Drug Discovery Today Volume 20, Number 2 February 2015
EDITORIAL
editorial Miguel Lo´pez-La´zaro
How many times should we screen a chemical library to discover an anticancer drug? Current cancer drug discovery and development are inefficient and no longer sustainable [1–3]. Despite the approval of numerous anticancer drugs over the past 15 years [4], the outcome of patients with advanced metastatic cancers continues to be poor [5]. For example, the five-year relative survival rates for patients with distant metastasis are 4% in lung cancer, 28% in prostate cancer, 24% in breast cancer, 13% in colorectal cancer, 3% in liver cancer and 2% in pancreatic cancer [5]. In addition, the pharmaceutical industry cannot maintain its level of profitability, and there have been many layoffs of drug discovery and development programmes in recent years. This inefficiency may be due to a poor selection of drug candidates in the early drug discovery process, which has encouraged debate about the most effective way of discovering drugs [3,4]. 1359-6446/ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.2014.12.006
The screening for potential anticancer agents is a decisive step in cancer drug discovery and development. An inadequate design may not only result in the selection of compounds with low therapeutic potential, but also in the failure to detect promising anticancer compounds. In the first case, we will waste resources trying to develop proof of concept in animal models and, possibly, the therapeutic potential of inefficient compounds in clinical trials. In the second case, we will miss compounds that could make an impact in the lives of patients with cancer. Lung cancer is the most common cancer in the world. Over 50% of people diagnosed with lung cancer have distant metastasis and, as mentioned previously, only 4% of them survive more than five years [5]. Let us imagine a chemical library of 5000 compounds: 4999 are less useful than the standard therapies and one can cure most of the patients with advanced lung cancers. How would we screen the library to identify this effective anticancer drug? Some researchers would screen the library against lung cancer cells, and would select the compound that killed or inhibited the proliferation of the cells at the lowest concentration. Others would screen the library for activity against any of the many molecular targets involved in lung cancer cell proliferation and survival and would select the best modulator of their chosen target. It is easy to predict that each of these screening approaches would result in the selection of a different compound; the most cytotoxic compound would not be the best modulator of all the molecular targets involved in lung cancer cell proliferation and survival. Many of the selected compounds would be tested in animal models and some could enter clinical trials. It is possible, however, that none of the selected compounds is the one we are looking for; the active compound could act on a molecular target not yet discovered. We would re-screen the chemical library any time we found a new and promising therapeutic target. This example illustrates the inefficiency of the current screening approaches. It also suggests that these approaches are so unreliable that we do not know when to stop screening the same compounds.
Understanding cancer patients’ needs Understanding patients’ needs is essential to increase screening efficiency and reliability. The majority of patients with advanced metastatic cancers die because the drugs used to treat them have a
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EDITORIAL
Editorial
narrow therapeutic window with respect to their ability to kill their cancer cells without significantly affecting their healthy cells. A consequence of this limited selectivity is that oncologists frequently cannot use those doses of drugs needed to eradicate the cancer cells. If they used such doses, they would also kill the normal cells and may cause the death of their patients. Instead, they use the maximum doses tolerated by the patients, which are usually insufficient to reach the drug concentrations required to eliminate all their cancer cells. The idea is to eliminate as many cancer cells as possible, generally with the aim of prolonging patient survival. Selectivity is the key feature of an efficient anticancer drug. Knowing how much selectivity a new drug should have to be clinically effective is important. Ideally, 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. A less ambitious aim is to at least improve the survival rates of cancer patients treated with the current pharmacological therapies. An experimental drug that improved the ability of the standard drugs to kill cancer cells selectively would probably increase patient survival. Cancer patients need drugs that improve the ability of the standard treatments to kill their cancer cells without significantly affecting their normal cells. Cancer patients do not need drugs that kill their cancer cells at low concentrations if they also kill their healthy cells at similar concentrations. Cancer patients do not need drugs that act on particular molecular targets involved in cancer cell proliferation and survival if they do not improve the selectivity of the current therapies. If a drug improves the ability of the standard treatments to kill cancer cells selectively, it does not really matter the concentrations at which it kills the cancer cells or the molecular targets involved in its pharmacological activity [6].
