Skip to content
#gene editing Review Open access

Beyond Precision: A Multidimensional Framework for Selecting Genetic Medicine Platforms

Sep 2026 · Cells · 0 citations · 95 references
CRISPR and Genetic Engineering

Abstract

Gene therapy is undergoing continued clinical translation and technological development. This progress has been marked by regulatory approvals and broadened therapeutic indications across genetic, metabolic, and oncologic diseases and disorders. The field has evolved over decades from early viral-mediated gene addition to approaches capable of targeted editing, regulation, or replacement of genetic information. These systems include base and prime editors, epigenetic modulators, CRISPR-Cas, RNA therapeutics and programmable integration platforms. When paired with increasingly sophisticated viral and nonviral delivery strategies, these technologies enable greater control over tissue targeting, duration of activity, and therapeutic exposure. Recent clinical successes, including approved ex vivo CRISPR-based therapies for hemoglobinopathies, in vivo CRISPR editing for transthyretin amyloidosis, and emerging clinical applications of base and prime editing, provide growing clinical evidence for the feasibility of genetic medicines. However, technological advancement has also made platform selection increasingly complex. Therapeutic performance is determined not by editing efficiency alone, but by the interaction among genetic precision, temporal control, dosage tunability, delivery efficiency, durability, and disease-specific safety requirements. A molecularly efficient platform may still have limited therapeutic value if it cannot reach the disease-relevant cell population at sufficient and safe exposure. In this review, we examine recent technological and clinical advances in genetic medicine with particular emphasis on developments during the past approximately five years. We propose a multidimensional framework in which gene therapy platforms are evaluated according to three intrinsic properties—genetic precision, temporal control, and dosage tunability—while delivery, clinical maturity, and disease context act as major translational constraints. This framework highlights that no single platform is universally optimal; rather, successful therapeutic design depends on matching the biological characteristics of the intervention to the requirements of the disease and target tissue. Remaining challenges in extrahepatic delivery, genomic safety, immunogenicity, manufacturing, and long-term monitoring remain important determinants of broader clinical implementation.

Read PDF

Similar papers

#computer vision Conference Aug 2008

A Preliminary Roadmap for Empirical Research on Agile Software Development

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 experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.

Torgeir Dingsøyr, T. Dybå, P. Abrahamsson · 92 citations · ⚡7
#computer vision Book Open access Mar 2017

On the Unhappiness of Software Developers

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. · 84 citations · ⚡6
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

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. · 78 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

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. · 62 citations · ⚡3
#computer vision Conference Aug 2008

Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges

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 adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.

A. Marchenko, P. Abrahamsson · 59 citations · ⚡11

Related blog posts

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.