Abstract
The prevalence of herbal medicines has gained widespread, particularly among cancer patients seeking adjunctive therapies. Co-administered with anticancer drugs (ACDs) frequently, herbal medicines result in increasing cases of herb-drug interactions (HDIs), following the serious clinical consequences. While herbal medicines pose negative impacts, such as limiting efficacy and increasing toxicity of ACDs, they also offer potential benefits, including enhancing bioavailability, reducing adverse reactions, and reversing tumor drug resistance. This review is the first to systematically characterize HDI molecular mechanisms at both pharmacodynamic (PD) and pharmacokinetic (PK) levels, elucidating how herbal medicines modulate ACDs efficacy and safety through antagonism/synergy/detoxification target, metabolic enzymes, and transporters. In particular, emerging risk prediction methodologies are proposed to assess the clinical occurrence of potential PD/PK-mediated HDIs. We provide a novel insight for promoting the mechanism study of HDIs, facilitating the safe and effective integration of herbal medicines into cancer treatment.
Graphical Abstract
Keywords: Herb-drug interactions, Herbal medicines, Cancer, Pharmacodynamic, Pharmacokinetic, Risk prediction
Introduction
Herbal medicines are becoming increasingly popular as alternative therapies around the world. The usage rate of herbal medicines among the healthy population in the United States was 12% in 1997 and increased to one-third in 2015 [1]. In Africa, 80% of the population has been treated with herbal remedies [2]. In Asian countries such as China, herbal medicines are essential to Traditional Chinese Medicines (TCMs) and are included in standard treatment guidelines for diseases and health insurance systems [3]. According to current World Health Organization statistics, there are a total of 21,000 herbs used for medicinal purposes worldwide [4]. In clinics, these herbal medicines are often co-administered with therapeutic drugs, increasing the potential risk of Herb-drug interactions (HDIs). A comparative study by Prely et al. included 294 patients, of whom 137 (46.6%) took at least one herb, with an average of 1.8 ± 0.8 herbs per patient. A total of 104 HDIs were identified in 68 patients (23.1%), and 36 interactions (34.6%) were identified as high-risk HDIs [5]. The effects of HDIs have a dual role. On the one hand, herbs can cause a decrease in efficacy, selectivity, and even trigger toxicity of therapeutic drugs. For example, Ginkgo biloba L. and Salvia miltiorrhiza Bunge induce adverse reactions (ADR), such as bleeding when co-administration with warfarin [6, 7]. On the other hand, appropriate application of herbs can increase the bioavailability of therapeutic drugs, improve their efficacy, and reduce ADR. Several studies have shown that Curcuma longa L. synergized with doxorubicin (DOX) in suppressing cancer cell proliferation, migration, and invasion, while reducing DOX-induced cardio-nephrotoxicity [8–13]. Researchers and clinicians have long been attempting to appropriately utilize the dual action of HDIs to maximize the clinical benefits of herbal medicines.
Cancer is a life-threatening disease characterized by the uncontrolled growth of abnormal cells, invading vital organs, and severe complications. As anticancer drugs (ACDs), especially chemotherapeutic drugs, have non-specific cell toxicity, cancer patients often experience severe adverse drug reactions [14]. Therefore, they often spontaneously seek alternatives and self-administer herbal therapies. Cancer is the second most common disease associated with herbal use, as reported by Rashrash et al. [1]. More than eight out of ten cancer patients used herbal medicines during chemotherapy, significantly increasing the probability of HDIs [15]. Recent real-world data studies indicated that 45.4% of herbal medicine users were found to be at risk of HDI in oncology treatment [16]. A study in the Middle East found that nearly 400 healthcare workers have prescribed 44 herbal medicines in one year, of which 15 were associated with HDIs with intravenous ACDs [17].
Since most ACDs are cytotoxic with a narrow therapeutic window, small blood concentration changes of drugs induced by herbal medicines are likely to produce profound alterations in efficacy or safety issues. Inappropriate herb-ACDs combinations can lead to severe clinical consequences, such as nausea, vomiting, constipation, diarrhea, pain, hepatotoxicity, nephrotoxicity, etc. [18, 19]. In addition, long-term medication may lead to drug resistance and hampers the efficacy of ACDs. Appropriate herb-ACDs combinations can effectively reverse multidrug resistance (MDR) [20–22]. Therefore, HDIs between herbal medicines and ACDs are more likely to have serious consequences while also offering more potential clinical benefits.
The mechanism of HDIs may involve a combination of pharmacodynamic (PD, antagonism/synergy/detoxification effect on the target) and pharmacokinetic (PK, alteration of the concentration of the ACDs), which in turn leads to alterations in the efficacy or toxicity of ACDs [23]. Thus, it is necessary to elucidate the potential PD/PK mechanisms of herbal medicines-ACDs interactions to avoid adverse effects and improve the safety and efficacy of ACDs. This review provides a first-time characterization and analysis of the mechanism of HDIs in herbal medicines and ACDs at both PD/PK levels. In particular, recent advances in risk prediction methods for HDIs are summarized while presenting the limitations. We aim to provide a novel insight for promoting the mechanism study of HDIs, facilitating the combination of clinical herbs and ACDs, and enriching clinical oncology treatment options.
Pharmacodynamic-mediated herb-drug interactions
HDIs occur due to competitive or complementary interactions at the same drug target, leading to antagonism or synergy effects between ACDs and herbal medicines. Synergy effects can enhance the therapeutic efficacy of ACDs, but may also lead to adverse toxicity and complicate dosing regimens. In contrast, antagonism effects may result in reduced efficacy and treatment failure. Furthermore, many herbal medicines are reported to reverse the MDR problem associated with the long-term use of ACDs. Some studies also suggest that HDIs can alleviate the ADR of ACDs, exhibiting potential benefits for herbs-ACDs combination. In this section, we explore the PD interactions between herbal medicines and ACDs, focusing on antagonism, synergy and detoxification effects, as shown in Fig. 1.
Fig. 1.
The pharmacodynamic-mediated mechanism of herb-drug interactions
Antagonism effect
Antagonism occurs when the overall effect of a drug combination is less than the sum of the pharmacological effects of each individual agent in the combination [24]. Herbal medicines may reduce the potency and toxicity of ACDs by directly binding to the drug or completely blocking specific receptors associated with ACDs. Limit studies have reported antagonism effects of herbal medicines on ACDs, as shown in Table 1. These studies mainly focus on the impact of herbal medicines on cancer cells in vitro. The carbamazepine is a potent histone deacetylase inhibitor of cancer cell growth, and Graviola extract was found to have an antagonism effect when combined with carbamazepine in MCF-7 and PC3 cells [25]. Additionally, in HepG2 cells, it was observed that the combination of curcumin and lovastatin increased the LC50 value of lovastatin, suggesting a potential antagonism effect [26]. The combination treatment of Tamarindus indica seeds extract with the tamoxifen (TMX) eliminates the cytotoxic effect in MCF-7 cells [27]. Encouse et al. found that green tea components, (−)-epigallocatechin gallate (EGCG), effectively prevent bortezomib (BZM)-induced cancer cells death both in vitro and in vivo [28]. The antagonism effect of EGCG was only evident in boronic acid-based proteasome inhibitors but not in non-boronic acid. Mechanism study indicated that EGCG directly interacts with BZM, blocking its proteasome inhibition function, thereby preventing BZM from triggering endoplasmic reticulum stress or caspase-7 activation.
Table 1.
Antagonism effect of herbal medicines on ACDs
| ACDs | Herbal medicines | Herb type | Subject and dosage | Interaction | Mechanism | References |
|---|---|---|---|---|---|---|
| BZM | EGCG | Active constituents | In vitro; Mice (25–50 mg/kg, p.o.) | Prevent cancer cell death induced by BZM in vitro and in vivo | React with BZM; Block proteasome inhibitory function | [28] |
| Carbamazepine | Graviola fruit | Herbal extract | In vitro | Decrease cytotoxicity activity of carbamazepine in MCF-7 and PC3 cells | NR | [25] |
| Lovastatin | Curcumin | Active constituents | In vitro | Decrease cytotoxicity activity of lovastatin in HepG2 cells | NR | [26] |
| TMX | Tamarindus indica L. | Herbal extract | In vitro | Eliminate the cytotoxic effect of TMX in MCF-7 cells | NR | [27] |
BZM bortezomib, EGCG (−)-epigallocatechin gallate, NR not reported, p.o. oral administration, TMX tamoxifen
Synergy effect and multitarget effect
Tumor progression involves complex molecular networks, making single-target therapies prone to failure due to interpatient heterogeneity and adaptive mechanisms such as target downregulation and MDR. Herbal medicines, characterized by multi-constituents, offer a unique advantage by synergistically acting on the same targets, complementarily unaffected targets, and MDR-related targets. Such multitarget effects underpin the therapeutic potential of herb-drug combinations [29]. Nagaprashantha et al. studied the combination of vicenin-2 (extracted from Ocimum sanctum) with docetaxel (DTX) synergistically suppresses prostate cancer progression in vitro and in vivo by inducing apoptosis, inhibiting angiogenesis and epithelial-mesenchymal transition (EMT). The authors reclaimed it is vicenin-2’s multitarget effects that provide a promising combinate therapeutic strategy for prostate cancer [30]. Above all, this section provides a systematic overview of the key mechanisms by which herbal medicines enhance the therapeutic efficacy of ACDs through synergistic interactions, as evidenced by existing studies. Common herbal medicines-mediated synergistic interactions with ACDs are summarized in Table 2.
Table 2.
