Synergistic Interactions of Cannabidiol with Chemotherapeutic Drugs in MCF7 Cells: Mode of Interaction and Proteomics Analysis of Mechanisms

Abstract

Cannabidiol (CBD), a nonpsychoactive phytocannabinoid, has recently emerged as a potential cytotoxic agent in addition to its ameliorative activity in chemotherapy-associated side effects. In this work, the potential interactions of CBD with docetaxel (DOC), doxorubicin (DOX), paclitaxel (PTX), vinorelbine (VIN), and 7-ethyl-10-hydroxycamptothecin (SN−38) were explored in MCF7 breast adenocarcinoma cells using different synergy quantification models. The apoptotic profiles of MCF7 cells after the treatments were assessed via flow cytometry. The molecular mechanisms of CBD and the most promising combinations were investigated via label-free quantification proteomics. A strong synergy was observed across all synergy models at different molar ratios of CBD in combination with SN−38 and VIN. Intriguingly, synergy was observed for CBD with all chemotherapeutic drugs at a molar ratio of 636:1 in almost all synergy models. However, discording synergy trends warranted the validation of the selected combinations against different models. Enhanced apoptosis was observed for all synergistic CBD combinations compared to monotherapies or negative controls. A shotgun proteomics study highlighted 121 dysregulated proteins in CBD-treated MCF7 cells compared to the negative controls. We reported the inhibition of topoisomerase II β and α, cullin 1, V-type proton ATPase, and CDK-6 in CBD-treated MCF7 cells for the first time as additional cytotoxic mechanisms of CBD, alongside sabotaged energy production and reduced mitochondrial translation. We observed 91 significantly dysregulated proteins in MCF7 cells treated with the synergistic combination of CBD with SN−38 (CSN−38), compared to the monotherapies. Regulation of telomerase, cell cycle, topoisomerase I, EGFR1, protein metabolism, TP53 regulation of DNA repair, death receptor signalling, and RHO GTPase signalling pathways contributed to the proteome-wide synergistic molecular mechanisms of CSN−38. In conclusion, we identified significant synergistic interactions between CBD and the five important chemotherapeutic drugs and the key molecular pathways of CBD and its synergistic combination with SN−38 in MCF7 cells. Further in vivo and clinical studies are warranted to evaluate the implementation of CBD-based synergistic adjuvant therapies for breast cancer.

1. Introduction

Medicinal cannabis and its secondary metabolites have garnered significant attention in recent years, and have been subject to extensive research and scrutiny [1,2,3,4,5,6,7]. From a medicinal and pharmaceutical standpoint, nonpsychoactive phytocannabinoids—such as cannabidiol (CBD)—are the most potentially beneficial components of cannabis’ chemical space [2,8]. CBD has displayed a wide range of pharmacological activities in several preclinical and clinical studies that enabled its potential application in inflammation, cancer, cardiovascular diseases, epilepsy, and neurodegenerative and psychiatric disorders [6,9,10,11,12,13,14,15,16,17,18]. The US Food and Drug Administration (FDA) has approved Epidiolex^®®—a pure CBD formulation—for use in patients with Lennox–Gastaut syndrome or Dravet syndrome seizures [19]. Sativex^®® (2.7 mg THC and 2.5 mg CBD per dose) has also been approved in around 30 countries for pain management in multiple sclerosis [18]. On a global scale, over 40 countries have approved medicinal marijuana/cannabis programmes. However, the Drug Enforcement Administration (DEA) has declared that CBD is a Schedule V banned substance, and its products must contain less than 0.1% ∆⁹-THC to be approved by the FDA [20]. The current legal status of CBD is neither clear nor harmonised worldwide, although there are widespread medicinal, food, or cosmetic products that contain CBD [21,22]. Intriguingly, liquid chromatography coupled with mass spectrometry enables the subnanomolar detection and quantitation of cannabinoids in different biological matrices [23].

CBD displays anticancer potential, blocking cancer initiation, progression, and invasion for various cancer types, including breast, lung, colon, prostate, brain, multiple myeloma, and skin cancers [6,7,9]. CDB, and the endocannabinoid system in general, have long been studied for their potential to treat cancer. Unlike the psychoactive cannabinoids, CBD has a comparatively lower affinity to both CB1 and CB2 receptors [24,25]. Nevertheless, it has been reported to act as a CB1 antagonist in murine brain tissue and vas deferens, or as an inverse agonist in human CB2 receptors [24,26]. Moreover, CBD interaction targets include 5-HT1A, GPR55, and PPAR-γ, in addition to TRPVs [27,28]. The basal respiration rate and ATP production in the gastric cancer cells were decreased by CBD, with suppressed proliferation and tumour growth, in a murine xenograft model [29]. CBD-induced mitochondrial stress in MCF7 cells, with a modulated mitochondrial redox and dynamics, was recently reported [30]. These are all suggestive of CBD’s impacts on the mitochondrial processes and energy production in cancer cells, where holistic multi-omics investigations are warranted in order to understand the underlying mechanisms. Other potential targets for CBD in cancers—such as cyclin-dependent kinases (CDK) and topoisomerases—are not well explored, despite the displayed selective inhibition of topoisomerase II by CBD derivatives such as HU-331 [31]. CBD’s anti-cancer mechanisms, its antiproliferative and pro-apoptotic effects, and its inhibitory effects on cancer metastasis, invasion, and migration were summarised in recent reviews [6,7,9]. CBD is also involved in the regulation of reactive oxygen species (ROS), endoplasmic reticulum (ER) stress, inflammation, and immunological modulation [6].

Breast cancer is the most common cancer type in women worldwide, with the highest regional incidence reported in Australia and New Zealand [32,33]. CBD was found to inhibit oestrogen-receptor-positive (ER+), -negative (ER−) or -triple-negative breast cancer cells (TNBC) in a dose-dependent manner, with relatively low IC₅₀ values [6,34,35,36,37,38,39]. This suggests that breast cancer cells are sensitive to CBD, while non-transformed epithelial breast cells (MCF10A) are not significantly affected [10,28,39,40]. Interestingly, the administration of CBD in several clinical trials, as a single dose of 150–900 mg [41,42,43,44,45,46,47,48,49] or 50–1000 mg/day for up to 13 weeks [50,51,52,53], showed overall good tolerability and safety profile, with mild side effects [54]. A variety of mechanisms for CBD’s antiproliferative effects have been reported, including autophagy, cell cycle arrest at the G₁/S checkpoint, and apoptosis in MCF7, MDA-MB-231, and 4T1 breast cancer cells [34,35,39,55]. Recent studies have found that the antiproliferative effect of CBD in breast cancer cells was independent of endocannabinoid receptors (to some extent), mediated primarily through ROS-induced cell death [34,35,39,56,57]. CBD’s pro-apoptotic actions were attributed to the downregulation of mTOR, AKT, 4EBP1, and cyclin D, as well as the overexpression of PPAR-γ [35,39], where CBD coordinated apoptosis and autophagy via Beclin-1 translocation and cleavage [39]. Furthermore, CBD inhibited the invasive and metastatic nature of aggressive TNBC breast cancer by suppressing the activation of the EGF/EGFR pathway and its downstream targets (AKT and NF-κB), along with Id-1 protein downregulation by ERK and ROS [34,36,37,55,56]. In comparison to daily administration, CBD administered three times per week boosted the longevity of mice while reducing the number of metastases [55,56]. CDB decreased TAM recruitment by downregulating CCL3, GM-CSF, and MIP-2, resulting in an overall reduction in angiogenesis [36]. Nevertheless, more research into the anticancer mechanisms of CBD at the proteome-wide level is needed in order to comprehensively understand and decode its complicated mechanisms to encourage future clinical trials.

Based on the anticancer actions of CBD, it has been suggested that combinations of CBD and chemotherapeutic drugs may overcome cancer resilience by targeting various cancers’ pathophysiological components [58,59]. Synergistic combinations offer the use of lower doses with reduced side effects and curtailed drug resistance [60,61]. For instance, CBD inhibited exosome and microvesicle release, making breast (MDA-MB-231), prostate (PC3), and hepatocellular (HEPG2) cancer cells more sensitive to cisplatin [57]. It also synergistically improved the effects of paclitaxel (PTX) and doxorubicin (DOX) in both MCF7 and MDA-MB-231 breast cancer cells [38]. Moreover, it reduced PTX-induced neurotoxicity via 5-HT1A receptors without affecting nervous system function or chemotherapeutic efficacy [62]. CBD amended DOX-induced cardiomyopathy by regulating the cardiac mitochondrial functions in murine models [63]. Nevertheless, the synergistic interactions of CBD with chemotherapeutic drugs have not been thoroughly investigated. In particular, limited data are available on effective doses and drug interactions, and further research is required [64,65]. In addition, the utilisation of different synergy metrics is fundamental, as there is no consensus on a gold standard synergy quantification model [66,67,68,69].

In the present study, MCF7 cell line was selected, as it is the most studied human breast cancer cell line [70]. In addition, breast cancer chemotherapeutic agents with different modes of action—taxane microtubule stabilisers (docetaxel, paclitaxel), anthracyclines (doxorubicin), microtubule-disruptive vinca-related alkaloids (vinorelbine), and topoisomerase I inhibitors (irinotecan metabolite = SN−38)—and their CBD combinations were studied. These chemotherapeutic agents represent the most common treatment options for stage I–IV breast cancer [71], along with other therapies, including hormonal therapy, radiotherapy, or surgery. We aimed to evaluate the synergistic interactions of CBD with chemotherapeutic drugs using different synergy quantitation metrics. The apoptotic profiles for the synergistic combinations were evaluated using flow cytometry. Furthermore, a shotgun proteomics study was conducted to decipher the proteome-level cytotoxic and synergistic mechanisms of CBD, and the most prominent (SN−38) synergistic combination.

