Efficacy and safety of once weekly selinexor 40 mg versus 60 mg with pomalidomide and dexamethasone in relapsed and/or refractory multiple myeloma.

Objective: To identify the optimal dose of selinexor in combination with pomalidomide and dexamethasone (SPd). Methods: An analysis of efficacy and safety of 2 once-weekly selinexor regimens (60 mg and 40 mg) with pomalidomide and dexamethasone (SPd-60 and SPd-40, respectively) given to patients with relapsed/refractory multiple myeloma (RRMM) in the STOMP (NCT02343042) and XPORT-MM-028 (NCT04414475) trials. Results: Twenty-eight patients (60.7% males, median age 67.5 years) and 20 patients (35.0% males, median age 65.5 years) were analyzed in the SPd-40 and SPd-60 cohorts, respectively. Overall response rate was 50% (95% confidence interval [CI] 30.6-69.4%) and 65% (95% CI 40.8-84.6%), respectively. Very good partial response or better was reported in 28.6% (95% CI 13.2-48.7%) and 30.0% (95% CI 11.9-54.3%) of patients, respectively. Among 27 responders in both cohorts, the 12-month sustained response rate was 83.3% (95% CI 64.7-100.0%) for SPd-40 and 28.1% (95% CI 8.9-88.8%) for SPd-60. Median progression-free survival was 18.4 months (95% CI 6.5 months, not evaluable [NE]) and 9.5 months (95% CI 7.6 months-NE) for SPd-40 and SPd-60, respectively. Twenty-four-month survival rates were 64.2% (95% CI 47.7-86.3%) for SPd-40 and 51.1% (95% CI 29.9-87.5%) for SPd-60. Treatment-emergent adverse events (TEAEs) included neutropenia (all grades: SPd-40 64.3% versus SPd-60 75.0%), anemia (46.4% versus 65.0%), thrombocytopenia (42.9% versus 45.0%), fatigue (46.4% versus 75.0%), nausea (32.1% versus 70.0%) and diarrhea (28.6% versus 35.0%). Conclusion: The all-oral combination of SPd exhibited preliminary signs of efficacy and was generally tolerable in patients with RRMM. The overall risk-benefit profile favored the SPd-40 regimen.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events.

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Motivation: Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. Results: The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28 151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. Availability and implementation: The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Spleen tyrosine kinase/FMS-like tyrosine kinase-3 inhibition in relapsed/refractory B-cell lymphoma, including diffuse large B-cell lymphoma: updated data with mivavotinib (TAK-659/CB-659).

We report an updated analysis from a phase I study of the spleen tyrosine kinase (SYK) and FMS-like tyrosine kinase 3 inhibitor mivavotinib, presenting data for the overall cohort of lymphoma patients, and the subgroup of patients with diffuse large B-cell lymphoma (DLBCL; including an expanded cohort not included in the initial report). Patients with relapsed/refractory lymphoma for which no standard treatment was available received mivavotinib 60-120 mg once daily in 28-day cycles until disease progression/unacceptable toxicity. A total of 124 patients with lymphoma, including 89 with DLBCL, were enrolled. Overall response rates (ORR) in response-evaluable patients were 45% (43/95) and 38% (26/69), respectively. Median duration of response was 28.1 months overall and not reached in DLBCL responders. In subgroups with DLBCL of germinal center B-cell (GCB) and non-GCB origin, ORR was 28% (11/40) and 58% (7/12), respectively. Median progression free survival was 2.0 and 1.6 months in the lymphoma and DLBCL cohorts, respectively. Grade ≥3 treatment-emergent adverse events occurred in 96% of all lymphoma patients, many of which were limited to asymptomatic laboratory abnormalities; the most common were increased amylase (29%), neutropenia (27%), and hypophosphatemia (26%). These findings support SYK as a potential therapeutic target for the treatment of patients with B-cell lymphomas, including DLBCL. Trial registration: ClinicalTrials.gov number: NCT02000934.

Selinexor-based regimens in patients with multiple myeloma after prior anti-B-cell maturation antigen treatment.

