Network Pharmacology-Based Exploration of Complementary Molecular Mechanisms of Heat-Clearing (Scutellariae Radix, Coptidis Rhizoma) and Blood-Tonifying Herbs (Angelicae Sinensis Radix, Paeoniae Radix Alba) in Cold Hypersensitivity in Hands and Feet

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript explores the molecular mechanisms of two heat-clearing herbs and two blood-tonifying herbs in cold hypersensitivity using network pharmacology. The topic is potentially interesting, but several methodological and interpretative issues require substantial revision.

Major Comments

• Angelicae Gigantis Radix was selected from Korean clinical prescriptions but replaced with Angelicae Sinensis Radix because of TCMSP availability. These are botanically and chemically distinct herbs, and the substitution substantially limits the clinical relevance of the comparison.
• The CHHF target set was generated from GeneCards using a single search term and included 2,588 proteins. The authors should report relevance-score thresholds and validate the disease targets using additional databases or clinically related terms.
• The use of OB ≥ 30% and DL ≥ 0.18 requires stronger justification. Sensitivity analyses should be performed because these thresholds may exclude pharmacologically relevant compounds.
• Defining core genes as nodes exceeding the median for four centrality indices is arbitrary. The authors should justify this criterion and test whether the results remain stable under alternative thresholds.
• The abstract mentions both Gene Ontology and Reactome analyses, whereas the Methods and Results primarily describe Reactome. This inconsistency should be corrected.
• Pathway enrichment does not establish whether a herb activates or inhibits a pathway. Directional and causal statements regarding MAPK, CXCL11, adrenergic receptors, inflammation, and vascular function should therefore be moderated.
• The proposed “synergistic” or “complementary” effects are not directly evaluated. Overlapping targets or pathways alone cannot demonstrate pharmacological synergy.
• The conclusion that these herbs may serve as alternative therapeutic strategies is premature without experimental or clinical validation. The conclusions should be reframed as hypothesis-generating.

Minor Comments

- Figure 6 is difficult to read because of its small labels and excessive pathway density. It should be redesigned or divided into clearer panels.

- The text refers to “Figure 5(A) and (B),” although the pathway heatmaps appear as Figure 6. Figure numbering should be corrected.

- Botanical names should be standardized throughout, particularly Angelicae Gigantis Radix versus Angelicae Sinensis Radix.

- The manuscript requires careful English editing to correct typographical and terminology errors, including “Raynard’s,” “commone,” “PII,” and inconsistent capitalization.

- The Discussion is repetitive and should distinguish more clearly between database-derived findings, biological hypotheses, and established evidence.

- The article type should be clarified, as the manuscript is labeled as a “Review” despite presenting an original network pharmacology analysis.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. We have thoroughly considered each comment and revised the manuscript accordingly. Our detailed point-by-point responses are provided below, and the corresponding changes are highlighted (red) and shown in the revised manuscript.

 

2. 2. Point-by-point response to Comments and Suggestions for Authors

Major Comments 1: Angelicae Gigantis Radix was selected from Korean clinical prescriptions but replaced with Angelicae Sinensis Radix because of TCMSP availability. These are botanically and chemically distinct herbs, and the substitution substantially limits the clinical relevance of the comparison.

Response 1: Thank you for your kind meaningful and helpful comment. As the reviewer correctly noted, the two medicinal herbs are botanically and chemically distinct. The lack of standardization in the Angelica species used across countries presents a challenge in this field of research. We agree that these two herbs cannot be regarded as directly equivalent.

However, the purpose of this study was not to characterize the pharmacological properties of a specific Angelica species, but rather to compare the potential molecular targets and pathways associated with two groups of medicinal herbs: heat-clearing herbs and blood-tonifying herbs.

Similar to Angelicae Gigantis Radix, which is used in Korea, Angelicae Gigantis Radix has traditionally been used in Chinese medicine for tonifying and activating the blood (Wei WL, Zeng R, Gu CM, Qu Y, Huang LF. Angelica sinensis in China—A review of botanical profile, ethnopharmacology, phytochemistry and chemical analysis. J Ethnopharmacol. 2016;190:116–141. doi:10.1016/j.jep.2016.05.023). Therefore, data on Angelicae Gigantis Radix available in the TCMSP database were included in our analysis.

Nevertheless, because of this substitution, the findings cannot be directly generalized to Angelicae Gigantis Radix used in Korean clinical practice. We have explicitly acknowledged this issue in the limitations section of the Discussion. We sincerely appreciate the reviewer’s valuable comment.

 

Major Comments 2: The CHHF target set was generated from GeneCards using a single search term and included 2,588 proteins. The authors should report relevance-score thresholds and validate the disease targets using additional databases or clinically related terms.

Response 2: Thank you for this important comment. We have clarified the disease-target retrieval strategy in the revised manuscript.

In the present study, no additional GeneCards relevance-score cutoff was applied. Given the exploratory nature of the present study, we adopted a broad target-retrieval strategy to avoid prematurely excluding potentially relevant disease-associated genes. Importantly, the application of a GeneCards relevance-score threshold is not uniform across network pharmacology studies. Several previous studies have retrieved disease-associated targets solely from GeneCards without reporting an additional relevance-score cutoff. For example, Xiang et al.(Xiang, B., Geng, R., Zhang, Z., Ji, X., Zou, J., Chen, L., & Liu, J. (2022). Identification of the effect and mechanism of Yiyi Fuzi Baijiang powder against colorectal cancer using network pharmacology and experimental validation. Frontiers in pharmacology, 13, 929836. https://doi.org/10.3389/fphar.2022.929836 ) retrieved 1,111 colorectal cancer-associated targets from GeneCards and directly used them for intersection analysis with drug-related targets. Similarly, Song et al.(Song, N., Tu, H., Li, Y., Xiong, W., Zhang, L., Liu, H., Ding, W., Long, M., Ren, D., & Zhong, J. (2022). Inhibitory Potential of Shen-Shuai-Ling Formulation on Renal Interstitial Fibrosis via Upregulation of PLZF. Evidence-based complementary and alternative medicine : eCAM, 2022, 5967804. https://doi.org/10.1155/2022/5967804) and Ai et al.(Ai, Z., Zhou, S., Li, W., Wang, M., Wang, L., Hu, G., Tao, R., Wang, X., Shen, Y., Xie, L., Ba, Y., Wu, H., & Yang, Y. (2020). "Fei Yan No. 1" as a Combined Treatment for COVID-19: An Efficacy and Potential Mechanistic Study. Frontiers in pharmacology, 11, 581277. https://doi.org/10.3389/fphar.2020.581277) retrieved disease-associated genes solely from GeneCards without reporting an additional relevance-score threshold.

Based on these precedents, we considered the use of the complete GeneCards-derived target set appropriate for the exploratory purpose of the present study, while recognizing that relevance-score filtering represents an alternative strategy for increasing target specificity.

At the same time, we agree that this approach may increase the inclusion of weakly associated or potentially false-positive disease–gene relationships. To address this concern, we have explicitly acknowledged this limitation in the revised Discussion and have revised the interpretation of the network results accordingly, emphasizing that the identified targets and pathways should be regarded as hypothesis-generating rather than as definitive disease mechanisms.

 

Major Comments 3: The use of OB ≥ 30% and DL ≥ 0.18 requires stronger justification. Sensitivity analyses should be performed because these thresholds may exclude pharmacologically relevant compounds.

Response 3: We sincerely thank the reviewer for the important comments regarding the OB and DL parameters in TCMSP. As the reviewer noted, the compounds included in a network pharmacology analysis may vary depending on the selected screening thresholds; therefore, the choice of cutoff values is an important methodological consideration. This comment prompted us to carefully re-examine the rationale for the criteria used in our study.

Accordingly, we reviewed the parameter guidance provided by TCMSP as well as previous network pharmacology studies. Although the TCMSP website currently suggests OB ≥ 20% and DL ≥ 0.1 as general screening criteria, our review of the literature confirmed that OB ≥ 30% and DL ≥ 0.18 have also been widely applied in numerous network pharmacology studies, including recent publications, as shown in references (A)–(F). Therefore, we applied OB ≥ 30% and DL ≥ 0.18 to select compounds with relatively high oral bioavailability and drug-likeness more stringently and to maintain methodological consistency with established network pharmacology studies.