2. Results and Discussion

2.1. Synergy Quantification of CBD and Standard Chemotherapeutic Drugs in MCF7 Human Breast Adenocarcinoma Cells

Since there is no agreement on a benchmark synergy model [69] to decipher the complex interactions between drugs, the exploration of different synergy models is advised. Therefore, multiple synergy quantification metrics were implemented to gain a comprehensive understanding of the potential synergistic interactions between CBD and the selected chemotherapeutic drugs. The combination index (CI) model can be utilised for pairwise or higher order drug combinations in constant and non-constant ratio combinations [72]. This model was used to quantify the cytotoxic interactions against MCF7 cells after 72 h of treatment. The CI < 1 and CI > 1 indicate synergism and antagonism, respectively, whereas additivity is indicated by CI = 1 [72]. CBD and the chemotherapeutic drugs were combined in molar ratios from 1431–18:1, respectively (

Table 1. Drug interaction analysis of cannabidiol (CBD) and the chemotherapeutic combinations in MCF7 breast cancer cells.

Combo ID, Molar Ratio	CI Values at					CSS	S	ZIP	BLISS	LOEWE	HSA
	IC50	IC75	IC90	IC95	IC97
CBD: Docetaxel
CDOC19, 18:1	1.23	1.35	1.64	2.07	2.60	35.5	0.33	−11.81	−19.12	−6.84	−15.76
CDOC28, 40:1	0.96	1.14	1.62	2.32	3.18	54.6	18.07	−10.9	−13.6	14.29	−9.42
CDOC37, 68:1	0.70	0.91	1.46	2.27	3.27	41.33	3.96	−9.74	−11.35	−8.14	−7.08
CDOC46, 106:1	0.49	0.70	1.23	2.04	3.03	49.82	13.09	−3.4	−5.61	−2.36	−1.38
CDOC55, 159:1	0.45	0.70	1.35	2.33	3.54	52.65	14.33	−3.34	−4.58	−2.05	−0.8
CDOC64, 238:1	0.50	0.86	1.80	3.20	4.94	51.66	12.22	−3.04	−4.2	−1.74	−0.29
CDOC73, 371:1	0.38	0.72	1.61	2.95	4.61	52.44	13.36	−2.36	−3.69	−1.35	0.07
CDOC82, 636:1	0.42	0.61	1.00	1.46	1.93	56.64	20.16	1.94	−0.1	1.86	3.4
CDOC91, 1431:1	0.48	0.73	1.17	1.65	2.12	56.04	20.19	1.96	−1.08	0.16	2.06
CI to DC	-	-	-	-	-	55.50	18.89	−8.41	−7.93	−1.46	−0.65
Checkerboard	-	-	-	-	-	72.91	−11.77	1.76	−5.07	−4.17	−0.66
Selected dose (39.75, 0.5 µM)	-	-	-	-	-	-	-	28.12	21.45	24.27	46.02
CBD: Doxorubicin
CDOX19, 18:1	1.17	1.16	1.15	1.14	1.14	64.78	46.12	6.07	4.52	−5.24	1.62
CDOX28, 40:1	1.12	1.11	1.10	1.10	1.09	66.93	46.23	3.32	1.97	−9.04	0
CDOX37, 68:1	1.07	1.06	1.05	1.05	1.05	66.94	43.69	0.18	−1.53	−10.92	−2.66
CDOX46, 106:1	1.42	1.41	1.40	1.39	1.38	65.5	42.45	0.61	−1.64	−11.27	−2.78
CDOX55, 159:1	1.34	1.33	1.32	1.31	1.31	24.4	1.7	−13.69	−46.05	−55.66	−48.18
CDOX64, 238:1	1.27	1.26	1.25	1.25	1.25	69.85	45.41	4.15	2.24	−6.37	0.99
CDOX73, 371:1	1.20	1.19	1.19	1.18	1.18	73.33	48.85	6.67	5.38	−2.87	4.15
CDOX82, 636:1	1.13	1.13	1.12	1.12	1.12	75.33	52.37	10.19	9.06	0.67	7.38
CDOX91, 1431:1	1.07	1.06	1.06	1.06	1.06	76.85	55.09	9.89	8.72	−2.69	6.75
CI to DC	-	-	-	-	-	70.24	53.04	4.96	4.05	−5.43	2.21
Checkerboard	-	-	-	-	-	54.90	−5.31	1.04	−1.05	−9.15	−4.31
Selected dose (38.42, 0.2 µM)	-	-	-	-	-	-	-	32.52	25.65	15.16	31.53
CBD: Paclitaxel
CPTX19, 18:1	1.50	1.61	1.89	2.35	2.95	35.94	0.09	−12.32	−20.86	−20.59	−19.3
CPTX28, 40:1	0.91	1.04	1.40	2.00	2.78	42.63	5.16	−7.42	−12.28	−11	−9.74
CPTX37, 68:1	0.70	0.85	1.31	2.05	3.03	40.73	2.11	−8.38	−11.49	−10.07	−8.88
CPTX46, 106:1	0.48	0.63	1.09	1.83	2.82	50.48	12.66	−2.18	−5.93	−4.42	−3.42
CPTX55, 159:1	0.37	0.54	1.04	1.85	2.93	55.54	16.97	−0.14	−3.26	−2.93	−1.55
CPTX64, 238:1	0.37	0.59	1.24	2.32	3.75	51.98	11.68	−1.04	−4.26	−3.37	−1.96
CPTX73, 371:1	0.34	0.61	1.41	2.73	4.49	52.76	12.71	−0.76	−3.19	−2.35	−1.01
CPTX82, 636:1 *	0.30	0.62	1.57	3.14	5.24	59.16	21.67	4.4	2.03	2.33	3.84
CPTX91, 1431:1 *	0.33	0.80	2.21	4.56	7.68	59.28	23.01	3.81	1.2	1.2	2.66
CI to DC	-	-	-	-	-	57.70	21.51	−6.44	−8.30	−3.69	−3.53
Checkerboard	-	-	-	-	-	53.78	−31.41	−5.35	−10.85	−7.90	−6.63
Selected dose (64.6, 0.1 µM)	-	-	0.68	-	-	-	-	~2.98	~−1.69	~6.1	~1.95
CBD:SN38
CSN19, 18:1	1.77	5.14	18.44	51.56	115.29	40.24	13.73	−0.43	−3.87	−3	−1.38
CSN28, 40:1	0.65	0.86	1.49	2.52	3.84	48.26	20.38	1.13	−0.58	1.23	2.95
CSN37, 68:1	0.57	0.76	1.33	2.19	3.23	45.18	15.31	−2.32	−4.31	−2.18	−0.62
CSN46, 106:1 *	0.47	0.59	0.95	1.44	1.98	50.63	21.35	1.96	0.13	2.04	3.69
CSN55, 159:1	0.47	0.60	0.94	1.37	1.82	50.13	19.75	1.08	−0.83	−0.05	1.7
CSN64, 238:1	0.45	0.60	0.97	1.40	1.85	49.6	18.07	0.23	−1.72	0.05	1.64
CSN73, 371:1 **	0.81	0.66	0.61	0.60	0.59	52.98	21.84	1.83	0.38	1.99	3.57
CSN82, 636:1 **	0.81	0.71	0.68	0.67	0.67	59.38	29.68	5.05	3.67	4.45	6.4
CSN91, 1431:1 **	0.92	0.93	0.91	0.91	0.90	58.73	29.9	3.43	2.21	2.79	4.66
CI to DC	-	-	-	-	-	48.39	23.86	2.37	1.35	3.58	4.46
Checkerboard	-	-	-	-	-	75.94	1.36	0.31	−3.24	−3.09	−0.08
Selected dose (42.45, 0.11 µM)	-	0.66	-	-	-	-	-	14.55	23.86	31.38	33.57
CBD: Vinorelbine
CVIN19, 18:1	1.30	1.39	1.64	2.02	2.53	31.94	−1.9	−9.19	−16.9	−16.72	−15.15
CVIN28, 40:1	1.03	1.18	1.58	2.25	3.12	37.98	2.98	−8.44	−12.37	−12.2	−10.12
CVIN37, 68:1	0.71	0.86	1.30	2.02	2.96	41.71	5.92	−6.9	−8.99	−7.59	−5.91
CVIN46, 106:1	0.56	0.74	1.26	2.12	3.25	44.84	9.79	−4.31	−6.29	−5.35	−3.24
CVIN55, 159:1	0.46	0.67	1.27	2.26	3.58	50.93	14.65	−1.54	−3.63	−3.13	−1.06
CVIN64, 238:1	0.45	0.72	1.52	2.84	4.58	49.75	12.98	−2.21	−5.08	−3.64	−1.84
CVIN73, 371:1	0.33	0.59	1.36	2.63	4.32	52.5	16.13	0.16	-2.16	−1.01	1.06
CVIN82, 636:1 *	0.32	0.65	1.65	3.30	5.49	55.57	21.19	3.31	1.6	1.94	4.38
CVIN91, 1431:1 **	0.73	0.60	0.56	0.55	0.55	56.32	25.34	5.55	3.36	2.56	5.09
CI to DC	-	-	-	-	-	57.45	23.54	−5.89	−6.49	−1.64	−0.70
Checkerboard	-	-	-	-	-	56.45	−30.43	−1.79	−6.39	−4.94	−4.07
Selected dose (42.4, 0.1 µM)	-	~0.62-	-	-	-	-	-	18.29	28.44	43.26	48.43

CI = combination index; CI to DC = all combined responses from CI model combinations were analysed via DrugComb server; CSS = combination sensitivity score; IC = inhibitory concentration killing the corresponding percentage of MCF7 cells; HSA = highest single agent model; ZIP = zero interaction potency model; ** = synergistic combinations in all models and inhibitory concentrations; * = synergistic combinations in all models and most effective doses; ~ = approximate score considered. Potential synergistic combinations with CI < 0.7 or synergy score > 1.5 in Loewe, HSA, ZIP, and Bliss models, or > 20 in the S model, are bold formatted. Potential effective combinations (CSS > 55) are bold formatted. Synergy metrics listed for the selected doses are derived from CI to DC.

). CompuSyn-calculated CI values at 50, 75, 90, 95, and 97% inhibitory concentrations are reported in Table 1. Each combination was denoted by an ID (e.g., CDOX19), where the last two digits indicate the corresponding molar ratio of CBD and DOX, as reported in Table 1. In addition, a checkerboard assay was used to combine the chemotherapeutic drugs with CBD in 1:10 and 1:2 serial dilutions, respectively (Figure 1). Therefore, synergy quantification in the Loewe, ZIP, BLISS, HSA, and S synergy score models along the combination sensitivity score (CSS) were scrutinised over a broader range of anticancer concentrations in order to comprehensively understand the synergistic interactions of CBD with the chemotherapeutic drugs against MCF7 cells.

