Reactor Design for Continuous Monoclonal Antibody Precipitation Based Upon Micro‐mixing

BACKGROUND: Precipitation has been applied for the processing of important therapeutics, including monoclonal antibodies (mAbs). The scale‐up has proven to be a challenging task due to the complexity of the reactions and transport processes involved. This requires a good understanding of the molecular processes underpinning precipitate formation. The aim of this study was to build a micro‐mixing model for the precipitation of a mAb in continuous tubular reactors using ammonium sulphate. The effect of micro‐mixing on precipitate formation (with respect to size, strength, and nature) was evaluated. An ultra scale‐down (USD) centrifugation methodology was applied to determine the ease of precipitate clarification. RESULTS: The results demonstrated that the final mean particle size decreased with increased micro‐mixing, and was obtained with short residence times. Antibody yields in the tubular reactors were consistently above 90% and were shown to be independent of the mixing. Similar particle sizes between a lab and pilot‐scale reactor were correlated with the average energy dissipation rate. The smaller particles obtained from improved micro‐mixing had higher fractal dimensions that correlated with minimal breakage upon exposure to turbulent shear. Precipitates were easily clarified at the USD scale (> 95% clarification), but less so at pilot‐scale (< 80% clarification). CONCLUSION: Precipitation is a rapid process where the final precipitate properties are controlled by the flow conditions. Therefore, the process can be manipulated to acquire a certain particle size range. A high‐throughput precipitation process is also possible. However, further investigation into large‐scale precipitate recovery is required. © 2020 Society of Chemical Industr

Precipitation as an Enabling Technology for the Intensification of Biopharmaceutical Manufacture

Advances in precipitation have demonstrated the capability of purifying therapeutic proteins such as antibodies from biological sources in a scalable and cost-effective manner. We discuss the latest developments in the unit operation for downstream processing applications and provide a perspective on exploring precipitation for bioprocess intensification

Corrigendum to: Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data (Aug, 10.1177/1471082X20936017, 2020)

Functional Genomics of Muscle, Nerve and Brain Disorder

Pathway testing for longitudinal metabolomics

We propose a top-down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom-up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo-likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.Functional Genomics of Muscle, Nerve and Brain Disorder

Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.Development and application of statistical models for medical scientific researc

Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models

Duchenne muscular dystrophy is a severe pediatric neuromuscular disorder caused by the lack of dystrophin. Identification of biomarkers is needed to support and accelerate drug development. Alterations of metabolites levels in muscle and plasma have been reported in pre-clinical and clinical cross-sectional comparisons. We present here a 7-month longitudinal study comparing plasma metabolomic data in wild-type and mdx mice. A mass spectrometry approach was used to study metabolites in up to five time points per mouse at 6, 12, 18, 24 and 30 weeks of age, providing an unprecedented in depth view of disease trajectories. A total of 106 metabolites were studied. We report a signature of 31 metabolites able to discriminate between healthy and disease at various stages of the disease, covering the acute phase of muscle degeneration and regeneration up to the deteriorating phase. We show how metabolites related to energy production and chachexia (e.g. glutamine) are affected in mdx mice plasma over time. We further show how the signature is connected to molecular targets of nutraceuticals and pharmaceutical compounds currently in development as well as to the nitric oxide synthase pathway (e.g. arginine and citrulline). Finally, we evaluate the signature in a second longitudinal study in three independent mouse models carrying 0, 1 or 2 functional copies of the dystrophin paralog utrophin. In conclusion, we report an in-depth metabolomic signature covering previously identified associations and new associations, which enables drug developers to peripherally assess the effect of drugs on the metabolic status of dystrophic mice.Development and application of statistical models for medical scientific researc

Exon Skipping-Mediated Dystrophin Reading Frame Restoration for Small Mutations

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Rimeporide as a first- in-class NHE-1 inhibitor: Results of a phase Ib trial in young patients with Duchenne Muscular Dystrophy

Rimeporide, a first-in-class sodium/proton exchanger Type 1 inhibitor (NHE-1 inhibitor) is repositioned by EspeRare for patients with Duchenne Muscular Dystrophy (DMD). Historically, NHE-1 inhibitors were developed for cardiac therapeutic interventions. There is considerable overlap in the pathophysiological mechanisms in Congestive Heart Failure (CHF) and in cardiomyopathy in DMD, therefore NHE-1 inhibition could be a promising pharmacological approach to the cardiac dysfunctions observed in DMD. Extensive preclinical data was collected in various animal models including dystrophin-deficient (mdx) mice to characterise Rimeporide’s anti-fibrotic and anti-inflammatory properties and there is evidence that NHE-1 inhibitors could play a significant role in modifying DMD cardiac and also skeletal pathologies, as the NHE-1 isoform is ubiquitous. We report here the first study with Rimeporide in DMD patients. This 4-week treatment, open label phase Ib, multiple oral ascending dose study, enrolled 20 ambulant boys with DMD (6–11 years), with outcomes including safety, pharmacokinetic (PK) and pharmacodynamic (PD) biomarkers. Rimeporide was safe and well-tolerated at all doses. PK evaluations showed that Rimeporide was well absorbed orally reaching pharmacological concentrations from the lowest dose, with exposure increasing linearly with dose and with no evidence of accumulation upon repeated dosing. Exploratory PD biomarkers showed positive effect upon a 4-week treatment, supporting its therapeutic potential in patients with DMD, primarily as a cardioprotective treatment, and provide rationale for further efficacy studies

Cross-sectional serum metabolomic study of multiple forms of muscular dystrophy

Development and application of statistical models for medical scientific researc