Synergy effect of herbal medicines on ACDs
| ACDs | Herbal medicines | Herb type | Subject and dosage | Interaction | Mechanism | References |
|---|---|---|---|---|---|---|
| 5-FU | Corilagin | Active constituents | In vitro | Increase cytotoxic effects of 5-FU in HCT-8 cells and SW480 cells | Inhibit the expression of GRP 78 | [176] |
| Curcumin | Active constituents | In vitro; Mice (100 mg/kg, i.p.) | Show synergy effects with 5-FU to MKN1, MKN74 and SNU668 cells and xenograft SNU668 cells tumors | Inhibit JAK/STAT3 pathway | [177] | |
| Magnolol | Active constituents | In vitro | Inhibit synergistically HeLa cells proliferation and metastasis | Inhibit EMT; Inhibit PI3K/AKT pathways | [44] | |
| Tangeretin | Active constituents | In vitro; Mice (25 mg/kg, i.p.) | Enhance anticaner effect of 5-FU against HCT116, SW480, SW620, and CT-26 cells and in vivo | Induce autophagy; Inhibit PI3K/AKT pathway | [178] | |
| Paris polyphylla Sm | Herbal extract | In vitro | Synergitize with 5-FU and CDDP in HCT-116 | Increase apoptosis | [179] | |
| Prunus spinosa L | Herbal extract | In vitro | Sensitize HCT116 cells to 5-FU | Inhibit PI3/AKT pathways | [180] | |
| Scutellaria baicalensis Georgi | Herbal extract | In vitro; Mice (4 g/kg, p.o.) | Enhance 5-FU-based chemotherapy to CRC cells and in vivo | Inhibit CDK-RB pathway | [181] | |
| Thai noni juice | Herbal extract | In vitro; Mice (125-250 mg/kg, i.p.) | Synergisticly enhance anti-cancer effect of 5-FU to CCA cells and in vivo | Increase apoptosis; Inhibit anti-apoptotic systems | [182] | |
| Jiedu Sangen decoction | Herbal prescription | In vitro | Inhibit chemoresistance to 5-FU of HCT-8/5-FU cells | Inhibit tumor glycolysis and PI3K/AKT pathway | [183] | |
| Fuzheng jiedu Quyu recipe | Herbal prescription | Human (15 g, p.o.) | Prolong mPFS significantly compared with the control group | Inhibit VEGF pathways | [53] | |
| Kanglaite injection | Herbal prescription | Human (200 mL, i.v.) | Improve DCR, ORR and QOL | Inhibit PI3K/AKT/mTOR pathways | [35] | |
| Afatinib | Ethoxy-erianin phosphate | Active constituents | In vitro | Synergisticly inhibit the proliferation, motion and angiogenesis of HepG2 and HUVECs cells | Inhibit VEGF and EGFR pathways | [184] |
| Ara-C | Ginsenoside compound K | Active constituents | In vitro | Synergistize cytotoxic effect of Ara-C in THP-1 and U937 cells | Increase apoptosis and DNA damage | [185] |
| Bevacizumab | Oxymatrine | Active constituents | In vitro | Enhance the anti-tumor effects of Bevacizumab in MDA-MB-231/MDA-MB-468 cells | Inhibt EMT; Deplete stem cells population | [43] |
| Bortezomib | Solamargine | Active constituents | In vitro; Mice (8 mg/kg, i.p.) | Enhance bortezomib activity in ARP-1 and NCI-H929 cells and xenograft mouse model | Induce autophagy | [186] |
| Cabazitaxel | Usnic Acid | Active constituents | In vitro | Enhance the efficacy of cabazitaxel to PC3 and DU145 cells | Increase apoptosis | [187] |
| CDDP | Triptolide | Active constituents | In vitro | Exhibit synergy effect with CDDP in MDA-MB-231 cells | Inhibit insulin-like growth factor 1 signaling | [188] |
| β-Asarone | Active constituents | In vitro | Increase sensitization of CDDP in MGC803, SGC7901, and MKN74 cells | Inhibit tumor glycolysis | [61] | |
| Centipeda minima (L.) A. Braun & Asch | Herbal extract | In vitro | Sensitize CDDP- or MMC-induced DNA damage and apoptosis against A549 and H1299 cells | Inhibit FA pathway | [58] | |
| Ocimum gratissimum L. | Herbal extract | In vitro; Mice (40 mg/kg, p.o.) | Sensitize HepG2 cells to the CDDP and in vivo | Inhibit EMT pathway | [189] | |
| Paris polyphylla Sm | Herbal extract | In vitro | Synergitize with 5-FU and CDDP in HCT-116 | Increase apoptosis | [179] | |
| Kanglaite injection | Herbal prescription | Human (200 mL, i.v.) | Improve DCR, ORR, 1-year survival rate, QOL | Inhibit PI3K/AKT/mTOR pathways | [76] | |
| Fufang Kushen injection | Herbal prescription | Zebrafish (5–50 μg/mL) | Enhance CDDP efficacy in zebrafish model | Inhibit angiogenesis | [54] | |
| Human (200 mL, i.v.) | Increase the ORR and DCR | [55] | ||||
| Shenmai injection | Herbal prescription | In vitro | Enhance CDDP cytotoxicity in CDDP-resistant A549/DDP cells | Inhibit tumor glycolysis and AKT-mTOR-c-Myc pathway | [190] | |
| Cetuximab | Honokiol | Active constituents | In vitro; Mice (100 mg/kg, i.p.) | Synergistic augment cetuximab’s sensitivity to KRAS mutant CRC cells and in vitro models | Inhibit autophagy | [40] |
| CTX | Parthenolide | Active constituents | In vitro; Mice (40 mg/kg, p.o.) | Synergistize with CTX in LLC cells and improve the survival rate of tumor-bearing mice | Increase apoptosis | [191] |
| Dichloroacetate | Albiziabioside A | Active constituents | In vitro | Exhibit synergy effect with dichloroacetate in MCF-7 cells | Increase apoptosis; Induce ferroptosis | [192] |
| DOX | Glycyrrhetinic acid | Active constituents | In vitro | Enhance cytotoxicity in MCF-7 cells, inhibit migration and tube formation | Increase apoptosis; Inhibit VEGF pathway | [51] |
| Magnoflorine | Active constituents | In vitro | Sensitize DOX to MCF-7 and MDA-MB-231 cells | Induce autophagy; Inhibit anti-apoptotic systems | [193] | |
| Oridonin | Active constituents | In vitro | Increase cytotoxic effect in osteosarcoma and MDA-MB-231 cells | Inhibit angiogenesis; Increase apoptosis | [52, 135] | |
| Parameritannin A-2 | Active constituents | In vitro | Increase cytotoxic effects of DOX in HGC27 cells | Increase apoptosis; Inhibit anti-apoptotic systems | [194] | |
| β-Asarone | Active constituents | In vitro | Synergisticly inhibit proliferation of Raji lymphoma cells | Increase apoptosis; Deplete stem cells population | [195] | |
| Cuban propolis | Herbal extract | In vitro | Increase cytotoxic effect in LoVo Dox cells | Increase apoptosis | [196] | |
| DTX | Capsaicin | Active constituents | In vitro; Mice (2 mg/kg, i.p.) | Synergisticly inhibit the growth of LNCaP and PC3 cells and reduce the tumor growth of PC3 in vivo | Inhibit PI3K/AKT pathway | [197] |
| Polyphyllin VII | Active constituents | In vitro | Enhance the inhibitory effect of DTX in DU-145 cells and DU145/DTX cells | Increase apoptosis; Induce ferroptosis | [198] | |
| Brazilian green propolis | Herbal extract | In vitro | Enhance the cytotoxicity of DTX alone against MCF-7 cells | Induce necrosis | [199] | |
| Entinostat | Tetrandrine | Active constituents | In vitro; Mice (25 mg/kg, p.o.) | Enhance antitumor effects of entinostat in vitro and in vivo | Increase apoptosis; Inhibit anti-apoptotic systems | [200] |
| GFT | Nitidumpeptins B | Active constituents | In vitro | Exhibit synergistic antiproliferative activity in acquired GFT-resistant NSCLC | Inhibt YAP expression | [201] |
| Yuanhuadine | Active constituents | In vitro | Exhibit a synergistic grwoth-inhibitory activity in GFT-resistant H1299 cells | Inhibit AXL expression | [202] | |
| GMC | Aloe-emodin | Active constituents | In vitro | Increase GMC cytotoxic effects in A549 and NCI-H1299 cells | Induce autophagy | [203] |
| Brucea javanica (L.) Merr | Herbal extract | Mice (1 g/kg, p.o.) | Reduce tumor growth rate in pancreatic cancer orthotopic xenograft mouse model | Increase apoptosis | [204] | |
| Aidi injection | Herbal prescription | Human (50–100 mL, i.v.) | Improve significantly ORR, DCR and QOL | Inhibit anti-apoptotic systems; Inhibit EMT | [45] | |
| MMC | Centipeda minima (L.) A. Braun & Asch | Herbal extract | In vitro | Sensitize CDDP- or MMC-induced DNA damage and apoptosis against A549 and H1299 cells | Inhibit FA pathway | [58] |
| MXT | Parthenolide | Active constituents | In vitro; Mice (40 mg/kg, p.o.) | Synergisticly inhibit the growth of LLC cells and reduce tumor growth rate in tumor-bearing mice | Inhibit angiogenesis and NF-kB signaling pathway | [191] |
| OVA | Gegen Qinlian decoction | Herbal prescription | In vitro; Mice (5 g/kg, p.o.) | Reverse OVA resistance in LoVo/OXAR cells and reduce tumor xenografts | Inhibit YTHDF1-regulated m6A modification of GLS1 | [205] |
| OXA | Genipin | Active constituents | In vitro; Mice (10 mg/kg, i.p.) | Exert synergistic antitumor effects HCT116 and DLD-1 cells and in vivo | Inhibit ROS/ER/BIM pathway | [206] |
| Rhein | Active constituents | In vitro | Enhance apoptosis of Panc-1 and MIAPaca-2 cells | Increase apoptosis; Inhibit PI3K/AKT pathway | [207] | |
| Regorafenib | Catalpol | Active constituents | In vitro | Increase cytotoxic effects of regorafenib in HepG2 and HUH-7 cells | Inhibit PI3K/AKT and VEGF pathways | [208] |
| SOF | EF24 | Active constituents | In vitro; Mice (10 mg/kg, i.p.) | Enhance the antitumor effects of SOF in vitro and in vivo | Inhibit angiogenesis | [50] |
| Leachianone A | Active constituents | In vitro | Increase cytotoxic effects of SOF to MHCC97H cells | Increase apoptosis; Inhibit anti-apoptotic systems | [209] | |
| Osthole | Active constituents | In vitro; Mice (100 mg/kg, p.o.) | Synergistic inhibit HCCLM3, HCCLM3-SR, and SK-Hep-1 cells and tumor grough of HCCLM3 cells | Inhibit cholesterol metabolism | [210] | |
| Tiliroside | Active constituents | In vitro; Mice (20 mg/kg, i.p.) | Enhance the synergistic anti-HCC activity of SOF HepG2, Hep3B, and SMMC-7721 cells and in vivo | Induce ferroptosis | [38] | |
| TMX | Elephantopus scaber L. | Herbal extract | In vitro | Increase cytotoxic effects of TMX to MCTS cells | Increase apoptosis | [211] |
| TMZ | Crocus sativus L. | Herbal extract | In vitro | Enhance the antitumor effect of TMZ against C6 glioma rat cell line | Induce autophagy | [212] |
| TPT | Safranal | Active constituents | In vitro | Enhance the growth inhibitory effects of TPT to HCT116 and A549 cells | Dysregulate the DNA repair machinery | [213] |
| Trifluorothymidine | Cryptotanshinone | Active constituents | In vitro | Enhance anticancer effect of trifluorothymidine in HGC-27 cells and AGS cells | Inhibit JAK/STAT3 pathway | [214] |
| VCR | Bunium persicum | Herbal extract | In vitro | Synergitize with VCR in MCF-7 and MDA-MB-231 cells | Increase apoptosis; Inhibit anti-apoptotic systems | [34] |
5-FU 5-fluorouracil, Ara-C cytarabine, CDDP cisplatin, CRC colorectal cancer, CTX cyclophosphamide, DCR disease control rate, DOX doxorubicin, DTX docetaxel, GFT gefitinib, GMC gemcitabine, i.p. intraperitoneal injection, i.v. intravenous injection, MMC mitomycin C, MTX methotrexate, NR not reported, ORR objective response rate, OXA oxaliplatin, p.o. oral administration, QOL quality of life, SOF sorafenib, TMZ temozolomide, TPT topotecan, VCR vincristine
Inhibit anti-apoptotic systems
Programmed cell death (PCD) is a form of cell death that can be regulated by various biomolecules [31]. PCD includes apoptosis, autophagy, necroptosis, ferroptosis, and pyroptosis [32]. Most ACDs eliminate tumor cells by inducing apoptosis and associated cell death networks. However, tumor cells can evade PCD through dysregulation of apoptotic signals, such as the activation of anti-apoptotic systems, leading to cancer recurrence. In this process, the Bcl-2 protein family plays a critical role. During apoptosis, pro-apoptotic protein Bax translocates to the outer mitochondrial membrane, forming pores that facilitate cytochrome c release into the cytoplasm, activating the caspase cascade and ultimately leading to cell apoptosis. This process is regulated by the transcription factor p53. Tumor cells can upregulate the anti-apoptotic protein Bcl-2 to inhibit Bax activation, evading ACDs-induced apoptosis [33]. Cell proliferation pathways such as PI3K/AKT/mTOR and NF-κB can further enhance anti-apoptotic systems, promoting tumor cell resistance to apoptosis. Recent studies have shown that certain natural products can restore drug-resistant tumor cells’ sensitivity to ACDs by inhibiting anti-apoptotic signals. For example, Bunium persicum seed extract synergizes with vincristine (VCR), enhancing VCR-induced cytotoxicity in MCF-7 cells. This effect is likely associated with inhibiting the NF-κB signaling pathway, upregulating the Bax/Bcl-2 ratio and p53 expression, and suppressing anti-apoptotic systems [34]. A meta-analysis showed that Kanglaite injection combined with 5-FU in treating esophageal, gastric, and colorectal cancers was significantly more effective than chemotherapy alone [35]. It can be attributed to the modulation of the PI3K/AKT/mTOR pathway and induction apoptosis by Kanglaite injection [36].
