A Web-Based Pharmacogenomics Search Tool for Precision Medicine in Perioperative Care.

Background: Precision medicine represents an evolving approach to improve treatment efficacy by modifying it to individual patient's gene variation. Pharmacogenetics, an applicable branch of precision medicine, identifies patient's predisposing genotypes that alter the clinical outcome of the drug, hence preventing serious adverse drug reactions. Pharmacogenetics has been extensively applied to various fields of medicine, but in the field of anesthesiology and preoperative medicine, it has been unexploited. Although the US Food and Drug Administration (FDA) has a table of pharmacogenomics biomarkers and pharmacogenetics, this table only includes general side effects of the included drugs. Thus, the existing FDA table offers limited information on genetic variations that may increase drug side effects. Aims: The purpose of this paper is to provide a web-based pharmacogenomics search tool composed of a comprehensive list of medications that have pharmacogenetic relevance to perioperative medicine that might also have application in other fields of medicine. Method: For this investigation, the FDA table of pharmacogenomics biomarkers in drug labeling was utilized as an in-depth of drugs to construct our pharmacogenetics drug table. We performed a literature search for drug-gene interactions using the unique list of drugs in the FDA table. Publications containing the drug-gene interactions were identified and reviewed. Additional drugs and extracted gene-interactions in the identified publications were added to the constructed drug table. Result: Our tool provides a comprehensive pharmacogenetic drug table including 258 drugs with a total of 461 drug-gene interactions and their corresponding gene variations that might cause modifications in drug efficacy, pharmacokinetics, pharmacodynamics and adverse reactions. This tool is freely accessible online and can be applied as a web-based search instrument for drug-gene interactions in different fields of medicine, including perioperative medicine. Conclusion: In this research, we collected drug-gene interactions in a web-based searchable tool that could be used by physicians to expand their field knowledge in pharmacogenetics and facilitate their clinical decision making. This precision medicine tool could further serve in establishing a comprehensive perioperative pharmacogenomics database that also includes different fields of medicine that could influence the outcome of perioperative medicine.

Immunoglobulin E as a Biomarker for the Overlap of Atopic Asthma and Chronic Obstructive Pulmonary Disease.

Asthma-COPD overlap (ACO) is a common clinical syndrome, yet there is no single objective definition. We hypothesized that immunoglobulin E (IgE) measurements could be used to refine the definition of ACO. In baseline plasma samples from 2870 participants in the COPD Genetic Epidemiology (COPDGene®) study, we measured total IgE levels and specific IgE levels to 6 common allergens. Compared to usual chronic obstructive pulmonary disease (COPD), participants with ACO (based on self-report of asthma) had higher total IgE levels (median 67.0 versus 42.2 IU/ml) and more frequently had at least one positive specific IgE (43.5% versus 24.5%). We previously used a strict definition of ACO in participants with COPD, based on self-report of a doctor's diagnosis of asthma before age 40. This strict ACO definition was refined by the presence of atopy, determined by total IgE > 100 IU/ml or at least one positive specific IgE, as was the broader definition of ACO based on self-reported asthma history. Participants with all 3 ACO definitions were younger (mean age 60.0-61.3 years), were more commonly African American (36.8%-44.2%), had a higher exacerbation frequency (1.0-1.2 in the past year), and had more airway wall thickening on quantitative analysis of chest computed tomography (CT) scans. Among participants with ACO, 37%-46% did not have atopy; these individuals had more emphysema on chest CT scan. Based on associations with exacerbations and CT airway disease, IgE did not clearly improve the clinical definition of ACO. However, IgE measurements could be used to subdivide individuals with atopic and non-atopic ACO, who might have different biologic mechanisms and potential treatments.

Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder. COPD patients may have different clinical features, imaging characteristics and natural history. Multiple studies have investigated heterogeneity using statistical methods such as unsupervised clustering to define different subgroups of COPD based largely on clinical phenotypes. Some studies have performed clustering using genetic data or limited numbers of blood biomarkers. Our primary goal was to use proteomic data to find subtypes of COPD within clinically similar individuals. In the Treatment of Emphysema with a gamma-Selective Retinoid Agonist (TESRA) study, multiplex biomarker panels were run in serum samples collected prior to randomization. After implementing an algorithm to minimize missing values, the dataset included 396 COPD individuals and 87 biomarkers. Using hierarchical clustering, we identified 3 COPD subgroups, containing 267 (67.4%), 104 (26.3%), and 25 (6.3%) individuals, respectively. The third cluster had less emphysema on quantitative analysis of chest computed tomography scans (p=0.03) and worse disease-related quality of life based on the St. George's Respiratory Questionnaire (total score cluster 1: 45.6, cluster 2: 45.4, cluster 3: 56.6; p=0.01), despite similar levels of lung function impairment (forced expiratory volume in 1 second (49.2%, 49.2%, 48.2 % predicted, respectively). Enrichment analysis showed the biomarkers distinguishing cluster 3 mapped to platelet alpha granule and cell chemotaxis pathways. Thus, we identified a subgroup which has less emphysema but may have greater inflammation, which could be potentially targeted with anti-inflammatory therapies.

Stochastic tracking of infection in a CF lung.

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan are the two ubiquitous imaging sources that physicians use to diagnose patients with Cystic Fibrosis (CF) or any other Chronic Obstructive Pulmonary Disease (COPD). Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures. In addition, even though both CT scan and MRI provide mesoscopic details of a lung, in order to obtain microscopic information a very high resolution is required. Neither MRI nor CT scans provide micro level information about the location of infection in a binary tree structure the binary tree structure of the human lung. In this paper we present an algorithm that enhances the current imaging results by providing estimated micro level information concerning the location of the infection. The estimate is based on a calculation of the distribution of possible mucus blockages consistent with available information using an offline Metropolis-Hastings algorithm in combination with a real-time interpolation scheme. When supplemented with growth rates for the pockets of mucus, the algorithm can also be used to estimate how lung functionality as manifested in spirometric tests will change in patients with CF or COPD.

Mucus distribution model in a lung with cystic fibrosis.

Cystic fibrosis (CF) is the most common autosomal recessive disease in Caucasians with a reported incidence of 1 in every 3200 live births. Most strikingly, CF is associated with early mortality. Host in flammatory responses result in airway mucus plugging, airway wall edema, and eventual destruction of airway wall support structure. Despite aggressive treatment, the median age of survival is approximately 38 years. This work is the first attempt to parameterize the distributions of mucus in a CF lung as a function of time. By default, the model makes arbitrary choices at each stage of the construction process, whereby the simplest choice is made. The model is sophisticated enough to fit the average CF patients' spirometric data over time and to identify several interesting parameters: probability of colonization, mucus volume growth rate, and scarring rate. Extensions of the model appropriate for describing the dynamics of single patient MRI data are also discussed.