A strong synergy was observed in different synergy models for the CDB combined with SN−38 (CSN91, CSN82, and CSN73) and vinorelbine (CVIN91). These combinations, unlike CPTX or CDOC, showed increased synergy in the CI model, with increased cell growth inhibition (Figure 2B), which is favoured for any anticancer treatment, where any antagonistic behaviour at a low fraction affected (Fa < 0.5 = 50% growth inhibition) will not be alarming. CBD and SN−38 combinations with a molar ratio of 1431, 636, or 371 to 1 displayed CI values of 0.9–0.93, 0.67–0.81, and 0.60–0.81, respectively, at the IC₅₀ to IC₉₇ modelled inhibitory concentrations. These combinations displayed synergistic scores in all DrugComb synergy models tested (Loewe, Bliss, HSA, ZIP, and S) when the same response data were imported to the DrugComb webserver. Moreover, CVIN91 (molar ratio of 1431:1 of CBD:VIN) showed a CI value of 0.55–0.73, with positive synergy scores in all DrugComb models. In addition, CVIN82 displayed synergy in all DrugComb models, with synergistic CI values of 0.32 and 0.65 at IC₅₀ and IC₇₅, respectively. Interestingly, all CBD chemotherapeutic combinations at a molar ratio of 636:1 were synergistic in the Loewe, Bliss, HSA, ZIP, and S models, in agreement with the CI model at different inhibitory concentrations. Nevertheless, discording interactions were observed for the Bliss model of DOC and the CI model of DOX (Figure 2B), together with the CI values at some inhibitory concentrations for DOC, DOX, PTX, and VIN (Table 1). Both checkerboard and CI design data reanalysed in DrugComb concurred on the captured synergy of around 39 µM CBD with various concentrations of different drugs (Figure 1 and Figure 2). All of the IC₅₀ values for the different combinations and monotherapies in the CI design are listed in Table S1. Discording CompuSyn- and GraphPad-calculated IC₅₀ values were observed—particularly for the standard chemotherapies, despite the same data having been implemented. These findings should be considered while using only CompuSyn for synergy evaluation.

Figure 3 and Table S2 show the Pearson and Spearman correlation matrices as linear and nonparametric rank correlation measurements, respectively, for different synergy quantification metrics and CSS. A non-significant weak correlation of the Loewe, Bliss, HSA, S, and Zip models with the CI model was indicated by the Pearson and Spearman correlation coefficients, except for the Loewe and HSA models, which were weakly and negatively correlated with the CI model at IC₅₀ (Pearson r −0.45, −0.39, respectively; p < 0.05). The same trend was observed for the CSS and S models’ Spearman correlation with the CI model at high inhibitory concentrations (IC₉₀–IC₉₇). The negative correlation displayed the different scaling, where the synergistic score of the CI model should be < 0, and that for the DrugComb synergy metrics should be > 0. However, moderate-to-strong Pearson and Spearman correlations were observed among the DrugComb synergy models and CSS (0.7–0.99, p < 0.05), except for Loewe with the CSS, S, and ZIP models (Pearson r = 0.5, 0.29, and 0.52, respectively; p < 0.05) and HSA with the S synergy model (Pearson r = 0.56). Taken together, different synergistic interactions could have been drawn, implementing different models—particularly the CI model versus others. Notably, various synergy metrics—including Loewe, ZIP, HSA, and Bliss—displayed a moderate correlation with strong disagreement instances when calculated for the O’Neil anticancer combination dataset (22,737 unique combinations) [67,69,73]. Moreover, substantial disagreements were reported when correlating synergy scores originating from different datasets [67]. Thus, the selection of synergistic doses needs to be validated against different models.

Unlike synergy, which captures the drug interactions, the combination sensitivity calculates the efficacy of the combined drugs, and its negligence may lead to biased synergistic combinations [74]. The relative IC₅₀ value and the area under the drug combination dose-response curve were utilised to derive the CSS, which was presented as a robust metric for efficacy quantification of drug combinations [75]. Table 1 summarises the combined sensitivity scores for the combinations of CBD with the chemotherapeutic drugs tested via checkerboard assay, or upon reprocessing of CI model data via DrugComb and their aggregated data (CI to DC). Notably, promising CSS values were observed for the synergistic combinations against different study designs. The highest CSS (>70) values were displayed for SN−38 and DOC combinations in the checkerboard assay, and DOX in the CI to DrugComb models. Considerably elevated CSS values were noticed for DOX combinations (CDOX19, CDOX28, CDOX37, CDOX64, and CDOX73), along with positive synergy scores in the ZIP, Bliss, and HSA synergy metrics, despite the marginally synergistic to weak antagonistic CI values (Table 1).

Previously, synergistic CBD interactions with DOX and PTX were studied against MCF7 and MDA-MB-231 cells in the CI model. A limited range of responses (total of nine doses), resulting from mixing three fixed doses of CBD with DOX (20, 1, and 0.1 uM) and PTX (0.5, 0.1, and 0.01 uM), were examined [38]. The molar ratio ranges from 20–2000 and 0.5–200 of CBD to 1 of PTX or DOX, respectively, were also explored in MCF7 cells [38]. Our findings are partially in agreement with the latter study in some synergy models, but we considered a broader range of responses (54 and 36 for our CI and checkerboard designs, respectively) in different synergy models, together with CSS calculations. Interestingly, CBD was previously reported to ameliorate DOX-induced cardiomyopathy via modulating cardiac mitochondrial functions such as complex I and complex II activities in mice [63,76]. In addition, CBD—or its combination with Δ⁹THC—reduced the PTX-induced neuropathic pain in mice without affecting the nervous system function or PTX efficacy [62,77]. Collectively, the cardioprotective and pain relief effects of CBD, along with its synergistic interactions with DOX or PTX, warrant further studies for its potential implementation in clinical practice.

CBD has been reported to potentiate DOC-inhibitory effects in LNCaP and DU-145 xenografts in vivo, and against LNCaP, 22RV1, DU-145, and PC-3 cells in vitro [78]. Interestingly, the coadministration of medicinal cannabis herbal tea did not affect the pharmacokinetics of irinotecan (and its SN−38 metabolite) and DOC [79]. Nevertheless, the potential synergistic interactions of CBD with DOC, VIN, and SN−38 have not previously been studied. The synergistic interactions of DOC, VIN, and SN−38 with CBD found in the current study require further in vivo investigations to evaluate the enhanced chemotherapeutic effects where dose reduction of chemotherapy may be expected.

In the present study, selected doses of combined CBD and chemotherapeutic drugs were validated against different models, including CI and DrugComb synergy metrics, with at least 90% inhibition of MCF7 cells. For DrugComb synergy models, both checkerboard design data and the combined dose responses of different combinations (CXYZ19 to CXYZ91) were considered for the selection of the synergistic doses. These synergistic doses were further utilised in the subsequent flow cytometry and shotgun proteomics studies.

2.2. Flow Cytometric Analyses of Apoptosis in MCF7 Human Breast Adenocarcinoma Cells Using Annexin V-CF Blue and 7-Aminoactinomycin D (7AAD)

The apoptotic profiles of MCF7 cells were evaluated using flow cytometry after 24 h of treatment with CBD, chemotherapeutic agents, or their combinations. The chemotherapeutic drugs (DOC, DOX, PTX, SN38, and VIN) and their most synergistic combinations with CBD were evaluated at the doses indicated in Table 1. A concurrent evaluation of apoptotic and necrotic MCF7 cells was carried out to detect whether the most synergistic combination had any enhanced apoptotic effects compared to monotherapy (Table S3). After 24 h, significant differences in the live, early or late apoptotic, total apoptotic, and necrotic cell populations were observed in the negative controls, monotherapies, and combined treatments in pairwise comparisons via two-way ANOVA and Tukey’s test for multiple comparisons correction (p < 0.05, n = 3) (Figure 4, Figure 5 and Figure S1, and Table S3).

CBD stimulated programmed cell death in the MDA-MB-231 breast cancer cells, with regulated crosstalk between apoptosis and autophagy [39]. In both the T-47D and MDA-MB-231 breast cancer cells, CBD treatment coordinated an interplay between PPAR-γ, mTOR, and cyclin D1 that favours apoptotic induction [35]. In the present study, CBD alone (38.42–64.6 µM) significantly reduced the live population of MCF7 cells from around 92% to 78.74–32.37% (p < 0.001), in a dose-dependent manner. The total apoptotic MCF7 cells were significantly increased from 5.94% to 15.38–58.94% by CBD in different concentrations (p < 0.0001) compared to the negative controls (Figure 4B and Figure 5B; p = 0.0058 for 38.42 µM CBD). However, different CBD doses significantly increased necrosis in MCF7 cells (p < 0.01), from 1.4% to 8.69%, with the highest CBD concentration (64.60 µM). Altogether, a significant 3–11-fold increase in the number of apoptotic cells was observed for different CBD doses in MCF7 cells compared to the untreated control cells, with a decreased living cell percentage and a slight increase in the number of necrotic cells (0.29–7.29%).

Similarly, a significant reduction in the number of live cells, and increased numbers of apoptotic and necrotic cells, were observed for DOC (0.5 µM) and VIN (0.1 µM) as monotherapies (p < 0.0001 in comparison to the untreated MCF7 cells; Figure 4A,E and Figure 5A,E). PTX (0.1 µM) showed the same trend, except with a non-significant difference for necrotic cells (p = 0.58). A substantial increase in necrotic cells (~20-fold change compared to the controls; p < 0.0001) was observed for 0.2 µM DOX (Figure 4B and Figure 5B). SN−38 showed a weak decrease in the number of living cells, but its effect on apoptosis was not statistically significant at the currently selected dose of 0.11 µM (Figure 4D and Figure 5D).

A significant decrease in the live MCF7 cell population, with enhanced apoptosis, was observed for all of the synergistic CDB combinations, compared to monotherapies or negative controls (p < 0.0001), except for apoptotic cell percentage of CDOX compared to CBD or DOX, which was slightly higher, with p = 0.0003 and 0.001, respectively (Figure 4, Figure 5 and Figure S1, Table S3). To this end, CBD enhanced the apoptotic activity of the studied chemotherapeutic drugs, which consequently contributed to the overall synergistic activity. For the following section, a shotgun proteomics discovery study was conducted to decipher the cytotoxic molecular mechanisms of CBD, and its possible synergistic mechanisms with SN−38 against MCF7 cells.

