Asynchronous haltere input drives specific wing and head movements in Drosophila.

Halteres are multifunctional mechanosensory organs unique to the true flies (Diptera). A set of reduced hindwings, the halteres beat at the same frequency as the lift-generating forewings and sense inertial forces via mechanosensory campaniform sensilla. Though haltere ablation makes stable flight impossible, the specific role of wing-synchronous input has not been established. Using small iron filings attached to the halteres of tethered flies and an alternating electromagnetic field, we experimentally decoupled the wings and halteres of flying Drosophila and observed the resulting changes in wingbeat amplitude and head orientation. We find that asynchronous haltere input results in fast amplitude changes in the wing (hitches), but does not appreciably move the head. In multi-modal experiments, we find that wing and gaze optomotor responses are disrupted differently by asynchronous input. These effects of wing-asynchronous haltere input suggest that specific sensory information is necessary for maintaining wing amplitude stability and adaptive gaze control.

Insulin purification-Innovation continuum via synthesis of fundamentals, technology, and modeling.

Advancement in all disciplines (art, science, education, and engineering) requires a careful balance of disruption and advancement of classical techniques. Often technologies are created with a limited understanding of fundamental principles and are prematurely abandoned. Over time, knowledge improves, new opportunities are identified, and technology is reassessed in a different light leading to a renaissance. Recovery of biological products is currently experiencing such a renaissance. Crystallization is one example of an elegant and ancient technology that has been applied in many fields and was employed to purify insulins from naturally occurring sources. Crystallization can also be utilized to determine protein structures. However, a multitude of parameters can impact protein crystallization and the "hit rate" for identifying protein crystals is relatively low, so much so that the development of a crystallization process is often viewed as a combination of art and science even today. Supplying the worldwide requirement for insulin (and associated variants) requires significant advances in process intensification to support scale of production and to minimize the overall cost to enable broader access. Expanding beyond insulin, the increasing complexity and diversity of biologics agents challenge the current purification methodologies. To harness the full potential of biologics, there is a need to fully explore a broader range of purification technologies, including nonchromatographic approaches. This impetus requires one to challenge and revisit the classical techniques including crystallization, chromatography, and filtration from a different vantage point and with a new set of tools, including molecular modeling. Fortunately, computational biophysics tools now exist to provide insights into mechanisms of protein/ligand interactions and molecular assembly processes (including crystallization) that can be used to support de novo process development. For example, specific regions or motifs of insulins and ligands can be identified and used as targets to support crystallization or purification development. Although the modeling tools have been developed and validated for insulin systems, the same tools can be applied to more complex modalities and to other areas including formulation, where the issue of aggregation and concentration-dependent oligomerization could be mechanistically modeled. This paper will illustrate a case study juxtaposing historical approaches to insulin downstream processes to a recent production process highlighting the application and evolution of technologies. Insulin production from Escherichia coli via inclusion bodies is an elegant example since it incorporates virtually all the unit operations associated with protein production-recovery of cells, lysis, solubilization, refolding, purification, and crystallization. The case study will include an example of an innovative application of existing membrane technology to combine three-unit operations into one, significantly reducing solids handling and buffer consumption. Ironically, a new separations technology was developed over the course of the case study that could further simplify and intensify the downstream process, emphasizing and highlighting the ever-accelerating pace of innovation in downstream processing. Molecular biophysics modeling was also employed to enhance the mechanistic understanding of the crystallization and purification processes.

Cationic polymer precipitation for enhanced impurity removal in downstream processing.

Precipitation can be used for the removal of impurities early in the downstream purification process of biologics, with the soluble product remaining in the filtrate through microfiltration. The objective of this study was to examine the use of polyallylamine (PAA) precipitation to increase the purity of product via higher host cell protein removal to enhance polysorbate excipient stability to enable a longer shelf life. Experiments were performed using three monoclonal antibodies (mAbs) with different properties of isoelectric point and IgG subclass. High throughput workflows were established to quickly screen precipitation conditions as a function of pH, conductivity and PAA concentrations. Process analytical tools (PATs) were used to evaluate the size distribution of particles and inform the optimal precipitation condition. Minimal pressure increase was observed during depth filtration of the precipitates. The precipitation was scaled up to 20L size and the extensive characterization of precipitated samples after protein A chromatography showed >75% reduction of host cell protein (HCP) concentrations (by ELISA), >90% reduction of number of HCP species (by mass spectrometry), and >99.8% reduction of DNA. The stability of polysorbate containing formulation buffers for all three mAbs in the protein A purified intermediates was improved at least 25% after PAA precipitation. Mass spectrometry was used to obtain additional understanding of the interaction between PAA and HCPs with different properties. Minimal impact on product quality and <5% yield loss after precipitation were observed while the residual PAA was <9 ppm. These results expand the toolbox in downstream purification to solve HCP clearance issues for programs with purification challenges, while also providing important insights into the integration of precipitation-depth filtration and the current platform process for the purification of biologics.

Haltere and visual inputs sum linearly to predict wing (but not gaze) motor output in tethered flying Drosophila.