There is a lack of consensus on therapy sequencing in previously treated multiple myeloma, particularly after anti-B-cell maturation antigen (BCMA) therapy. Earlier reports on selinexor (X) regimens demonstrated considerable efficacy in early treatment, and after anti-BCMA-targeted chimeric antigen receptor-T cell therapy. Here, we present data from 11 heavily pretreated patients who predominantly received BCMA-antibody-drug conjugate therapy. We observe that X-containing regimens are potent and achieve durable responses with numerically higher overall response and clinical benefit rates, as well as median progression free survival compared to patients' prior anti-BCMA therapies, despite being used later in the treatment course. In an area of evolving unmet need, these data reaffirm the efficacy of X-based regimens following broader anti-BCMA therapy.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Critical assessment of transformer-based AI models for German clinical notes.

Objective: Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods: We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results: General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion: The domain-adaptation strategy's potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion: General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Phase 1 Study Evaluating Pharmacokinetics and Tolerability of Ofatumumab Combined With Bendamustine in Patients With Indolent B-Cell Non-Hodgkin's Lymphoma.

The pharmacokinetics (PK) and safety of ofatumumab and bendamustine alone and in combination were evaluated in patients with treatment-naive or relapsed indolent B-cell non-Hodgkin lymphoma (iNHL). Patients were randomly assigned to ofatumumab and bendamustine or ofatumumab alone. Ofatumumab PK concentration profiles and parameters were similar, alone or in combination with bendamustine. A decrease of 14% in the maximum observed plasma concentration (Cmax ) and 15% in the area under the plasma concentration-time curve (AUC) from time 0 to the last measurable concentration sampling time (AUClast ) was observed for ofatumumab coadministered with bendamustine, which was not considered clinically relevant. Bendamustine PK concentration profiles and parameters were similar with or without ofatumumab. The most frequent treatment-related adverse event was infusion-related reaction in 53% in the combination arm and 47% in the ofatumumab arm. No relevant drug-drug interaction was observed between ofatumumab and bendamustine. Ofatumumab alone or in combination with bendamustine had a manageable safety profile.

Clinical safety and pharmacokinetic evaluation of aqueous extract of Cocculus hirsutus, an anti-viral phytopharmacetical drug as a potential for the treatment of dengue and COVID-19.

Background and aim: Dengue a worldwide concern for public health has no effective vaccine or drug available for its prevention or treatment. There are billions of people who are at risk of contracting the dengue virus (DENV) infections with only anti-mosquito strategies to combat this disease. Based on the reports, particularly in vitro studies and small animal studies showing anti-viral activity of aqueous extract of Cocculus hirsutus (AQCH), studies were conducted on AQCH tablets as a potential for the treatment of dengue and COVID-19 infections. The current study was part of the research on AQCH tablet formulation and was aimed to evaluate safety and pharmacokinetics in healthy human subjects. Materials and methods: Sixty healthy adult human subjects were divided into 5 groups (cohorts: I to V; n = 12 per cohort) and randomized in the ratio of 3:1 to receive active treatment or placebo in a blinded manner. Five doses 100 mg, 200 mg, 400 mg, 600 mg and 800 mg tablets were administered three times daily at an interval of 8 h for days 01-09 under fasting conditions and a single dose in morning on day 10. Safety assessment was based on monitoring the occurrence, pattern, intensity, and severity of adverse events during study period. Blood samples were collected for measurement of the bio-active marker Sinococuline concentrations by a validated LC-MS/MS method followed by pharmacokinetic evaluation. Results and conclusion: The test formulation was well tolerated in all cohorts. Sinococuline peak plasma concentration (Cmax) and total exposure of plasma concentration (AUC) demonstrated linearity up to 600 mg and saturation kinetics at 800 mg dose. There was no difference observed in elimination half-life for all the cohorts, suggesting absence of saturation in rate of elimination. Dose accumulation was observed and steady state was achieved within 3 days. The information on human pharmacokinetics of AQCH tablets would assist in further dose optimization with defined pharmacokinetic-pharmacodynamic relationship.

Deep Learning-based detection of psychiatric attributes from German mental health records.