Nevertheless, we fully agree with the reviewer that prespecified OB and DL thresholds may exclude pharmacologically relevant compounds and thereby influence the results of downstream analyses. Although we retained the criteria of OB ≥ 30% and DL ≥ 0.18, which have been widely and consistently used in previous network pharmacology studies, we have added threshold-dependent compound selection as a methodological limitation in the revised manuscript. We have also clearly stated that future studies should evaluate the robustness of the findings by applying alternative screening criteria or conducting sensitivity analyses. We once again thank the reviewer for providing this valuable perspective, which allowed us to re-evaluate and more clearly articulate the methodological rationale of our study.

• Tong J, Ding Y, Zhang D. Mechanisms of Actinidia chinensis Planch roots in the treatment of breast cancer based on network pharmacology and molecular docking. Medicine (Baltimore). 2025;104(31):e43560. doi:10.1097/MD.0000000000043560 
• Mohammad S, Karim MR, Iqbal S, et al. Network pharmacology and molecular docking approach to predict the oral and topical therapeutic mechanisms of Panax ginseng's active compounds on atopic dermatitis. J Biomol Struct Dyn. 2025;43(18):11146-11161. doi:10.1080/07391102.2025.2497467 
• Ye XW, Wang HL, Cheng SQ, Xia LJ, Xu XF, Li XR. Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease. Front Aging Neurosci. 2022;14:890046. Published 2022 Jun 21. doi:10.3389/fnagi.2022.890046 
• Liu H, Cao M, Jin Y, et al. Network pharmacology and experimental validation to elucidate the pharmacological mechanisms of Bushen Huashi decoction against kidney stones. Front Endocrinol (Lausanne). 2023;14:1031895. Published 2023 Feb 14. doi:10.3389/fendo.2023.1031895 
• Sheng P, Xie J, Wu Y, Xia X, Li B, Wu M. A Network Pharmacology Approach for Uncovering the Mechanism of 'Kouchuangling' in Radiation-induced Oral Mucositis Treatment. Comb Chem High Throughput Screen. 2023;26(5):1042-1057. doi:10.2174/1386207325666220617151600 
• Hu X, Yu Y, Wei C, Sun J, Lin X, Chen R. Multi-component synergy of safflower (Carthamus tinctorius L.) against hypertension-dyslipidemia: Network pharmacology and molecular docking study. Comput Biol Chem. 2026;121:108831. doi:10.1016/j.compbiolchem.2025.108831 

 

Major Comments 4: Defining core genes as nodes exceeding the median for four centrality indices is arbitrary. The authors should justify this criterion and test whether the results remain stable under alternative thresholds.

Response 4: We sincerely thank the reviewer for the important comment regarding the criteria used to define core genes. In this study, to reduce the potential topological bias associated with reliance on a single metric and to identify nodes that were consistently important from different perspectives, we defined core genes as the intersection of nodes with values above the median for all four centrality measures. This approach represents a relatively stringent, conservative multi-criteria filtering strategy.

Our review of references (A)–(E) confirmed that using the median values of four centrality measures to identify core genes is not a universally established or absolute criterion, but rather an empirical approach commonly adopted in previous network pharmacology studies. Accordingly, we have expanded the revised manuscript to provide a clearer rationale for selecting these criteria and to further address the robustness of the results.

• Zhang, N.; Zhang, D.; Zhang, Q.; Zhang, R.; Wang, Y. Mechanism of Danggui Sini underlying the treatment of peripheral nerve injury based on network pharmacology and molecular docking: A review. Medicine 2023, 102, e33528, doi:10.1097/MD.0000000000033528.
• Zhao, J.; Mo, C.; Shi, W.; Meng, L.; Ai, J. Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy. Evid Based Complement Alternat Med 2021, 2021, 9980981, doi:10.1155/2021/9980981.
• Zeng, T.; Ling, C.; Liang, Y. Exploring active ingredients and mechanisms of Coptidis Rhizoma-ginger against colon cancer using network pharmacology and molecular docking. Technol Health Care 2024, 32, S523–S542, doi:10.3233/THC-248046.]
• Zhou, L.; Zhang, L.; Tao, D. Investigation on the Mechanism of Qubi Formula in Treating Psoriasis Based on Network Pharmacology. Evid Based Complement Alternat Med 2020, 2020, 4683254, doi:10.1155/2020/4683254.
• Wu, Y.; Fang, Y.; Li, Y.; Au, R.; Cheng, C.; Li, W.; Xu, F.; Cui, Y.; Zhu, L.; Shen, H. A network pharmacology approach and experimental validation to investigate the anticancer mechanism of Qi-Qin-Hu-Chang formula against colitis-associated colorectal cancer through induction of apoptosis via JNK/p38 MAPK signaling pathway. J Ethnopharmacol 2024, 319, 117323, doi:10.1016/j.jep.2023.117323.

 

Major Comments 5: The abstract mentions both Gene Ontology and Reactome analyses, whereas the Methods and Results primarily describe Reactome. This inconsistency should be corrected.

Response 5: We sincerely thank the reviewer for carefully identifying the inconsistency between the analytical methods described in the abstract and those presented in the main text. We fully agree with the reviewer and have removed the corresponding statement from the abstract. We once again thank the reviewer for pointing out this important error, which has helped improve the accuracy and consistency of the manuscript.

 

Major Comments 6: Pathway enrichment does not establish whether a herb activates or inhibits a pathway. Directional and causal statements regarding MAPK, CXCL11, adrenergic receptors, inflammation, and vascular function should therefore be moderated.

Response 6: We sincerely thank the reviewer for this important comment. Although the conclusions of this study are based on network pharmacology analyses—that is, statistical associations involving predicted targets and pathways—we agree that some statements in the Discussion were phrased as though the medicinal herbs directly regulated specific biological processes.

These statements could therefore be interpreted as making claims stronger than warranted by the currently available evidence. Accordingly, we thoroughly revised the Discussion by replacing expressions implying causality or direct biological effects, such as “modulate,” “directly regulate,” “provides mechanistic evidence,” and “effects,” with terms indicating prediction or association, including “is enriched in,” “is associated with,” “is predicted to be associated with,” and “suggests a potential association.” For example, the original statement, “Coptidis Rhizoma may directly modulate α2C-adrenergic receptor-mediated cold-induced vasoconstrictive mechanisms,” was revised to “ADRA2C may be a candidate target through which Coptidis Rhizoma is predicted to be associated with α2C-adrenergic receptor-mediated cold-induced vasoconstrictive mechanisms.” We consistently applied the same principle to statements concerning MAPK, CXCL11, and immune- and inflammation-related processes throughout the Discussion.

These revisions clearly distinguish predicted molecular associations from experimentally demonstrated biological effects throughout the manuscript. We once again thank the reviewer for identifying this important issue.

 

Major Comments 7: The proposed “synergistic” or “complementary” effects are not directly evaluated. Overlapping targets or pathways alone cannot demonstrate pharmacological synergy.

Response 7: We sincerely thank the reviewer for the important comment regarding our interpretation of synergistic and complementary effects in this study. As the reviewer correctly noted, we fully agree that the overlap of molecular targets or pathways identified through network pharmacology alone is insufficient to demonstrate synergistic or complementary effects between the two groups of medicinal herbs.

In response to this valuable comment, we carefully re-examined the manuscript and revised the following two statements that could have been interpreted as implying synergistic or complementary effects:

1. The original statement:

“because it emphasizes the synergistic relationships among multiple components, pathways, and targets [19,20].”

was revised to:

“because it enables the systematic analysis of interactions among multiple components, pathways, and targets [19,20].”

2. The original statement:

“These results suggest that while HCHs (Scutellariae Radix and Coptidis Rhizoma) primarily act through anti-inflammatory, cytokine-suppressive, and hemostatic mechanisms, BTHs (Paeoniae Radix Alba and Angelicae Sinensis Radix) contribute through metabolic regulation, neurovascular modulation, and partial inhibition of TNF-mediated signaling. Therefore, the combination of heat-clearing and BTHs may act synergistically through complementary mechanisms to restore microcirculatory balance in disorders such as CHHF.”

was revised to:

“These results suggest that HCHs and BTHs exhibit distinct but partially overlapping molecular profiles associated with CHHF. HCHs showed greater representation of pathways related to inflammation, cytokine signaling, and hemostasis, whereas BTHs showed associations with pathways related to metabolic regulation, neurovascular processes, and TNF signaling.”

Through these revisions, we removed interpretations implying synergistic or complementary effects and limited our conclusions to the molecular associations supported by the network pharmacology analysis. We once again thank the reviewer for this valuable comment, which helped us present our findings more accurately and cautiously.

 

Major Comments 8: The conclusion that these herbs may serve as alternative therapeutic strategies is premature without experimental or clinical validation. The conclusions should be reframed as hypothesis-generating.

Response 8: Thank you for this important comment. We agree that, in the absence of experimental or clinical validation, it would be premature to suggest that HCHs may currently serve as complementary or alternative therapeutic strategies for CHHF.

Accordingly, we have revised the conclusion to avoid overinterpretation of the network pharmacology findings and to explicitly frame the results as hypothesis-generating rather than confirmatory. We now state that the identified molecular pathways may suggest potential mechanistic differences and complementarities between HCHs and BTHs, while emphasizing that further experimental and clinical studies are required before any therapeutic implications can be established. =

Revised text: “These network-pharmacology-based findings suggest that HCHs such as Scutellariae Radix and Coptidis Rhizoma may be associated with molecular pathways distinct from, and potentially complementary to, those of BTHs in CHHF. These results should be regarded as hypothesis-generating rather than confirmatory, and further experimental and clinical studies are warranted before HCHs can be considered as complementary or alternative therapeutic strategies to conventional BTH therapies.”

Minor Comments 1: Figure 6 is difficult to read because of its small labels and excessive pathway density. It should be redesigned or divided into clearer panels.

Response 1: Thank you for this helpful comment. In response to the reviewer’s suggestion, we redesigned Figure 6 to improve its readability and visual clarity. Specifically, the original two-panel figure was reorganized into three panels, allowing each panel to be displayed at a larger scale with improved label visibility. We also revised the layout to make the grouping of related pathways more visually distinct and readily identifiable. These changes reduce visual density and facilitate a clearer interpretation of the pathway enrichment results. The revised Figure 6 has been incorporated into the manuscript.

 

Minor Comments 2: The text refers to “Figure 5(A) and (B),” although the pathway heatmaps appear as Figure 6. Figure numbering should be corrected.

Response 2: We thank the reviewer for pointing out the error in the figure numbering. We have corrected this error in the revised manuscript.

 

Minor Comments 3: Botanical names should be standardized throughout, particularly Angelicae Gigantis Radix versus Angelicae Sinensis Radix.

Response 3: We sincerely thank the reviewer for the careful comment regarding the need to use accurate and consistent botanical nomenclature for Angelica-derived medicinal herbs throughout the manuscript. As the reviewer noted, Angelicae Gigantis Radix and Angelicae Sinensis Radix are botanically distinct medicinal herbs. We fully agree that it is important to clearly distinguish between these two terms and avoid using them interchangeably.

In response to this comment, we re-examined the relevant terminology throughout the manuscript and standardized the nomenclature. Specifically, the Korean Pharmacopoeia designates the relevant medicinal herb as Angelicae Gigantis Radix, whereas the Chinese Pharmacopoeia designates it as Angelicae Sinensis Radix. Based on these definitions, we used the respective terms consistently throughout the manuscript. We also clearly stated that Angelicae Sinensis Radix was used in the present network pharmacology analysis because data for Angelicae Gigantis Radix were unavailable in the TCMSP database.

Furthermore, we revised the Discussion to avoid implying that these two medicinal herbs are identical. We explicitly stated that they are botanically and chemically distinct and acknowledged as a limitation that the findings derived from Angelicae Sinensis Radix cannot be directly generalized to Angelicae Gigantis Radix used in Korean clinical practice.

We once again thank the reviewer for carefully identifying this important issue, which has helped improve the botanical accuracy and clarity of the manuscript.

 

Minor Comments 4: The manuscript requires careful English editing to correct typographical and terminology errors, including “Raynard’s,” “commone,” “PII,” and inconsistent capitalization.

Response 4: We sincerely thank the reviewer for carefully identifying the typographical and terminology errors in the manuscript. In response to this comment, we thoroughly reviewed and edited the English throughout the manuscript. Specifically, we corrected “Raynard’s” to “Raynaud’s,” “commone” to “common,” and “PII” to “PPI,” and standardized inconsistent capitalization and terminology throughout the manuscript. We appreciate the reviewer’s valuable comment, which helped improve the accuracy, clarity, and consistency of the manuscript.

 

Minor Comments 5: The Discussion is repetitive and should distinguish more clearly between database-derived findings, biological hypotheses, and established evidence.

Response 5: Thank you for this important comment. We revised the Discussion to more clearly distinguish database-derived findings, established biological evidence, and hypothesis-generating interpretations.

Specifically, pathway- and target-level findings involving MAPK signaling, CXCL11-associated signaling, and α2-adrenergic receptors are now explicitly described as predicted associations or pathway-enrichment results rather than demonstrated biological effects. Relevant evidence from previous experimental and physiological studies is discussed separately, and any proposed links between these findings and CHHF- or RP-related mechanisms are clearly framed as biological hypotheses requiring experimental validation.

We also revised potentially overinterpretive statements using more cautious terms such as “candidate pathway associations,” “raises the hypothesis,” and “requires experimental validation,” while removing repetitive mechanistic explanations. These changes clarify the distinction between our database-derived results, established evidence, and hypothesis-generating interpretations.

 

 

Minor Comments 6: The article type should be clarified, as the manuscript is labeled as a “Review” despite presenting an original network pharmacology analysis.

Response 6: We sincerely thank the reviewer for pointing out the need to clarify the article type. We agree that, because this manuscript presents an original network pharmacology analysis, it should not be classified as a “Review.” Accordingly, we have changed the article type from “Review” to “Article.” We appreciate the reviewer’s valuable comment, which helped ensure the appropriate classification of the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

This study used network pharmacology to explore two heat-clearing (HCHs, Scutellariae Radix and Coptidis Rhizoma) and two blood-tonifying herbs (BTHs, Angelicae Sinensis Radix and Paeoniae Radix Alba) for the management of cold hypersensitivity in hands and feet (CHHF). The authors reported that HCHs and BTHs may act through distinct yet complementary mechanisms in CHHF.

The topic of this manuscript is good, however, substantial revision is needed.

1. The authors selected two HCHs and two BTHs as the research focus. As we know, herbal formulas are widely applied under the framework of complementary medicine theory, in contrast to arbitrarily selecting individual herbs for patients without theoretical support. Although the four herbs were chosen via text mining, the authors should provide convincing justifications for neglecting the therapeutic effects of herbal formulas.
2. The parameter provided by the TCMSP database has been changed a long time ago. The authors should use the updated standard for their research (OB: ≥20%; DL ≥1).
3. Considering the authors did not perform any lab experiment, molecular docking and molecular dynamics simulation should be conducted to receive a more accurate results.
4. Network pharmacology relies on predictions, which can sometimes be wrong. Acknowledge limitations, like false positives in protein interactions, and suggest future lab experiments to confirm the findings.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. We have thoroughly considered each comment and revised the manuscript accordingly. Our detailed point-by-point responses are provided below, and the corresponding changes are highlighted (red) and shown in the revised manuscript.

 

2. 2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: The authors selected two HCHs and two BTHs as the research focus. As we know, herbal formulas are widely applied under the framework of complementary medicine theory, in contrast to arbitrarily selecting individual herbs for patients without theoretical support. Although the four herbs were chosen via text mining, the authors should provide convincing justifications for neglecting the therapeutic effects of herbal formulas.

Response 1: We fully agree with the reviewer’s observation that, in clinical Korean medicine practice, herbal medicines are administered as multi-herb formulas based on theoretical and clinical rationale. We consider this a highly important perspective when interpreting the clinical implications of our study. However, the primary objective of this study was not to evaluate the therapeutic effects of a specific herbal formula, but to compare the potential molecular targets and pathways of medicinal herbs conventionally used to treat cold hypersensitivity in the hands and feet (CHHF) with those of heat-clearing herbs (HCHs), which have not traditionally been used primarily for CHHF.

As discussed in the manuscript, we focused on HCHs because of their potential relevance to the inflammatory mechanisms underlying CHHF and the associated circulatory and vascular dysfunction. Therefore, we sought to identify potential candidate agents for the treatment of CHHF. These findings may provide a hypothesis-generating basis for future investigations of HCHs in CHHF, including their potential incorporation into clinically relevant herbal formulas.