2.3. Bottom-Up Label-Free Quantification Proteomics Study of MCF7 Cell Lysates after Treatment with CBD or Its SN−38 Synergistic Combination

A shotgun proteomics study of MCF7 cells treated with 42.45 µM CBD, 0.11 µM SN−38, and their synergistic combination (CSN−38) was performed, with relative quantification, implementing the Hi-N method. The expressed proteins in CBD-treated MCF7 cells were analysed in pairwise comparisons to the control group to trace for the difference in the proteome-wide dysregulated expressions that may be associated with the cytotoxic effects of CBD. To highlight the possible CSN−38 synergistic mechanisms, the differentially expressed proteins in the combination-treated cells were analysed in pairwise comparison to the monotherapies. Among the total 3569 identified proteins, 1256 and 758 were found to be significantly different in CBD vs. Control and CSN−38 vs. monotherapies, respectively, with p and Q values ≤ 0.05 (Q-values are the adjusted p-values, and are derived based on the optimised false discovery rate (FDR) approach). Figure 6 displays the quality control metrics and overlapped proteins among different experimental groups. Decent tryptic digestion could be speculated with minimal missed cleavages, and most of the peptides were eluted in the 3rd and 4th quartiles of the LC run. In addition, the peptides with an absolute mass error of 20 ppm were disregarded based on the mass error distribution of the identified peptides (Figure 6A). The peptide counts, unique peptide counts, m/z, confidence scores, statistics, and fold change (FC) calculations are listed in Supplementary File 2.

2.3.1. Proteome-Wide Elucidation of the Cytotoxic Effects of CBD in MCF7 Cells: Pilot Shotgun Proteomics Study

The differentially expressed proteins in CBD-treated MCF7 cells, compared to the controls, were selected implementing p- and Q-values of ≤ 0.01 alongside an absolute FC ≥ 2 (absolute log2 FC ≥ 1) cutoff. A total of 25 upregulated and 96 downregulated proteins were identified in CBD-treated cells (

Table 2. Significantly upregulated proteins in cannabidiol-treated MCF7 cells.

Uniprot ID *	HGNC Gene ID	Protein Name	Log2 Fold Change
P04733	MT1F	Metallothionein-1F	7.03
A0A0J9YWD4	RIMS2	Regulating synaptic membrane exocytosis protein 2	5.80
Q8WX94	NLRP7	NACHT_ LRR and PYD domains-containing protein 7	5.75
P02795	MT2A	Metallothionein-2	3.78
Q9Y2Z9	COQ6	Ubiquinone biosynthesis monooxygenase COQ6	2.60
A6NCE7	MAP1LC3B2	Microtubule-associated proteins 1A/1B light chain 3 beta 2	1.68
E9PRY0	EIF3M	Eukaryotic translation initiation factor 3 subunit M	1.66
Q9NX24	NHP2	H/ACA ribonucleoprotein complex subunit 2	1.51
Q5JPF3	ANKRD36C	Ankyrin repeat domain-containing protein 36C	1.50
A0A0B4J1X2	CACNA1G	Voltage-dependent T-type calcium channel subunit α-1G	1.48
P12645	BMP3	Bone morphogenetic protein 3	1.45
Q9BQ39	DDX50	ATP-dependent RNA helicase DDX50	1.44
P08910	ABHD2	Monoacylglycerol lipase ABHD2	1.40
Q8TCU6	PREX1	Phosphatidylinositol 3_4_5-trisphosphate-dependent Rac exchanger 1 protein	1.35
Q3KQV9	UAP1L1	UDP-N-acetylhexosamine pyrophosphorylase-like protein 1	1.34
Q14139	UBE4A	Ubiquitin conjugation factor E4 A	1.26
M0QZQ3	SPTBN4	Spectrin beta chain	1.26
F5GZS6	SLC3A2	4F2 cell-surface antigen heavy chain	1.25
P35527	KRT9	Keratin_ type I cytoskeletal 9	1.23
F5GX23	PSMD9	26S proteasome non-ATPase regulatory subunit 9	1.22
Q5XKE5	KRT79	Keratin_ type II cytoskeletal 79	1.07
Q9NRV9	HEBP1	Heme-binding protein 1	1.05
H0YMI4	USP3	Ubiquitin carboxyl-terminal hydrolase	1.01
B4DIG0	CDC25B	Protein-tyrosine-phosphatase	1.00
P29992	GNA11	Guanine nucleotide-binding protein subunit alpha-11	1.00

* Significantly upregulated proteins in 42.45 µM CBD-treated MCF7 cells (p and Q ≤ 0.01 alongside Progenesis QIP-calculated maximum fold change ≥ 2 = log2 fold change ≥ 1) compared to the vehicle-treated control cells.

and

Table 3. Significantly downregulated proteins in cannabidiol-treated MCF7 cells.

Uniprot ID *	HGNC Gene ID	Protein Name	Log2 Fold Change
P02538	KRT6A	Keratin 6A	−28.33
E9PC85	LRRK2	Leucine-rich repeat kinase 2	−30.08
Q9BRK5	SDF4	45 kDa calcium-binding protein	−10.86
P28331	NDUFS1	NADH-ubiquinone oxidoreductase 75 kDa subunit_ mitochondrial	−8.44
K7EPA3	TMEM161A	Transmembrane protein 161A	−7.83
Q14249	ENDOG	Endonuclease G_ mitochondrial	−7.53
Q9H501	ESF1	ESF1 homologue	−4.39
Q9NRY5	FAM114A2	Protein FAM114A2	−4.24
C9J2C7	NT5C1B-RDH14	NT5C1B-RDH14 readthrough	−3.95
Q9NX14	NDUFB11	NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 11_ mitochondrial	−3.49
A0A3B3ISF0	LARP1B	La-related protein 1B	−3.35
Q9UP79	ADAMTS8	A disintegrin and metalloproteinase with thrombospondin motifs 8	−3.34
Q8IYA6	CKAP2L	Cytoskeleton-associated protein 2-like	−3.31
P60602	ROMO1	Reactive oxygen species modulator 1	−3.26
P55317	FOXA1	Hepatocyte nuclear factor 3-alpha	−3.25
Q96G01	BICD1	Protein bicaudal D homologue 1	−3.23
O43291	SPINT2	Kunitz-type protease inhibitor 2	−3.21
O00762	UBE2C	Ubiquitin-conjugating enzyme E2 C	−3.04
O00291	HIP1	Huntingtin-interacting protein 1	−2.83
Q02818	NUCB1	Nucleobindin-1	−2.81
E9PJC6	AHNAK	Neuroblast differentiation-associated protein AHNAK	−2.58
Q2TV78	MST1L	Putative macrophage stimulating 1-like protein	−2.51
A0A6Q8PFY3	FANCD2	Fanconi anemia group D2 protein	−2.45
A0A0U1RR07	SYTL2	Synaptotagmin-like protein 2	−2.43
Q8IVV2	LOXHD1	Lipoxygenase homologuey domain-containing protein 1	−2.31
Q9BUB7	TMEM70	Transmembrane protein 70_ mitochondrial	−2.28
Q8TCT8	SPPL2A	Signal peptide peptidase-like 2A	−2.27
Q96DV4	MRPL38	39S ribosomal protein L38_ mitochondrial	−2.27
Q9Y646	CPQ	Carboxypeptidase Q	−2.23
Q15036	SNX17	Sorting nexin-17	−2.22
O75306	NDUFS2	NADH dehydrogenase [ubiquinone] iron–sulfur protein 2_ mitochondrial	−2.17
G3V0I5	NDUFV1	NADH dehydrogenase [ubiquinone] flavoprotein 1_ mitochondrial	−2.12
A0A590UJ21	CUL1	Cullin-1	−1.97
Q00534	CDK6	Cyclin-dependent kinase 6	−1.96
Q9P1A6	DLGAP2	Disks large-associated protein 2	−1.95
K7EJV0	SEPTIN9	Septin-9	−1.92
E7EN73	KIAA0319L	Dyslexia-associated protein KIAA0319-like protein	−1.92
C9JJ19	MRPS34	28S ribosomal protein S34_ mitochondrial	−1.92
Q92665	MRPS31	28S ribosomal protein S31_ mitochondrial	−1.88
Q96C01	FAM136A	Protein FAM136A	−1.86
Q16540	MRPL23	39S ribosomal protein L23_ mitochondrial	−1.83
A0A2R8YGD3	RAPGEF2	Cyclic nucleotide ras GEF	−1.81
G8JLA1	RDH13	Retinol dehydrogenase 13	−1.80
P82650	MRPS22	28S ribosomal protein S22_ mitochondrial	−1.76
O00217	NDUFS8	NADH dehydrogenase [ubiquinone] iron–sulfur protein 8_ mitochondrial	−1.75
Q9Y2Z2	MTO1	Protein MTO1 homologue_ mitochondrial	−1.68
A0A0A0MRM2	NRAP	Nebulin-related-anchoring protein	−1.64
Q9UKX2	MYH2	Myosin-2	−1.64
Q6X4W1	NSMF	NMDA receptor synaptonuclear signaling and neuronal migration factor	−1.62
O75363	BCAS1	Breast carcinoma-amplified sequence 1	−1.62
O94913	PCF11	Pre-mRNA cleavage complex 2 protein Pcf11	−1.62
Q92823	NRCAM	Neuronal cell adhesion molecule	−1.60
Q96EY7	PTCD3	Pentatricopeptide repeat domain-containing protein 3_ mitochondrial	−1.57
Q6ZXV5	TMTC3	Protein O-mannosyl-transferase TMTC3	−1.53
Q6IBS0	TWF2	Twinfilin-2	−1.50
Q9NY74	ETAA1	Ewing’s tumour-associated antigen 1	−1.50
Q8N961	ABTB2	Ankyrin repeat and BTB/POZ domain-containing protein 2	−1.42
A0A1B0GU86	ACY1	N-acyl-L-amino-acid amidohydrolase	−1.42
P55011	SLC12A2	Solute carrier family 12 member 2	−1.42
H7BYU6	ZNF521	Zinc finger protein 521	−1.38
P04181	OAT	Ornithine aminotransferase_ mitochondrial	−1.36
Q8N565	MREG	Melanoregulin	−1.33
Q14802	FXYD3	FXYD domain-containing ion transport regulator 3	−1.33
Q7L2E3	DHX30	ATP-dependent RNA helicase DHX30	−1.32
Q12767	TMEM94	Transmembrane protein 94	−1.31
Q8N0X2	SPAG16	Sperm-associated antigen 16 protein	−1.30
P14854	COX6B1	Cytochrome c oxidase subunit 6B1	−1.30
Q92820	GGH	Gamma-glutamyl hydrolase	−1.28
M0QY24	ZNF546	Zinc finger protein 546	−1.26
Q02880	TOP2B	DNA topoisomerase 2-beta	−1.25
P50579	METAP2	Methionine aminopeptidase 2	−1.23
Q8NEZ3	WDR19	WD repeat-containing protein 19	−1.23
G5E9Z9	LRP2BP	LRP2 binding protein_ isoform CRA_a	−1.22
Q8TDY2	RB1CC1	RB1-inducible coiled-coil protein 1	−1.22
H0Y5K5	ERGIC3	Endoplasmic reticulum-Golgi intermediate compartment protein 3	−1.20
Q9H869	YY1AP1	YY1-associated protein 1	−1.20
A7XYQ1	SOBP	Sine oculis-binding protein homologue	−1.18
P49006	MARCKSL1	MARCKS-related protein	−1.18
H3BLV9	SRPK1	SRSF protein kinase 1	−1.17
Q9Y487	ATP6V0A2	V-type proton ATPase 116 kDa subunit a2	−1.16
Q9BZG8	DPH1	2-(3-amino-3-carboxypropyl)histidine synthase subunit 1	−1.15
H3BV16	NDUFB10	Complex I-PDSW	−1.15
Q99797	MIPEP	Mitochondrial intermediate peptidase	−1.15
O95294	RASAL1	RasGAP-activating-like protein 1	−1.14
P23921	RRM1	Ribonucleoside-diphosphate reductase large subunit	−1.14
Q8IVG5	SAMD9L	Sterile alpha motif domain-containing protein 9-like	−1.13
P31350	RRM2	Ribonucleoside-diphosphate reductase subunit M2	−1.13
Q8WVV9	HNRNPLL	Heterogeneous nuclear ribonucleoprotein L-like	−1.08
A0A2R8YET2	PRRC2C	Protein PRRC2C	−1.07
Q9HCM1	RESF1	Retroelement silencing factor 1	−1.07
F8VWW7	SPRYD3	SPRY domain-containing protein 3	−1.06
P54296	MYOM2	Myomesin-2	−1.03
Q86XE3	MICU3	Calcium uptake protein 3_ mitochondrial	−1.02
P49736	MCM2	DNA replication licensing factor MCM2	−1.01
Q9H4E5	RHOJ	Rho-related GTP-binding protein RhoJ	−1.01
Q9HAZ2	PRDM16	Histone-lysine N-methyltransferase PRDM16	−1.00

