A clinician's guide for conducting randomized trials in individual patients.

In determining optimal treatment for a patient conventional trials of therapy are susceptible to bias. Large-scale randomized trials can provide only a partial guide and have not been or cannot be carried out for most clinical disorders. However, randomized controlled trials (RCTs) in individual patients (N of 1 RCTs) may in some circumstances provide a solution to this dilemma. In an N of 1 RCT a patient undergoes pairs of treatment periods (one period of each pair with the active drug and one with matched placebo, assigned at random); both the patient and the clinician are blind to allocation, and treatment targets are monitored. N of 1 RCTs are useful for chronic, stable conditions for which the proposed treatment, which has a rapid onset of action and ceases to act soon after it is discontinued, has shown promise in an open trial of therapy. The monitoring of treatment targets usually includes quantitative measurement of the patient''s symptoms with the use of simple patient diaries or questionnaires. Pairs of treatment periods are continued until effectiveness is proved or refuted. The cooperation of a pharmacy is required for the preparation of matching placebos and conduct of the trial. Formal statistical analysis may be helpful for interpreting the results. The practical approach presented in this paper allows clinicians to conduct their own N of 1 RCTs.     

A statistical framework for haplotype block inference

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.     

Reward ignorant modeling of dynamic treatment regimes

Personalized medicine optimizes patient outcome by tailoring treatments to patient‐level characteristics. This approach is formalized by dynamic treatment regimes (DTRs): decision rules that take patient information as input and output recommended treatment decisions. The DTR literature has seen the development of increasingly sophisticated causal inference techniques that attempt to address the limitations of our typically observational datasets. Often overlooked, however, is that in practice most patients may be expected to receive optimal or near‐optimal treatment, and so the outcome used as part of a typical DTR analysis may provide limited information. In light of this, we propose considering a more standard analysis: ignore the outcome and elicit an optimal DTR by modeling the observed treatment as a function of relevant covariates. This offers a far simpler analysis and, in some settings, improved optimal treatment identification. To distinguish this approach from more traditional DTR analyses, we term it reward ignorant modeling, and also introduce the concept of multimethod analysis, whereby different analysis methods are used in settings with multiple treatment decisions. We demonstrate this concept through a variety of simulation studies, and through analysis of data from the International Warfarin Pharmacogenetics Consortium, which also serve as motivation for this work.     

A Generalized Estimator of the Attributable Benefit of an Optimal Treatment Regime

Summary For many diseases where there are several treatment options often there is no consensus on the best treatment to give individual patients. In such cases, it may be necessary to define a strategy for treatment assignment; that is, an algorithm that dictates the treatment an individual should receive based on their measured characteristics. Such a strategy or algorithm is also referred to as a treatment regime. The optimal treatment regime is the strategy that would provide the most public health benefit by minimizing as many poor outcomes as possible. Using a measure that is a generalization of attributable risk (AR) and notions of potential outcomes, we derive an estimator for the proportion of events that could have been prevented had the optimal treatment regime been implemented. Traditional AR studies look at the added risk that can be attributed to exposure of some contaminant; here we will instead study the benefit that can be attributed to using the optimal treatment strategy. We will show how regression models can be used to estimate the optimal treatment strategy and the attributable benefit of that strategy. We also derive the large sample properties of this estimator. As a motivating example, we will apply our methods to an observational study of 3856 patients treated at the Duke University Medical Center with prior coronary artery bypass graft surgery and further heart‐related problems requiring a catheterization. The patients may be treated with either medical therapy alone or a combination of medical therapy and percutaneous coronary intervention without a general consensus on which is the best treatment for individual patients.     

Tumor-derived lactate induces M2 macrophage polarization via the activation of the ERK/STAT3 signaling pathway in breast cancer

Tumor-associated macrophages (TAM) are prominent components of tumor microenvironment (TME) and capable of promoting cancer progression. However, the mechanisms for the formation of M2-like TAMs remain enigmatic. Here, we show that lactate is a pivotal oncometabolite in the TME that drives macrophage M2-polarization to promote breast cancer proliferation, migration, and angiogenesis. In addition, we identified that the activation of ERK/STAT3, major signaling molecules in the lactate signaling pathway, deepens our molecular understanding of how lactate educates TAMs. Moreover, suppression of ERK/STAT3 signaling diminished tumor growth and angiogenesis by abolishing lactate-induced M2 macrophage polarization. Finally, research data of the natural compound withanolide D provide evidence for ERK/STAT3 signaling as a potential therapeutic strategy for the prevention and treatment of breast cancer. These findings suggest that the lactate-ERK/STAT3 signaling pathway is a driver of breast cancer progression by stimulating macrophage M2-like polarization and reveal potential new therapeutic targets for breast cancer treatment.     