Accordingly, to directly compare the distinct characteristics and shared molecular features of the two groups, we selected individual medicinal herbs, rather than multi-herb formulas, as the units of analysis. At the same time, given that multi-herb formulas are used in actual clinical practice, we have added to the revised manuscript that this study design may limit the clinical applicability of our findings.

We sincerely thank the reviewer for providing this important perspective, which allowed us to clarify the purpose and scope of the study design and to more carefully consider the limitations regarding its clinical applicability.

 

Comments 2: The parameter provided by the TCMSP database has been changed a long time ago. The authors should use the updated standard for their research (OB: ≥20%; DL ≥1).

Response 2: We sincerely thank the reviewer for the important comment regarding the OB and DL screening thresholds used in the TCMSP database. As the reviewer noted, the number and composition of compounds included in subsequent network pharmacology analyses may vary depending on the screening criteria selected. We therefore fully agree that threshold selection is an important methodological consideration. This comment prompted us to carefully re-examine the current guidance provided by TCMSP and the screening criteria used in previous network pharmacology studies.

Our review confirmed that TCMSP-based network pharmacology studies applying the thresholds suggested by the reviewer—OB ≥ 20% and DL ≥ 0.1—have been published, as shown in references (A)–(C). At the same time, references (D)–(I), including recent studies, have continued to apply OB ≥ 30% and DL ≥ 0.18. These findings indicate that screening thresholds have not been uniformly standardized across network pharmacology studies and that both sets of criteria have been used depending on the study design and screening strategy.

Accordingly, in the present study, we retained the thresholds of OB ≥ 30% and DL ≥ 0.18. These relatively stringent criteria have been widely used in previous TCMSP-based network pharmacology studies and allow for the selection of compounds with relatively high oral bioavailability and drug-likeness.

Nevertheless, we fully acknowledge the reviewer’s concern that applying prespecified thresholds may exclude some pharmacologically relevant compounds and consequently influence the results of downstream analyses. Therefore, in the revised Discussion, we identified threshold-dependent compound selection as a methodological limitation of this study. We also added that future studies should assess the robustness of the findings by applying alternative screening thresholds or conducting sensitivity analyses.

We once again sincerely thank the reviewer for providing this important perspective, which prompted us to re-evaluate the methodological rationale for our screening strategy and to present both its basis and limitations more clearly.

• Geng, H., Xue, Y., Yan, B. et al. Network Pharmacology and Molecular Docking Study on the Mechanism of the Therapeutic Effect of Strychni Semen in NSCLC. Biol Proced Online 26, 33 (2024). https://doi.org/10.1186/s12575-024-00259-w 
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• Chen J-F, Wu S-W, Shi Z-M, Qu Y-J, Ding M-R and Hu B (2023) Exploring the components and mechanism of Solanum nigrum L. for colon cancer treatment based on network pharmacology and molecular docking. Front. Oncol. 13:1111799. doi: 10.3389/fonc.2023.1111799
• Tong J, Ding Y, Zhang D. Mechanisms of Actinidia chinensis Planch roots in the treatment of breast cancer based on network pharmacology and molecular docking. Medicine (Baltimore). 2025;104(31):e43560. doi:10.1097/MD.0000000000043560 
• Mohammad S, Karim MR, Iqbal S, et al. Network pharmacology and molecular docking approach to predict the oral and topical therapeutic mechanisms of Panax ginseng's active compounds on atopic dermatitis. J Biomol Struct Dyn. 2025;43(18):11146-11161. doi:10.1080/07391102.2025.2497467 
• Ye XW, Wang HL, Cheng SQ, Xia LJ, Xu XF, Li XR. Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease. Front Aging Neurosci. 2022;14:890046. Published 2022 Jun 21. doi:10.3389/fnagi.2022.890046 
• Liu H, Cao M, Jin Y, et al. Network pharmacology and experimental validation to elucidate the pharmacological mechanisms of Bushen Huashi decoction against kidney stones. Front Endocrinol (Lausanne). 2023;14:1031895. Published 2023 Feb 14. doi:10.3389/fendo.2023.1031895 
• Sheng P, Xie J, Wu Y, Xia X, Li B, Wu M. A Network Pharmacology Approach for Uncovering the Mechanism of 'Kouchuangling' in Radiation-induced Oral Mucositis Treatment. Comb Chem High Throughput Screen. 2023;26(5):1042-1057. doi:10.2174/1386207325666220617151600 
• Hu X, Yu Y, Wei C, Sun J, Lin X, Chen R. Multi-component synergy of safflower (Carthamus tinctorius L.) against hypertension-dyslipidemia: Network pharmacology and molecular docking study. Comput Biol Chem. 2026;121:108831. doi:10.1016/j.compbiolchem.2025.108831

 

Comments 3: Considering the authors did not perform any lab experiment, molecular docking and molecular dynamics simulation should be conducted to receive a more accurate results.

Response 3: We sincerely appreciate the Reviewer’s valuable suggestion to incorporate molecular docking and molecular dynamics (MD) simulations. We fully agree that these approaches could further strengthen the present study. As the Reviewer appropriately pointed out, molecular docking can be used as a computational tool to predict binding conformations and affinities between ligands and receptors, whereas MD simulations can provide a more detailed assessment of molecular interactions and the stability of ligand–target complexes at the atomic level. Accordingly, these approaches could provide valuable complementary evidence for evaluating the compound–target relationships predicted through network pharmacology.

However, the primary focus of the present study was not to validate individual ligand–target interactions, but rather to comparatively explore the potential molecular targets and pathway-level characteristics of heat-clearing herbs (HCHs) and blood-tonifying herbs (BTHs) in CHHF. In particular, we focused on Reactome-based pathway analysis to characterize and compare the molecular profiles of the two herbal categories. A relatively large number of target proteins and active compounds were identified in this results, and the first step to successfully conducting a molecular docking and molecular dynamics simulation is selecting the appropriate critical target protein. In this study, critical protein targets were not selected.

Thus, rather than validating the binding of individual compounds to specific targets, the present study aimed to identify broader molecular associations and pathway patterns and thereby generate hypotheses regarding the potential mechanisms of HCHs and BTHs in CHHF. Given this scope and analytical focus, molecular docking and MD simulations were not included in the present study.

Nevertheless, we agree with the Reviewer that incorporating molecular docking and MD simulations in future studies could provide more detailed in silico validation of the predicted compound–target interactions and further strengthen the mechanistic interpretation of our findings.

In addition, to provide indirect support for the key mechanisms identified for the four herbs in relation to CHHF, we conducted an additional review of relevant previous studies and summarized the available supporting evidence in the Appendix A5 (table). Although the available evidence remains limited and cannot substitute for molecular docking, MD simulations, or direct experimental validation, we believe that it provides additional context and supporting evidence for interpreting the molecular associations predicted in the present study.

We greatly appreciate the Reviewer’s constructive suggestion, which helped us more clearly define the analytical scope of the present study and identify an important direction for strengthening mechanistic validation in future research.

 

Comments 4: Network pharmacology relies on predictions, which can sometimes be wrong. Acknowledge limitations, like false positives in protein interactions, and suggest future lab experiments to confirm the findings.

Response 4: Thank you for this important comment. We agree that network pharmacology is inherently prediction-based and that database-derived protein interactions and target–pathway associations may include false-positive or biologically irrelevant relationships. Accordingly, we have revised the limitations section to clarify that the identified targets and pathways should be interpreted as predicted molecular associations rather than experimentally demonstrated mechanisms of action or therapeutic effects. We have also emphasized the need for future experimental validation to determine the direction, magnitude, and biological relevance of these associations, followed by clinical investigation where appropriate.

The revised text is as follows: “Finally, this study was based solely on database-derived network pharmacology analysis without experimental or clinical validation. Accordingly, the identified targets and pathways should be regarded as predicted molecular associations rather than demonstrated mechanisms of action or therapeutic effects. Experimental studies are required to determine the direction, magnitude, and biological relevance of these associations, followed by clinical investigation where appropriate.”

