Clinical Relevance of Dissolution Testing in Quality by Design

Quality by design (QbD) has recently been introduced in pharmaceutical product development in a regulatory context and the process of implementing such concepts in the drug approval process is presently on-going. This has the potential to allow for a more flexible regulatory approach based on understanding and optimisation of how design of a product and its manufacturing process may affect product quality. Thus, adding restrictions to manufacturing beyond what can be motivated by clinical quality brings no benefits but only additional costs. This leads to a challenge for biopharmaceutical scientists to link clinical product performance to critical manufacturing attributes. In vitro dissolution testing is clearly a key tool for this purpose and the present bioequivalence guidelines and biopharmaceutical classification system (BCS) provides a platform for regulatory applications of in vitro dissolution as a marker for consistency in clinical outcomes. However, the application of these concepts might need to be further developed in the context of QbD to take advantage of the higher level of understanding that is implied and displayed in regulatory documentation utilising QbD concepts. Aspects that should be considered include identification of rate limiting steps in the absorption process that can be linked to pharmacokinetic variables and used for prediction of bioavailability variables, in vivo relevance of in vitro dissolution test conditions and performance/interpretation of specific bioavailability studies on critical formulation/process variables. This article will give some examples and suggestions how clinical relevance of dissolution testing can be achieved in the context of QbD derived from a specific case study for a BCS II compound

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Regions of interest analysis in pharmacological fMRI: How do the definition criteria influence the inferred result?

Prior hypotheses in functional brain imaging are often formulated by constraining the data analysis to regions of interest (ROIs). In this context, this approach yields higher sensitivity than whole brain analyses, which could be particularly important in drug development studies and clinical decision making. Here we systematically examine the effects of different ROI definition criteria on the results inferred from a hypothesis-driven pharmacological fMRI experiment, with the aim of maximising sensitivity and providing a recommended procedure for similar studies. In order to achieve this, we compared different criteria for selecting both anatomical and functional ROIs. Anatomical ROIs were defined (i) specifically for each subject, (ii) at group level, and (iii) using a Talairach-like atlas, in order to assess the effects of inter-subject anatomical variability. Functional ROIs (fROIs) were defined, both for each subject and at group level, by (i) selecting the voxels with the highest Z-score from each study session, and (ii) selecting an inclusive union of significantly active voxels across all sessions. A single value was used to summarise the response within each ROI. For anatomical ROIs we used the mean of the parameter estimates (β values) of either all voxels or the top 20% active voxels. For fROIs we used the mean β value of all voxels constituting the ROI. The results were assessed in terms of the achieved sensitivity in detecting the experimental effect. The use of single-subject anatomical ROIs combined with a summary value calculated from the top 20% fraction of active voxels was the most reliable and sensitive approach for detecting the experimental effect. The use of fROIs from individual sessions introduced unacceptable biases in the results, while the use of union fROIs yielded a lower sensitivity than anatomical ROIs. For these reasons, fROIs should be employed with caution when it is not possible to make clear anatomical prior hypotheses

Pharmacological modulation of pain-related brain activity during normal and central sensitization states in humans.

Abnormal processing of somatosensory inputs in the central nervous system (central sensitization) is the mechanism accounting for the enhanced pain sensitivity in the skin surrounding tissue injury (secondary hyperalgesia). Secondary hyperalgesia shares clinical characteristics with neurogenic hyperalgesia in patients with neuropathic pain. Abnormal brain responses to somatosensory stimuli have been found in patients with hyperalgesia as well as in normal subjects during experimental central sensitization. The aim of this study was to assess the effects of gabapentin, a drug effective in neuropathic pain patients, on brain processing of nociceptive information in normal and central sensitization states. Using functional magnetic resonance imaging (fMRI) in normal volunteers, we studied the gabapentin-induced modulation of brain activity in response to nociceptive mechanical stimulation of normal skin and capsaicin-induced secondary hyperalgesia. The dose of gabapentin was 1,800 mg per os, in a single administration. We found that (i) gabapentin reduced the activations in the bilateral operculoinsular cortex, independently of the presence of central sensitization; (ii) gabapentin reduced the activation in the brainstem, only during central sensitization; (iii) gabapentin suppressed stimulus-induced deactivations, only during central sensitization; this effect was more robust than the effect on brain activation. The observed drug-induced effects were not due to changes in the baseline fMRI signal. These findings indicate that gabapentin has a measurable antinociceptive effect and a stronger antihyperalgesic effect most evident in the brain areas undergoing deactivation, thus supporting the concept that gabapentin is more effective in modulating nociceptive transmission when central sensitization is present

