Overlap coefficient for assessing the similarity of pharmacokinetic data between ethnically different populations

Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis

Diagnostic tests play an important role in medical research and clinical practice. The ultimate goal of a diagnostic test is to distinguish between diseased and nondiseased individuals and before a test is routinely used in practice, it is a pivotal requirement that its ability to discriminate between these two states is thoroughly assessed. The overlap coefficient, which is defined as the proportion of overlap area between two probability density functions, has gained popularity as a summary measure of diagnostic accuracy. We propose two Bayesian nonparametric estimators, based on Dirichlet process mixtures, for estimating the overlap coefficient. We further introduce the covariate-specific overlap coefficient and develop a Bayesian nonparametric approach based on Dirichlet process mixtures of additive normal models for estimating it. A simulation study is conducted to assess the empirical performance of our proposed estimators. Two illustrations are provided: one concerned with the search for biomarkers of ovarian cancer and another one aimed to assess the age-specific accuracy of glucose as a biomarker of diabetes.

Inference on the overlap coefficient: The binormal approach and alternatives

The overlap coefficient (OVL) measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. The overlap coefficient has received scant attention in the literature since it lacks readily available software for its implementation, while inferential procedures that can cover the whole range of distributional scenarios for the two underlying distributions are missing. Such methods, both parametric and non-parametric are developed in this article, while R-code is provided for their implementation. Parametric approaches based on the binormal model show better performance and are appropriate for use in a wide range of distributional scenarios. Methods are assessed through a large simulation study and are illustrated using a dataset from a study on human immunodeficiency virus-related cognitive function assessment.

Identification of Candidate Genes Associated with Charcot-Marie-Tooth Disease by Network and Pathway Analysis

Charcot-Marie-Tooth Disease (CMT) is the most common clinical genetic disease of the peripheral nervous system. Although many studies have focused on elucidating the pathogenesis of CMT, few focuses on achieving a systematic analysis of biology to decode the underlying pathological molecular mechanisms and the mechanism of its disease remains to be elucidated. So our study may provide further useful insights into the molecular mechanisms of CMT based on a systematic bioinformatics analysis. In the current study, by reviewing the literatures deposited in PUBMED, we identified 100 genes genetically related to CMT. Then, the functional features of the CMT-related genes were examined by R software and KOBAS, and the selected biological process crosstalk was visualized with the software Cytoscape. Moreover, CMT specific molecular network analysis was conducted by the Molecular Complex Detection (MCODE) Algorithm. The biological function enrichment analysis suggested that myelin sheath, axon, peripheral nervous system, mitochondrial function, various metabolic processes, and autophagy played important roles in CMT development. Aminoacyl-tRNA biosynthesis, metabolic pathways, and vasopressin-regulated water reabsorption were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in CMT occurrence and development. According to the crosstalk, the biological processes could be roughly divided into a correlative module and two separate modules. MCODE clusters showed that in top 3 clusters, 13 of CMT-related genes were included in the network and 30 candidate genes were discovered which might be potentially related to CMT. The study may help to update the new understanding of the pathogenesis of CMT and expand the potential genes of CMT for further exploration.

Population pharmacokinetic modeling to facilitate dose selection of tapentadol in the pediatric population

OBJECTIVE

The main aim of this analysis was to characterize the pharmacokinetics (PK) of the strong analgesic tapentadol in 2-year-old to <18-year-old patients with acute pain and to inform the optimal dosing strategy for a confirmatory efficacy trial in this patient population.

METHODS

The analysis dataset included tapentadol concentrations obtained from 92 pediatric patients receiving a single tapentadol oral solution (OS) dose of 1.0 mg/kg bodyweight in two single-dose PK clinical trials. Population PK analysis was performed using nonlinear mixed effects modeling. Simulations were performed to identify tapentadol OS doses in pediatric subjects (2 to <18 years) that would produce exposures similar to those in adults receiving safe and efficacious doses of tapentadol IR (50-100 mg every 4 hrs).

RESULTS

Tapentadol PK in children aged from 2 to <18 years was best described by a one-compartment model. Mean population apparent clearance and apparent volume of distribution for a typical subject weighing 45 kg were 170 L/h and 685 L, respectively. Clearance, expressed in bodyweight units as L/h/kg, decreased with increasing age whereas total clearance (L/h) increased with increasing age. Model-based simulations suggested that a tapentadol OS dose of 1.25 mg/kg to children and adolescents aged 2 to <18 years would result in efficacious tapentadol exposures similar to those in adults receiving tapentadol immediate release 50-100 mg every 4 hrs. The proposed tapentadol OS dose was subsequently applied in a confirmatory efficacy trial in 2 to <18-year-old patients suffering from acute postsurgical pain.

