \textit{dynamical graph prior} (DYGR) for relational inference. The reason we call it a prior is that, contrary to established practice, it constructively uses error amplification in high-degree non-local polynomial filters to generate good gradients for graph learning. To deal with non-uniqueness, DYGR simultaneously fits a ``shallow'' one-step model with shared graph topology. Experiments show that DYGR reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Since appropriate sampling rates for unknown dynamical systems are not known a priori, this robustness makes DYGR suitable for real applications in scientific machine learning