Induce non-apoptotic cell death
In addition to inducing apoptosis, herbal medicines can also trigger non-apoptotic cell death. In combination with ACDs, they can kill cancer cells through multiple cell death, overcoming the resistance of tumors induced by a single PCD mechanism. For instance, oridonin enhances the cytotoxicity of 5-FU against renal cancer both in vitro and in vivo by inducing necroptosis [37]. Tiliroside induces ferroptosis, making hepatocellular carcinoma (HCC) cells sensitive to sorafenib (SOF) through the Keap1/Nrf2 pathway [38].
However, it is noteworthy that autophagy appears to exhibit a dual role in the mechanism of herbal synergy. Under stress conditions such as drug exposure and nutrient deprivation, protective autophagy maintains the genomic stability of cancer cells by degrading damaged organelles and proteins, thereby reducing apoptosis induced by ACDs and promoting MDR [39]. Herbal medicines can enhance the cytotoxicity of ACDs by inhibiting protective autophagy and disrupting the survival mechanisms of cancer cells. Honokiol has been shown to inhibit autophagy in KRAS-mutant cells and synergize cetuximab to colorectal cancer (CRC) both in vitro and in vivo [40]. On the other hand, herbal medicines can excessively activate autophagy pathways, leading to excessive self-degradation of cancer cells and triggering autophagic cell death. Zhang et al. reported that oridonin can enhance cellular autophagy by reducing p62 expression and upregulating the conversion of LC3-II. Combined with NVP-BEZ235, it significantly induces apoptosis in neuroblastoma cells and inhibits the growth of neuroblastoma xenografts [41].
Inhibit the EMT
EMT refers to the transformation of epithelial cells into mesenchymal cells, which endows cancer cells with the ability to migrate and invade. Drug stimulation triggers EMT, further contributing to tumor progression, metastasis, and drug resistance [42]. Herbal medicines can inhibit EMT, reduce cancer cell migration and invasion, reverse drug resistance, and synergize ACDs. For example, bevacizumab induces EMT in triple-negative breast cancer (TNBC) cells by activating the Wnt/β-catenin pathway, leading to limited efficacy against TNBC. Oxymatrine reverses the EMT phenotype, depletes the bevacizumab-induced TNBC stem cell subpopulation, and enhances the antitumor effect of bevacizumab both in vitro and in vivo [43]. Similarly, magnolol can target the EMT and PI3K/AKT/mTOR pathways to synergistically inhibit cervical cancer cell metastasis when combined with 5-FU [44]. In non-small cell lung cancer (NSCLC) patients, the combination of Aidi injection and gemcitabine significantly improved the objective response rate (ORR) and disease control rate (DCR), possibly be related to inhibiting cell migration and invasion by Aidi injection [45].
Inhibit angiogenesis
Oxygen is critical for energy metabolism, driving cellular bioenergetics. The rapid and uncontrolled growth of tumors restricts oxygen availability, making inadequate blood flow, or hypoxia, a common characteristic of nearly all solid tumors [46]. Hypoxic conditions result in a more aggressive phenotype of cancer cells, likely due to hypoxia-induced changes in gene expression and subsequent proteomic alterations [47]. Additionally, there is an intriguing correlation between the hypoxic microenvironment and MDR. Sustained drug treatments suppress tumor angiogenic activity, leading to tumor-associated hypoxia, which promotes the selection of resistant cell clones adapted to oxygen and nutrient deprivation, thereby limiting drug efficacy [48]. A key factor in this process is the activation of HIF-1α/VEGF pathways [49]. Herbal medicines could disrupt hypoxia-angiogenesis crosstalk to achieve synergy effects. For example, hypoxia induced by the anti-angiogenic effects of SOF can lead to SOF -resistance in HCC cells. EF24, a structural analog of curcumin, can promote the degradation of cytoplasmic HIF-1α, thereby synergistically enhancing the antitumor effects of SOF in vitro and in vivo [50]. Additionally, Oridonin and glycyrrhetinic acid have been reported to inhibit angiogenesis, downregulate VEGFR, and increase DOX sensitivity [51, 52]. Fuzheng jiedu Quyu recipe inhibited VEGF and significantly improved progression-free survival combined with 5-FU in the real world [53]. Fufang Kushen injection has been reported to inhibit angiogenesis and significantly improve ORR and DCR in NSCLC patients when combined with platinum-based chemotherapy [54, 55].
Other possible synergy mechanism
Chemotherapy typically kills cancer cells by causing DNA damage, such as DNA double-strand breaks or crosslinking. Cancer cells utilize the DNA repair mechanism mediated by the fanconi anemia pathway (FA) to resist the damaging effects of DNA crosslinker [56, 57]. Fan et al. discovered that Centipeda minima ethanol extract can inhibit the formation of FANCD2 in the FA pathway, thereby making NSCLC more sensitive to DNA damage and apoptosis induced by cisplatin (CDDP) or mitomycin C (MMC) both in vitro and in vivo [58]. It has been recently shown that cancer stem cells (CSCs) also participate in cancer MDR [59]. CSCs are in the G0 quiescent state of the cell cycle, allowing them to evade the cytotoxic effects of ACDs that target proliferating cells. Additionally, CSCs can reduce the efficacy of ACDs by highly expressing P-gp, inducing anti-apoptotic systems [60]. Drug stimulation regulates the expression of stem cell-related genes and promotes the activation and enrichment of tumor stem cells, ultimately leading to MDR. β-asarone could increase the sensitization of DOX in gastric carcinoma cell lines by abolishing DOX-induced enrichment of the stem-like population and inducing apoptosis [61].
Detoxification effect
ACDs induce cancer cell death. However, the same mechanisms can also damage normal cells, resulting in side effects and a decline in quality of life. Cancer patients receiving ACDs may experience a variety of ADR, including fatigue, nausea, vomiting, mucositis, alopecia, dry skin, rashes, bowel changes, reduced blood cell counts, and an increased risk of infection [62]. Tissue damage is the most serious side effect of ACDs, leading to various organs toxicity, including kidneys, liver, heart, nerves, etc. [63]. Reducing ACDs-related organ toxicity is of critical importance [64]. Several clinical trials have demonstrated that TCMs can serve as an adjuvant therapy to ACDs and mitigate ADR [35, 45, 55, 65, 66]. Based on ACDs’ special organ toxicity, herbal medicines can provide protective effects for different tissues and organs, as shown in Table 3.
Table 3.