 

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

This manuscript presents a network pharmacology analysis comparing two heat-clearing herbs, Scutellariae Radix and Coptidis Rhizoma, with two blood-tonifying herbs, Angelicae Sinensis Radix and Paeoniae Radix Alba, to investigate their potential molecular mechanisms in cold hypersensitivity in hands and feet (CHHF). Rather than examining a traditional herbal formula, the study compares two pharmacological herb categories and attempts to interpret their complementary therapeutic roles through target overlap, protein-protein interaction networks, and pathway enrichment analysis. The manuscript also integrates information from multiple publicly available databases, including TCMSP, GeneCards, UniProt, STRING, Cytoscape, and Reactome, into a coherent analytical workflow. The topic is clinically relevant because CHHF lacks well-established molecular therapeutic strategies, and the study offers a modern systems biology perspective for understanding traditional Korean medicine. Overall, the manuscript is clearly organized, the workflow is logical, and the figures generally facilitate understanding of the analytical pipeline. With these merits in mind, I provide several suggestions below to further strengthen the manuscript.

Comments

1. Avoid overinterpreting computational predictions.

The conclusions are based entirely on network pharmacology analyses. Several statements in the Discussion describe the herbs as directly modulating biological processes, which is stronger than the evidence supports. Please revise the language to emphasize that these are predicted mechanisms rather than experimentally validated effects. (Discussion, particularly lines 468 to 551).

I recommend consistently distinguishing predicted molecular associations from experimentally demonstrated biological effects. Expressions such as "may regulate," "are predicted to influence," or "are associated with" would better reflect the level of evidence.

2. Strengthen the rationale for herb selection.

The selection of heat-clearing herbs is based on PubMed publication frequency (Table 2), whereas blood-tonifying herbs (Methods 2.1) were selected from clinical guidelines. Please explain why these different selection strategies are appropriate and discuss the potential selection bias.

3. Discuss database limitations.

The study relies heavily on TCMSP and GeneCards, both of which have known limitations regarding target prediction and database completeness. Please include a brief discussion of these limitations and their potential impact on the results.

Although the authors acknowledge the absence of experimental validation (lines 567 to 577), they should also discuss:

• database dependency,
• possible omission of newly identified compounds,
• uncertainty of predicted herb-target interactions,
• potential bias introduced by GeneCards relevance scoring.

Such discussion would substantially improve methodological transparency.

4. Moderate the mechanistic interpretation.

Some discussions, particularly those involving p38 MAPK (lines 468 to 485), CXCL11 (lines 486 to 525), and adrenergic signaling (lines 504 to 525), extend beyond what pathway enrichment alone can demonstrate. Please clearly distinguish pathway enrichment results from proposed biological mechanisms.

5. Better emphasize the novelty.

The Introduction and Discussion should more clearly explain how this study advances previous network pharmacology research, particularly regarding the comparison between heat-clearing and blood-tonifying herbs in CHHF.

Specifically, the authors should clarify whether the novelty lies in:

• the comparison between herb categories rather than individual herbs,
• the disease context of CHHF,
• the pathway-based comparison strategy, or
• the integration of Korean Medicine clinical guidelines into herb selection.

Emphasizing this distinction would strengthen the manuscript's scientific contribution and help readers appreciate its originality beyond another network pharmacology analysis.

Overall, the manuscript addresses an interesting and clinically relevant topic and presents a well-organized network pharmacology workflow. However, several conceptual issues require revision, particularly regarding the interpretation of computational predictions, justification of herb selection, discussion of database limitations, and moderation of mechanistic claims. Addressing these points would substantially improve the scientific rigor and overall impact of the study.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. We have thoroughly considered each comment and revised the manuscript accordingly. Our detailed point-by-point responses are provided below, and the corresponding changes are highlighted (red) and shown in the revised manuscript.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Avoid overinterpreting computational predictions.

The conclusions are based entirely on network pharmacology analyses. Several statements in the Discussion describe the herbs as directly modulating biological processes, which is stronger than the evidence supports. Please revise the language to emphasize that these are predicted mechanisms rather than experimentally validated effects. (Discussion, particularly lines 468 to 551).

I recommend consistently distinguishing predicted molecular associations from experimentally demonstrated biological effects. Expressions such as "may regulate," "are predicted to influence," or "are associated with" would better reflect the level of evidence.

Response 1: We sincerely thank the reviewer for this important comment. Although the conclusions of this study are based on network pharmacology analyses—that is, statistical associations involving predicted targets and pathways—we agree that some statements in the Discussion were phrased as though the medicinal herbs directly regulated specific biological processes. These statements could therefore be interpreted as making claims stronger than warranted by the currently available evidence.

Accordingly, we thoroughly revised the Discussion by replacing expressions implying causality, such as “modulate,” “directly regulate,” “provides mechanistic evidence,” and “effects,” with expressions indicating prediction or association, including “is enriched in,” “is associated with,” “is predicted to be associated with,” and “suggests a potential association.” For example, the original statement, “Coptidis Rhizoma may directly modulate α2C-adrenergic receptor-mediated cold-induced vasoconstrictive mechanisms,” was revised to “ADRA2C may be a candidate target through which Coptidis Rhizoma is predicted to be associated with α2C-adrenergic receptor-mediated cold-induced vasoconstrictive mechanisms.” We consistently applied the same principle throughout the Discussion to statements concerning MAPK, CXCL11, and immune- and inflammation-related processes.

Through these revisions, we clearly distinguished predicted molecular associations from experimentally demonstrated biological effects throughout the manuscript. We once again thank the reviewer for identifying this important issue.

Comments 2: Strengthen the rationale for herb selection.

The selection of heat-clearing herbs is based on PubMed publication frequency (Table 2), whereas blood-tonifying herbs (Methods 2.1) were selected from clinical guidelines. Please explain why these different selection strategies are appropriate and discuss the potential selection bias.

Response 2: Thank you for this important comment. We agree that the rationale for applying different selection strategies to heat-clearing herbs (HCHs) and blood-tonifying herbs (BTHs) required further clarification. Because HCHs are not commonly recommended as a principal therapeutic category in the Clinical Practice Guideline of Korean Medicine for Cold Hypersensitivity in Hands and Feet (CPG of CHHF), the guideline-based frequency approach used to identify commonly used BTHs could not be applied equivalently to HCHs. Therefore, we used a literature-based screening strategy for HCHs to identify herbs with the greatest existing research relevance to CHHF-related clinical and pathophysiological features.

We also agree that the use of different evidence sources may introduce selection bias, because publication frequency and guideline-based prescription frequency reflect different dimensions of evidence and may preferentially identify well-studied herbs and commonly used clinical herbs, respectively. Accordingly, we have revised the Methods section to explicitly explain this rationale and to clarify that the selected HCHs and BTHs should be interpreted as representative candidates derived from different evidence sources rather than as directly equivalent groups selected under identical criteria.

Comments 3: Discuss database limitations.

The study relies heavily on TCMSP and GeneCards, both of which have known limitations regarding target prediction and database completeness. Please include a brief discussion of these limitations and their potential impact on the results.

Although the authors acknowledge the absence of experimental validation (lines 567 to 577), they should also discuss:

database dependency,

possible omission of newly identified compounds,

uncertainty of predicted herb-target interactions,

potential bias introduced by GeneCards relevance scoring.

Such discussion would substantially improve methodological transparency.

Response 3: We sincerely thank the reviewer for this important comment. We agree that both the TCMSP and GeneCards databases have limitations. In response to the reviewer’s comment, we revised the limitations described in the Discussion to address our reliance on TCMSP and GeneCards and the resulting uncertainties.

Since no related experiments were conducted in the laboratory, we investigated the related studies (linked to the active compounds and core biological activities in CHHF diseases such as anti-inflammatory, anti-coagulant, blood flow, microcirculation) and added them in the text Appendix A5 (table A5).  We think that added Appendix A5 (table A5) may indirectly support our results.

Specifically, we acknowledged the following database-related limitations:

1. The compound–target and disease-associated gene data used in this study were derived from TCMSP and GeneCards, respectively.
2. TCMSP may not include recently reported or insufficiently studied compounds.
3. TCMSP includes both experimentally validated compound–target associations and associations predicted using SysDT; therefore, the level of evidence supporting individual associations may vary.
4. The GeneCards relevance score is derived from multiple data sources and may preferentially rank genes that have been studied more extensively.

We also stated that this reliance on databases and the uncertainty associated with predicted relationships may affect not only the composition of the initial target datasets but also the subsequent PPI and pathway-enrichment results.

Comments 4: Moderate the mechanistic interpretation.

