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    ABSTRACT: We investigated the population pharmacokinetics and pharmacogenetics of efavirenz in 307 patients coinfected with human immunodeficiency virus and tuberculosis and included in the Cambodian Early vs Late Initiation of Antiretrovirals trial (CAMELIA) in Cambodia. Efavirenz (600 mg/d) and stavudine plus lamivudine were ad-ministered in addition to standard antituberculosis treatment, including rifampicin and isoniazid. Blood samples were obtained a mean of 14 hours after efavirenz intake at weeks 2 and 6 after initiation of efavirenz and weeks 22 (efavirenz plus antituberculosis drugs) and 50 (efavirenz alone) after initiation of antituberculosis treatment. Ten patients participated in an extensive pharmacokinetic study after week 50. CYP2B6 G516T and C485-18T polymorphisms were the most significant covariates, with weight showing a significant minor effect. Change in efavirenz apparent clearance in patients taking both efavirenz and antituberculosis treatment was highly dependent on NAT2 polymorphism, as a possible surrogate of isoniazid exposure. Patients carrying the CYP2B6 516 TT genotype and slow-acetylation NAT2 phenotype had the lowest efavirenz apparent clearance. These data suggest that the inducing effect of rifampicin is counterbalanced by a concentration-dependant in-hibitory effect of isoniazid on efavirenz clearance. Efavirenz is a nonnucleoside reverse-transcriptase inhib-itor of human immunodeficiency virus (HIV) type 1 and one of the preferred components of the first-line an-tiretroviral treatment (ART) regimen of HIV infection worldwide. Current guidelines recommend efavirenz at a dosage of 600 mg/d combined with 2 nucleoside (or nucleotide) analogues as one of the preferred options for first-line therapy in developed as well as resource-limited countries [1]. Furthermore, it was demonstrated that efavirenz can be coadministered safely with stan-dard antituberculosis therapy that includes rifampicin, a potent drug enzyme inducer, and isoniazid for 6 months and ethambutol plus pyrazinamide for the first 2 months. Earlier studies recommended increasing the efavirenz dosage to 800 mg/d in patients receiving efa-virenz and rifampicin concomitantly [2, 3]. Later studies demonstrated the efficacy of efavirenz at a dosage of 600 mg/d along with antituberculosis drugs [4]; recently, it has been suggested that the efavirenz dosage be in-creased to 800 mg/d in patients weighing >50 kg [5]. Efavirenz is metabolized mainly through CYP2B6 [6], which has been demonstrated to be inducible
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    ABSTRACT: Enhancements in sensitivity now allow DNA profiles to be obtained from only tens of picograms of DNA, corresponding to a few cells, even for samples subject to degradation from environmental exposure. However, low-template DNA (LTDNA) profiles are subject to stochastic effects, such as "dropout" and "dropin" of alleles, and highly variable stutter peak heights. Although the sensitivity of the newly developed methods is highly appealing to crime investigators, courts are concerned about the reliability of the underlying science. High-profile cases relying on LTDNA evidence have collapsed amid controversy, including the case of Hoey in the United Kingdom and the case of Knox and Sollecito in Italy. I argue that rather than the reliability of the science, courts and commentators should focus on the validity of the statistical methods of evaluation of the evidence. Even noisy DNA evidence can be more powerful than many traditional types of evidence, and it can be helpful to a court as long as its strength is not overstated. There have been serious shortcomings in statistical methods for the evaluation of LTDNA profile evidence, however. Here, I propose a method that allows for multiple replicates with different rates of dropout, sporadic dropins, different amounts of DNA from different contributors, relatedness of suspected and alternate contributors, "uncertain" allele designations, and degradation. R code implementing the method is open source, facilitating wide scrutiny. I illustrate its good performance using real cases and simulated crime scene profiles.
    Proceedings of the National Academy of Sciences 07/2013; 110(30). DOI:10.1073/pnas.1219739110
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    ABSTRACT: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network.
    Artificial intelligence in medicine 02/2013; 57(3). DOI:10.1016/j.artmed.2012.12.006
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    ABSTRACT: CONTEXT: Studies on the influence of single nucleotide polymorphisms (SNPs) on drug pharmacokinetics (PK) have usually been limited to the analysis of observed drug concentration or area under the concentration versus time curve. Nonlinear mixed effects models enable analysis of the entire curve, even for sparse data, but until recently, there has been no systematic method to examine the effects of multiple SNPs on the model parameters. OBJECTIVE: The aim of this study was to assess different penalized regression methods for including SNPs in PK analyses. METHODS: A total of 200 data sets were simulated under both the null and an alternative hypothesis. In each data set for each of the 300 participants, a PK profile at six sampling times was simulated and 1227 genotypes were generated through haplotypes. After modelling the PK profiles using an expectation maximization algorithm, genetic association with individual parameters was investigated using the following approaches: (i) a classical stepwise approach, (ii) ridge regression modified to include a test, (iii) Lasso and (iv) a generalization of Lasso, the HyperLasso. RESULTS: Penalized regression approaches are often much faster than the stepwise approach. There are significantly fewer true positives for ridge regression than for the stepwise procedure and HyperLasso. The higher number of true positives in the stepwise procedure was accompanied by a higher count of false positives (not significant). CONCLUSION: We find that all approaches except ridge regression show similar power, but penalized regression can be much less computationally demanding. We conclude that penalized regression should be preferred over stepwise procedures for PK analyses with a large panel of genetic covariates.
    Pharmacogenetics and Genomics 01/2013; 23(3). DOI:10.1097/FPC.0b013e32835dd22c
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    ABSTRACT: We consider the comparison of hypotheses "parent-child" or "full siblings" against the alternative of "unrelated" for pairs of individuals for whom DNA profiles are available. This is a situation that occurs repeatedly in familial database searching. A decision rule that uses both the kinship index (KI), also known as the likelihood ratio, and the identity-by-state statistic (IBS) was advocated in a recent report as superior to the use of KI alone. Such proposal appears to conflict with the Neyman-Pearson Lemma of statistics, which states that the likelihood ratio alone provides the most powerful criterion for distinguishing between any two simple hypotheses. We therefore performed a simulation study that was two orders of magnitude larger than in the previous report, and our results corroborate the theoretical expectation that KI alone provides a better decision rule than KI combined with IBS.
    Forensic Science International: Genetics 06/2012; 7(1). DOI:10.1016/j.fsigen.2012.06.001
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    ABSTRACT: This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. Stumpf, M. Girolami, eds.)
    Handbook of Statistical Systems Biology, 09/2011: pages 39 - 65; , ISBN: 9781119970606
  • Handbook of Statistical Systems Biology, 09/2011: pages 66 - 82; , ISBN: 9781119970606
  • Handbook of Statistical Systems Biology, 09/2011: pages 467 - 476; , ISBN: 9781119970606
  • Handbook of Statistical Systems Biology, 09/2011: pages 339 - 358; , ISBN: 9781119970606
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    Handbook of Statistical Systems Biology, 09/2011: pages 1 - 14; , ISBN: 9781119970606
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