Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV1 50–80% predicted, FEV1/FVC < 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug–proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder characterized by prolonged periods of fatigue, chronic pain, depression, and a complex constellation of other symptoms. Currently, ME/CFS has no known cause, nor are the mechanisms of illness well understood. Therefore, with few exceptions, attempts to treat ME/ CFS have been directed mainly toward symptom management. These treatments include antivirals, pain relievers, antidepressants, and oncologic agents as well as other single-intervention treatments. Results of these trials have been largely inconclusive and, in some cases, contradictory. Contributing factors include a lack of well-designed and -executed studies and the highly heterogeneous nature of ME/ CFS, which has made a single etiology difficult to define. Because the majority of single-intervention treatments have shown little efficacy, it may instead be beneficial to explore broader-acting combination therapies in which a more focused precision-medicine approach is supported by a systems-level analysis of endocrine and immune co-regulation. (Clin Ther. 2019;41:798e805)

Early patient discontinuation from adjuvant endocrine treatment (ET) is multifactorial and complex: Patients must adapt to various challenges and make the best decisions they can within changing contexts over time. Predictive models are needed that can account for the changing influence of multiple factors over time as well as decisional uncertainty due to incomplete data. AtlasTi8 analyses of longitudinal interview data from 82 estrogen receptor-positive (ER+) breast cancer patients generated a model conceptualizing patient-, patient-provider relationship, and treatment-related influences on early discontinuation. Prospective self-report data from validated psychometric measures were discretized and constrained into a decisional logic network to refine and validate the conceptual model. Minimal intervention set (MIS) optimization identified parsimonious intervention strategies that reversed discontinuation paths back to adherence. Logic network simulation produced 96 candidate decisional models which accounted for 75% of the coordinated changes in the 16 network nodes over time. Collectively the models supported 15 persistent end-states, all discontinued. The 15 end-states were characterized by median levels of general anxiety and low levels of perceived recurrence risk, quality of life (QoL) and ET side effects. MIS optimization identified 3 effective interventions: reducing general anxiety, reinforcing pill-taking routines, and increasing trust in healthcare providers. Increasing health literacy also improved adherence for patients without a college degree. Given complex regulatory networks’ intractability to end-state identification, the predictive models performed reasonably well in identifying specific discontinuation profiles and potentially effective interventions.

A significant cause of morbidity in COVID-19 infected patients admitted to the hospital is a severe dysregulated inflammatory response characterized as a cytokine storm, a key component of acute respiratory distress syndrome (ARDS). Here we have assembled a basic immune regulatory model from a list of 19 immune mediators with reported involvement in cytokine storm. Automated text-mining of over 2,500 full text journal publications using the MedScan natural language processing (NLP) engine identified 112 documented regulatory interactions coordinating the dynamic response of this network. This same text mining highlighted reported bi-directional associations between Coronavirus infection and a broad set of immune mediators producing a complex feedback pattern of host-pathogen interaction. Decisional logic parameters supporting the network's dynamic response were identified such that observed responses to SARS-CoV infection in an in vitro system of Calu3 human lung adenocarcinoma cells could be accurately predicted. Of the 19 competing models, 2 supported a dominant inactive immune resting state, with a predicted onset of cytokine storm in 63% and 26% of simulated infections respectively. Discrete event simulation based on the latter suggest that some repurposing strategies might outperform popular use of hydroxychloroquine as a companion to anti-viral therapy.