BACKGROUND: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in mental health. Therefore, application of text mining to the electronic health records - especially mental state examination - is a key approach for detection of psychiatric disease phenotypes that relate to treatment outcomes. METHODS: We focused on the mental state examination (MSE) in the patients' discharge summaries as the key part of the psychiatric records. We prepared a sample of 150 text documents that we manually annotated for psychiatric attributes and symptoms. These documents were further divided into training and test sets. We designed and implemented a system to detect the psychiatric attributes automatically and linked the pathologically assessed attributes to AMDP terminology. This workflow uses a pre-trained neural network model, which is fine-tuned on the training set, and validated on the independent test set. Furthermore, a traditional NLP and rule-based component linked the recognized mentions to AMDP terminology. In a further step, we applied the system on a larger clinical dataset of 510 patients to extract their symptoms. RESULTS: The system identified the psychiatric attributes as well as their assessment (normal and pathological) and linked these entities to the AMDP terminology with an F1-score of 86% and 91% on an independent test set, respectively. CONCLUSION: The development of the current text mining system and the results highlight the feasibility of text mining methods applied to MSE in electronic mental health care reports. Our findings pave the way for the secondary use of routine data in the field of mental health, facilitating further clinical data analyses.

Once weekly selinexor, carfilzomib and dexamethasone in carfilzomib non-refractory multiple myeloma patients.

BACKGROUND: Proteasome inhibitors (PIs), including carfilzomib, potentiate the activity of selinexor, a novel, first-in-class, oral selective inhibitor of nuclear export (SINE) compound, in preclinical models of multiple myeloma (MM). METHODS: The safety, efficacy, maximum-tolerated dose (MTD) and recommended phase 2 dose (RP2D) of selinexor (80 or 100 mg) + carfilzomib (56 or 70 mg/m2) + dexamethasone (40 mg) (XKd) once weekly (QW) was evaluated in patients with relapsed refractory MM (RRMM) not refractory to carfilzomib. RESULTS: Thirty-two patients, median prior therapies 4 (range, 1-8), were enrolled. MM was triple-class refractory in 38% of patients and 53% of patients had high-risk cytogenetics del(17p), t(4;14), t(14;16) and/or gain 1q. Common treatment-related adverse events (all/Grade 3) were thrombocytopenia 72%/47% (G3 and G4), nausea 72%/6%, anaemia 53%/19% and fatigue 53%/9%, all expected and manageable with supportive care and dose modifications. MTD and RP2D were identified as selinexor 80 mg, carfilzomib 56 mg/m2, and dexamethasone 40 mg, all QW. The overall response rate was 78% including 14 (44%) ≥ very good partial responses. Median progression-free survival was 15 months. CONCLUSIONS: Weekly XKd is highly effective and well-tolerated. These data support further investigation of XKd in patients with MM.

Cocculus hirsutus-Derived Phytopharmaceutical Drug Has Potent Anti-dengue Activity.

Dengue is a serious public health concern worldwide, with ∼3 billion people at risk of contracting dengue virus (DENV) infections, with some suffering severe consequences of disease and leading to death. Currently, there is no broad use vaccine or drug available for the prevention or treatment of dengue, which leaves only anti-mosquito strategies to combat the dengue menace. The present study is an extension of our earlier study aimed at determining the in vitro and in vivo protective effects of a plant-derived phytopharmaceutical drug for the treatment of dengue. In our previous report, we had identified a methanolic extract of aerial parts of Cissampelos pareira to exhibit in vitro and in vivo anti-dengue activity against all the four DENV serotypes. The dried aerial parts of C. pareira supplied by local vendors were often found to be mixed with aerial parts of another plant of the same Menispermaceae family, Cocculus hirsutus, which shares common homology with C. pareira. In the current study, we have found C. hirsutus to have more potent anti-dengue activity as compared with C. pareira. The stem part of C. hirsutus was found to be more potent (∼25 times) than the aerial part (stem and leaf) irrespective of the extraction solvent used, viz., denatured spirit, hydro-alcohol (50:50), and aqueous. Moreover, the anti-dengue activity of stem extract in all the solvents was comparable. Hence, an aqueous extract of the stem of C. hirsutus (AQCH) was selected due to greater regulatory compliance. Five chemical markers, viz., Sinococuline, 20-Hydroxyecdysone, Makisterone-A, Magnoflorine, and Coniferyl alcohol, were identified in fingerprinting analysis. In a test of primary dengue infection in the AG129 mice model, AQCH extract at 25 mg/kg body weight exhibited protection when administered four and three times a day. The AQCH was also protective in the secondary DENV-infected AG129 mice model at 25 mg/kg/dose when administered four and three times a day. Additionally, the AQCH extract reduced serum viremia and small intestinal pathologies, viz., viral load, pro-inflammatory cytokines, and vascular leakage. Based on these findings, we have undertaken the potential preclinical development of C. hirsutus-based phytopharmaceutical, which could be studied further for its clinical development for treating dengue.