* Significantly downregulated proteins in 42.45µM CBD-treated MCF7 cells (p and Q ≤ 0.01 alongside maximum Progenesis QIP-calculated maximum fold change ≥2 = log2 fold change ≤−1) compared to the vehicle-treated control cells.

, and Supplementary File 2 (CBD vs. control sheet)). These 121 dysregulated proteins represent the proteome-level variance acquired upon CBD treatment. Therefore, these proteins may reflect the possible underlying mechanisms of its cytotoxic effects in MCF7 cells.

STRING [80], Reactome [81], g:Profiler [82], and IMPaLA [83] overrepresentation analyses identified a subset of downregulated proteins encoding genes such as NDUFV1, NDUFB11, NDUFB10, NDUFS1, NDUFS2, and NDUFS8. These proteins were involved in complex I biogenesis, respiratory electron transport, mitochondrial ATP synthesis, the citric acid (TCA) cycle, oxidative phosphorylation, and retrograde endocannabinoid signalling, together with COX6B and ATP6V0A2, for all of the aforementioned processes except for retrograde endocannabinoid signalling (Table 3, Figure 7, and Supplementary File 2, ORA_Enrich. of CBD vs. C sheet).

Aerobic respiration occurs exclusively in the mitochondria of the eukaryotic cells and starts with glycolysis and the TCA cycle; followed by complexes I–IV, which form the electron transport chains (ETC) within the inner membrane of the mitochondria [84,85]. Complex I is the largest complex of the mitochondrial ETC, offering approximately 40% contribution of the redox-driven proton pump necessary for ATP production in the mitochondria [86]. Notably, complex I is required for biosynthesis and redox regulation during cancer cell growth, resistance to cell death, and metastasis [86]. CBD’s paradoxical regulatory effects on mitochondrial functions were recently reviewed in brain tissue, isolated mitochondria, and hippocampal cells [87]. CBD (4 µM) was previously found to significantly undermine the basal respiration rate and ATP production in gastric cancer cells with suppressed proliferation and tumour growth in xenografted mice [29]. A similar trend was observed in the HCT116 and DLD-1 colorectal cancer cells (CRC) with Noxa- and ROS-dependent apoptosis [88].

V-ATPase inhibitors were found to facilitate the control of the tumour microenvironment, with reduced tumour acidity and metastasis, along with the prevention of chemoresistance [89].

In light of the aforementioned downregulated proteins and their involvement in the biological processes, CBD-mediated apoptosis and cytotoxicity in MCF7 cells may involve a decreased mitochondrial energy production via the downregulation of complex I- and IV-related proteins (FC = −8.44 to −1.15). In addition, CBD can potentially reduce the tumour acidic microenvironment and metastasis by downregulating V-type proton ATPase (FC = −1.16). Here, CBD was reported for the first time among V-ATPase inhibitors.

In our study, CBD repressed the mitochondrial translation in MCF7 cells. Briefly, the initiation, elongation, and termination of the mitochondrial translation were also over-represented in recruiting the 121 dysregulated proteins in different platforms (Table 3, Figure 7, and Supplementary File 2). The downregulated PTCD3-, MRPS22-, MRPS34-, MRPL38-, MRPS31-, and MRPL23-encoded proteins (FC = −2.27 to −1.57) were involved in the inhibited mitochondrial translation. Mitochondrial translation inhibition has been suggested as a therapeutic strategy for ovarian cancer [90] and human acute myeloid leukemia [91]. Altogether, CBD mediated the mitochondrial power outage alongside the inhibited mitochondrial translation in MCF7 breast cancer cells.

Expression dysregulation was observed for proteins involved in DNA synthesis, the cell cycle, mitosis checkpoints, and G₁/S transition, such as the upregulated CDC25B-, NHP2-, and PSMD9-encoded proteins and the downregulated RRM2-, CUL1-, MCM2-, UBE2C-, and CDK6-encoded proteins (Table 2 and Table 3, Figure 7, and Supplementary File 2). In addition, D-type cyclins and their binding partner kinases are important regulators of cell cycle progression, tumour development, and proliferation in breast cancer and normal breast epithelial cells [92]. Multiple mitogenic signalling pathways regulate and influence CDK4/6 and cyclin D activity, including oestrogen receptors and receptor tyrosine kinase (RTK), as well as the PI3K–AKT–mTOR or RAS–RAF–MEK–ERK pathways [92]. Of note, CDK6 knockdown was shown to overcome the abemaciclib resistance in MCF7 cells [93]. In this context, CBD-mediated inhibition of CDK-6 in MCF7 cells (FC = −1.96) could be utilised to enhance the sensitivity of CDK4/6 inhibitors such as abemaciclib, palbociclib, and ribociclib for the treatment of hormone-receptor-positive breast cancer. Additionally, the downregulated cullin 1 (FC = −1.97), CUL1-encoded protein may be correlated with curtailed proliferation, migration, and invasion of breast cancer [94,95,96]. Furthermore, CBD-downregulated minichromosome maintenance proteins (MCM), such as the DNA replication licensing factor MCM2, may elucidate the effects of CBD on replication and cell cycle development, while a recent meta-analysis postulated the overexpression of MCM2-7 as a predictive biomarker of poor cancer prognosis [97]. Similarly, a growing body of evidence supports the oncogenic potential of the ubiquitin-conjugating enzyme E2C (UBE2C)’s overexpression in breast cancer, which is linked to the loss of BRCA1 function and induced chemical resistance in MCF7 and MDA-MB-231 cells [98]. Notably, the enhanced effects of radiation, doxorubicin, tamoxifen, and letrozole were observed in breast cancer cells upon the downregulation of UBE2C [99]. Taken together, the CBD-mediated downregulation of MCM2- and UBE2C-encoded proteins contributed to the overall cytotoxicity of CBD, along with the suppressed proliferation, migration, and invasion via the declined expression of cyclin-dependent kinase 6 (CDK6) and cullin 1 in MCF7 cells.

Topoisomerase II (TOP2) inhibitors are potent chemotherapeutic drugs that regulate DNA replication [100]. DNA Topoisomerase IIβ and α (TOP2B and TOP2A) were significantly downregulated in CBD-treated MCF7 cells compared to the controls, with FC = −1.25 and −0.72, respectively (Table 3 and Supplementary File 2). Previously, CBD derivatives such as HU-331 displayed selective inhibition to topoisomerase II isoforms via non-competitive inhibition of their ATPase activity [31]. Here, we report the inhibition of TOP2 isoforms in CBD-treated MCF7 cells for the first time, as an additional cytotoxic mechanism, along with the aforementioned downregulation of CDK-6-, MCM2-, and UBE2C-encoded proteins. In addition, the mitochondria-related mechanisms were underlined, including sabotaged energy production and mitochondrial translation.

2.3.2. Proteome-Wide Elucidation of Synergistic Mechanisms of SN−38 Synergistic Combination with CBD in MCF7 Cells: Pilot Shotgun Proteomics Study

The differentially expressed proteins in the synergistic CBD and SN−38 combination-treated cells, compared to the monotherapies, were selected implementing p- and Q-values of ≤ 0.01 alongside a maximum FC ≥ 2 (absolute log2 FC ≥ 1) cutoff. A total of 5 upregulated and 31 downregulated proteins were identified in CBD-treated cells. These 36 dysregulated proteins represent the proteome-level variance acquired upon CSN−38 treatment (

Table 4. Significantly downregulated proteins in CSN38-treated MCF7 cells compared to the monotherapies.

