Dataset Information

A pathway-driven predictive model of tramadol pharmacogenetics.

ABSTRACT: Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype-phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs genetic data from uridine diphosphate glucuronosyltransferase, family 1, polypeptide B7 (UGT2B7), adenosine triphosphate (ATP)-binding cassette, subfamily B, number 1 (ABCB1), opioid receptor mu 1 (OPRM1), and catechol-O-methyltransferase (COMT) to predict the tramadol to primary metabolite ratio (T:M1) and the resulting toxicologically inferred MP (t-MP). These data were then combined with CYP2D6 data to evaluate performance of a fully combinatorial model relative to CYP2D6 alone. These data identify UGT2B7 as a potentially significant explanatory marker for T:M1 variability in a population of tramadol-exposed individuals of Finnish ancestry. Supervised machine learning and feature selection were used to demonstrate that a set of 16 loci from 5 genes can predict t-MP with over 90% accuracy, depending on t-MP category and algorithm, which was significantly greater than predictions made by CYP2D6 alone.

SUBMITTER: Wendt FR

PROVIDER: S-EPMC6777452 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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A pathway-driven predictive model of tramadol pharmacogenetics.

Wendt Frank R FR Novroski Nicole M M NMM Rahikainen Anna-Liina AL Sajantila Antti A Budowle Bruce B

European journal of human genetics : EJHG 20190301 7

Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype-phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs g ...[more]

PMID: 30824817

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Project description:BackgroundEmergency department (ED) visits contribute substantially to health care expenditures. Case management has been proposed as a strategy to address the medical and social needs of complex patients. However, strong research designs to evaluate the effectiveness of such interventions are limited.ObjectivesTo evaluate whether a community-based case management program was associated with reduced ED utilization among complex patients.DesignPatients whose risk exceeded a threshold were randomly assigned to a group offered case management or to the control group. Assignment occurred at five intervals between November 2017 and January 2019. Program effectiveness for all assigned patients was assessed using an intention-to-treat effect. Program effectiveness among those who received treatment was assessed using a local average treatment effect, estimated using instrumental variables. Both estimators were adjusted for baseline characteristics using linear models.ParticipantsAdults over age 18 with at least one health care encounter with Michigan Medicine or St. Joseph Mercy Health System between June 2, 2016, and November 27, 2018.InterventionsIntervention arm participants (n = 486) were offered coordinated case management across medical, mental health, and social service organizations. Control arm participants (n = 409) received usual care.Main measuresThe primary outcome was the number of ED visits in the 6 months following randomization into the study. Secondary outcomes were 6-month counts of inpatient and outpatient visits.Key resultsOf the 486 patients assigned to the intervention, 131 (27%) consented to receive case management. The intention-to-treat effect on ED visits was + 0.14 (95% CI: - 0.27 to + 0.55). The local average treatment effect among those who consented and received case management was + 0.53 (95% CI: - 1.00 to + 2.05). Intention-to-treat and local average treatment effects were not significant for secondary outcomes.ConclusionsThe community case management intervention targeting ED visits was not associated with reduced utilization. Future case management interventions may benefit from additional patient engagement strategies and longer evaluation time periods.Trial registrationClinicaltrials.gov Identifier: NCT03293160.

Project description:BackgroundThe rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration.ResultsThe efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer.ConclusionsAn efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This "pathway architecture designer" workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.

Dataset Information

A pathway-driven predictive model of tramadol pharmacogenetics.

Publications

A pathway-driven predictive model of tramadol pharmacogenetics.

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