A patient-oriented screening approach This screening approach is based on addressing the following research question: Can any compound of the library improve the ability of the standard drugs to kill cancer cells without significantly affecting nonmalignant cells from appropriate tissues? To answer this question we need cancer cells, nonmalignant cells and drugs used to treat patients with the selected cancers. The cancer cells and nonmalignant cells should be human and from the same tissues to avoid species and tissue differences in sensitivity [6–8]. The simplest approach to addressing this research question would be (1) to expose a cancer cell line and a nonmalignant cell line from the same tissue to the experimental drugs and to the standard anticancer drugs, (2) to calculate one or several cytotoxicity parameters (e.g. IC50 and LC50) for each drug in both cell lines after estimating cell viability with a cytotoxicity test (e.g. SRB assay), (3) to calculate a selectivity index for each drug, for example, by dividing the IC50 value in the nonmalignant cell line by that in the cancer cell line, and (4) to compare the selectivity index of the compounds of the library with that of the current drugs. A drug that cured the majority of lung cancer patients with advanced disease would show a high selectivity towards cancer 168
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Drug Discovery Today Volume 20, Number 2 February 2015
cells. With this approach, we would probably identify this drug by screening our chemical library only once. Screening reliability could be undoubtedly improved including additional cancer and nonmalignant cells. If the screening is focused on a particular cancer, we should consider using several well-characterized cancer cell lines representative of the most common cancer subtypes and a variety of nonmalignant cells representative of tissues usually affected by pharmacotherapy. If the screening is not focused on a particular cancer, we should include additional cancer cell lines from other tissues. With this panel of cell lines, we would reliably predict whether or not any compound of a library has anticancer potential. This screening approach has the typical limitations of all in vitro methods. Because in vitro conditions cannot represent in vivo conditions faithfully, drugs that work in vitro may not work in vivo and vice versa. The implementation of this screening approach also seems to be complicated and expensive. Unlike the target-based screening methods, in which each compound is typically tested at one concentration against one target, this approach requires each compound to be tested at several concentrations against several cell types. This would increase costs and require changes in the implementation and organisation of the drug discovery process. However, the costs of screening the same library many times and of assessing the in vivo anticancer activity of inefficient compounds are probably higher. On top of this, the implementation and organisation of the drug discovery process should never have priority over solid scientific foundations [3]. The only way forward to cure patients when their cancer cells have spread to unknown locations is to develop drugs that kill these cells without significantly affecting healthy cells. If we cannot achieve this, we should at least improve the selectivity of the existing drugs. Selectivity is not a new concept in cancer drug discovery and development. Researchers use non-malignant cells to assess if active concentrations of their selected compounds induce toxicity against these cells. Despite providing useful information, this strategy is inadequate to identify the most selective compounds of a chemical library. Another limitation of assessing selectivity this way is that the magnitude of this parameter can be rather different depending on the type of cancer cells and non-malignant cells we use. What matters in the screening approach discussed here is if any of the compounds of the library improves the selectivity of the standard anticancer drugs, and not the magnitude of the cytotoxic effect induced by specific compounds in particular cancer cells and non-malignant cells. Finally, it is important to note that understanding the molecular and phenotypic differences between cancer cells and healthy cells is crucial for designing novel anticancer agents, but it is not essential for detecting the best anticancer agent of a library of known compounds. As discussed elsewhere, we should accept that drug discovery is the art of developing effective treatments against diseases we do not fully understand using drugs we do not fully know how work [3]. References 1 Hutchinson, L. and Kirk, R. (2011) High drug attrition rates – where are we going wrong? Nat. Rev. Clin. Oncol. 8, 189–190 2 Begley, C.G. and Ellis, L.M. (2012) Drug development: raise standards for preclinical cancer research. Nature 483, 531–533
Drug Discovery Today Volume 20, Number 2 February 2015
7 Lopez-Lazaro, M. (2009) Digoxin, HIF-1, and cancer. Proc. Natl. Acad. Sci. U. S. A. 106, E26 8 Calderon-Montano, J.M. et al. (2014) The in vivo antitumor activity of cardiac glycosides in mice xenografted with human cancer cells is probably an experimental artifact. Oncogene 33, 2947–2948
Miguel Lo´pez-La´zaro PhD Department of Pharmacology, Faculty of Pharmacy, University of Seville, Spain email: [email protected]
Editorial
3 Sams-Dodd, F. (2013) Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug Discov. Today 18, 211–217 4 Moffat, J.G. et al. (2014) Phenotypic screening in cancer drug discovery – past, present and future. Nat. Rev. Drug Discov. 13, 588–602 5 Siegel, R. et al. (2014) Cancer statistics, 2014. CA Cancer J. Clin. 64, 9–29 6 Lopez-Lazaro, M. (2014) Experimental Cancer Pharmacology for Researchers: At What Concentration Should My Drug Kill Cancer Cells So That It Has Potential for Cancer Therapy? Amazon Digital Services, Inc ASIN: B00MMO25NM http:// www.amazon.com/dp/B00MMO25NM/