UniProt ID	HGNC Gene ID	Description	Log2 Fold Change *
Downregulated proteins
O75363	BCAS1	Breast carcinoma-amplified sequence 1	−27.75
Q9NVH1	DNAJC11	DnaJ homologue subfamily C member 11	−24.68
O00762	UBE2C	Ubiquitin-conjugating enzyme E2 C	−4.84
O94913	PCF11	Pre-mRNA cleavage complex 2 protein Pcf11	−4.32
A0A1B0GU86	ACY1	N-acyl-L-amino-acid amidohydrolase	−4.16
A0A2R8YGD3	RAPGEF2	Cyclic nucleotide ras GEF	−2.48
Q9P1V8	SAMD15	Sterile alpha motif domain-containing protein 15	−2.44
Q8TDI0	CHD5	Chromodomain-helicase-DNA-binding protein 5	−2.42
K7ER88	ACAA2	3-Ketoacyl-CoA thiolase_ mitochondrial (Fragment)	−2.05
P20701	ITGAL	Integrin alpha-L	−2.01
A0A0D9SG95	CCT7	T-complex protein 1 subunit eta	−1.78
P03952	KLKB1	Plasma kallikrein	−1.72
Q92823	NRCAM	Neuronal cell adhesion molecule	−1.63
O75582	RPS6KA5	Ribosomal protein S6 kinase alpha-5	−1.56
Q6UXG2	ELAPOR1	Endosome/lysosome-associated apoptosis and autophagy regulator 1	−1.56
Q5H9M0	PWWP3B	PWWP domain-containing DNA repair factor 3B	−1.47
P55199	ELL	RNA polymerase II elongation factor ELL	−1.46
Q9NPB8	GPCPD1	Glycerophosphocholine phosphodiesterase GPCPD1	−1.46
Q2TV78	MST1L	Putative macrophage stimulating 1-like protein	−1.42
A0A0U1RQX8	CBL	E3 ubiquitin-protein ligase CBL	−1.35
Q6P4H8	ATPSCKMT	ATP synthase subunit C lysine N-methyltransferase	−1.34
O14513	NCKAP5	Nck-associated protein 5	−1.33
Q8WVV9	HNRNPLL	Heterogeneous nuclear ribonucleoprotein L-like	−1.32
A0A2R8Y5P9	SHROOM3	Protein Shroom3	−1.22
O43303	CCP110	Centriolar coiled-coil protein of 110 kDa	−1.18
Q9C091	GREB1L	GREB1-like protein	−1.13
Q9BQ52	ELAC2	Zinc phosphodiesterase ELAC protein 2	−1.12
A0A075B757	NBPF14	Neuroblastoma breakpoint family member 14	−1.11
O14497	ARID1A	AT-rich interactive domain-containing protein 1A	−1.10
Q96C90	PPP1R14B	Protein phosphatase 1 regulatory subunit 14B	−1.04
Q6IEG0	SNRNP48	U11/U12 small nuclear ribonucleoprotein 48 kDa protein	−1.00
Upregulated proteins
O94973	AP2A2	AP-2 complex subunit alpha-2	1.20
O14949	UQCRQ	Cytochrome b-c1 complex subunit 8	1.11
A0A1C7CYZ1	MAPK15	Mitogen-activated protein kinase 15 (Fragment)	1.10
Q5T9A4	ATAD3B	ATPase family AAA domain-containing protein 3B	1.03
P49189	ALDH9A1	4-Trimethylaminobutyraldehyde dehydrogenase	1.01

*: p and Q ≤ 0.01 alongside maximum Progenesis QIP-calculated fold change ≥ 2 (absolute log2 fold change ≥ 1), compared to the monotherapies (CBD and SN−38); CSN38: the synergistic combination of 42.45 µM cannabidiol and 0.11 µM SN−38.

). Therefore, they may reflect the possible underlying synergistic mechanisms of action against MCF7 breast adenocarcinoma cells. Relaxed p- and Q-values of ≤ 0.05, together with the same FC cutoff, identified 91 dysregulated proteins recruited in further pathway analyses, where no significant pathway enrichment was identified implementing the 36 most significant proteins.

A subset of downregulated proteins encoding genes such as CCT7, ATM, and MAP3K4, alongside the overexpressed MAPK15 involved in the positive regulation of telomere maintenance by telomerase, was identified upon the recruitment of the 91 dysregulated proteins (Supplementary File 2, CSN38 vs. Monotherapies sheet) in STRING [80] and g:Profiler [82] overrepresentation analyses (Figure 8).

We observed overexpressed chaperonin-containing TCP-1s (CCTs), such as CCT7, which was shown to be involved in breast cancer progression, associated with poor overall survival in breast cancer patients, and was recently speculated as a potential breast cancer therapeutic target and valuable prognostic marker [101]. In addition, ATM serine/threonine kinase encoded by ATM is involved in DNA repair, cell cycle arrest, and apoptosis as activated and recruited upon DNA double-strand breaks (DSBs) [102,103]. ATM gene mutations result in ataxia–telangiectasia, where disrupted recognition and repairs of DNA double-strand breaks (DSBs) take place, with increased risk of cancer [104,105]. CSN−38 significantly downregulated both T-complex protein 1 subunit eta (CCT7) and ATM in MCF7 cells (−1.78 and −2.84 FC, respectively) compared to the monotherapies, where impaired DNA DSB repairs and tumour progression could be anticipated through the synergism of CBD and SN−38.

Mitogen-activated protein kinase 4 (MAP3K4) was also downregulated and was highlighted as a potential radiation response biomarker in both post- and preoperative settings in breast cancer patients [106]. Unlike healthy tissue, tumours increase extracellular acidification as cancer cells ferment glucose with increased lactate production, and maintain the same level of mitochondrial respiration even in the presence of oxygen, which was hypothesized by Otto Warburg as a hallmark of cancer [107,108]. Interestingly, the MAP3K4 downregulation inhibited the Warburg effect by regulating lactate secretion and lactate receptor expression through the HER2/HER3 signalling pathways in MCF7 cells [109]. In our study, CSN−38 significantly decreased the expression of MAP3K4 (FC −1.15, Q = 0.025; Supplementary File 2, CSN38 vs. Monotherapies sheet), which might inhibit the migration and extracellular acidity of MCF7 cells. In addition to MAP3K4, MAPK15 was also involved in the MAPK signalling pathway and was overexpressed in the CSN38-treated cells compared to the monotherapies. Increased migration of breast and lung cancer cells was reported earlier upon the downregulation of MAPK15 (alias ERK8), which acts as a negative regulator of cell migration and O-glycosylation of N-acetylgalactosamine (GalNAc) [110]. O-GalNAc glycosylation in the endoplasmic reticulum was reported as a driving mechanism of cancer invasiveness [111]. Based on the current findings, the CSN−38 treatment might inhibit the invasiveness and migration of MCF7 cells by increasing MAPK15 expression (FC 1.1).

Several proteins involved in the cell cycle, EGFR1, metabolism of proteins, TP53 regulation of DNA repair, death receptor signalling, and RHO GTPase signalling pathways were significantly over-represented or enriched (p ≤ 0.05) in IMPaLA or Reactome analyses upon recruiting the 91 dysregulated proteins in the combination-treated MCF7 cells. However, a high false discovery rate or Q value ≥ 0.05 was observed for these pathways (Supplementary File 2, ORA_Enrich of CSN38 vs. Monotherapies sheet). All of the involved proteins identified in Reactome-enriched pathways were downregulated (encoded by ELL, ATM, POLR2H, PCF11, SNAPC3, ABR, PREX1, IRAK1, SPPL2A, NGEF, ITGAL, KLKB1, TMEM87A, ELAC2, UBE2C, ARID1A, NUP93, WDR5, ZNF546, ZNF521, CCT7, ACY1, AJUBA, NRCAM, and CBL), except for FBXW10-encoded protein, which was overexpressed.

The most downregulated proteins in the combination-treated MCF7 cells were breast carcinoma-amplified sequence 1, DnaJ homologue subfamily C member 11, NADH:ubiquinone oxidoreductase subunit A2, and ubiquitin-conjugating enzyme E2 C (FC −27.75, −24.86, −5.01, and −4.84, respectively), compared to the monotherapies. The synergistic combination significantly diminished the expression of breast carcinoma-amplified sequence 1—a breast cancer oncogenic protein that is linked to mammary tumourigenesis [112]. Complex I-related proteins (NDUFA2) and UBE2C-encoded protein are discussed in Section 2.3.1. DnaJ homologue subfamily C member 11 is a heat shock protein, recently found to be upregulated in breast cancers such as basal, luminal B, and HER2 breast cancer subtypes, and downregulated in other tumour types; however, its functional role in breast cancer has not yet been deciphered [113,114].

Three downregulated proteins encoded by ABR, PREX1, and NGEF (FC −1.84, −1.06 and −1.0, respectively) were involved in death receptor signalling, p75NTR receptor-mediated signalling (NGFR), and cell death signalling via NRAGE, NRIF, and NADE, as identified in Reactome enrichment analysis (p-values 2.82 × 10⁻³, 8.25 × 10⁻⁴ and 4.51 × 10⁻³, respectively). IRAK1-encoded interleukin 1 receptor-associated kinase 1 was also significantly involved in death receptor signalling, p75NTR receptor-mediated signalling, and p75NTR signals through NF-κB, as identified by Reactome and IMPaLA. IRAK1 silencing in MCF7 cells was previously reported to decrease invasion, proliferation, and migration, with enhanced sensitivity to PTX [115]. Aggressive growth, PTX resistance, and metastasis were also reported in triple-negative breast cancer cells mediated via IRAK1 overexpression [116]. Recently, a large study including 1085 breast cancer patients showed a positive correlation of decreased IRAK1 expression and reduced tumour size following neoadjuvant chemotherapy [117]. A synergistic effect was also noticed through the downregulated phosphatidylinositol 3,4,5-triphosphate-dependent Rac exchanger 1 (PREX1, FC -1.06). Briefly, decreased MCF7 viability was reported for PREX1 loss by positive feedback to upstream phosphatidylinositol 3-kinase (P13K) activators [118]. In addition, a positive correlation was found between PREX1 overexpression and poor prognosis in breast cancer patients via neuregulin–ErbB signalling [119]. Similarly, the neuronal guanine nucleotide exchange factor (NGEF) overexpression was correlated with tumour progression and metastasis, including in breast carcinomas [120]; therefore, its decreased expression in the synergistic combination-treated cells will contribute to the overall synergistic effect of CBD with SN−38 against MCF7 cells.