In vitro and in vivo characterization of a novel,highly potent p53-MDM2 inhibitor

Small molecule inhibitors of the p53-MDM2 protein complex are under intense investigation in clinical trials as anti-cancer agents, including our first generation inhibitor NVP-CGM097. We recently described the rational design of a novel pyrazolopyrrolidinone core as a new lead structure and now we report on the synthesis and optimization of this to provide a highly potent lead compound. This new compound displayed excellent oral efficacy in our preclinical mechanistic in vivo model and marked a significant milestone towards the identification of our second generation clinical candidate NVP-HDM201.     

Synthesis and biological evaluation of pyrrolidine-based T-type calcium channel inhibitors for the treatment of neuropathic pain

The treatment of neuropathic pain is one of the urgent unmet medical needs and T-type calcium channels are promising therapeutic targets for neuropathic pain. Several potent T-type channel inhibitors showed promising in vivo efficacy in neuropathic pain animal models and are being investigated in clinical trials. Herein we report development of novel pyrrolidine-based T-type calcium channel inhibitors by pharmacophore mapping and structural hybridisation followed by evaluation of their Ca_v3.1 and Ca_v3.2 channel inhibitory activities. Among potent inhibitors against both Ca_v3.1 and Ca_v3.2 channels, a promising compound 20n based on in vitro ADME properties displayed satisfactory plasma and brain exposure in rats according to in vivo pharmacokinetic studies. We further demonstrated that 20n effectively improved the symptoms of neuropathic pain in both SNL and STZ neuropathic pain animal models, suggesting modulation of T-type calcium channels can be a promising therapeutic strategy for the treatment of neuropathic pain.     

Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups

This paper discusses a number of methods for adjusting treatment effect estimates in clinical trials where differential effects in several subpopulations are suspected. In such situations, the estimates from the most extreme subpopulation are often overinterpreted. The paper focusses on the construction of simultaneous confidence intervals intended to provide a more realistic assessment regarding the uncertainty around these extreme results. The methods from simultaneous inference are compared with shrinkage estimates arising from Bayesian hierarchical models by discussing salient features of both approaches in a typical application.     

Discovery of novel isoflavone derivatives as AChE/BuChE dual-targeted inhibitors: synthesis,biological evaluation and molecular modelling

AChE and BuChE are druggable targets for the discovery of anti-Alzheimer’s disease drugs, while dual-inhibition of these two targets seems to be more effective. In this study, we synthesised a series of novel isoflavone derivatives based on our hit compound G from in silico high-throughput screening and then tested their activities by in vitro AChE and BuChE bioassays. Most of the isoflavone derivatives displayed moderate inhibition against both AChE and BuChE. Among them, compound 16 was identified as a potent AChE/BuChE dual-targeted inhibitor (IC₅₀: 4.60?μM for AChE; 5.92?μM for BuChE). Molecular modelling study indicated compound 16 may possess better pharmacokinetic properties, e.g. absorption, blood–brain barrier penetration and CYP2D6 binding. Taken together, our study has identified compound 16 as an excellent lead compound for the treatment of Alzheimer’s disease.     

基于SVM 的药物靶点预测方法及其应用

目的:基于已知药物靶点和潜在药物靶点蛋白的一级结构相似性,结合SVM技术研究新的有效的药物靶点预测方法。方法:构造训练样本集,提取蛋白质序列的一级结构特征,进行数据预处理,选择最优核函数,优化参数并进行特征选择,训练最优预测模型,检验模型的预测效果。以G蛋白偶联受体家族的蛋白质为预测集,应用建立的最优分类模型对其进行潜在药物靶点挖掘。结果:基于SVM所建立的最优分类模型预测的平均准确率为81.03%。应用最优分类器对构造的G蛋白预测集进行预测,结果发现预测排位在前20的蛋白质中有多个与疾病相关。特别的,其中有两个G蛋白在治疗靶点数据库(TTD)中显示已作为临床试验的药物靶点。结论:基于SVM和蛋白质序列特征的药物靶点预测方法是有效的,应用该方法预测出的潜在药物靶点能够为发现新的药靶提供参考。     

Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

MOTIVATION: To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. RESULTS: We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.     

Optimal Strategies for Controlling Riverine Tsetse Flies Using Targets: A Modelling Study

BackgroundTsetse flies occur in much of sub-Saharan Africa where they transmit the trypanosomes that cause the diseases of sleeping sickness in humans and nagana in livestock. One of the most economical and effective methods of tsetse control is the use of insecticide-treated screens, called targets, that simulate hosts. Targets have been ~1m², but recently it was shown that those tsetse that occupy riverine situations, and which are the main vectors of sleeping sickness, respond well to targets only ~0.06m². The cheapness of these tiny targets suggests the need to reconsider what intensity and duration of target deployments comprise the most cost-effective strategy in various riverine habitats.Conclusion/SignificanceSeasonal use of tiny targets deserves field trials. The ability to recognise habitat that contains tsetse populations which are not self-sustaining could improve the planning of all methods of tsetse control, against any species, in riverine, savannah or forest situations. Criteria to assist such recognition are suggested.     

