Osmolality of Excipients for Parenteral Formulation Measured by Freezing Point Depression and Vapor Pressure - A Comparative Analysis.

PURPOSE: To investigate the difference in methods to determine the osmolality in solutions of stabilizers used for long-acting injectable suspensions. METHODS: The osmolality was measured by freezing point depression and vapor pressure for 11 different polymers and surfactants (PEG 3350, 4000, 6000, 8000, 20,000, PVP K12, K17 and K30, poloxamer 188, 388 and 407, HPMC E5, Na-CMC, polysorbate 20 and 80, vitamin E-TPGS, phospholipid, DOSS and SDS) in different concentrations. RESULTS: Independently of the measuring method, an increase in osmolality with increasing concentration was observed for all polymers and surfactants, as would be expected due to the physicochemical origin of the osmolality. No correlation was found between the molecular weight of the polymers and the measured osmolality. The osmolality values were different for PVPs, PEGs, and Na-CMC using the two different measurement methods. The values obtained by the freezing point depression method tended to be similar or higher than the ones provided by vapor pressure, overall showing a significant difference in the osmolality measured by the two investigated methods. CONCLUSIONS: For lower osmolality values (e.g. surfactants), the choice of the measuring method was not critical, both the freezing point depression and vapor pressure could be used. However, when the formulations contained higher concentrations of excipients and/or thermosensitive excipients, the data suggests that the vapor pressure method would be more suited.

Lipid Based Formulations in Hard Gelatin and HPMC Capsules: a Physical Compatibility Study.

PURPOSE: To investigate the compatibility between hard gelatin and HPMC capsules with a range of different isotropic lipid based formulations containing multiple excipients. METHODS: The miscibility was investigated for 350 systems applying five different oils (Labrafac ™ lipophile WL1349, Maisine® CC, Captex 300 EP/NF, olive oil, and Capmul MCM EP/NF), five different surfactans (Labrasol ® ALF, Labrafil M 2125 CS, Kolliphor ® ELP, Kolliphor ® HS 15, Tween 80) and three different cosolvents (propylene glycol, polyethylene glycol 400, and Transcutol ® HP). For the isotropic systems capsule compatibility was investigated in both gelatin and HPMC capsules at 25°C at 40% and 60% relative humidity by examining physical damages to the capsules and weight changes after storage. RESULTS: The miscibility of lipid based vehicles was best when the formulation contained monoglycerides and surfactants with a hydrophilic-lipophilic balance value <12. Gelatin capsules in general resulted in a better compatibility when compared to HPMC capsules for the evaluated formulations. Addition of water to the formulation improved the capsule compatibility for both capsule types. The expected capsule mass change could partly be predicted in binary systems using the provided data of the single excipients weighted for its formulation proportion. CONCLUSIONS: The capsule compatibility was driven by the components incorporated into the formulations, where more was compatible with gelatin than HPMC capsules. Prediction of the mass change from individual excipient contributions can provide a good first estimate if a vehicle is compatible with a capsule, however, this needs to be proved experimentally.

A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate.

A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.

Can We Simplify Liposome Manufacturing Using a Complex DoE Approach?

Microfluidic liposome production presents a streamlined pathway for expediting the translation of liposomal formulations from the laboratory setting to clinical applications. Using this production method, resultant liposome characteristics can be tuned through the control of both the formulation parameters (including the lipids and solvents used) and production parameters (including the production speed and mixing ratio). Therefore, the aim of this study was to investigate the relationship between not only total flow rate (TFR), the fraction of the aqueous flow rate over the organic flow rate (flow rate ratio (FRR)), and the lipid concentration, but also the solvent selection, aqueous buffer, and production temperature. To achieve this, we used temperature, applying a design of experiment (DoE) combined with machine learning. This study demonstrated that liposome size and polydispersity were influenced by manipulation of not only the total flow rate and flow rate ratio but also through the lipids, lipid concentration, and solvent selection, such that liposome attributes can be in-process controlled, and all factors should be considered within a manufacturing process as impacting on liposome critical quality attributes.

R Shiny App for the Automated Deconvolution of NMR Spectra to Quantify the Solid-State Forms of Pharmaceutical Mixtures.

Bioavailability and chemical stability are important characteristics of drug products that are strongly affected by the solid-state structure of the active pharmaceutical ingredient (API). In pharmaceutical development and quality control activities, solid-state NMR (ssNMR) has proved to be an excellent tool for the detection and accurate quantification of undesired solid-state forms. To obtain correct quantitative outcomes, the resulting spectrum of an analytical sample should be deconvoluted into the individual spectra of the pure components. However, the ssNMR deconvolution is particularly challenging due to the following: the relatively large line widths that may lead to severe peak overlap, multiple spinning sidebands as a result of applying Magic Angle Spinning (MAS), and highly irregular peak shapes commonly observed in mixture spectra. To address these challenges, we created a tailored and automated deconvolution approach of ssNMR mixture spectra that involves a linear combination modelling (LCM) of previously acquired reference spectra of pure solid-state components. For optimal model performance, the template and mixture spectra should be acquired under the same conditions and experimental settings. In addition to the parameters controlling the contributions of the components in the mixture, the proposed model includes terms for spectral processing such as phase correction and horizontal shifting that are all jointly estimated via a non-linear, constrained optimisation algorithm. Finally, our novel procedure has been implemented in a fully functional and user-friendly R Shiny webtool (hence no local R installation required) that offers interactive data visualisations, manual adjustments to the automated deconvolution results, and the traceability and reproducibility of analyses.

