Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records

We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice

Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The “count” indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies

Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three-component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug-ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other

Effects of pregnancy on the pharmacokinetics of metformin

This study's primary objective was to fully characterize the pharmacokinetics of metformin in pregnant women with gestational diabetes mellitus (GDM) versus nonpregnant controls. Steady-state oral metformin pharmacokinetics in pregnant women with GDM receiving either metformin monotherapy (n 5 24) or a combination with glyburide (n 5 30) as well as in nonpregnant women with type 2 diabetes mellitus (T2DM) (n 5 24) were determined utilizing non-compartmental techniques. Maternal and umbilical cord blood samples were collected at delivery from 38 women. With both 500- and 1000-mg doses, metformin bioavailability, volume of distribution beta (Vb), clearance, and renal clearance were significantly increased during pregnancy. In addition, in the women receiving metformin 500 mg, significantly higher metformin apparent oral clearance (CL/F) (27%), weight-adjusted renal secretion clearance (64%), and apparent oral volume of distribution beta (Vb/F) (33%) were seen during pregnancy. Creatinine clearance was significantly higher during pregnancy. Increasing metformin dose from 500 to 1000 mg orally twice daily significantly increased Vb/F by 28%, weight-adjusted Vb/F by 32% and CL/F by 25%, and weight-adjusted CL/F by 28% during pregnancy. Mean metformin umbilical cord arterial-to-venous plasma concentration ratio was 1.0 6 0.1, venous umbilical cord-to-maternal concentration ratio was 1.4 6 0.5, and arterial umbilical cord-to-maternal concentration ratio was 1.5 6 0.5. Systemic exposure after a 500-mg dose of metformin was lower during pregnancy compared with the nonpregnant women with T2DM. However, in patients receiving metformin 1000 mg, changes in estimated bioavailability during pregnancy offset the changes in clearance leading to no significant change in CL/F with the higher dose. SIGNIFICANCE STATEMENT Gestational diabetes mellitus complicates 5%-13% of pregnancies and is often treated with metformin. Pregnant women undergo physiological changes that alter drug disposition. Preliminary data suggest that pregnancy lowers metformin concentrations, potentially affecting efficacy and safety. This study definitively describes pregnancy's effects on metformin pharmacokinetics and expands the mechanistic understanding of pharmacokinetic changes across the dosage range. Here we report the nonlinearity of metformin pharmacokinetics and the increase in bioavailability, clearance, renal clearance, and volume of distribution during pregnancy

Pharmacodynamics of Glyburide, Metformin, and Glyburide/Metformin Combination Therapy in the Treatment of Gestational Diabetes Mellitus

In gestational diabetes mellitus (GDM), women are unable to compensate for the increased insulin resistance during pregnancy. Data are limited regarding the pharmacodynamic effects of metformin and glyburide during pregnancy. This study characterized insulin sensitivity (SI), β-cell responsivity, and disposition index (DI) in women with GDM utilizing a mixed-meal tolerance test (MMTT) before and during treatment with glyburide monotherapy (GLY, n = 38), metformin monotherapy (MET, n = 34), or GLY and MET combination therapy (COMBO; n = 36). GLY significantly decreased dynamic β-cell responsivity (31%). MET and COMBO significantly increased SI (121% and 83%, respectively). Whereas GLY, MET, and COMBO improved DI, metformin (MET and COMBO) demonstrated a larger increase in DI (P = 0.05) and a larger decrease in MMTT peak glucose concentrations (P = 0.03) than subjects taking only GLY. Maximizing SI with MET followed by increasing β-cell responsivity with GLY or supplementing with insulin might be a more optimal strategy for GDM management than monotherapy

Pharmacodynamics of Metformin in Pregnant Women With Gestational Diabetes Mellitus and Nonpregnant Women With Type 2 Diabetes Mellitus

Gestational diabetes mellitus is a condition similar to type 2 diabetes mellitus (T2DM) in that patients are unable to compensate for the degree of insulin resistance, and both conditions are often treated with metformin. The comparative pharmacodynamic response to metformin in these 2 populations has not been studied. This study characterized insulin sensitivity, β-cell responsivity, and disposition index following a mixed-meal tolerance test utilizing a minimal model of glucose, insulin, and C-peptide kinetics before and during treatment with metformin. The study included women with gestational diabetes mellitus (n = 34), T2DM (n = 14), and healthy pregnant women (n = 30). Before treatment, the gestational diabetes mellitus group had significantly higher baseline (45%), dynamic (68%), static (71%), and total β-cell responsivity (71%) than the T2DM group. Metformin significantly increased insulin sensitivity (51%) as well as disposition index (97%) and decreased mixed-meal tolerance test peak glucose concentrations (8%) in women with gestational diabetes mellitus after adjustment for gestational age–dependent effects; however, in women with T2DM metformin only significantly affected peak glucose concentrations (22%) and had no significant effect on any other parameters. Metformin had a greater effect on the change in disposition index (Δ disposition index) in women with gestational diabetes mellitus than in those with T2DM (P =.01). In conclusion, response to metformin in women with gestational diabetes mellitus is significantly different from that in women with T2DM, which is likely related to the differences in disease severity

Supplementary Material for: Predicting the Glomerular Filtration Rate in Bariatric Surgery Patients

<b><i>Background/Aims:</i></b> Identifying the best method to estimate the glomerular filtration rate (GFR) in bariatric surgery patients has important implications for the clinical care of obese patients and research into the impact of obesity and weight reduction on kidney health. We therefore performed such an analysis in patients before and after surgical weight loss. <b><i>Methods:</i></b> Fasting measured GFR (mGFR) by plasma iohexol clearance before and after bariatric surgery was obtained in 36 severely obese individuals. Estimated GFR was calculated using the Modification of Diet in Renal Disease equation, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation using serum creatinine only, the CKD-EPI equation using serum cystatin C only and a recently derived equation that uses both serum creatinine and cystatin C (CKD-EPI_creat-cystC) and then compared to mGFR. <b><i>Results:</i></b> Participants were primarily middle-aged white females with a mean baseline body mass index of 46 ± 9, serum creatinine of 0.81 ± 0.24 mg/dl and mGFR of 117 ± 40 ml/min. mGFR had a stronger linear relationship with inverse cystatin C before (r = 0.28, p = 0.09) and after (r = 0.38, p = 0.02) surgery compared to the inverse of creatinine (before: r = 0.26, p = 0.13; after: r = 0.11, p = 0.51). mGFR fell by 17 ± 35 ml/min (p = 0.007) following surgery. The CKD-EPI_creat-cystC was unquestionably the best overall performing estimating equation before and after surgery, revealing very little bias and a capacity to estimate mGFR within 30% of its true value over 80% of the time. This was true whether or not mGFR was indexed for body surface area. <b><i>Conclusions:</i></b> In severely obese bariatric surgery patients with normal kidney function, cystatin C is more strongly associated with mGFR than is serum creatinine. The CKD-EPI_creat-cystC equation best predicted mGFR both before and after surgery

Clinical Data Combined With Modeling and Simulation Indicate Unchanged Drug-Drug Interaction Magnitudes in the Elderly

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities