This chapter provides a comprehensive overview of genomic safety considerations for both small molecules and cell and gene therapies. For small chemical entities, testing is essential to identify a possible genotoxic potential early in drug development. Mutations in somatic and germline cells can lead to cancer or heritable diseases, necessitating robust testing strategies. Standard assays include in vitro assays such as bacterial reverse mutation tests (e.g., Ames test), mammalian cell-based gene mutation, and chromosomal damage assays, and in vivo models. Emerging technologies (e.g., error-corrected sequencing) enhance sensitivity for detecting ultra-rare mutations and provide opportunities for testing in cells and tissues under conditions otherwise unavailable. Regulatory frameworks such as ICH S2(R1) guide preclinical testing, whereas exploratory and screening strategies help interpret ambiguous results and support early candidate selection. Impurity testing, especially for mutagenic contaminants (e.g., N-nitrosamines), employs in silico tools and enhanced Ames protocols. In addition, the chapter addresses the genotoxicity risks associated with gene and cell therapies, particularly those involving designer nucleases (e.g., CRISPR/Cas9) and viral vectors. It explores mechanisms such as off-target editing and insertional mutagenesis and presents a multi-tiered genotoxicity assessment strategies combining in silico prediction (for off-target editing), genome-wide experimental methods, and the evaluation of functional consequences. Regulatory guidance emphasizes case-by-case risk assessment and supports the use of innovative in vitro transformation assays. Several such assays are reviewed in this chapter, including SACF, GILA, IVIM, and SAGA, which demonstrate suitability for assessing the oncogenic potential across different therapeutic modalities. These approaches also align with the 3Rs principles by reducing reliance on animal testing. Overall, the chapter explains the need for modality-specific, mechanistically informed safety strategies to ensure genomic integrity across therapeutic platforms.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on e...
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 92 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.