Targeting Cancer Cell Proliferation Using Piperazine‐Linked Quinolinequinones: Mechanism and Metabolic Profile

ABSTRACT

A series of aminated quinolinequinones linked to piperazine analogs (QQ1‐7) were synthesized and screened against the full panel of National Cancer Institute (NCI) cancer cell lines for their potential as cytotoxic agents. The Developmental Therapeutics Program of the NCI analyzed the NCI‐60 screening results and revealed that seven QQs were potent inhibitors of cancer cell growth across several cell lines, advancing them to the five‐dose assay. Encouraged by the NCI five‐dose assay results, the cytotoxicity of the selected QQs (QQ1 and QQ4) was further studied in three cancer cell lines—HCT‐116 (colon cancer), ACHN (renal cancer), MCF7, and T‐47D (breast cancer)—as well as in a normal cell line (HUVEC) for a deeper understanding. QQ1 was the hit compound for ACHN cells with an IC₅₀ value of 1.55 μM. QQ1 could inhibit ACHN cell proliferation, induce oxidative stress, and cause cell cycle arrest in ACHN cells. QQ1 did not affect the apoptotic value in ACHN cells. Oral bioavailability was poor for both QQ1 and QQ4 in vivo in rats due to faster intrinsic hepatic clearance in comparison with humans, as evidenced by in vitro metabolic studies with rat and human liver microsomes. Molecular docking simulation with putative target CDC25A revealed the interaction of QQ1 and QQ4 with active site residues responsible for substrate recognition.

We examined the quinolinequinones' (QQ1‐ 7) effects on cancer cell lines. The most potent compound was QQ1 against ACHN cells with an IC50 value of 1.5 ± 0.16 μM. For better understanding, the effects of QQ1 on apoptosis, cell cycle, and oxidative stress were investigated. QQ1 inhibited ACHN cell proliferation via cell cycle arrest. The host‐guest interactions of quinolinequinones were also studied in detail using thorough in silico docking simulations with pharmacokinetic studies.

1. Introduction

Cancer continues to be at the top of the list of diseases that seriously threaten human health and quality of life with its high incidence and mortality rate. According to data based on the latest GLOBOCAN estimates produced by the International Agency for Research on Cancer (IARC) and published in the Global Cancer Observatory as Cancer Today, 9,743,832 of the 19,976,499 cancer cases observed in 2022 resulted in death (WHO ²⁰²²). This number makes cancer one of the leading causes of death worldwide today, and it gives strong signals that it will continue to increase in the future due to various risk factors such as population ageing, alcohol and tobacco use, environmental pollution, unhealthy diet, and physical inactivity (Bray et al. 2024).

When the incidence rate of cancer in both genders is examined by continent in 2022, the Asia continent (49.2%) ranks first, followed by Europe (22.4%), North America (13.4%), Latin America and the Caribbean (7.8%), Africa (5.9%), and Oceania (1.3%), respectively. When the death percentages are evaluated, it is observed that Asia (56.1%), Europe (20.4%), LAC (7.7%), and Oceania are in the same place in the ranking, while North America (7.2%) and Africa (7.8%) have changed places in the ranking (WHO). This situation is probably due to the inadequacy of cancer preventive measures and treatments in Africa, as well as the inability to fully report cancer (Jianhui et al. 2023). A study that takes into account the productivity losses due to cancer‐related mortality and morbidity among people with different levels of education and experience and the costs of cancer treatments to estimate the macroeconomic costs of cancer highlights the global economic costs of cancer from 2020 to 2050 (S. Chen et al. ²⁰²³). Although advances in treatments, especially chemotherapy drugs targeting DNA, RNA, and protein of cancer cells, have yielded positive results in cancer patients, metastasis and treatment resistance remain major challenges associated with poor prognosis. The successful use of the non‐cytotoxic, therapeutic, and small‐molecule tamoxifen in clinical trials against breast cancer in 1970 was considered the beginning of molecularly targeted cancer treatments (Li et al. 2023).

Heterocyclic structures represent an important class of compounds and play a crucial role as lead molecules in drug development, including vital biomolecules, such as DNA and RNA, natural products, numerous drugs, synthetic pharmaceuticals, essential vitamins, and biologically active agents (Eftekhari‐Sis and Zirak ²⁰¹⁵; Eftekhari‐Sis et al. ²⁰¹³). The design of quinolinequinones containing a heterocyclic moiety was divided into three moieties. The first one was the quinolinequinone moiety, which was associated with the biological activity (Kruschel et al. 2024; Kruschel et al. ²⁰²⁰). The second moiety was a heterocyclic structure containing a nitrogen atom in particular, such as substituted piperazines, while the third moiety was the variable substituent on the phenyl ring of the heterocyclic structure. Over the last 10 years, our group has mostly concentrated on creating novel compounds based on the (hetero)cyclic structures and examining their biological efficacy to explore the structure–activity relationships (SAR). We have targeted possible anticancer lead structures by employing the 1,4‐quinone moiety as the primary pharmacophore (Ciftci et al. 2022, 2023; Jannuzzi et al. ²⁰²⁴; Yildirim, Yildiz, Bayrak, Mataraci‐Kara, Ozbek‐Celik, et al. ²⁰²²; Yildirim, Yildiz, Bayrak, Mataraci‐Kara, Radwan, et al. ²⁰²²). One example of a commercially available anticancer drug is LY83583, which contains a quinolinequinone moiety, used to treat cancers linked to redox cycling and reactive oxygen species overproduction (Kontos and Wei 1993; Mülsch et al. ¹⁹⁸⁸). The SAR analyses of our earlier research showed that the presence of a chlorine atom within the quinone moiety and the presence of the group(s), such as EDGs or EWGs, within the aryl amino moiety were significant. Using LY83583 as the lead compound for the design, we synthesized novel AQQs for cancer therapy research by further realization, keeping in mind the previously given facts (Jannuzzi et al. 2024, 2023; Yilmaz Goler et al. ²⁰²³). In this study, we decided to examine the anticancer potency of the aminated quinolinequinones by the linkage of piperazine analogs containing electron‐donating group(s). This structural manipulation combines the main biological features of aminated quinolinequinone analogs based on the electron‐donating group(s) effect in the aryl amino moiety.

2. Materials and Methods

2.1. Biological Evaluation

2.1.1. In Vitro Anticancer Screening by NCI

QQ1‐7 were submitted to the National Cancer Institute (NCI), Bethesda, USA, and as per the standard protocol of the NCI, all compounds were evaluated for their anticancer activity against a panel of 60 cancer cell lines derived from leukemia, non–small‐cell lung, colon, CNS, melanoma, ovarian, renal, prostate, and breast as per protocol. Tested compounds were added to the microtiter culture plates followed by incubation for 48 h at 37°C. Sulforhodamine B (SRB), a protein‐binding dye, is used for endpoint determination. The percentage of growth of the treated cells was determined in comparison to the untreated control cells, and the results of each tested compound were reported. Data from one‐dose experiments pertain to the percentage growth at 10 μM (Boyd and Pauli 1995; Grever et al. ¹⁹⁹²; Monks et al. ¹⁹⁹¹). To evaluate the dose–response curves in the 60‐cell line panel, the NCI advanced the QQs that satisfied certain requirements established by the DTP NCI to the five‐dose screening stage. A serial 5 × 10‐fold dilution (100, 10, 1.0, 0.1, and 0.01 μM) from an initial DMSO stock solution was performed against each cell line, before incubation at each individual concentration. Different parameters (GI₅₀, TGI, and LC₅₀) for each cell line were determined from the dose–response relationship.

2.1.2. Cell Culture and Treatments

HCT‐116 colon cancer cells, T‐47D and MCF7 breast cancer cells, ACHN renal carcinoma cells, and HUVEC human umbilical vein cells obtained from the American Type Culture Collection (ATCC, USA). HCT‐116, T‐47D, ACHN, and HUVEC cells were maintained in DMEM medium (GIBCO, USA) supplemented with 10% fetal bovine serum (FBS, GIBCO, USA), 100 units/mL penicillin, and 100 μg/mL streptomycin (GIBCO, USA). MCF7 cells were cultured in DMEM:F12, 10% fetal bovine serum (FBS, GIBCO, USA), 100 units/mL penicillin, and 100 μg/mL streptomycin (GIBCO, USA). The cells were grown at 37°C in a humidified atmosphere containing 5% CO₂.

2.1.3. MTT Assay

All of the cells were seeded in 96‐well plates at 1 × 10⁴ cells/well and incubated overnight. Then the medium was discarded, and the cells were treated with 1, 2.5, 5, 10, 25, 50, and 100 μM of QQ1, QQ4, or doxorubicin HCl (DOXO) for 24 h. Control cells received solvent (1% DMSO). 20 μL of 5 mg/mL (4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium (MTT) solution (Biomatik, Canada) was added to the wells after 3 h of incubation. The medium was discarded, and the formazan precipitate was dissolved with DMSO. A microplate reader (BioTek) was used to detect absorbance at 590 nm. Viability is expressed as a percentage of control. The concentration resulting in a 50% inhibition (IC₅₀ value) was calculated from the dose–response curve with GraphPad Prism 8.

