The research and development trends in the biosynthesis of 7-aminocephalosporanic acid based on patent analysis

Assessment of the enological potential of several strains of Lachancea thermotolerans. A proteomic approach.

ABSTRACTThe SNP Consortium (‘TSC’) was a nonprofit research collaboration formed in 1999 to identify, map, and publicly release human genomic markers known as ‘single nucleotide polymorphisms’ (SNPs). The project was funded by a group of pharmaceutical, biotechnology, and information technology companies, together with the Wellcome Trust, which collectively contributed $53 million to the project. TSC was the rare scientific undertaking that completed its work ahead of schedule, under budget, and with far more results than planned. This essay describes TSC, its background, legal structure, and novel ‘protective’ patent strategy, which have served as models for later research collaborations in a range of scientific fields.

The impending patent cliff projected between 2028-2030 poses significant commercial and strategic challenges for innovative pharmaceutical and biotechnology companies. To sustain growth and maintain competitive positioning, organizations are increasingly relying on strategic mergers, acquisitions, and partnerships to replenish pipelines. However, systematic quantitative strategies and framework for asset evaluation remain limited. This review outlines how clinical pharmacology and pharmacometrics (CPP) can support asset evaluation and decision-making during the asset due diligence. First, CPP spans the entire drug development continuum, providing a quantitative framework for evaluating external assets, including pharmacological plausibility, dosing feasibility, and overall development risks. Second, Model-informed drug development (MIDD) approaches can be applied to predict human pharmacokinetics, inform dose selection, and estimate the probability of technical and regulatory success. Third, we examine the emerging role of artificial intelligence and machine learning in asset evaluation and portfolio decision-making, by discovering prognostic and predictive factors, and identifying the patient sub-group. We also introduce NewCo as an emerging drug-development and business model, where quantitative strategies may be deployed. Further, we address cognitive biases, such as confirmation bias and sunk cost fallacies that can influence acquisition outcomes. Importantly, we propose the development of a bias-aware, fit-for-purpose corporate template to integrate CPP and MIDD insights, standardize evaluation criteria, and support cross-functional decision-making during asset due diligence. Embedding quantitative and bias-mitigated CPP frameworks into due diligence workflows, can help identify high-value opportunities, de-risk development uncertainties, and accelerate delivery of innovative therapies to patients with unmet medical needs.

This study aims to review the development trends of clinical trials for lung cancer cellular therapy systematically, analyze the spatiotemporal distribution, research entities, cell types, target selection quantitatively, thus, trial design characteristics and reveal the evolutionary patterns, driving factors, and scientific challenges in this field. The findings are intended to provide a reference for subsequent research, policy formulation, and clinical translation. Clinical trials of cellular therapies for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) worldwide up to July 2025 were retrieved. A cross-referencing strategy was applied to include interventional clinical studies in the process of excluding non-cellular therapies and records with missing key information. Two researchers independently screened and extracted data, 452 trials were included ultimately. Descriptive statistical methods were used to make analysis of core indicators, such as annual trends, geographical distribution, funding entities, target mechanisms, research phases, and trial status. Results Global clinical trials for lung cancer cellular therapy entered a rapid development phase, which started in 2016, peaked in 2024 (51 trials). Trials were highly concentrated on early stages, with Phase I and Phase I/II studies accounting for 79.9% (361/452). Only 7 Phase III studies were conducted, this gave a highlight of significant translational bottlenecks. Geographically, a bipolar driving pattern emerged between China and the United States. China experienced explosive growth post-2016 and leaded globally in trial numbers by 2024 (31 trials). Funding entities shifted from early public sector dominance to industry-led by small and medium-sized biotech companies. In terms of targets, PD-1 (25 trials) and IL-2Rα (22 trials) were the most popular, while emerging targets, for instance, cancer-testis antigen 1B and mesothelin gradually gained prominence. Mechanistically, immuno-oncology therapies and T-cell stimulators predominated. Among therapies such as CAR-T, NK, TCR-T, TIL, and DC vaccines, CAR-T and TIL, all showed the highest recent activity, while DC vaccines were relatively mature. This study systematically analyzed the current status and trends of global clinical trials for lung cancer cellular therapy, which demonstrated the rapid development driven by technological breakthroughs, policy support, and capital investment. Nevertheless, lung cancer cellular therapy still faces scientific challenges, such as target selection, efficacy enhancement, and safety optimization. Future research should explore deeply to give a promotion of clinical translation and application.