FABIA: factor analysis for bicluster acquisition.

MOTIVATION: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques. RESULTS: On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches. AVAILABILITY: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Picking up good vibrations: Exploration of the intensified vibratory mill via a modern design of experiments.

The aim of this work was to strengthen the understanding of the intensified vibratory mill by unravelling the milling process in terms of the particle size reduction and heat generation via a modern design of experiments approach. Hence, the influence of five process parameters (acceleration, breaks during milling, bead size, milling time and bead-suspension ratio) was investigated via an I-optimal design. Particle size was measured via laser diffraction and the temperature of the sample after milling was computed. To advance our understanding, a mechanistic model for the set-up of wet-stirred media milling processes was applied on the observed milling trends. A generic approach for the optimisation of the milling process was retrieved and included the optimisation of the bead size and intermittent pausing for effective cooling. To finetune the remaining process parameters, the present work provides contour plots and strong predictive models. With these models, the particle size and the temperature after milling of suspensions manufactured with the intensified vibratory mill could be forecasted for the first time.

The impact of the method of extracting metabolic signal from 1H-NMR data on the classification of samples: A case study of binning and BATMAN in lung cancer.

Nuclear magnetic resonance (NMR) spectroscopy is a principal analytical technique in metabolomics. Extracting metabolic information from NMR spectra is complex due to the fact that an immense amount of detail on the chemical composition of a biological sample is expressed through a single spectrum. The simplest approach to quantify the signal is through spectral binning which involves subdividing the spectra into regions along the chemical shift axis and integrating the peaks within each region. However, due to overlapping resonance signals, the integration values do not always correspond to the concentrations of specific metabolites. An alternate, more advanced statistical approach is spectral deconvolution. BATMAN (Bayesian AuTomated Metabolite Analyser for NMR data) performs spectral deconvolution using prior information on the spectral signatures of metabolites. In this way, BATMAN estimates relative metabolic concentrations. In this study, both spectral binning and spectral deconvolution using BATMAN were applied to 400 MHz and 900 MHz NMR spectra of blood plasma samples from lung cancer patients and control subjects. The relative concentrations estimated by BATMAN were compared with the binning integration values in terms of their ability to discriminate between lung cancer patients and controls. For the 400 MHz data, the spectral binning approach provided greater discriminatory power. However, for the 900 MHz data, the relative metabolic concentrations obtained by using BATMAN provided greater predictive power. While spectral binning is computationally advantageous and less laborious, complementary models developed using BATMAN-estimated features can add complementary information regarding the biological interpretation of the data and therefore are clinically useful.

The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models.

Investigating whether metabolites regulate the co-expression of a predefined gene module is one of the relevant questions posed in the integrative analysis of metabolomic and transcriptomic data. This article concerns the integrative analysis of the two high-dimensional datasets by means of multivariate models and statistical tests for the dependence between metabolites and the co-expression of a gene module. The general linear model (GLM) for correlated data that we propose models the dependence between adjusted gene expression values through a block-diagonal variance-covariance structure formed by metabolic-subset specific general variance-covariance blocks. Performance of statistical tests for the inference of conditional co-expression are evaluated through a simulation study. The proposed methodology is applied to the gene expression data of the previously characterized lipid-leukocyte module. Our results show that the GLM approach improves on a previous approach by being less prone to the detection of spurious conditional co-expression.

Quality control of Platinum Spike dataset by probe-level mixed models.

Benchmark datasets are important for the validation and optimization of the analysis routes. Lately, a new benchmark dataset, 'Platinum Spike', for the Affymetrix GeneChip experiments has been introduced. We performed a quality check of the Platinum Spike dataset by using probe-level linear mixed models. The results have shown that there are 'empty' probe sets detecting transcripts, spiked in at different concentrations, and, reversely, there are probe sets that do not detect transcripts, spiked in at different concentrations, even though they were designed to do so. We proposed a formal inference procedure for testing the assumption of independence of all technical replicates in the data and concluded that for almost 10% of probe sets arrays cannot be treated independently, which has strong implications for the normalization procedures and testing for the differential expression. The proposed diagnostics procedure is used to facilitate a thorough exploration of gene expression Affymetrix data beyond the preprocessing and differential expression analysis.