In the true flies (Diptera), the hind wings have evolved into specialized mechanosensory organs known as halteres, which are sensitive to gyroscopic and other inertial forces. Together with the fly's visual system, the halteres direct head and wing movements through a suite of equilibrium reflexes that are crucial to the fly's ability to maintain stable flight. As in other animals (including humans), this presents challenges to the nervous system as equilibrium reflexes driven by the inertial sensory system must be integrated with those driven by the visual system in order to control an overlapping pool of motor outputs shared between the two of them. Here, we introduce an experimental paradigm for reproducibly altering haltere stroke kinematics and use it to quantify multisensory integration of wing and gaze equilibrium reflexes. We show that multisensory wing-steering responses reflect a linear superposition of haltere-driven and visually driven responses, but that multisensory gaze responses are not well predicted by this framework. These models, based on populations, extend also to the responses of individual flies.

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening.

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

Prediction of lab and manufacturing scale chromatography performance using mini-columns and mechanistic modeling.

Chromatography is a cornerstone of biologics downstream purification processes, and there is an ever increasing demand for improved speed and efficiency in process development. Scale-down models are used in process development to optimize operating conditions and study process robustness while expending as little time and material as possible. The advent of automated liquid handling systems and miniature columns has taken the efficiency of process development to another level by allowing up to eight column runs in parallel with column volumes under 1 ml. As expected, results between these miniature columns and typical lab/manufacturing scale columns can deviate due to scale dependent and/or configuration dependent differences. Regulatory guidelines do not require an exact match in scale-down and large scale data, but do require that small scale models account for scale effects, be representative of the commercial process, and be scientifically justified. Therefore, it is important to gain insight into what causes differences between scales and account for them during development. Mechanistic models can be used to understand the physics of the process (fluid flow, mass transfer, etc.) as a function of scale, and provide explanation for deviations that may be observed. We have used mechanistic modeling to study the factors leading to differences in pool sizes observed between scales, and to make predictions on lab scale pool sizes from miniature column data. Results indicate that changes in mass transfer parameters, specifically axial dispersion, between scales leads to the observed differences in pool size. Additionally, we have studied the effect of system differences between automated liquid handling systems and conventional preparative chromatography systems on elution pool volume. This work provides new insight into the fundamental differences observed between scales and overcomes the challenge of enabling the use of miniature column chromatography as a scale-down model for process characterization.

Inertial Sensing and Encoding of Self-Motion: Structural and Functional Similarities across Metazoan Taxa.

To properly orient and navigate, moving animals must obtain information about the position and motion of their bodies. Animals detect inertial signals resulting from body accelerations and rotations using a variety of sensory systems. In this review, we briefly summarize current knowledge on inertial sensing across widely disparate animal taxa with an emphasis on neuronal coding and sensory transduction. We outline systems built around mechanosensory hair cells, including the chordate vestibular complex and the statocysts seen in many marine invertebrates. We next compare these to schemes employed by flying insects for managing inherently unstable aspects of flapping flight, built around comparable mechanosensory cells but taking unique advantage of the physics of rotating systems to facilitate motion encoding. Finally, we highlight fundamental similarities across taxa with respect to the partnering of inertial senses with visual senses and conclude with a discussion of the functional utility of maintaining a multiplicity of encoding schemes for self-motion information.

Targeted purification development enabled by computational biophysical modeling.

Chromatographic and non-chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid-liquid interface. These interactions are dominated by the protein-surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein-surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non-chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein-ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform.

Honeybees (Apis mellifera) learn to discriminate the smell of organic compounds from their respective deuterated isotopomers.

The understanding of physiological and molecular processes underlying the sense of smell has made considerable progress during the past three decades, revealing the cascade of molecular steps that lead to the activation of olfactory receptor (OR) neurons. However, the mode of primary interaction of odorant molecules with the OR proteins within the sensory cells is still enigmatic. Two different concepts try to explain these interactions: the 'odotope hypothesis' suggests that OR proteins recognize structural aspects of the odorant molecule, whereas the 'vibration hypothesis' proposes that intra-molecular vibrations are the basis for the recognition of the odorant by the receptor protein. The vibration hypothesis predicts that OR proteins should be able to discriminate compounds containing deuterium from their common counterparts which contain hydrogen instead of deuterium. This study tests this prediction in honeybees (Apis mellifera) using the proboscis extension reflex learning in a differential conditioning paradigm. Rewarding one odour (e.g. a deuterated compound) with sucrose and not rewarding the respective analogue (e.g. hydrogen-based odorant) shows that honeybees readily learn to discriminate hydrogen-based odorants from their deuterated counterparts and supports the idea that intra-molecular vibrations may contribute to odour discrimination.

Self assembly of nanoislands on YSZ-(001) surface: a mechanistic approach toward a robust process.

We experimentally investigate the mechanism of formation of self-assembled arrays of nanoislands surrounding dopant sources on the (001) surface of yttria-stabilized zirconia. Initially, we used lithographically defined thin-film patches of gadolinia-doped ceria (GDC) as dopant sources. During annealing at approximately one-half the melting temperature of zirconia, surface diffusion of dopants leads to the breakup of the surface around the source, creating arrays of epitaxial nanoislands with a characteristic size (~100 nm) and alignment along elastically compliant directions, <110>. The breakup relieves elastic strain energy at the expense of increasing surface energy. On the basis of understanding the mechanism of island formation, we introduce a simple and versatile powder-based doping process for spontaneous surface patterning. The new process bypasses lithography and conventional vapor-source doping, opening the door to spontaneous surface patterning of functional ceramics and other refractory materials. In addition to using GDC solid-solution powders, we demonstrate the effectiveness of the process in another system based on Eu2O3.