Detoxification effect of herbal medicines on ACDs
| ACDs | Herbal medicine | Herb type | Subject and dosage | Interaction | Mechanism | References |
|---|---|---|---|---|---|---|
| 5-FU | Bletilla striata polysaccharide | Active constituents | Mice (120 mg/kg, i.p.) | Ameliorate the toxic and side effects of 5-FU in the intestinal tract and bone marrow | Regulate nucleotide synthesis, inflammatory damage, and hormone production | [215] |
| Martynoside | Active constituents | In vitro; Mice (20 mg/kg, p.o.) | Protect against 5-FU-induced myelosuppression in vitro and in vivo | NR | [216] | |
| Xianglian Pill | Herbal prescription | Mice (2.5–0.625 g/kg, p.o.) | Alleviate its gastrointestinal side effects | Regulate the p38 MAPK/NF-κB pathway | [217] | |
| Kanglaite injection | Herbal prescription | Human (200 mL, i.v.) | Reduce AEs such as vomiting, diarrhea, hematoxicity, hepatotoxicity, neurotoxicity | NR | [35] | |
| Fuzheng jiedu Quyu recipe | Herbal prescription | Human (15 g, p.o.) | Lighter AEs and reduce the incidence of grade III-IV AEs | NR | [53] | |
| CDDP | Betulin | Active constituents | Rats (8 mg/kg, i.p.) | Reverses CDDP-induced liver injury | Inhibit apoptosis and the NLRP3 inflammasome pathway | [78] |
| Chiisanoside | Active constituents | Mice (25–100 mg/kg) | Reverse CDDP-induced ototoxicity | Regulate actin homeostasis; Inhibit ferroptosis | [218] | |
| Curcumin | Active constituents | Mice (100 mg/kg, i.p.) | Prevent CDDP-induced renal inflammatory injury | Modulate the NF-κB signaling pathway | [70] | |
| Panduratin A | Active constituents | In vitro; Mice (50 mg/kg) | Protects against CDDP-induced nephrotoxicity | Diminish mitochondrial dysfunction and ROS generation | [71] | |
| Umbelliferone | Active constituents | Mice (40 mg/kg, i.p.) | Prevent CDDP-induced nephrotoxicity | Regulate Nrf2 pathway | [72] | |
| Ursolic acid | Active constituents | Mice (80 mg/kg, i.p.) | Attenuate CDDP-induced hearing loss | Inhibit the TRPV1/Ca2+/calpain oxidative stress pathway | [219] | |
| Kanglaite injection | Herbal prescription | Human (200 mL, i.v.) | Reduce severe toxicities by 59%, including hematoxicity, vomiting, neurotoxicity and hepatotoxicity | NR | [76] | |
| Fufang Kushen injection | Herbal prescription | Human (200 mL, i.v.) | Reduce the frequency of gastrointestinal reaction, hepatoxicity and hematoxiciy, | NR | [55] | |
| Salvia officinalis L. | Herbal extract | Rats (250 mg/kg, p.o.) | Ameliorate CDDP-induced hepatotoxic effects | NR | [77] | |
| CPT-11 | Kangai injection | Herbal prescription | Rats (4 mL/kg, i.v.) | Alleviate the severe weight loss induced by CPT-11 in tumor-bearing mice | NR | [220] |
| CTX | Pithecellobium dulce (Roxb.) Benth | Herbal extract | Mice (40 mg/kg, p.o.) | Overcome CTX-induced immunosuppression accompanied with urotoxicity, hepatotoxicity, and nephrotoxicity | NR | [221] |
| DOX | Glycyrrhetinic acid | Herb active constituents | In vitro; Zebrafish Embryo (40 μM); Mice (40 mg/kg, p.o.) | Protect the heart from DOX-induced cardiotoxicity | Inhibit ferroptosis | [85] |
| Hyperoside | Active constituents | Mice (15–30 mg/kg, p.o.) | Prevent DOX-induced cardiotoxicity | Inhibit the NOXs/ROS/NLRP3 inflammasome signaling pathway | [84] | |
| Piper nigrum L. | Herbal extract | Rats (100–200 mg/kg, p.o.) | Ameliorate DOX toxicity of blood chemical and immunological properties in mammary tumor rats | NR | [222] | |
| Orange peel | Herbal extract | Mice (50 mg/kg, i.p.) | Protect the cellular toxicity of DOX | Inhibit cellular apoptosis | [223] | |
| Tripterygium glycoside | Active constituents | Rats (10 mg/kg, p.o.) | Ameliorate NS induced by DOX in rats | NR | [73] | |
| Qishen Granule | Herbal prescription | in vitro; Mice (1.67–6.66 g/kg, p.o.) | Protect against DOX-induced cardiotoxicity | Coordinate MDM2-p53-mediated mitophagy and mitochondrial biogenesis | [82] | |
| Shenmai injection | Herbal prescription | Rats (4.5–9 mL/kg, i.v.) | Alleviate the myocardial injury induced by DOX | Inhibit myocardial autophagy | [81] | |
| GMC | Aidi injection | Herbal prescription | Human (50–100 mL, i.v.) | Reduce the risk of gastrointestinal toxicity, hepatotoxicity, nephrotoxicity | NR | [45] |
| MTX | Morinda officinalis iridoid glycosides | Active constituents | Rats (50–100 mg/kg, p.o.) | Attenuate MTX induced-liver injury | Reverse metabolism disturbance, inhibit the apoptosis and increase the formation of autophagosome | [79] |
| Egyptian propolis | Herbal extract | Mice (500 mg/kg, i.p.) | Improve the hepatic and renal biochemical and toxicity parameters of MTX in EAC-bearing mice | NR | [224] | |
| OXA | Curcumin | Active constituents | Mice (10 mg/kg, i.p.) | Alleviate OXA-induced neuropathic pain | Enhance Nrf2-antioxidant responses | [225] |
| JianPi-BuShen | Herbal prescription | Human (136 g, p.o.) | Reduce gastrointestinal reaction and neurotoxicity, improve completion rate in chemotherapy | Inhibit pyroptosis | [226] | |
| PTX | Goshajinkigan | Herbal prescription | Human (7.5 mg, p.o.) | Less significantly frequent persistent CIPN 6 months post-chemotherapy | Act on spinal kappa-opioid receptors | [91, 92] |
5-FU 5-fluorouracil, AEs adverse events, Ara-C cytarabine, CDDP cisplatin, CIPN chemotherapy-induced peripheral neurotoxicity, CPT-11 irinotecan, CTX cyclophosphamide, DOX doxorubicin, GFT gefitinib, GMC gemcitabine, i.p. intraperitoneal injection, i.v. intravenous injection, MMC mitomycin C, MTX methotrexate, NR not reported, OXA oxaliplatin, PTX paclitaxel, SOF sorafenib, TMZ temozolomide, TNBC triple-negative breast cancer, TPT topotecan, VCR vincristine
The kidneys are the primary excretory organs in the human body, and most ACDs are eliminated through renal excretion. Consequently, ACDs remain in prolonged contact with various renal regions, leading to nephrotoxicity in different parts. CDDP and methotrexate (MTX) can induce acute kidney injury through direct toxicity to renal tubular epithelial cells, apoptosis activation, oxidative stress, mitochondrial damage, or drug crystallization within renal tubules [67–69]. DOX also causes nephrotic syndrome (NS) by exerting direct toxicity on podocyte cells and inducing oxidative stress and inflammatory responses. Herbal medicines mitigate nephrotoxicity through inhibiting renal cell apoptosis, oxidative stress, and mitochondrial damage. Curcumin prevents CDDP-induced renal inflammatory injury by modulating the NF-κB signaling pathway and reducing the expression of IL-1β, IL-6, IL-8, and TNF-α [70]. Panduratin A, a bioactive compound derived from Boesenbergia rotunda (L.) Mansf., protects against CDDP-induced nephrotoxicity by diminishing mitochondrial dysfunction and intracellular ROS generation. [71]. Umbelliferone prevents CDDP-induced nephrotoxicity through regulating Nrf2. [72]. Tripterygium glycoside protects against DOX-induced NS in rats by alleviating podocyte morphological damage, inhibiting caspase-3 activity, and reducing apoptosis [73].
The liver is the primary site for drug metabolism and is significantly exposed to ACDs. Chemotherapeutic agents such as CDDP, MTX and cyclophosphamide (CTX) have been reported to cause hepatotoxicity, which is primarily manifested as elevated transaminase levels, lipid disorders, and cholestasis [74]. Herbal prescriptions Kanglaite injection and Kushen injection have been found in clinical trials to reduce the hepatotoxicity of CDDP significantly and are expected to be used as adjunctive therapies with CDDP [55, 75, 76]. Salvia officinalis ethanolic extract effectively ameliorates CDDP-induced hepatotoxicity by reducing liver enzyme activity, alleviating oxidative stress, and improving histopathological changes [77]. By targeting apoptosis and the NLRP3 inflammasome pathway independently of Nek7, Betulin effectively reverses CDDP-induced liver injury [78]. Morinda officinalis iridoid glycosides alleviate MTX-induced hepatotoxicity by inhibiting hepatocyte apoptosis, modulating oxidative stress, and reversing lipid metabolism disorders [79]. Pithecellobium dulce extract mitigates CTX-induced hepatotoxicity and nephrotoxicity by improving immunosuppression [77].
Cardiotoxicity is one of the most severe ADR caused by ACDs, contributing to treatment-related mortality. It is commonly observed with anthracycline-based drugs, such as DOX. Certain mechanisms of cardiotoxicity remain unclear but may involve oxidative stress, apoptosis, abnormal expression of related genes, calcium overload, and the generation of toxic metabolites [80]. Some TCMs and natural products have demonstrated protective effects against DOX-induced cardiac dysfunction. Shenmai injection alleviates myocardial injury by inhibiting excessive myocardial autophagy by regulating the miR-30a/Beclin 1 pathway [81]. Qishen Granule protects against DOX-induced cardiotoxicity by coordinating MDM2-p53-mediated mitophagy and mitochondrial biogenesis [82]. Hyperoside protects HL-1 cells from DOX-induced cardiotoxicity by inhibiting the ASK1/p38 signaling pathway and NOXs/ROS/NLRP3 inflammasome signaling pathway [83, 84]. Glycyrrhizic acid protects the heart from DOX-induced cardiotoxicity by activating the Nrf2/HO-1 signaling pathway [85].
Platinum and paclitaxel (PTX) could induce neurological dysfunctions or autonomic nerves, referred to as chemotherapy-induced peripheral neurotoxicity (CIPN) [86]. The incidence of CIPN is predicted to be around 68.1% within the first month following treatment [87]. Oxaliplatin (OXA) causes severe acute and chronic peripheral neuropathies, which may be attributed to altered ion channel activity and significant internal calcium ion depletion [88]. PTX undermines microtubule dynamics, leading to mitochondrial malfunction and the induction of oxidative stress in peripheral neurons [89]. TCMs such as Astragalus membranaceus (Fisch.) Bunge and Atractylodes Macrocephala Koidz. have been widely used in clinical practice to prevent chronic OXA-induced CIPN. This may be attribute to the modulation of the inflammatory response induced by NF-κB on microglial activation [90]. Goshajinkigan (GJG), which consists of 10 types of herbal medicines, showed potential for mitigating CIPN symptoms of PTX in a randomized comparative trial [91]. The mechanism may be GJG’s action on spinal kappa-opioid receptors through dynorphin release, hence diminishing the perception of pain [92].
Pharmacokinetic-mediated herb-drug interactions
The therapeutic efficacy of ACDs is closely related to their concentration in the tumor or blood circulation. Most ACDs in the clinic, especially cytotoxic drugs, are usually used at the maximum tolerated dose to achieve maximum killing of cancer cells [93]. However, the therapeutic index of ACDs is minimal, and a 20% increase in blood peak concentration (Cmax) or area under curve (AUC) can lead to a substantial increase in toxicity [2]. Specifically, the main cause is the inhibition or induction of drug metabolizing enzymes (DME) and drug transporters (DT). Figure 2 provides a schematic diagram of the PK-mediated HDIs mechanism. A summary of common herbal medicines-mediated pharmacokinetic interactions with ACDs is shown in Table 4.
Fig. 2.
The pharmacokinetic-mediated mechanism of herb-drug interactions
Table 4.
