Advances in predicting and manipulating the immunogenicity of biotherapeutics and vaccines

Therapeutic proteins are vital to the future of human health provision and the survival and profitability of the global pharmaceutical industry. Returns from protein therapeutics are experiencing unprecedented growth: both their number and their economic dividend have increased by an order of magnitude in the last 10 years. The potential immunogenicity of protein therapeutics raises many clinical and safety concerns. Many poorly understood factors relating to both product and host affect immune responses. Available laboratory measurement of immunogenicity is of little utility for predicting the clinical properties of biotherapeutics. Coupled with assay variability and standardization issues, this precludes adequate prediction of the biological or clinical responses of therapeutic proteins, arguing for the utilization of informatic strategies in the analysis and prediction of protein immunogenicity. Currently, many unresolved issues must be addressed and thus circumvented before effective prediction can become routine.

Analysis of powder caking in multicomponent powders using atomic force microscopy to examine particle properties

Atomic force microscopy has been used to study the surface properties of model spray dried powders. Phase imaging, nanoindentation and force modulation microscopy have differentiated between the different surface material properties of the particles, revealing a regular dispersion of soft, oil rich areas distributed across the particles' surface. Humidity and temperature cycling effects on the caking behavior of the particles have also been investigated, with significant morphology changes and onset of caking found to occur within relatively short periods of time.

Predicting adult occupational environments from childhood personality traits

To test aspects of a theory of the role of personality and gender on the development of vocational interests and their subsequent effects on adult occupational choices, the authors of this study examined associations among childhood personality traits, gender, and occupational environments more than 40 years later. Participants (N = 587) were assessed on the Big Five by their teachers when the participants were between 6 and 12 years old. In middle-age (late 40s), the participants reported their occupation. Holland's (1997) RIASEC vocational types (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) were used to characterize the job environments of reported occupations. Childhood Openness/Intellect and Conscientiousness, but no other Big Five traits, were associated with occupational environments. For the most strongly sex-typed work environments, associations with Openness/Intellect were moderated by gender. These findings suggest that the roots of the strongest gender-stereotyping effects in occupations may be found not only in the social factors associated with gender but also in the individual differences of children related to Openness/Intellect.

Predicting success with silicone-hydrogel contact lenses in new wearers

Purpose: to evaluate changes in tear metrics and ocular signs induced by six months of silicone-hydrogel contact lens wear and the difference in baseline characteristics between those who successfully continued in contact lens wear compared to those that did not. Methods: Non-invasive Keratograph, Tearscope and fluorescein tear break-up times (TBUTs), tear meniscus height, bulbar and limbal hyperaemia, lid-parallel conjunctival folds (LIPCOF), phenol red thread, fluorescein and lissamine-green staining, and lid wiper epitheliopathy were measured on 60 new contact lens wearers fitted with monthly silicone-hydrogels (average age 36 ± 14 years, 40 females). Symptoms were evaluated by the Ocular Surface Disease Index (OSDI). After six months full time contact lens wear the above metrics were re-measured on those patients still in contact lens wear (n= 33). The initial measurements were also compared between the group still wearing lenses after six months and those who had ceased lens wear (n= 27). Results: There were significant changes in tear meniscus height (p= 0.031), bulbar hyperaemia (p= 0.011), fluorescein TBUT (p= 0.027), corneal (p= 0.007) and conjunctival (p= 0.009) staining, LIPCOF (p= 0.011) and lid wiper epitheliopathy (p= 0.002) after six months of silicone-hydrogel wear. Successful wearers had a higher non-invasive (17.0 ± 8.2. s vs 12.0 ± 5.6. s; p= 0.001) and fluorescein (10.7 ± 6.4. s vs 7.5 ± 4.7. s; p= 0.001) TBUT than drop-outs, although OSDI (cut-off 4.2) was also a strong predictor of success. Conclusion: Silicone-hydrogel lenses induced significant changes in the tear film and ocular surface as well as lid margin staining. Wettability of the ocular surface is the main factor affecting contact lens drop-out. © 2013 British Contact Lens Association.

