ANTONIO PEÓN LÓPEZ

Abstract Background: Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking. Methods: Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers. Results: The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium. Conclusions: Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS-FLI1 (sensitivity). © 2018 The Author(s).

Abstract Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC50 10 μM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions. © 2017 The Author(s).

Abstract A multidisciplinary approach was used to identify and optimize a quinazolinedione-based ligand that would decrease the flexibility of the substrate-covering loop (catalytic loop) of the type II dehydroquinase from Helicobacter pylori. This enzyme, which is essential for the survival of this bacterium, is involved in the biosynthesis of aromatic amino acids. A computer-aided fragment-based protocol (ALTA) was first used to identify the aromatic fragments able to block the interface pocket that separates two neighboring enzyme subunits and is located at the active site entrance. Chemical modification of its non-aromatic moiety through an olefin cross-metathesis and Seebach's self-reproduction of chirality synthetic principle allowed the development of a quinazolinedione derivative that disables the catalytic loop plasticity, which is essential for the enzyme′s catalytic cycle. Molecular dynamics simulations revealed that the ligand would force the catalytic loop into an inappropriate arrangement for catalysis by strong interactions with the catalytic tyrosine and by expelling the essential arginine out of the active site. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Abstract Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict. © 2016 Peón, Dang and Ballester.

Abstract The irreversible inhibition of type I dehydroquinase (DHQ1), the third enzyme of the shikimic acid pathway, is investigated by structural, biochemical and computational studies. Two epoxides, which are mimetics of the natural substrate, were designed as irreversible inhibitors of the DHQ1 enzyme and to study the binding requirements of the linkage to the enzyme. The epoxide with the S configuration caused the covalent modification of the protein whereas no reaction was obtained with its epimer. The first crystal structure of DHQ1 from Salmonella typhi covalently modified by the S epoxide, which is reported at 1.4 Å, revealed that the modified ligand is surprisingly covalently attached to the essential Lys170 by the formation of a stable Schiff base. The experimental and molecular dynamics simulation studies reported here highlight the huge importance of the conformation of the C3 carbon of the ligand for covalent linkage to this type of aldolase I enzyme, revealed the key role played by the essential His143 as a Lewis acid in this process and show the need for a neatly closed active site for catalysis. © The Royal Society of Chemistry 2015.