Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks

Explainable artificial intelligence for patient stratification and drug repositioning

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Exploratory mining mimicking the trial recruitment process as well as network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The method represents a human-in-the-loop framework, where medical experts use data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. To examine the validity of our method, we implemented our method on individual cancer types and on pan-cancer data to consider the inter- and intra-heterogeneity within a cancer type and among cancer types. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups showed that most of these drugs are FDA-approved drugs for cancer, and others are non-cancer related, but have the potential to be repurposed for cancer. We have discovered novel cancer-related mechanisms that these drugs can target in different cancer types to reduce cancer treatment costs and improve patient survival. Further wet lab experiments to validate these findings are required prior to initiating clinical trials using these repurposed therapies.Includes bibliographical references

A review on machine learning approaches and trends in drug discovery

Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4

Toxicity prediction using multi-disciplinary data integration and novel computational approaches

Current predictive tools used for human health assessment of potential chemical hazards rely primarily on either chemical structural information (i.e., cheminformatics) or bioassay data (i.e., bioinformatics). Emerging data sources such as chemical libraries, high throughput assays and health databases offer new possibilities for evaluating chemical toxicity as an integrated system and overcome the limited predictivity of current fragmented efforts; yet, few studies have combined the new data streams. This dissertation tested the hypothesis that integrative computational toxicology approaches drawing upon diverse data sources would improve the prediction and interpretation of chemically induced diseases. First, chemical structures and toxicogenomics data were used to predict hepatotoxicity. Compared with conventional cheminformatics or toxicogenomics models, interpretation was enriched by the chemical and biological insights even though prediction accuracy did not improve. This motivated the second project that developed a novel integrative method, chemical-biological read-across (CBRA), that led to predictive and interpretable models amenable to visualization. CBRA was consistently among the most accurate models on four chemical-biological data sets. It highlighted chemical and biological features for interpretation and the visualizations aided transparency. Third, we developed an integrative workflow that interfaced cheminformatics prediction with pharmacoepidemiology validation using a case study of Stevens Johnson Syndrome (SJS), an adverse drug reaction (ADR) of major public health concern. Cheminformatics models first predicted potential SJS inducers and non-inducers, prioritizing them for subsequent pharmacoepidemiology evaluation, which then confirmed that predicted non-inducers were statistically associated with fewer SJS occurrences. By combining cheminformatics' ability to predict SJS as soon as drug structures are known, and pharmacoepidemiology's statistical rigor, we have provided a universal scheme for more effective study of SJS and other ADRs. Overall, this work demonstrated that integrative approaches could deliver more predictive and interpretable models. These models can then reliably prioritize high risk chemicals for further testing, allowing optimization of testing resources. A broader implication of this research is the growing role we envision for integrative methods that will take advantage of the various emerging data sources.Doctor of Philosoph

Clinical application of genomics- and phosphoproteomics-based selection of targeted therapy in patients with advanced solid tumors

Precision oncology has come a long way since the introduction of the first targeted drug (trastuzumab) in 1999. Broad molecular testing of tumor tissue has vastly expanded our knowledge of the biology of cancer, leading to a steep increase in the number of approved targeted drugs and an expansion of the labeled indications of these drugs. Off-label use of these new classes of targeted drugs is nowadays better documented and often performed in clinical trials to maximize the learning potential of these experimental treatments for the medical community. As long as no “cure for cancer” exists, there will be room for improvement of our knowledge and approach to treating patients with cancer. General improvements in the logistics, availability of targeted drugs and access to diagnostics and expertise will likely have the greatest impact on direct benefit for patients. In the future, standardized processing and conservation of tumor tissue/biopsies should be possible in all healthcare facilities, and collaborations and sharing of knowledge and resources with the academic institutes will be viable to delivering precision oncology to all patients. If these conditions are met, more patients may potentially benefit from the knowledge and new treatment options resulting from the precision oncology trials. Also, medical oncologists may learn more about molecular testing and interpreting test results from participation in MTBs. To maximize the impact of precision oncology, international collaborations are of utmost importance and research groups throughout the world are encouraged to share best practices and creative solutions to overcome the hurdles that still hamper new initiatives in the field today. Future clinical research may focus on prospective therapy selection using molecular information from other –omics fields, such as phosphoproteomics, especially in patients where no clear monogenetic driver mutations is identified and a comprehensive pathway analysis may give more direction for potential therapeutic strategies. More knowledge on the best method of prioritizing targets for treatments will be essential, as well as clinical trials investigating new combinations of targeted agents. With an increasing understanding of cancer biology and improved strategies for treatment selection, precision oncology will be accessible for patients with advanced cancer and more patients will benefit from the knowledge that we gain today and tomorrow. In the future, treatments based on histology alone may be considered old-fashioned, and multi-omics diagnostics may result in a comprehensible report that can be easily interpreted, and will directly guide treatment decisions for individual patients