CONCLUSION

This analysis provides an example of a model-based approach for a dose recommendation to be used in an efficacy trial in the pediatric population. Uniform dosing based on bodyweight was proposed for the treatment of acute pain in children aged from 2 to <18 years.

The core genes involved in the promotion of depression in patients with ovarian cancer

The present study aimed to identify the core genes and pathways involved in depression in patients with ovarian cancer (OC) who suffer from high or low-grade depression. The dataset GSE9116 from Gene Expression Omnibus database was analyzed to identify differentially expressed genes (DEGs) in these patients. To elucidate how certain genes could promote depression in patients with OC, pathway crosstalk, protein-protein interaction (PPI) and comprehensive gene-pathway analyses were determined using WebGestalt, ToppGene and Search Tool for the Retrieval of Interacting Genes and gene ontology analysis. Key genes and pathways were extracted from the gene-pathway network, and gene expression and survival analysis were evaluated. A total of 93 DEGs were identified from GSE9116 dataset, including 84 upregulated genes and nine downregulated genes. The PPI, pathway crosstalk and comprehensive gene-pathway analyses highlighted C-C motif chemokine ligand 2 (CCL2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serpin family E member 1 (SERPINE1) and serpin family G member 1 (SERPING1) as core genes involved in the promotion of depression in patients with OC. These core genes were involved in the following four pathways 'Ensemble of genes encoding ECM-associated proteins including ECM-affiliated proteins', 'ECM regulators and secreted factors', 'Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins' and 'MAPK signaling pathway and IL-17 signaling pathway'. The results from gene expression and survival analysis demonstrated that these four key genes were upregulated in patients with OC and high-grade depression and could worsen patients' survival. These results suggested that CCL2, FOS, SERPINE1 and SERPING1 may serve a crucial role in the promotion of depression in patients with OC. This finding may provide novel markers for predicting and treating depression in patients with OC; however, the underlying mechanisms remain unknown and require further investigation.

A Quantitative Comparison Approach for Methylphenidate Drug Regimens in Attention-Deficit/Hyperactivity Disorder Treatment

OBJECTIVE

Different methylphenidate (MPH) formulations, immediate release (IR) or extended release (ER), have been developed to treat Attention-Deficit/Hyperactivity Disorder (ADHD). A better use of these formulations, with a proper choice of their timing, dosage, and combination, can help to attain optimal therapeutic effect while maintaining a good quality of life. In this study, we aim at presenting a quantitative comparison approach to help identify drug regimens that provide best therapeutic performances and respect patients' specific needs.

METHODS

Using pharmacokinetic (PK) models of various MPH formulations constructed with data in hand and a formerly developed performance metric for MPH regimens, we proposed a statistical integral strategy for regimen comparison, which comprises a sequential, a relative, and a probability-over-threshold method. The first is hierarchical in nature and sequentially compares the regimen performance, the total daily dose, and the administration frequency. The second compares two regimens by quantifying their similarity. The third computes the probability of an incremental regimen performance over a specified threshold. The first two comparison approaches are used for naive patients, whereas the third one is for patients under treatment.

RESULTS

PK models of one compartment effectively describe both the IR and ER data. Applied to three frequent MPH clinical situations, the three-methods strategy is able to distinguish the regimens proposed for each. A combined regimen of IR and ER taken at the same time performs better than a single ER dose.

CONCLUSION

The proposed statistical strategy is able to differentiate ADHD regimens in various clinically relevant situations, and adapt the use of MPH drugs to a patient's daily routine. Since it does not compare fixed doses and formulations but rather any MPH regimen, our approach generalizes the current context of bioequivalence study and provides an accessible computational tool for objectively selecting MPH regimens.