PCF11-encoded protein was among the top downregulated proteins in the combination-treated MCF7 cells, with a fold change of −4.32. PCF11 downregulation is associated with favourable outcomes in neuroblastoma patients [121], but its functional role in breast carcinoma is still unexplored. However, it was found to be involved in the alternative polyadenylation (APA), causing three prime untranslated region (3′ UTR) shortening of mRNA by cancer-specific ubiquitin ligase [17]. Additionally, aminoacylase 1 (ACY1) was also significantly downregulated (FC −4.16) in the CSN38-treated MCF7 cells compared to the monotherapies. ACY1 is responsible for amino acid deacylation during protein degradation and has been reported to be upregulated in colorectal cancer patients and HCT116 cells, promoting tumour progression [122,123]. ACY1 has been proposed as a tumour suppressor in small-cell lung cancer, renal cell carcinoma, and hepatocellular cancer cells, as its decreased expression in these cells may accumulate acylated peptide growth factors [124,125,126]. ACY1 was also downregulated in MCF7 cells treated with 17β-estradiol, suggesting its role as a tumour suppressor in oestrogen-dependent breast carcinomas, but further studies are needed in order to evaluate its clinical relevance in breast cancer [127].

The downregulation of integrin subunit alpha L, nucleoporin 93, and signal peptide peptidase-like 2A encoded by the ITGAL, NUP93, and SPPL2A genes (FC −2.01, −2.30, and −2.0, respectively) contributed to the synergistic mechanisms of CBD and SN−38 against MCF7 cells. Briefly, ITGAL overexpression was reported in lymph node metastases of breast cancer patients, compared to the primary breast tumours, highlighting its role in breast cancer metastasis [128]. On the other hand, the overexpression of nucleoporin 93—a nuclear envelope protein—enhanced the migration and invasion of the triple-negative and claudin-low breast cancer cells alongside metastasis in animal models [129,130]. Moreover, the signal peptide peptidase was highly induced in breast and lung cancer cell lines, and its role in tumour progression is anticipated via FKBP8 degradation [131]. Collectively, in addition to its synergistic activity against MCF7-related breast adenocarcinomas, these findings indicate the potential therapeutic role of CSN−38 against other breast cancer subtypes, including triple-negative breast cancers. However, further studies involving a wide range of in vitro and in vivo breast cancer models are necessary in order to develop potential CBD-based adjuvants for breast cancer in the future. Moreover, CBD significantly synergized the SN−38- mediated inhibition of DNA topoisomerase 1 (TOP1) in MCF7 cells (FC −1; p = 0.01 and Q = 0.03), which contributed to the overall synergy between CBD and SN−38 (Figure 8A).

3. Material and Methods

3.1. Chemicals and Drug Preparation

DOC, DOX, PTX, VIN, SN38, and CBD of > 98% purity were purchased from Sigma-Aldrich, NSW, Australia. A freshly prepared stock solution in dimethyl sulfoxide (DMSO) of 1 and 158.998 mM was prepared for chemotherapeutic drugs and CBD, respectively. CBD was combined with drugs in nine different ratios (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, and 9:1, v/v) for combination index (CI) analyses. Moreover, 50, 1, 2, 1, and 2 mM stock in DMSO of DOC, DOX, PTX, VIN, and SN38, respectively, were prepared for synergy studies in a checkerboard design.

3.2. Breast Adenocarcinoma Cell Line Culture Conditions

MCF7 human breast adenocarcinoma cells were obtained from the American Type Culture Collection (ATCC: Manassas, VA, USA). Dulbecco’s modified Eagle’s medium (DMEM; Lonza, NSW, Australia) with 4.5 g/L glucose, L-glutamine, and sodium pyruvate (Lonza Australia Pty Ltd., Mount Waverley, VIC, Australia), supplemented with 10% foetal bovine serum (FBS; Interpath, Heidelberg West, VIC, Australia) and 100 U/mL of penicillin and streptomycin (Gibco^TM BRL, Scoresby, VIC, Australia) was used to culture MCF7 cell line at 37 °C in the presence of 5% CO₂.

3.3. Cell Viability Determination

Cellular viability was determined using the alamarBlue (resazurin) assay [132,133,134]. Briefly, in a 96-well plate, 100 μL of the suspended MCF7 cells was seeded at 1 × 10⁴/well, and incubated at 37 °C in the presence of 5% CO₂ overnight to adhere. The cells were treated with different concentrations of CBD, chemotherapeutic drugs, and their combinations in different ratios, together with the vehicle control (0.5% dimethyl sulfoxide). After 72 h, the medium was removed from the wells, and then 100 μL of alamarBlue (0.1 mg/mL in FBS free media) solution was added to each well and incubated for 4 h at 37 °C in the presence of 5% CO₂. Using a microplate spectrophotometer (BMG CLARIO star, Victoria, Australia), the fluorescence was measured at an excitation wavelength of 555 nm and an emission wavelength of 595 nm. Cell viability was determined as a percentage of vehicle-treated cells (control; 0.5% DMSO).

3.4. Synergy Quantification of CBD and Standard Chemotherapeutics against MCF7 Human Breast Adenocarcinoma Cells

The potential interactions between CBD and the chemotherapeutic drugs were analysed using the combination index (CI) model and the DrugComb portal [75]. CompuSyn version 2.0 (Biosoft, San Francisco, CA, USA) was used for the CI calculations based on the median-effect equation, which was derived from the mass–action law [135,136,137]. In the current study, nine pairwise combinations of chemotherapeutic drugs with CBD were studied in a constant ratio design, with a six-point dose–response curve in 1:2 serial dilution (n = 3), using the CI model (Chou–Talalay method) (

Table 5. Molar ratios, concentrations, and codes of cannabidiol (CBD) combinations.

Combination Code *	Highest Concentration (µM)		Molar Ratio (CBD:Drug)
	CBD	Drug
CXYZ19	15.9	0.9	18:1
CXYZ28	31.8	0.8	40:1
CXYZ37	47.7	0.7	68:1
CXYZ46	63.6	0.6	106:1
CXYZ55	79.5	0.5	159:1
CXYZ64	95.4	0.4	238:1
CXYZ73	111.3	0.3	371:1
CXYZ82	127.2	0.2	636:1
CXYZ91	143.1	0.1	1431:1

*: C refers to CBD; XYZ indicates initials of the chemotherapeutic drugs; the two-digit numbers refer to the combination with the corresponding molar ratio.

) and 1:10 serially diluted chemotherapeutic drugs in the checkerboard design (n = 3) for better exploration of the interaction over a wider range of doses. The response data obtained from the CI model were further analysed in DrugComb, where the mean percentage of cell inhibition and the concentrations of the combined drugs were used as inputs for synergy scores in different models and combination sensitivity score (CSS) evaluation.

3.5. Flow Cytometric Analyses of Apoptosis in MCF7 Human Breast Adenocarcinoma Cells Using Annexin V-CF Blue and 7-Aminoactinomycin D (7AAD)

The apoptotic profiles of MCF7 cells were examined using Abcam Apoptosis Detection Kit (#ab214663, Abcam, VIC, Australia) according to the manufacturer’s protocol, after 24 h of treatment with CBD, chemotherapeutic drugs, and their most synergistic combination. Briefly, MCF7 cells were seeded at a density of 1 × 10⁶ per mL in T75 cell culture flasks and treated with the vehicle control (0.5% DMSO), selected synergistic CBD+ chemotherapeutic drug combinations, and monotherapies (individual treatment with CBD or chemotherapeutic drugs). The cell culture media were collected after 24 h of treatment, and cells were detached using 0.25% w/v of trypsin for 3 min at 37 °C. Trypsin was neutralised with an equal volume of 10% FBS-containing DMEM, and combined with the previously collected media. Cell pellets were collected by centrifugation at 500× g for 5 min at room temperature (RT), rinsed twice in PBS, resuspended in 1 mL PBS, and centrifuged again for 5 min at 500× g. The cell pellets harvested from all treatment and control groups were instantaneously resuspended in 0.5 mL of 1X binding buffer, and 5 µL of annexin V-CF blue and 7-AAD staining solutions were added to each 100 µL of cell suspension. After 15 min of incubation in the dark, 400 µL of 1X binding buffer was added. The cells were then examined using an ACEA Biosciences NovoCyte 3000 flow cytometer (ACEA Biosciences Inc., San Diego, CA, USA). For analysis and processing, the NovoExpress (version 1.5.0, ACEA Biosciences Inc., USA) software was implemented, where cells were gated on FSC vs. SSC to exclude the cell debris and aggregates. The cells were then gated on a dot plot of Annexin V-CF in Pacific Blue vs. 7-AAD fluorescence in PerCP, with a quadrant placed indicating live cells (+Annexin V and −7-AAD) in the lower-left quadrant, early apoptotic cells (+Annexin V and −7-AAD) in the lower-right quadrant, late apoptotic cells (+Annexin V and +7-AAD) in the upper-right quadrant, and necrotic cells (−Annexin V and +7-AAD) in the upper-left quadrant. Cell percentage data from each quadrant after each treatment (n = 3) were exported to GraphPad Prism for statistical analysis and visualisation. Annexin V and 7-AAD (7-aminoactinomycin D) were combined to distinguish necrotic cells from early and late apoptotic cells. Annexin V binds to the phosphatidylserine (PS) phospholipids, which are translocated to the outer surface of cells during apoptosis [138]. 7-AAD, on the other hand, is a fluorescent dye that intercalates in double-stranded DNA, with a high affinity for guanine–cytosine residues, and is used as a fluorescent DNA marker in flow cytometry and fluorescence microscopy [139,140]. The PerCP and Pacific Blue channels were used for Annexin V and 7-AAD, respectively, as the emission spectra of these dyes do not overlap; therefore, no compensation is necessary [68].

