Personalized Drug Dosage – Closing the Loop

A brief account is given of various approaches to the individualization of drug dosage, including the use of pharmacodynamic markers, therapeutic monitoring of plasma drug concentrations, genotyping, computer-guided dosage using ‘dashboards’, and automatic closed-loop control of pharmacological action. The potential for linking the real patient to his or her ‘virtual twin’ through the application of physiologically-based pharmacokinetic modeling is also discussed

5-Hydroxymethylcytosine is a predominantly stable DNA modification.

5-Hydroxymethylcytosine (hmC) is an oxidation product of 5-methylcytosine which is present in the deoxyribonucleic acid (DNA) of most mammalian cells. Reduction of hmC levels in DNA is a hallmark of cancers. Elucidating the dynamics of this oxidation reaction and the lifetime of hmC in DNA is fundamental to understanding hmC function. Using stable isotope labelling of cytosine derivatives in the DNA of mammalian cells and ultrasensitive tandem liquid-chromatography mass spectrometry, we show that the majority of hmC is a stable modification, as opposed to a transient intermediate. In contrast with DNA methylation, which occurs immediately during replication, hmC forms slowly during the first 30 hours following DNA synthesis. Isotopic labelling of DNA in mouse tissues confirmed the stability of hmC in vivo and demonstrated a relationship between global levels of hmC and cell proliferation. These insights have important implications for understanding the states of chemically modified DNA bases in health and disease.We would like to acknowledge the CRUK CI Flow Cytometry and Histopathology/ISH core facilities for their contributions, David Oxley, Clive d’Santos and Donna Michelle-Smith for their support with mass spectrometry, Xiangang Zou for his help with mES cells and David Tannahill for critical reading of the manuscript. This work was funded by Cancer Research UK (all authors) and the Wellcome Trust Senior Investigator Award (S.B.).This is the accepted manuscript. The final version is available from Nature Chemistry at http://www.nature.com/nchem/journal/vaop/ncurrent/full/nchem.2064.html

HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

<p>Abstract</p> <p>Background</p> <p>The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.</p> <p>Results</p> <p>We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.</p> <p>Conclusion</p> <p>Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.</p

Cost effectiveness of potential ART adherence monitoring interventions in sub-saharan Africa

Background Interventions based around objective measurement of adherence to antiretroviral drugs for HIV have potential to improve adherence and to enable differentiation of care such that clinical visits are reduced in those with high adherence. It would be useful to understand the approximate upper limit of cost that could be considered for such interventions of a given effectiveness in order to be cost effective. Such information can guide whether to implement an intervention in the light of a trial showing a certain effectiveness and cost. Methods An individual-based model, calibrated to Zimbabwe, which incorporates effects of adherence and resistance to antiretroviral therapy, was used to model the potential impact of adherence monitoring-based interventions on viral suppression, death rates, disability adjusted life years and costs. Potential component effects of the intervention were: enhanced average adherence when on ART, reduced risk of ART discontinuation, and reduced risk of resistance acquisition. We considered a situation in which viral load monitoring is not available and one in which it is. In the former case, it was assumed that care would be differentiated based on the adherence level, with fewer clinic visits in those demonstrated to have high adherence. In the latter case, care was assumed to be primarily differentiated according to viral load level. The maximum intervention cost required to be cost effective was calculated based on a cost effectiveness threshold of $500 per DALY averted. Findings In the absence of viral load monitoring, an adherence monitoring-based intervention which results in a durable 6$50 per person-year on ART, mainly driven by the cost savings of differentiation of care. In the presence of viral load monitoring availability, an intervention with a similar effect on viral load suppression was cost-effective when costing $23-$32 per year, depending on whether the adherence intervention is used to reduce the level of need for viral load measurement. Conclusion The cost thresholds identified suggest that there is clear scope for adherence monitoringbased interventions to provide net population health gain, with potential cost-effective use in situations where viral load monitoring is or is not available. Our results guide the implementation of future adherence monitoring interventions found in randomized trials to have health benefit

Poor medication adherence in clinical trials: consequences and solutions

Poor adherence to medicines in clinical trials can undermine the value of the trials; for example, by compromising estimates of the benefits and risks of a medicine. In this article, we highlight such consequences and also discuss approaches to tackle this problem

Poor medication adherence in clinical trials: consequences and solutions

Poor adherence to medicines in clinical trials can undermine the value of the trials; for example, by compromising estimates of the benefits and risks of a medicine. In this article, we highlight such consequences and also discuss approaches to tackle this problem