ABSTRACT Nucleic acids are generally recognized as the carriers of genetic information, but they are undergoing a profound conceptual transformation toward programmable information materials. Here, we introduce and delineate the emerging field of nucleic acid information materials (NAIMs), defined as engineered systems that repurpose DNA and RNA, the quintessential molecules of life, from their biological genetic roles into programmable materials for information technology and biotechnology. This paradigm shift leverages their inherent molecular recognition, predictable self‐assembly, and vast information‐encoding capacity. We propose the development of molecular building blocks, the materials fabrication, the function design, and the system integration, as the evolution of NAIMs. We discuss how nucleic acids are used to create static nanostructures and then to construct dynamic intelligent systems, such as molecular robots, computational circuits, and next‐generation data storage media. We also explore the application of NAIMs in two distinct areas: (1) Biotechnology (BT), where they enable precision diagnostics, targeted drug delivery, and logic‐based computational diagnosis and (2) Information technology (IT), where they enable energy‐efficient molecular computation and ultra‐dense long‐term data storage; the combination of BT and IT serves as the cornerstone of the emerging field of bio‐semiconductors. Finally, we outline the main challenges for NAIMs regarding synthesis scalability, system integration, and in vivo stability, and offer a perspective on the future BT–IT convergence that NAIMs are rewiring.

How biotech companies evolve intellectual property management capabilities: lessons from Cambridge Antibody Technology.

International trade in egg products has increased in recent decades to meet the rising demands of a global population, the food industry, and consumers. Egg white is a rich source of proteins and peptides suitable for various food applications. Processing egg whites is important in the food industry. This review thoroughly examines different aspects of egg white technology, including the definition of egg white, the natural microflora of eggs, and potential contaminants that may be present during processing. The impact of raw materials on the quality and features of egg products is also discussed, along with traditional and modern processing technologies regarding their applicability and efficiency. The study shows recent advancements and innovative approaches that could enhance egg white processing. Additionally, the fermentation of egg whites is explored as a promising method to modify their functional properties and produce bioactive compounds. The review shows the health benefits and significance of bioactive peptides derived from egg white in the development of functional foods. Overall, this review provides a comprehensive overview of current research in egg white processing, demonstrating how this valuable raw material can be utilized to create innovative products with enhanced nutritional and functional properties.

Purpose of reviewWith an estimated 41.1B digital devices, the term “digital biomarkers” has been increasingly bandied about in the research literature. There is, however, a significant disconnect between the presumption of digital biomarkers and the reality of digital biomarkers.Recent findingsThe research literature embraces the concept of digital biomarkers without concomitant evidence for validation of digital measures as biomarkers. Unlike imaging or blood-based biomarkers, there is a woeful lack of research dedicated to validating digital measures as biomarkers. This gap also presents an opportunity. Regulatory agencies worldwide have long-standing protocols used by pharmaceutical and biotech companies to stand up quality management systems (QMS) that track research from inception to regulatory approved submissions. The recent United States (US) Food and Drug Administration (FDA) approval of Alzheimer's disease (AD) plasma biomarkers is another example where successful QMS implementation provided the processes and transparency necessary to obtain approval. Regulatory guidelines for digital technology validation are more circumspect on validation pathways of AD digital biomarkers, but FDA provides a framework for building a QMS that could potentially do so.SummaryBuilding an open source QMS for AD digital biomarker validation will be a critical breakthrough for harnessing the potential of digital technologies for detection, monitoring and treatment of AD and related disorders.

