Carbonic anhydrase IX (CAIX) is a transmembrane enzyme that regulates pH in hypoxic tumors and promotes tumor cell survival. Its expression is associated with the occurrence of metastases and poor prognosis. Here, we present nine derivatives of the cobalt bis­(dicarbollide)(1−) anion substituted at the boron or carbon sites by alkysulfamide group(s) as highly specific and selective inhibitors of CAIX. Interactions of these compounds with the active site of CAIX were explored on the atomic level using protein crystallography. Two selected derivatives display subnanomolar or picomolar inhibition constants and high selectivity for the tumor-specific CAIX over cytosolic isoform CAII. Both derivatives had a time-dependent effect on the growth of multicellular spheroids of HT-29 and HCT116 colorectal cancer cells, facilitated penetration and/or accumulation of doxorubicin into spheroids, and displayed low toxicity and showed promising pharmacokinetics and a significant inhibitory effect on tumor growth in syngenic breast 4T1 and colorectal HT-29 cancer xenotransplants.

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Schlafen 11 (SLFN11) is an interferon-inducible antiviral restriction factor with tRNA endoribonuclease and DNA binding functions. It is recruited to stalled replication forks in response to replication stress and inhibits replication of certain viruses such as the human immunodeficiency virus 1 (HIV-1) by modulating the tRNA pool. SLFN11 has been identified as a predictive biomarker in cancer, as its expression correlates with a beneficial response to DNA damage inducing anticancer drugs. However, the mechanism and interdependence of these two functions are largely unknown. Here, we present cryo-electron microscopy (cryo-EM) structures of human SLFN11 in its dimeric apoenzyme state, bound to tRNA and in complex with single-strand DNA. Full-length SLFN11 neither hydrolyses nor binds ATP and the helicase domain appears in an autoinhibited state. Together with biochemical and structure guided mutagenesis studies, our data give detailed insights into the mechanism of endoribonuclease activity as well as suggestions on how SLFN11 may block stressed replication forks.

et al.. A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications.. Computer Methods and Programs in Biomedicine, Elsevier, 2019, 170, pp.a b s t r a c tBackground and objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models. Methods:We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create lowrank solutions using a non-intrusive sparse sampling of the feature space.Results: In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue. Conclusions:A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the training phase, the procedure can be easily extended to more complex nonlinear constitutive behaviors -such as hyperelasticity -and more general load cases. Therefore it can be integrated with little intrusiveness to generic simulation software including more sophisticated and realistic models.