Indatuximab ravtansine plus dexamethasone with lenalidomide or pomalidomide in relapsed or refractory multiple myeloma: a multicentre, phase 1/2a study.

BACKGROUND: Indatuximab ravtansine (BT062) is an antibody-drug conjugate that binds to CD138 and synergistically enhances the antitumor activity of lenalidomide in preclinical models of multiple myeloma. This phase 1/2a study was done to determine the safety, activity, and pharmacokinetics of indatuximab ravtansine in combination with immunomodulatory drugs in patients with relapsed or refractory multiple myeloma. METHODS: This open-label, phase 1/2a study took place at nine hospital sites in the USA. Eligible patients were aged 18 years or older, had relapsed or refractory multiple myeloma, and ECOG performance status or Zubrod score of 2 or below. Patients who received indatuximab ravtansine with lenalidomide and dexamethasone (indatuximab ravtansine plus lenalidomide) had failure of at least one previous therapy. Patients treated with indatuximab ravtansine with pomalidomide and dexamethasone (indatuximab ravtansine plus pomalidomide) had failure of at least two previous therapies (including lenalidomide and bortezomib) and had progressive disease on or within 60 days of completion of their last treatment. In phase 1, patients received indatuximab ravtansine intravenously on days 1, 8, and 15 of each 28-day cycle in escalating dose levels of 80 mg/m2, 100 mg/m2, and 120 mg/m2, with lenalidomide (25 mg; days 1 to 21 every 28 days orally) and dexamethasone (20-40 mg; days 1, 8, 15, and 22 every 28 days). In phase 2, the recommended phase 2 dose of indatuximab ravtansine was given to an expanded cohort of patients in combination with lenalidomide and dexamethasone. The protocol was amended to allow additional patients to be treated with indatuximab ravtansine plus pomalidomide (4 mg; days 1 to 21 every 28 days orally) and dexamethasone, in a more heavily pretreated patient population than in the indatuximab ravtansine plus lenalidomide group. The phase 1 primary endpoint was to determine the dose-limiting toxicities and the maximum tolerated dose (recommended phase 2 dose) of indatuximab ravtansine, and the phase 2 primary endpoint was to describe the objective response rate (ORR; partial response or better) and clinical benefit response (ORR plus minor response). All patients were analysed for safety and all patients with post-treatment response assessments were analysed for activity. This study is registered with ClinicalTrials.gov, number NCT01638936, and is complete. FINDINGS: 64 (86%) of 74 screened patients were enrolled between July 3, 2012, and June 30, 2015. 47 (73%) patients received indatuximab ravtansine plus lenalidomide (median follow-up 24·2 months [IQR 19·9-45·4]) and 17 (27%) received indatuximab ravtansine plus pomalidomide (24·1 months [17·7-36·7]). The maximum tolerated dose of indatuximab ravtansine plus lenalidomide was 100 mg/m2, and defined as the recommended phase 2 dose for indatuximab ravtansine plus pomalidomide. An objective response for indatuximab ravtansine plus lenalidomide was observed in 33 (71·7%) of 46 patients and in 12 (70·6%) of 17 patients in the indatuximab ravtansine plus pomalidomide group. The clinical benefit response for indatuximab ravtansine plus lenalidomide was 85% (39 of 46 patients) and for indatuximab ravtansine plus pomalidomide it was 88% (15 of 17 patients). The most common grade 3-4 adverse events in both groups were neutropenia (14 [22%] of 64 patients), anaemia (10 [16%]), and thrombocytopenia (seven [11%]). Treatment-emergent adverse events (TEAEs) that led to discontinuation occurred in 35 (55%) of the 64 patients. Five (8%) patients with a TEAE had a fatal outcome; none was reported as related to indatuximab ravtansine. INTERPRETATION: Indatuximab ravtansine in combination with immunomodulatory drugs shows preliminary antitumor activity, is tolerated, and could be further evaluated in patients with relapsed or refractory multiple myeloma. FUNDING: Biotest AG.

COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints.

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

The COVID-19 Ontology.

MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data-Driven Modeling of Knowledge Assemblies in Understanding Comorbidity Between Type 2 Diabetes Mellitus and Alzheimer's Disease.

BACKGROUND: Recent studies have suggested comorbid association between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) through identification of shared molecular mechanisms. However, the inference is pre-dominantly literature-based and lacks interpretation of pre-disposed genomic variants and transcriptomic measurables. OBJECTIVE: In this study, we aim to identify shared genetic variants and dysregulated genes in AD and T2DM and explore their functional roles in the comorbidity between the diseases. METHODS: The genetic variants for AD and T2DM were retrieved from GWAS catalog, GWAS central, dbSNP, and DisGeNet and subjected to linkage disequilibrium analysis. Next, shared variants were prioritized using RegulomeDB and Polyphen-2. Afterwards, a knowledge assembly embedding prioritized variants and their corresponding genes was created by mining relevant literature using Biological Expression Language. Finally, coherently perturbed genes from gene expression meta-analysis were mapped to the knowledge assembly to pinpoint biological entities and processes and depict a mechanistic link between AD and T2DM. RESULTS: Our analysis identified four genes (i.e., ABCG1, COMT, MMP9, and SOD2) that could have dual roles in both AD and T2DM. Using cartoon representation, we have illustrated a set of causal events surrounding these genes which are associated to biological processes such as oxidative stress, insulin resistance, apoptosis and cognition. CONCLUSION: Our approach of using data as the driving force for unraveling disease etiologies eliminates literature bias and enables identification of novel entities that serve as the bridge between comorbid conditions.

Systems Toxicology Approach for Testing Chemical Cardiotoxicity in Larval Zebrafish.

Transcriptomic approaches can give insight into molecular mechanisms underlying chemical toxicity and are increasingly being used as part of toxicological assessments. To aid the interpretation of transcriptomic data, we have developed a systems toxicology method that relies on a computable biological network model. We created the first network model describing cardiotoxicity in zebrafish larvae-a valuable emerging model species in testing cardiotoxicity associated with drugs and chemicals. The network is based on scientific literature and represents hierarchical molecular pathways that lead from receptor activation to cardiac pathologies. To test the ability of our approach to detect cardiotoxic outcomes from transcriptomic data, we have selected three publicly available data sets that reported chemically induced heart pathologies in zebrafish larvae for five different chemicals. Network-based analysis detected cardiac perturbations for four out of five chemicals tested, for two of them using transcriptomic data collected up to 3 days before the onset of a visible phenotype. Additionally, we identified distinct molecular pathways that were activated by the different chemicals. The results demonstrate that the proposed integrational method can be used for evaluating the effects of chemicals on the zebrafish cardiac function and, together with observed cardiac apical end points, can provide a comprehensive method for connecting molecular events to organ toxicity. The computable network model is freely available and may be used to generate mechanistic hypotheses and quantifiable perturbation values from any zebrafish transcriptomic data.

Proposal of an Architecture for Terminology Management in a Research Project.

Clinical and medical knowledge evolve and this causes changes in concepts and terms that describe them. The objective of this work is to formally present an ontology-based standard architecture that will be used in the scenario of neurodegeneration research to maintain terminologies and their relations updated and coherent over the time. The proposed structure is composed by three elements that will allow the user to do a list of operations on the terminology resources explicitly contemplated by the Common Terminology Service Release 2 (CTS2).