Qualitative methods in early-phase drug trials : data and methods from a trial of N-acetylcysteine in schizophrenia

Objective: The pharmacokinetic profile of a drug often gives little indication of its potential therapeutic application, with many therapeutic uses of drugs being discovered serendipitously while being studied for different indications. As hypothesis-driven, quantitative research methodology is exclusively used in early-phase trials, unexpected but important phenomena may escape detection. In this context, this study aimed to examine the potential for integrating qualitative research methods with quantitative methods in early-phase drug trials. To our knowledge, this mixed methodology has not previously been applied to blinded psychopharmacologic trials.

Method: We undertook qualitative data analysis of clinical observations on the dataset of a randomized, double-blind, placebo-controlled trial of N-acetylcysteine (NAC) in patients with DSM-IV-TR–diagnosed schizophrenia (N = 140). Textual data on all participants, deliberately collected for this purpose, were coded using NVivo 2, and emergent themes were analyzed in a blinded manner in the NAC and placebo groups. The trial was conducted from November 2002 to July 2005.

Results: The principal findings of the published trial could be replicated using a qualitative methodology. In addition, significant differences between NAC- and placebo-treated participants emerged for positive and affective symptoms, which had not been captured by the rating scales utilized in the quantitative trial. Qualitative data in this study subsequently led to a positive trial of NAC in bipolar disorder.

Conclusions: The use of qualitative methods may yield broader data and has the potential to complement traditional quantitative methods and detect unexpected efficacy and safety signals, thereby maximizing the findings of early-phase clinical trial research.

A role for glutathione in the pathophysiology of bipolar disorder and schizophrenia? Animal models and relevance to clinical practice.

The tripeptide, glutathione (glutamylcysteinylglycine) is the primary endogenous free radical scavenger in the human body. When glutathione (GSH) levels are reduced there is an increased potential for cellular oxidative stress, characterised by an increase and accruement of reactive oxygen species (ROS). Oxidative stress has been implicated in the pathology of schizophrenia and bipolar disorder. This could partly be caused by alterations in dopaminergic and glutamatergic activity that are implicated in these illnesses. Glutamate and dopamine are highly redox reactive molecules and produce ROS during normal neurotransmission. Alterations to these neurotransmitter pathways may therefore increase the oxidative burden in the brain. Furthermore, mitochondrial dysfunction, as a source of oxidative stress, has been documented in both schizophrenia and bipolar disorder. The combination of altered neurotransmission and this mitochondrial dysfunction leading to oxidative damage may ultimately contribute to illness symptoms. Animal models have been established to investigate the involvement of glutathione depletion in aspects of schizophrenia and bipolar disorder to further characterise the role of oxidative stress in psychopathology. Stemming from preclinical evidence, clinical studies have recently shown antioxidant precursor treatment to be effective in schizophrenia and bipolar disorder, providing a novel clinical angle to augment often suboptimal conventional treatments.

Deep bed filtration : mathematical models and observations

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. The O’Melia and Ali1 model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This article evaluates those models and their applicability under different operating conditions of filtration.

Intellectual property, creativity and innovation : IP models and the political economy of developing Asia

In advanced capitalist societies, intellectual property laws protecting such subject matter as copyright and patents are justified by a combination of theories, which include the provision of economic incentives to foster creativity and innovation and the prevention of unfair competition. IP academics and policy makers have differing views about the appropriate balance between these objectives and public interest considerations such as health, education and the protection of the environment. These different views entered the policy debate in Asian developing countries in connection with an unprecedented introduction and expansion of IP laws over the last 25 years. This paper will use case studies of law reform from Asia, in particular Southeast Asia, to show that the policy considerations of governments in reforming their laws were often quite different from the standard rationale mentioned above. As much of the IP was, at least initially, held by foreigners and introduced to attract foreign investment, national development considerations were joined with the more commonly quoted objectives to promote the rights, creativity and innovation of individuals. Such national development objectives at times coincided and at other times collided with official explanations and received wisdom about the effects of stronger IP rights.

Especially in the early postcolonial period, copyright laws and other IP laws were frequently restricted or simply not implemented, if they conflicted with development policies in areas such as education or public health. Such policies were slowly changing in the wake of WTO-TRIPS and other international agreements. Nevertheless, the implementation and enforcement of the IP laws has been uneven. Specialised institutions such as courts and IP administering agencies compete with other branches of government and administration for limited funding and a rich repertoire of informal dispute settlement procedures has kept the number of court cases relatively low. In some countries, censorship laws have influenced freedom of expression and led to quite idiosyncratic interpretations of intellectual property laws. Governments often also retain a role in the assessment of licensing and technology transfer contracts. And while there are many programs to foster individual creativity, in most cases R & D activities are still largely taking place in government institutions and this has influenced the thinking about intellectual property rights and creativity in the context of employment.

The paper uses a few case studies to examine the implementation of IP laws in selected Asian developing countries to point to the quite different institutional setting for IP law reform in comparison to European or American models. It reaches some tentative conclusions as to the likely effects on creativity and innovation under these different circumstances.

Emerging hypothesis verification using function-based geometric models and active vision strategies

This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is that the camera can foveate and track particular features. It is argued that simple 2D analytic descriptions of the movement of edges can infer 3D shape while the camera is moved. This uses an advantage of foveation i.e. the problem becomes object centred. The problem of correspondence for numerous edge points is overcome by the use of a tree based representation for the competing hypotheses. Numerous hypothesis are maintained simultaneously and it does not rely on a single kinematic model which assumes constant velocity or acceleration. The numerous advantages of this strategy are described.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN