Emerging Technologies / Biotechnology

The growth of China's biotech sector is attributed to several factors, including accelerating out-licensing momentum, breakthroughs in innovative drug developments, improving profitability, and favorable macroeconomic conditions. Additionally, domestic policy support and ongoing globalization themes are bolstering the sector's revival (Global X China Biotech ETF, 2025; Invesco, 2025).

China's biotech sector holds a smaller share of the global biomanufacturing market compared to the US and Europe. However, significant investments in R&D and improving competitiveness suggest potential for future growth. The sector's ability to innovate and collaborate globally will be crucial for maintaining momentum (Merics, 2025).

The integration of AI into MedTech products faces challenges such as regulatory hurdles, data quality issues, and societal mistrust. Additionally, AI applications are often narrowly focused on diagnostics, particularly in radiology and cardiology, which can make monetization difficult without additional reimbursement[1][5].

AI can significantly enhance operational efficiency by automating tasks, analyzing large datasets, and allowing healthcare professionals to focus on high-value activities. Additionally, AI can improve patient outcomes by enabling predictive analytics for preventative care and enhancing patient engagement through personalized communication[3][5].

DNA computing utilizes DNA molecules to perform computational tasks, offering high parallel processing capabilities and operating under mild conditions at ambient temperatures, which significantly reduces energy consumption compared to traditional computing systems. This makes DNA computing an attractive option for sustainable AI applications.

AI can help improve energy efficiency by optimizing processes and enabling the use of more efficient computing systems, such as GPU-accelerated systems. Additionally, AI can be used to analyze and optimize energy distribution networks, and it can facilitate the integration of renewable energy sources. AI models can also be deployed in locations with excess renewable energy, reducing operational carbon footprints.

Tevogen.AI is an artificial intelligence initiative by Tevogen Bio that integrates machine learning and predictive modeling to enhance its ExacTcell technology. This integration aims to improve target identification and streamline pre-clinical processes, potentially accelerating clinical timelines and reducing development costs. The initiative is supported by partnerships with Microsoft and Databricks.

The partnerships with Microsoft and Databricks provide Tevogen.AI with advanced AI tools and capabilities. Microsoft's AI tools help develop proprietary algorithms that decode interactions between human leukocyte antigens (HLA) and T cells, expanding Tevogen's understanding of immune responses and identifying new therapeutic opportunities. Databricks supports the integration of machine learning into Tevogen's pre-clinical pipeline, enabling faster and more accurate target identification.

Biostate AI aims to make biology predictable and enhance precision medicine by leveraging generative AI and RNA sequencing. This involves developing technologies that can predict how diseases behave and respond to treatments, similar to how ChatGPT operates in language processing.

Biostate AI uses RNA sequencing data to build a generative AI service that predicts disease behavior and treatment responses. The company has already shown success in predicting leukemia recurrence and plans to expand into cancer, autoimmune, and cardiovascular diseases.

Biosensors detect AMR by identifying microorganisms, resistance genes, or antibiotics in clinical, bacterial, or environmental samples. They use optical, electrochemical, mechanical, or thermal sensors to provide rapid, precise results, enabling real-time monitoring and early intervention.

WGS identifies antimicrobial resistance genes and mutations across the entire genome of pathogens, enabling comprehensive detection of resistance mechanisms. It supports real-time surveillance and helps researchers understand complex resistance pathways that traditional methods might miss.

AI enhances the development of regenerative therapies by analyzing extensive datasets to identify optimal cell types for individual patients, facilitating personalized medicine. It also aids in the discovery and development of new biotherapeutics and improves manufacturing processes through automation and machine learning algorithms.

AI plays a crucial role in improving the scalability of regenerative medicine by enabling AI-driven manufacturing approaches. This allows for the automation of processes such as cell reprogramming, expansion, and differentiation, making personalized regenerative medicines viable at a large scale.

Evo 2 is a generative AI tool developed by researchers from the Arc Institute, NVIDIA, and leading universities. It is trained on DNA sequences from over 128,000 genomes and can predict disease-causing mutations, analyze evolutionary patterns, and design new genomic structures. This capability significantly advances AI-powered genetic research by potentially accelerating medical research and bioengineering.

Evo 2 offers significant benefits by accelerating medical research and potentially identifying disease-causing mutations with high accuracy. However, its ability to design new gene sequences also raises concerns about potential misuse in genetic engineering, such as unintended consequences or ethical issues related to creating artificial life forms.

BCIs help paralyzed individuals by translating brain signals into commands that can control external devices such as computers, prosthetic limbs, or robotic exoskeletons. This technology allows people with severe paralysis to communicate and regain some level of mobility and independence.

BCIs use various types of brain signals, including electroencephalographic (EEG) potentials, electrocorticographic (ECoG) signals, and signals from implanted microelectrodes. These signals are recorded either non-invasively from the scalp or invasively through surgical implants.

Researchers have demonstrated that it is technically feasible to encode malicious software exploits within synthetic DNA strands. When these strands are sequenced and analyzed by vulnerable DNA sequencing software, the embedded malware can be executed, potentially compromising the computer system processing the genetic data. This attack requires extensive knowledge of both computer science and microbiology, as well as tailored preparation of the DNA sample for specific sequencing devices.

Next-generation DNA sequencing (NGS) involves complex workflows with multiple digital touchpoints, making it vulnerable to cyberattacks that can lead to breaches of personal genetic data, manipulation of genomic information, and threats to scientific integrity and public health. Hackers could exploit these vulnerabilities to compromise patient privacy, corrupt genetic data, or even introduce biological threats. The risks extend beyond individual privacy to national security and global biosecurity, highlighting the urgent need for improved cyber-biosecurity measures.

CRISPR/Cas9 is a gene editing technology that allows for precise modifications to specific genes. In the context of autoimmune diseases, it offers a targeted approach to treat these conditions by altering genes implicated in the disorders, potentially providing more effective treatments than traditional immunosuppressive drugs.

Engineered immune cells, such as CAR T cells, are being developed to target specific cells involved in autoimmune responses. These cells can help reduce inflammation and prevent the immune system from attacking the body's own tissues. Novel CAR technologies are enhancing the safety and efficacy of these treatments, offering promising solutions for conditions like systemic lupus erythematosus.

Emerging bio-friendly materials include algae, mushroom mycelium, and agricultural waste. These materials are being developed into biodegradable plastics, sustainable textiles, and innovative building materials. For instance, mycelium is used to create biodegradable foams and leather-like materials, while algae and agricultural waste are being explored for their potential in biodegradable plastics[1][3][5].

Synthetic biology (Synbio) can play a crucial role in developing bio-friendly materials by enabling the design and engineering of biological systems to produce novel materials efficiently. Despite challenges in cost and efficiency, Synbio offers the potential to revolutionize bio-based manufacturing by creating tailored biological pathways for the production of sustainable materials. This could significantly enhance the production of bio-based materials, aligning with ambitious targets like those set by the U.S. government for biotechnology and biomanufacturing.