Pharmacokinetic interaction of herbal medicines on ACDs
| ACDs | Herbal medicines | Herb Type | Subject and dosage | Interaction | Mechanism | References |
|---|---|---|---|---|---|---|
| 5-FU | Furanodiene | Active constituents | Zebrafish | Synergistize anti-cancer effect for both MDA-MB-231 cells and BEL-7402 cells xenotransplanted zebrafish | Inhibit P-gp | [227] |
| Terpenoids | Active constituents | In vitro; Mice (50 mg/kg, p.o.) | Enhance the chemotherapy sensitivity of HCT-8/Fu to 5-FU; Enhanced 5-FU accumulation in vitro and in vivo | Inhibit P-gp | [228] | |
| Apatinib | Huosu Yangwei oral liquid | Herbal prescription | Rats (10 mL/kg) | Prolong the plasma half-life; Increase AUC | Inhibit CYP1A, CYP2A6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A | [229] |
| Ara-C | Proanthocyanidin | Active constituents | In vitro | Lower the IC50 of Ara-C and DOX in HL-60/DOX cells | Inhibit MRP1, P-gp, LRP | [230] |
| CDDP | Astragaloside IV | Active constituents | In vitro; Mice (50 mg/kg, p.o.) | Enhance the antitumor effect of CDDP in HepG2 cells and H22 tumor-bearing mice | Inhibit MRP2 | [231] |
| Furanodiene | Active constituents | Zebrafish | Synergistize anti-cancer effect for both MDA-MB-231 cells and BEL-7402 cells xenotransplanted zebrafish | Inhibit P-gp | [227] | |
| Kanglaite injection | Herbal prescription | In vitro | Increase the antitumor effects of CDDP on HepG2 cells | Inhibit P-gp, MRP2, BCRP | [75] | |
| Wedelolactone | Active constituents | In vitro; Mice (20 mg/kg) | Reduce kidney accumulation; Ameliorate CDDP-induced kidney injury | Inhibit OCT2 | [110] | |
| 6-methylflavone | Active constituents | In vitro; Mice (50 mg/kg) | Decrease CDDP-induced cytotoxicity and kidney injury | Inhibit OCT2 | [109] | |
| CPT-11 | Psoralidin | Active constituents | Mice (500 mg/kg, p.o.) | Increase gastrointestinal toxicity and body weight loss of CPT-11 | Inhibit UGT1A1 | [103] |
| Caffeic acid | Active constituents | Mice (400 mg/L p.o.) | Increase SN-38 glucuronide excretion; Improve the leukopenia, intestinal oxidative stress and inflammation of CPT-11 | Induce UGT1A1 | [232] | |
| CTX | Rhizoma paridis saponins | Active constituents | Rats (200 mg/kg, p.o.) | Reduce CTX anticancer effect and toxicity in hepatocarcinoma model | Inhibit CYP2B6, CYP3A4 | [233] |
| Dasatinib | Sinapic acid | Active constituents | Rats (20 mg/kg, p.o.) | Increase Cmax, AUC, MRT, Tmax, systemic bioavailability; Decrease Vd and CL | Inhibit CYP3A2, P-gp and BCRP | [234] |
| DOX | Dioscorea bulbifera L | Herbal extract | Mice (0.1 mL/10 g, p.o.) | Decrease survival rate, induce elevated levels of toxicity in the heart and kidneys and delay excretion of DOX | Inhibit P-gp | [18] |
| Glabratephrin | Active constituents | In vitro; Mice (5 μM, i.v.) | Increase DOX accumulation and cytotoxicity in breast cancer cells; Reduce the growth of Pgp-expressing tumors | Inhibit P-gp | [118] | |
| Saikosaponin D | Active constituents | In vitro; Mice (10 mg/kg, i.p.) | Enhance DOX anticancer efficacy in vitro and in vivo; Increase DOX accumulation in breast cancer cells | Inhibit P-gp | [235] | |
| Tetrandrine | Active constituents | In vitro | Increase the cytotoxicity of DOX, VCR and PTX; Increase the intracellular accumulation in KB-C2 cells | Inhibit P-gp | [123] | |
| Voacamine | Active constituents | In vitro | Increase the cytotoxicity of PTX or DOX in A2780 DX and LoVo DX drug-resistant cells | Inhibit P-gp | [121] | |
| Algerian propolis | Herbal extract | In vitro | Increase Dox content in MDA-MB-231 cells; Decrease the IC50 of Dox | Inhibit P-gp | [116] | |
| Euryops pectinatus L | Herbal extract | In vitro | Increased the potency of DOX in MCF/Dox, CEM/ADR500 cells and Caco2 cells | Inhibit P-gp | [117] | |
| Proanthocyanidin | Active constituents | In vitro | Lower the IC50 of Ara-C and DOX in HL-60/DOX cells | Inhibit MRP1, P-gp, LRP | [230] | |
| Scabiosa atropurpure L. | Herbal extract | In vitro | Increase anti-proliferative effects in Caco-2 cells | Inhibit P-gp and MRPs | [120] | |
| Shenmai injection | Herbal prescription | In vitro; Mice (5 mL/kg, i.v.) | Strengthened the toxicity to MCF-7/DOX cells; Increase intracellular concentrations; Reduce the weight and volume of tumor | Inhibit P-gp | [122] | |
| Cinnamophilin | Active constituents | In vitro | Enhance cytotoxic effects of DTX, VCR, or PTX in MDR human cervical cancer cell line | Inhibit P-gp | [236] | |
| DTX | Wogonin | Active constituents | Rats (10–40 mg/kg) | Increase Cmax and AUC of DTX in rats with mammary tumors | Inhibit CYP3A4, P-gp | [237] |
| St John’s wort | Herbal extract | Human (300 mg) | Decrease AUC; Increase clearance | Inhibit CYP3A4 | [238] | |
| Marsdenia tenacissima (Roxb.) Moon | Herbal extract | In vitro | Restore GFT sensitivity in GFT-resistant NSCLC cells | Inhibit CYP3A4, CYP2D6 | [239] | |
| GFT | Trigonella foenum-graecum L | Herbal prescription | Human | Exacerbate ribociclib-induced hepatotoxicity | Inhibit CYP3A4 | [19] |
| Ribociclib | St John’s wort | Herbal extract | Human (300 mg) | Decrease AUC; Reduce Cmax and half-life | Inhibit CYP3A4 | [240] |
| IM | Panax ginseng C. A. Mey | Herbal extract | Human | Cause hepatotoxicity | Inhibit CYP3A4 | [96] |
| Botryllamide G | Active constituents | Mice (13.4 mg/kg, i.v.) | Increase brain exposure to lapatinib in mice | Inhibit BCRP | [115] | |
| Lapatinib | Luteolin | Active constituents | Rat (20 mg/kg, p.o.) | Decrease MTX-induced cytotoxicity; Increase AUC | Inhibit OATP1B1 | [112] |
| MTX | Isosinensetin | Active constituents | Rat (20 mg/kg, p.o.) | Attenuate MTX-induced nephrotoxicity; Reduce MTX renal concentrations | Inhibit OAT3 | [111] |
| Dihydromyricetin | Active constituents | In vitro; Mice (100 mg/kg, i.p.) | Restore chemosensitivity (OXA and VCR) in HCT116/OXA and HCT8/VCR cell lines and in vivo | Inhibit MRP2 | [241] | |
| OXA | Cinnamophilin | Active constituents | In vitro | Enhance cytotoxic effects of DTX, VCR, or PTX in MDR human cervical cancer cell line | Inhibit P-gp | [236] |
| PTX | Tetrandrine | Active constituents | In vitro | Increase the cytotoxicity of DOX, VCR and PTX; Increase the intracellular accumulation in KB-C2 cells | Inhibit P-gp | [123] |
| Voacamine | Active constituents | In vitro | Increase the cytotoxicity of PTX or DOX in A2780 DX and LoVo DX drug-resistant cells | Inhibit P-gp | [121] | |
| Xiaoaiping injection | Herbal prescription | in vitro; Mice (20-40 mL/kg) | Enhances anti-tumor effect of PTX in SK-OV-3 cells and xenograft tumor model | Inhibit CYP2C8, CYP3A4, P-gp | [242] | |
| Xiang-Sha-Liu-Jun-Zi Tang | Herbal prescription | Rats (250 mg/kg) | Increase AUC; Prolong the half-life | Inhibit CYP3A1, CYP3A2, CYP3A4 | [243] | |
| Cinnamophilin | Active constituents | In vitro | Enhance cytotoxic effects of DTX, VCR, or PTX in MDR human cervical cancer cell line | Inhibit P-gp | [236] | |
| VCR | Dihydromyricetin | Active constituents | In vitro; Mice (100 mg/kg, i.p.) | Restore chemosensitivity (OXA and VCR) in HCT116/OXA and HCT8/VCR cell lines and in vivo | Inhibit MRP2 | [241] |
| Tetrandrine | Active constituents | In vitro | Increase the cytotoxicity of DOX, VCR and PTX; Increase the intracellular accumulation in KB-C2 cells | Inhibit P-gp | [123] |
5-FU 5-fluorouracil, Ara-C cytarabine, CDDP cisplatin, CTP-11 irinotecan, CTX cyclophosphamide, DOX doxorubicin, DTX docetaxel, GFT gefitinib, IM imatinib, NSCLC non-small cell lung cancer, OXA oxaliplatin, PTX paclitaxel, VCR vincristine
Drug metabolism enzymes-mediated herb-drug interactions
ACDs undergo phase I and/or phase II metabolic reactions in the body, producing inactive or active metabolites. Among all phase I drug-metabolizing enzymes, cytochrome P450 enzymes (CYPs) play a key role in the metabolism of ACDs. In particular, CYP3A4, CYP2D6, CYP1A2, and CYP2C8 are the main CYP isoenzymes involved in ACDs metabolism. Investigations by Gougis et al. indicated that approximately 50% of anticancer drugs are metabolized via CYP3A4 [2]. Many herbal medicines could either induce or inhibit CYP3A4 activity, which is the most frequently reported cause of PK-mediated HDIs.
Induction of CYP3A4 activity enhances the metabolism of substrate drugs, leading to decreased AUC and Cmax, which often results in reduced therapeutic efficacy and lower toxicity. St. John’s Wort (SJW) is a typical inducer of CYP3A4 and CYP2B6, with its main active compound, hyperforin, increasing CYP3A4 activity via activation of the pregnane X receptor [94]. Clinical practice has long proven that SJW reduces the bioavailability, Cmax, and half-life of imatinib (IM), irinotecan (CPT-11), and DTX by inhibiting CYP3A4, reducing their efficacy and ADR [95]. In contrast, inhibition of CYP3A4 activity can increase AUC to substrate drugs, which may lead to severe safety concerns, particularly for ACDs with a narrow therapeutic window. For instance, Panax ginseng has been shown to inhibit the CYP3A4 metabolism of IM in humans, ultimately causing hepatotoxicity in chronic myeloid leukemia patients; the hepatotoxic reactions diminished after discontinuation of Panax ginseng [96]. Similarly, a recent case report described a female metastatic breast cancer patient developed grade III ribociclib-induced liver injury, which was caused by the Trigonella foenumgraecum L.’s CYP3A4 inhibition, and hepatotoxicity improved after stopping supplement [19]. In addition, CYP3A4-mediated interactions were also observed in a male NSCLC cancer patient treated with echinacea and etoposide, resulting in severe thrombocytopenia [97].
