A Simple and Reliable Approach for Assessing

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A Simple and Reliable Approach for Assessing Anticancer Activity In Vitro ARTICLE in CURRENT MEDICINAL CHEMISTRY · FEBRUARY 2015 Impact Factor: 3.85 · DOI: 10.2174/0929867322666150209150639 · Source: PubMed

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Current Medicinal Chemistry, 2015, 22, 1324-1334

A Simple and Reliable Approach for Assessing Anticancer Activity In Vitro Miguel López-Lázaro* Department of Pharmacology, Faculty of Pharmacy, University of Seville, Spain Abstract: Cancer patients need better anticancer drugs, and medicinal chemistry can play a critical role in the discovery of these drugs. For an efficient drug discovery process, chemists working on the synthesis of potential anticancer agents need to use reliable screening methods. These methods should not only detect the compounds with the highest therapeutic potential, but should also predict whether such potential is high enough to deserve additional attention. Unfortunately, the current strategies for assessing anticancer activity in vitro are unable to do this reliably. This review article analyzes these strategies and describes an alternative screening approach. It is based on establishing suitable experimental conditions to detect compounds that improve the selective cytotoxicity of the drugs used in cancer therapy. This patient-oriented approach is easy to implement and may help medicinal chemists, and other researchers involved in cancer drug discovery, assess in vitro anticancer activity more reliably.

Keywords: Antitumor activity, cancer, cytotoxic activity, drug discovery, screening, selectivity. 1. INTRODUCTION The chances of survival of people diagnosed with localized tumors are relatively high. Unless the tumor is in a vital organ or problematic location, surgery and radiation therapies can generally eliminate the tumor cells and cure the disease. These therapeutic modalities are not curative when cells from primary tumors have already metastasized to unknown locations. When this very common situation arises, oncologists need to administer drugs systemically to try to reach and kill the cancer cells. Unfortunately, the ability of the existing anticancer drugs to eliminate the cancer cells in patients with metastasis is low, and these patients do not usually overcome the disease. For example, the five-year relative survival rate for patients with distant metastasis is 4% in lung cancer, 28% in prostate cancer, 24% in breast cancer, 13% in colorectal cancer, 3% in liver cancer, 16% in melanoma, 12% in renal cancer, 27% in ovarian cancer, 5% in bladder cancer, 4% in esophageal cancer, and 2% in pancreatic cancer [1]. Many patients within these percentages eventually die of the disease despite surviving five years after diagnosis. Because pharmacotherapy is the main form of treatment for patients with advanced metastatic cancers, it is vital to discover better anticancer drugs. Medicinal chemistry plays an important role in cancer drug discovery and development. For an efficient drug discovery process, chemists working on the synthesis of potential anticancer agents need to use reliable screening methods. These methods should not only detect the compounds with the highest therapeutic potential, but should also predict whether such potential is high enough to deserve additional attention. *Address correspondence to this author at the Department of Pharmacology, Faculty of Pharmacy, University of Seville, Spain; C/ Profesor Garcia Gonzalez 2, 41012, Sevilla, Spain; Tel: +34 954 55 63 48; Fax: + 34 954 55 60 74; E-mail: [email protected] 1875-533X/15 $58.00+.00

Medicinal chemists involved in cancer drug discovery typically follow a cytotoxic potency-based screening approach: the lower the concentration required to kill cancer cells the higher the potential for cancer therapy. If the most cytotoxic compound of the series improves the cytotoxic potency of the anticancer drug used as control, the new compound deserves additional attention [2-7]. Another common screening strategy consists of assessing the ability of the compounds to act on particular therapeutic targets. For example, because DNA topoisomerases and tubulin are the targets of well-established anticancer drugs, the most potent modulators of these targets are considered to be the most promising compounds of their series [8-16]. Recently, compounds have been screened and selected based on their ability to target molecular differences between cancer cells and healthy cells, e.g., particular protein kinases [17-22]. Several recent studies have also focused on the screening of small-molecule modulators of microRNAs [23, 24]. Researchers usually combine cytotoxic potency-based approaches with target-based approaches to identify the best compounds of their series, and use animal models to confirm the in vitro anticancer activity of their selected compounds [8, 9, 11, 14, 16, 19, 20]. After analyzing the type of drugs that cancer patients need, this article shows that the current screening methods are unreliable to identify this type of drugs. Then, it describes a simple and patient-oriented screening approach, which may help researchers assess the in vitro anticancer activity of their compounds more reliably. 2. CANCER PATIENTS NEED DRUGS THAT IMPROVE THE ABILITY OF THE EXISTING DRUGS TO KILL THEIR CANCER CELLS SELECTIVELY When one treats cancer cells with standard anticancer drugs and examines the cells under the microscope, one observes that specific concentrations and exposure times result © 2015 Bentham Science Publishers