2.1.4. Colony Formation Assay

1 × 10³ ACHN cells/well were seeded in 6 well plates and incubated overnight. 0.5, 1, and 2.5 μM QQ1 and 2.5 μM DOXO were added to wells, and 24 h later, the medium was refreshed. The cells were grown for 10 days. Afterward, the cells were fixed with cold methanol for 5 min. The cells were dyed with 0.5% crystal violet (10% methanol) for 20 min. Then the wells were washed with distilled water and air‐dried. Colonies were counted manually and photographed under normal light.

2.1.5. Apoptosis Necrosis Analysis

Apoptotic and necrotic cell rates were determined using Annexin V‐FITC and PI staining (Sony Biotechnology, USA), following the manufacturer's protocol. 5 × 10⁵ ACHN cells were treated with 0.5 μM, 1 μM, and 2.5 μM QQ1, 2.5 μM DOXO, or control in 6‐well plates for 24 h. After treatment, the cells were harvested by trypsinization and centrifuged at 1500 rpm for 10 min. The cell pellets were stained with Annexin V‐FITC and PI, followed by a 15‐min incubation at room temperature in the dark. Flow cytometric analysis was performed immediately using a FACS Calibur flow cytometer (BD Biosciences, USA), and data were quantified using BD Biosciences software.

2.1.6. Cell Cycle Assay

The impact of QQ1 on cell‐cycle arrest was assessed using the Muse Cell Cycle Kit (Merck Millipore, USA) following the manufacturer's instructions. 5 × 10⁵ ACHN cells were treated with 0.5 μM, 1 μM, and 2.5 μM QQ1, 2.5 μM DOXO, or control in 6‐well plates for 24 h. After treatment, cells were harvested by trypsinization and fixed in ice‐cold 70% ethanol for 3 h. Following fixation, the cells were centrifuged at 1500 rpm for 5 min, and the pellet was resuspended in 200 μL of assay buffer. The cells were then incubated in the dark for 30 min. The distribution of cells across different cell‐cycle phases (G0/G1, S, G2/M, and sub‐G1) was analyzed using a FACS Calibur flow cytometer (BD Biosciences), and the data were processed using BD Biosciences software.

2.1.7. Oxidative Stress

The impact of QQ1 on oxidative stress was assessed using (5‐(and‐6)‐chloromethyl‐2′,7′)‐dichlorodihydrofluorescein diacetate (H₂DCFDA) staining (Sigma‐Aldrich, USA). For this experiment, 5 × 10⁵ ACHN cells per well were plated in 6‐well plates and allowed to adhere overnight. The cells were then treated with QQ1 at concentrations of 0.5, 1, and 2.5 μM for 24 h, alongside controls. The positive control was 100 μM hydrogen peroxide (H₂O₂) treatment for 30 min. After treatment, the cells were harvested by trypsinization, and 20 μM H₂DCFDA dye was added to the cell suspension. Following a 30‐min incubation, the cells were centrifuged at 1500 rpm for 5 min, the supernatant was discarded, and the cells were resuspended in PBS. Changes in oxidative stress were evaluated and analyzed using flow cytometry (BD Biosciences, USA).

2.1.8. Statistical Analysis

All quantitative data are expressed as mean ± standard error (SEM). Statistical analyses were performed using GraphPad Prism 8 software. Unpaired t‐tests or one‐way analysis of variance (ANOVA), followed by Tukey's test, were used to evaluate differences between groups where appropriate. A significance threshold of p < 0.05 was applied.

2.2. ADME and PK Profiling

2.2.1. In Vitro logP and logD Determination

A 10 mM stock solution of QQ1 and QQ4 was prepared in dimethyl sulfoxide (DMSO). The stock was further serially diluted in DMSO to prepare working stock solutions of 5, 2.5, 1.25, and 0.625 mM. The experiment was conducted at final concentrations of 100, 50, 25, 12.5, and 6.25 μM. A mixture of 1‐octanol and sodium phosphate buffer (50 mM, pH 7.4) was prepared and used for the preparation of calibration curve standards. The mixture was kept in the plasma extractor for 24 h. After pre‐saturation, the mixture was separated in a separating funnel to separate the two phases.

The test solutions were prepared by spiking 10 μL of working stock solutions into 990 μL of pre‐saturated 1‐octanol:buffer (1:1) mixture to produce test item final concentrations of 6.25, 12.5, 25, 50, and 100 μM. The solutions were kept in the plasma extractor for 1 h for mixing and partitioning at room temperature (24°C). The samples from both the organic (1‐octanol) and aqueous layers (buffer) were separately used to read using the UV–vis spectrophotometer at 280 nm, the λ _max of both QQ1 and QQ4. Similarly, the pre‐saturated 1‐octanol:water (1:1) was used for the logP experiment. Verapamil and Atenolol were used as controls for the experiment (Make: Sigma Aldrich).

The logD/logP was calculated from the slope of the graph:

$$\log D/\log P=\log \left(\frac{1}{\text{slope}}\right)$$

2.2.2. In Vitro Metabolic Stability Study

Liver microsomes from mouse (Cat. No. M1000, Lot. No. 1710069), rat (Sprague–Dawley, male, Cat. No. R1000, Lot. No. 1610290), dog (Beagle, male, Cat. No. D1000, Lot. No. 1310086), and human (Cat. No. H0610, Lot. No. 1610016) were obtained from Xeno Tech LLC, Kansas, USA, to evaluate the metabolic stability of QQ1 and QQ4. The study used a liver microsomal protein concentration of 0.5 mg/mL and a test compound concentration of 0.5 μM. Microsomes (12.5 μL per well) were mixed with 2.5 μL of QQ1 or QQ4 (100 μM solution prepared in acetonitrile: dimethyl sulfoxide, 96:4) in the presence or absence of NADPH (50 μL, 10 mM), and the final volume was adjusted to 500 μL with 50 mM sodium phosphate buffer (pH 7.4). The reactions without NADPH served as controls. Samples (50 μL) were collected at 0, 5, 10, 15, and 30 min for the test groups and at 0 and 30 min for the controls. The reactions were quenched with 150 μL acetonitrile, followed by the addition of an internal standard (Rolipram). After vortexing and centrifugation, the supernatants were analyzed using LC–MS/MS. All experiments were conducted in duplicate.

2.2.3. In Vivo Bioavailability Study of QQ1 and QQ4 in Male Sprague–Dawley Rats

Sprague–Dawley rats (HyLasco Biotechnology (India) Pvt. Ltd., a subsidiary of Charles River from the US) of 8–12 weeks of age, weighing between 220 and 320 g, were used in this study. Animals were housed in polysulfonate cages in a typical research laboratory environment at 25°C ± 3°C and 50%–70% relative humidity with approximately 12 h light and dark cycles maintained with an enrichment device. During the study, the animals had access to a rat maintenance diet (Altromin Spezialfutter GmbH, Germany) and purified water (UV‐treated, charcoal‐filtered). Animal experiments were conducted in accordance with the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Ministry of Social Justice and Environment, Government of India, and approved by the Institutional Animal Ethics Committee. All rats were cannulated with the jugular vein, fasted overnight before dosing (except IV dose), and had access to food and water for ~4 h post‐dosing. QQ1 in NMP (10%) + PBS (pH 7.4) qs was administered intravenously at a dose of 1 mg/kg bw (10 mL/kg). QDM01 in NMP (10%) + Cremophor EL (5%) + PEG400 (30%) + PG (20%) + PBS (pH 7.4) qs was administered PO at a dose of 5 mg/kg bw (10 mL/kg). The animal weight on the day of dosing was used to calculate the required volume of the formulation and administered to the rats. After dosing, blood samples (~0.250 mL) were collected from the jugular vein at the following time points: 0.083 (IV only), 0.25, 0.5, 1, 2, 4, 6, 8, and 24 h using a serial sampling design (equal volumes of heparinized saline were replaced after each sample collection). The samples were collected in pre‐labeled microcentrifuge tubes containing K₂EDTA (20 μL of 200 mM solution per mL of blood) as an anticoagulant. The blood samples were stored at −60°C until bioanalysis using the fit‐for‐purpose LC–MS/MS method. Pharmacokinetic parameters were calculated using the non‐compartmental analysis (NCA) tool of the validated Phoenix WinNonlin 8.3.

