The brain is a central hub for integration of internal and external conditions and, thus, a regulator of the stress response. Glucocorticoids are the essential communicators of this response. Aberrations in glucocorticoid signaling are a common symptom in patients with psychiatric disorders. The gene FKBP5 encodes a chaperone protein that functionally inhibits glucocorticoid signaling and, thus, contributes to the regulation of stress. In the context of childhood trauma, differential expression of FKBP5 has been found in psychiatric patients compared to controls. These variations in expression levels of FKBP5 were reported to be associated with differences in stress responsiveness in human carriers of the single nucleotide polymorphism (SNP) rs1360780. Understanding the mechanisms underlying FKBP5 polymorphism‐associated glucocorticoid responsiveness in the CNS will lead to a better understanding of stress regulation or associated pathology. To study these mechanisms, two novel humanized mouse lines were generated. The lines carried either the risk (A/T) allele or the resilient (C/G) allele of rs1360780. Primary cells from CNS (astrocytes, microglia, and neurons) were analyzed for their basal expression levels of FKBP5 and their responsiveness to glucocorticoids. Differential expression of FKBP5 was found for these cell types and negatively correlated with the cellular glucocorticoid responsiveness. Astrocytes revealed the strongest transcriptional response, followed by microglia and neurons. Furthermore, the risk allele (A/T) was associated with greater induction of FKBP5 than the resilience allele. Novel FKBP5‐humanized mice display differential glucocorticoid responsiveness due to a single intronic SNP. The vulnerability to stress signaling in the shape of glucocorticoids in the brain correlated with FKBP5 expression levels. The strong responsiveness of astrocytes to glucocorticoids implies astrocytes play a prominent role in the stress response, and in FKBP5‐related differences in glucocorticoid signaling. The novel humanized mouse lines will allow for further study of the role that FKBP5 SNPs have in risk and resilience to stress pathology.

The cumulative load of genetic predisposition, early life adversity (ELA) and lifestyle shapes the prevalence of psychiatric disorders. Single nucleotide polymorphisms (SNPs) in the human FKBP5 gene were shown to modulate disease risk. To enable investigation of disease-related SNPs in behaviourally relevant context, we generated humanised mouse lines carrying either the risk (AT) or the resiliency (CG) allele of the rs1360780 locus and exposed litters of these mice to maternal separation. Behavioural and physiological aspects of their adult stress responsiveness displayed interactions of genotype, early life condition, and sex. In humanised females carrying the CG- but not the AT-allele, ELA led to altered HPA axis functioning, exploratory behaviour, and sociability. These changes correlated with differential expression of genes in the hypothalamus, where synaptic transmission, metabolism, and circadian entrainment pathways were deregulated. Our data suggest an integrative role of FKBP5 in shaping the sex-specific outcome of ELA in adulthood.

The analysis of data collected using design of experiments (DoE) is the current gold standard to determine the influence of input parameters and their interactions on process performance and product quality. In early development, knowledge on the bioprocess of a new product is limited. Many input parameters need to be investigated for a thorough investigation. For eukaryotic cell cultures, intensified DoE (iDoE) has been proposed as efficient tool, requiring fewer bioreactor runs by introducing setpoint changes during the bioprocess. We report the first successful application of iDoE to mammalian cell culture, performing sequential setpoint changes in the growth phase for the selected input parameters temperature and dissolved oxygen. The process performance data were analyzed using ordinary least squares regression. Our results indicate iDoE to be applicable to mammalian bioprocesses and to be a cost‐efficient option to inform modeling early on during process development. Even though only half the number of bioreactor runs were used in comparison to a classical DoE approach, the resulting models revealed comparable input‐output relations. Being able to examine several setpoint levels within one bioreactor run, we confirm iDoE to be a promising tool to speed up biopharmaceutical process development.

Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes.Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization.Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential.Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.

Understanding the relationship between structure and function is crucial in drug development. Recombinant proteins present unique challenges when establishing structure-function relationships (SFR) due to the extensive options for potential modifications across numerous residues. Even simple stress studies lack degradation selectivity as they lead to multiple, simultaneous modifications. Co-occurring modifications at amino acid residues make direct correlations with a specific stressor and resolving the causal impact on functionality difficult. The introduction of more variance into the data set would reduce the correlation structure. In this study, we demonstrate the feasibility and benefits of a multifactorial design approach for SFR establishment. Design of experiments (DoE) allows for the parallel investigation of stress factors via combined experiments, resulting in a higher variance of stress conditions and consequently a broader variation in degradation. This approach enables a more insightful correlation analysis along with model-based data evaluation strategies, thereby facilitating significantly improved data interpretation results during SFR studies.

For biotherapeutic analytics, robust and reliable potency assays are required. Design of experiment (DoE) approaches are used to investigate the impact of multiple assay parameters. Currently, specific assay features (e.g., half effective concentration) are modelled independently from each other. A joint interpretation of several assay features is thus difficult. In our functional DoE approach, we use the functional relationship of the assay features to describe the sigmoidal dose-response curve. With the composed functional form, the direct impact of assay parameters on the dose-response curve shape was modelled. Moreover, a multivariate desirability was defined and used for assay optimization. We believe that functional modelling contributes to understanding the joint influence of assay parameters and helps to design robust biotherapeutic analytics.