Machine Learning-Assisted Screening of Herbal Medicine Extracts as Vaccine Adjuvants

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Single-walled carbon nanotube-protein complex: A strategy to improve the immune response to protein in mice

Vaccines represent an effective tool for controlling disease infection. As a key component of vaccines, many types of adjuvants have been developed and used today. This study is designed to investigate the efficacy of single-walled carbon nanotubes (SWCNTs) as a new adjuvant. The results showed that SWCNT could adsorb the antigen by intermolecular action, and the adsorption rate was significantly higher after dispersion of the SWCNTs in a sonic bath. The titer of specific antibody of mice in the SWCNTs group was higher than that of the mice in the antigen control group, confirming the adjuvant efficacy of SWCNTs. During immunisation, the specific antibody was detected earlier in the mice of the SWCNTs group, especially when the amount of antigen was reduced. And it was proved that the titer of antibodies was higher after subcutaneous and intraperitoneal injection compared to intramuscular injection. Most importantly, the mice immunised with SWCNTs showed almost the same level of immunity as the mice in the FCA (Freund's complete adjuvant) group, indicating that the SWCNTs were an effective adjuvant. In addition, the mice in the SWCNT group maintained antibody levels for 90 days after the last booster vaccination and showed a good state of health during the observed period. We also found that the SWCNTs were able to induce macrophages activation and enhance antigen uptake by mouse peritoneal macrophages.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Deciphering anti-infectious compounds from Peruvian medicinal Cordoncillos extract library through multiplexed assays and chemical profiling

High prevalence of parasitic or bacterial infectious diseases in some world areas is due to multiple reasons, including a lack of an appropriate health policy, challenging logistics and poverty. The support to research and development of new medicines to fight infectious diseases is one of the sustainable development goals promoted by World Health Organization (WHO). In this sense, the traditional medicinal knowledge substantiated by ethnopharmacology is a valuable starting point for drug discovery. This work aims at the scientific validation of the traditional use of Piper species ("Cordoncillos") as firsthand anti-infectious medicines. For this purpose, we adapted a computational statistical model to correlate the LCMS chemical profiles of 54 extracts from 19 Piper species to their corresponding anti-infectious assay results based on 37 microbial or parasites strains. We mainly identified two groups of bioactive compounds (called features as they are considered at the analytical level and are not formally isolated). Group 1 is composed of 11 features being highly correlated to an inhibiting activity on 21 bacteria (principally Gram-positive strains), one fungus (C. albicans), and one parasite (Trypanosoma brucei gambiense). The group 2 is composed of 9 features having a clear selectivity on Leishmania (all strains, both axenic and intramacrophagic). Bioactive features in group 1 were identified principally in the extracts of Piper strigosum and P. xanthostachyum. In group 2, bioactive features were distributed in the extracts of 14 Piper species. This multiplexed approach provided a broad picture of the metabolome as well as a map of compounds putatively associated to bioactivity. To our knowledge, the implementation of this type of metabolomics tools aimed at identifying bioactive compounds has not been used so far.

Potentials of saponins-based adjuvants for nasal vaccines

Respiratory infections are a major public health concern caused by pathogens that colonize and invade the respiratory mucosal surface. Nasal vaccines have the advantage of providing protection at the primary site of pathogen infection, as they induce higher levels of mucosal secretory IgA antibodies and antigen-specific T and B cell responses. Adjuvants are crucial components of vaccine formulation that enhance the immunogenicity of the antigen to confer long-term and effective protection. Saponins, natural glycosides derived from plants, shown potential as vaccine adjuvants, as they can activate the mammalian immune system. Several licensed human vaccines containing saponins-based adjuvants administrated through intramuscular injection have demonstrated good efficacy and safety. Increasing evidence suggests that saponins can also be used as adjuvants for nasal vaccines, owing to their safety profile and potential to augment immune response. In this review, we will discuss the structure-activity-relationship of saponins, their important role in nasal vaccines, and future prospects for improving their efficacy and application in nasal vaccine for respiratory infection.