2.2.4. Bioanalytical Method (LC/MS/MS) for QQ1 and QQ4

2.2.4.1. Chemicals and Reagents

MS‐grade (99.0% pure) ammonium formate and formic acid were obtained from Sigma Aldrich. HPLC‐grade acetonitrile (ACN) and dimethyl sulfoxide solvents were purchased from Merck, Germany. Milli‐Q water used for the preparation of the mobile phase, rinsing solvent, and seal washes was obtained from the in‐house (Eurofins Advinus Limited) Milli‐Q system. The compounds QQ4 (C₂₀H₁₈ClN₃O₂, 367.82, purity > 97%) and AQQ2 (C₁₈H₁₃ClN₂O₄, 356.75, purity > 97%) (Jannuzzi et al. 2023) were used as internal standards for QQ1 and QQ4, respectively. A SCIEX API 4000 LC/MS/MS triple quadrupole mass spectrometer system equipped with a negative electrospray ionization (ESI) source and Shimadzu prominence HPLC comprising binary pumps, a column oven, and a SIL‐HTC autosampler was used in this study. Data acquisition, integration, and quantification were performed using Analyst 1.6.3.

Chromatographic and mass spectrometric conditions Liquid chromatographic separations of QQ1, QQ4, and AQQ2 were achieved on a reverse‐phase Synergi Fusion RP 50 × 4.6 mm, 5 μm column operating at 40°C. The gradient mobile phase was used, starting with an initial 90:10, 3 min:10:90, 6 min:10:90, and 6–10 min:90:10 for up to 8 min. Additionally, 5 mM ammonium formate with 0.1% formic acid (mobile phase) and 0.1% formic acid in Acetonitrile (mobile phase) were delivered at a flow rate of 0.6 mL/min without a splitter.

The mass spectrometer was operated in negative electrospray ionization mode with unit mass resolution in a quadrupole analyzer with a dwell time of 200 ms, and the analytes were detected using multiple reaction monitoring (MRM). The compound parameters QQ1, QQ4, and AQQ2 were optimized along with the MRM transition (m/z) to achieve sensitivity. The source parameters were optimized to a curtain gas N₂ flow of 25 psi (CUR), nebulizer N₂ gas at 40 psi (gas 1), ion spray voltage of +5500 V (IS), auxiliary N₂ gas of 60 psi (gas 2) with a turbo spray temperature of 450°C (TEMP), and collision‐activated dissociation gas (CAD) of 10 psi. The MRM transition (m/z) selected for the analyte QQ1 was 356.20/237.20, 356.20/132.20, and 356.20/104.10; for QQ4 it was 370.10/148.10 and 370.10/237.20; and for AQQ2 it was 359.10/300.30. A system suitability test was performed before sample analysis. The system suitability test comprised six replicate injections of the extracted ULOQ and an extracted blank and LLOQ sample from rat plasma. The percentage coefficient of variation (CV (%)) for the peak area ratio (analyte to internal standard) of six replicate injections was < 5%, meeting the acceptance criteria. The retention time was within a ±0.5 min variation in each analytical run.

Sample preparation: QQ1 and QQ4 and their respective internal standards were extracted from rat plasma samples using the protein precipitation (crashing) method. To 50 μL of CC/QC/study samples, 200 μL of Internal Standard Working Solution in acetonitrile was added to all the tubes except for the Standard Blank sample and vortexed to mix. Vortex (Vibramax 100, Heidolph Instruments) was applied for approximately 10 min. The samples were centrifuged (Eppendorf 5810R) for 10 min at 10,000 rpm at a set temperature of 4°C. A sufficient volume of supernatant was aliquoted into autosampler vials for LC–MS/MS analysis (SCIEX API 4000).

2.2.4.2. Preparation of Calibration Standards and Quality Control Samples

Stock solutions of QQ1 and QQ4 and their respective internal standards were prepared in dimethyl sulfoxide (DMSO) and acetonitrile at a concentration of 1 mg/mL. The stock solutions of QQ1 and QQ4 were further diluted using DMSO to prepare calibration standard solutions in the concentration range of 22–25,000 ng/mL. Acetonitrile was used as an internal standard to prepare a working solution with a concentration of 500 ng/mL. These solutions were then spiked into interference‐free rat blank plasma to obtain calibration standards in the pharmacologically relevant range (1.10–1250 ng/mL). Similarly, the quality control (QC) samples were prepared using independent stock solutions of analytes to obtain concentrations of 3.00, 500, and 1000 ng/mL in rat plasma, representing low, medium, and high concentration QC samples, respectively. Stock solutions, diluted standard solutions, quality control solutions, and internal standard solutions were stored at 2°C–8°C. Spiked plasma samples (calibration standards and quality controls) were freshly prepared before sample analysis.

2.2.4.3. Pharmacokinetic Data Analysis

Pharmacokinetic parameters were calculated using an NCA tool of the validated Phoenix WinNonlin 8.3. C _max, T _max, and exposures (AUC_last and AUC_infinity) were calculated as applicable. Additionally, pharmacokinetic parameters for the parenteral route, like C ₀, elimination half‐life (T_1/2), hepatic clearance (CL), and distribution volume (Vd) were estimated. Oral bioavailability (%F) was calculated with dose‐normalized exposure of non‐intravenous against dose‐normalized intravenous exposure.

2.3. Molecular Docking

AutoDock Vina (Eberhardt et al. 2021; Trott and Olson ²⁰¹⁰) implemented in the SwissDock server (https://www.swissdock.ch/) was used to run the simulation. The SMILES string for compound QQ1 was generated through the Molinspiration server (https://www.molinspiration.com/) and was supplied along with the pdb code (PDB: 1C25) of the target protein in the SwissDock webform. The center of the active site residues (CYS384, TYR386, CYS430, GLU431, PHE432, ARG436, MET488, HIS490) (Fauman et al. 1998) was obtained using the “measure centre” command available in UCSF Chimera (Pettersen et al. 2004) and was assigned as the center of the grid box (14.38–39.95—66.16) with a box dimension of 15–15—15 and a sampling exhaustivity of 50. The top‐scoring conformer was then analyzed using the Protein‐Ligand Interaction Profiler (PLIP) server (https://plip‐tool.biotec.tu‐dresden.de/) (Adasme et al. 2021).

3. Results and Discussion

3.1. Chemistry

The available dichloroquinolinequinone (QQ) was chosen as the starting substance for synthesizing aminated QQ (QQ1‐7). The chemical structures of the obtained compounds are shown in Figure 1. For the synthesis of target molecules (QQ1‐7), a reaction of QQ, obtained via chlorooxidation of commercial hydroxyquinoline using a previously reported procedure, with an excess of piperazines containing an aryl group was used in the presence of the catalyst CeCl₃.7H₂O according to previously reported literature (Kim et al. 2003; Yildiz et al. ²⁰²²).

FIGURE 1.

Structures of QQs (QQ1‐7) containing EDG(s) in the aryl amino moiety.

3.2. Anticancer Activity

3.2.1. Preliminary Single‐Dose Screening Against a Panel of Sixty Cancer Cell Lines

The Developmental Therapeutics Program (DTP) of the National Cancer Institute (NCI) in the United States of America assessed the previously obtained QQs' (QQ1‐7) impact on the proliferation of 60 different cancer cell lines tested at a 10 μM single dose (Razaghi et al. 2018; Ricci and Zong ²⁰⁰⁶). The cell lines were derived from nine human cancer cell types represented by leukemia, non–small‐cell (NSC) lung cancer, colon cancer, central nervous system (CNS) cancer, melanoma, ovarian cancer, renal cancer, prostate cancer, and breast cancers (Boyd and Pauli 1995). Among seven synthesized molecules, all QQs inhibited the growth of some cancer cell lines shown in Table 1. In general, some QQs containing a methyl or methoxy group in different positions on the piperazine moiety resulted in little to no growth percentage of most cancer cell lines, such as non–small‐cell lung cancer (except that NCI‐H522), colon cancer (except that HCT‐116 and HCT‐15), CNS cancer, melanoma (except that LOX IMVI, MALME‐3 M, MDA‐MB‐435, and UACC‐257), ovarian cancer, prostate cancer (except that DU‐145), and breast cancer (except that MCF7 and T‐47D). In some cases, a down to 30% growth percentage was observed. Interestingly, most QQs displayed excellent anticancer activity against leukemia cell lines. On the other hand, fewer activities were observed at the leukemia panel against RPMI‐8226 (with 20.58 to 88.49 inhibition) cell line. Two colon cancer cell lines (HCT‐116 and HCT‐15) were overly sensitive to all QQs except QQ5 and QQ6 against HCT‐15. QQ2 showed a GI% value of 98.81 for the sub‐panel cell line of melanoma LOX IMVI, whereas QQ6 exhibited a GI% value of 96.64 for the MDA‐MB‐435 cell line. Regarding the renal cancer subpanel, five QQs (QQ1‐4 and QQ7) displayed potent anticancer activity (GI% ≥ 72.20% and up to 98.59%) on five types of renal cancer cell lines. All QQs showed sensitivity against the renal cell line ACHN, except that QQ5. Regarding the breast cancer, subpanel results showed that all QQs have potent anticancer activity on the MCF7, T‐47D, and MDA‐MB‐468 breast cancer cell lines.

