Florian Lipsmeier

Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions.

Monoclonal antibodies (mAbs) and proteins containing antibody domains are the most prevalent class of biotherapeutics in diverse indication areas. Today, established techniques such as immunization or phage display allow for an efficient generation of new mAbs. Besides functional properties, the stability of future therapeutic mAbs is a key selection criterion which is essential for the development of a drug candidate into a marketed product. Therapeutic proteins may degrade via asparagine (Asn) deamidation and aspartate (Asp) isomerization, but the factors responsible for such degradation remain poorly understood. We studied the structural properties of a large, uniform dataset of Asn and Asp residues in the variable domains of antibodies. Their structural parameters were correlated with the degradation propensities measured by mass spectrometry. We show that degradation hotspots can be characterized by their conformational flexibility, the size of the C-terminally flanking amino acid residue, and secondary structural parameters. From these results we derive an accurate in silico prediction method for the degradation propensity of both Asn and Asp residues in the complementarity-determining regions (CDRs) of mAbs.

Aggregation and Chemical Modification of Monoclonal Antibodies under Upstream Processing Conditions

PurposeTo investigate antibody stability and formation of modified species under upstream processing conditions.MethodsThe stability of 11 purified monoclonal human IgG1 and IgG4 antibodies, including an IgG1-based bispecific CrossMab, was compared in downscale mixing stress models. One of these molecules was further evaluated in realistic bioreactor stress models and in cell culture fermentations. Analytical techniques include size exclusion chromatography (SEC), turbidity measurements, cation exchange chromatography (cIEX), dynamic light scattering (DLS) and differential scanning calorimetry (DSC).ResultsSensitivity in downscale stress models varies among antibodies and results in formation of high molecular weight (HMW) aggregates. Stability is increased in cell culture medium and in bioreactors. Media components stabilizing the proteins were identified. Extensive chemical modifications were detected both in stress models as well as during production of antibodies in cell culture fermentations.ConclusionsProtective compounds must be present in chemically defined fermentation media in order to stabilize antibodies against the formation of HMW aggregates. An increase in chemical modifications is detectable in bioreactor stress models and over the course of cell culture fermentations; this increase is dependent on the expression rate, pH, temperature and fermentation time. Consequently, product heterogeneity increases during upstream processing, and this compromises the product quality.

Prediction of VH–VL domain orientation for antibody variable domain modeling

The antigen‐binding site of antibodies forms at the interface of their two variable domains, VH and VL, making VH–VL domain orientation a factor that codetermines antibody specificity and affinity. Preserving VH–VL domain orientation in the process of antibody engineering is important in order to retain the original antibody properties, and predicting the correct VH–VL orientation has also been recognized as an important factor in antibody homology modeling. In this article, we present a fast sequence‐based predictor that predicts VH–VL domain orientation with Q2 values ranging from 0.54 to 0.73 on the evaluation set. We describe VH–VL orientation in terms of the six absolute ABangle parameters that have recently been proposed as a means to separate the different degrees of freedom of VH–VL domain orientation. In order to assess the impact of adjusting VH–VL orientation according to our predictions, we use the set of antibody structures of the recently published Antibody Modeling Assessment (AMA) II study. In comparison to the original AMAII homology models, we find an improvement in the accuracy of VH–VL orientation modeling, which also translates into an improvement in the average root‐mean‐square deviation with regard to the crystal structures. Proteins 2015; 83:681–695.

VH-VL orientation prediction for antibody humanization candidate selection: A case study.

abstract Antibody humanization describes the procedure of grafting a non-human antibodys complementarity-determining regions, i.e., the variable loop regions that mediate specific interactions with the antigen, onto a β-sheet framework that is representative of the human variable region germline repertoire, thus reducing the number of potentially antigenic epitopes that might trigger an anti-antibody response. The selection criterion for the so-called acceptor frameworks (one for the heavy and one for the light chain variable region) is traditionally based on sequence similarity. Here, we propose a novel approach that selects acceptor frameworks such that the relative orientation of the 2 variable domains in 3D space, and thereby the geometry of the antigen-binding site, is conserved throughout the process of humanization. The methodology relies on a machine learning-based predictor of antibody variable domain orientation that has recently been shown to improve the quality of antibody homology models. Using data from 3 humanization campaigns, we demonstrate that preselecting humanization variants based on the predicted difference in variable domain orientation with regard to the original antibody leads to subsets of variants with a significant improvement in binding affinity.

