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Lessons learned from merging wet lab experiments with molecular simulation to improve mAb humanization.

ABSTRACT: Humanized monoclonal antibodies (mAbs) are among the most promising modern therapeutics, but defined engineering strategies are still not available. Antibody humanization often leads to a loss of affinity, as it is the case for our model antibody Ab2/3H6 (PDB entry 3BQU). Identifying appropriate back-to-mouse mutations is needed to restore binding affinity, but highly challenging. In order to get more insight, we have applied molecular dynamics simulations and correlated them to antibody binding and expression in wet lab experiments. In this study, we discuss six mAb variants and investigate a tyrosine conglomeration, an isopolar substitution and the improvement of antibody binding towards wildtype affinity. In the 3D structure of the mouse wildtype, residue R94h is surrounded by three tyrosines which form a so-called 'tyrosine cage'. We demonstrate that the tyrosine cage has a supporting function for the CDRh3 loop conformation. The isopolar substitution is not able to mimic the function appropriately. Finally, we show that additional light chain mutations can restore binding to wildtype-comparable level, and also improve the expression of the mAb significantly. We conclude that the variable light chain of Ab2/3H6 is of underestimated importance for the interaction with its antigen mAb 2F5.

SUBMITTER: Schwaigerlehner L

PROVIDER: S-EPMC6277173 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Lessons learned from merging wet lab experiments with molecular simulation to improve mAb humanization.

Schwaigerlehner L L Pechlaner M M Mayrhofer P P Oostenbrink C C Kunert R R

Protein engineering, design & selection : PEDS 20180701 7-8

Humanized monoclonal antibodies (mAbs) are among the most promising modern therapeutics, but defined engineering strategies are still not available. Antibody humanization often leads to a loss of affinity, as it is the case for our model antibody Ab2/3H6 (PDB entry 3BQU). Identifying appropriate back-to-mouse mutations is needed to restore binding affinity, but highly challenging. In order to get more insight, we have applied molecular dynamics simulations and correlated them to antibody binding ...[more]

PMID: 29757445

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Project description:BackgroundThe search for diagnostic biomarkers has been profiting from a growing number of high quality sequenced genomes and freely available bioinformatic tools. These can be combined with wet lab experiments for a rational search. Improved, point-of-care diagnostic tests for visceral leishmaniasis (VL), early case detection and surveillance are required. Previous investigations demonstrated the potential of IgG1 as a biomarker for monitoring clinical status in rapid diagnostic tests (RDTs), although using a crude lysate antigen (CLA) as capturing antigen. Replacing the CLA by specific antigens would lead to more robust RDTs.MethodologyImmunoblots revealed L. donovani protein bands detected by IgG1 from VL patients. Upon confident identification of these antigens by mass spectrometry (MS), we searched for evidence of constitutive protein expression and presence of antigenic domains or high accessibility to B-cells. Selected candidates had their linear epitopes mapped with in silico algorithms. Multiple high-scoring predicted epitopes from the shortlisted proteins were screened in peptide arrays. The most promising candidate was tested in RDT prototypes using VL and nonendemic healthy control (NEHC) patient sera.ResultsOver 90% of the proteins identified from the immunoblots did not satisfy the selection criteria and were excluded from the downstream epitope mapping. Screening of predicted epitope peptides from the shortlisted proteins identified the most reactive, for which the sensitivity for IgG1 was 84% (95% CI 60-97%) with Sudanese VL sera on RDT prototypes. None of the sera from NEHCs were positive.ConclusionWe employed in silico searches to reduce drastically the output of wet lab experiments, focusing on promising candidates containing selected protein features. By predicting epitopes in silico we screened a large number of peptides using arrays, identifying the most promising one, for which IgG1 sensitivity and specificity, with limited sample size, supported this proof of concept strategy for diagnostics discovery, which can be applied to the development of more robust IgG1 RDTs for monitoring clinical status in VL.