TABLE 1.

Percentage cell growth of QQs against nine human cancer cell lines at 10 μM concentration (QQ1‐7).

Molecules		QQ1	QQ2	QQ3	QQ4	QQ5	QQ6	QQ7
Panel/cancer cell line
Leukemia
GP	CCRF‐CEM	6.08	0.31	4.71	5.41	11.48	10.00	6.79
	HL‐60 (TB)	4.34	4.21	7.30	3.39	12.52	9.82	−4.99
	K‐562	6.19	7.01	9.90	6.28	9.05	7.16	6.59
	MOLT‐4	9.67	10.43	9.85	13.90	38.31	18.21	4.55
	RPMI‐8226	35.46	11.51	46.80	11.78	79.42	55.76	23.20
	SR	3.69	−24.09	3.39	4.58	12.36	4.42	0.31
Non–small‐cell lung cancer
GP	A549/ATCC	91.52	99.61	93.81	91.34	112.23	97.25	96.02
	EKVX	101.34	95.38	103.83	97.05	98.25	102.35	99.93
	HOP‐62	63.90	59.50	83.26	73.14	93.43	83.46	85.42
	HOP‐92	127.92	118.46	143.61	84.39	134.56	134.95	96.18
	NCI‐H226	91.32	94.23	90.33	93.19	91.01	94.71	93.98
	NCI‐H23	46.29	38.08	55.32	35.59	56.33	60.28	55.93
	NCI‐H322M	95.16	89.86	93.78	97.37	92.42	95.44	97.16
	NCI‐H460	38.71	53.07	58.38	44.83	75.02	62.98	52.68
	NCI‐H522	−16.18	−28.01	26.75	45.17	9.14	42.59	−9.71
Colon cancer
GP	COLO 205	65.24	31.84	90.42	75.33	105.68	91.90	30.47
	HCC‐2998	97.80	104.70	107.38	103.98	107.62	105.96	106.90
	HCT‐116	−56.09	−95.15	−16.27	−78.08	27.06	1.40	−59.66
	HCT‐15	20.28	4.38	24.79	20.99	55.54	42.26	17.09
	HT29	106.28	94.90	103.88	105.71	111.02	107.89	87.31
	KM12	92.26	87.20	92.20	80.00	90.70	94.45	95.67
	SW‐620	−35.02	−27.83	11.04	−79.66	43.80	14.34	−4.75
CNS cancer
GP	SF‐268	79.44	80.18	82.86	80.25	87.58	84.13	85.97
	SF‐295	102.54	86.80	100.54	97.46	106.52	109.91	108.24
	SF‐539	81.23	78.88	92.08	86.65	95.04	93.83	89.63
	SNB‐19	81.49	74.86	79.53	70.35	88.81	90.85	94.71
	SNB‐75	83.66	82.56	84.33	99.11	85.96	85.06	85.17
	U251	79.47	71.28	85.24	72.41	102.14	90.55	90.95
Melanoma
GP	LOX IMVI	10.39	1.19	24.17	−32.58	50.21	28.59	20.96
	MALME‐3 M	−1.87	−14.46	24.37	52.78	58.84	61.57	23.73
	M14	69.61	36.21	75.25	70.31	88.10	81.74	45.57
	MDA‐MB‐435	−66.98	−62.88	11.85	−81.24	57.70	3.36	−59.89
	SK‐MEL‐2	97.11	81.10	99.91	94.35	95.52	98.36	95.05
	SK‐MEL‐28	83.00	35.91	92.07	80.85	98.41	105.42	78.30
	SK‐MEL‐5	95.08	88.83	95.16	90.40	96.41	97.26	95.03
	UACC‐257	55.44	21.65	61.47	29.56	95.04	70.20	24.63
	UACC‐62	61.77	66.22	74.29	29.16	83.02	84.35	69.31
Ovarian cancer
GP	IGROV1	69.98	77.34	73.06	58.58	86.33	80.76	80.69
	OVCAR‐3	NT	NT	NT	NT	NT	NT	NT
	OVCAR‐4	65.96	59.03	67.03	48.29	85.72	80.50	48.20
	OVCAR‐5	107.42	98.44	110.57	109.86	114.49	123.79	112.16
	OVCAR‐8	73.53	69.98	80.64	67.89	90.54	82.85	74.83
	NCI/ADR‐RES	76.98	73.66	78.72	55.84	81.67	81.92	86.64
	SK‐OV‐3	91.46	95.83	92.95	111.50	90.51	93.84	81.00
Renal cancer
GP	786–0	104.75	91.01	103.76	99.48	107.30	114.21	107.11
	A498	104.39	110.84	103.37	110.55	114.80	103.69	102.91
	ACHN	−3.31	−22.55	−32.91	1.41	45.67	−23.75	−18.71
	CAKI‐1	39.87	25.44	52.03	44.31	63.56	57.85	32.34
	RXF 393	27.79	−10.88	52.20	12.46	70.97	64.54	15.17
	SN12C	58.18	70.09	75.09	27.80	88.31	95.82	84.73
	TK‐10	122.53	68.67	117.43	132.88	148.16	125.97	99.61
	UO‐31	14.88	2.66	18.30	32.02	44.50	38.03	19.24
Prostate cancer
GP	PC‐3	71.89	66.06	72.93	63.31	78.98	78.66	68.14
	DU‐145	45.66	20.25	44.73	51.67	48.05	59.19	5.58
Breast cancer
GP	MCF7	−44.65	−29.32	5.92	−61.98	36.86	19.08	−27.59
	MDA‐MB‐231/ATCC	75.33	88.25	84.85	24.54	86.42	85.05	62.47
	HS 578 T	79.83	57.40	70.56	79.75	87.23	84.45	70.22
	BT‐549	97.32	99.06	97.92	103.43	119.34	105.40	110.64
	T‐47D	−54.16	−52.48	−39.52	−56.07	19.27	−48.09	−53.27
	MDA‐MB‐468	−62.82	−68.75	−66.60	−63.93	−55.31	−63.55	−57.75

Abbreviation: NT, not tested.

3.2.2. Five‐Dose Screening Against a Panel of Sixty Cancer Cell Lines

The five‐dose screening approach compares the active QQs included in the full panel of an NCI sixty cancer cell line to five different doses (ranging from 0.01 μM to 100 μM) discovered in the single‐dose screening to determine threshold inhibition criteria parameters: GI₅₀ (drug concentration at which 50% of growth is inhibited), TGI (total growth inhibition), and LC₅₀ (drug concentration at which 50% of tumor cells are killed) shown in Tables 2, 3, 4 (all in μM), respectively (Boyd and Pauli 1995; Monks et al. ¹⁹⁹¹). GI₅₀ data were used to perform a comprehensive NCI 65‐dose panel trend analysis, as shown in Table 2. The most sensitive cancer cell lines were HL‐60 (TB) and SR from the leukemia panel; EKVX, HOP‐92, and NCI‐H522 from the non–small‐cell lung cancer panel; HCT‐116, HCT‐15, and SW‐620 from the colon cancer panel; LOX IMVI, MALME‐3 M, and MDA‐MB‐435 from the melanoma panel; OVCAR‐4 from the ovarian cancer panel; ACHN, RXF 393, and UO‐31 from the renal cancer panel; and MCF7, MDA‐MB‐231/ATCC, T‐47D, and MDA‐MB‐468 from the breast cancer panel. CNS cancer, ovarian cancer, and prostate cancer cell lines were partly responsive to treatment with all QQs, with GI₅₀ values more than 2 μM. Excellent potency was seen for all QQs with respect to leukemia cell lines, achieving a GI₅₀ of less than 2 μM. The most responsive cancer cell line against QQ2 and QQ4 was the SR cell line (1.00 and 0.50 μM, respectively). All selected QQs (QQ1‐4 and QQ6‐7) displayed maximum sensitivity toward EKVX (except for QQ7), HOP‐92, and NCI‐H522 cancer cell lines of non–small‐cell lung cancer and HCT‐116, HCT‐15, and SW‐620 cancer cell lines of colon cancer with less than 2 μM GI₅₀. All QQs showed promising cytotoxic activity with TGI values less than 10 μM (Table 2) particularly for all of the leukemia cell lines, EKVX, HOP‐62, and NCI‐H522 cell lines of non–small‐cell lung cancer, HCT‐116, HCT‐15, and SW‐620 cell lines of colon cancer, LOX IMVI, MALME‐3 M, and MDA‐MB‐435 cell lines of melanoma, OVCAR‐4 cell line of ovarian cancer, ACHN, RXF 393, and UO‐31 cell lines of renal cancer, and MCF7, MDA‐MB‐231/ATCC, T‐47D, and MDA‐MB‐468 cell lines of breast cancer. Despite the fact that the QQs exhibited high anticancer potency in inhibiting the growth of some cancer cell lines (leukemia, non–small‐cell lung cancer, colon cancer, melanoma, renal cancer, and breast cancer), as indicated by the low values of GI₅₀ in Table 2, they were generally not lethal against leukemia cell lines, as indicated by the high values of LC₅₀ in Table 4. Some non–small‐cell lung cancer cell lines (EKVX, HOP‐92, and NCI‐H522), colon cancer cell lines (HCT‐116 and SW‐620), melanoma cell lines (LOX IMVI and MDA‐MB‐435), ovarian cancer cell line (OVCAR‐4), renal cancer cell lines (ACHN, RXF 393, and UO‐31), and breast cancer cell lines (MDA‐MB‐231/ATCC and MDA‐MB‐468) were killed by most of the QQs with low micromolar LC₅₀ (< 10 μM). According to the results, QQ1 and QQ4 have high to exceptional cytotoxic efficacy. More intriguingly, these structures are excellent candidates for more research and development as a new class of anticancer drugs (Table 3).