A Simple and Reliable Approach for Assessing Anticancer Activity

in a 100% cancer cell death. Many of these drugs kill all the cancer cells at micromolar concentrations, and others do it at nanomolar or millimolar concentrations. Researchers have also found numerous compounds not used in cancer therapy despite their ability to kill cancer cells at very low concentrations. These observations raise questions that chemists working on the synthesis of potential anticancer agents ask cancer pharmacologists: If we already have drugs that are excellent at killing cancer cells, why do these drugs not save the lives of patients with metastatic cancers? Why do oncologists often use drugs that kill cancer cells at high concentrations instead of compounds that do it at lower concentrations? At what concentration should a new compound kill cancer cells to have potential for cancer therapy? The current drugs do not usually save the lives of patients with metastatic cancers because they have a limited ability to kill cancer cells at concentrations that do not significantly affect healthy cells. This narrow selectivity implies that oncologists cannot use the doses of the drugs required to kill all the cancer cells of their patients. If they used such doses, they would also kill the cells from their normal tissues and would cause the death of the patients. As a poor alternative, oncologists have to use the maximum doses tolerated by the patients, which are generally insufficient to reach the drug concentrations required to eliminate all their cancer cells. The purpose of this strategy is to eliminate as many cancer

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cells as possible without killing the patient, usually with the final aim of extending patient survival (Fig. 1). The answer to the second question is that oncologists often use drugs that kill cancer cells at high concentrations because the key feature of an effective anticancer drug is its ability to kill cancer cells selectively and not its ability to kill cancer cells at low concentrations. A drug that kills cancer cells at 1 mM without affecting healthy cells at 100 mM will probably kill more cancer cells in patients than a drug that kills both cell types at 1 nM. The answer to the third question is that the new compound should kill cancer cells at concentrations that do not significantly affect nonmalignant cells in order to have potential for cancer therapy [25]. Selectivity is the most important feature of an effective anticancer drug. Understanding how much selectivity a new drug should have to be clinically effective is essential. If possible, the drug should kill all the cancer cells of the patients without significantly affecting their healthy cells. It should match the selectivity of antibiotics, which can kill the bacterial cells of our bodies at concentrations that do not significantly affect our cells. An anticancer drug with this selectivity would probably save the lives of patients with metastatic cancers, like antibiotics commonly save the lives of patients with bacterial infections. If this is not possible, the new drug should at least improve the survival rates of cancer patients treated with the current pharmacological

Fig. (1). The anticancer drug sorafenib has a limited ability to kill cancer cells selectively. Sorafenib is the standard of care for patients with unresectable or metastatic hepatocellular carcinoma (liver cancer). This oral multikinase inhibitor is known to induce direct and indirect cytotoxic effects against cancer cells. Skin toxicity is one of the most common dose-limiting side effects of sorafenib. The recommended maximum tolerated dose for patients receiving sorafenib is 400 mg twice daily, which results in mean plasma concentrations of approximately 10 µM. In the experiments shown in this Figure, human liver hepatocellular carcinoma cells (HepG2) and human skin nonmalignant cells (VH10) were exposed to sorafenib 10 µM and 100 µM for 24 h. Sorafenib was then eliminated and the cells were exposed to drug-free medium for an additional period of 48 h. At this point, representative photographs were taken, and the percentage of cell viability (shown at the right bottom of the photographs) relative to the untreated cells was estimated with the MTT assay. At high concentrations (100 µM), sorafenib killed all the liver cancer cells, but also killed the nonmalignant skin cells. At the maximum concentrations tolerated by patients (10 µM), sorafenib showed some selectivity for the cancer cell line, but could not kill all the liver cancer cells. This limited selectivity probably explains why most of the patients with hepatocellular carcinoma receiving sorafenib live several months longer than untreated patients but do not usually overcome the disease (the five-year survival rate in patients diagnosed with advanced metastatic liver disease is approximately 3%) [1, 65-67]. Figure taken from reference [25].

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therapies. A drug that improved the ability of the standard drugs to kill cancer cells selectively would probably increase patient survival. We should therefore look for drugs that improve the selectivity of the existing anticancer drugs [25]. 3. CURRENT SCREENING APPROACHES CANNOT RELIABLY IDENTIFY THE TYPE OF DRUGS THAT CANCER PATIENTS NEED Cancer patients need drugs that improve the ability of the standard drugs to kill their cancer cells without significantly affecting their healthy cells. The most common screening strategies cannot detect this type of drugs reliably. Chemists involved in cancer drug discovery typically follow a cytotoxic potency-based screening approach: a compound is promising if it kills cancer cells at low concentrations and improves the potency of the standard drugs. For example, several compounds have recently been considered promising anticancer agents based on their ability to kill cancer cells at lower concentrations than those of the standard drugs etoposide [26], cytarabine (ara-C) [27], 5-fluorouracyl [28], doxorubicin [29] and cisplatin [7, 30]. Despite its widespread use, this approach is unsound and unreliable. It is unsound because the aim of this approach is to find something that we do not need. Cancer patients do not need drugs that kill their cancer cells at low concentrations. As explained previously, they need drugs that kill their cancer cells at concentrations that do not significantly affect their healthy cells. If a drug kills cancer cells at low concentrations and also kills normal cells at similar concentrations, the maximum doses tolerated by the patients will be insufficient to reach the drug concentrations required to eliminate their cancer cells. If a drug kills cancer cells without significantly affecting nonmalignant cells, it does not matter much at what concentrations it kills the cancer cells. It is unreliable because the ability of a compound to kill cancer cells at low concentrations is not related to its ability to kill cancer cells selectively. For example, after reporting that several isoprenyl-thiourea and urea derivatives killed colon cancer cells at low concentrations and improved the potency of the anticancer drug 5-fluorouracil, we found that these compounds were equally cytotoxic towards nonmalignant cells [31]. More recently, we assessed the anticancer activity of a series of new aziridine derivatives, and found that the most cytotoxic compound against a cancer cell line did not show any selectivity versus a nonmalignant cell line from the same tissue (it was actually 7 times more cytotoxic against the normal cell line). We selected for further studies a compound that was less cytotoxic in the cancer cell line, but more selective. When we tested the cytotoxicity of this compound against three pairs of human cancer cell lines and nonmalignant cell lines, we observed that the selectivity of this aziridine against breast cancer cells versus normal cells from three different tissues was approximately 50-100 fold [32]. These experimental data demonstrate that cytotoxic potency and selective cytotoxicity are two unrelated parameters. Therefore, when we show that our compounds kill cancer cells at low concentrations, we are not revealing whether or not they have potential for cancer therapy.

