Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

A network pharmacology integrated pharmacokinetics strategy to investigate the pharmacological mechanism of absorbed components from crude and processed Zingiberis Rhizoma on deficiency-cold and hemorrhagic syndrome

ETHNOPHARMACOLOGICAL RELEVANCE

Zingiberis Rhizoma (ZR) and Zingiberis Rhizoma Carbonisata (ZRC), as two forms of ginger-based herbal drugs used in China for at least 2000 years, have been recorded in Chinese Pharmacopoeia and applied for specific indications in traditional Chinese medicine (TCM).

AIM OF THE STUDY

The present study aimed to explore the underlying therapeutic and processing mechanism of the absorbed components of ZR and ZRC on deficiency-cold and hemorrhagic syndrome (DCHS) using network pharmacological technique combined with pharmacokinetics strategy.

MATERIALS AND METHODS

In this study, a rapid and sensitive approach was conceived to simultaneously determine the seven components (zingiberone, 6-gingerol, 8-gingerol, 6-shogaol, 6-paradol, diacetyl-6-gingerol and 10-gingerol) in rat serum by HPLC-DAD-MS. The network pharmacological technique was employed to evaluate the effect of the absorbed components of ZR and ZRC on DCHS. Also, the vitro experiments were carried out to validate the functions of the seven compounds on coagulation and other major haematological effects.

RESULTS

The values of intra-assay and inter-assay precision were determined to be less than 7.44%, with an accuracy value ranging from 83.64% to 107.99%. Analysis of rat plasma revealed that the extraction recoveries and matrix effects of the seven analytes were >85.76%. The method for validation following oral administration of ZR and ZRC to rats was proved to be a success in the pharmacokinetic study of the seven ingredients. Pharmacokinetics showed that ZR processing could enhance the absorption and utilization of 6-shogaol, 6-paradol and diacetyl-6-gingerol, meanwhile reduce the absorption of 6-gingerol, 8-gingerol, and 10-gingerol. Through the pathway enrichment analysis, it was found that the significant biological process of ZR and ZRC on DCHS was primarily associated with complement, coagulation cascades and platelet activation pathways. The vitro experiments indicated that zingiberone, 6-paradol and diacetyl-6-gingerol had a hemostatic effect by upregulating the expression of one or more targets such as TNF-α, FⅩa, FⅫ, FⅧ, ICAM-1, vWF and ITGB3. While 6-gingerol, 6-shogaol, 8-gingerol and 10-gingerol played a critical role in promoting blood circulation by increasing the expression of TM and/or PORC, and/or reducing the expression of ITGB3.

CONCLUSION

In brief, network pharmacological technique in combination with pharmacokinetics strategy provided an applicable method for pharmacological mechanism study of ZR and ZRC, which, also, could be used as reference for quality control of the two drugs. In a broader sense, this combined strategy might even be valuable in uncovering the therapeutic and processing mechanism of Chinese herbs on a systematic level.

UPLC-Q-Exactive/MS-Based Metabonomics revealed protective effect of Zingiberis Rhizome and Its Processed Product on Deficiency-cold and Hemorrhagic Syndrome rats

Zingiberis Rhizome carbonisata (ZRC) is the processed product of Zingiberis Rhizome (ZR). ZR is mainly used for warming spleen and stomach for dispelling cold, while ZRC is commonly applied as a treatment for Deficiency-cold and Hemorrhagic Syndrome (DCHS). Although they have long been used to serve different clinical purposes, the specific action mechanism of the drugs and molecular changes underlying ZR processing are not totally clear. In this study, metabolomics study was carried out to analyze the alterations of endogenous metabolites in serum and urine samples of DCHS rat models using ultra-high-performance liquid chromatography coupled with quadrupole-Exactive mass spectrometry (UPLC Q-Exactive MS) technique, followed by the construction of PCA score plots which showed that the ZRC group was completely separated from the DCHS and ZR groups but demonstrated a highly close plotting to the NC group. The results revealed that ZR and ZRC both intervened in the metabolic pathways of DCHS models, but in a varying degree and with different influencing factors. In addition, ZRC was found to function as a treatment for the metabolic disorders of DCHS through 15 pharmacodynamic biomarkers involving a series of pathways, such as glycine, serine and threonine metabolic pathway, as well as arachidonic acid metabolic pathway. This study showed that metabolomics method based on UPLC Q-Exactive MS could preliminarily illuminate the therapeutic mechanism of ZR and ZRC on DCHS, and the changes in ZR processing from the perspective of molecular level. The results also provided a new insight for further research on DCHS treatment.

Screening and identification of Euphorbiae pekinensis Rupr. anti-angiogenic multi-components with UPLC-QTOF-MS in zebrafish

Euphorbia pekinensis Rupr. (EP) (Euphorbiaceae), as Traditional Chinese Medicine (TCM), exhibits therapeutic effects on tumors in clinical practice. Anti-angiogenesis may be an underlying molecular mechanism of EP's actions. However, the anti-angiogenic active ingredients of EP remain unclear. The screening and analysis of anti-angiogenic agents were essential for the sufficient utilization and development of EP. Thus, we established a UPLC-QTOF-MS method based on a transgenic zebrafish model to screen anti-angiogenesis activity components in EP. UPLC-QTOF-MS was used to characterize compounds from EP and in vivo compounds in Tg (flk1: mCherry) zebrafish larvae treated with EP. Based on the identification results, five components were selected, and their anti-angiogenesis activity were investigated via assessment of intersegmental blood vessels during the development of the transgenic zebrafish. Three of these components (3,3'-O-dimethoxy ellagic acid, quercetin, and ingenol) are active components of EP with anti-angiogenic effects. Among them, 3, 3'-O-dimethoxy ellagic acid and ingenol were first demonstrated with anti-angiogenesis effects. UPLC-PDA analysis was performed on EP water extracts to determine anti-angiogenesis active ingredients quantitatively. In the concentration range of 100-200 μg/mL, EP and the active ingredient compositions, mixed according to the content of EP, had equivalent anti-angiogenesis activities. These experimental results indicate that the UPLC-QTOF-MS method, combined with a transgenic zebrafish model, is rapid, sensitive and reliable. The combination in TCM offers the potential to achieve certain effect levels with lower concentrations of the individual compound.

iTRAQ-based quantitative proteomics and network pharmacology revealing hemostatic mechanism mediated by Zingiberis Rhizome Carbonisata in deficiency-cold and Hemorrhagic Syndrome rat models

Zingiberis Rhizome Carbonisata (ZRC) has been used as a hemostatic agent in traditional Chinese medicine (TCM). However, the underlying molecular mechanism remains unclear. In this study, network pharmacology method was used to predict the potential mechanism of ZRC on hemostasis, based on the structures of the main compounds. Then, iTRAQ-based quantitative proteomics analysis was used for verification of the candidate target proteins and pathways to illustrate the underlying mechanisms. Furthermore, the differentially expressed proteins (DEPs) in the enriched pathways were validated by Enzyme-linked immunosorbent assay. The results showed that the hemostasis mechanism of ZRC may be related to Platelet activation, Rap1 signaling pathway and Complement and coagulation cascades. And 10 proteins (Fermt3, ACTB, Talin, αIIbβ3, Fga, Fgb, Fgg, FXIIIb, Kng and PLC-β were identified as the target DEPs) are considered as the key factors related to hemostatic efficacy of ZRC. Thus, integrated network pharmacology and quantitative proteomics technology were applied for the effective illuminating the molecular mechanisms of Chinese material medica.

A Song of Ice and Fire: Cold and Hot Properties of Traditional Chinese Medicines

The theory of cold and hot properties is the basic theory of traditional Chinese medicines (TCMs) and has been successfully applied to combat human diseases for thousands of years. Although the theory of cold and hot is very important to guide the clinical application of TCMs, this ancient theory remains an enigma for a long time. In recent years, more and more researchers have tried to uncover this ancient theory with the help of modern techniques, and the cold and hot properties of a myriad of TCMs have been studied. However, there is no review of cold and hot properties. In this review, we first briefly introduced the basic theories about cold and hot properties, including how to distinguish between the cold and hot properties of TCMs and the classification and treatment of cold and hot syndromes. Then, focusing on the application of cold and hot properties, we take several important TCMs with cold or hot property as examples to summarize their traditional usage, phytochemistry, and pharmacology. In addition, the mechanisms of thermogenesis and antipyretic effect of these important TCMs, which are related to the cold and hot properties, were summarized. At the end of this review, the perspectives on research strategies and research directions of hot and cold properties were also offered.

Pharmacokinetic-pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Moutan Cortex (MC) and Moutan Cortex charcoal (MCC) are two kinds of Chinese medicinal materials widely used in traditional Chinese medicine (TCM) with opposite drug efficacy. And the contributions of the chemical component to the drug efficacy are still not clear. In our study, a support vector regression (SVR) model with particle swarm optimization (PSO) has been developed for simultaneously characterizing the pharmacokinetics (PK) and pharmacodynamics (PD) of MC/MCC. Then the contributions of the chemical component to the drug efficacy of MC/MCC are calculated by the weight analysis of SVR. The experimental results show that the effective substances found by the PSO-SVR model in MC and MCC are consistent with TCM theory. And the PSO-SVR model is a better model for PK-PD compared with the back-propagation neural network (BPNN). In conclusion, the PSO-SVR is a valuable tool that linked PK and PD profiles of MC/MCC with multiple components and identified the contributions of multiple therapeutic materials to the drug efficacy.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.