Computationally restoring the potency of a clinical antibody against Omicron.

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3 and revealed how quickly viral escape can curtail effective options4,5. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab4-6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.

Perspectives for self-driving labs in synthetic biology.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants.

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data.

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

A modular approach to create a neurovascular unit-on-a-chip.

In this work, we describe the fabrication and working of a modular microsystem that recapitulates the functions of the "Neurovascular Unit". The microdevice comprised a vertical stack of a poly(dimethylsiloxane) (PDMS) neural parenchymal chamber separated by a vascular channel via a microporous polycarbonate (PC) membrane. The neural chamber housed a mixture of neurons (~4%), astrocytes (~95%), and microglia (~1%). The vascular channel was lined with a layer of rat brain microvascular endothelial cell line (RBE4). Cellular components in the neural chamber and vascular channel showed viability (>90%). The neural cells fired inhibitory as well as excitatory potentials following 10 days of culture. The endothelial cells showed diluted-acetylated low density lipoprotein (dil-a-LDL) uptake, expressed von Willebrand factor (vWF) and zonula occludens (ZO-1) tight junctions, and showed decreased Alexafluor™-conjugated dextran leakage across their barriers significantly compared with controls (p < 0.05). When the vascular layer was stimulated with TNF-α for 6 h, about 75% of resident microglia and astrocytes on the neural side were activated significantly (p < 0.05 compared to controls) recapitulating tissue-mimetic responses resembling neuroinflammation. The impact of this microsystem lies in the fact that this biomimetic neurovascular platform might not only be harnessed for obtaining mechanistic insights for neurodegenerative disorders, but could also serve as a potential screening tool for central nervous system (CNS) therapeutics in toxicology and neuroinfectious diseases.

Bifurcations: focal points of particle adhesion in microvascular networks.

OBJECTIVE: Particle adhesion in vivo is dependent on the microcirculation environment, which features unique anatomical (bifurcations, tortuosity, cross-sectional changes) and physiological (complex hemodynamics) characteristics. The mechanisms behind these complex phenomena are not well understood. In this study, we used a recently developed in vitro model of microvascular networks, called SMN, for characterizing particle adhesion patterns in the microcirculation. METHODS: SMNs were fabricated using soft-lithography processes followed by particle adhesion studies using avidin and biotin-conjugated microspheres. Particle adhesion patterns were subsequently analyzed using CFD-based modeling. RESULTS: Experimental and modeling studies highlighted the complex and heterogeneous fluid flow patterns encountered by particles in microvascular networks resulting in significantly higher propensity of adhesion (>1.5×) near bifurcations compared with the branches of the microvascular networks. CONCLUSION: Bifurcations are the focal points of particle adhesion in microvascular networks. Changing flow patterns and morphology near bifurcations are the primary factors controlling the preferential adhesion of functionalized particles in microvascular networks. SMNs provide an in vitro framework for understanding particle adhesion.

Flow and adhesion of drug carriers in blood vessels depend on their shape: a study using model synthetic microvascular networks.

Development of novel carriers and optimization of their design parameters has led to significant advances in the field of targeted drug delivery. Since carrier shape has recently been recognized as an important design parameter for drug delivery, we sought to investigate how carrier shape influences their flow in the vasculature and their ability to target the diseased site. Idealized synthetic microvascular networks (SMNs) were used for this purpose since they closely mimic key physical aspects of real vasculature and at the same time offer practical advantages in terms of ease of use and direct observation of particle flow. The attachment propensities of surface functionalized spheres, elliptical/circular disks and rods with dimensions ranging from 1microm to 20microm were compared by flowing them through bifurcating SMNs. Particles of different geometries exhibited remarkably different adhesion propensities. Moreover, introduction of a bifurcation as opposed to the commonly used linear channel resulted in significantly different flow and adhesion behaviors, which may have important implications in correlating these results to in vivo behavior. This study provides valuable information for design of carriers for targeted drug delivery.

A physiologically realistic in vitro model of microvascular networks.

Existing microfluidic devices, e.g. parallel plate flow chambers, do not accurately depict the geometry of microvascular networks in vivo. We have developed a synthetic microvascular network (SMN) on a polydimethalsiloxane (PDMS) chip that can serve as an in vitro model of the bifurcations, tortuosities, and cross-sectional changes found in microvascular networks in vivo. Microvascular networks from a cremaster muscle were mapped using a modified Geographical Information System, and then used to manufacture the SMNs on a PDMS chip. The networks were cultured with bovine aortic endothelial cells (BAEC), which reached confluency 3-4 days after seeding. Propidium iodide staining indicated viable and healthy cells showing normal behavior in these networks. Anti-ICAM-1 conjugated 2-mum microspheres adhered to BAEC cells activated with TNF-alpha in significantly larger numbers compared to control IgG conjugated microspheres. This preferential adhesion suggests that cultured cells retain an intact cytokine response in the SMN. This microfluidic system can provide novel insight into characterization of drug delivery particles and dynamic flow conditions in microvascular networks.

Synthetic microvascular networks for quantitative analysis of particle adhesion.

We have developed a methodology to study particle adhesion in the microvascular environment using microfluidic, image-derived microvascular networks on a chip accompanied by Computational Fluid Dynamics (CFD) analysis of fluid flow and particle adhesion. Microfluidic networks, obtained from digitization of in vivo microvascular topology were prototyped using soft-lithography techniques to obtain semicircular cross sectional microvascular networks in polydimethylsiloxane (PDMS). Dye perfusion studies indicated the presence of well-perfused as well as stagnant regions in a given network. Furthermore, microparticle adhesion to antibody coated networks was found to be spatially non-uniform as well. These findings were broadly corroborated in the CFD analyses. Detailed information on shear rates and particle fluxes in the entire network, obtained from the CFD models, were used to show global adhesion trends to be qualitatively consistent with current knowledge obtained using flow chambers. However, in comparison with a flow chamber, this method represents and incorporates elements of size and complex morphology of the microvasculature. Particle adhesion was found to be significantly localized near the bifurcations in comparison with the straight sections over the entire network, an effect not observable with flow chambers. In addition, the microvascular network chips are resource effective by providing data on particle adhesion over physiologically relevant shear range from even a single experiment. The microfluidic microvascular networks developed in this study can be readily used to gain fundamental insights into the processes leading to particle adhesion in the microvasculature.

Simulation-based analysis of fluid flow and electrokinetic phenomena in microfluidic devices.

Recent advances in microfabrication techniques, sensing methods, and miniaturization have enabled automated analysis of samples using microfluidic systems. Each unique application requires successful custom development of integrated lab-on-a-chip devices. This involves design, analysis and characterization of individual components, (pumps, valves, mixers, separators, sensors) and the integrated system. In this regard, first-principle-based simulations of the underlying complex multiphysics phenomena can provide detailed understanding of device function. An overview of modeling and simulation-based analysis for the design and development of microfluidic devices is presented. In particular, the authors highlight some key factors affecting the performance of lab-on-a-chip systems such as surface tension effects, analyte dispersion, Joule heating, and mass transport limitations, and delineate the parameters that influence them. The limitations of these modeling techniques and future needs are discussed.