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Molecular Mechanisms of Androgen Receptor Function In Vivo

Androgens are steroid hormones that regulate the development and function of male reproductive organs as well as physiology of many non-reproductive tissues, such as muscle, bone, liver, and kidney. Moreover, androgen signaling is involved in several pathological conditions, most common of which is prostate cancer. In its target cells, testosterone or its more potent metabolite 5α-dihydrotestosterone regulates cellular processes by modulating gene expression through the androgen receptor (AR). Ligand-activated AR translocates to nucleus and binds to specific DNA sequences, called androgen response elements (AREs), at the regulatory regions of its target genes. AR cistromes, i.e., global maps of genomic AR occupancy, comprise thousands of AR-binding events primarily located at distal enhancers. AR-binding sites are characterized by distinct histone modifications, and AR recruitment is primed by pioneer factors capable of binding to compact chromatin. AR interacts with a plethora of coregulatory proteins that modify the local chromatin environment and interact with basal transcription machinery. These elements create the complex cellular landscape for androgen action. The purpose of this thesis was to study molecular determinants of context-specific AR functions in vivo in murine androgen-responsive tissues. The advantage of in vivo studies is that unlike in cancer cell models androgen target cells reside within their physiological environment with an intact AR pathway. In the first part of this work, an androgen reporter mouse line with the luciferase gene under androgenic control was created. In this model, luciferase activity is a measure of AR function, and it can be used for assessing in vivo effects of chemical compounds on AR signaling. The androgen reporter mice were treated with genistein, a phytoestrogenic compound to which people consuming soy products are also exposed. Previous studies have implied that genistein plays a potential role in prostate cancer prevention. The results in this thesis work showed that genistein exhibits tissue-specific effects on AR signaling in vivo. Furthermore, genistein modulates endogenous AR-mediated gene expression in prostate, supporting its potentially beneficial role in prostate carcinogenesis. In the second part of this work, genomic AR occupancy was examined using chromatin immunoprecipitation (ChIP) coupled with massively parallel sequencing (ChIP-seq). Distinct AR cistromes were identified in three androgen-responsive tissues: prostate, kidney, and epididymis. AR-binding events associate with tissue-specific transcription programs responsible for distinct physiological functions of androgens in these tissues. The key finding in this work was that tissue-specific AR binding is directed by divergent pioneer factors, and that previously identified forkhead box protein A1 (FoxA1) is prostate-specific rather than general pioneer factor for AR. Two novel pioneer factors for AR were identified in this study hepatocyte nuclear factor 4 alpha (Hnf4α) in murine kidney and activating protein 2 alpha (AP-2α) in murine epididymis. ChIP-seq was also utilized to study in vivo role and characteristics of selective AREs cis-elements not bound by other steroid receptors. Transgenic SPARKI mice have the second zinc finger of the AR DNA-binding domain swapped with the respective part of glucocorticoid receptor, resulting in a chimeric AR unable to bind to selective AREs. A significant proportion of in vivo binding events of wild-type AR were not shared by SPARKI AR in prostate and epididymis, highlighting the importance of selective AREs in AR-specific functions in vivo. Differential receptor binding was also linked to differentially expressed genes in the epididymides of wild-type and SPARKI mice. De novo sequence analysis revealed that the selective AREs are characterized by decreased sequence conversation, indicating that, counter-intuitively, AR selectivity in vivo is achieved by relaxed rather than increased cis-element stringency. In conclusion, both the AREs and the collaborating proteins contribute to precise AR-regulated transcriptional outcome in the context of native chromatin and distinct tissues. Overall, the results clarified several