TCM formula optimization for treating diabetic peripheral neuropathy: Network pharmacology, machine learning, and experimental verification
10.1097/st9.0000000000000108
- Author:
Yang DU
1
;
Juqin PENG
1
;
Fuzhi ZHANG
1
;
Qingyuan YU
2
;
Xuezhong ZHOU
3
;
Kuo YANG
3
;
Junguo REN
1
Author Information
1. Beijing Key Laboratory of Pharmacology of Traditional Chinese Medicine, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
2. Beijing Key Laboratory of Pharmacology of Traditional Chinese Medicine, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China; Xiyuan Clinical Medical College, Beijing University of Chinese Medicine, Beijing, China
3. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
- Publication Type:Journal Article
- Keywords:
Diabetic peripheral neuropathy (DPN);
Machine learning;
Network pharmacology;
TCM formula optimization;
Traditional Chinese medicine (TCM)
- From:
Science of Traditional Chinese Medicine
2026;4(2):152-162
- CountryChina
- Language:English
-
Abstract:
Background: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes mellitus that significantly impairs patients’ quality of life. Traditional Chinese medicine (TCM), as a major component of complementary and alternative medicine, has accumulated numerous effective formulas for the clinical management of DPN. However, systematic approaches for optimizing TCM formulas remain limited. Objective: To establish a pathway-oriented approach for TCM formula optimization and to evaluate the efficacy and mechanisms of the optimized formula in DPN. Methods: We developed 2 formula optimization algorithms that defined pathway-oriented herbal correlation (HC) and herbal contribution (HO) to screen and construct a new herbal formula, Qihongtongbi (QHTB), for treating Qi deficiency and blood stasis syndrome complicated with DPN. An animal experiment was subsequently conducted to evaluate the therapeutic effectiveness of QHTB. A total of 32 specific-pathogen-free male ob/ob mice (18–20 g) were randomly divided into 4 groups: model, Mudan Granules (MDG), QHTB low- and high-dose groups (n = 8). Ten male C57BL/6J mice (18–20 g) served as the control group. Metabolomics analysis was further employed to elucidate the biological mechanisms underlying the effects of QHTB. Results: Based on our previous study on TCM medication patterns for treating DPN, 22 candidate herbs were selected for formula optimization. The top 5 candidate herbs (total HC = 25.24 × 10
) exhibited HC values comparable to those of MDG (total HC = 26.1 × 10
). These herbs were Carthamus tinctorius L. (HC = 5.40 × 10
), Astragalus mongholicus Bunge (HC = 5.23 × 10
), Salvia miltiorrhiza Bunge (HC = 5.15 × 10
), Commiphora myrrha (T. Nees) Engl. (HC = 5.02 × 10
), and Glycyrrhiza glabra L. (HC = 4.44 × 10
). HO analysis showed that the cumulative HO of these 5 herbs exceeded 50%. In vivo experiments demonstrated that, compared with the model group, QHTB significantly lowered blood glucose (P < 0.05), enhanced nerve conduction velocity (P < 0.01), improved nociceptive hypersensitivity (P < 0.01), and ameliorated sciatic nerve morphology, with efficacy comparable to MDG. Serum and fecal metabolomics further revealed that QHTB exerted multipathway regulatory effects, among which nicotinate and nicotinamide metabolism represented a key mechanism. Conclusion: In summary, this study provides a methodological reference for the systematic optimization of TCM formulas.
