Long-term stability predictions of therapeutic monoclonal antibodies in solution using Arrhenius-based kinetics.

Long-term stability of monoclonal antibodies to be used as biologics is a key aspect in their development. Therefore, its possible early prediction from accelerated stability studies is of major interest, despite currently being regarded as not sufficiently robust. In this work, using a combination of accelerated stability studies (up to 6 months) and first order degradation kinetic model, we are able to predict the long-term stability (up to 3 years) of multiple monoclonal antibody formulations. More specifically, we can robustly predict the long-term stability behaviour of a protein at the intended storage condition (5 °C), based on up to six months of data obtained for multiple quality attributes from different temperatures, usually from intended (5 °C), accelerated (25 °C) and stress conditions (40 °C). We have performed stability studies and evaluated the stability data of several mAbs including IgG1, IgG2, and fusion proteins, and validated our model by overlaying the 95% prediction interval and experimental stability data from up to 36 months. We demonstrated improved robustness, speed and accuracy of kinetic long-term stability prediction as compared to classical linear extrapolation used today, which justifies long-term stability prediction and shelf-life extrapolation for some biologics such as monoclonal antibodies. This work aims to contribute towards further development and refinement of the regulatory landscape that could steer toward allowing extrapolation for biologics during the developmental phase, clinical phase, and also in marketing authorisation applications, as already established today for small molecules.

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning.

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange ( http://orange.biolab.si ) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scOrange-a tool for hands-on training of concepts from single-cell data analytics.

MOTIVATION: Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve. RESULTS: We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training. AVAILABILITY AND IMPLEMENTATION: scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

Knowledge-based bioinformatics for the study of mammalian oocytes.

Bioinformatics tools have been recently applied to study the differentiation of the mammalian oocyte during folliculogenesis. In this review, we will summarize our knowledge of 1) the use of biological databases for the extraction of relevant information, 2) bioinformatics methods for knowledge extraction and representation, 3) the application of these methods to the study of mammalian oocyte differentiation and 4) state-of the-art prediction approaches for the assessment and estimation of the cell differentiation status.

Stage prediction of embryonic stem cell differentiation from genome-wide expression data.

MOTIVATION: The developmental stage of a cell can be determined by cellular morphology or various other observable indicators. Such classical markers could be complemented with modern surrogates, like whole-genome transcription profiles, that can encode the state of the entire organism and provide increased quantitative resolution. Recent findings suggest that such profiles provide sufficient information to reliably predict the cell's developmental stage. RESULTS: We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 datasets from the Gene Expression Omnibus, we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines. AVAILABILITY: Model inference and computational evaluation procedures in the form of Python scripts and accompanying datasets are available at http://www.biolab.si/supp/stagerank. CONTACT: blaz.zupan@fri.uni-lj.si SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conserved developmental transcriptomes in evolutionarily divergent species.

BACKGROUND: Evolutionarily divergent organisms often share developmental anatomies despite vast differences between their genome sequences. The social amoebae Dictyostelium discoideum and Dictyostelium purpureum have similar developmental morphologies although their genomes are as divergent as those of man and jawed fish. RESULTS: Here we show that the anatomical similarities are accompanied by extensive transcriptome conservation. Using RNA sequencing we compared the abundance and developmental regulation of all the transcripts in the two species. In both species, most genes are developmentally regulated and the greatest expression changes occur during the transition from unicellularity to multicellularity. The developmental regulation of transcription is highly conserved between orthologs in the two species. In addition to timing of expression, the level of mRNA production is also conserved between orthologs and is consistent with the intuitive notion that transcript abundance correlates with the amount of protein required. Furthermore, the conservation of transcriptomes extends to cell-type specific expression. CONCLUSIONS: These findings suggest that developmental programs are remarkably conserved at the transcriptome level, considering the great evolutionary distance between the genomes. Moreover, this transcriptional conservation may be responsible for the similar developmental anatomies of Dictyostelium discoideum and Dictyostelium purpureum.