|Year : 2019 | Volume
| Issue : 1 | Page : 17-21
Research and development of anticancer agents under the guidance of biomarkers
Xiaohui Xu1, Guoyu Qiu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Ruling Jia1, Bo Zhao1
1 Department of Chemical Drug and Traditional Chinese Medicine, Lanzhou Institutes for Food and Drug Control, Lanzhou, Gansu, China
2 Department of Medicine, The Fifth People's Hospital of Zhuhai, Zhuhai, Guangdong, China
3 Department of Food, Gansu Province Product Quality Supervision and Inspection Research Institute, Lanzhou, Gansu, China
4 Department of Pharmacy, First Hospital of Lanzhou University, Lanzhou, Gansu, China
|Date of Submission||18-Feb-2019|
|Date of Acceptance||03-Mar-2019|
|Date of Web Publication||28-Mar-2019|
Assoc. Prof. Guoyu Qiu
Department of Chemical Drug and Traditional Chinese Medicine, Lanzhou Institutes for Food and Drug Control, No 988, Peng Jia Ping Town, Lanzhou 730050, Gansu
Source of Support: None, Conflict of Interest: None
At present, cancer ranks first as the cause of death in the world, necessitating the need to develop new anticancer agents. As a probe, biomarkers can indicate the biological and pharmacological activity of anticancer agents and are thus valuable in predicting their effectiveness during the research and development phase. This paper reviews the research on the biomarker-guided prediction of the efficacy of anticancer agents. We infer that, in the process of the development of anticancer agents, reasonable selection of biomarkers can improve the accuracy of the development of anticancer agents.
Keywords: Anticancer agents, biomarkers, research and development
|How to cite this article:|
Xu X, Qiu G, Ji L, Ma R, Dang Z, Jia R, Zhao B. Research and development of anticancer agents under the guidance of biomarkers. Cancer Transl Med 2019;5:17-21
|How to cite this URL:|
Xu X, Qiu G, Ji L, Ma R, Dang Z, Jia R, Zhao B. Research and development of anticancer agents under the guidance of biomarkers. Cancer Transl Med [serial online] 2019 [cited 2020 May 28];5:17-21. Available from: http://www.cancertm.com/text.asp?2019/5/1/17/255121
| Introduction|| |
Now, more than half of all cancer patients live in low- and middle-income countries, and that number is expected to rise to 70% by 2030, according to a report by the International Union against Cancer. Cancer has become the first cause of death in urban and rural residents in China. The incidence of cancer is rising rapidly, with about 2.6 million people suffering from cancer and 1.8 million dying every year in China. Currently, there are about 130–150 types of approved anticancer drugs in the market around the world., Anticancer drugs are the primary mode to fight against cancer and represent the highest level of research in cancer therapeutics. As shown in [Figure 1], the failure in the process of development of anticancer agents is due to wrong drug target, wrong molecule, wrong conclusion, and wrong patient. However, under the guidance of biomarkers, the success rate of developing anticancer agents can be three times higher than those without biomarkers. The development of anticancer agents is still slow, expensive, and inefficient. One way to solve this problem is to use predictive biomarkers. During the research, biomarkers are involved in a wide range of disciplines, models, and developmental stages, from the identification and evaluation of drug targets to solving the problem during the clinical trials. Biomarker can be used as the probe to play an important role in determining drug target, drug action mechanism, and other aspects, which is of great significance in predicting the accuracy of the anticancer molecule. In the context of preclinical drug development, biomarker can objectively measure and evaluate the normal biological process, pathological process, and process of drug reaction, which is helpful to monitor the effectiveness of the drug on the disease. [Figure 2] shows the research designs of anticancer agents, differentiated by the presence or absence of biomarkers. With the completion of human genome sequencing and the development of metabonomics technology, biomarkers are more and more widely used in drug development. Major organ systems where biomarkers are reliably applied are the hepatobiliary system, renal system, cardiac system, muscle system, reproductive system, nervous system, and immune system. This paper reviews the biomarker-guided screening of anticancer agents, which we hope to be assistance in further research and development of new anticancer agents.