EDITORIAL
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169
EDITORIAL
editorial Miguel Lo´pez-La´zaro
How many times should we screen a chemical library to discover an anticancer drug? Current cancer drug discovery and development are inefficient and no longer sustainable [1–3]. Despite the approval of numerous anticancer drugs over the past 15 years [4], the outcome of patients with advanced metastatic cancers continues to be poor [5]. For example, the five-year relative survival rates for patients with distant metastasis are 4% in lung cancer, 28% in prostate cancer, 24% in breast cancer, 13% in colorectal cancer, 3% in liver cancer and 2% in pancreatic cancer [5]. In addition, the pharmaceutical industry cannot maintain its level of profitability, and there have been many layoffs of drug discovery and development programmes in recent years. This inefficiency may be due to a poor selection of drug candidates in the early drug discovery process, which has encouraged debate about the most effective way of discovering drugs [3,4]. 1359-6446/ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.2014.12.006
The screening for potential anticancer agents is a decisive step in cancer drug discovery and development. An inadequate design may not only result in the selection of compounds with low therapeutic potential, but also in the failure to detect promising anticancer compounds. In the first case, we will waste resources trying to develop proof of concept in animal models and, possibly, the therapeutic potential of inefficient compounds in clinical trials. In the second case, we will miss compounds that could make an impact in the lives of patients with cancer. Lung cancer is the most common cancer in the world. Over 50% of people diagnosed with lung cancer have distant metastasis and, as mentioned previously, only 4% of them survive more than five years [5]. Let us imagine a chemical library of 5000 compounds: 4999 are less useful than the standard therapies and one can cure most of the patients with advanced lung cancers. How would we screen the library to identify this effective anticancer drug? Some researchers would screen the library against lung cancer cells, and would select the compound that killed or inhibited the proliferation of the cells at the lowest concentration. Others would screen the library for activity against any of the many molecular targets involved in lung cancer cell proliferation and survival and would select the best modulator of their chosen target. It is easy to predict that each of these screening approaches would result in the selection of a different compound; the most cytotoxic compound would not be the best modulator of all the molecular targets involved in lung cancer cell proliferation and survival. Many of the selected compounds would be tested in animal models and some could enter clinical trials. It is possible, however, that none of the selected compounds is the one we are looking for; the active compound could act on a molecular target not yet discovered. We would re-screen the chemical library any time we found a new and promising therapeutic target. This example illustrates the inefficiency of the current screening approaches. It also suggests that these approaches are so unreliable that we do not know when to stop screening the same compounds.
Understanding cancer patients’ needs Understanding patients’ needs is essential to increase screening efficiency and reliability. The majority of patients with advanced metastatic cancers die because the drugs used to treat them have a
www.drugdiscoverytoday.com
167
EDITORIAL
Editorial
narrow therapeutic window with respect to their ability to kill their cancer cells without significantly affecting their healthy cells. A consequence of this limited selectivity is that oncologists frequently cannot use those doses of drugs needed to eradicate the cancer cells. If they used such doses, they would also kill the normal cells and may cause the death of their patients. Instead, they use the maximum doses tolerated by the patients, which are usually insufficient to reach the drug concentrations required to eliminate all their cancer cells. The idea is to eliminate as many cancer cells as possible, generally with the aim of prolonging patient survival. Selectivity is the key feature of an efficient anticancer drug. Knowing how much selectivity a new drug should have to be clinically effective is important. Ideally, 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. A less ambitious aim is to at least improve the survival rates of cancer patients treated with the current pharmacological therapies. An experimental drug that improved the ability of the standard drugs to kill cancer cells selectively would probably increase patient survival. Cancer patients need drugs that improve the ability of the standard treatments to kill their cancer cells without significantly affecting their normal cells. Cancer patients do not need drugs that kill their cancer cells at low concentrations if they also kill their healthy cells at similar concentrations. Cancer patients do not need drugs that act on particular molecular targets involved in cancer cell proliferation and survival if they do not improve the selectivity of the current therapies. If a drug improves the ability of the standard treatments to kill cancer cells selectively, it does not really matter the concentrations at which it kills the cancer cells or the molecular targets involved in its pharmacological activity [6].