In addition to CYP-mediated phase I metabolism, many ACDs undergo phase II conjugation reactions, in which drug molecules combine with endogenous substances such as glucuronic acid or sulfate to form highly polar inactive metabolites for excretion. UDP-glucuronosyltransferases (UGTs) are the most important enzymes in phase II conjugation reactions, mediating approximately 35% of phase II metabolism [98]. UGTs are the largest detoxification enzymes in the body. Many herbal products rich in flavonoids, lignans, and anthraquinones have been found to inhibit UGTs [99]. Co-administration of such herbal medicines with UGT substrate drugs may result in severe safety issues. CPT-11 is converted by carboxylesterases into its active metabolite, SN-38, which induces gastrointestinal toxicity through mucosal damage. Hepatic UGTs (UGT1A1 and UGT1A9) detoxify SN-38 by converting it into its inactive glucuronide metabolite. Thus, co-administration of herbal medicines that inhibit UGT1A1 and UGT1A9 is risky [100, 101]. Studies have shown that baicalein (extracted from Scutellaria baicalensis Georgi) competitively inhibits the glucuronidation of SN-38 by inhibiting UGT1A1 [102]. Zhang et al. also demonstrated significant HDIs between CPT-11 and herbal medicines containing psoralen or flavonols, resulting in increased gastrointestinal toxicity of CPT-11 [103]. Additionally, UGT1A1 is responsible for bilirubin metabolism in the body, and inhibition of UGT1A1 activity by herbal medicines may lead to hyperbilirubinemia, especially when co-administered with UGT1A1-inhibiting ACDs (e.g. SOF). Multiple studies have suggested that the concurrent use of SOF with UGT1A1 herbal inhibitors should be avoided [104]. Reports on the induction of UGTs by herbal medicines are relatively limited. Increased UGT activity can enhance detoxification, thereby reducing the ADR of ACDs. SJW accelerates the glucuronidation of SN-38, significantly reducing hematological and gastrointestinal toxicity. This effect may be attributed to decreased SN-38 exposure levels, as well as the anti-inflammatory and anti-apoptotic properties of SJW [105–107].
Other Phase II metabolizing enzymes include glutathione S-transferase (GST), sulfotransferases (SULTs), catechol-O-methyltransferases (COMTs), and N-acetyltransferases (NATs) [23]. Although research on these metabolizing enzymes in HDIs is limited, their importance should be underscored. For example, GST catalyzes the conjugation of glutathione with ACDs, therefore reducing their efficacy [27]. Oridonin overcomes PANC-1/Gem cells gemcitabine resistance by inhibiting GST metabolic activity [108].
Drug transporters-mediated herb-drug interactions
DTs are transmembrane proteins widely expressed in various tissues and organs, playing a critical role in drug absorption, distribution, metabolism, and excretion. Transporters represent another key factor influencing drug concentrations in the human body. In recent years, an increasing number of clinical and basic studies have emphasized the significant role of transporters in the occurrence of HDIs. Numerous ACDs and their metabolites have been identified as substrates of DTs, and herbal medicines can modulate drug concentrations in the blood, tissues, or even within tumors by inhibiting or inducing the function and expression of transporters. DTs can be classified into uptake transporters and efflux transporters based on their transport direction.
Uptake transporters, including organic anion transporting polypeptides (OATPs), organic anion transporters (OATs), and organic cation transporters (OCTs), belong to the solute carrier transporters family. These transporters are widely distributed in various tissues and organs, such as the liver, kidneys, and intestines. They are primarily responsible for the uptake of small-molecule drugs and endogenous substances. They mediate the process of transporting substrates from the extracellular environment (typically the blood circulation) into the intracellular via passive diffusion or secondary active transport.
Many herbs or natural products have been reported to inhibit the function or expression of uptake transporters. This inhibition often prevents drug molecules from entering non-target organs such as the kidneys and liver, thereby reducing the organ toxicity of ACDs. For example, CDDP and MTX are taken up into renal epithelial cells through OCTs located on the basolateral membrane of renal tubules. The drug molecule accumulation in the kidney induces oxidative stress and ROS production, leading to proximal tubular necrosis and acute kidney injury. Natural products such as 6-methoxyflavone and wedelolactone have been shown to protect the kidneys of mice by inhibiting OCT2-mediated CDDP uptake [109, 110]. Similarly, Wang et al. reported that isosinensetin attenuated MTX-induced nephrotoxicity by inhibiting OCT3 uptake function and reducing MTX renal concentrations [111]. In the basolateral membrane of hepatocytes, OATPs mediate the uptake of various ACDs into liver cells. Inhibiting OATP activity can similarly reduce the ADR of hepatotoxic ACDs. Natural products luteolin have been shown to mitigate MTX-induced cytotoxicity by inhibiting the transport activity of OATP1B1 [112].
Efflux transporters, including P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), multidrug resistance proteins (MRPs), etc., are primarily members of the ATP-binding cassette transporters family. These proteins are expressed on the apical membranes of many secretory cell types, such as intestines, liver, kidneys, adrenal glands, and physiological barriers, including the blood–brain barrier (BBB) and blood-testis barrier. They actively transport substrates out of cells, performing a detoxification function by excreting substances into the intestinal lumen, bile canaliculi, and renal tubules, thereby protecting the body from exogenous compounds.
Inhibition of efflux transporters by herbal medicines influences the bioavailability of ACDs, increases drug concentrations in tumors or blood circulation, and ultimately enhances therapeutic efficacy. However, this is always accompanied by increased toxicity. P-gp and BCRP are highly expressed in intestinal epithelial cells, and their inhibition by herbal products can increase the oral bioavailability of ACDs. Boonnop et al. found that black ginger extract and its active compound, 5,7-dimethoxyflavone, enhanced the oral absorption of PTX by inhibiting P-gp and BCRP [113]. Similarly, Korean red ginseng extracts increased PTX bioavailability by inhibiting P-gp, thereby enhancing PTX’s anticancer effects against breast cancer [114]. In physiological barriers such as the BBB, P-gp and BCRP are highly expressed and efflux exogenous toxins, exhibiting neuroprotective effects. However, this also limits the entry of therapeutic drugs into the brain, diminishing efficacy. Numerous studies have focused on inhibiting one or both of these transporters to prolong the mean residence time of ACDs in the brain. For instance, the natural product botryllamide G, a potent BCRP inhibitor, when combined with tariquidar (a P-gp inhibitor), nearly doubled the brain exposure of lapatinib in mice [115]. Beyond normal physiological tissues, efflux transporters are also highly expressed in various cancer cells. Efflux transporters actively expel ACDs and prevent intracellular drug accumulation in cancer cells, thus contributing to the development of MDR. Herbal medicines can inhibit P-gp, BCRP, or MRPs, thereby reversing tumor MDR. This is particularly relevant for cytotoxic ACDs, such as DOX, PTX, and vincristine (VCR). Recent studies have demonstrated that various herbal medicines, such as Scabiosa atropurpurea L., inhibit the function of P-gp or downregulate its expression. These interventions have increased DOX accumulation and enhanced its antitumor activity in drug-resistant cancer cell lines (e.g., A2780/DX, LoVo/DX) [116–123]. On the other hand, increased DOX exposure also exacerbates side effects. Dioscorea bulbifera L. has been reported to delay DOX excretion by inhibiting P-gp, leading to worsening cardiac and renal toxicity and decreased survival rates [18].
In contrast, studies regarding herb-induced transporter activity are few and mainly associated with SJW. Low doses of SJW have been shown to induce intestinal P-gp expression in clinical practice, reducing drug absorption and consequently decreasing the bioavailability and therapeutic efficacy of co-administered drugs [124]. However, SJW does not appear to induce P-gp expression at the BBB. Therefore, co-administration of SJW with P-gp substrate drugs targeting the central nervous system is not expected to result in HDIs [125].
It is worth noting that genetic polymorphisms in DME or DT are likely to influence the extent of interactions with herbal medicines. Compared to individuals with weak metabolic or transport phenotypes, those with strong phenotypes are more susceptible to inhibition by DME or DT, and the degree of inhibition is greater [126]. For example, Radix Astragali had no statistically significant effect on the Cmax and AUC of fexofenadine in ABCB1 3435T carriers, but it significantly prolonged the half-life in individuals with the ABCB1 345CC genotype [127]. Similarly, garlic reduces the systemic clearance of DTX in patients with the CYP3A5 expressive phenotype (CYP3A5*1/*1), but this effect was not observed in patients with the non-expressive phenotype (CYP3A5*3/*3) [128]. It is important to note that the impact of genetic polymorphisms on HDIs has not been as thoroughly characterized as DDIs. The mechanisms underlying the association between genotype and HDIs in oncology treatment require further research.
Limitation of current herb-drug interactions studies
Unknown chemical composition
Unlike most pharmaceutical products with well-defined chemical structures and a robust foundation of pharmacological research, herbal medicines are typically composed of multiple chemical constituents. The net effect of the interaction could contribute to the additive effects of these individual components. Thus, merely studying HDIs between whole herbal preparations and ACDs is insufficient. It is essential to analyze the whole chemical constituents of the herbs, evaluate their effects individually, and identify the causative chemical constituents (CCCs) that contribute to the HDIs. This approach can provide a reference for HDI studies involving other herb medicines with similar CCCs structure. However, limited herbal medicines, such as SJW and milk thistle, have been fully characterized by chemical composition. Many HDIs studies are confined to herbal extracts or focus on a single major constituent of the herb, resulting in research bias. Consequently, the findings of such studies often have limited research value and fail to establish correlations between chemical structures and the occurrence of HDIs. HDI researchers are encouraged to foster interdisciplinary collaborations in fields such as medicinal chemistry, ensuring that the results are more scientifically robust and valuable.
Variability in herbal component
For herb medicine characterized by chemical composition, this composition is not fixed. The chemical constituents of the same herb may vary significantly depending on the place of origin. This variability often renders research findings of HDIs for the same herb non-generalizable. For example, the use of ginseng (250 mg/day) in NSCLC patients induced CYP3A4 activity, thereby increasing the metabolism of gefitinib and reducing efficacy [129]. However, this finding contradicts the CYP3A4 inhibitory effect observed in the co-administration of ginseng with IM. Audrey et al. attributed this variability to differences in the ginsenoside content of commercial ginseng preparations [130, 131]. In addition, different extraction methods also lead to variability in chemical composition, ultimately affecting research outcomes. Benkovic et al. reported that the combination of ethanol or water extracts of propolis with irinotecan significantly extended the median survival time of tumor-bearing mice [132]. However, only the ethanol extract exhibited a significantly synergistic antitumor effect. Research indicates that the total flavonoid and polyphenol contents in the ethanol extract of propolis were significantly higher than those in the water extract, which might explain the stronger anticancer activity of the ethanol extract in combination therapy [133]. Therefore, in research, it is crucial to meticulously document the manufacturer, origin, batch number, preparation method, and chemical composition of the herbs to ensure reproducibility of the results and facilitate comparison with other studies.