Miguel López-Lázaro

The ability of a compound to modulate molecular targets involved in cancer cell proliferation and survival can neither reliably predict its selectivity towards cancer cells. For example, the enzymes DNA topoisomerases are necessary for cancer cell proliferation, and several topoisomerase inhibitors are useful anticancer drugs. This does not mean, however, that a good inhibitor of these enzymes is a promising anticancer compound. We have reported that curcumin is an efficient inhibitor of DNA topoisomerases [33] with a very limited selectivity towards cancer cells [34, 35]. Wang et al. recently screened a chemical library of 359,484 compounds to identify potential inhibitors of the steroid receptor coactivators SRC-3 and SRC-1; these coactivators are involved in cancer cell proliferation and survival. They identified the cardiac glycoside bufalin as a potent inhibitor of SRC-3 and SRC-1. This compound also inhibited breast cancer cell growth at nanomolar concentrations and reduced tumor growth in mice xenotransplanted with breast cancer cells. Based on these observations, the authors discussed that bufalin could be useful for cancer therapy [36]. Clifford and Kaplan observed, however, that human breast nonmalignant cells were more sensitive than human breast cancer cells to the cytotoxic activity of bufalin [37]. This suggests that bufalin would probably be ineffective in breast cancer therapy [38]. Researchers do not routinely use healthy cells for assessing anticancer activity in vitro because they may not think it is necessary. In the end, no matter what screening approach we follow, we will have to use animal models to assess preclinical anticancer activity properly. These models will confirm the anticancer activity of the selected compounds, and will also reveal if they induce toxicity against healthy tissues. Even if performed properly, in vivo animal experiments cannot compensate for a deficient screening approach. First, although animal models can show if the chosen compounds induce selective anticancer activity in vivo, they cannot reveal the anticancer activity of the compounds left behind in the screening process. Assessing selectivity after the screening step is an inadequate approach to identify the most selective compound of a chemical series. If we accept that selectivity is the key feature of an effective anticancer drug, we should assess selectivity as part of, and not after, the screening process so that we can detect the best anticancer compounds of our series. Second, rodent models can show the selectivity of a compound in human cancer cells versus rodent normal cells (xenograft models), or in rodent cancer cells versus rodent normal cells (allograft and spontaneous models). However, animal models cannot show if a compound kills human cancer cells versus human normal cells selectively. This distinction is important to avoid experimental artifacts caused by species differences in sensitivity. For example, we have shown that rodent cells are extremely resistant (over 1000-fold) to the cytotoxicity of some cardiac glycosides in relation to human cancer and nonmalignant cells [39]. This means that if we assess the anticancer activity of these compounds in mice xenotransplanted with human cancer cells, we will find a marked in vivo anticancer activity [36, 40-43]. This activity, however, is probably caused by the ability of cardiac glycosides to selectively kill human cells versus rodent cells rather than by their ability to


selectively kill human cancer cells versus human healthy cells [39, 44, 45]. This can explain why the cardiac glycoside bufalin inhibited tumor growth in mice xenografted with human breast cancer cells [36] despite its lack of selectivity [38]. These observations suggest that animal models cannot substitute a proper in vitro design. An inadequate in vitro design may result in the selection of useless compounds and in the failure to detect promising anticancer agents. In the first case, we will waste resources trying to prove in animal models, and too many times in cancer patients, the anticancer activity of ineffective compounds. In fact, most of the compounds selected for further studies based on the current screening strategies do not show relevant anticancer effects when tested in animal models of metastasis and in clinical trials [46-48]. Drug attrition rates for cancer are actually much higher than in other therapeutic areas, and only 5% of compounds that enter clinical trials are licensed after demonstrating sufficient efficacy in phase III testing [47]. But the most worrying consequence of an inadequate in vitro screening is that we are probably throwing away useful anticancer drugs. Thousands of novel compounds are routinely discarded for not being the most cytotoxic of their series or the best modulators of particular molecular targets. Since these drug features are poor predictors of therapeutic potential, an unknown number of these compounds might be better than the existing anticancer drugs. Finally, let us consider the following situation to examine the ability of the current screening strategies to detect effective anticancer drugs reliably. Let us imagine that one of us is diagnosed with advanced metastatic liver disease. Like most of the patients in this situation, we would only survive several months after diagnosis despite being treated with novel anticancer drugs such as sorafenib (see Fig. 1 legend). Let us imagine that we have a collection of 1000 compounds, and that one of them is an effective cure for patients with our cancer. Could we identify this effective anticancer compound using the current in vitro approaches?