|Figure 1: Comparison of success rate of drug research under the guidance of the presence or absence of biomarkers|
Click here to view
|Figure 2: Two designs of anticancer agent research; (a) without the guidance from biomarkers and (b) under the guidance of the biomarkers|
Click here to view
| Biomaker|| |
Biomarkers that are based on immunology and molecular biology technology, relating to cell growth and proliferation, are being developed. Many molecules, including proteins, nucleic acids, and metabolic derivatives, can be potential biomarkers. Cancer biomarkers can be the molecules that are directly secreted by the cancer tissues or secreted by the body in response to cancer. The presence of these molecules indicates the presence of cancer in the human body.,,, Identification and detection of these cancer biomarkers are of great importance in understanding the activity and mechanism of action of the drug, which aids in better prediction of the pharmacological activity of the test drug, thus reducing the time and cost of drug research and clinical trials. Drug activity screening is the first step in the discovery of new anticancer agents,in vitro and in vivo. Common biomarkers include cytotoxic biomarkers,,,,,,,, gene biomarkers, protein expression biomarkers, and imaging biomarkers. At present, most of the studies on anticancer agents use biomarkers to study the pharmacological activity. Based on the type of biomarkers studied, it is either upregulated or downregulated posttreatment, aiding in prediction of the pharmacological effects of the studied anticancer agent., Metabolomics offer the opportunity to identify endogenous biomarkers and are applicable to the drugs that produce urinary metabolite. However, the protocol is not applicable to the drug that cannot be metabolized or eliminated through different pathway. In a study, stable isotope labeling and unlabeled drug administration were performed on mice, and urine samples were collected, and on the basis of principal component analysis (PCA), nontargeted mass spectrometric metabolomic analysis was performed. Currently, the methods used to find and identify biomarkers include second-generation genome sequencing, bioinformatic mutation analysis, PD marker identification in vivo and in vitro, quantitative Western Blot, FACS analysis, immunohistochemical detection, and DNA sequencing.
| Studying Anticancer Agent Based on Biomarker|| |
The search for biomarker includes two key processes. One is to find the biomarker and establish an applicable detection method for it, while the second is to establish the correlation or causal relationship between biomarker and anticancer agent. Only the biomarker that meets both the processes is eligible to be used in the research of anticancer agent. [Table 1] lists the highly cited literature on the research of anticancer agents based on prediction and prognostic biomarkers in recent years. At present, the cost of developing new drugs is rising, technology is improving, but the declaration of new drug is on the decline. In the face of this situation, it is important to identify few biomarkers based on computer design for specific classes of anticancer agent with both biosafety and efficacy. Under the guidance of biomarker, pharmacological activity of anticancer agents, including pharmacokinetic and pharmacodynamic properties and drug efficacy, can also be predicted.
|Table 1: Highly cited references about recent studies on anticancer agents by prediction and prognostic biomarkers,,,,,,,,|
Click here to view
Predict pharmacological activity
A compound must undergo a series of bioactivity screening in vitro and in vivo before it can be considered as a candidate for anticancer agent. In the early stage of screening such a candidate agent, the related specific biomarker plays an important role in its evaluation. Currently, the targets of anticancer agent screening mainly include microtubulin, telomerase, DNA topoisomerase, and those regulating cell signal transduction pathways. The primary research objective of anticancer agents is to find the target, while the application of biomarker discovery is to predict and evaluate the efficacy of anticancer agents and their targets. [Table 2] lists such examples where biomarkers were used to screen anticancer agents.
