Identifying Novel Drug Indications through Automated Reasoning

abstract: Background With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process. Methodology In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications. Conclusion/Significance To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.004094

Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

Motivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

Adaptation of High-Throughput Screening in Drug Discovery—Toxicological Screening Tests

High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses of chemical compounds to be conducted in a short time and the affinity of biological structures which is often related to toxicity to be defined. Since 2008 we have implemented the automation of this technique and as a consequence, the possibility to examine 100,000 compounds per day. The HTS method is more frequently utilized in conjunction with analytical techniques such as NMR or coupled methods e.g., LC-MS/MS. Series of studies enable the establishment of the rate of affinity for targets or the level of toxicity. Moreover, researches are conducted concerning conjugation of nanoparticles with drugs and the determination of the toxicity of such structures. For these purposes there are frequently used cell lines. Due to the miniaturization of all systems, it is possible to examine the compound’s toxicity having only 1–3 mg of this compound. Determination of cytotoxicity in this way leads to a significant decrease in the expenditure and to a reduction in the length of the study

Emerging radiopharmaceuticals for PET-imaging gliomas. A multi-: radiopharmaceutical, camera, modality, model, and modelling assessment

Gliomas, which are a type of brain tumour derived from the non-neuronal and nutrient-supplying glial cells of the brain, are particularly devastating disease due to the importance and delicate nature of cerebral matter. Surgical removal, chemotherapy, and radiation therapy often have unwanted consequences depending on a variety of physiological and probability factors. With the human life expectancy averaging 12-15 months after clinical diagnosis (with treatment) for aggressive brain tumours, accurately detecting and characterizing these tumours non-invasively is important for treatment planning. Currently, the highest anatomical resolution imaging modality available for brain imaging is magnetic resonance imaging (MRI), but this lacks biochemical information. Positron emission tomography paired with computed tomography for anatomical reference (PET-CT) divulges quantifiable biochemical information. By selecting imaging radiopharmaceuticals for PET imaging that have relevance to tumour surface proteins or other cellular metabolic processes it is possible to not only aid in detecting or delineating gliomas, but also gain specific biochemical-property insight into these lesions. The aim of these studies was to evaluate the two emerging radiopharmaceuticals (2S, 4R)-4-[18F]fluoroglutamine ([18F]FGln) and Al[18F]F-NOTA-Folate ([18F]FOL) and to directly compare them with routinely clinically-used radiopharmaceuticals 2-deoxy-2-[18F]fluoro-ᴅ-glucose ([18F]FDG) and ʟ-[11C]methionine ([11C]Met) for the PET imaging of gliomas in animal models. Other parameters, such as the in vivo stability, ex vivo biodistribution, in vitro binding and blocking, and the presence of relevant receptors on human tissue samples were investigated in to divulge additional information. The results demonstrated that both [18F]FGln and [18F]FOL provided an enhanced level of contrast between tumour and adjacent non-tumour brain tissue versus that of the clinically used radiopharmaceuticals [18F]FDG and [11C]Met in animal models.Uudet radiolääkeaineet glioomien PET-kuvantamiseen. Tutkimuksia radiolääkeaineista, modaliteeteista, kameroista, kokeellisista malleista ja mallintamisesta Glioomat ovat aivokasvaimia, jotka syntyvät ravinteiden kuljetusta hoitavista glia- eli hermotukisoluista. Ne ovat erityisen tuhoisia sairauksia aivokudoksen tärkeyden ja herkkyyden vuoksi. Kirurgisella leikkauksella, kemoterapialla, ja sädehoidolla on usein ei-toivottuja seurauksia riippuen fysiologisista ja todennäköisyystekijöistä. Koska elinajanodote aggressiivisen aivokasvaimen diagnoosin jälkeen on keskimäärin 12–15 kuukautta (hoidon kanssa), ei-invasiivinen tarkka havaitseminen ja karakterisointi on tärkeää hoidon suunnittelussa. Tällä hetkellä parhaat työkalut aivojen kuvantamiseen ovat magneettikuvaus (MRI), joka mahdollistaa parhaimman anatomisen tarkkuuden, ja positroniemissiotomografia (PET), joka paljastaa biokemiallisen informaation. Valitsemalle PET-kuvantamiseen radiolääkeaine, joka kiinnittyy syöpäsolun pintaproteiineihin tai liittyy solun aineenvaihduntaprosessiin on mahdollista paitsi havaita tai rajata glioomia, myös saada erityistä biokemiallista tietoa näistä leesioista. Tämän tutkimuksen tavoitteena oli arvioida kahta uutta radiolääkeainetta; (2S, 4R)-4-[18F]fluoriglutamiinia ([18F]FGln) ja Al[18F]F-NOTA-folaattia ([18F]FOL) ja verrata niitä kliinisessä käytössä oleviin 2-deoxy-2-[18F]fluori-ᴅ-glukoosiin ([18F]FDG) ja ʟ-[11C]metioniiniin ([11C]Met) glioomien PET-kuvantamisessa. Stabiilisuutta, biologista jakautumista, sitoutumista ja sitoutumisen salpautumista, sekä farmakokineettista mallintamista tutkittiin in vivo, ex vivo ja in vitro olosuhteissa eläinmalleissa ja kudosnäytteillä. Tulokset osoittivat, että eläinmalleissa sekä [18F]FGln että [18F]FOL mahdollistavat paremman kontrastin tuumorin ja viereisen tuumorittoman aivokudoksen välillä verrattuna kliinisessä käytössä oleviin [18F]FDG ja [11C]Met radiolääkeaineisiin

Personalised antimicrobial management in secondary care

Background: The growing threat of Antimicrobial Resistance (AMR) requires innovative methods to promote the sustainable effectiveness of antimicrobial agents. Hypothesis: This thesis aimed to explore the hypothesis that personalised decision support interventions have the utility to enhance antimicrobial management across secondary care. Methods: Different research methods were used to investigate this hypothesis. Individual physician decision making was mapped and patient experiences of engagement with decision making explored using semi-structured interviews. Cross-specialty engagement with antimicrobial management was investigated through cross-sectional analysis of conference abstracts and educational training curricula. Artificial intelligence tools were developed to explore their ability to predict the likelihood of infection and provide individualised prescribing recommendations using routine patient data. Dynamic, individualised dose optimisation was explored through: (i) development of a microneedle based, electrochemical biosensor for minimally invasive monitoring of beta-lactams; and (ii) pharmacokinetic (PK)-pharmacodynamic (PD) modelling of a new PK-PD index using C-Reactive protein (CRP) to predict the pharmacodynamics of vancomycin. Ethics approval was granted for all aspects of work explored within this thesis. Results: Mapping of individual physician decision making during infection management demonstrated several areas where personalised, technological interventions could enhance antimicrobial management. At specialty level, non-infection specialties have little engagement with antimicrobial management. The importance of engaging surgical specialties, who have relatively high rates of antimicrobial usage and healthcare associated infections, was observed. An individualised information leaflet, co-designed with patients, to provide personalised infection information to in-patients receiving antibiotics significantly improved knowledge and reported engagement with decision making. Artificial intelligence was able to enhance the prediction of infection and the prescribing of antimicrobials using routinely available clinical data. Real-time, continuous penicillin monitoring was demonstrated using a microneedle based electrochemical sensor in-vivo. A new PK-PD index, using C-Reactive Protein, was able to predict individual patient response to vancomycin therapy at 96-120 hours of therapy. Conclusion: Through co-design and the application of specific technologies it is possible to provide personalised antimicrobial management within secondary care.Open Acces

Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in

Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in