3.6. Bottom-Up Label-Free Quantification Proteomics Study of MCF7 Cell Lysates after Treatment with the Most Synergistic Combination

3.6.1. Cell Culture, Treatment, and Protein Extraction

MCF7 cells were seeded at a density of 1.0 × 10⁶ per mL cells in T75 flasks and incubated overnight at 37 °C in the presence of 5% CO₂. The media were aspirated and replaced with fresh media containing 0.5% DMSO as the vehicle control, and the selected doses of chemotherapeutic drugs, CBD, and their synergistic combinations, in triplicate, and incubated for 24 h at 37 °C in the presence of 5% CO₂. Each cell flask was treated with 0.25% w/v trypsin for 3 min at 37 °C, and the cell culture media were collected. Trypsin was neutralised with an equal volume of 10% FBS-containing DMEM before mixing with the previously collected media. The cells were centrifuged at 500× g for 5 min at RT, and then the pellets were rinsed twice with ice-cold PBS and centrifuged again at 500× g for 5 min. The cell pellets were then resuspended in 100 µL of lysis buffer including 1 µL of universal nuclease and fully mass spectrometry (MS)-compatible Halt™ Protease and Phosphatase Inhibitor Cocktail, EDTA-Free (Thermo Scientific, Waltham, MA, USA). The cells were pipetted up and down 10–15 times until the sample viscosity was reduced, and then placed on ice for 20 min. The lysate was then centrifuged at 14,000 rpm for 20 min at 4 °C and collected.

3.6.2. Protein Quantification

The Pierce™ Rapid Gold BCA Protein Assay Kit (#A53226, Thermo Scientific, USA) was employed for the determination of protein concentration of the cell lysate, in triplicate, against bovine serum albumin (BSA) standard, as per the manufacturer’s protocol. Briefly, 1 µL of each sample replicate was diluted 1:20 in water together with 20 µL of each standard, and then placed in a 96-well plate with 200 µL of working reagent per well. Samples were diluted until they were within the working range of 20–2,000 µg mL⁻¹. The plate was mixed thoroughly on a plate shaker for 30 s and incubated at RT for 5 min, and then the absorbance was measured within 20 min at 480 nm using a microplate spectrophotometer (BMG CLARIOstar, Melbourne, VIC, Australia). The blank absorbance was subtracted from all other readings of standards and samples, and sample concentration was determined against the established BSA standard calibration curve. Samples were stored at −80 °C for further analysis.

3.6.3. Preparation and Clean-Up of Peptides

One-hundred-microgram protein samples were used for chemical and enzymatic sample processing using the EasyPep™ Mini MS Sample Prep Kit, as per the manufacturer’s protocol (Thermo Fisher Scientific, USA). The final volume was adjusted to 100 µL using lysis buffer in a microcentrifuge tube. The reduction and alkylation solutions (50 µL each) were added, gently mixed, and incubated at 95 °C using a heat block for 10 min. The samples were allowed to cool at RT, and then 50 µL of the reconstituted trypsin/lys-C protease mixture was added to each sample and incubated with shaking at 37 °C for 3 h. After incubation, 50 µL of digestion stop solution was added and mixed gently. Peptide clean-up columns were implemented to remove hydrophilic and hydrophobic contaminants. Clean peptide samples were dried using a vacuum centrifuge and resuspended in 100 µL of 0.1% formic acid in water for LC–MS analysis, and then carefully placed in maximum recovery sample vials (Waters Corp, Milford, MA, USA).

3.6.4. Label-Free Bottom-Up Quantification Proteomics Analysis via Nano-Ultra-High-Performance Liquid Chromatography Coupled with Quadruple Time-of-Flight Mass Spectrometry (Nano-UPLC-qTOF-MS)

A nanoACQUITY UPLC system (Waters Corp., Milford, MA, USA) coupled with a Synapt G2-S high-definition mass spectrometer (HDMS) (Waters Corp., Manchester, UK) operating in positive electron spray ion mode (ESI+) and equipped with a hybrid quadrupole time of flight (qTOF) analyser was used to analyse tryptic peptides. A Waters NanoLockSpray Exact Mass Ionization Source was utilised to maintain mass accuracy. Briefly, 100 fg mL-1 Glu-fibrinopeptide B (GFP) dissolved in 50% aqueous acetonitrile containing 0.1% formic acid with a lock mass m/z of 785.84.26 was infused as a lock spray solution at 0.5 μL min⁻¹ and calibrated against a sodium iodide solution. For the chromatographic separation of peptides, the nanoEase M/Z BEH C18 (1.7 μm, 130 Å, 75 μm × 100 mm, Waters Corp., Milford, MA, USA) at 40 °C was utilised and coupled with a nanoEase M/Z Symmetry C18 Trap Column (100 Å, 5 µm, 180 µm × 20mm, Waters Corp., Milford, MA, USA). Milli-Q water and acetonitrile containing 0.1% formic acid were used as mobile phases A and B, respectively (LCMS grade, Merck, Germany). An injection volume of 1 µL at a 300 nL min⁻¹ flow rate was used throughout the 50 min gradient. Samples were injected into the trapping column at 5 μL min⁻¹ at 99% mobile phase A for 3 min, before being eluted to the analytical column. An initial 1% of mobile phase B was ramped to 85% B over 50 min with the following gradient: 10% B at 2 min, 40% B at 40 min, and 85% B at 42 min. All samples were kept at 4 °C and were injected in duplicates. The ion source block temperature was set to 80 °C, and capillary voltage was maintained at 3 kV. Ions were acquired with m/z between 50 and 2000, scanning time of 0.5 sec, sample cone voltage and source offset at 30 V, nanoflow gas at 0.3 Bar, purge gas at 20 Lh⁻¹, and cone gas flow at 20 L h⁻¹. A data–independent acquisition (DIA) method by MS^E multiplex mode was used for sample acquisition with a T-wave collision-induced dissociation cell filled with argon gas, using MassLynx Mass Spectrometry Software (Waters Corporation, Milford, MA, USA).

3.6.5. Data Processing and Availability

Progenesis QI software (Waters Corporation, Milford, MA, USA) was used to import and further process the MassLynx-acquired data. Automatic selection of alignment references among QC samples was set, and peptides were identified against the UniProt human proteome database (October 2020 version) using the ion accounting method, with a 250 kDa protein mass maximum. One fragment per peptide or one peptide per protein together with 3 fragments per protein were set as ion matching requirements using relative quantification, implementing the Hi-N method (n = 3). Auto peptide and fragment tolerance, and less than 4% FDR, were set as search tolerance parameters. Peptides with absolute mass error >20 ppm or a single charge were further filtered out. Pairwise comparisons of the identified proteins in the treated groups were performed against the control group for cytotoxic potential exploration, while the most synergistic combination samples were compared against monotherapy-treated samples for the elucidation of possible synergistic mechanisms. In each experimental design, proteins with analysis of variance (ANOVA)-derived p-values of at least≤ 0.05 and q-values ≤ 0.05, with a fold change ≥2, were considered significant and included for further pathway analyses. Differentially expressed proteins identified by the quantitative processing of the LC–MS/MS analysis of the proteome tryptic digestion were analysed by STRING [80], Reactome [81], g:Profiler [82], and IMPaLA [83] to identify the relevant pathways responsible for the synergistic effect against MCF7 cells. The G: SCS algorithm was used for multiple testing corrections in the g:Profiler platform, with an adjusted p-value threshold of 0.05. The raw and processed data have been deposited to the ProteomeXchange Consortium via the PRoteomics IDEntifications (PRIDE) repository [141], with the dataset identifiers PXD026587 and 10.6019/PXD026331.

3.7. Statistical Analysis

All statistical comparisons were performed using GraphPad Prism Version 9 (San Diego, CA, USA), except for the shotgun proteomics study, where MetaboAnalyst 5.0 was used together with Progenesis QIP (Waters Corporation, Milford, MA, USA). The significance was analysed via ANOVA and t-tests for multiple and pairwise comparisons, respectively. Data were expressed as means  ±  SD. Differences of at least p  <  0.05 between the mean values in the experiments were considered statistically significant.

4. Conclusions and Future Directions

Synergistic interactions were observed between CBD and the five selected chemotherapeutic drugs at different molar ratios in MCF7 cells, using different synergy quantitation models. We highlighted the promising molar ratios of CBD and chemotherapeutic drug combinations that were validated against different synergy metrics. CBD synergistically enhanced the antiproliferative activity of the standard chemotherapeutic drugs by promoting apoptosis. Moreover, the presented pilot label-free quantification-driven proteomics study highlighted the cytotoxic mechanisms of CBD against MCF7 cells, along with the underlying synergistic mechanisms of its combination with SN−38. However, further targeted proteomics studies with absolute quantification are warranted in order to validate the presented targets and pathways. Good tolerability and minimal adverse effects are reported for CBD in clinical trials (50–1000 mg/day up to 13 weeks), with an increased easing of legislation to approve CBD products. Nevertheless, further validation of the proposed combinations in normal, non-transformed breast epithelial cells is required.

The present study highlights potential new opportunities in breast cancer treatment with other chemotherapeutic agents, such as CDK or topoisomerase inhibitors. For example, we speculated the potential CBD combinations with CDK4/6 inhibitors such as abemaciclib, palbociclib, and ribociclib based on CBD-mediated inhibition of CDK-6 in MCF7 cells. The presented cytotoxic mechanisms of CBD are encouraging for further investigation of CBD’s interaction with hormonal therapies, including aromatase inhibitors (such as anastrozole, letrozole, and exemestane), selective oestrogen receptor modulators (SERM such as tamoxifen or toremifene), and new generations of drugs such as fulvestrant and goserelin.

Collectively, CBD presents an opportunity to potentially enhance the effectiveness of the breast cancer treatment regimens containing doxorubicin, docetaxel, paclitaxel, SN−38, and vinorelbine. Furthermore, the proposed combinations with CBD may be able to alleviate chemotherapy-induced adverse effects by reducing the dosage of chemotherapeutic drugs, or via CBD’s ameliorative or protective activities. However, further in vivo and clinical studies are warranted in order to validate these in vitro findings.