StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit.

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

Application of structured statistical analyses to identify a biomarker predictive of enhanced tralokinumab efficacy in phase III clinical trials for severe, uncontrolled asthma.

BACKGROUND: Tralokinumab is an anti-interleukin (IL)-13 monoclonal antibody investigated for the treatment of severe, uncontrolled asthma in two Phase III clinical trials, STRATOS 1 and 2. The STRATOS 1 biomarker analysis plan was developed to identify biomarker(s) indicative of IL-13 activation likely to predict tralokinumab efficacy and define a population in which there was an enhanced treatment effect; this defined population was then tested in STRATOS 2. METHODS: The biomarkers considered were blood eosinophil counts, fractional exhaled nitric oxide (FeNO), serum dipeptidyl peptidase-4, serum periostin and total serum immunoglobulin E. Tralokinumab efficacy was measured as the reduction in annualised asthma exacerbation rate (AAER) compared with placebo (primary endpoint measure of STRATOS 1 and 2). The biomarker analysis plan included negative binomial and generalised additive models, and the Subgroup Identification based on Differential Effect Search (SIDES) algorithm, supported by robustness and sensitivity checks. Effects on the key secondary endpoints of STRATOS 1 and 2, which included changes from baseline in standard measures of asthma outcomes, were also investigated. Prior to the STRATOS 1 read-out, numerous simulations of the methodology were performed with hypothetical data. RESULTS: FeNO and periostin were identified as the only biomarkers potentially predictive of treatment effect, with cut-offs chosen by the SIDES algorithm of > 32.3 ppb and > 27.4 ng/ml, respectively. The FeNO > 32.3 ppb subgroup was associated with greater AAER reductions and improvements in key secondary endpoints compared with the periostin > 27.4 ng/ml subgroup. Upon further evaluation of AAER reductions at different FeNO cut-offs, ≥37 ppb was chosen as the best cut-off for predicting tralokinumab efficacy. DISCUSSION: A rigorous statistical approach incorporating multiple methods was used to investigate the predictive properties of five potential biomarkers and to identify a participant subgroup that demonstrated an enhanced tralokinumab treatment effect. Using STRATOS 1 data, our analyses identified FeNO at a cut-off of ≥37 ppb as the best assessed biomarker for predicting enhanced treatment effect to be tested in STRATOS 2. Our findings were inconclusive, which reflects the complexity of subgroup identification in the severe asthma population. TRIAL REGISTRATION: STRATOS 1 and 2 are registered on ClinicalTrials.gov ( NCT02161757 registered on June 12, 2014, and NCT02194699 registered on July 18, 2014).

Study on risk factors and phenotypes of acute exacerbations of chronic obstructive pulmonary disease in Guangzhou, China-design and baseline characteristics.

BACKGROUND: To describe a study design that focuses on risk factors and patterns of chronic obstructive pulmonary disease (COPD) exacerbations. METHODS: A 2-year, single centre, observational study was conducted in Guangzhou in China. The study enrolled 318 subjects with COPD aged 40-79 years, stratified into different but equally sized groups according to global initiative for chronic obstructive lung disease (GOLD) stage (including Stage 0) and 86 lung healthy controls. An assessment each year was scheduled including questionnaires, lung function testing, Chest X-ray and blood collection. A sub-group, called sub-group X, consisting of 203 subjects with COPD and 51 lung healthy controls, was selected to answer a symptom questionnaire daily (EXACT-PRO) via a BlackBerry Personal Digital Assistant (PDA) device. Upon an alert that indicated a change in daily symptom pattern, the patients were contacted by the clinic to decide whether they had experienced an exacerbation and should have an extra visit within 24-48 hours. At an extra visit, nasal and throat swabs, induced sputum and blood were collected. Air pollution, temperature and humidity were also monitored daily. A subset of sub-group X, called sub-group M that consisted of 52 COPD patients and 15 healthy controls was dedicated to measure muscle strength and a dexa scan. RESULTS: More than 78% of the enrolled patients completed the study successfully. There appeared a difference between the patient groups and the controls in gender, age, body mass index (BMI), forced expiratory volume in 1 second (FEV1), FEV1/FVC and smoking at baseline. In sub-group X 90 out of 203 (44.4%) selected COPD patients developed one or more exacerbations in the 2-year observation period. They were more severe COPD patients according to GOLD stage at study start. On average most exacerbations occurred in the month March and the least number of exacerbations occurred in October. CONCLUSIONS: This study with the obtained patient dataset will allow a better insight in many aspects of exacerbations in COPD (e.g., the identification, the risk factors, phenotypes and the biomarkers).