Predicting binge-drinking behaviour using an extended TPB:examining the impact of anticipated regret and descriptive norms

Aims: To investigate the utility of an extended Theory of Planned Behaviour (TPB), including descriptive norms and anticipated regret, in predicting binge-drinking intentions and behaviour. Methods: A total of178 undergraduates completed a questionnaire containing measures of TPB variables, descriptive norms, anticipated regret, and previous binge-drinking behaviour. One week later, 104 students completed a measure of binge-drinking behaviour. Results: Hierarchical regression demonstrated that attitudes (beta = 0.30, P < 0.001) and anticipated regret (beta = 0.47, P < 0.001) were significant predictors of intentions, with the final equation accounting for 58% of the variance. Hierarchial regression found that intentions (beta = -0.21, P < 0.05) and previous binge-drinking behaviour (beta = 0.36, P < 0.01) predicted current drinking behaviour, accounting for 33% of the variance. Conclusions: The study suggests that modifying attitudes and inducing regret may be effective strategies for reducing binge-drinking intentions among undergraduates, which should reduce subsequent binge-drinking behaviour. © The Author 2006. Published by Oxford University Press on behalf of the Medical Council on Alcohol. All rights reserved.

Breakage model development and application with CFD for predicting breakage of whey protein precipitate particles

Particle breakage due to fluid flow through various geometries can have a major influence on the performance of particle/fluid processes and on the product quality characteristics of particle/fluid products. In this study, whey protein precipitate dispersions were used as a case study to investigate the effect of flow intensity and exposure time on the breakage of these precipitate particles. Computational fluid dynamic (CFD) simulations were performed to evaluate the turbulent eddy dissipation rate (TED) and associated exposure time along various flow geometries. The focus of this work is on the predictive modelling of particle breakage in particle/fluid systems. A number of breakage models were developed to relate TED and exposure time to particle breakage. The suitability of these breakage models was evaluated for their ability to predict the experimentally determined breakage of the whey protein precipitate particles. A "power-law threshold" breakage model was found to provide a satisfactory capability for predicting the breakage of the whey protein precipitate particles. The whey protein precipitate dispersions were propelled through a number of different geometries such as bends, tees and elbows, and the model accurately predicted the mean particle size attained after flow through these geometries. © 2005 Elsevier Ltd. All rights reserved.

Predicting university success in psychology:are subject-specific skills important?

The relationship between previous academic achievement and subsequent success at university was explored in a retrospective study of 56 UK psychology students. It was found that the subjects studied at A-level, and the grades obtained, did not predict performance at university. In contrast, GCSE grades, in particular those achieved in Science and English, were significant predictors of final year marks. Once at university, first and second year results had an incremental ability to predict final year performance, with an additional effect of undertaking a work placement. The implications of the results are discussed within the context of recent literature relating to cognitive and non-cognitive predictors of academic performance.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Immunoinformatics:predicting immunogenicity in silico

This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. It addresses databases, HLA supertypes, MCH binding, and other properties of immune systems. The book contains chapters written by leaders in the field and provides a firm background for anyone working in immunoinformatics in one easy-to-use, insightful volume.

Predicting the effect of mixing on oxygen transfer and nutrient removal in activated sludge basins