Some discussions, particularly those involving p38 MAPK (lines 468 to 485), CXCL11 (lines 486 to 525), and adrenergic signaling (lines 504 to 525), extend beyond what pathway enrichment alone can demonstrate. Please clearly distinguish pathway enrichment results from proposed biological mechanisms.

Response 4: We sincerely thank the reviewer for this important comment. We agree that the Discussion did not sufficiently distinguish between the findings actually identified through pathway-enrichment analysis and the biological mechanisms proposed by our research team based on those findings. Upon review, we determined that this issue arose primarily from overly definitive wording in the relevant paragraphs concerning p38 MAPK, CXCL11, and adrenergic signaling, where the enrichment results were described using expressions such as “regulates” and “provides mechanistic evidence.”

Accordingly, we revised these paragraphs throughout by using expressions indicating prediction or association, such as “was predicted to be potentially associated with” and “suggests an association with.” These revisions clearly distinguish, within each statement, the findings directly identified through enrichment analysis—for example, the enrichment of targets in specific pathways—from the biological interpretations proposed on the basis of those findings.

We also revised the Limitations section to clarify that pathway enrichment reflects the statistical distribution of predicted targets across biological pathways rather than directly measuring pathway activation or inhibition. Accordingly, we added the following statement to emphasize that the biological mechanisms proposed in this study should be interpreted as hypothesis-generating rather than confirmatory:

“As pathway enrichment reflects the statistical distribution of predicted targets across biological pathways rather than a direct measurement of pathway activation or inhibition, the biological mechanisms proposed in this study should be interpreted as hypothesis-generating rather than confirmatory.”

Comments 5: Better emphasize the novelty.

The Introduction and Discussion should more clearly explain how this study advances previous network pharmacology research, particularly regarding the comparison between heat-clearing and blood-tonifying herbs in CHHF.

Specifically, the authors should clarify whether the novelty lies in:

the comparison between herb categories rather than individual herbs,

the disease context of CHHF,

the pathway-based comparison strategy, or

the integration of Korean Medicine clinical guidelines into herb selection.

Emphasizing this distinction would strengthen the manuscript's scientific contribution and help readers appreciate its originality beyond another network pharmacology analysis.

Response 4: Thank you for this important comment. We agree that the novelty of the present study needed to be more clearly distinguished from the general application of network pharmacology. Accordingly, we have revised the Discussion to explicitly clarify the principal scientific contribution of this study.

Specifically, the primary novelty lies in examining heat-clearing herbs as potential therapeutic candidates for CHHF, a condition that has conventionally been understood within a cold-related pathological framework and approached mainly through blood-tonifying or warming strategies in Korean Medicine. Rather than focusing on the molecular mechanisms of individual herbs alone, we directly compared representative heat-clearing herbs with clinically relevant blood-tonifying herbs at the herbal-category level within the specific disease context of CHHF. This comparison allowed us to identify both distinct and overlapping molecular pathway profiles and to explore the counterintuitive possibility that heat-clearing herbs, despite their traditionally cold nature and heat-clearing actions, may also have molecular relevance to CHHF.

We have also clarified that the pathway-based comparison is the analytical strategy used to characterize this category-level distinction, rather than the methodological novelty itself. Likewise, the Korean Medicine Clinical Practice Guideline was used to provide clinical relevance to the selection of the blood-tonifying comparator herbs, but the integration of the guideline itself is not presented as the principal source of novelty.

Accordingly, we added a paragraph to the Discussion emphasizing that the originality of this study lies primarily in broadening the conventional therapeutic perspective on CHHF beyond blood-tonifying and warming approaches through a pathway-based comparison of traditionally contrasting herbal categories. We further framed these findings as hypothesis-generating, providing a basis for subsequent experimental and clinical investigation of heat-clearing herbs as potential therapeutic candidates for CHHF.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Except for some minor formatting issues, such as misaligned tables, which should be corrected, the authors have adequately addressed the previous comments and revised the manuscript accordingly. I therefore recommend acceptance.

Author Response

Comment 1; Except for some minor formatting issues, such as misaligned tables, which should be corrected, the authors have adequately addressed the previous comments and revised the manuscript accordingly. I therefore recommend acceptance.

Response 1; We sincerely appreciate the Reviewer’s valuable comment.  We think that point can be resolved through the editing process, or if necessary, by using an editing service. 

 

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have provided point-by-point responses and a revised manuscript. Some comments have been satisfactorily addressed, but critical methodological problems still exist and major revision is required.

1. The authors have not adequately responded to the official updated TCMSP screening criteria OB≥20% and DL≥1 suggested in the last comment. They only discussed DL ≥0.1 from published papers, and defended their original thresholds OB≥30% and DL≥0.18 only by citing previous publications. Published studies using legacy thresholds cannot override official database standards, and adding this issue as a limitation is not an adequate substitute for re-analysis. The authors must re-filter compounds according to the official OB≥20% and DL≥0.1 thresholds, redo target prediction, network and pathway analyses, and update all relevant results, figures and tables.
2. Since this study lacks wet-lab experimental validation, molecular docking and molecular dynamics simulation are necessary to verify predicted compound-target interactions. The authors’excuse that no pre-selected critical targets are available is invalid because core hub targets can be extracted from their own PPI and pathway analysis outputs. The supplementary literature table in Appendix A5 only offers indirect background evidence and cannot replace in silico binding validation. The authors must perform molecular docking on core compound-target pairs, conduct molecular dynamics simulation for key complexes, and report binding energy, docking poses and MD results in the revised manuscript, and these analyses cannot be deferred to future work.
3. The authors’response regarding single-herb versus herbal-formula comparison is acceptable, but the limitation that conclusions from single-herb analysis cannot be directly transferred to clinical multi-herb formula use should be clearly stated in both Abstract and Discussion.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. We have thoroughly considered each comment and revised the manuscript accordingly. Our detailed point-by-point responses are provided below, and the corresponding changes are highlighted and shown in the revised manuscript.

 

2. Point-by-point response to Comments and Suggestions for Authors

Comment 1; The authors have not adequately responded to the official updated TCMSP screening criteria OB≥20% and DL≥1 suggested in the last comment. They only discussed DL ≥0.1 from published papers, and defended their original thresholds OB≥30% and DL≥0.18 only by citing previous publications. Published studies using legacy thresholds cannot override official database standards, and adding this issue as a limitation is not an adequate substitute for re-analysis. The authors must re-filter compounds according to the official OB≥20% and DL≥0.1 thresholds, redo target prediction, network and pathway analyses, and update all relevant results, figures and tables.

Response 1:

We agree with the reviewer’s comment a certain point.

As reveiwer’ comment, we believe that published studies using legacy thresholds cannot override official database standards, and adding this issue as a limitation is not an adequate substitute for re-analysis. According to TCMSP User Guide, in TCMSP site (https://www.tcmsp-e.com/tcmsp.php), there is an example of the whole compounds-targets interactions that associated with the ingredients (under the criterion of OB ≥20% or DL ≥ 0.1) in licorice. However, It is understood that the standard is not absolute rather as a standard sample or a recommended standard. Even if OB≥20% and DL≥0.1 is considered the standard criterion, they set the criterion of OB ≥ 40% and DL ≥ 0.18 in research of licorice. Also, it is described in “3) ADME screening“ that the most powerful function of TCMSP is that we can obtain the ideal active compounds under various screening criterions, with the filter rules of “equal to”,“greater than” and “less than”, and etc. This example shows that it can be used flexibly depending on the analyst.

Importantly, before conducting the present study, we reviewed approximately 100 previous network pharmacology studies to understand the prevailing methodological approaches in the field. Among these studies, 49 studies used TCMSP for compound screening. Of these 49 studies, 46 studies applied the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Only three studies used different screening strategies. Their criteria were as follows:

(1) OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.18;

(2) OB ≥ 20%, DL ≥ 0.18, Caco-2 ≥ 0, and BBB ≥ −0.3; and

(3) no predefined OB or DL cutoff in TCMSP, with active compounds instead collected from multiple databases and subsequently screened according to SwissADME criteria.

This literature survey indicates that although alternative screening strategies have been adopted, OB ≥ 30% and DL ≥ 0.18 has remained the predominant screening strategy in TCMSP-based network pharmacology studies.