Studying pharmacokinetics and pharmacodynamics
The relationship between targets and diseases was confirmed by the biomarkers understanding pharmacodynamic combined with pharmacokinetics. The expression of biological effects was studied and correlated at several levels, including tissue, organ, and whole animal, which were targeted by anticancer agent receptors, to elucidate the pharmacodynamic basis and mechanism of anticancer agents. At the same time, based on the comprehensive and systematic research results, biomarkers closely related to the expression of medicinal properties were found for quality control and guiding the selection of dosage. Pharmacodynamic results of therapeutic anticancer agent are often used to confirm targeted biological reactions, and its analytical methods range from mass spectrometry to immunohistochemistry. So far, detection of antigen-specific antibody was widely used. Membrane transporters, belonging to two superfamilies of ATP-binding cassette transporter (ABC) and solute transporters, play a core role in homeostasis regulation of the body. The drug carriers that can associate with these membrane proteins are widely studied. Drug transporters involve all aspects of drug absorption, distribution, and excretion. Several ABC transporters are highly expressed in a variety of cancers and play an important role in the formation of multidrug resistance phenotypes. Some polymorphisms of transporter have been shown to alter the pharmacokinetic characteristics of clinical drug. Therefore, drug transporters are reliable biomarkers of pharmacokinetics and pharmacodynamics. In the meantime, when the function of transporter is impaired, the substrates of transporter can also serve as a biomarker. Due to the environmental factors and genetic factors of cancer heterogeneity, the response of anticancer drug varies among individuals. Genetic factors may influence the pharmacokinetics and pharmacodynamics of anticancer drug, which results in systemic drug exposure or changes in the function of drug targets that alter drug response. Therefore, some pharmacogenetic biomarkers have been applied in the practice of pharmacodynamics or pharmacokinetics of anticancer agents. However, many genetic biomarkers are in the stage of discovery, and some problems need to be solved to improve the translation of genetic biomarkers, which will involve a multidisciplinary approach using standardized phenotypic and genotyping strategies to work together to identify and replicate associations.
Predicts and prognosis drug efficacy
The function of combination and inhibition of targeted anticancer agents plays a role as molecular targets that are vital in the development and progression of cancer and intracellular signaling pathways. So far, hundreds of different cancer-targeting drugs are approved for clinical use in different countries. Compared with previous chemotherapy, recent anticancer-targeted drugs have fewer side effects, higher efficiency, and higher cost. However, the therapeutic efficiency of the targeted anticancer drugs against advanced cancer is still insufficient. Different targeted anticancer drugs have different mechanisms of action and show curative effect in different patients. Therefore, a personalized approach is needed to select the best candidate of targeted anticancer drugs for individual patients. Now, researchers have developed a new generation of biomarkers, which are molecular pathway activators that can predict the response of individual cancer to targeted anticancer drugs. The successful application of high-throughput gene expression profiles and the emergence of new biological information tools have promoted the rapid development of the fields related to molecular pathways. Predictive biomarkers for cancers are necessary to accurately identify patients who will benefit from such cancer treatment. Approved anticancer drugs target discrete molecular aberrations or pathways in tumor cells, active in a subset of the patient population, but clinical studies have shown that not all patients showed positive response to biomarkers. To predict the emerging resistance mechanism of biomarkers, it is necessary to not only to guide the selection of patient subgroups in specific treatments but also to identify new therapeutic targets. Beyond the “one marker, one drug” model, the integration of genomics transcriptome and receptor status assessment into the joint development of biomarker will contribute to the successful application of molecular marker in cancer therapy. On the other hand, the intrinsic or acquired drug resistance, adverse drug reactions, and heterogeneity between cancer and patients limit the clinical efficacy of anticancer drugs in the treatment of advanced cancer. To overcome these obstacles, the application of predictive biomarkers was used to guide medical oncologist to choose a variety of anticancer treatment for cancer patients and improve the efficiency against toxicity. However, for some anticancer agents from natural sources, such as tribetidine, carbamazepine, and alvocidib, the application of biomarkers to predict the clinical therapeutic effect is still slow.
| Discussion|| |
Biomarkers have received extensive attention due to their importance in anticancer agent research as they can be used to screen anticancer agent of high efficiency and low toxicity. However, the mechanism of action of anticancer agent is mostly established in cell line and animal model. Human body is a highly complex system with many cells and organs, where different diseases involve multiple biological signaling pathways and different organs have different mechanisms, which are very difficult to simulate in the preclinical drug development system. In addition, biological signal transduction pathways often involve multiple genes other than the one target, which makes it very limited to use a single original drug target as a biomarker. On the other hand, the majority of anticancer agents has in common DNA-damaging properties and affects not only target cells but also nontumor cells. Its genotoxicity has been demonstrated in experimental models and in cancer patients treated with chemotherapy. This phenomenon makes that some anticancer agents can fail in the latter stages of development because of toxicity and lack of efficacy. To improve anticancer agent safety during its developmental phase, new biomarkers are needed, which can reduce the time-consuming process and cost of drug development. Therefore, it is essential to develop anticancer agent under the guidance of few biomarkers and verify with each other.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
CanTreat International. The Informal Working Group on Cancer Treatment in Developing Countries. The 21st
World Cancer Congress; 2010.