Activated sludge basins (ASBs) are a key-step in wastewater treatment processes that are used to eliminate biodegradable pollution from the water discharged to the natural environment. Bacteria found in the activated sludge consume and assimilate nutrients such as carbon, nitrogen and phosphorous under specific environmental conditions. However, applying the appropriate agitation and aeration regimes to supply the environmental conditions to promote the growth of the bacteria is not easy. The agitation and aeration regimes that are applied to activated sludge basins have a strong influence on the efficacy of wastewater treatment processes. The major aims of agitation by submersible mixers are to improve the contact between biomass and wastewater and the prevention of biomass settling. They induce a horizontal flow in the oxidation ditch, which can be quantified by the mean horizontal velocity. Mean values of 0.3-0.35 m s-1 are recommended as a design criteria to ensure best conditions for mixing and aeration (Da Silva, 1994). To give circulation velocities of this order of magnitude, the positioning and types of mixers are chosen from the plant constructors' experience and the suppliers' data for the impellers. Some case studies of existing plants have shown that measured velocities were not in the range that was specified in the plant design. This illustrates that there is still a need for design and diagnosis approach to improve process reliability by eliminating or reducing the number of short circuits, dead zones, zones of inefficient mixing and poor aeration. The objective of the aeration is to facilitate the quick degradation of pollutants by bacterial growth. To achieve these objectives a wastewater treatment plant must be adequately aerated; thus resulting in 60-80% of all energetic consummation being dedicated to the aeration alone (Juspin and Vasel, 2000). An earlier study (Gillot et al., 1997) has illustrated the influence that hydrodynamics have on the aeration performance as measure by the oxygen transfer coefficient. Therefore, optimising the agitation and aeration systems can enhance the oxygen transfer coefficient and consequently reduce the operating costs of the wastewater treatment plant. It is critically important to correctly estimate the mass transfer coefficient as any errors could result in the simulations of biological activity not being physically representative. Therefore, the transfer process was rigorously examined in several different types of process equipment to determine the impact that different hydrodynamic regimes and liquid-side film transfer coefficients have on the gas phase and the mass transfer of oxygen. To model the biological activity occurring in ASBs, several generic biochemical reaction models have been developed to characterise different biochemical reaction processes that are known as Activated Sludge Models, ASM (Henze et al., 2000). The ASM1 protocol was selected to characterise the impact of aeration on the bacteria consuming and assimilating ammonia and nitrate in the wastewater. However, one drawback of ASM protocols is that the hydrodynamics are assumed to be uniform by the use of perfectly mixed, plug flow reactors or as a number of perfectly mixed reactors in series. This makes it very difficult to identify the influence of mixing and aeration on oxygen mass transfer and biological activity. Therefore, to account for the impact of local gas-liquid mixing regime on the biochemical activity Computational Fluid Dynamics (CFD) was used by applying the individual ASM1 reaction equations as the source terms to a number of scalar equations. Thus, the application of ASM1 to CFD (FLUENT) enabled the investigation of the oxygen transfer efficiency and the carbon & nitrogen biological removal in pilot (7.5 cubic metres) and plant scale (6000 cubic metres) ASBs. Both studies have been used to validate the effect that the hydrodynamic regime has on oxygen mass transfer (the circulation velocity and mass transfer coefficient) and the effect that this had on the biological activity on pollutants such as ammonia and nitrate (Cartland Glover et al., 2005). The work presented here is one part to of an overall approach for improving the understanding of ASBs and the impact that they have in terms of the hydraulic and biological performance on the overall wastewater treatment process. References CARTLAND GLOVER G., PRINTEMPS C., ESSEMIANI K., MEINHOLD J., (2005) Modelling of wastewater treatment plants ? How far shall we go with sophisticated modelling tools? 3rd IWA Leading-Edge Conference & Exhibition on Water and Wastewater Treatment Technologies, 6-8 June 2005, Sapporo, Japan DA SILVA G. (1994). Eléments d'optimisation du transfert d'oxygène par fines bulles et agitateur séparé en chenal d'oxydation. PhD Thesis. CEMAGREF Antony ? France. GILLOT S., DERONZIER G., HEDUIT A. (1997). Oxygen transfer under process conditions in an oxidation ditch equipped with fine bubble diffusers and slow speed mixers. WEFTEC, Chicago, USA. HENZE M., GUJER W., MINO T., van LOOSDRECHT M., (2000). Activated Sludge Models ASM1, ASM2, ASM2D and ASM3, Scientific and Technical Report No. 9. IWA Publishing, London, UK. JUSPIN H., VASEL J.-L. (2000). Influence of hydrodynamics on oxygen transfer in the activated sludge process. IWA, Paris - France.

Encoding order and developmental dyslexia:a family of skills predicting different orthographic components

We investigated order encoding in developmental dyslexia using a task that presented nonalphanumeric visual characters either simultaneously or sequentially—to tap spatial and temporal order encoding, respectively—and asked participants to reproduce their order. Dyslexic participants performed poorly in the sequential condition, but normally in the simultaneous condition, except for positions most susceptible to interference. These results are novel in demonstrating a selective difficulty with temporal order encoding in a dyslexic group. We also tested the associations between our order reconstruction tasks and: (a) lexical learning and phonological tasks; and (b) different reading and spelling tasks. Correlations were extensive when the whole group of participants was considered together. When dyslexics and controls were considered separately, different patterns of association emerged between orthographic tasks on the one side and tasks tapping order encoding, phonological processing, and written learning on the other. These results indicate that different skills support different aspects of orthographic processing and are impaired to different degrees in individuals with dyslexia. Therefore, developmental dyslexia is not caused by a single impairment, but by a family of deficits loosely related to difficulties with order. Understanding the contribution of these different deficits will be crucial to deepen our understanding of this disorder.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.