TABLE 2.

In vitro GI₅₀ (μM) values as per the five‐dose assay of the selected QQs.

Molecules	QQ1	QQ2	QQ3	QQ4	QQ6	QQ7
Panel/cancer cell line
Leukemia
CCRF‐CEM	2.33	1.73	2.03	2.21	2.46	2.80
HL‐60 (TB)	1.36	1.61	1.27	1.35	1.75	1.48
K‐562	2.04	1.28	1.80	1.35	2.30	3.03
MOLT‐4	1.97	1.78	2.01	2.09	NT	NT
RPMI‐8226	2.10	2.00	2.08	2.14	2.27	2.69
SR	1.89	1.00	1.36	0.504	1.80	2.27
Non–small‐cell lung cancer
A549/ATCC	16.5	23.5	15.7	15.0	15.5	15.9
EKVX	1.73	1.68	1.78	1.73	2.05	2.45
HOP‐62	17.3	35.1	17.0	15.4	18.5	19.1
HOP‐92	1.49	1.36	1.48	1.63	1.63	1.49
NCI‐H226	14.3	20.8	12.9	10.1	14.0	16.2
NCI‐H23	5.21	3.83	4.24	1.77	4.15	5.11
NCI‐H322M	15.0	22.3	14.4	8.70	15.3	15.3
NCI‐H460	6.73	6.68	5.49	4.49	7.26	5.86
NCI‐H522	1.73	1.68	1.74	1.76	1.73	1.70
Colon cancer
COLO 205	4.53	3.14	1.94	1.84	1.87	2.01
HCC‐2998	12.0	3.33	8.53	1.77	12.1	11.7
HCT‐116	1.73	1.56	1.67	1.62	1.73	1.92
HCT‐15	2.08	1.69	1.71	1.54	2.05	2.66
HT29	12.0	5.33	6.09	5.05	12.2	4.71
KM12	20.7	48.1	19.0	17.5	20.3	18.4
SW‐620	1.67	1.48	1.42	1.47	1.64	1.63
CNS cancer
SF‐268	15.2	23.7	14.4	5.91	13.1	19.2
SF‐295	14.2	14.7	12.1	2.12	12.5	13.9
SF‐539	6.96	1.80	3.91	1.85	11.4	7.66
SNB‐19	16.1	30.3	14.4	13.4	16.2	16.9
SNB‐75	17.5	17.4	10.9	12.8	11.8	14.3
U251	10.3	6.00	6.96	1.79	5.46	10.7
Melanoma
LOX IMVI	1.59	1.65	1.57	1.52	1.62	1.75
MALME‐3 M	1.95	2.03	1.83	1.77	1.84	2.06
M14	3.92	2.20	2.69	1.84	3.27	3.04
MDA‐MB‐435	1.77	1.78	1.81	1.91	1.79	1.94
SK‐MEL‐2	11.2	2.75	6.33	2.11	6.25	12.0
SK‐MEL‐28	3.31	2.19	2.60	2.35	3.30	3.80
SK‐MEL‐5	13.1	4.98	9.63	1.86	11.5	12.8
UACC‐257	2.21	1.98	2.05	1.75	2.12	2.86
UACC‐62	3.43	3.92	3.49	1.84	3.48	5.99
Ovarian cancer
IGROV1	3.48	1.97	2.53	1.74	2.93	3.25
OVCAR‐3	NT	NT	NT	NT	NT	NT
OVCAR‐4	1.79	1.75	1.74	1.68	1.73	2.18
OVCAR‐5	16.6	6.89	15.0	3.36	15.4	16.2
OVCAR‐8	4.72	1.91	2.66	1.60	2.00	4.87
NCI/ADR‐RES	4.33	2.51	2.44	1.89	3.36	6.74
SK‐OV‐3	13.8	37.2	12.8	12.8	15.4	12.1
Renal cancer
786–0	6.43	1.71	2.33	3.35	5.01	15.9
A498	13.5	> 100	13.1	15.0	13.5	15.0
ACHN	2.21	1.85	1.72	1.61	1.75	1.78
CAKI‐1	5.50	2.78	2.86	1.68	2.64	4.72
RXF 393	1.87	1.74	1.72	1.79	1.77	2.61
SN12C	4.97	3.56	4.28	1.79	5.19	11.1
TK‐10	12.4	4.76	3.96	7.15	4.18	4.11
UO‐31	1.91	1.49	1.48	1.53	1.64	2.00
Prostate cancer
PC‐3	9.21	1.63	4.48	2.85	8.55	13.8
DU‐145	11.3	3.80	6.86	6.05	11.1	5.93
Breast cancer
MCF7	1.65	1.46	1.63	1.22	1.98	1.66
MDA‐MB‐231/ATCC	1.68	1.71	1.62	1.59	1.68	1.66
HS 578 T	13.6	4.94	5.74	3.64	10.0	14.1
BT‐549	10.2	1.96	3.61	5.51	6.75	4.23
T‐47D	1.76	1.90	1.67	1.61	1.82	1.78
MDA‐MB‐468	1.59	1.60	1.54	1.49	1.75	1.84

Abbreviation: NT, not tested.

TABLE 3.

In vitro TGI (μM) values as per the five‐dose assay of the selected QQs.

Molecules	QQ1	QQ2	QQ3	QQ4	QQ6	QQ7
Panel/cancer cell line
Leukemia
CCRF‐CEM	6.65	5.19	5.79	7.09	NT	> 100
HL‐60 (TB)	3.94	4.05	3.82	3.87	4.47	4.46
K‐562	5.51	4.08	5.31	8.53	6.28	12.0
MOLT‐4	5.38	4.95	5.45	5.59	NT	NT
RPMI‐8226	5.37	5.22	5.47	5.79	5.92	7.32
SR	6.41	5.00	NT	4.80	5.38	6.59
Non–small‐cell lung cancer
A549/ATCC	32.6	> 100	36.0	33.3	33.0	35.2
EKVX	3.38	3.10	3.34	3.14	4.42	7.05
HOP‐62	31.5	> 100	31.0	29.4	33.2	38.0
HOP‐92	2.93	2.70	2.86	3.17	3.07	3.03
NCI‐H226	31.9	> 100	33.3	26.5	31.9	35.9
NCI‐H23	19.6	16.8	NT	NT	NT	NT
NCI‐H322M	28.2	> 100	28.2	21.0	29.1	28.7
NCI‐H460	22.4	46.5	21.3	22.6	26.7	19.9
NCI‐H522	3.55	3.31	3.42	3.53	3.39	3.34
Colon cancer
COLO 205	17.1	10.8	3.80	3.33	4.01	4.04
HCC‐2998	24.9	13.8	22.1	3.38	25.5	26.5
HCT‐116	3.11	2.89	3.07	2.97	3.18	3.85
HCT‐15	6.22	4.52	4.05	3.28	5.60	11.3
HT29	27.3	> 100	21.0	19.3	28.6	18.2
KM12	43.6	> 100	36.4	32.0	41.7	34.7
SW‐620	3.90	3.45	3.00	3.08	3.32	3.79
CNS cancer
SF‐268	29.6	> 100	32.2	22.2	29.6	50.1
SF‐295	27.3	> 100	25.4	4.86	26.2	28.3
SF‐539	20.0	3.32	15.6	3.50	23.6	21.2
SNB‐19	30.6	> 100	29.4	26.4	32.9	32.9
SNB‐75	38.0	> 100	27.9	33.3	24.7	28.8
U251	22.7	25.6	21.1	3.42	19.5	24.1
Melanoma
LOX IMVI	3.07	3.21	3.02	2.91	3.12	3.56
MALME‐3 M	4.25	4.64	3.77	3.34	4.06	6.05
M14	14.7	6.45	9.36	3.69	13.0	10.5
MDA‐MB‐435	3.29	3.45	3.35	3.51	3.34	3.68
SK‐MEL‐2	23.6	14.8	19.2	4.48	19.3	25.4
SK‐MEL‐28	10.3	5.06	6.61	5.25	9.58	13.2
SK‐MEL‐5	25.7	17.7	21.5	3.77	23.8	25.5
UACC‐257	6.22	4.25	4.97	3.30	5.23	8.41
UACC‐62	12.4	42.2	13.0	3.60	12.3	19.3
Ovarian cancer
IGROV1	12.3	4.09	6.58	3.21	9.52	11.6
OVCAR‐3	NT	NT	NT	NT	NT	NT
OVCAR‐4	3.44	3.23	3.26	3.29	3.26	5.36
OVCAR‐5	32.0	21.2	31.5	11.4	30.3	30.3
OVCAR‐8	17.6	4.35	9.30	2.99	5.01	17.9
NCI/ADR‐RES	20.7	7.18	7.45	4.21	15.5	28.6
SK‐OV‐3	29.9	> 100	42.9	25.8	46.2	25.2
Renal cancer
786–0	18.8	3.09	5.36	10.2	16.3	29.5
A498	34.1	> 100	26.5	28.4	26.4	29.1
ACHN	5.80	3.89	3.51	2.96	3.19	3.37
CAKI‐1	18.2	> 100	11.6	3.09	8.15	17.1
RXF 393	3.43	3.17	3.15	3.23	3.25	8.37
SN12C	17.8	78.9	21.9	3.39	20.2	25.6
TK‐10	25.5	> 100	8.89	18.1	13.2	11.6
UO‐31	4.30	2.86	2.81	3.11	3.40	6.83
Prostate cancer
PC‐3	23.1	3.70	17.5	11.9	22.3	29.1
DU‐145	23.6	> 100	26.3	18.3	23.1	18.4
Breast cancer
MCF7	3.60	3.22	3.75	3.23	5.86	3.97
MDA‐MB‐231/ATCC	3.21	3.27	3.12	3.09	3.20	3.26
HS 578 T	47.4	85.3	38.5	34.5	51.5	39.0
BT‐549	21.8	3.41	9.58	17.3	18.3	14.1
T‐47D	3.95	4.35	3.92	4.04	4.13	4.27
MDA‐MB‐468	3.16	3.18	3.08	3.04	3.43	3.50