We fully acknowledge the Reviewer’s point that OB ≥ 20% and DL ≥ 0.1 have recently been recommended in TCMSP. Nevertheless, the criteria of OB ≥ 30% and DL ≥ 0.18 have been extensively used for many years and continue to be applied in recent network pharmacology studies. Moreover, there is a methodological rationale for these thresholds. Yao et al. (2024) [1] reported that because the average oral bioavailability (OB) and drug-likeness (DL) values of molecules in DrugBank are approximately 30% and 0.18, respectively, OB ≥ 30% and DL ≥ 0.18 have been regarded as meaningful screening criteria in network pharmacology studies. Thus, our selection of these thresholds was not arbitrary but was based on both established practice in previous TCMSP-based studies and a previously reported pharmacokinetic rationale.

Moreover, many recent studies are still using screening criteria OB≥30% and DL≥0.18, and other different threshold (OB≥33% and DL≥0.18) [2] and the updated standard is thought to be gradually being utilized at present. Moreover, at present, a few papers published used updated standard criteria (OB ≥20% or DL ≥ 0.1) are identified in pubmed.

We believe there may be an advantage to the updated standard, but we don’t think it is absolute and essential criteria. Although there may be parts that need improvement in the legacy screening criteria, we think that legacy screening can be used as an analytical criteria (As long as it’s not a method lacking scientific significance). Both methods have their own scientifical meanings, but they will differ in terms of significance and statistical meaning. Also, we believe that analyzing under new conditions (updated criteria) is similar to conducting another separate analyses of various experimental conditions in wet-lab experimental validation. Drug-likeness cannot be simply quantified through experimental means directly [3]. Over the years, researchers have developed various methods to characterize drug-likeness [4]. Some compounds may only slightly exceed the set thresholds, they still have the potential to become effective drugs.  This approach could lead to missing some promising drug candidates [5]. We believe that the reviewer’s comments and the updated standard take these issues into consideration. We agree with the reviewer’s observation a certain point, and our legacy screening cirteria is more strict standard that possibly narrows down the candidate pool. Nevertheless, this legacy method, which is still widely used today, is also thought to have significance in the strict aspects.

 

[1] Yao, T., Wang, Q., Han, S., Xu, Y., Chen, M., & Wang, Y. (2024). Exploring the therapeutic mechanism of Yuebi decoction on nephrotic syndrome based on network pharmacology and experimental study. Aging (Albany NY), 16(18), 12623.

[2] Pei T, Zheng C, Huang C, Chen X, Guo Z, Fu Y, Liu J, Wang Y. Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula. J Ethnopharmacol. 2016 Aug 22;190:272-87.

[3] Vamathevan J, Clark D, Czodrowski P. et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019;18:463–77.

[4] Clark DE, Pickett SD. Computational methods for the prediction of ‘drug-likeness’. Drug Discov Today 2000;5:49–58.

[5] Hughes JD, Blagg J, Price DA. et al. Physiochemical drug proper-ties associated with in vivo toxicological outcomes. Bioorg Med Chem Lett 2008;18:4872–5.

 

oral bioavailability 30% & network pharmacology

(Recent journal MDPI journal, high impact journals related to OB ≥ 30% and DL ≥ 0.18.)

 

Lu Y. Research on neuroimmune regulatory mechanisms and intervention strategies for chronic insomnia based on network pharmacology. Pak J Pharm Sci. 2026 Jan;39(1):249-260.

Yang A, Luo X, Guo Y, Zhang H, Zhang W, Chang S, Wen S, Yang W, Sun Y. The network pharmacology prediction and experiment validation of Astragalus membranaceus for alleviating silicosis fibrosis via decreasing MMP9 and EGFR expression. Sci Rep. 2026 Mar 6;16(1):12255.

Wang WL, Chen Y. Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction. Genes (Basel). 2024 Mar 22;15(4):392.

Arif R, Bukhari SA, Mustafa G, Ahmed S, Albeshr MF. Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Nigella sativa for the Treatment of Breast Cancer. Pharmaceuticals (Basel). 2024 May 10;17(5):617.

Lin Y, Chen XJ, He L, Yan XL, Li QR, Zhang X, He MH, Chang S, Tu B, Long QD, Zeng Z. Systematic elucidation of the bioactive alkaloids and potential mechanism from Sophora flavescens for the treatment of eczema via network pharmacology. J Ethnopharmacol. 2023 Jan 30;301:115799.

Li T, Li W, Guo X, Tan T, Xiang C, Ouyang Z. Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata-Dipsacus asper herb pair: a network pharmacology and experimental study. Front Pharmacol. 2023 Oct 2;14:1242194.

Liu F, Cao B, Zhang H, Zou Q, Liu G, Dong Y, Su D, Ren DL. Exploring the mechanism of Tengli Kangliu Decoction in the prevention and treatment of colorectal cancer precancerous based on network pharmacology. Medicine (Baltimore). 2022 Nov 18;101(46):e31690.

Liu Y, Yin P, Yang X, Li J, Li W. Network Pharmacology and Rat Model Analysis of Yougui Pill in Carrageenan-Induced Prostatitis. J Vis Exp. 2026 May 19;(231).

 

etc.

Comment 2:

Since this study lacks wet-lab experimental validation, molecular docking and molecular dynamics simulation are necessary to verify predicted compound-target interactions. The authors’excuse that no pre-selected critical targets are available is invalid because core hub targets can be extracted from their own PPI and pathway analysis outputs. The supplementary literature table in Appendix A5 only offers indirect background evidence and cannot replace in silico binding validation. The authors must perform molecular docking on core compound-target pairs, conduct molecular dynamics simulation for key complexes, and report binding energy, docking poses and MD results in the revised manuscript, and these analyses cannot be deferred to future work.

Response 2: We sincerely appreciate the Reviewer’s valuable suggestion to incorporate molecular docking and molecular dynamics (MD) simulations once again. We agree that these approaches could further strengthen the present study.

Even if our manuscript’s biological validity has some weakness, as reviewer’s comment, we believe that our network pharmacology based analytical methods are sound and reasonable. Moreover, we think that our manuscript’s originality, novelty, and significance can be highly evaluated.

Rebuttal comment as follows;

When planning this thesis, the first step is to rationally select the many prescriptions and herbal medicines related to CHHF. This was also accomplished through extensive literature researches.

The primary focus of the present study was not to validate individual ligand–target interactions, but rather to comparatively explore the potential molecular targets and pathway-level characteristics of heat-clearing herbs (HCHs) and blood-tonifying herbs (BTHs) in CHHF.

Unless it comes down to special formulations, herbal medicine, or ingredients, after wet-lab experimental validation, we aimed to narrow down the candidate substances and target proteins as much as possible and proceed.

To carefully verify and taken into consideration critical target compounds and target proteins related to CHHF (nonarbitrary selection) and to perform molecular docking on core compound-target pairs, conduct molecular dynamics simulation etc., we think it will take a lot of time.  

Previously described in first response to reviewer’s comment, a relatively large number of target compounds (about 60) were identified in this results from 4 important Herbs, a number of proteins related to CHHF, and the first step to successfully conducting a molecular docking and molecular dynamics simulation is selecting the appropriate critical target protein. In this study, critical protein targets were not selected. Because, we thought that biological validity, relevance (effectiveness verification, validation of adjustability), structural feasibility, active site information), technical requirements etc. about critical target compounds and target proteins must be carefully verified and taken into consideration (nonarbitrary selection).

In most studies, to proceed with molecular docking and molecular dynamics (MD) simulations, it is conducted on up to 10 components and several critical target protiens based on earlier researches and other informations.

Moreover, in a more precise and special paper, molecular dynamics is performed for a single compound and relative potential key proteins.

If we proceed MD by selecting many components and proteins, from the clinical goal of treating CHHF, the core disease of this paper, validity may rather be lacking.

After thoroughly reviewing the above validity, selecting key oral compound, critical proteins, and additional wet-lab experimental validation, it will be accomplished in separate study focused on this theme in the future (focused on minimum ingredients and proteins).

 

For example;

Ref. A – 1 single compound and single tarpet protein (Forsythoside A and MMP-2)

Ref. B - 3 compounds and 1 protein target (TNF)

Ref. C - Gomisin B, Kaempferol, Celabenzine, and Panaxadiol etc. (about 10)

Ref. D - 11 active components (e.g., baicalein, baicalin, kaempferol)

Ref. E - 26 active components

Ref. F - 4 compounds (formononetin, isorhamnetin, β-Sitosterol, and kaempferol)

Ref. G - 14 active compounds

Ref. H - 14 active compounds

etc.