Risk evaluation strategy in herb-drug interactions
Numerous investigations on the mechanisms of HDIs focus solely on either PK or PD aspects, rendering their conclusions less applicable to clinical practice, particularly for ACDs that could benefit from synergy effects. For instance, oridonin has been found to enhance the mRNA expression of various CYP enzyme families (1a, 2a, 2d, 2e, 2c, and 3a) and induce CYP enzyme activity. Zhang and colleagues suggested that the clinical application of oridonin might pose potential risks for HDIs [134]. However, subsequent studies revealed that oridonin exhibits synergy effects with ACDs in various cancer cells, likely due to its anti-angiogenic effect and inhibition of the anti-apoptotic protein Bax-2 [52, 135]. Due to SJW’s strong CYP3A4-inducing effect, early studies have cautioned against the co-administration of SJW with medications. However, Chen et al. found that hypericin (extracted from SJW) synergized with DOX to kill MDA-MB-231 cells and alleviated DOX-induced cardiac toxicity [84], suggesting the potential benefits of combination therapy. This bias is primarily due to the complexity arising from the multi-component of herbal medicines and their multi-target effects when combined with ACDs. Judging the compatibility of a herb with ACDs based solely on one aspect of research is overly simplistic and risks overlooking a promising ACD sensitizer. HDIs-related research should be conducted from a holistic and dialectical perspective.
In recent years, advancements in computer technology have led to the broth of various DDI risk prediction methods in silico. These methods quantitatively predict the net effect of combination therapy by calculating the effects of drugs on DME, DT, and targets. This research method, which integrates multidimensional information from PK and PD, is particularly well-suited for the complex study of HDIs. Therefore, we further summarize the current comprehensive risk prediction strategies to assess the suitability of combining herbs with ACDs from a holistic, systemic perspective.
Modelling and simulation approaches
Modelling and Simulation (MS) approaches are emerging computational techniques that abstract key factors influencing drug disposition and pharmacological effects through various quantitative methods. These approaches establish mathematical models and integrate specific information about exogenous substances to predict potential drug-drug interactions (DDIs), support decision-making in drug development, and optimize clinical treatment regimens. Since 2012, regulatory agencies such as the FDA and EMA have officially recognized the pivotal role of MS approaches in predicting the risk of DDIs during new drug development and have issued corresponding regulatory guidance [136, 137]. Notably, physiologically based pharmacokinetic (PBPK) modelling is particularly emphasized in these guidelines for its ability to predict the risk of DDIs.
The PBPK model can forecast the effects of perpetrator compounds on drug-metabolizing enzymes and transporters, enabling the prediction of potential clinical DDIs. Establishing a PBPK model requires two sets of parameters: physiological parameters (e.g., organ weight, organ blood flow rate) can be obtained from literature, while compound-specific parameters (e.g., tissue partition coefficients, absorption rate constants, and metabolic clearance rates) need to be determined through in vitro, in vivo studies, or clinical trials. These parameters can also be estimated based on the physicochemical properties of the respective compounds. PBPK models for both victim and perpetrator compounds can be linked through appropriate interaction mechanisms, such as reversible inhibition (RI) or time-dependent inhibition (TDI), to simulate PK-mediated DDIs [136]. Various commercial software platforms are available to facilitate PBPK model development. Differential equation-solving software packages include MATLAB Simulink, Berkeley Madonna, Wolfram Mathematica, and acslX. These programs do not contain predefined model structures or differential equations, making the complexity and flexibility of the model dependent on the researcher’s objectives and programming capabilities. On the other hand, software such as Simcyp, PK-Sim, GastroPlus, and MATLAB SimBiology provide template-based model structures, although at the cost of complete customization [138].
Although PBPK model-based predictions of DDIs are well-established, research on predicting HDIs remains relatively limited. Current studies mainly focus on herbs or natural products with well-defined clinical PK profiles, such as SJW, milk thistle, and Wuzhi capsules. Furthermore, existing research predominantly centres on the effects of herbs on DME.
Pilla Reddy et al. developed a PBPK-based HDI risks decision tree by integrating in vitro and clinical PK data of the main components of SJW-bergamottin, curcumin, and hyperforin. This model successfully predicted a moderate clinical HDI risks (1.57-fold) between SJW and anticancer drugs such as acalabrutinib, osimertinib, and olaparib [139]. Similarly, Adiwidjaja et al. established a PBPK model for hyperforin to predict the effects of SJW on CYPs and evaluated interactions between hyperforin and substrates of CYP3A, CYP2C9, and CYP2C19. Their study revealed that hyperforin concentrations are significantly higher in the intestine compared to the liver, with intestinal CYPs induction being more pronounced than hepatic CYPs induction (15.5-fold vs. 1.1-fold, respectively) [140].
Gufford et al. developed a quantitative model to assess the risk of clinical interactions between silybin, the main active ingredient of milk thistle, and raloxifene. The model result indicated that silybin could increase the AUC and Cmax of raloxifene by 30% by affecting intestinal glucuronidation [141]. This predictive modelling was further validated in a clinical trial, where silybin caused a 9% increase in the AUC of raloxifene in healthy volunteers, with one subject experiencing a twofold increase in AUC and a threefold increase in Cmax., further demonstrating the potential applicability of PBPK modelling in predicting clinical HDI risks.
The main active ingredient of Wuzhi capsules is extracted from Schisandra sphenanthera plant (Magnoliaceae family), which has hepatoprotective effects and is clinically approved to be used in combination with the immunosuppressant Cyclosporin A (CsA) to alleviate CsA-induced hepatotoxicity. Fan et al. used PBPK modelling to predict that multiple doses of two major active components of Wuzhi capsules, schisandrin A and schisandrol B, could increase CsA’s AUC by 226 and 36%, respectively. This suggests that when combined clinically, CsA doses can be reduced to lower the risks of ADR [142]. Multiple doses of schisandrin A and schisandrol B were also predicted to increase the AUC of MTX by 29 and 301%, and Cmax by 7 and 75%, respectively [143]. He et al.’s PBPK model predicted that schisantherin A from Wuzhi capsules exhibited TDI of CYP3A4, while schisantherin B exhibited both RI and TDI of CYP3A4 and CYP3A5. Multiple doses of schisantherin B were predicted to increase the AUC of tacrolimus by 26% in CYP3A5 expressers and by 57% in non-expressers [144]. Additionally, Adiwidjaja’ PBPK model predicted that schisandrol B and schisantherin B could effectively inhibit CYP3A4-mediated metabolism of IM and bosutinib. Co-administration in clinical practice could increase bosutinib exposure (AUC ratio of 3.0), but would not affect IM exposure [145]. These PBPK models collectively provide a framework for the prospective evaluation of HDI potential, offering evidence-based insights into the risks or safety of herb-drug combinations.
Herb-drug interactions database
Herb-drug interactions database is a structured data collection system designed to rapidly retrieve critical HDI risks extracted from a large body of literature, providing essential information for clinical practitioners. Since the public became aware of the clinical significance of HDIs, researchers have been attempting to establish HDIs databases using various information technologies as early as the 1990s [146–148]. Those databases enhance clinical decision-making by systematically compiling scientific evidence of HDIs, providing risks assessment management strategies to enable clinical practitioners avoid ADR in herb-drug combinations. More importantly, the categorical compilation of HDI mechanisms of action allows for rapid identification of interaction characteristics and thus prediction of unobserved interactions [149].
To date, more than ten free or commercial HDIs databases have been developed [150]. Among the free databases, prominent examples include the Chi Mei Search System (CMSS), the Chinese-Western Medicine Integrative Information Network (CWMIN), the Drug Herb Interaction Query Website (DHIQW), the Center of Excellence for Natural Product-Drug Interaction Research, and the Probot Chinese Medicine-Drug Interaction Database. Commercial databases primarily include the UW Drug Interaction Database (DIDB), Hédrine, Lexicomp Drug Interactions, the Natural Medicines Comprehensive Database, and Stockley’s Herbal Medicines Interactions.
As research on HDIs progresses, these databases require regular updates, with update frequencies ranging from daily to annually. However, HDIs information is often buried in various textual sources such as research papers, conference reports, books, and drug evaluation reports, making structured data extraction highly challenging [150]. This process requires a combination of extensive medical research expertise and strong information technologies support. Due to a lack of sustained funding, most free databases (e.g., CMSS, CWMIN, and DHIQW) have stopped receiving updates after their initial release. In contrast, commercial databases have continued to be maintained and updated. Among them, DIDB is currently the largest and is updated daily. Established in 2002 by Dr. Renée Levy at the University of Washington [151], DIDB contains the most extensive manually curated in vitro and clinical data, encompassing interaction events under various conditions, including co-administered drugs, excipients, food products, herbal medicines, tobacco, organ damage, and genetic factors. This database integrates information from literature, drug labels, FDA new drug applications, and biologics license applications. Through manual extraction, the data are presented in a structured format based on their underlying mechanisms within the DIDB [149, 152]. Interaction outcomes in DIDB include not only PK but also PD and safety data. As of June 2021, the application contained a total of 2,539 natural products (including herbal and food products) and 15,864 drug interaction experiments/studies [150].
Despite the advancements represented by HDIs databases, significant limitations persist. Poor consistency of predictive results across databases [153]. A study using Hédrine and the MSKCC database to prospectively assess the occurrence of HDIs in outpatients taking oral ACDs identified 46 HDIs in Hédrine and 22 in the MSKCC database, with only 9.5% of interactions common to both [5]. This low concordance can be explained by the absence of certain herbal medicine in one or the other database. In addition, high maintenance costs and non-standardised herb nomenclature likewise contribute to the limitations of the database risks assessment [150]. Future advancements require expanding data coverage and integrating AI models to address complex therapeutic scenarios globally.
Network pharmacology
Network pharmacology based on systems biology, genomics, proteomics, and other disciplines has emerged recently. It utilizes omics data analysis and computer simulation technologies to reveal the network relationship of drug-gene-target-disease interactions, and predicts the action mechanism, drug efficacy and ADR. Network pharmacology’s holistic, systematic and complete nature makes it very suitable for studying herbal medicine with multiple components and multitargets [154, 155]. In particular, the gene targets screening of tumor and drug metabolism has made a unique contribution in predicting HDI risks and explaining the mechanism of PD/PK interaction. Herb pair of Radix Astragali and Rhizoma Curcumae Phaeocaulis (HQEZ) has been found to reduce toxicity and increase the therapeutic effect of 5-FU [156]. Network analysis of HQEZ revealed that the 4 core compounds (folate, curcumin, quercetin and kaempferol) could affect chemoresistance and 5-FU sensitivity related targets, such as AKT1, EGFR, P-gp, BCRP, MMP2, TLR4, TLR9 and so on [157]. Systems pharmacology screening of Phyllanthus fraternus (PF) predicted 51 genes related to drug metabolism or drug transport, including ABCB1, CYP1A1, CYP1A2, CYP2C9, and CYP3A4, suggesting potential HDIs interactions [158]. Molecular docking and molecular dynamics further demonstrated that the three core components (2,4-bis(1,1-dimethylethyl)-phenol, 5-mecyloxy-N-[(5-methylpyridin-2-yl)sulphonyl]-1H-indole-2-carboxamide and E, E. Z-1,3,12-ninene-5,14-diol) interact more with the target.