molecular mechanisms employed by AR in vivo that can potentially contribute to the development of better treatments and diagnostic tools for hormone-dependent disorders in the future.Steroidihormoneihin kuuluvat androgeenit säätelevät sekä miehen lisääntymiselinten kehittymistä ja toimintaa että useiden muiden kudosten, kuten munuaisten, lihasten ja luiden fysiologiaa. Normaalien fysiologisten säätelytehtäviensä lisäksi androgeenit vaikuttavat myös useiden sairauksien kehittymiseen, joista yleisin on eturauhassyöpä. Androgeenit muokkaavat kohdesolujensa toimintaa säätelemällä geenien luentaa androgeenireseptorin (AR) välityksellä. AR sitoutuu kohdegeeniensä säätelyalueille tunnistaen tietyn DNA-jakson, jota kutsutaan androgeenien vaste-elementiksi. AR:n vaikutus geenien luentaan välittyy vuorovaikutuksessa muiden säätelyproteiinien kanssa, ja yhdessä nämä muuttavat kromatiinin rakennetta ja toimivat transkriptiokoneiston kanssa. Vaikka androgeenien fysiologiset vaikutukset eri kudoksissa tunnetaan melko hyvin, kudosspesifisen hormonivasteen molekyylimekanismit eivät ole vielä selviä. Tässä väitöskirjassa AR:n toimintaa tutkittiin hiiren androgeenivasteisissa kudoksissa erilaisten hiirimallien ja modernien menetelmien avulla. Kromatiini-immunosaostukseen yhdistetyllä massiivisen rinnakkaisella DNA:n sekvensoinnilla (ChIP-seq) voidaan analysoida transkriptiotekijän DNA:n sitomispaikkoja koko genomin laajuudessa. Syöpäsoluviljelmiin verrattuna hiirikokeiden vahvuus on niiden mahdollistama AR:n vaikutusten tutkiminen fysiologisessa, monia solutyyppejä sisältävässä toimintaympäristössä. Tässä väitöskirjatyössä valmistettiin uusi siirtogeeninen hiirimalli, jossa androgeenit säätelevät ns. raportoijageenin ilmentymistä. Raportoijageenin aktiivisuutta mittaamalla voidaan tutkia erilaisten kemiallisten yhdisteiden vaikutusta AR:n toimintaan elävän hiiren kudoksissa. Raportoijahiiret altistettiin kasviestrogeeni genisteiinille, jota myös ihmiset saavat soijavalmisteita sisältävästä ravinnosta. Aiempien tutkimustulosten mukaan genisteiini saattaa alentaa riskiä sairastua eturauhassyöpään. Tämän työn tulokset osoittivat, että genisteiini vaikuttaa AR:n signalointiin kudosspesifisellä tavalla. Lisäksi havaittiin genisteiinin säätelevän AR-riippuvaisten geenien aktiivisuutta eturauhasessa, mikä tukee hypoteesia sen vaikutuksista eturauhassyövän kehittymiseen. Androgeenialtistuksen aikaansaamat AR:n genominlaajuiset sitoutumisprofiilit havaittiin hyvin erilaisiksi hiiren eturauhasessa, munuaisessa ja lisäkiveksessä. AR:n sitoutumispaikat liittyivät kudoskohtaisten geenien ilmentymiseen, selittäen androgeenien erilaisia fysiologisia vaikutuksia näissä kudoksissa. Aiemmissa soluviljelytutkimuksissa on havaittu FoxA1-proteiinin toimivan AR:n pioneeritekijänä, eli sen ajatellaan avaavan tiukkaan paketoitua kromatiinirakennetta AR:n sitoutumista varten. Yksi tämän työn tärkeistä havainnoista on, että AR:n sitoutumista ohjaavat eri pioneeritekijät eri kudoksissa ja että FoxA1:n vaikutus rajoittuu eturauhaseen. Tässä työssä tunnistettiin kaksi uutta pioneeritekijää AR:lle: Hnf4α munuaisessa ja AP-2α lisäkiveksessä. Muut steroidireseptorit eivät sitoudu AR-selektiivisiin vaste-elementteihin in vitro-olosuhteissa, ja tässä työssä niitä tutkittiin in vivo siirtogeenisen SPARKI-hiiren ja ChIP-seq -menetelmän avulla. SPARKI-hiiren AR on kimeerinen proteiini, jonka DNA:ta sitovan osan toinen sinkkisormi on vaihdettu glukokortikoidireseptorin vastaavaan osaan, minkä johdosta SPARKI-AR ei sitoudu AR-selektiivisiin elementteihin. Merkittävä osa villityypin AR:n genominlaajuisista sitoutumispaikoista eturauhasessa ja lisäkiveksessä todettiin AR-selektiivisiksi in vivo -olosuhteissa. Yksi tämän työn mielenkiintoisista havainnoista on, että AR-selektiivisen DNA-sitoutumisen mahdollistaa reseptorin kyky sitoutua emäsjärjestykseltään vaihtelevampiin vaste-elementteihin kuin muut steroidireseptorit. Yhteenvetona tässä väitöskirjatyössä osoitettiin, että androgeenien aktivoiman AR:n vuorovaikutus säätelyalueiden vaste-elementtien ja kudosspesifisten pioneeritekijöiden kanssa on edellytys fysiologisesti tarkoituksenmukaiselle geenien ilmentymiselle. Uudet havainnot AR:n toiminnan molekyylimekanismeista voivat tulevaisuudessa mahdollistaa entistä parempien hoitomuotojen kehittämisen ihmisen hormoniriippuvaisiin sairauksiin