Wang Z, Yang HW, Wang X, Wang L, Cheng YD, Zhang YS, Tu YY. The molecular mechanism and regulatory pathways of cancer stem cells. Cancer Transl Med
2016; 2 (5): 147–53.
Wong KK, Qian ZR, Le Y. The role of precision medicine in pancreatic cancer: challenges for targeted therapy, immune modulating treatment, early detection, and less invasive operations. Cancer Transl Med
2016; 2 (2): 41–7.
Townsend MJ, Arron JR. Reducing the risk of failure: biomarker-guided trial design. Nat Rev Drug Discov
2016; 15 (8): 517–8.
Qi J, Yang HW, Wang X, Tu YY. The Progress in molecular biomarkers of gliomas. Cancer Transl Med
2016; 2 (4): 125–9.
Hu J, Yin BB. Advances in biomarkers of biliary tract cancers. Biomed Pharmacot
2016; 81: 128–35.
Huang L, Lv WG, Zhao XF. CD24 as a molecular marker in ovarian cancer: a literature review. Cancer Transl Med
2016; 2((1): 29–32.
Guo F, Chen H, Gong Q, Zuo H, Xiong J, Liu S. The involvement of p53-miR-34a-CDK4 signaling during the development of cervical cancer. Cancer Transl Me
d 2015; 1 (2): 67–71.
Lieggi NT, Edvardsson A, O'Brien PJ. Translation of novel anti-cancer cytotoxicity biomarkers detected with high content analysis from an in vitro
predictive model to an in vivo
cell mode. Toxicol In Vitro
2010; 24: 2063–71.
Jackson DB. Clinical and economic impact of the nonresponder phenomenon-implications for systems based discovery. Drug Discov Today
2009; 14: 380–5.
Yoon YS, Kim JC. Recent applications of chemosensitivity tests for colorectal cancer treatment. World J Gastroenterol
2014; 20: 16398–408.
KubotaTW. Chemotherapy sensitivity and resistance testing: to be standard or to be individualized, that is the question. Gastric Cancer
2006; 9: 82–7.
Wang W, Baggerly KA, Knudsen S, Askaa J, Mazin W, Coombes KR. Independent validation of a model using cell line chemosensitivity to predict response to therapy. J Natl Cancer Inst
2013; 105: 1284–91.
Mehta RS, Bornstein R, Yu IR, Parker RJ, McLaren CE, Nguyen KP, Li KT, Fruehauf JP. Breast cancer survival and in vitro
tumor response in the extreme drug resistance assay. Breast Cancer Res Treat
2001; 66: 225–37.
Ruppen J, Wildhaber FD, Strub C, Hall SR, Schmid RA, Geiser T, Guenat OT. Towards personalized medicine: chemosensitivity assays of patient lung cancer cell spheroids in a perfused microfluidic platform. Lab Chip
2015; 15: 3076–85.
Cree IA. Chemosensitivity and chemoresistance testing in varian cancer. Curr Opin Obstet Gynecol
2009; 21: 39–43.
Samatova TR, Galatenko VV, Block A, Shkurnikov MY, Tonevitsky AG, Schumacher U. Novel biomarkers in cancer: the whole is greater than the sum of its parts. Semin Cancer Biol
2016; 1270 (8): 1–8.
O'Conno JP, Aboagye E, Adams J, Aerts HJ, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G, Buckley DL, Chenevert TL, Clarke LP, Collette S, Cook GJ, deSouza NM, Dickson JC, Dive C, Evelhoch JL, Faivre-Finn C, Gallagher FA, Gilbert FJ, Gillies RJ, Goh V, Griffiths JR, Groves AM, Halligan S, Harris AL, Hawkes DJ, Hoekstra OS, Huang EP, Hutton BF, Jackson EF, Jayson GC, Jones A, Koh DM, Lacombe D, Lambin P, Lassau N, Leach MO, Lee TY, Leen EL, Lewis JS, Liu Y, Lythgoe MF, Manoharan P, Maxwell RJ, Miles KA, Morgan B, Morris S, Ng T, Padhani AR, Parker GJ, Partridge M, Pathak AP, Peet AC, Punwani S, Reynolds AR, Robinson SP, Shankar LK, Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol
2017; 14: 169–86.