Although the existing network pharmacology has been well used in predicting the mechanism of action of herbal medicines and the risks of HDIs. However, insufficient reliable data on herbal compounds remains an obvious obstacle to constructing sufficiently predictable herbal networks [159]. Another important challenge lies in the scientific and reliable validation of predictive results. The integration of technologies such as artificial intelligence is expected to bridge the network pharmacology limitations and strengthen the predictive reliability of herbal networks.
Machine learning and artificial intelligence
In recent years, with the development of artificial intelligence, researchers have tried to use machine learning (ML) to predict risks of drug interactions in clinical practice. ML leverages complex algorithms and mathematical methods to cluster and normalize large datasets, followed by calculations and predictions from large-scale omics data. Tools such as BestComboScore [160], DrugComb [161], DrugComboRanker [162], and DeepSynergy [163] have been used to predict drug combinations by employing deep learning (DL) and statistical models. These ML-based tools for drug combinations are of great significance in understanding HDIs.
The development of ML-based HDIs prediction models requires two types of information: biological (e.g., synergy datasets from herb-drug combination matrices, gene expression, microRNA expression, and proteome) and chemical (e.g., chemical fingerprints and molecular descriptors of herbal products) [154]. However, due to the lack of comprehensive material basis information, variability in the chemical composition of herbal medicines, and other related factors, ML-based HDIs prediction remains relatively underdeveloped and requires further advancement.
In addition, AI technology has been applied to the development of databases, as exemplified by SUPP.AI, which is currently the only HDIs database utilizing AI technology. In 2019, a team from the Allen Institute for Artificial Intelligence (AI2) developed the SUPP.AI database to identify and catalog supplement-drug interactions, including those involving herbal medicines, using machine learning and natural language processing (NLP) techniques [164]. By leveraging AI, SUPP.AI can automatically extract HDIs information from biomedical literature, significantly reducing labor costs and enabling the database to remain freely accessible and regularly updated. As of November 2024, SUPP.AI had recorded approximately 2044 supplements, 2866 drugs, and 59,096 interactions [165]. Researchers and clinicians can search the database using keywords such as the name of an herb or drug. Search results include potential HDIs outcomes on entity pages, as well as related evidence sentences on interaction pages, where herbs and drugs are highlighted in each sentence. The evidence sentences are accompanied by their sources, with further source details linked through the semantic scholar tool. However, challenges such as the ambiguous differentiation between drugs and herbs, the lack of standardized terminology for herbal products, and limitations in NLP technology seem to restrict SUPP.AI’s ability to identify potential HDIs information from the literature [164].
ML promotes data-driven decision-making, which is highly compatible with the multi-level data integration approach of network pharmacology. Although there is currently no successful practical application, several recent reviews have proposed the concept of combining ML with network pharmacology and elucidated its underlying logic [166–168]. ML methods such as support vector machines, random forests, and DL have been proposed to enhance decision-making levels and accuracy of network pharmacology [169–171]. In this context, network pharmacology will benefit from ML-driven artificial intelligence models to represent drug-target, herb-target, drug-pathway, and herb-pathway interactions; supervised learning for interaction prediction; generative modelling; and hybrid models for integrating various data sources [168]. Particularly for herbs, this can encompass all known compounds with any pharmacological effects. This approach not only identifies direct HDIs but also secondary interactions that may arise due to downstream effects of metabolism or signaling pathways. In this process, obtaining high-quality, manually verified datasets to train ML models is currently an urgent challenge that needs to be addressed [166].
Summaries and future perspectives
TCM physicians have recognized the drugs interactions, leading to the creation of multi-herb prescriptions to achieve synergy or detoxification effect [23]. For example, there are prescriptions designed to enhance the efficacy of herbal medicines based on compatible TCM theories ‘Monarch, Minister, Assistant, and Guide’, as well as prohibited combinations based on “Nineteen Medicaments of Mutual Antagonism” [172, 173]. These ancient theories of TCM are rooted in extensive clinical practice, indicating that the paradigm of drug combinations has a long history. In recent years, the medical community has increasingly recognized the importance of drug interactions, shifting focus from DDIs to an increasing number of reported HDIs. This reflects a growing awareness of the prevalence of herbal medicines and the significant clinical benefits/risks associated with HDIs, particularly in oncology treatment. Early researchers generally regarded HDIs as adverse events, believing that herbs could affect the efficacy of ACDs, leading to unpredictable clinical outcomes. However, with the advancement of HDIs research, increasing evidence suggests that the combination of herbs and ACDs may provide beneficial effects for cancer patients. Nevertheless, due to the lack of comprehensive elucidation of HDIs mechanisms, combining herbal medicines and ACDs remains a topic of considerable debate.
Although modelling simulations, databases, network pharmacology and machine learning tools are widely used in drug discovery and have found some applications in herbal medicines research, their application in predicting HDIs is still in its early stages. For example, while PBPK models for predicting DDIs are well-established, HDIs predictions remain relatively underdeveloped. This is largely due to the complexity of herbal compositions, the lack of identification of CCCs, and insufficient clinical PK data for critical compounds. Furthermore, much of the existing research centres on the effects of herbs on DME, with relatively little attention to other potential mechanisms of HDIs. Additionally, challenges such as the unclear composition of many herbal medicines, the lack of standardized terminology for herbal products, and limitations in NLP technology significantly hinder the development of comprehensive and accurate HDIs databases. These issues complicate the extraction and organization of reliable data from literature, which is crucial for constructing databases that can accurately predict herb-drug interactions. Advancing this field requires collaborative efforts between systems biology experts, bioinformaticians, AI researchers, and herbal medicines specialists. Such interdisciplinary cooperation is essential to overcoming current challenges and accelerating progress in HDIs research, ultimately leading to more accurate predictions and safer clinical applications.
In clinical oncology, based on the risk prediction methods, we believe that some clinical strategies can be practiced to reduce the risks associated with HDIs. First, pre-treatment risk assessment. Prioritize herbs with established PK/PD profiles and avoid combinations with narrow therapeutic index ACDs (e.g., CPT-11, IM) using modelling simulations, HDIs databases, etc. Second, during-treatment endogenous biomarkers monitoring. Since the main physiological role of DME and DT is to dispose of various endogenous compounds, some studies in recent years have proposed to characterize the activity of DME and DT by detecting changes in the levels of these biomarkers, which has been well applied in the early DDI studies of drug discovery [174]. The real-time monitoring biomarkers during the course of herb-drug combination therapy can timely terminate the occurrence of ADR, which is even more promising in ACDs mostly metabolized by CYP3A4. Finally, dynamic dose adjustment, using AI algorithms to integrate real-world data to improve the combination drug delivery scheme, to achieve individualized and precise oncology treatment [175].
Conclusion
In conclusion, this review systematically analyzes the PD/PK mechanisms by which herbs influence ACDs and summarizes current predict methods for HDI risks, aiming to promote these predictive approaches to address existing limitations in HDIs research. Finally, we propose clinical mitigation frameworks to harness the therapeutic potential of herb-ACD combinations while minimizing risks in precision oncology.
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (No. 82074104, 82474169).
Abbreviations
- TCMs
Traditional Chinese Medicines
- HDIs
Herb–drug interactions
- ACDs
Anticancer drugs
- MDR
Multidrug resistance
- PK
Pharmacokinetic
- PD
Pharmacodynamic
- Cmax
Blood peak concentration
- AUC
Area under curve
- DME
Drug metabolizing enzymes
- DT
Drug transporters
- CYPs
Cytochrome P450 enzymes
- SJW
St. John’s Wort
- IM
Imatinib
- CPT-11
Irinotecan
- DTX
Docetaxel
- UGTs
UDP-glucuronosyltransferases
- SOF
Sorafenib
- OATPs
Organic anion transporting polypeptides
- OATs
Organic anion transporters
- OCTs
Organic cation transporters
- CDDP
Cisplatin
- MTX
Methotrexate
- ROS
Reactive oxygen species
- P-gp
P-glycoprotein
- BCRP
Breast cancer resistance protein
- MRPs
Multidrug resistance proteins
- BBB
Blood-brain barrier
- PTX
Paclitaxel
- DOX
Doxorubicin
- VCR
Vincristine
- 5-FU
5-Fluorouracil
- CTX
Cyclophosphamide
- Ara-C
Cytarabine
- GFT
Gefitinib
- EGCG
(−)-Epigallocatechin gallate
- BZM
Bortezomib
- EMT
Epithelial-mesenchymal transition
- PCD
Programmed cell death
- TNBC
Triple-negative breast cancer
- HCC
Hepatocellular carcinoma
- FA
Fanconi anemia pathway
- NSCLC
Non-small cell lung cancer
- CSCs
Cancer stem cells
- GC
Gastric carcinoma
- AKI
Acute kidney injury
- NS
Nephrotic syndrome
- CIPN
Chemotherapy-induced peripheral neurotoxicity
- OXA
Oxaliplatin
- GJG
Goshajinkigan
- CTX
Cyclophosphamide
- TPT
Topotecan
- MMC
Mitomycin C
- TMZ
Temozolomide
- MS
Modelling and simulation
- DDIs
Drug–drug interactions
- PBPK
Physiologically based pharmacokinetic
- RI
Reversible inhibition
- TDI
Time-dependent inhibition
- CsA
Cyclosporin A
- IT
Information technologies
- CMSS
Chi Mei search system
- CWMIN
Chinese-Western Medicine Integrative Information Network
- DHIQW
Drug Herb Interaction Query Website
- AI
Artificial intelligence
- ML
Machine learning
- NLP
Natural language processing
- ORR
Objective response rate
- DCR
Disease control rate
- QOL
Quality of life
Author contributions
Xiaoyan Duan: Writing – original draft, Writing – review & editing, Investigation. Xiaoyu Fan: Writing – review & editing, Formal analysis, Data curation. Haiyan Jiang: Supervision, Data curation. Jie Li: Data curation. Xue Shen: Supervision. Zeao Xu: Investigation. Ziqi Zhou: Formal analysis. Jia Xu: Writing – review & editing, Conceptualization. Chongze Chen: Writing – review & editing, Conceptualization. Hongtao Jin: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.
Funding
This work was supported by the National Natural Science Foundation of China (No. 82074104, 82474169).
Availability of data and materials
Not applicable.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Jia Xu, Email: doctorxujia@163.com.
Chongze Chen, Email: chongze@126.com.
Hongtao Jin, Email: jinhongtao@imm.ac.cn.
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Data Availability Statement
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