In this paper, four sphingosine-1-phosphate (S1P) targeted small molecule drugs for the treatment of multiple sclerosis which have been marketed worldwide, and their patent information were unscrambled and researched. The development progress of fingolimod, siponimod, ozanimod and ponesimod, as well as their published patents about product, formulation, new use, et al, were studied and analyzed to provide effective patent information and references for pharmaceutical enterprises in China in the process of establishing and developing related drugs, as well as new patent mining, patent application and layout.

By analyzing the relevant patent documents and judgment documents of the current patent authorization opposition cases of traditional Chinese medicine, the overall characteristics of such cases in China can be known. The current patent authorization opposition cases of traditional Chinese medicine in China show the following features: a relatively high proportion of individual applicants, more formal disputes, a high proportion of composition inventions and a high rejection rate, a low proportion of patent agencies, and less participation of technical investigators. It is suggested that the research and development entities of traditional Chinese medicine patents should adjust the research and development priorities, strengthen research and development cooperation, standardize the research and development process, and enhance the awareness of intellectual property management. The examination department should pay attention to the guiding role of examination rules, improve the examination rules besides compositions, and improve the examination rules for experimental data of traditional Chinese medicine. The judicial department should promote the regular participation of technical investigators in patent authorization opposition cases of traditional Chinese medicine.

Claudin 18.2 (CLDN18.2) has emerged as a promising molecular target for anti-tumor therapy, drawing considerable attention in recent years. Although several CLDN18.2-targeted therapeutics have already entered clinical practice and shown good efficacy, their efficacy is closely correlated with the expression level of the target. Consequently, the development of molecular imaging probes with high sensitivity and specificity for CLDN18.2 is of great significance for the early detection of tumors, stratification of patients, and real-time monitoring of therapeutic response. This review systematically summarizes recent advances in the development of CLDN18.2-targeted molecular imaging probes, aiming to provide a theoretical framework to support the future design and clinical translation of diagnostic radiopharmaceuticals.

Active parts and active ingredients are the material basis for therapeutic effects of traditional Chinese medicines (TCMs), and the combinations formed by rational compatibility can achieve the purposes of enhancing efficacy, reducing toxicity, reducing dosage, expanding new therapeutic effects, and developing drug formulations, etc. The relevant research has become an important means for compatibility research, explanation of material basis of pharmacodynamics, screening of precise active ingredients, and research and development of new drugs of TCMs. The compatibility forms include combination of active parts, combination of active ingredients, and combination of active parts and active ingredients. The screening methods for the best combination mainly adopt traditional experimental design methods such as orthogonal design, uniform design, and baseline geometric design, combined with animal models, cell models, and enzyme activity inhibition tests. The new drug formulations of TCMs developed based on combinations formed by active ingredients mainly include nano-particles, nano-micelles, co-amorphous systems, and co-crystals. However, there are still problems in current research, such as lack of innovation in the screening methods, insufficient explanation of synergistic mechanisms, and insufficient guidance for the development of new drug formulations and new drugs. In the future, artificial intelligence (AI) should be used to screen the composition ratio of TCMs, multi-omics technologies should be used to investigate the synergistic effects and molecular regulatory networks of composition formed by compatibility, and the research of new drug formulations based on composition formed by compatibility should be actively expanded, so as to provide references for efficient, in-depth, and comprehensive revelation of the material basis and the research and development of new drugs of TCMs.

To identify safer and more effective opioid analgesics, κ-opioid receptor (KOR) selective agonists have been widely investigated for their potent analgesic properties and lack of the abuse liability typically associated with μ-opioid receptor activation. The clinical approval of the peptide-based drug difelikefalin (Korsuva) established a pivotal milestone, creating new pathways for the therapeutic application of KOR-selective agonists. Subsequently, diverse compound scaffolds, including small molecules and peptides, along with innovative research approaches, have been systematically explored through experimental studies. This review examines the latest chemical advancements and challenges related to KOR agonists, as well as their clinical and potential applications.

Infectious diseases pose a serious threat to global public health, making the research, development, and quality control of anti-infective drugs of critical importance. In recent years, artificial intelligence (AI) and big data have accelerated the drug discovery process and improved drug quality control levels by leveraging their efficient data processing capabilities and pattern recognition abilities. This article reviews the application of AI and big data in anti-infective drug molecular screening, resistance mechanism research, and broad-spectrum candidate drug development, while also summarizing their key roles in pharmaceutical production and quality control processes. However, the application of AI and big data technology in the development and quality control of anti-infective drugs still has limitations. In the future, it is necessary to promote the integration of interdisciplinary approaches and improve algorithm accuracy to drive the development and quality control of anti-infective drugs toward a new phase of efficiency and precision.

Tarlatamab is an innovative targeted immunotherapy agent that simultaneously binds to delta-like ligand 3 (DLL3) expressed on tumor cell surfaces and CD3 expressed on cytotoxic T-cell surfaces. As a DLL3/CD3-targeted bispecific T-cell engager (BiTE), it activates T cells to release inflammatory cytokines, thereby killing tumor cells expressing DLL3 protein. In May 2024, tarlatamab received accelerated approval from the U.S. FDA for the treatment of adult patients with extensive-stage small cell lung cancer that has progressed during or after platinum-based chemotherapy. This article provides an overview of tarlatamab's basic information, mechanism of action, pharmacokinetics, clinical trials, and safety, to provide a reference for clinical application.