A significant limitation of numerous current genetic engineering therapy approaches is their limited control over the strength, timing, or cellular context of their therapeutic effect. Synthetic gene/genetic circuits are synthetic biology approaches that can control the generation, transformation, or depletion of a specific DNA, RNA, or protein and provide precise control over gene expression and cellular behavior. They can be designed to perform logical operations by carefully selecting promoters, repressors, and other genetic components. Patent search was performed in Espacenet, resulting in 38 selected patents with 15 most frequent international classifications. Patent embodiments were categorized into applications for the delivery of therapeutic molecules, treatment of infectious diseases, treatment of cancer, treatment of bleeding, and treatment of metabolic disorders. The logic gates of selected genetic circuits are described to comprehensively demonstrate their therapeutic applications. Synthetic gene circuits can be customized for precise control of therapeutic interventions, leading to personalized therapies that respond specifically to individual patient needs, enhancing treatment efficacy and minimizing side effects. They can be highly sensitive biosensors that provide real-time therapy by accurate monitoring various biomarkers or pathogens and appropriately synthesizing a therapeutic molecule. Synthetic gene circuits may also lead to the development of advanced regenerative therapies and to implantable biodevices that produce on-demand bioactive molecules. However, this technology faces challenges for commercial profitability. The genetic circuit designs need adjustments for specific applications, and may have disadvantages like toxicity from multiple regulators, homologous recombination, context dependency, resource overuse, and environmental variability.

Discussions around global equity and justice in science typically emphasize the lack of diversity in the editorial boards of scientific journals, inequities in authorship, \"parachute research,\" dominance of the English language, or scientific awards garnered predominantly by Global North scientists. These inequities are pervasive and must be redressed. But there is a bigger problem. The legacy of colonialism in scientific research includes an intellectual property system that favors Global North countries and the big corporations they support. This unfairness shows up in who gets access to the fruits of science and raises the question of who science is designed to serve or save.

This study examines the role of intellectual property protection (IPP) in enhancing radical technological innovation (RTI) within national research project teams, using an innovation-driven theory and an ability-motivation-opportunity (AMO) perspective. This study utilizes a sample of 336 national research project team members from various Chinese universities, research institutes, and corporations to analyze the theoretical model. Additionally, a two-stage hybrid partial least squares structural equation modeling (PLS-SEM) approach, combined with artificial neural network techniques (ANN), is employed to evaluate the hypotheses. The empirical findings of this study reveal a positive association between the intensity of IPP and RTI within national research project teams. Research and development investment intensity (R&DII) is identified as the primary predictor, while integrated leadership (IL) and group potential (GP) play crucial moderating roles. These groundbreaking findings extend the scope of innovation-driven and AMO theories, providing a proactive model for national research project teams to propose improvements to the IPP system, ultimately enhancing the realization of RTI.

China\'s high-quality development cannot be achieved without high-quality research. As the university is an indispensable source of advanced research, analyzing the impact of university-industry collaboration (UIC) on firm performance helps us understand the significance of universities for China\'s economic development and innovation activities. As existing research does not pay attention to the impact of UIC on the productivity of Chinese firms, we examine the impact of such collaboration on firm productivity using natural language processing and by matching China\'s intellectual property and listed firms\' operations databases. The empirical results show that UIC can promote firm productivity by improving the quality of their innovations, strengthening internalization efficiency, and broadening their research horizons. Moreover, the UIC process has a pronounced effect on promoting firm productivity in technology- and intellectual property-intensive industries. From the UIC perspective, we interpret China\'s economic development and provide new insights for developing countries regarding using universities to alleviate the insufficiency of private R&D investments.

In the following narrative review, we discuss the potential role of large language models (LLMs) in medical device innovation, specifically examples using generative pretrained transformer-4. Throughout the biodesign process, LLMs can offer prompt-driven insights, aiding problem identification, knowledge assimilation and decision-making. Intellectual property analysis, regulatory assessment and market analysis emerge as key LLM applications. Through case examples, we underscore LLMs\' transformative ability to democratise information access and expertise, facilitating inclusive innovation in medical devices as well as its effectiveness with providing real-time, individualised feedback for innovators of all experience levels. By mitigating entry barriers, LLMs accelerate transformative advancements, fostering collaboration among established and emerging stakeholders.