This study proposes a novel data-driven machine learning (ML) framework for multi-criteria environmental, social, and governance (ESG) evaluation. The framework aims to address the transparency, consistency, and subjectivity limitations of existing ESG evaluation systems by employing a fully data-driven, modular, and ML-supported architecture. It comprises three main modules: (i) ESG data preprocessing with missing-data imputation by the MissForest algorithm; (ii) a three-plane ESG feature selection workflow that integrates clustering, feature importance, and classification algorithms to identify representative ESG indicators; and (iii) a hybrid weighting and ranking procedure that combines unsupervised principal component analysis (PCA), criteria importance through inter-criteria correlation (CRITIC), and technique for order preference by similarity to ideal solution (TOPSIS) methods. A recent 2024 real-world application involving 57 listed Chinese pharmaceutical and biotechnology companies and 70 ESG indicators demonstrates the framework’s practical utility in producing transparent and objective ESG rankings. The main contributions of this work are fourfold: (1) the development of an end-to-end, entirely data-driven ML framework for ESG evaluation; (2) the introduction of an innovative three-plane ESG feature selection workflow within the framework; (3) the first study for designing a hybrid PCA-CRITIC-TOPSIS approach in ESG weighting and ranking; (4) the validation of the framework through a real-world industry application using recent and authentic ESG data.

Purpose This paper develops a systemic framework for investment decision-making in pharmaceutical and biotech companies, addressing growing complexity driven by regulatory uncertainty, technological disruption, and evolving market needs. It seeks to move beyond traditional, fragmented approaches toward more integrated and viable innovation strategies. Design/methodology/approach The study adopts a systemic perspective that combines risk management, strategic partnerships and technological enablers such as artificial intelligence and predictive analytics. Drawing on case studies from AstraZeneca and Eli Lilly, as well as on notable failures such as Pfizer's torcetrapib and repeated setbacks in Alzheimer's research, the analysis illustrates how systemic approaches enhance portfolio resilience and decision quality through cross-functional governance. Findings The findings demonstrate that a systemic model built around four interrelated pillars – scientific merit, regulatory feasibility, commercial viability and competitive positioning – supports better risk-adjusted decisions and stronger alignment between R&D, financial, and strategic functions. By integrating these dimensions, organizations can improve the robustness of their investment portfolios and foster long-term innovation capability. Originality/value This study contributes a novel conceptual and practical framework that links strategic intelligence, technological tools and governance mechanisms in the investment decision-making process. It offers actionable insights for managers, investors, and policymakers seeking to align capital allocation with sustainable innovation. By adopting systemic approaches, pharmaceutical and biotech firms can transition from reactive, fragmented decision-making toward proactive, evidence-based strategies that optimize risk-adjusted returns and accelerate the delivery of transformative therapies.

Medical science liaisons (MSLs) serve a critical role in pharmaceutical, biotechnology, medical device, diagnostics, and healthcare companies by facilitating scientific exchange with key opinion leaders (KOLs) and health care professionals (HCPs). Despite their growing strategic importance, evaluating MSL impact remains challenging due to the complexity of their responsibilities. This study investigates current practices for assessing MSL impact, explores how their value is defined, and examines the challenges in measuring MSL performance. A cross-sectional online survey was conducted, targeting MSLs, MSL managers/directors, MSL Excellence and Operations, executive management, and other medical affairs professionals. Participants were recruited via professional networks, social media, pharmaceutical industry conferences, online platforms dedicated to medical affairs, and posting on LinkedIn, resulting in a non-probability convenience sample. The survey comprised multiple-choice and Likert-scale questions covering KOL engagement impact scores, definitions of MSL value and impact, and challenges in performance, measurement, and assessment. A total of 1023 respondents participated. Geographically, the majority of respondents were based in the United States (56%), followed by Spain (6%), Brazil (5%), and Canada (5%), with additional contributions from Europe, Latin America, and other regions. Overall, 52% indicated that KOL engagement impact scores should be utilized as a performance metric. MSL value was most often defined as building and maintaining strong KOL relationships (27%), whereas MSL impact was most frequently linked to influencing clinical practice and improving patient care (46%). Key challenges included difficulty defining and measuring the quality of MSL activities (76%) and reliance on quantitative standards that do not reflect MSLs' value (61%). Only 40% reported that their organizations measure MSL impact, and 41% suggested that the assessment should combine individual achievements with team contributions. Furthermore, 67% considered measuring MSL performance "difficult" or "very difficult". This study highlights the challenges of measuring MSLs' impact and value. Many organizations continue to struggle with capturing the quality of MSL activities and their broader strategic contributions.

