Clinical trial design in non-invasive brain stimulation psychiatric research

Major depressive disorder (MDD) trials - investigating either non-pharmacological or pharmacological interventions - have shown mixed results. Many reasons explain this heterogeneity, but one that stands out is the trial design due to specific challenges in the field. We aimed therefore to review the methodology of non-invasive brain stimulation (NIBS) trials and provide a framework to improve clinical trial design. We performed a systematic review for randomized, controlled MDD trials whose intervention was transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) in MEDLINE and other databases from April 2002 to April 2008. We created an unstructured checklist based on CONSORT guidelines to extract items such as power analysis, sham method, blinding assessment, allocation concealment, operational criteria used for MDD, definition of refractory depression and primary study hypotheses. Thirty-one studies were included. We found that the main methodological issues can be divided in to three groups: (1) issues related to phase II/small trials, (2) issues related to MDD trials and, (3) specific issues of NIBS studies. Taken together, they can threaten study validity and lead to inconclusive results. Feasible solutions include: estimating the sample size a priori; measuring the degree of refractoriness of the subjects; specifying the primary hypothesis and statistical tests; controlling predictor variables through stratification randomization methods or using strict eligibility criteria; adjusting the study design to the target population; using adaptive designs and exploring NIBS efficacy employing biological markers. In conclusion, our study summarizes the main methodological issues of NIBS trials and proposes a number of alternatives to manage them. Copyright (C) 2011 John Wiley & Sons, Ltd.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

CONTROL OF GLYPHOSATE RESISTANT HAIRY FLEABANE (Conyza bonariensis) WITH DICAMBA AND 2,4-D

Auxyn type herbicides such as dicamba and 2,4-D are alternative herbicides that can be used to control glyphosate-resistant hairy fleabane. With the forthcoming possibility of releasing dicamba-resistant and 2,4-D-resistant crops, use of these growth regulator herbicides will likely be an alternative that can be applied to the control of glyphosate resistant hairy fleabane (Conyza bonariensis). The objective of this research was to model the efficacy, through dose-response curves, of glyphosate, 2,4-D, isolated dicamba and glyphosate-dicamba combinations to control a brazilian hairy fleabane population resistant to glyphosate. The greenhouse dose-response studies were conducted as a completely randomized experimental design, and the rates used for dose response curve construction were 0, 120, 240, 480, 720 and 960 ga.i. ha(-1) for 2,4-D, dicamba and the dicamba combination, with glyphosate at 540 g a. e. ha(-1). The rates for glyphosate alone were 0, 180, 360, 540, 720 and 960 g a. e. ha(-1). Herbicides were applied when the plants were in a vegetative stage with 10 to 12 leaves and height between 12 and 15 cm. Hairy fleabane had low sensitivity to glyphosate, with poor control even at the 960 g a. e. ha(-1) rate. Dicamba and 2,4-D were effective in controlling the studied hairy fleabane. Hairy fleabane responds differently to 2,4-D and dicamba. The combination of glyphosate and dicamba was not antagonistic to hairy fleabane control, and glyphosate may cause an additive effect on the control, despite the population resistance.

Control of glyphosate resistant hairy fleabane (Conyza bonariensis) with dicamba and 2,4-D

Auxyn type herbicides such as dicamba and 2,4-D are alternative herbicides that can be used to control glyphosate-resistant hairy fleabane. With the forthcoming possibility of releasing dicamba-resistant and 2,4-D-resistant crops, use of these growth regulator herbicides will likely be an alternative that can be applied to the control of glyphosate resistant hairy fleabane (Conyza bonariensis). The objective of this research was to model the efficacy, through dose-response curves, of glyphosate, 2,4-D, isolated dicamba and glyphosatedicamba combinations to control a brazilian hairy fleabane population resistant to glyphosate. The greenhouse dose-response studies were conducted as a completely randomized experimental design, and the rates used for dose response curve construction were 0, 120, 240, 480, 720 and 960 g a.i. ha-1 for 2,4-D, dicamba and the dicamba combination, with glyphosate at 540 g a.e. ha-1. The rates for glyphosate alone were 0, 180, 360, 540, 720 and 960 g a.e. ha-1. Herbicides were applied when the plants were in a vegetative stage with 10 to 12 leaves and height between 12 and 15 cm. Hairy fleabane had low sensitivity to glyphosate, with poor control even at the 960 g a.e. ha-1 rate. Dicamba and 2,4-D were effective in controlling the studied hairy fleabane. Hairy fleabane responds differently to 2,4-D and dicamba. The combination of glyphosate and dicamba was not antagonistic to hairy fleabane control, and glyphosate may cause an additive effect on the control, despite the population resistance.