of the many potential mechanisms of action of the active compound would we select for our screening protocol? What if the anticancer drug that we are trying to identify acted on a molecular target not yet discovered? This example suggests that the most common screening approaches do not reliably detect the best anticancer compound in a group of potential anticancer agents. It also suggests that we need to develop novel strategies to assess in vitro anticancer activity more reliably [25]. 4. A SIMPLE APPROACH FOR ASSESSING ANTICANCER ACTIVITY IN VITRO Cancer patients need drugs that improve the ability of the existing drugs to kill their cancer cells without significantly affecting their healthy cells. This patient-oriented approach aims to establish the most adequate experimental conditions to answer the following question: Can any of my compounds improve the ability of the current drugs to kill cancer cells without affecting nonmalignant cells from suitable tissues? The general approach consists of 1) exposing cancer cells and nonmalignant cells to the experimental drugs and to the standard anticancer drugs, 2) estimating cell viability with a simple cytotoxicity test, 3) calculating one or several cytotoxicity parameters (e.g., IC50 values) for each drug in each cell type, 4) calculating a selectivity index for each drug (e.g., dividing the IC50 value in the nonmalignant cells by that in the cancer cells) and 5) comparing the selectivity indices of the experimental drugs with those of the standard anticancer drugs [25, 49, 50]. Several aspects need to be considered for implementing this approach. 4.1. Selection of Cancer Cells, Nonmalignant Cells and Standard Anticancer Drugs

Some researchers would screen the collection against liver cancer cells and would select the most cytotoxic compound. Others would select the best modulator of a particular therapeutic target. For example, we could choose the best inhibitor of a specific protein kinase of a particular signal transduction pathway involved in liver cancer cell proliferation and survival. We could also opt for a more classic therapeutic target and select, for instance, the best topoisomerase inhibitor or antitubulin compound. We could also combine several of these screening strategies. It is easy to predict that the most cytotoxic compound would not be the best topoisomerase inhibitor, the best antitubulin compound, and the best inhibitor of all the molecular targets of the multiple signal transduction pathways involved in liver cancer cell proliferation and survival.

The first important aspect to consider for implementing this approach is the selection of the cancer cells and nonmalignant cells. Distinguishing between cancer cells and nonmalignant cells and being familiar with several basic terms and concepts is important for this purpose. Unlike nonmalignant cells, cancer cells proliferate and disseminate in an uncontrolled manner and are able to induce tumors following inoculation into susceptible animals. Primary cells are cells taken directly from organisms, and can be regarded as such until they are successfully subcultured for the first time. Cell lines are cells derived from organisms after the first successful subculture. The term nonmalignant cells can be used for primary nonmalignant cells, for cell lines derived from primary nonmalignant cells that may have been altered to facilitate cell growth in culture (e.g., cells that have been immortalized through transfection of the catalytic component of the enzyme telomerase reverse transcriptase hTERT), or for fetal cells. The terms normal cells and healthy cells commonly refer to non-diseased cells and, therefore, to nonmalignant cells.

What would we think if a problem only has one solution and each of the methods used to solve it leads to different solutions? We would probably think that only one of the methods is adequate, or that all of them are inadequate. If we think that only one is adequate, which one would it be? Would it be the one based on assessing cytotoxic potency or the one based on assessing mechanisms of action? If we think that the second option is the most suitable one, which

Ideally, we should use primary cancer cells and primary nonmalignant cells for implementing this approach. Because primary cells are freshly derived from living organisms, they mimic the physiological state of cells in vivo more closely than cell lines, which may develop changes (e.g., unstable karyotypes) as a result of subculturing. However, primary cells are less convenient to use than cell lines; one needs at least one subject to obtain the primary cells required for each


