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Cancer Drugs and Target Prediction in Human. