Design of multi-drug combinations for poly-pharmacological effects using composition-activity relationship modeling and multi-objective optimization approach: Application in traditional Chinese medicine

A precise efficacy determination strategy of traditional Chinese herbs based on Q-markers: Anticancer efficacy of Astragali radix as a case

BACKGROUND

As a "multi-components and multi-efficacy" complex system, traditional Chinese herbs are universally distributed and applied in treating clinical diseases. However, the efficacy deviation and ambiguous clinical location are affected by different effects and content of components caused by uncertain factors in the production process. It further restricts resource allocation and clinical medication and hinders modernization and globalization. In this study, a precise efficacy determination strategy was innovatively proposed, aiming to quantitatively predict the efficacy of herbs and obtain precise medicinal materials. Quality-markers (Q-markers) characterizing the efficacy are conducive to achieving precise efficacy determination.

PURPOSE

With the anticancer efficacy of Astragali radix (AR) as a case, the present study was designed to establish a methodology for precise efficacy determination based on Q-markers characterizing specific efficacy.

METHODS

Guided by the basic principles of Q-markers, the potential Q-markers characterizing the anticancer efficacy of AR were screened through molecular simulation and network pharmacology. The activity of Q-markers was evaluated on MDA-MB-231 cells, and the content of Q-markers was determined by HPLC. A quantitative efficacy prediction model of the relationship between the influencing factors and anticancer efficacy was further constructed through the effect-constituents index (ECI) and machine learning and verified by biotechnology, which can be directly applied to predict the efficacy in numerous samples.

RESULTS

Astragaloside I, astragaloside II, and astragaloside III inhibited the proliferation of MDA-MB-231 cells and were successfully quantified in AR samples, reflecting the effectiveness and measurability of Q-markers. Gradient Boost Regression showed the best performance in the quantitative efficacy prediction model with EV_test= 0.815, R²_test= 0.802. The results of precise efficacy determination indicated that 1-2-3 (Wuzhai, Shanxi, two years, C segment) sample performed best in 54 batches of AR samples with biased anticancer efficacy. Furthermore, AR samples with higher ECI had higher anticancer efficacy and vice versa.

CONCLUSION

The precise efficacy determination strategy established in the present study is reliable and proved in the AR case, which is expected to support resource allocation optimization, efficacy stability improvement, and precise clinical medication achievement.

Dose Optimization of Anxiolytic Compounds Group in Valeriana jatamansi Jones and Mechanism Exploration by Integrating Network Pharmacology and Metabolomics Analysis

Anxiety disorder impacts the quality of life of the patients. The 95% ethanol extract of rhizomes and roots of Valeriana jatamansi Jones (Zhi zhu xiang, ZZX) has previously been shown to be effective for the treatment of anxiety disorder. In this study, the dose ratio of each component of the anxiolytic compounds group (ACG) in a 95% ethanol extract of ZZX was optimized by a uniform design experiment and mathematical modeling. The anxiolytic effect of ACG was verified by behavioral experiments and biochemical index measurement. Network pharmacology was used to determine potential action targets, as well as predict biological processes and signaling pathways, which were then verified by molecular docking analysis. Metabolomics was then used to screen and analyze metabolites in the rat hippocampus before and after the administration of ZZX-ACG. Finally, the results of metabolomics and network pharmacology were integrated to clarify the anti-anxiety mechanism of the ACG. The optimal dose ratio of ACG in 95% ethanol extract of ZZX was obtained, and our results suggest that ACG may regulate ALB, AKT1, PTGS2, CYP3A4, ESR1, CASP3, CYP2B6, EGFR, SRC, MMP9, IGF1, and MAPK8, as well as the prolactin signaling pathway, estrogen signaling pathway, and arachidonic acid metabolism pathway, thus affecting the brain neurotransmitters and HPA axis hormone levels to play an anxiolytic role, directly or indirectly.

A Bioinspired Hemostatic Powder Derived from the Skin Secretion of Andrias davidianus for Rapid Hemostasis and Intraoral Wound Healing

High-performance hemostasis has become increasingly essential in treating various traumas. However, available topical hemostats still have various drawbacks and side-effects. Herein, hemostatic powders derived from the skin secretion of Andrias davidianus (SSAD) with controllable particle size are prepared using feasible frozen-ball milling following lyophilization for hemorrhage-control. Scanning electron microscopy, rheometry, and Brunauer-Emmett-Teller test are used to characterize the coagulation-promoting surface topography, rheological properties, and porous structure of the SSAD particles. The blood-coagulation assays showed that the SSAD powders can induce blood-absorption in a particle size-dependent manner. Particle sizes of the SSAD powders larger than 200 µm and smaller than 800 µm greatly affect the blood-clotting rate. Associated with the thromboelastography (TEG) and amino acid/protein composition analyses, the accessibility and diffusion of blood are mainly dependent on the wettability, adhesivity, and clotting factors of the SSAD particles. Rapid hemostasis in vivo further involves three hemorrhage models (liver, femoral artery, and tail) as well as an oral wound model, which suggest favorable hemostatic and simultaneous regenerative effects of the SSAD hemostatic powder. Considering its degradability and good biocompatibility, SSAD can be an optimal candidate for a new class of inexpensive, natural, and promising hemostatic and wound-dressing agent.

A strategy for component-based Chinese medicines design approach of Polygonum orientale L. against hypoxia/reoxygenation based on uniform design-stepwise regression-simulated annealing

Presently, optimal proportions and synergistic mechanisms of component-based Chinese medicines are critical for developing novel strategies to treat cardiovascular diseases (CVDs). A new multi-objective optimization algorithm based on uniform design (UD) and stepwise regression (SR) modeling is proposed to find the synergistic effect of orientin (Ori), quercitrin (Que) and vitexin (Vit), the three effective components from Polygonum orientale L., using the H9c2 cells injury induced by hypoxia/reoxygenation (H/R). The optimal proportion of these three components was calculated by simulated annealing (SA). In this research, the excellent combination named OQV-e (Ori: Que: Vit =12.55 μM: 39.99 μM: 19.99 μM) could exert significant cardioprotection against the H9c2 cells injury induced by H/R through increasing cell viability, decreasing leakage rate of lactate dehydrogenase (LDH) and the level of nitric oxide (NO). Moreover, western blot analysis revealed that OQV-e could activate autophagy by inhibiting the p-JNK/JNK signaling pathway, which showed that the method (UD-SR-SA) was a feasible strategy. Mathematical system modeling may be a considerable approach for the powerful mathematical analysis of the complex pharmacological effects of component-based Chinese medicines from herbal medicines, which might greatly enhance the efficiency to find new modern Chinese drugs for CVDs based on Chinese herbal medicine (CHM) with affirmative therapeutic effect.

The effects of aqueous extract of Maca on energy metabolism and immunoregulation

Background

In the present work, we investigated the effects of aqueous extract of Maca (AEM) on energy metabolism and immunoregulation in spleen-deficient mice.

Method

We established a cyclophosphamide-induced spleen-deficiency model with ginseng, a herb that strengthens splenic function, as a control. Sixty male Kunming mice were randomly divided among 5 groups: normal, model, ginseng control (1.5 g/kg), AEM high dose (1.5 g/kg), and AEM low dose (0.75 g/kg). All animals, except those in the normal group, were injected with cyclophosphamide to induce spleen deficiency. Furthermore, we investigated differences in the thermotropic behaviors of mice using the Animal Thermotropism Behavior Surveillance System to detect energy metabolism-related assays and immune regulation assays.

Results

Mice given AEM exhibited tropism in response to hot plate exposure. AEM inhibited loss of body weight and immune organ atrophy caused by cyclophosphamide, increased the cAMP/cGMP ratio in blood, and enhanced the activities of Na⁺–K⁺-ATPase, Ca²⁺–Mg²⁺-ATPase, lactate dehydrogenase, and hepatic glycogen. AEM significantly reversed declining white blood cells and platelet counts, and increased the hemoglobin content within peripheral blood cells. AEM improved the protein levels of IFN-γ, TNF-β, IL-2, and IL-4 in the spleen.

Conclusions

Maca possesses the Traditional Chinese Medicine (TCM) property of warm and appears to strengthen spleen function.

A preliminary strategy for screening the combination of active components based on Bu–Shen–Yi–Qi Decoction for bronchial asthma

Objective: The anti-asthma herbal medicine Bu–Shen–Yi–Qi Decoction (BSYQ Decoction), a combination of three traditional Chinese medicine herbs developed in our lab, has shown demonstrated efficacy in Ovalbumin (OVA)-induced acute bronchial asthma. However, the obscure substances, multiple potential targets and the unclear molecular mechanisms are obstacles to control drug quality, stability and standardization. Multi-component formulae with a clear pharmacodynamic material and specific mechanism are an innovation worth exploring. They may also partly reserve a potential synergistic and additive effect compared with single components extracted from traditional Chinese herbs. This study was designed to select three standard multi-component formulae of the combination of effective components preliminarily based on four effective components [total flavonoids of Herba Epimedii ([Formula: see text]; Astragalus polysaccharide ([Formula: see text]; Astragaloside ([Formula: see text]; and Catalpol ([Formula: see text]] in BSYQ Decoction.

Methods: OVA-induced asthmatic murine models were established. A uniform design was applied to select 10 proportions from four target components, and 3 formulae which showed best effect aimed at IFN-[Formula: see text], IL-4 and IgE in the serum respectively were screened based on stepwise regression analysis.

Results: According to the regression analysis, Formula I ([Formula: see text]:[Formula: see text]:[Formula: see text]:6:29), Formula II ([Formula: see text]:[Formula: see text]:[Formula: see text]:8:2) and Formula III ([Formula: see text]:[Formula: see text]:[Formula: see text]:[Formula: see text]:50:8:2) are the three potential proper ratios for the new multi-component formula aimed at serum IFN-[Formula: see text], IL-4 and IgE in OVA-induced asthma mice, respectively.

Conclusion: Three multi-component formulae derived from BSYQ Decoction could exert anti-inflammatory effect against OVA-induced asthma, which might provide evidences and lay foundations for further study of standard modern Chinese drug for treating asthma based on BSYQ Decoction.

MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics

The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel software tool for addressing this task in the context of regression and classification modelling is presented. The methodology that implements the tool is organized into two phases. The first phase uses a multiobjective evolutionary technique to perform the selection of subsets of descriptors. The second phase performs an external validation of the chosen descriptors subsets in order to improve reliability. The tool functionalities have been illustrated through a case study for the estimation of the ready biodegradation property as an example of classification QSAR modelling. The results obtained show the usefulness and potential of this novel software tool that aims to reduce the time and costs of development in the drug discovery process.