independent experiment. Their value in the laboratory setting is limited, especially when large quantities of cells are required. Well-characterized cancer and nonmalignant cell lines are suitable for implementing this approach. Both the cancer cells and the nonmalignant cells should be human to avoid species differences in sensitivity. For example, as discussed previously, rodent cells are extremely resistant to the cytotoxicity of cardiac glycosides. Therefore, if we use a human cancer cell line and a rodent nonmalignant cell line to evaluate the selectivity of a cardiac glycoside, we will mistakenly find a selective anticancer effect caused by species differences in sensitivity. The cancer cells and nonmalignant cells should also be from the same tissues to avoid tissue differences in sensitivity. Imagine a compound that kills cancer and normal cells from the lungs at 1 µM and cancer and normal cells from the skin at 100 µM. If we assess the selectivity of this compound using lung cancer cells versus skin normal cells, we will find a selective anticancer effect caused by tissue differences in sensitivity. Another important aspect to consider is the number of cell lines to use. Ideally, we should assemble a panel of wellcharacterized cancer cell lines representative of all cancer types, and a variety of nonmalignant cells. The normal cells should be from the same tissues than those of the cancer cells and from tissues commonly affected by cancer pharmacotherapy. With this panel of cell lines, we would reliably predict whether or not any of the compounds has potential for cancer therapy. If we use fewer cancer cell lines, useful compounds for specific types of cancer may be missed. If we use fewer nonmalignant cell lines, we may miss toxicity towards particular tissues that could limit the therapeutic potential of the compounds. Despite these limitations, we can assess anticancer activity reliably using a much lower number of cell lines. If the study is focused on a particular cancer, the minimum requirement for implementing this screening approach is to use a human cancer cell line, a human nonmalignant cell line from the same tissue, and an anticancer drug used to treat patients with the selected cancer. This simple approach is suitable to screen rapidly the in vitro anticancer activity of large series of compounds against a particular cancer. If none of the compounds improves the selectivity index of the standard anticancer drugs, the compounds have a little potential for the treatment of the selected cancer [51]. If one of the compounds improves the selectivity index of the standard drugs, the compound is promising; however, we should use more nonmalignant cell lines to assess anticancer activity more robustly. Using more normal cell types is essential to detect possible toxicity to normal tissues that could limit the potential use of the new compound. For example, a compound that kills lung cancer cells at 1 µM and lung nonmalignant cells at 100 µM will not be clinically effective if it also kills normal cells from other tissues (e.g., skin cells or blood cells) at 1 µM. If the study is focused on one cancer, in vitro anticancer activity can be assessed robustly with several cancer cell lines representative of the most common cancer subtypes and with several nonmalignant cell lines originated from a variety of normal tissues. This little panel of cell lines could be used to confirm the activity of compounds passing the initial screening step.


If the study is not focused on a particular cancer, we could use several pairs of cancer cell lines and nonmalignant cell lines, as well as standard drugs used to treat patients with the selected cancers. It is important to note that specific anticancer drugs are used only in particular cancers. Therefore, we should calculate selectivity indices for the experimental compounds in each cancer and compare them with the selectivity indices of the standard drugs used in those cancers. For example, imagine that we decide to screen a series of compounds against cancer and healthy cells from the colon, lungs and liver, and that we use the standard anticancer drugs 5-fluorouracil, cisplatin and sorafenib. To show that one of our compounds could be useful for treating patients with colon cancer, we should use the IC50 values calculated for our compound and for the standard drug 5fluorouracil in the colon cancer cell line and in the three nonmalignant cell lines (i.e., colon, lung and liver). Then, we should calculate a selectivity index for our compound and for 5-fluorouracil dividing the mean IC50 value in the three nonmalignant cell lines by the IC50 value in the colon cancer cell line. Finally, we should see if the selectivity index of our compound is higher than that of 5-fluorouracil. Depending on the aim of the study, we can use particular types of cancer cells. If the experimental treatment is being developed for specific patients, primary cells from these patients could be used to better predict the effectiveness of the treatment or to avoid the administration of inactive chemotherapeutics to them [52, 53]. Treatment failure has been associated with the ability of cancer cells to develop drug resistance. We, therefore, could also use resistant cancer cells for implementing this approach. Evidence also suggests that resistance to treatment may be due to the fact that current treatments preferentially target non-stem cancer cells. Cancer stem cells have been associated with resistance to treatment, worse prognosis and recurrence of the disease. Another way that could help to identify potential anticancer drugs would be to evaluate their effects on cancer stem cells [54-56]. 4.2. Cell Treatment Conditions Cell density is an important parameter to consider to obtain reliable results. The cytotoxicity of a drug in cells grown at low density may be different from that in cells grown at high density. For example, the IC50 values for 5-fluorouracil and a novel merosesquiterpene were approximately 6 times lower in MCF7 breast cancer cells seeded at 5 x103 cells/cm2 than in the same cells seeded at 5 x104 cells/cm2 [57]. Because nonmalignant cells generally grow slower than cancer cells, they are often seeded at higher densities than cancer cells, which may originate unreal selectivity values. The screening approach discussed in this manuscript minimizes these false positives. Provided that the density at which we seed each cell line is the same for the experimental drugs and the standard drugs, we can seed different cell lines at different densities. What matters in this approach is whether or not the experimental compounds improve the selectivity of the current anticancer drugs and not the magnitude of their IC50 values or selectivity indices in particular cell types. The compounds should be tested at concentrations that allow the determination of the cytotoxic parameters required to calculate the selectivity indices. For example, if we show


