Blending biology and chemistry to enable systems pharmacology

The avalanche of data that followed the sequencing of the human genome has revealed an overwhelming biological complexity. No simple molecular explanation exists for most of the diseases and, in consequence, simple therapies have low probability of success. The emerging field of systems pharmacology seeks drugs of broad impact on molecular networks. To achieve so, it is necessary to integrate heterogeneous data, at different levels of complexity, and find correlations between them. This translational exercise is, perhaps, the major concern of current biomedical research. In this Thesis we undertake part of this challenge through cases that orbit the drug discovery endeavor. Using computational methods in various areas of bioinformatics and chemoinformatics, we link chemical, biomolecular and phenotypic data to provide a more holistic view of pharmacology.L’allau de dades que ha seguit la seqüenciació del genoma humà està revelant una increïble complexitat biològica. No existeix una explicació simple per a la majoria de les malalties i, en conseqüència, les teràpies simples tenen baixes probabilitats d’èxit. L’emergent camp de la farmacologia de sistemes busca medicaments d’ampli impacte en les xarxes moleculars. Per a aconseguir-ho, és necessària la integració de dades heterogènies, a diferents nivells de complexitat, i la capacitat de trobar correlacions entre elles. Aquest exercici translacional és, probablement, la major preocupació de la recerca biomèdica d’avui. En aquesta Tesi assumim part d’aquest repte a través de casos que orbiten el descobriment de fàrmacs. Mitjançant mètodes computacionals en àrees diverses de la bioinformàtica i la quimioinformàtica, connectem dades químiques, biomoleculars i fenotípiques per a facilitar una visió més holística de la farmacologia

Encircling the regions of the pharmacogenomic landscape that determine drug response

Background: The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. Methods: To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. Results: We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. Conclusions: Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.A.F-T. is a recipient of an FPI fellowship. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R) and the European Research Council (SysPharmAD: 614944)

Patient-reported reasons for declining same-day antiretroviral therapy initiation in routine HIV care settings in Lusaka, Zambia: results from a mixed-effects regression analysis

Introduction: In the current "test and treat" era, HIV programmes are increasingly focusing resources on linkage to care and same-day antiretroviral therapy (ART) initiation to meet UNAIDS 95-95-95 targets. After observing sub-optimal treatment indicators in health facilities supported by the Centre for Infectious Disease Research in Zambia (CIDRZ), we piloted a "linkage assessment" tool in facility-based HIV testing settings to uncover barriers to same-day linkage to care and ART initiation among newly identified people living with HIV (PLHIV) and to guide HIV programme quality improvement efforts. Methods: The one-page, structured linkage assessment tool was developed to capture patient-reported barriers to same-day linkage and ART initiation using three empirically supported categories of barriers: social, personal and structural. The tool was implemented in three health facilities, two urban and one rural, in Lusaka, Zambia from 1 November 2017 to 31 January 2018, and administered to all newly identified PLHIV declining same-day linkage and ART. Individuals selected as many reasons as relevant. We used mixed-effects logistic regression modelling to evaluate predictors of citing specific barriers to same-day linkage and ART, and Fisher's Exact tests to assess differences in barrier citation by socio-demographics and HIV testing entry point. Results: A total of 1278 people tested HIV positive, of whom 126 (9.9%) declined same-day linkage and ART, reporting a median of three barriers per respondent. Of these 126, 71.4% were female. Females declining same-day ART were younger, on average, (median 28.5 years, interquartile range (IQR): 21 to 37 years) than males (median 34.5 years, IQR: 26 to 44 years). The most commonly reported barrier category was structural, "clinics were too crowded" (n = 33), followed by a social reason, "friends and family will condemn me" (n = 30). The frequency of citing personal barriers differed significantly across HIV testing point (χ2 p = 0.03). Significant predictors for citing ≥1 barrier to same-day ART were >50 years of age (OR: 12.59, 95% CI: 6.00 to 26.41) and testing at a rural facility (OR: 9.92, 95% CI: 4.98 to 19.79). Conclusions: Given differences observed in barriers to same-day ART initiation reported across sex, age, testing point, and facility type, new, tailored counselling and linkage to care approaches are needed, which should be rigorously evaluated in routine programme settings

Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients' progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.Funding: L.M. is a recipient of an FPI fellowship. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R), the European Research Council (SysPharmAD: 614944), and the Generalitat de Catalunya (VEIS 001-P-001). V.S. is a recipient of a Miguel Servet grant from ISCIII (CPII19/00033) and receives funds from AGAUR (2017 SGR 540). The PDX program is supported by a GHD-Pink (FERO foundation) grant to V.S., A.G.-O. and M.P. received a FI-AGAUR and a Juan de la Cierva (MJCI-2015-25412) fellowship, respectively. M.S., P.R., and S.C. acknowledge the support of the NIH grants P30 CA008748, RO1CA19064

Bioactivity descriptors for uncharacterized chemical compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.We would like to thank the SB&NB lab members for their support and helpful discussions. We are grateful to T.O. Botelho, I. Ramos, and C. Gonzalez for giving us access to the IRB Barcelona and Prestwick libraries. P.A. acknowledges the support of the Generalitat de Catalunya (RIS3CAT Emergents CECH: 001-P-001682 and VEIS: 001-P-001647), the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R), the European Research Council (SysPharmAD: 614944), and the European Commission (RiPCoN: 101003633). A.G.d.H. acknowledges support by Agencia Estatal de Investigación (AEI) and Fondos FEDER (PID2019-104698RB-I00)

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/