Abbreviation: NT, not tested.

TABLE 4.

In vitro LC₅₀ (μM) values as per the five‐dose assay of the selected QQs.

Molecules	QQ1	QQ2	QQ3	QQ4	QQ6	QQ7
Panel/cancer cell line
Leukemia
2003CCRF‐CEM	> 100	> 100	> 100	> 100	> 100	> 100
HL‐60 (TB)	> 100	15.1	> 100	> 100	> 100	> 100
K‐562	> 100	> 100	> 100	> 100	> 100	> 100
MOLT‐4	> 100	> 100	> 100	> 100	NT	NT
RPMI‐8226	> 100	> 100	> 100	> 100	> 100	> 100
SR	> 100	> 100	> 100	> 100	> 100	> 100
Non–small‐cell lung cancer
A549/ATCC	64.4	> 100	82.6	74.0	70.1	77.7
EKVX	6.61	5.72	6.28	5.70	9.52	28.4
HOP‐62	57.0	> 100	56.5	56.0	59.6	75.6
HOP‐92	5.76	5.34	5.54	6.18	5.79	6.15
NCI‐H226	70.9	> 100	85.7	69.8	72.5	79.7
NCI‐H23	53.7	75.0	48.5	6.94	48.0	51.3
NCI‐H322M	53.2	> 100	55.2	46.0	55.4	53.9
NCI‐H460	62.2	> 100	62.9	> 100	84.2	52.2
NCI‐H522	7.29	6.55	6.73	7.07	6.64	NT
Colon cancer
COLO 205	47.6	> 100	7.42	6.00	8.58	8.08
HCC‐2998	51.8	71.5	51.8	6.44	53.8	60.0
HCT‐116	5.58	5.38	5.67	5.45	5.85	7.74
HCT‐15	34.2	56.2	9.60	6.96	52.3	56.7
HT29	61.9	> 100	58.8	56.4	67.3	54.5
KM12	91.9	> 100	69.9	58.5	86.0	65.5
SW‐620	9.11	8.04	6.32	6.46	6.73	8.82
CNS cancer
SF‐268	57.3	> 100	71.9	62.0	66.7	> 100
SF‐295	52.7	> 100	53.3	13.2	54.9	57.4
SF‐539	45.6	6.11	50.2	6.61	49.1	49.2
SNB‐19	58.2	> 100	60.1	51.8	66.9	64.0
SNB‐75	82.5	> 100	70.9	86.7	51.7	57.9
U251	49.7	89.5	51.9	6.52	50.3	54.6
Melanoma
LOX IMVI	5.92	6.25	5.79	5.58	6.01	7.26
MALME‐3 M	9.28	20.8	7.74	6.32	8.95	33.8
M14	38.9	> 100	30.8	7.40	36.9	35.1
MDA‐MB‐435	6.10	NT	6.20	6.45	6.22	6.96
SK‐MEL‐2	49.8	> 100	44.6	9.48	44.7	53.7
SK‐MEL‐28	34.4	62.0	24.5	15.7	31.3	40.2
SK‐MEL‐5	50.7	46.4	46.6	7.65	49.0	50.7
UACC‐257	22.6	9.13	15.4	6.23	17.1	33.0
UACC‐62	37.8	> 100	37.7	7.08	35.7	46.7
Ovarian cancer
IGROV1	40.6	8.49	27.0	5.90	33.2	35.0
OVCAR‐3	NT	NT	NT	NT	NT	NT
OVCAR‐4	6.59	5.96	6.10	6.42	6.13	27.7
OVCAR‐5	61.8	54.0	65.8	47.8	59.3	56.6
OVCAR‐8	44.8	9.91	33.5	5.59	16.3	45.4
NCI/ADR‐RES	79.4	> 100	66.4	NT	66.4	> 100
SK‐OV‐3	64.5	> 100	> 100	51.8	> 100	52.3
Renal cancer
786–0	43.3	5.56	15.8	32.1	40.4	54.7
A498	85.7	> 100	53.7	54.0	51.3	56.1
ACHN	20.1	8.16	7.15	5.44	5.79	6.40
CAKI‐1	42.7	> 100	34.5	5.68	28.6	42.3
RXF 393	6.27	5.76	5.74	5.82	5.99	43.1
SN12C	44.9	> 100	94.9	NT	63.4	59.1
TK‐10	52.7	> 100	29.0	42.8	36.5	35.0
UO‐31	9.72	5.52	5.34	6.33	7.06	25.6
Prostate cancer
PC‐3	54.9	8.39	46.2	47.9	52.7	61.6
DU‐145	49.2	> 100	89.1	42.8	48.2	44.6
Breast cancer
MCF7	7.84	7.09	8.64	8.51	> 100	NT
MDA‐MB‐231/ATCC	6.16	6.22	NT	6.01	6.09	6.41
HS 578 T	> 100	> 100	> 100	> 100	> 100	> 100
BT‐549	46.7	5.94	30.8	41.6	43.2	37.8
T‐47D	8.87	9.96	NT	> 100	NT	> 100
MDA‐MB‐468	6.28	6.30	6.16	6.20	6.72	6.67

Abbreviation: NT, not tested.

3.2.3. Cytotoxic Evaluation of QQ1 and QQ4 With MTT Assay

The cytotoxic effects of QQ1, QQ4, and DOXO were assessed in a range of cancer cell lines, including HCT‐116 (colon cancer), MCF7 and T‐47D (breast cancers), ACHN (renal carcinoma), and HUVEC (non‐cancerous endothelial cells). The half‐maximal inhibitory concentration (IC₅₀) values for each compound are summarized in Table 5, and dose inhibition curves can be seen in Figure 2. The selectivity of the compounds was calculated against HUVEC. QQ1 demonstrated moderate activity with IC₅₀ values ranging from 1.55 μM in ACHN cells to 4.79 μM in HCT‐116 cells. QQ4 was generally less potent than QQ1 in the cancer cell lines, with IC₅₀ values ranging from 3.24 μM in MCF7 cells to 6.78 μM in HCT‐116 cells. This result indicates that having a methyl group as the EDG on the para position causes a decrease in the cytotoxic activity of dichloroquinolinequinones. Remarkably, the IC₅₀ value of QQ1 against ACHN cells was 1.55 μM, and 4.72 μM against HUVEC, which resulted in ~3‐fold higher selectivity than DOXO.

TABLE 5.

IC₅₀ values of QQ1 and QQ4 were determined from dose–response curves of the MTT assay.