Future technologies in biotech and pharmaceutical companies

This editorial examines how artificial intelligence (AI)—including machine learning, generative AI, and natural language processing—is reshaping biomedical research and pharmaceutical R&D. It outlines distinct adoption archetypes emerging among large pharmaceutical organizations: partnership-driven acceleration through strategic technology alliances; culture-centric transformation that embeds AI into everyday scientific and operational decision-making; and production-first democratization that makes AI tools broadly usable across functions. In parallel, AI is lowering entry barriers for smaller biotech companies, enabling faster iteration in molecular design and earlier clinical translation, while cloud and federated approaches expand access to powerful pre-trained models without compromising proprietary data. The editorial also emphasizes the limiting factors that will determine whether "democratized discovery" translates into sustained impact: high-quality, interoperable data; rigorous model validation; transparency and auditability; workforce upskilling; ethical oversight; and alignment with evolving regulatory expectations. Together, these elements define a pragmatic pathway toward an AI-integrated biomedical ecosystem focused on speed, safety, and equitable innovation.

Indonesia is increasingly challenged by the management of organic solid waste, especially in Bali Province, where organic waste accounts for about 68% of the total municipal waste produced. The current waste management strategies mainly depend on landfilling and basic composting techniques, which are inadequate to mitigate the environmental and socio-economic effects. This research utilizes the Analytical Hierarchy Process (AHP) to systematically assess and prioritize eight bioconversion and thermal-conversion technologies for managing organic waste in Bali. The evaluation considers four main criteria, environmental, social, technical, and economic, along with their sub-criteria, based on expert opinions and literature review. The results reveal that bioconversion technologies, particularly composting, black soldier fly (BSF) processing, and eco-enzyme production, are the most appropriate choices, as they offer high community acceptance, reduced greenhouse gas emissions, and better compatibility with local waste characteristics and socio-economic conditions. Thermal technologies like incineration and gasification are less favored due to their higher environmental risks and capital expenses. The findings offer a comprehensive decision-support framework for policymakers and practitioners to create sustainable organic waste management strategies tailored to Indonesia’s context.

This article examines the development of a biotechnological industry in the context of China's transition from planned to market economy since 1979. It focuses on two biotech companies, Weiguang Biological Products Co., Ltd., and BGI, both headquartered in Shenzhen—China's first and most successful Special Economic Zone (SEZ). We argue that China's incomplete and incremental reform significantly shaped the forms and practices of a “post-socialist biotech.” This distinguishes it from Western counterparts in terms of labor governance, value creation, and the conceptualization of risk. The institutional inheritance of socialism allows the two Shenzhen-based biotech companies to organize labor in unique ways. As a result, their approach to innovation differs from the capital-intensive model of scientific research in the West. Beyond monetary profits, the products of these two firms also generate values that are ideological and social, contributing to the Chinese state's political agenda and aiming to strengthen national identity. While conforming to state agendas helps these enterprises counter economic risks, their growth remains confined by state domination in strategic sectors. Altogether, the three axes (labor, value, and risk) comprise a model of biotech that reflects the economic structures of gradual and incomplete marketization.

This paper examines the factors motivating scientists and engineers to return to India after working in information technology and biotechnology companies in the United States of America (USA). Drawing on a qualitative study, the findings reveal that decisions to return are influenced by family considerations, career opportunities, entrepreneurial aspirations, challenges associated with immigration to the USA, and an affinity for Indian culture. Conversely, migrants who choose to remain in the USA cite superior career prospects, favorable work environments, and a high quality of life. Despite their differing choices, both returnees and stayers actively maintain transnational professional relationships with scientists and engineers in the USA and India, respectively, fostering connections across borders.

The "Drug Administration Law of the People's Republic of China" has a profound impact on the innovation of pharmaceutical companies. This study analyzes Chinese pharmaceutical and biotechnology companies from 2014 to 2023 and employs a sharp regression discontinuity design (RDD) based on the 2019 revision of the Drug Administration Law to examine changes in innovation output before and after the policy, while exploring impact mechanisms from external conditions (financing constraints) and internal factors (human capital structure), with further heterogeneity analysis by firm size, region, and ownership. The results show that the implementation of the 2019 revised Drug Administration Law significantly increased the number of patent applications by pharmaceutical and biotechnology enterprises, thereby promoting innovation output. Further analysis finds that the policy promotes corporate innovation through two pathways: easing financing constraints and facilitating adjustments in human capital structure. This study provides empirical evidence on the effectiveness of the policy, offering insights for future legislation and suggesting pathways to enhance innovation capacity in the context of ongoing healthcare reform in China.

