Common characteristics of open source software development and applicability for drug discovery: a systematic review

<p>Abstract</p> <p>Background</p> <p>Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery.</p> <p>Methods</p> <p>A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project.</p> <p>Results</p> <p>Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined.</p> <p>Conclusions</p> <p>We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.</p

Automation and control of laser wakefield accelerators using Bayesian optimisation

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%

Chronic Citalopram Administration Causes a Sustained Suppression of Serotonin Synthesis in the Mouse Forebrain

BACKGROUND:Serotonin (5-HT) is a neurotransmitter with important roles in the regulation of neurobehavioral processes, particularly those regulating affect in humans. Drugs that potentiate serotonergic neurotransmission by selectively inhibiting the reuptake of serotonin (SSRIs) are widely used for the treatment of psychiatric disorders. Although the regulation of serotonin synthesis may be an factor in SSRI efficacy, the effect of chronic SSRI administration on 5-HT synthesis is not well understood. Here, we describe effects of chronic administration of the SSRI citalopram (CIT) on 5-HT synthesis and content in the mouse forebrain. METHODOLOGY/PRINCIPAL FINDINGS:Citalopram was administered continuously to adult male C57BL/6J mice via osmotic minipump for 2 days, 14 days or 28 days. Plasma citalopram levels were found to be within the clinical range. 5-HT synthesis was assessed using the decarboxylase inhibition method. Citalopram administration caused a suppression of 5-HT synthesis at all time points. CIT treatment also caused a reduction in forebrain 5-HIAA content. Following chronic CIT treatment, forebrain 5-HT stores were more sensitive to the depleting effects of acute decarboxylase inhibition. CONCLUSIONS/SIGNIFICANCE:Taken together, these results demonstrate that chronic citalopram administration causes a sustained suppression of serotonin synthesis in the mouse forebrain. Furthermore, our results indicate that chronic 5-HT reuptake inhibition renders 5-HT brain stores more sensitive to alterations in serotonin synthesis. These results suggest that the regulation of 5-HT synthesis warrants consideration in efforts to develop novel antidepressant strategies

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization

Scale-up of freeze-drying cycles, the use of process analytical technology (PAT), and statistical analysis

Chapter 10Traditionally, the quality of pharmaceutical drugs is tested on the final freeze-dried product following a regulatory framework known as Quality-by-Testing (QbT) (Yu, Pharm Res 25: 781–91, 2008). In this system, product quality and performance are ensured by performing extensive tests on the final product, and by using a fixed formulation and manufacturing process. In contrast, the US Food and Drug Administration (FDA) proposed the Quality by Design (QbD) initiative with the idea that quality cannot be “tested into” the product, but it should be built into it (FDA, Guidance for industry, Q8(R2) pharmaceutical development. Dept. of Health and Human Services, Center for Drug Evaluation and Research. Rockville, MD, 2009). Quality by Design consists of a systematic approach to pharmaceutical product development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management (FDA, Guidance for industry, Q8(R2) pharmaceutical development. Dept. of Health and Human Services, Center for Drug Evaluation and Research. Rockville, MD, 2009; Mockus et al, Pharm Dev Technol 16: 549–76, 2011; Yu, Pharm Res 25: 781–91, 2008). In this chapter, a statistical model for the sublimation step in freeze-drying was used to construct the design space for the cycle development and to select adequate parameters for scaling up from pilot to commercial scale. Three critical operating variables of the process were tested: freezing rate, shelf temperature, and chamber pressure in primary drying. The model was used to predict the sublimation rate and the product temperature, since their selection is of paramount importance to obtain a product of high quality. The obtained results were then used to define the design space of the product at pilot scale