that the IC50 value of a compound is e.g. 50 µM in cancer cells and >100 µM in nonmalignant cells, we are not truly revealing its anticancer potential. We are simply showing that its selectivity index is higher than 2, which could be 3 (low therapeutic potential) or 10000 (high therapeutic potential). When the solubility of the compounds is not a problem, we should use wide concentration ranges to allow the calculation of the required cytotoxic parameters in all the cell lines. Drug exposure times and drug-free recovery periods may change the cytotoxic potency and selectivity of particular compounds. Some drugs need more time to kill cells than other drugs. If we use short exposure times and recovery periods, we may underestimate the cytotoxicity of drugs inducing a slow cytotoxic effect. Other compounds may trigger selective cytotoxic effects at short exposure times and nonselective cytotoxic effects at long exposure times. The selectivity of these compounds could be missed if cell viability is assessed after a long drug incubation period (e.g., 72 h), but could be detected after a shorter drug exposure time (e.g., 4 h) followed by a recovery period (e.g., 68 h). In addition, it should be noted that many anticancer drugs used in patients have a very short biological half life (time required to eliminate 50% of the drug from the plasma). Short drug exposure times followed by drug-free recovery periods may therefore mimic the in vivo activity of these drugs more closely than long drug exposure times. Drug exposure times and drugfree recovery periods should be designed flexibly. However, if we test the experimental compound and the standard drug following several treatment schedules, we should not only compare the selectivity indices obtained under the same schedules, but also under the most selective schedules. 4.3. Methods for Assessing Cell Viability Once the cells have been exposed to the experimental compounds and the standard anticancer drugs, we need to estimate how many viable cells remain at the end of the experiments. This section does not seek to review all the available assays used to estimate cell viability in culture. It briefly discusses several standard assays that can be used to implement the screening approach shown in this article. Each assay has its own set of advantages and disadvantages, and the selection of a particular method depends on many factors such as the number of samples to screen, its suitability for high-throughput screening (HTS), or the infrastructure and research funds of the laboratory. The assays discussed next can be used in multi-well formats, and have been reviewed or described in detail elsewhere [58-63]. References [58] (freely available at http://www.ncbi.nlm.nih.gov/books/ NBK144065/) and [63] describe in detail several common protocols for assessing cell viability. The MTT assay is an inexpensive and widely used colorimetric assay. It was the first convenient 96-well method developed for screening large numbers of samples. The MTT assay is based on the ability of viable cells to convert the MTT compound (3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide) into an insoluble and purple formazan product; dead cells are metabolically inactive and cannot reduce the MTT into the colored product. After an incubation period (generally 1-4 h) of the cells with the MTT


and a solubilization step, the quantity of the colored product is measured at 570 nm using a plate reading spectrophotometer. Like all assays that rely on an active cellular metabolism, any change in culture conditions that alters the metabolism of the cells (e.g., high cell confluence, changes in pH, depletion of essential nutrients) will likely affect the rate of MTT reduction into the formazan product and will lead to a loss of linearity between absorbance and cell number. In addition, like all assays that rely on a reduction reaction, the MTT is subject to interferences from compounds with inherent reductive capacity (e.g., ascorbic acid and sulfhydrylcontaining compounds such as reduced glutathione). At high concentrations and exposure times, the compound MTT induces cytotoxic effects. The solubilization step required for the MTT assay can be eliminated using newer tetrazolium reagents (e.g. XTT) that are reduced by viable cells into soluble formazan products. The assays based on these newer tetrazolium reagents also have their advantages and disadvantages [58-60]. The resazurin assay is a redox-based colorimetric or fluorometric assay, which employs a methodology similar to that of the tetrazolium assays (MTT, XTT). This method relies on the capacity of viable cells to reduce the blue compound resazurin into the pink, fluorescent and soluble product resorufin. The quantity of resorufin produced is proportional to the number of viable cells. Fluorescence can be quantified using a fluorometer equipped with a 560 nm excitation / 590 nm emission filter set. Although resorufin can also be quantified by measuring a change in absorbance, spectrophotometric determination is not often used because of its much lower sensitivity. When used as a fluorometric assay, its sensitivity is higher than that of the tetrazolium assays. However, the resazurin assay is also subject to chemical interferences caused by reducing compounds. Resazurin is also cytotoxic at high concentrations and exposure times. Fluorescent compounds may also interfere with the resorufin product. An important advantage is that resazurin reagents probably generate the most cost-effective data on a per well basis [58-60]. The sulforhodamine B (SRB) assay is a cost-effective and widely used colorimetric method for screening, in which cell density determination is based on the measurement of cellular protein content. After drug treatments, cells are fixed with trichloroacetic acid and stained with the SRB compound. The excess dye is then removed by washing the plates with acetic acid, and the protein-bound dye is dissolved in Tris base solution for spectrophotometric determination at 510 nm. Its sensitivity is comparable to that of some fluorometric methods. Another important advantage is that SRB staining is independent of cell metabolic activity; therefore, fewer steps are required to optimize assay conditions for cell lines with different metabolic activities. In addition, the SRB assay is not subject to chemical interferences caused by reducing compounds. The SRB method, however, does not distinguish between viable and dead cells, although this does not seem to compromise its ability to detect cytotoxic effects of a drug. Several studies have shown that results from the SRB assay correlate well with those of the MTT assay, although the IC50 values of compounds tested using the SRB method generally are slightly higher. The SRB assay is the screening method used by the National