A method for the inference of cytokine interaction networks

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.     

Multiple Hypotheses in the Analysis of a Crossover Trial

Crossover trials are used in a variety of fields, such as medicine, biology, psychology, and some commercial goods investigations. The aim of this paper is to extend a methodology for multiple comparisons to the problem of testing in crossover trials with two treatments. These two treatments are given in two orderings, treatment A first or treatment B first. We perform inference on the effect of one treatment relative to the effect of the other, without assuming that these effects are independent of treatment ordering, using techniques from order-restricted inference and multiple comparisons, and compare to some existing multiple comparison tests.     

Novel β-dicarbonyl derivatives as inhibitors of aminopeptidase N (APN)

Most zinc metalloproteases are over-expressed in tumor cells and play a critical role in the genesis, development, and metastasis of tumors. Novel zinc binding groups (ZBGs) represent a novel strategy to obtain optimal potency and selectivity for zinc metalloproteases inhibitors. Here we described the design, synthesis, and biological studies of novel β-dicarbonyl derivatives as aminopeptidase N (APN/CD13) inhibitors. The results demonstrated that some compounds exhibited moderate to good inhibitory activities against APN with compound 5c being the most potent, suggesting that 5c could serve as new lead for the future APN inhibitor development. The results further confirm our design rationale of β-dicarbonyl moiety as a new ZBG, which may provide a new direction for the design and discovery of zinc metalloproteases inhibitors as new anti-tumor agents.     

Targeting Apoptosis and Multiple Signaling Pathways with Icariside II in Cancer Cells

Cancer is the second leading cause of deaths worldwide. Despite concerted efforts to improve the current therapies, the prognosis of cancer remains dismal. Highly selective or specific blocking of only one of the signaling pathways has been associated with limited or sporadic responses. Using targeted agents to inhibit multiple signaling pathways has emerged as a new paradigm for anticancer treatment. Icariside II, a flavonol glycoside, is one of the major components of Traditional Chinese Medicine Herba epimedii and possesses multiple biological and pharmacological properties including anti-inflammatory, anti-osteoporosis, anti-oxidant, anti-aging, and anticancer activities. Recently, the anticancer activity of Icariside II has been extensively investigated. Here, in this review, our aim is to give our perspective on the current status of Icariside II, and discuss its natural sources, anticancer activity, molecular targets and the mechanisms of action with specific emphasis on apoptosis pathways which may help the further design and conduct of preclinical and clinical trials.Icariside II has been found to induce apoptosis in various human cancer cell lines of different origin by targeting multiple signaling pathways including STAT3, PI3K/AKT, MAPK/ERK, COX-2/PGE2 and β-Catenin which are frequently deregulated in cancers, suggesting that this collective activity rather than just a single effect may play an important role in developing Icariside II into a potential lead compound for anticancer therapy. This review suggests that Icariside II provides a novel opportunity for treatment of cancers, but additional investigations and clinical trials are still required to fully understand the mechanism of therapeutic effects to further validate it in anti-tumor therapy.     

The compliance score as a regressor in randomized trials

The compliance score in randomized trials is a measure of the effect of randomization on treatment received. It is in principle a group-level pretreatment variable and so can be used where individual-level measures of treatment received can produce misleading inferences. The interpretation of models with the compliance score as a regressor of interest depends on the link function. Using the identity link can lead to valid inference about the effects of treatment received even in the presence of nonrandom noncompliance; such inference is more problematic for nonlinear links. We illustrate these points with data from two randomized trials.     

Advances in endophenotyping schizophrenia

The search for the genetic architecture of schizophrenia has employed multiple, often converging strategies. One such strategy entails the use of tracing the heritability and neurobiology of endophenotypes. Endophenotypes are quantifiable traits not visible to the eye, which are thought to reflect an intermediate place on the path from genes to disorder. Endophenotype abnormalities in domains such as neurophysiology or neurocognition occur in schizophrenia patients as well as their clinically “unaffected” relatives, and reflect polymorphisms in the DNA of schizophrenia spectrum subjects which create vulnerability to developing schizophrenia. By identifying the single nucleotide polymorphisms (SNPs) associated with endophenotypes in schizophrenia, psychiatric neuroscientists can select new strong inference based molecular targets for the treatment of schizophrenia.