A patient-oriented screening approach This screening approach is based on addressing the following research question: Can any compound of the library improve the ability of the standard drugs to kill cancer cells without significantly affecting nonmalignant cells from appropriate tissues? To answer this question we need cancer cells, nonmalignant cells and drugs used to treat patients with the selected cancers. The cancer cells and nonmalignant cells should be human and from the same tissues to avoid species and tissue differences in sensitivity [6–8]. The simplest approach to addressing this research question would be (1) to expose a cancer cell line and a nonmalignant cell line from the same tissue to the experimental drugs and to the standard anticancer drugs, (2) to calculate one or several cytotoxicity parameters (e.g. IC50 and LC50) for each drug in both cell lines after estimating cell viability with a cytotoxicity test (e.g. SRB assay), (3) to calculate a selectivity index for each drug, for example, by dividing the IC50 value in the nonmalignant cell line by that in the cancer cell line, and (4) to compare the selectivity index of the compounds of the library with that of the current drugs. A drug that cured the majority of lung cancer patients with advanced disease would show a high selectivity towards cancer 168
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Drug Discovery Today Volume 20, Number 2 February 2015
cells. With this approach, we would probably identify this drug by screening our chemical library only once. Screening reliability could be undoubtedly improved including additional cancer and nonmalignant cells. If the screening is focused on a particular cancer, we should consider using several well-characterized cancer cell lines representative of the most common cancer subtypes and a variety of nonmalignant cells representative of tissues usually affected by pharmacotherapy. If the screening is not focused on a particular cancer, we should include additional cancer cell lines from other tissues. With this panel of cell lines, we would reliably predict whether or not any compound of a library has anticancer potential. This screening approach has the typical limitations of all in vitro methods. Because in vitro conditions cannot represent in vivo conditions faithfully, drugs that work in vitro may not work in vivo and vice versa. The implementation of this screening approach also seems to be complicated and expensive. Unlike the target-based screening methods, in which each compound is typically tested at one concentration against one target, this approach requires each compound to be tested at several concentrations against several cell types. This would increase costs and require changes in the implementation and organisation of the drug discovery process. However, the costs of screening the same library many times and of assessing the in vivo anticancer activity of inefficient compounds are probably higher. On top of this, the implementation and organisation of the drug discovery process should never have priority over solid scientific foundations [3]. The only way forward to cure patients when their cancer cells have spread to unknown locations is to develop drugs that kill these cells without significantly affecting healthy cells. If we cannot achieve this, we should at least improve the selectivity of the existing drugs. Selectivity is not a new concept in cancer drug discovery and development. Researchers use non-malignant cells to assess if active concentrations of their selected compounds induce toxicity against these cells. Despite providing useful information, this strategy is inadequate to identify the most selective compounds of a chemical library. Another limitation of assessing selectivity this way is that the magnitude of this parameter can be rather different depending on the type of cancer cells and non-malignant cells we use. What matters in the screening approach discussed here is if any of the compounds of the library improves the selectivity of the standard anticancer drugs, and not the magnitude of the cytotoxic effect induced by specific compounds in particular cancer cells and non-malignant cells. Finally, it is important to note that understanding the molecular and phenotypic differences between cancer cells and healthy cells is crucial for designing novel anticancer agents, but it is not essential for detecting the best anticancer agent of a library of known compounds. As discussed elsewhere, we should accept that drug discovery is the art of developing effective treatments against diseases we do not fully understand using drugs we do not fully know how work [3]. References 1 Hutchinson, L. and Kirk, R. (2011) High drug attrition rates – where are we going wrong? Nat. Rev. Clin. Oncol. 8, 189–190 2 Begley, C.G. and Ellis, L.M. (2012) Drug development: raise standards for preclinical cancer research. Nature 483, 531–533
Drug Discovery Today Volume 20, Number 2 February 2015
7 Lopez-Lazaro, M. (2009) Digoxin, HIF-1, and cancer. Proc. Natl. Acad. Sci. U. S. A. 106, E26 8 Calderon-Montano, J.M. et al. (2014) The in vivo antitumor activity of cardiac glycosides in mice xenografted with human cancer cells is probably an experimental artifact. Oncogene 33, 2947–2948
Miguel Lo´pez-La´zaro PhD Department of Pharmacology, Faculty of Pharmacy, University of Seville, Spain email: [email protected]
Editorial
3 Sams-Dodd, F. (2013) Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug Discov. Today 18, 211–217 4 Moffat, J.G. et al. (2014) Phenotypic screening in cancer drug discovery – past, present and future. Nat. Rev. Drug Discov. 13, 588–602 5 Siegel, R. et al. (2014) Cancer statistics, 2014. CA Cancer J. Clin. 64, 9–29 6 Lopez-Lazaro, M. (2014) Experimental Cancer Pharmacology for Researchers: At What Concentration Should My Drug Kill Cancer Cells So That It Has Potential for Cancer Therapy? Amazon Digital Services, Inc ASIN: B00MMO25NM http:// www.amazon.com/dp/B00MMO25NM/
EDITORIAL
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