Cancer Institute (NCI) in their anticancer drug discovery program [62, 63]. The ATP assay is a fast and highly sensitive luminescence method widely used for estimating cell viability in high-throughput screening. Cells contain regulated levels of ATP, and drug-induced cytotoxicity results in a loss of ability to synthesize new ATP along with a rapid depletion of cellular ATP by endogenous ATPases. ATP is quantified by measuring the light produced through its reaction with the firefly enzyme luciferase using a luminometer. The amount of light produced is directly proportional to the number of viable cells. Commercial kits contain a detergent to lyse the cells, ATPase inhibitors to stabilize the ATP that is released from the lysed cells, luciferin as a substrate, and luciferase to catalyze the reaction. The main advantage of this assay is its high sensitivity, which enables miniaturization into 1536 well formats for HTS protocols. In addition, luminescence signals are not altered by intrinsically fluorescent test compounds. Another advantage of this assay is that, like the SRB assay and unlike the tetrazolium and resazurin assays, it does not require an incubation step of the substrate with a population of viable cells to yield the product that generates the signal, which eliminates the step of returning the cells to the incubator. However, the assay would detect possible changes in cellular ATP levels not related to changes in cell viability. In addition, the luciferase reaction is susceptible to temperature changes and may also be altered by luciferase inhibitors [58-60]. The lactate dehydrogenase (LDH) assay has been widely used to detect in vitro cytotoxicity. LDH is a cytosolic enzyme that is released into cell culture media when the plasma membrane is damaged. The released LDH can be quantified by a coupled enzymatic reaction. First, LDH catalyzes the conversion of lactate to pyruvate via reduction of NAD+ to NADH. Second, diaphorase uses NADH to reduce an appropriate redox reagent such as a tetrazolium compound or resazurin into a colored formazan or fluorescent product. The levels of this product is directly proportional to the amount of released LDH in the medium and, therefore, to the amount of dead cells. Although LDH activity can be measured spectrophotometrically, fluorescence detection is advisable to increase sensitivity. This assay is susceptible to background signal from serum sources of LDH found in supplemented growth media, which elevate the background signal. This method is also subject to assay artifacts by compounds that inhibit LDH activity and to fluorescence or color interferences. An important inconvenient is that activity-based surrogates of cell death such as LDH have a finite enzymatic half-life. The loss of LDH activity over time can lead to underestimation of cytotoxicity in cells exposed for relatively long incubation periods (48-72 h) to drugs (or drug concentrations) that induce a fast cytotoxic effect [59]. Although these assays are based on different concepts, there is usually a good correlation between the results obtained with different assays. For example, although the MTT assay measures cellular activity and the SRB assay measures cellular content, comparisons of data from several hundred compounds screened in parallel by MTT and SRB assays yielded quite similar results [63]. The possible experimental artifacts caused by a particular assay could be prevented and


detected using two independent assays [59, 60]. Observing the cells under the microscope is also a good practice to detect these possible artifacts, particularly when the number of test compounds is small. If we use a particular assay, and the results do not faithfully represent what we see under the microscope (cell number and morphology), we should consider repeating the experiments using another assay. 4.4. Expression and Discussion of Results After estimating cell viability with a cytotoxicity assay, we need to calculate one or several cytotoxicity parameters (e.g., IC50, IC90, LC50) for each drug in each cell type. Then, we should use these cytotoxicity parameters to calculate one or several selectivity indices for each drug (e.g., dividing the IC50 value in the nonmalignant cells by that in the cancer cells). Finally, we should compare the selectivity indices of the experimental drugs with those of the standard anticancer drugs. Selectivity indices based on only one cytotoxic parameter (e.g., IC50) may be misleading when dose-response curves are not analyzed carefully. Fig. (2) represents dose-response curves of three hypothetical compounds in a cancer cell line and a nonmalignant cell line. Each panel also shows two selectivity indices based on IC50 values and IC90 values. In Fig. (2A), the selectivity index based on the IC50 values represents faithfully the selectivity of the compound observed in the dose-response curves. In Fig. (2B and 2C), however, the selectivity index based on the IC50 values may lead to misinterpretation of results. Dose-response curves in Fig. (2B) show that the selectivity of the compound is very limited; however, the selectivity index based on the IC50 values (171,3) suggests that the compound has a high selectivity towards the cancer cells. In Fig. (2C), the selectivity index based on the IC50 values (3,0) suggests that the compound has a limited selectivity towards the cancer cell line. However, the dose-response curves show that this compound kills all the cancer cells at 0.1 µM and all the healthy cells at 100 µM. This compound could be useful if the reduced cell viability (40%) observed in the normal cells in the 0.1-10 µM range is caused by reversible inhibition of cell proliferation. False positives (Fig. 2B) and false negatives (Fig. 2C) obtained when selectivity indices are calculated based on only one cytotoxic parameter (e.g., IC50) could be prevented by using additional selectivity indices based on other cytotoxic parameters (e.g., IC90) or by showing the dose-response curves. In addition to comparing the selectivity indices of the experimental compounds with those of the standard anticancer drugs, it is important to observe that none of the healthy cell lines is highly sensitive to the cytotoxicity of the experimental compounds. We should not forget that a high toxicity in normal cells from just one particular tissue may cause that the maximum doses tolerated by the patients are insufficient to reach the drug concentrations required to eliminate their cancer cells. 4.5. Final Considerations The screening approach discussed throughout this article has the typical limitations of all in vitro methods. Because in vitro conditions cannot represent in vivo conditions faithfully, compounds that work in vitro may not work in vivo