Timing and locations of reef fish spawning off the southeastern United States

<div><p>Managed reef fish in the Atlantic Ocean of the southeastern United States (SEUS) support a multi-billion dollar industry. There is a broad interest in locating and protecting spawning fish from harvest, to enhance productivity and reduce the potential for overfishing. We assessed spatiotemporal cues for spawning for six species from four reef fish families, using data on individual spawning condition collected by over three decades of regional fishery-independent reef fish surveys, combined with a series of predictors derived from bathymetric features. We quantified the size of spawning areas used by reef fish across many years and identified several multispecies spawning locations. We quantitatively identified cues for peak spawning and generated predictive maps for Gray Triggerfish (<i>Balistes capriscus</i>), White Grunt (<i>Haemulon plumierii</i>), Red Snapper (<i>Lutjanus campechanus</i>), Vermilion Snapper (<i>Rhomboplites aurorubens</i>), Black Sea Bass (<i>Centropristis striata</i>), and Scamp (<i>Mycteroperca phenax</i>). For example, Red Snapper peak spawning was predicted in 24.7–29.0°C water prior to the new moon at locations with high curvature in the 24–30 m depth range off northeast Florida during June and July. External validation using scientific and fishery-dependent data collections strongly supported the predictive utility of our models. We identified locations where reconfiguration or expansion of existing marine protected areas would protect spawning reef fish. We recommend increased sampling off southern Florida (south of 27° N), during winter months, and in high-relief, high current habitats to improve our understanding of timing and location of reef fish spawning off the southeastern United States.</p></div

Logistic regression model fit statistics for probability of encountering a fish within 48 hours of spawning, with percent deviance explained (i.e. percent variability explained by inclusion of additional variable); cross-validation results for Area Under the Curve (AUC), False Positive Rate (FPR) and False Negative Rate (FNR); and percentage of 500 runs where random variable inclusion in model outperformed model-selected bathymetric variable (‘Random test’).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#sec002" target="_blank">Methods</a> for additional details.</p

External validation of spawning predictions.

<p>Boxplots of model-predicted Z-score standardized probabilities of collecting a spawning female underlying locations where spawning females were collected by Florida Fish and Wildlife Conservation Commission (FWC; Lowerre-Barbieri et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#pone.0172968.ref039" target="_blank">39</a>]), LGL Ecological Research Associates ([<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#pone.0172968.ref067" target="_blank">67</a>]), MARMAP Fishery Dependent Sampling (MMFD), and anecdotal reports from fishers (‘Tishler’) collected by Tishler-Meadows [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#pone.0172968.ref066" target="_blank">66</a>]. Z-Scores above zero were interpreted as providing support for model predictions. Inset numbers denote sample sizes.</p

Summary statistics (mean ± standard deviation) for apparent use of multi-year spawning locations.

<p>Multi-year spawning location size computed as minimum convex polygon containing all collections within a site.</p

Probability of encountering a spawning condition female Vermilion Snapper.

<p>Predicted mean (left) and standard error (right) probabilities of observing spawning condition female Vermilion Snapper at time and conditions of peak spawning, relative to external validation observations (+). Raster color-coding based on 2.5 standard deviations from the mean. Green boxes denote no-take marine protected areas and SMZs. Basemap courtesy ESRI Ocean Basemap and partners.</p

Number of gear deployments with multispecies observations of spawning females from SERFS.

<p>Number of gear deployments with multispecies observations of spawning females from SERFS.</p