Alymani NA, Smith MD, Williams DJ, Petty RD. Predictive biomarkers for personalised anti-cancer drug use: discovery to clinical implementation. Eur J Cancer
2010; 46: 869–79.
Ranjan R, Esimbekova EN, Kratasyuk VA. Rapid biosensing tools for cancer biomarkers. Biosens Bioelectron
2017; 87: 918–30.
Beyoǧlu D, Zhou YY, Chen C, Idle JR. Mass isotopomer-guided decluttering of metabolomic data to visualize endogenous biomarkers of drug toxicity. Biochem Pharmacol
2018; 156: 491–500.
Bairi KE, Atanasov AG, Amrani M, Afqir S. The arrival of predictive biomarkers for monitoring therapy response to natural compounds in cancer drug discovery. Biomed Pharmacother
2019; 109: 2492–8.
Dugger SA, Platt A, Goldstein DB. Drug development in the era of precision medicine. Nat Rev Drug Discov
2018; 17 (3): 183–96.
Pritzker KP. Predictive and prognostic cancer biomarkers revisited. Expert Rev Mol Diagn
2015; 15 (8): 971–4.
Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet
2014; 15: 349–70.
Stephenson D, Sauer JM. The predictive safety testing consortium and the coalition against major diseases. Nat Rev Drug Discov
2014; 13 (11): 793–4.
Sistare FD, Dieterle F, Troth S, Holder DJ, Gerhold D, Andrews-Cleavenger D, Baer W, Betton G, Bounous D, Carl K, Collins N, Goering P, Goodsaid F, Gu YZ, Guilpin V, Harpur E, Hassan A, Jacobson-Kram D, Kasper P, Laurie D, Lima BS, Maciulaitis R, Mattes W, Maurer G, Obert LA, Ozer J, Papaluca-Amati M, Phillips JA, Pinches M, Schipper MJ, Thompson KL, Vamvakas S, Vidal JM, Vonderscher J, Walker E, Webb C, Yu Y. Towards consensus practices to qualify safety biomarkers for use in early drug development. Nat Biotechnol
2010; 28 (5): 446–54.
Zhao X, Modur V, Carayannopoulos LN, Laterza OF. Biomarkers in pharmaceutical research. Clin Chem
2015; 61 (11): 1343–53.
Waterton JC, Pylkkanen L. Qualification of imaging biomarkers for oncology drug development. Eur J Cancer
2012; 48 (4): 409–15.
Kraus VB. Biomarkers as drug development tools: discovery, validation, qualification and use. Nat Rev Rheumatol
2018; 14 (6): 354–62.
Asadullah K, Busch A, Gottwald M, Reinke P, Landeck L. Industry-academia collaborations for biomarkers. Nat Rev Drug Discov
2015; 14 (12): 805–6.
Twomey JD, Brahme NN, Zhang BL. Drug-biomarker co-development in oncology-20 years and counting. Drug Resist Updat
2017; 30: 48–62.
Suspiro A, Prista J. Biomarkers of occupational exposure do anticancer agents: a minireview. Toxicol Lett
2011; 207: 42–52.
Yan ZH, Madison LL, Burkhardt A, Yu J, Tayber O, Li Z, Wu D, Loke HK, Wysong DR, Iartchouk N, Smith MD, Kuranda M, Li P, Bembenek ME. Analysis of two pharmacodynamic biomarkers using acoustic micro magnetic particles on the ViBE bioanalyzer. Anal Biochem
2011; 410 (1): 13–8.
Tóth B, Krajcsi P, Magnan R. Biomarkers in Toxicology. Parow: Academic Press; 2014. p. 947–63.
Buzdin A, Sorokin M, Garazha A, Sekacheva M, Kim E, Zhukov N, Wang Y, Li X, Kar S, Hartmann C, Samii A, Giese A, Borisov N. Molecular pathway activation – New type of biomarkers for tumor morphology and personalized selection of target drugs. Semin Cancer Biol
2018; 53: 110–24.
[Figure 1], [Figure 2]
[Table 1], [Table 2]