A


B

SI (IC50) = 141,3 SI (IC90) = 120,1

SI (IC50) = 171,3 SI (IC90) = 1,2

100

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100

90

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90

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80

70 60

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50 40 30

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Fig. (2). Selectivity indices based on IC50 values do not always represent selective anticancer activity faithfully. In panel A, the selectivity index based on IC50 values, SI (IC50), represents the selectivity of the hypothetical compound faithfully. In panels B and C, however, this index does not truly represent what dose-response curves show. Graph B shows that the compound has a very limited selectivity towards the cancer cells, and the SI (IC50) indicates a high selectivity (false positive). Graph C suggests that the compound can kill cancer cells selectively, and the SI (IC50) indicates a very low selectivity towards cancer cells (false negative). Misinterpretation of results could be prevented by showing the dose-response curves or by calculating an additional selectivity index, e.g., SI (IC90). IC50 values (concentration of compound required to inhibit cell viability by 50%) and IC90 values (concentration of compound required to inhibit cell viability by 90%) for each compound are not shown for simplicity. Selectivity indices (SI) are calculated by dividing the IC50 (or IC90) values in the healthy cells by those in the cancer cells. See text for further details.

and vice versa. Another limitation is that we cannot exclude toxicity to all types of normal tissues by using a variety of nonmalignant cells. Therefore, animal models are essential to assess preclinical anticancer activity properly. In addition to detecting pharmacokinetic limitations of the experimental compounds, animal models are important to confirm or detect possible in vivo activity and toxicity. To assess in vivo anticancer activity robustly, we should evaluate if the experimental drug improves the survival rates of the standard drugs when tested under equivalent experimental conditions in animal models representative of the patients who would eventually receive the drugs (e.g., animal models of metastasis). Unfortunately, most in vivo studies do not use animal models of metastasis, do not assess survival as the endpoint of the experiments (they typically assess reduction

of tumor volumes), or do not test the experimental drugs and the standard drugs under comparable experimental conditions [25, 46]. The screening strategy discussed in this article is based on assessing selectivity. Evaluating selectivity is not a novel idea in anticancer drug discovery. Researchers from many laboratories use healthy cells to assess if active concentrations of their selected compounds induce toxicity against these cells. Although this strategy provides useful information about the selected compounds, it is inadequate to identify the most selective compound in a group of potential anticancer agents. This strategy is also inadequate to show if the chosen compound improves the selectivity of the drugs used in cancer patients. Another negative aspect


of assessing selectivity this way is that the magnitude of this parameter changes much depending on the type of cancer cells and healthy cells that we choose; this makes interpretation and extrapolation of results problematic. This problem is minimized in the screening approach described in this article. What matters in the screening approach discussed here is if any of the compounds improves the selectivity of the drugs used to treat cancer patients, and not the magnitude of their cytotoxic effects in particular cancer cells and healthy cells. This approach can be used for high-throughput screening [50]. Current high-throughput screening approaches are typically based on testing library compounds at one concentration against one specific target. The same chemical libraries are continuously screened against different anticancer targets. The approach discussed in this manuscript requires each compound to be tested at several concentrations against several cell types. This would require changes in the implementation and organization of the drug discovery process and would increase costs. However, the cost of screening the same libraries many times and of assessing the in vivo anticancer activity of inefficient compounds is probably higher. On top of this, the implementation and organization of the drug discovery process should not have priority over solid scientific foundations [64]. The only way forward to make an impact in the lives of cancer patients when their tumor cells have spread to unknown locations is to find drugs that improve the ability of the current drugs to kill these cells selectively. High-throughput screening approaches should be designed to detect this type of drugs [50]. CONCLUSION Cancer patients need drugs that improve the ability of the existing pharmacological treatments to kill their cancer cells without significantly affecting their normal cells. Instead of looking for this type of drugs, the current screening strategies typically look for drugs that kill cancer cells at low concentrations or that modulate particular targets involved in cancer cell proliferation and survival. Because cytotoxic potency and ability to modulate these targets do not reliably predict selectivity towards cancer cells, the current screening approaches result in the selection of ineffective compounds and probably in the failure to detect promising anticancer drugs. In the first case, researchers waste resources trying to prove in animal models and in cancer patients the anticancer activity of ineffective compounds. In the second case, researchers discard compounds that could change the lives of cancer patients. This article describes an alternative screening approach. It is based on establishing the most adequate conditions to answer the question: Can the experimental compounds improve the ability of the existing anticancer drugs to kill cancer cells without significantly affecting nonmalignant cells? This patient-oriented approach is easy to implement and can help researchers assess in vitro anticancer activity more reliably. CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest.


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Received: November 04, 2014

Revised: January 18, 2015

Accepted: January 25, 2015

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