		HCT‐116	MCF7	T‐47D	ACHN	HUVEC
QQ1	IC50	4.79 ± 0.34	3.40 ± 0.37	4.61 ± 0.52	1.55 ± 0.16	4.72 ± 0.44
	SI	0.99	1.39	1.03	3.04	—
QQ4	IC50	6.78 ± 0.56	3.24 ± 0.36	4.94 ± 0.43	4.30 ± 0.51	11.39 ± 1.24
	SI	1.68	3.51	2.31	2.65	—
DOXO	IC50	10.53 ± 2.29	18.44 ± 2.55	75.93 ± 14.77	36.08 ± 9.42	31.25 ± 4.74
	SI	2.97	1.69	0.41	0.87	—

Note: The values are expressed as the mean ± SEM. Selectivity index [SI = IC₅₀ (HUVEC)/IC₅₀ (cancer cell line)] of QQ1, QQ4, and DOXO. HCT‐116 colon cancer, MCF7 and T‐47D breast cancer cells, ACHN renal carcinoma cells, and the HUVEC non‐cancerous cell line. IC₅₀: The compound concentration required to inhibit cell viability by 50%.

FIGURE 2.

Cytotoxic effect of QQ1, QQ4, and DOXO on HCT116, MCF‐7, T47D, ACHN, and HuVeC is evaluated by MTT assay after 24 h treatment. Values are expressed as mean ± SEM, n = 6.

Renal adenocarcinoma is the most common type of kidney cancer in adults and accounts for 2%–3% of cancers in adults (Capitanio and Montorsi 2016). In recent years, targeted therapies such as kinase inhibitors and mTOR inhibitors have been used for first‐ and second‐line treatment. Still, the median survival for patients with metastatic renal cell carcinoma remains less than 3 years when treated with systemic therapy (Greef and Eisen 2016). Thus, novel targeted molecules should be developed to improve the efficacy of kidney cancer treatment. Eventually, the results from NCI drug screening and our MTT assay encouraged us to make further investigations of QQ1 in ACHN cells, given its relatively higher selectivity and potency in this renal carcinoma model compared to other cell lines.

To further investigate the inhibitory effect of QQ1 on ACHN cell proliferation, we performed a colony formation assay at 0.5–1‐2.5 μM concentrations. Our results demonstrated a dose‐dependent loss of colony‐forming ability in ACHN cells (Figure 3).

FIGURE 3.

Results of the colony formation assays. Bar graphs show relative differences in the number of colonies in ACHN cell lines treated with QQ1 or DOXO. The number of colonies is normalized to the mean number of colonies of controls from the respective day of the experiment. Differences that are statistically different, according to Tukey's multiple comparisons, are marked with # meaning p < 0.0001.

3.2.4. Assessment of Flow Cytometric Experiments

By evaluating apoptosis and necrosis measurements, we aimed to determine the effectiveness of the new drug candidate molecule. However, despite increasing drug concentrations, no apoptosis or necrosis‐induced cell death, as measured by Annexin V staining, was observed in ACHN cells, as seen with doxorubicin, which was used as a positive control (Figure 4). Piperazine is used in the synthesis of numerous bioactive molecules, and its derivatives possess various pharmacological activities (Bassetto et al. 2017; Chen et al. ²⁰⁰⁹; Xu et al. ²⁰¹¹). Additionally, some derivatives exhibit anti‐cancer properties (Fytas et al. 2015). Certain piperazine derivatives have also demonstrated specific activities such as HDAC (histone deacetylase) inhibition. Aminoflavones can induce different types of cell death, including apoptosis in kidney cells (Turcotte et al. 2008). Studies in the early 2000s showed that certain drugs induce autophagy rather than apoptosis in kidney cancer cells. Aminoflavones' antiproliferative effects were observed in a study involving various kidney cancer cells. In conclusion of this study, cells like TK‐10, CAKI‐1, and SN‐12 experienced apoptosis, whereas ACHN cells did not exhibit this effect (Callero et al. 2012). In another study published in 2016, the effects of chlorinated pyrimidine compounds in HeLa, T47D, and HCT15 cells were examined. All chlorinated groups exhibited cytotoxic effects, which were explained by topoisomerase I and II inhibition (Kadayat et al. 2016). Another study that was published in 2024 investigated how 20 novel isoquinoline quinones affected 52 distinct cancer cells. They reported that synthesizing nine distinct compounds increased the amount of ROS while inhibiting cell growth. Additionally, they asserted that they altered the cell cycle through p21. They were unable to identify any impact of these substances on apoptosis, though. They claimed that a variety of death pathways were impacted by the elevated ROS (Kruschel et al. 2024).

FIGURE 4.

Representative image from flow cytometric apoptosis/necrosis assay, along with quantitative analysis for ACHN cells treated with QQ1. Values expressed as mean ± SEM **p < 0.01, ***p < 0.001, ****p < 0.0001, n = 4.

ACHN cells were treated with different doses of QQ1 and analyzed for changes in the cell cycle using flow cytometry. We found that only at a dose of 0.5 μM QQ1, the proportion of cells in the G0/G1 phase decreased significantly compared to the control group. There were no significant changes in the other phases of the cell cycle. However, when the cell cycle phases were evaluated as sub‐G1 + G0/G1 and S + G2 + M phases versus control groups, a significant difference was observed in the total proportion of both phases in the group treated with 0.5 μM QQ1 (Figure 5). Deregulated cell‐cycle progression has been linked to cancer, making cell‐cycle inhibition a potentially significant target for cancer treatment (Jacks and Weinberg 1996; Sherr and Roberts ¹⁹⁹⁹).

FIGURE 5.

Representative images along with quantitative analysis of cell cycle disturbance in ACHN cells following QQ1 treatment. Values expressed as mean ± SEM. *p < 0.05, n = 4.

These antiproliferative effects raise the possibility that naftopidil could be useful in treating RCC. Importantly, positive regulators of cell cycle progression through the G1‐S checkpoint are cyclin‐dependent kinases (CDK; e.g., CDK2). In mammalian cells, CDK activation and S‐phase entry are inhibited by CDK inhibitors (Weinberg 1995).

Reactive oxygen species (ROS) accumulation can lead to intracellular oxidative stress. Therefore, we evaluated ROS levels after treatments. According to the results, QQ1 caused significant ROS induction in ACHN cells (Figure 6). Among the applied groups, the highest amount of ROS was observed at 0.5 μM (Figure 6). In our studies, changes in the cell cycle were also observed in this group. By producing more ROS, QQ1 is believed to have an impact on the cell cycle. The ability of quinone compounds to directly induce ROS has been attributed to C at position 6, despite the lack of a direct molecular target (Delgado et al. 2013; Delgado et al. ²⁰¹²; Kruschel et al. ²⁰²⁰).

FIGURE 6.

Representative images with quantitative analysis of ROS production in ACHN cells following QQ1. Values expressed as mean ± SEM. *p < 0.05, ***p < 0.001, n = 3.

3.3. Pharmacokinetic Profiling

3.3.1. In Vitro ADME Studies

The LogP values for QQ1 (1.56) and QQ4 (1.58) indicate that both compounds are notably more lipophilic than the positive controls, Verapamil (−0.21) and Atenolol (−0.33). Similarly, their LogD values (1.76 for QQ1 and 2.35 for QQ4) further underscore their enhanced lipophilicity at physiological pH. This suggests that QQ1 and QQ4 are more likely to partition into lipid‐rich biological membranes, resulting in greater tissue penetration and a higher volume of distribution (Vd) compared to Verapamil and Atenolol (Table 6).

TABLE 6.

In vitro pharmacokinetic profile of QQ1 and QQ4.

Source	Parameters	QQ1	QQ4	Verapamil a	Atenolol a
	LogP b	1.56	1.58	−0.21	−0.33
	LogD b	1.76	2.35	1.87	−1.77
Mouse liver microsomes	% metabolism in 30 min	100	100	80
	Half‐life (min)	6.5	0	11
	Clint (μL/min/mg protein)	212.5	2944	130
Rat liver microsomes	% metabolism in 30 min	100	97	79
	Half‐life (min)	3	6.5	11
	Clint (μL/min/mg protein)	491	207	121
Dog liver microsomes	% metabolism in 30 min	100	48	81
	Half‐life (min)	24.5	32	11
	Clint (μL/min/mg protein)	57.5	45.5	131
Human liver microsomes	% metabolism in 30 min	100	77.5	79
	Half‐life (min)	12.5	11	11
	Clint (μL/min/mg protein)	111.5	121	129

^a Positive controls.

^b All the test items partitioned toward octanol (lipophilic).

