Preclinical safety and efficacy of a therapeutic antibody that targets SARS-CoV-2 at the sotrovimab face but is escaped by Omicron

The recurrent emerging of novel viral variants of concern (VOCs) with evasion of preexisting antibody immunity upholds severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case numbers and maintains a persistent demand for updated therapies. We selected the patient-derived antibody CV38-142 based on its potency and breadth against the VOCs Alpha, Beta, Gamma, and Delta for preclinical development into a therapeutic. CV38-142 showed in vivo efficacy in a Syrian hamster VOC infection model after post-exposure and therapeutic application and revealed a favorable safety profile in a human protein library screen and tissue cross-reactivity study. Although CV38-142 targets the same viral surface as sotrovimab, which maintains activity against Omicron, CV38-142 did not neutralize the Omicron lineages BA.1 and BA.2. These results highlight the contingencies of developing antibody therapeutics in the context of antigenic drift and reinforce the need to develop broadly neutralizing variant-proof antibodies against SARS-CoV-2

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. 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