 (A) Liu W, Wu T, Wang S, Zhu W, Li Y, Wan J, Sun M, Du J, Wu P. Treatment of heterotopic ossification via inhibiting the MMP-2/CDH5 axis through oral delivery of network pharmacology-predicted Chinese medicine. Mater Today Bio. 2026 Apr 9;38:103107.

(B) Liu M, Gu Y, Yang Y, Zhang K, Yang J, Wang W, Li W, Wang X, Dong X, Yin X, Qu C, Ni B, Ni J. Network Pharmacology, Molecular Dynamics Simulation, and Biological Validation Insights into the Potential of Ligustri Lucidi Fructus for Diabetic Nephropathy. Int J Mol Sci. 2025 Jun 30;26(13):6303.

(C) Mohammad S, Karim MR, Iqbal S, et al. Network pharmacology and molecular docking approach to predict the oral and topical therapeutic mechanisms of Panax ginseng's active compounds on atopic dermatitis. J Biomol Struct Dyn. 2025;43(18):11146-11161. doi:10.1080/07391102.2025.2497467

(D) Hu X, Yu Y, Wei C, Sun J, Lin X, Chen R. Multi-component synergy of safflower (Carthamus tinctorius L.) against hypertension-dyslipidemia: Network pharmacology and molecular docking study. Comput Biol Chem. 2026;121:108831. doi:10.1016/j.compbiolchem.2025.108831

(E) Wang WL, Chen Y. Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction. Genes (Basel). 2024 Mar 22;15(4):392.

(F) Li Z, Liu S, Zhang R, Li B. Exploring the mechanism of Danggui Sini Decoction in the treatment of myocardial infarction: A systematic review, network pharmacology, and molecular docking. Medicine (Baltimore). 2024 Oct 18;103(42):e40073.

(G) Tong J, Ding Y, Zhang D. Mechanisms of Actinidia chinensis Planch roots in the treatment of breast cancer based on network pharmacology and molecular docking. Medicine (Baltimore). 2025;104(31):e43560. doi:10.1097/MD.0000000000043560

(H) Liu Y, Guo Z, Lang F, Li J, Jiang J. Anticancer Effect of Active Component of Astragalus Membranaceus Combined with Olaparib on Ovarian Cancer Predicted by Network-Based Pharmacology. Appl Biochem Biotechnol. 2023 Nov;195(11):6994-7020.

 

 

 

Comment 3:

The authors’response regarding single-herb versus herbal-formula comparison is acceptable, but the limitation that conclusions from single-herb analysis cannot be directly transferred to clinical multi-herb formula use should be clearly stated in both Abstract and Discussion.

Response 3: We sincerely appreciate the Reviewer’s valuable suggestion. We have revised in the abstract and discussion as reviewer’ comment.

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

1. The authors argued that the large number of compounds and target pairs prevents them from performing molecular docking and molecular dynamics simulations. This justification is not acceptable. Modern computational platforms and supercomputing resources support high-throughput molecular docking for hundreds or even thousands of compounds. The sheer size of the candidate dataset cannot serve as a valid reason to skip these essential validation analyses. Molecular docking and molecular dynamics simulations are still required to complete this work, and relevant results should be supplemented in the revised manuscript.
2. The handling of TCMSP database parameters remains problematic in the current revision. The official, predefined screening thresholds of TCMSP are widely recognized and adopted in published network-pharmacology studies. The authors should strictly follow these official parameters for compound screening rather than adjusting filtering criteria arbitrarily according to their own dataset. Re-screening of active ingredients based on standard TCMSP thresholds needs to be completed, and all corresponding results should be updated throughout the manuscript.

Several key methodological weaknesses have not been fully resolved after revision. Without standardized TCMSP screening outputs and mandatory molecular docking together with molecular dynamics simulation validation, the predicted compound-target interactions remain purely computational predictions lacking necessary structural-level verification. The manuscript cannot reach the acceptable publication standard in its current status.

Author Response

Comment 1: The authors argued that the large number of compounds and target pairs prevents them from performing molecular docking and molecular dynamics simulations. This justification is not acceptable. Modern computational platforms and supercomputing resources support high-throughput molecular docking for hundreds or even thousands of compounds. The sheer size of the candidate dataset cannot serve as a valid reason to skip these essential validation analyses. Molecular docking and molecular dynamics simulations are still required to complete this work, and relevant results should be supplemented in the revised manuscript.

Response 1: We sincerely appreciate the Reviewer’s valuable suggestion to incorporate molecular docking and molecular dynamics (MD) simulations once again. We agree that these approaches could further strengthen the present study to some extent.

The reviewer’s point is correct, but within the scope of this study, we acknowledge that it has been limited to analysis of this level.

As previously responded, the primary focus of the present study was not to validate individual ligand–target interactions, but rather to comparatively explore the potential molecular targets and pathway-level characteristics of heat-clearing herbs (HCHs) and blood-tonifying herbs (BTHs) in CHHF. In most studies, to proceed with molecular docking and molecular dynamics (MD) simulations, it is conducted on up to 10 components and several critical target protiens based on earlier researches and other informations. To carefully verify and taken into consideration critical target compounds and target proteins related to CHHF (nonarbitrary selection) and to perform molecular docking on core compound-target pairs, conduct molecular dynamics simulation etc., it will take a lot of time.  Instead, it was specified in the main text as a limitation and added as a future research task.

Nevertheless, the current our results provide a valuable baseline for identifying potential candidates.

Comment 2: The handling of TCMSP database parameters remains problematic in the current revision. The official, predefined screening thresholds of TCMSP are widely recognized and adopted in published network-pharmacology studies. The authors should strictly follow these official parameters for compound screening rather than adjusting filtering criteria arbitrarily according to their own dataset. Re-screening of active ingredients based on standard TCMSP thresholds needs to be completed, and all corresponding results should be updated throughout the manuscript.

 

Response 2: First of all, thank you for your kind important and helpful comment.

We agree with the reviewer’s comment to some extent.

We believe that published studies using legacy thresholds cannot override official database standards.

As previously responded, according to TCMSP User Guide, in TCMSP site (https://www.tcmsp-e.com/tcmsp.php), there is an example of the whole compounds-targets interactions that associated with the ingredients (under the criterion of OB ≥20% or DL ≥ 0.1) in licorice. However, it is understood that the standard is not absolute rather as a standard sample or a recommended standard.

Accordingly, we reviewed the parameter guidance provided by TCMSP as well as previous network pharmacology studies. Although the TCMSP website currently suggests OB ≥ 20% and DL ≥ 0.1 as general screening criteria, our review of the literature confirmed that OB ≥ 30% and DL ≥ 0.18 have also been widely applied in numerous network pharmacology studies, including recent publications, as shown in second round response. Therefore, we applied OB ≥ 30% and DL ≥ 0.18 to select compounds with relatively high oral bioavailability and drug-likeness more stringently and to maintain methodological consistency with established network pharmacology studies.

Importantly, before conducting the present study, we reviewed approximately 100 previous network pharmacology studies to understand the prevailing methodological approaches in the field. Among these studies, 49 studies used TCMSP for compound screening. Of these 49 studies, 46 studies applied the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. A few studies used different screening strategies. Their criteria were as follows:

(1) OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.18;

(2) OB ≥ 20%, DL ≥ 0.18, Caco-2 ≥ 0, and BBB ≥ −0.3; and

(3) no predefined OB or DL cutoff in TCMSP, with active compounds instead collected from multiple databases and subsequently screened according to SwissADME criteria.

Nevertheless, we fully agree with the reviewer’s comment that prespecified OB and DL thresholds may exclude pharmacologically relevant compounds and thereby influence the results of downstream analyses. Although we retained the criteria of OB ≥ 30% and DL ≥ 0.18, which have been widely and consistently used in previous network pharmacology studies, we have added threshold-dependent compound selection as a methodological limitation in the revised manuscript.  

As editor’s comment (and opinions of reviewer2, reviewer3), in the revised discussion and conclusion, we identified threshold-dependent compound selection as a methodological limitation of this study as follows.

“Although this study demonstrated the potential binding affinity between active compounds and predicted protein targets, a limitation of this work is that molecular docking and dynamics simulations were not performed to evaluate the dynamic stability of the ligand-receptor complexes. Moreover, this study have limitations regarding the interpretation of computational predictions, screening thresholds and discussion of database limitations. ”