QQ1 and QQ4 exhibit varied metabolic profiles across species in liver microsomes. In mouse liver microsomes, both compounds are completely metabolized (100% metabolism in 30 min), with QQ1 showing a half‐life of 6.5 min and QQ4 being metabolized almost instantly (half‐life = 0 min). In rat liver microsomes, QQ1 again undergoes complete metabolism (100%) with a short half‐life (3 min), while QQ4 demonstrates slightly improved stability with 97% metabolism and a half‐life of 6.5 min. Dog liver microsomes show significant differences, where QQ1 is fully metabolized (100%) with a half‐life of 24.5 min, whereas QQ4 exhibits slower metabolism (48% in 30 min) and a longer half‐life of 32 min, suggesting greater stability in dogs. In human liver microsomes, both compounds show moderate metabolic stability, with QQ1 being fully metabolized (100%) and QQ4 showing 77.5% metabolism within 30 min. The intrinsic clearance (Cl_int) values indicate that QQ1 generally has a higher clearance rate than QQ4 across all species, with the exception of human microsomes, where their Cl_int values are comparable (111.5 μL/min/mg protein for QQ1 vs. 121 μL/min/mg protein for QQ4). These findings highlight the rapid clearance of QQ1 and the relatively improved stability of QQ4 in some species, particularly dogs and humans (Table 6).

3.3.2. In Vivo Pharmacokinetic Studies

Following a single IV bolus administration (1 mg/kg) of the QQ1 formulation to male Sprague–Dawley rats, the mean plasma clearance is very high, 199 mL/min/kg, which is approximately 3.61‐fold higher than the normal hepatic blood flow of rats. The mean volume of distribution was 59.2 L/kg, which was approximately 84.57‐fold greater than 0.7 L/kg of total body fluids, indicating high distribution in tissues. The mean terminal plasma half‐life was 3.60 h in male rats. The median time to reach peak plasma concentration of QQ1 with a single peroral administration (5 mg/kg) of QQ1 dose formulation to male Sprague–Dawley rats was 0.5 h with C _max of 473 ng/mL, and the AUC_last (plasma exposure) was 2050 ng*h/mL. Calculated oral bioavailability was 5.40% (Table 7).

TABLE 7.

Mean pharmacokinetic parameters following IV and PO administration of QQ1 and QQ4.

Parameters	QQ1		QQ4
	IV	PO	IV	PO
C 0 (IV only)/C max (PO only) (ng/mL)	56.8 ± 31	473 ± 70.5	30.8 ± 17.7	579 ± 31.5
T max (h)	NA	0.5	NA	0.5
AUClast (h*ng/mL)	75.7 ± 14.3	2050 ± 404	113 ± 29.7	2360 ± 255
AUCINF (h*ng/mL)	82.7 ± 22	2050 ± 405	123 ± 30.5	2380 ± 265
T 1/2 (h)	3.60 ± 0.96	2.92 ± 0.0731	5.73 ± 1.77	4.28 ± 1.28
Vd (L/kg)	59.2 ± 10.5	NC	59.1 ± 8.45	NC
Cl (mL/min/kg)	199 ± 59.2	NC	124 ± 24.8	NC
MRTlast (h)	3.45 ± 0.445	4.26 ± 0.41	7.09 ± 1.82	4.04 ± 0.13
%F	NA	5.40	NA	4.17

Abbreviaitons: NA, not applicable; NC, not calculated due to insufficient elimination phase.

Following a single IV bolus administration (1 mg/kg) of the QQ4 formulation to male Sprague–Dawley rats, the mean plasma clearance is very high, 124 mL/min/kg, which is approximately 2.25‐fold higher than the normal hepatic blood flow of rats. The mean volume of distribution was 59.1 L/kg, which was approximately 84.43 times greater than 0.7 L/kg of total body fluids, indicating high distribution in tissues. The mean terminal plasma half‐life was 5.73 h in male rats. The median time to reach peak plasma concentration of QQ4 with a single peroral administration (5 mg/kg) of QQ4 dose formulation to male Sprague–Dawley rats was 0.5 h with C _max of 579 ng/mL and the AUC_last (plasma exposure) was 2360 ng*h/mL. Calculated oral bioavailability was 4.17% (Table 7).

3.4. In Silico Molecular Interaction Analysis

Swiss Target Prediction server (http://www.swisstargetprediction.ch/) predicted dual specificity phosphatases CDC25B and CDC25A as highly probable target proteins with probabilities of 0.13 and 0.09, respectively, based on the similarity of QQ1 with known ligands of CDC25B and CDC25A. Considering the fact that QQ1 affects the progression from G0/G1 to S‐phase and also generates ROS, the probable putative target must be CDC25A (Shen and Huang 2012). Accordingly, we selected the experimental X‐ray crystallographic 3D structure of the human CDC25A catalytic domain (PDB: 1C25) (Fauman et al. 1998) for molecular docking simulation with compound QQ1. The active site of the CDC25A catalytic domain was extremely shallow and largely exposed to solvent, with the C‐terminal extending away from the protein. While catalytic CYS430 is buried in the active site, the side chains of GLU431, PHE432, and ARG436 were pointed above the active site and function in recognizing the substrate. It is observed that QQ1 interacts with these three residues along with GLU491 in the C‐terminal extension (Figure 7). A hydrogen bonding interaction was noted between the side chain guanyl nitrogen of ARG436 and C8 quinone oxygen of QQ1. Although hydrophobic interactions were observed between the side chains of GLU431, PHE432, and the nitrogen‐containing ring of QQ1. This orients the phenylpiperazine substitution at the C6 position of QQ1 toward the C‐terminal extension, where it establishes a hydrophobic interaction with the side chain of GLU491 (Figure 7). In the case of QQ4, there was a ~180° flip around the bond connecting C8 carbon quinone with nitrogen of piperazine. Due to this, the N1 nitrogen of QQ4 established two hydrogen bonding interactions with side chain guanyl nitrogen and backbone amide nitrogen of ARG436. This has favored the π–cation interaction between the nitrogen‐containing ring of QQ4 with guanyl nitrogen of ARF436. Additionally, an ionic interaction was observed between the C7 chloro substitution and the side chain carboxylic acid group of GLU431. In this case also, the phenyl piperazine gets orientated toward the C‐terminal extension, establishing a hydrophobic interaction between the tolyl methyl group and the side chain of GLU491 (Figure 7). The study clearly shows that both compounds QQ1 and QQ4 interact with active site residues lining the shallow cavity that are responsible for substrate recognition.

FIGURE 7.

Interaction of compounds QQ1 (left) and QQ4 (right) with active site residues of human CDC25A (PDB:1C25). Interacting amino acid residues (grey) and ligands (green) are depicted in the model colored by atom type. Hydrogen bonding interaction shown as a solid blue line, hydrophobic interaction shown as a dashed grey line, ionic interaction shown as a dashed magenta line, and π–cation interaction shown as a dashed saffron line connecting the positive yellow sphere with the hydrophobic grey sphere.

4. Conclusions

Herein, seven QQs (QQ1‐7) by the linkage of piperazine analogs containing EDG, such as methyl and methoxy group previously described by our group, were studied for their anticancer activity in the NCI‐60 cell panel (Yildiz et al. 2022). Some of the QQs showed remarkable growth inhibition effects against leukemia, colon cancer, renal cancer, and breast cancer cell lines at 10 μM concentration. As a result, six of them were chosen for further testing in the five‐dose assay conducted by the NCI. When assessing the efficacy of QQs, an agent is deemed potent if its GI₅₀ value is below 2 μM, as this suggests potential selectivity toward a specific cancer cell line. Consequently, two QQs (QQ1 and QQ4) were evaluated for cytotoxicity in vitro against the HCT‐116 colon cancer, ACHN renal cancer, and MCF7, T‐47D breast cancer cell lines, following the promising results from the NCI. QQ1 showed good inhibitory activity and selectivity for ACHN renal cancer cells. Deregulated cell‐cycle progression has been linked to cancer, making cell‐cycle inhibition a potentially significant target for cancer management (Jacks and Weinberg 1996; Sherr and Roberts ¹⁹⁹⁹). These antiproliferative effects raised the possibility that naftopidil could be useful in treating RCC. Important positive regulators of cell cycle progression through the G1‐S checkpoint are cyclin‐dependent kinases (CDK; e.g., CDK2). In mammalian cells, CDK activation and S‐phase entry are inhibited by CDK inhibitors (Weinberg 1995). Both the compounds, QQ1 and QQ4, were found to be metabolically stable when tested against human liver microsomes in vitro. Oral bioavailability in rats was found to be poor for both the compounds and is due to higher intrinsic hepatic clearance (Cl_int) as evidenced by in vitro metabolic stability studies with rat liver microsomes. A molecular docking simulation of compounds QQ1 and QQ4 against the putative target CDC25A identified through the Swiss Target Prediction server revealed the interaction of QQs with residues lining (GLU431, PHE432, ARG436) the shallow active site pocket. Nevertheless, future research will focus on a thorough analysis of the mechanism and a review of the decline in cell proliferation. Molecular hybridization produced compounds with better antitumor activity than the original molecules in several studies, proving the efficacy of this strategy. An optimal process which includes a stronger correlation between organic synthesis, computational analysis, and experimental biological investigation could provide promising perspectives and help the development of new potential drugs to be evaluated in clinical trials.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data will be made available on request.