Prediction of the effect of formulation on the toxicity of chemicals

Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

Bridging analysis of the efficacy and safety of bazedoxifene in Japanese and global populations of postmenopausal women with osteoporosis

This study examined whether the global clinical data for bazedoxifene could be extrapolated to a Japanese population by evaluating the results of a phase 2 study in postmenopausal Japanese women with osteoporosis as compared to those of a pivotal, phase 3 study. The efficacy of bazedoxifene 20 and 40 mg versus placebo on lumbar spine bone mineral density (BMD), bone turnover markers, lipid profile, incidence of fractures, and safety parameters was compared between the Japanese phase 2 study (N = 429) and the global phase 3 study (N = 7,492) during a 2-year period. In the primary population for assessment of bridging, differences in the mean percent change from baseline in lumbar spine BMD at 2 years relative to placebo were greater for women treated with bazedoxifene 20 and 40 mg in the phase 2 study than in the phase 3 study. BMD changes in the bazedoxifene groups were confirmed to be similar between the phase 2 study population and a subset of the phase 3 study population with similar baseline characteristics. The effects of bazedoxifene on incidence of fractures, bone turnover markers, and lipid metabolism were similar between studies. There were no major differences in safety parameters between studies. The greater improvement in lumbar spine BMD and similar results in bone turnover markers, fracture incidence, and safety profile observed with bazedoxifene in the phase 2 study compared with the phase 3 study confirmed the feasibility of extrapolating the global clinical data to a Japanese population.

A new framework to enhance the interpretation of external validation studies of clinical prediction models

OBJECTIVES

It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from "different but related" samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models.

STUDY DESIGN AND SETTING

We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting.

RESULTS

We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings.

CONCLUSION

The proposed framework enhances the interpretation of findings at external validation of prediction models.

Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology

Important understanding can be gained from using molecular biology-based and chemistry-based techniques together. Bayesian classifiers have thus been developed in the present work using several statistically significant molecular properties of compiled datasets of drugs and non-drugs, including their disease category or organ. The results show they provide a useful classification and simplicity of several different ligand efficiencies and molecular properties. Early recall of drugs among non-drugs using the classifiers as a ranking tool is also provided. As the chemical space of compounds is addressed together with their anatomical characterization, chemical libraries can be improved to select for specific organ or disease. Eventually, by including even finer detail, the method may help in designing libraries with specific pharmacological or toxicological target chemical space. Alternatively, a lack of statistically significant differences in property density distributions may help in further describing compounds with possibility of activity on several organs or disease groups, and given their very similar or considerably overlapping chemical space, therefore wanted or unwanted side-effects. The overlaps between densities for several properties of organs or disease categories were calculated by integrating the area under the curves where they intersect. The naïve Bayesian classifiers are readily built, fast to score, and easily interpretable.

Visualizing and assessing discrimination in the logistic regression model

Logistic regression models are widely used in medicine for predicting patient outcome (prognosis) and constructing diagnostic tests (diagnosis). Multivariable logistic models yield an (approximately) continuous risk score, a transformation of which gives the estimated event probability for an individual. A key aspect of model performance is discrimination, that is, the model's ability to distinguish between patients who have (or will have) an event of interest and those who do not (or will not). Graphical aids are important in understanding a logistic model. The receiver-operating characteristic (ROC) curve is familiar, but not necessarily easy to interpret. We advocate a simple graphic that provides further insight into discrimination, namely a histogram or dot plot of the risk score in the outcome groups. The most popular performance measure for the logistic model is the c-index, numerically equivalent to the area under the ROC curve. We discuss the comparative merits of the c-index and the (standardized) mean difference in risk score between the outcome groups. The latter statistic, sometimes known generically as the effect size, has been computed in slightly different ways by several different authors, including Glass, Cohen and Hedges. An alternative measure is the overlap between the distributions in the outcome groups, defined as the area under the minimum of the two density functions. The larger the overlap, the weaker the discrimination. Under certain assumptions about the distribution of the risk score, the c-index, effect size and overlap are functionally related. We illustrate the ideas with simulated and real data sets.

Evaluating statistical methods to establish clinical similarity of two biologics

The design, endpoints, statistical methods, and equivalence boundary for studies seeking to demonstrate clinical similarity between biologics are not standardized by any regulatory agency. We describe our experience in conducting a phase III study of a biologic product before and after a manufacturing change, focusing on statistical considerations for claiming equivalence for the dosing endpoint. We discuss and evaluate traditional statistical methods like two one-sided testing and the Kolmogorov-Smirnov test, as well as the newly proposed overlap coefficient method. We conclude that establishing clinical similarity of biologics is complex and demands more thought from regulatory agencies and the biopharmaceutical industry.