Bone is a primary site for metastasis in breast cancer, with up to 70 % of patients with metastatic breast cancer developing osteolytic bone lesions, wherein cancer cells drive osteoclast resorption of bone. However, progress in developing therapies is limited by the absence of predictive in vitro models. This study developed a unique organ-on-a-chip model to simulate osteolytic bone metastasis and utilised a multi-omics approach for characterisation/qualification and validation against in vivo data. Using the Emulate S1 platform, we co-cultured murine osteocytes and osteoclasts to recreate the bone microenvironment, alongside breast cancer cells in a separate channel separated by a porous membrane. Using RNA sequencing, cytokine profiling, and fluorescence staining, we demonstrated the importance of the complete tri-culture model in replicating key aspects of in vivo biology, and uncovered critical pathways involved in metastasis. A synergistic effect was observed in the tri-culture organ-chip model, leading to increased cancer cell migration and the upregulation of pro-metastatic and pro-inflammatory pathways that promote bone degradation and cancer progression. This study validates an organ-chip model of osteolytic breast cancer bone metastasis as a scalable alternative to traditional animal models. Furthermore, we show how multi-omics and bioinformatics techniques may be used for qualification and validation of organ-chip models; for unpicking the relative contribution of the different cell types; and to identify signalling pathways and therapeutic targets. STATEMENT OF SIGNIFICANCE: In this study, we develop a 3D organ-on-a-chip tri-culture model of the osteolytic metastatic niche, in which we verify expected bone and breast cancer cell behaviours. Importantly, we successfully validate our organ-chip against a dataset from the gold standard in vivo preclinical model of osteolytic breast metastases, using transcriptomics and proteomics to confirm strong alignment of gene expression profiles with in vivo mouse expression. Additionally, our multi-omics analysis sheds new light on both expected and novel molecular pathways for therapeutic targeting, demonstrating the utility of the organ-chip as a potential replacement for preclinical mouse models of breast cancer metastases in bone. Therefore, this study represents a key marker in the field of organ-chip research, demonstrating the importance of biomaterials technologies for preclinical science. Most importantly, our work demonstrates for biotech and pharma companies that qualified organ-chip devices can play a role as intermediate medium-throughput technologies for screening lead drug candidates.

Connected drug delivery devices such as combination products that integrate traditional drug delivery systems with digital connectivity features represent an opportunity to improve treatment outcomes and disease management. This online survey study was conducted to explore the evolving landscape of digitally connected subcutaneous (SC) drug delivery devices, including the perspectives of pharmaceutical stakeholders regarding the promise of these technologies, particularly in relation to the expansion of traditional mobile companion applications and their integration in drug delivery systems. A total of 80 employees of pharmaceutical, biotechnology, or digital health companies with primary roles in medical affairs, commercial, combination product development, or digital health who had experience working on SC drug-device combination products completed the survey. Survey questions explored the value propositions of connected SC drug delivery devices for patients, providers, and payers; barriers to the adoption of these technologies; and strategies for gaining internal support for connected healthcare initiatives. Responses demonstrated that industry professionals recognize the potential value of connected SC drug delivery devices and associated companion mobile applications and are investing in bringing them to market. Nearly all respondents (97.5%) reported that connectivity is at least moderately important to achieving important objectives, including acquiring real-world data, improving medication adherence, and enhancing ease-of-use for patients. Equal potential value was noted for using connectivity in clinical trials or commercial settings, with neither considered more beneficial than the other. Indications in oncology and endocrinology were considered to be the most likely to benefit from connected SC drug delivery devices. Key barriers to the adoption of connected SC drug delivery devices were development cost, data security, and patient and payer acceptance, while generating evidence of internal and external value was noted as a significant barrier to gaining company endorsement. These results should guide strategies for the effective integration of connected healthcare solutions within the pharmaceutical sector.