A new drug can have its origin in either pharma, biotech or academia. In general, discovery scientists working in pharma and biotech are advantaged over their academic counterparts and the relative advantages and disadvantages associated are discussed in depth. Against all odds, an increasing number of important drugs have had their origins in academia. This article reports three case studies from the Liotta Research Group (LRG), which explores the special circumstances that allowed these drug development campaigns to be successful. The first involves the antiretroviral agent, emtricitabine. In this case efficient synthetic methodology, developed in the LRG, coupled with some key university and commercial sector partnerships, enabled a group of academic collaborators to discover and develop a highly effective HIV reverse transcriptase inhibitor. The second case study involves the discovery and development of the breakthrough hepatitis C drug, sofosbuvir. Based on key input from Professors Schinazi and Liotta at Emory University, scientists at the Emory startup, Pharmasset, identified the nucleoside core of the drug that would become sofosbuvir. Subsequent analysis of its phosphorylation profile by Pharmasset scientists suggested that converting it to its corresponding monophosphate prodrug would circumvent a kinase block and enable it to be an effective hepatitis C polymerase inhibitor. The third case study describes the formation of DRIVE (Drug Innovation Ventures at Emory)/EIDD (Emory Institute for Drug Development), which were created to circumvent unintended impediments for carrying out academic drug discovery and development. Although DRIVE/EIDD is a wholly-owned, not-for-profit subsidiary of Emory University, it contains many attributes that enables it to operate much more nimbly than a typical academic laboratory. With an experienced drug development team and no shareholders to distract them, DRIVE/EIDD was able to focus its attention of the development of drugs to address viral diseases of global concern. In particular, their strategy to identify and develop an antiviral agent active against multiple single-stranded RNA viruses led to molnupiravir, a broadly active, oral drug that received Emergency Use Authorization for the treatment of SARS-CoV-2 infections (i.e., COVID-19).

This article provides an early analysis of the potential for creating future biosimilar competition for cell and gene therapies (CGTs) to lower prices and improve patient access, building on a unique set of interviews with relevant experts. Our discussion addressed regulatory, manufacturing, intellectual property, and market size challenges. Due to CGTs\' complexity, meeting the regulatory requirement of \'high similarity with no clinically meaningful differences\' will be difficult. Gene therapies are likely better candidates for biosimilar development than cell therapies. Biosimilarity should be met when gene therapy biosimilars contain the same genetic sequence as a reference product, and the variability in the vector meets the high similarity standard. Manufacturing challenges, including the lack of standardized platforms, high production costs, and complexity, pose significant obstacles. It may also be important to demonstrate biosimilarity within the manufacturing process. Intellectual property barriers, specifically patenting, trade secrecy, and regulatory exclusivity, could hinder biosimilars\' ability to gain market share, although recent Supreme Court decisions limiting the breadth of patent claims could ease barriers to future CGT competition, including from biosimilars. Finally, inadequate market sizes might create hurdles, especially for curative treatments, as patient pools shrink following treatment by the reference CGT.

Three-dimensional printing (3D printing) or \"additive manufacturing\" first came to prominence in the field of engineering, in particular in the transport sector where the value of its fast and accurate prototyping and manufacture of spare parts was quickly recognised. However, over the last ten years, this revolutionary technology has disrupted established manufacture in an increasingly diverse range of technical areas. Perhaps the most unexpected of these is pharmaceuticals - not merely the manufacture of products such as surgically inserted implants, but also of dosage formulations themselves - now available in all manner of printed delivery forms and vehicles and showing promising control of release properties though 3D printing process choices. This review will provide an overview of how 3D printing technology has developed and expanded across technological boundaries during the past decade, with a closer look at the current opportunities and barriers to its widespread adoption, particularly in the medical and pharmaceutical sectors. Special attention has been paid to patents as a boost and barrier to the expansion of 3D printing in the medical and pharmaceutical sector, with a focus on the patent literature.

During the COVID-19 pandemic, intellectual property licensing through bilateral agreements and the Medicines Patent Pool were used to facilitate access to new COVID-19 therapeutics in low- and middle-income countries (LMICs). The lessons learnt from the application of the model to COVID-19 could be relevant for preparedness and response to future pandemics and other health emergencies.The speed at which affordable versions of a new product are available in LMICs is key to the realization of the potential global impact of the product. When initiated early in the research and development life cycle, licensing could facilitate rapid development of generic versions of innovative products in LMICs during a pandemic. The pre-selection of qualified manufacturers, for instance building on the existing network of generic manufacturers engaged during the COVID-19 pandemic, the sharing of know-how and the quick provision of critical inputs such as reference listed drugs (RLDs) could also result in significant time saved. It is important to find a good balance between speed and quality. Necessary quality assurance terms need to be included in licensing agreements, and the potentials of the new World Health Organization Listed Authority mechanism could be explored to promote expedited regulatory reviews and timely access to safe and quality-assured products.The number, capacity, and geographical distribution of licensed companies and the transparency of licensing agreements have implications for the sufficiency of supply, affordability, and supply security. To foster competition and support supply security, licenses should be non-exclusive. There is also a need to put modalities in place to de-risk the development of critical pandemic therapeutics, particularly where generic product development is initiated before the innovator product is proven to be effective and approved. IP licensing and technology transfer can be effective tools to improve the diversification of manufacturing and need to be explored for regional manufacturing for accelerated access at scale in in LMICs and supply security in future pandemics.

A trademark\'s image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.