Project description:BACKGROUND:Electronic health (eHealth) solutions are considered to relieve current and future pressure on the sustainability of primary health care systems. However, evidence of the effectiveness of eHealth in daily practice is missing. Furthermore, eHealth solutions are often not implemented structurally after a pilot phase, even if successful during this phase. Although many studies on barriers and facilitators were published in recent years, eHealth implementation still progresses only slowly. To further unravel the slow implementation process in primary health care and accelerate the implementation of eHealth, a 3-year Living Lab project was set up. In the Living Lab, called eLabEL, patients, health care professionals, small- and medium-sized enterprises (SMEs), and research institutes collaborated to select and integrate fully mature eHealth technologies for implementation in primary health care. Seven primary health care centers, 10 SMEs, and 4 research institutes participated. OBJECTIVE:This viewpoint paper aims to show the process of adoption of eHealth in primary care from the perspective of different stakeholders in a qualitative way. We provide a real-world view on how such a process occurs, including successes and failures related to the different perspectives. METHODS:Reflective and process-based notes from all meetings of the project partners, interview data, and data of focus groups were analyzed systematically using four theoretical models to study the adoption of eHealth in primary care. RESULTS:The results showed that large-scale implementation of eHealth depends on the efforts of and interaction and collaboration among 4 groups of stakeholders: patients, health care professionals, SMEs, and those responsible for health care policy (health care insurers and policy makers). These stakeholders are all acting within their own contexts and with their own values and expectations. We experienced that patients reported expected benefits regarding the use of eHealth for self-management purposes, and health care professionals stressed the potential benefits of eHealth and were interested in using eHealth to distinguish themselves from other care organizations. In addition, eHealth entrepreneurs valued the collaboration among SMEs as they were not big enough to enter the health care market on their own and valued the collaboration with research institutes. Furthermore, health care insurers and policy makers shared the ambition and need for the development and implementation of an integrated eHealth infrastructure. CONCLUSIONS:For optimal and sustainable use of eHealth, patients should be actively involved, primary health care professionals need to be reinforced in their management, entrepreneurs should work closely with health care professionals and patients, and the government needs to focus on new health care models stimulating innovations. Only when all these parties act together, starting in local communities with a small range of eHealth tools, the potential of eHealth will be enforced.

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Project description:BACKGROUND:To develop effective and sustainable simulation training programs in low-resource settings, it is critical that facilitators are thoroughly trained in debriefing, a critical component of simulation learning. However, large knowledge gaps exist regarding the best way to train and evaluate debrief facilitators in low-resource settings. METHODS:Using a mixed methods approach, this study explored the feasibility of evaluating the debriefing skills of nurse mentors in Bihar, India. Videos of obstetric and neonatal post-simulation debriefs were assessed using two known tools: the Center for Advanced Pediatric and Perinatal Education (CAPE) tool and Debriefing Assessment for Simulation in Healthcare (DASH). Video data was used to evaluate interrater reliability and changes in debriefing performance over time. Additionally, twenty semi-structured interviews with nurse mentors explored perceived barriers and enablers of debriefing in Bihar. RESULTS:A total of 73 debriefing videos, averaging 18?min each, were analyzed by two raters. The CAPE tool demonstrated higher interrater reliability than the DASH; 13 of 16 CAPE indicators and two of six DASH indicators were judged reliable (ICC >?0.6 or kappa >?0.40). All indicators remained stable or improved over time. The number of 'instructors questions,' the amount of 'trainee responses,' and the ability to 'organize the debrief' improved significantly over time (p?<?0.01, p?<?0.01, p?=?0.04). Barriers included fear of making mistakes, time constraints, and technical challenges. Enablers included creating a safe learning environment, using contextually appropriate debriefing strategies, and team building. Overall, nurse mentors believed that debriefing was a vital aspect of simulation-based training. CONCLUSION:Simulation debriefing and evaluation was feasible among nurse mentors in Bihar. Results demonstrated that the CAPE demonstrated higher interrater reliability than the DASH and that nurse mentors were able to maintain or improve their debriefing skills overtime. Further, debriefing was considered to be critical to the success of the simulation training. However, fear of making mistakes and logistical challenges must be addressed to maximize learning. Teamwork, adaptability, and building a safe learning environment enhanced the quality enhanced the quality of simulation-based training, which could ultimately help to improve maternal and neonatal health outcomes in Bihar.

Project description:BackgroundVulnerable communities are susceptible to and disproportionately affected by the impacts of the COVID-19 pandemic. Understanding the challenges faced, perceptions, lessons learned, and recommendations of the organizations that provide services in response to COVID-19 to vulnerable communities is critical to improving emergency response and preparedness in these communities.MethodsThis study employed GIS mapping to identify the needs and assets that exist in communities in Baltimore City, where vulnerabilities related to social determinants of health and the burden of the COVID-19 pandemic were greatest. We also conducted an online survey between September 1, 2021, and May 30, 2022, to assess the COVID-19-related services provided by local organizations, challenges faced, perceptions, lessons learned, and recommendations to inform policies, programs, and funding related to improving the COVID-19 response in underserved communities. The survey was disseminated through the online Kobo Toolbox platform to leaders and representatives of organizations in Baltimore City.ResultsBased on GIS mapping analysis, we identified three communities as the most vulnerable and 522 organizations involved in the COVID-19 response across Baltimore City. 247 surveys were disseminated, and 50 survey responses were received (20.24% response rate). Out of these organizations, nearly 80% provided services in response to COVID-19 to the identified vulnerable communities. Challenges experienced ranged from funding (29%), and outreach/recruitment (26%), to not having access to updated and accurate information from local officials (32%).ConclusionsThis research highlights critical insights gained related to the experiences of vulnerable populations and suggests ways forward to address challenges faced during the emergency response by providing recommendations for policy and program changes. Furthermore, the findings will help better prepare vulnerable communities for public health emergencies and build more community resilience.

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Lessons learned from merging wet lab experiments with molecular simulation to improve mAb humanization.

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Lessons learned from merging wet lab experiments with molecular simulation to improve mAb humanization.

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