Molecular polygamy: The promiscuity of l‐phenylalanyl‐tRNA‐synthetase triggers misincorporation of meta‐ and ortho‐tyrosine in monoclonal antibodies expressed by Chinese hamster ovary cells

In‐depth analytical characterization of biotherapeutics originating from different production batches is mandatory to ensure product safety and consistent molecule efficacy. Previously, we have shown unintended incorporation of tyrosine (Tyr) and leucine/isoleucine (Leu/Ile) at phenylalanine (Phe) positions in a recombinant produced monoclonal antibody (mAb) using an orthogonal MASCOT/SIEVE based approach for mass spectrometry data analysis. The misincorporation could be avoided by sufficient supply of phenylalanine throughout the process. Several non‐annotated signals in the primarily chromatographic peptide separation step for apparently single Phe→Tyr sequence variants (SVs) suggest a role for isobar tyrosine isoforms. Meta‐ and ortho‐Tyr are spontaneously generated during aerobic fed‐batch production processes using Chinese hamster ovary (CHO) cell lines. Process induced meta‐ and ortho‐Tyr but not proteinogenic para‐Tyr are incorporated at Phe locations in Phe‐starved CHO cultures expressing a recombinant mAb. Furthermore, meta‐ and ortho‐Tyr are preferably misincorporated over Leu. Structural modeling of the l‐phenylalanyl‐tRNA‐synthetase (PheRS) substrate activation site indicates a possible fit of non‐cognate ortho‐Tyr and meta‐Tyr substrates. Dose‐dependent misincorporations of Tyr isoforms support the hypothesis that meta‐ and ortho‐Tyr are competing, alternative substrates for PheRS in CHO processes. Finally, easily accessible at‐line surrogate markers for Phe→Tyr SV formation in biotherapeutic production were defined by the calculation of critical ratios for meta‐Tyr/Phe and ortho‐Tyr/Phe to support early prediction of SV probability, and finally, to allow for immediate process controlled Phe→Tyr SV prevention. Biotechnol. Bioeng. 2015;112: 1187–1199.

A hybrid approach identifies metabolic signatures of high‐producers for chinese hamster ovary clone selection and process optimization

In‐depth characterization of high‐producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data‐driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO‐K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high‐producing CHO‐K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high‐producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched‐chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high‐content selection of CHO producer clones in a high‐throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high‐performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005–2019.

Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial: Remote PD Testing with Smartphones

Background: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers.

Large-Scale Continuous Mobility Monitoring of Parkinson’s Disease Patients Using Smartphones

Smartphone-based assessments have been considered a potential solution for continuously monitoring gait and mobility in mild to moderate Parkinson’s disease (PD) patients. Forty-four PD patients from cohorts 4 to 6 of the Multiple Ascending Dose (MAD) study of PRX002/RG7935 and thirty-five age- and gender-matched healthy individuals (i.e. healthy controls - HC) in a separate study performed smartphone-based assessments for up to 24 weeks and up to 6 weeks, respectively. The assessments included “active gait tests”, where all participants were asked to walk for 30 s with at least one 180\(^\circ \) turn, and “passive monitoring”, in which subjects carried the smartphone in a pocket or fanny pack as part of their daily routine. In total, over 6,600 active gait tests and over 30,000 h of passive monitoring data were collected. A mobility analysis indicates that patients with PD are less mobile than HCs, as manifested in time spent in gait-related activities, number of turns and sit-to-stand transitions, and power per step. It supports the potential use of smartphones for continuous mobility monitoring in future clinical practice and drug development.