Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity

Cholesteryl Ester Transfer Protein (CETP) as a Drug Target for Cardiovascular Disease

Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer’s disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration

Security theory and practice: Medical rescue and disater medicne in crisis response

Ze wstępu: Nieznany jest autor powiedzenia, że wojny wygrywa się nie liczbą żołnierzy, lecz liczbą sanitariuszy – na pewno jednak nie należy traktować tej sentencji wyłącznie jako zręcznego bon mot, lecz jako głęboką mądrość. Od liczebności, sprawności i skuteczności służb ratowniczych zależy bowiem, czy przetrwamy kataklizmy, tj. czy będziemy umieli minimalizować liczbę ofiar katastrof, zamachu terrorystycznego lub konfliktu zbrojnego, gdyby do takiego miało dojść. Po raz pierwszy zajęliśmy się w naszym czasopiśmie problematyką ratownictwa, rozważanego na tle reagowania kryzysowego i medycyny katastrof, postanawiając przyjrzeć się kondycji polskiego ratownictwa oraz głównym wyzwaniom, jakie stoją przed służbami ratowniczymi RP, wreszcie – nowym trendom rozwojowym ratownictwa. Do podjęcia tematyki skłoniły nas także narastające w społeczeństwie obawy przed pogarszaniem się jakości zarówno podstawowych usług medycznych oraz szpitalnictwa, jak i medycyny ratunkowej1. Zmiany w systemie medycyny ratunkowej podyktowane są względami oszczędnościowymi, a także szczupłością kadry lekarzy specjalistów medycyny ratunkowej, których liczba nie przekracza dwóch tysięcy wobec czternastu tysięcy ratowników medycznych. Bieżący rok przebiega pod znakiem akcji protestacyjnych różnych grup pracowniczych służby zdrowia – lekarzy rezydentów, pielęgniarek i ratowników medycznych. Polska plasuje się dziś na przedostatnim miejscu w Europie, jeśli chodzi o dostępność specjalistycznej pomocy medycznej, i nic nie zapowiada, by w najbliższym czasie sytuacja miała ulec poprawie. Tym większe znaczenie społecznych organizacji ratowniczych i postawa tysięcy wolontariuszy gotowych nieść pomoc potrzebującym. W numerze zarysowujemy obraz współczesnego polskiego ratownictwa, piszemy o ratownictwie medycznym, wodnym i górskim, a także o psychologicznym wsparciu poszkodowanych w formie ratownictwa psychologicznego. Sygnalizujemy znaczenie nowoczesnych technologii, które pozwalają na skuteczne prowadzenie akcji poszukiwawczych i ratunkowych w górach, jaskiniach i nad wodą. Całości numeru dopełniaja recenzje książek związanych tematycznie z profilem numeru oraz seria komunikatów

Animals used in <i>in vivo</i> efficacy assays.

<p>Other mammals include mainly laboratory rodents (<i>e</i>.<i>g</i>. hamster, gerbil), carnivores (cat), lagomorphs (rabbit), and primates (<i>e</i>.<i>g</i>. rhesus monkey); the latter were used in 1,157 assays. The main classes of non-mammal animals include arthropods, nematodes, and birds.</p

Semantic similarities between animal models and phenotypes.

<p>A hierarchically clustered heatmap showing pairwise semantic similarities between 35 animal models and 35 phenotypes frequently mentioned in the assay descriptions. Red color corresponds to higher, blue—to lower semantic similarity; values in each row are Z-score normalized. Both rows and columns are hierarchically clustered (using average linkage and Euclidean distance) and the results are represented as dendrograms. Semantic clusters, shown as red regions on the heatmap, correspond to distinct disease areas including epilepsy, pain, inflammation, hypertension, diabesity, and cancer. The figure provides an automatically-generated summary of the use of common animal models to study the effect of drugs on different types of disease-related phenotypes.</p

Differential use of rats and mice across <i>in vivo</i> assays.

<p><b>(A)</b> Number of assays involving rats and mice for eight example experimental systems. <b>(B)</b> Differential use of the two rodent species in assays testing drugs from the 10 most common drug classes (based on the second level of the ATC classification). Classes are ordered by the difference in the number of assays involving rats and mice. The images of the animals used in the figure were obtained under the open license from Gene Expression Atlas <a href="https://www.ebi.ac.uk/gxa" target="_blank">https://www.ebi.ac.uk/gxa</a>.</p

Processing of assay descriptions, with an illustrative example case.

<p><b>(A)</b> The input data: raw assay descriptions retrieved from the ChEMBL database. <b>(B)</b> Shallow grammatical analysis (shallow parsing). GENIA tagger annotates each word with its corresponding part-of-speech (POS) category (e.g. noun, adjective, verb). The POS annotations are then used to find longer chunks of text corresponding to noun phrases; here represented as yellow blocks in the shallow parse tree. <b>(C)</b> Custom chunking. Noun phrases detected by GENIA are simplified using custom tags and chunking rules. <b>(D)</b> Named entity recognition (NER). Strains, experimental animal models, and phenotypic terms are identified in terms using a combination of dictionary and rule-based NER methods. <b>(E)</b> Learning distributed vector representations. The entire dataset of preprocessed assay descriptions is used to train a neural network language model, Word2Vec. Thus, words and noun phrases from each assay description are converted to high-dimensional numerical vectors that can be used as input for clustering and machine learning models. <i>S</i>, sentence; <i>NP</i>, noun phrase; <i>PP</i>, prepositional phrase; <i>VP</i>, verb phrase; <i>JJ</i>, adjective; <i>NN</i>, noun; <i>IN</i>, preposition; <i>NNP</i>, proper noun; <i>VBN</i>, verb, past participle.</p

Major component of the animal model—Drug network with detailed “diabesity” cluster.

<p>The nodes in the graph correspond to approved drugs and animal models of disease, including induced, spontaneous, and transgenic disease models text-mined from assay descriptions. A drug is linked to an animal model if it was tested in at least five assays involving this model. Drug nodes are colored according to the assigned ATC (level 2) codes, while animal model nodes are blue; node size is proportional to the number of assays involving a given drug or model. Animal model-drug relationships visualized in the graph are listed in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005641#pcbi.1005641.s007" target="_blank">S5 Dataset</a>. STZ, streptozotocin-induced model; GTT, glucose tolerance test; ZDF, Zucker Diabetic Fatty rat; glucose, glucose-loaded model.</p

Most common rodent strains and experimental disease models mentioned in the descriptions of <i>in vivo</i> efficacy assays in ChEMBL.

<p><b>(A)</b> Twenty strains that are most frequently mentioned in assay descriptions; outbred strains are marked with an asterisk (*). Upon identification in the text of assay descriptions, the strain names were normalized using strain synonym listings maintained by rodent genome databases. For instance, C57BL mouse was described in various descriptions with more than 30 different terms including names that do not follow official nomenclature guidelines: “BL6”, “Black6”, or “C57/Black”. <b>(B)</b> Bar plot showing twenty experimental models that are most frequently mentioned in assay descriptions. The models were manually annotated with disease area.</p