Modeling and Synergy Testing of Drug Combination Data: A Pharmacokinetic Analysis

In this paper, we present and implement a method to assess the mathematical synergy of two-drug combinations based on a stochastic model. The drugs in question are two isomers that are applied to the human eye via a liquid eye drop. Techniques applied to the data in this paper can be applied to other two-drug combination studies. We derive the mean and the variance terms of the drug combination effects in closed form using Ito\u27s method of stochastic differential equations. The model fit of the data to the individual subject is examined by both statistical and graphical methods. Two estimation methods in SAS, PROC NUN and PROC NLMIXED, are used to estimate model parameters. We perform simulation and power studies using R software to show the strengths of the proposed approach in estimating the model parameters. From this research, we find that the combination of drugs under study is synergistic in nature. We also confirm that the proposed stochastic model is appropriate

Mixture Model for Individual and Combined Data Using Estimating Equations

Abstract When performing analysis of individual data on the application of a particu lar drug, it is useful to study the within variability. But when two drugs are used in combination, it is of more interest to study any combination effects on the subjects. In this paper we consider a new analytical framework that is a comb ination of the individual and co mbined data analyses, based on an estimating equation approach. The proposed analyses utilize a stochastic model for a two-drug combination and derive the mean and the variance terms based on Ito's calculus. The proposed estimation methods are used to estimate model parameters fro m both individual and co mbined data, and they provide the basis for model free synergy tests. The strength of the fit of the model to the data is examined by statistical measures and the graphical method. Simu lation studies were performed to show the strengths of the proposed approach in estimat ing the model parameters. A synergy test of the model fitted by the individual subjects confirmed that the combination of the isomers under study is synergistic in nature

Deep resequencing reveals excess rare recent variants consistent with explosive population growth

Accurately determining the distribution of rare variants is an important goal of human genetics, but resequencing of a sample large enough for this purpose has been unfeasible until now. Here, we applied Sanger sequencing of genomic PCR amplicons to resequence the diabetes-associated genes KCNJ11 and HHEX in 13,715 people (10,422 European Americans and 3,293 African Americans) and validated amplicons potentially harbouring rare variants using 454 pyrosequencing. We observed far more variation (expected variant-site count ∼578) than would have been predicted on the basis of earlier surveys, which could only capture the distribution of common variants. By comparison with earlier estimates based on common variants, our model shows a clear genetic signal of accelerating population growth, suggesting that humanity harbours a myriad of rare, deleterious variants, and that disease risk and the burden of disease in contemporary populations may be heavily influenced by the distribution of rare variants

Association of Low-Frequency and Rare Coding-Sequence Variants with Blood Lipids and Coronary Heart Disease in 56,000 Whites and Blacks

Low-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the “Exome Array” to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121∗], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited

Small Dense Low-Density Lipoprotein-Cholesterol Concentrations Predict Risk for Coronary Heart Disease

OBJECTIVE: To investigate the relationship between plasma levels of small dense low-density lipoprotein cholesterol (sdLDL-C) and risk for incident coronary heart disease (CHD) in a prospective study among Atherosclerosis Risk in Communities (ARIC) study participants. APPROACH AND RESULTS: Plasma sdLDL-C was measured in 11,419 men and women of the biracial ARIC study using a newly developed homogeneous assay. A proportional hazards model was used to examine the relationship between sdLDL-C, vascular risk factors, and risk for CHD events (n=1,158) over a period of ≈11 years. Plasma sdLDL-C levels were strongly correlated with an atherogenic lipid profile and were higher in diabetics than nondiabetics (49.6 vs. 42.3 mg/dL, p<0.0001). In a model that included established risk factors, sdLDL-C was associated with incident CHD with a hazard ratio (HR) of 1.51 (95%CI: 1.21–1.88) for the highest versus the lowest quartile, respectively. Even in individuals considered to be at low cardiovascular risk based on their LDL-C levels, sdLDL-C predicted risk for incident CHD (HR 1.61; 95% CI 1.04–2.49). Genome-wide association analyses identified genetic variants in 8 loci associated with sdLDL-C levels. These loci were in or close to genes previously associated with risk for CHD. We discovered 1 novel locus, PCSK7, for which genetic variation was significantly associated with sdLDL-C and other lipid factors. CONCLUSIONS: sdLDL-C was associated with incident CHD in ARIC study participants. The novel association of genetic variants in PCSK7 with sdLDL-C and other lipid traits may provide new insights into the role of this gene in lipid metabolism

Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: The atherosclerosis risk in communities (ARIC) study

Objective: To investigate the relationship between plasma levels of small dense low-density lipoprotein-cholesterol (sdLDL-C) and risk for incident coronary heart disease (CHD) in a prospective study among Atherosclerosis Risk in Communities (ARIC) study participants.Approach and results: Plasma sdLDL-C was measured in 11 419 men and women of the biracial ARIC study using a newly developed homogeneous assay. A proportional hazards model was used to examine the relationship among sdLDL-C, vascular risk factors, and risk for CHD events (n=1158) for a period of ≈11 years. Plasma sdLDL-C levels were strongly correlated with an atherogenic lipid profile and were higher in patients with diabetes mellitus than non-diabetes mellitus (49.6 versus 42.3 mg/dL; P\u3c0.0001). In a model that included established risk factors, sdLDL-C was associated with incident CHD with a hazard ratio of 1.51 (95% confidence interval, 1.21-1.88) for the highest versus the lowest quartile, respectively. Even in individuals considered to be at low cardiovascular risk based on their LDL-C levels, sdLDL-C predicted risk for incident CHD (hazard ratio, 1.61; 95% confidence interval, 1.04-2.49). Genome-wide association analyses identified genetic variants in 8 loci associated with sdLDL-C levels. These loci were in or close to genes previously associated with risk for CHD. We discovered 1 novel locus, PCSK7, for which genetic variation was significantly associated with sdLDL-C and other lipid factors.Conclusions: sdLDL-C was associated with incident CHD in ARIC study participants. The novel association of genetic variants in PCSK7 with sdLDL-C and other lipid traits may provide new insights into the role of this gene in lipid metabolism

Whole Exome Sequencing Identifies Novel Genes for Fetal Hemoglobin Response to Hydroxyurea in Children with Sickle Cell Anemia

<div><p>Hydroxyurea has proven efficacy in children and adults with sickle cell anemia (SCA), but with considerable inter-individual variability in the amount of fetal hemoglobin (HbF) produced. Sibling and twin studies indicate that some of that drug response variation is heritable. To test the hypothesis that genetic modifiers influence pharmacological induction of HbF, we investigated phenotype-genotype associations using whole exome sequencing of children with SCA treated prospectively with hydroxyurea to maximum tolerated dose (MTD). We analyzed 171 unrelated patients enrolled in two prospective clinical trials, all treated with dose escalation to MTD. We examined two MTD drug response phenotypes: HbF (final %HbF minus baseline %HbF), and final %HbF. Analyzing individual genetic variants, we identified multiple low frequency and common variants associated with HbF induction by hydroxyurea. A validation cohort of 130 pediatric sickle cell patients treated to MTD with hydroxyurea was genotyped for 13 non-synonymous variants with the strongest association with HbF response to hydroxyurea in the discovery cohort. A coding variant in <i>Spalt-like transcription factor</i>, or <i>SALL2</i>, was associated with higher final HbF in this second independent replication sample and <i>SALL2</i> represents an outstanding novel candidate gene for further investigation. These findings may help focus future functional studies and provide new insights into the pharmacological HbF upregulation by hydroxyurea in patients with SCA.</p></div

Comparison of discovery and validation cohorts.

<p>WBC: white blood cell count; ANC: absolute neutrophil count; ARC: absolute reticulocyte count; MCV: mean corpuscular volume; HU: hydroxyurea.</p><p>The discovery cohort was composed of 120 patients from HUSTLE and 51 from SWiTCH. The validation cohort was collected from patients treated at TCCH.</p><p>Comparison of discovery and validation cohorts.</p