Exploring a role for flow-induced aggregation assays in platform formulation optimisation for antibody-based proteins

The development time of therapeutic monoclonal antibodies (mAbs) has been shortened by formulation platforms and the assessment of ‘protein stability’ using ‘developability’ assays. A range of assays are used to measure stability to a variety of stresses, including forces induced by hydrodynamic flow. We have previously developed a low-volume Extensional Flow Device (EFD) which subjects proteins to defined fluid flow fields in the presence of glass interfaces and used it to identify robust candidate sequences. Here, we study the aggregation of mAbs and Fc-fusion proteins using the EFD and orbital shaking under different formulations, investigating the relationship between these assays and evaluating their potential in formulation optimisation. EFD experiments identified the least aggregation-prone molecule using a fraction of the material and time involved in traditional screening. We also show that the EFD can differentiate between different formulations and that protective formulations containing polysorbate 80 stabilised poorly developable Fc-fusion proteins against EFD-induced aggregation up to two-fold. Our work highlights common platform formulation additives that affect the extent of aggregation under EFD-stress, as well as identifying factors that modulate the underlying aggregation mechanism. Together, our data could aid the choice of platform formulations early in development for next-generation therapeutics including fusion proteins

Dataset associated with 'The effect of mutation on an aggregation-prone protein: An in vivo, in vitro and in silico analysis'.

Aggregation of initially stably structured proteins is involved in more than 20 human amyloid diseases. Despite intense research, however, how this class of proteins assembles into amyloid fibrils remains poorly understood. We address this question using β2-microglobulin (β2m) as a model system, focusing on D76N-β2m that is involved in hereditary amyloidosis. Here, we identify the residues key to protect β2m from aggregation and we show that residue 76 has a unique ability to drive β2m aggregation in vivo and in vitro

Substitution of Met-38 to Ile in γ-synuclein found in two patients with amyotrophic lateral sclerosis induces aggregation into amyloid

α-, β-, and γ-Synuclein are intrinsically disordered proteins implicated in physiological processes in the nervous system of vertebrates. α-synuclein (αSyn) is the amyloidogenic protein associated with Parkinson’s disease and certain other neurodegenerative disorders. Intensive research has focused on the mechanisms that cause αSyn to form amyloid structures, identifying its NAC region as being necessary and sufficient for amyloid assembly. Recent work has shown that a 7-residue sequence (P1) is necessary for αSyn amyloid formation. Although γ-synuclein (γSyn) is 55% identical in sequence to αSyn and its pathological deposits are also observed in association with neurodegenerative conditions, γSyn is resilient to amyloid formation in vitro. Here, we report a rare single nucleotide polymorphism (SNP) in the SNCG gene encoding γSyn, found in two patients with amyotrophic lateral sclerosis (ALS). The SNP results in the substitution of Met38 with Ile in the P1 region of the protein. These individuals also had a second, common and nonpathological, SNP in SNCG resulting in the substitution of Glu110 with Val. In vitro studies demonstrate that the Ile38 variant accelerates amyloid fibril assembly. Contrastingly, Val110 retards fibril assembly and mitigates the effect of Ile38. Substitution of residue 38 with Leu had little effect, while Val retards, and Ala increases the rate of amyloid formation. Ile38 γSyn also results in the formation of γSyn-containing inclusions in cells. The results show how a single point substitution can enhance amyloid formation of γSyn and highlight the P1 region in driving amyloid formation in another synuclein family member

Análise de agrupamento para implementação da meta-análise em estimativas de herdabilidade para características de crescimento em bovinos de corte Cluster analysis for meta-analysis implementation for heritability of estimates growth traits in beef cattle

Estimativas de herdabilidade de características de crescimento são informações muito utilizadas em programas de melhoramento genético de bovinos de corte. Neste trabalho, foram compiladas 869 estimativas de herdabilidade, provenientes de 186 artigos publicados, das características peso ao nascimento, peso à desmama, peso aos 365 dias e peso aos 550 dias, de bovinos de corte de origem indiana. As estimativas foram divididas em grupos, em cada uma das quatro características, utilizando-se o método de agrupamento de Ward, e combinadas, dentro de cada grupo, por intermédio da meta-análise. Observou-se, para todas as características, que os grupos compostos por animais da raça Nelore presentes, em sua maioria, no Brasil, apresentaram maiores estimativas combinadas de herdabilidade que os demais grupos. Foram utilizados dois métodos, o da máxima verossimilhança restrita e o proposto por DerSimonian e Laird, para estimar a variância entre os estudos, tendo o primeiro apresentado valores superiores.<br>Heritability estimates of growth traits are essential informations in animal breeding programs. In this paper, 869 heritability estimates of birth weight, weaning weight, weight at 365 days old and weight at 550 days old, of 186 reports were compiled. The estimates were divided in groups using the Ward method of cluster analysis, and pooled by meta-analysis. It was observed, for all traits, that groups from Nelore breed, majority in Brasil, showed greater pooled heritability estimates than the other groups. The methods, restricted maximum likelihood and DerSimonian and Laird, were used to estimate the variance between studies, where the first method showed higher variances

Isolamento e caracterização do vírus da influenza pandêmico H1N1 em suínos no Brasil

A infecção causada pelo vírus Influenza A (IAV) é endêmica em suínos no mundo inteiro. O surgimento da pandemia de influenza humana pelo vírus A/H1N1 (pH1N1) em 2009 levantou dúvidas sobre a ocorrência deste vírus em suínos no Brasil. Durante o desenvolvimento de um projeto de pesquisa do vírus de influenza suína em 2009-2010, na Embrapa Suínos e Aves (CNPSA), foi detectado em um rebanho de suínos em Santa Catarina, Brasil, um surto de influenza altamente transmissível causado pelo subtipo viral H1N1. Este vírus causou uma doença leve em suínos em crescimento e em fêmeas adultas, sem mortalidade. Tres leitões clinicamente afetados foram eutanasiados. As lesões macroscópicas incluiam consolidação leve a moderada das áreas cranioventrais do pulmão. Microscopicamente, as lesões foram caracterizadas por bronquiolite necrosante obliterativa e pneumonia broncointersticial. A imunohistoquímica, utilizando um anticorpo monoclonal contra a nucleoproteína do vírus influenza A, revelou marcação positiva no núcleo das células epiteliais bronquiolares. O tecido pulmonar de três leitões e os suabes nasais de cinco fêmeas e quatro leitões foram positivos para influenza A pela RT-PCR. O vírus influenza foi isolado de um pulmão, mais tarde sendo confirmado pelo teste de hemaglutinação (título HA 1:128) e por RT-PCR. A análise das seqüências de nucleotídeos dos genes da hemaglutinina (HA) e proteína da matriz (M) revelou que o vírus isolado foi consistente com o vírus pandêmico A/H1N1/2009 que circulou em humanos no mesmo período. Este é o primeiro relato de um surto de influenza causado pelo vírus pandêmico A/H1N1 em suínos no Brasil

Likelihood-Based Clustering of Meta-Analytic SROC Curves

Meta-analysis of diagnostic studies experience the common problem that different studies mightnot be comparable since they have been using a different cut-off value for the continuous or orderedcategorical diagnostic test value defining different regions for which the diagnostic test is defined to bepositive. Hence specificities and sensitivities arising from different studies might vary just because theunderlying cut-off value had been different. To cope with the cut-off value problem interest is usuallydirected towards the receiver operating characteristic (ROC) curve which consists of pairs of sensitivitiesand false-positive rates (1-specificity). In the context of meta-analysis one pair represents one study andthe associated diagram is called an SROC curve where the S stands for “summary”. In meta-analysis ofdiagnostic studies emphasis has traditionally been placed on modelling this SROC curve with the intentionof providing a summary measure of the diagnostic accuracy by means of an estimate of the summary ROCcurve. Here, we focus instead on finding sub-groups or components in the data representing differentdiagnostic accuracies. The paper will consider modelling SROC curves with the Lehmann family whichis characterised by one parameter only. Each single study can be represented by a specific value of thatparameter. Hence we focus on the distribution of these parameter estimates and suggest modelling apotential heterogeneous or cluster structure by a mixture of specifically parameterised normal densities.We point out that this mixture is completely nonparametric and the associated mixture likelihood is welldefinedand globally bounded. We use the theory and algorithms of nonparametric mixture likelihoodestimation to identify a potential cluster structure in the diagnostic accuracies of the collection of studiesto be analysed. Several meta-analytic applications on diagnostic studies, including AUDIT and AUDIT-Cfor detection of unhealthy alcohol use, the mini-mental state examination for cognitive disorders, as wellas diagnostic accuracy inspection data on metal fatigue of aircraft spare parts, are discussed to illustratethe methodology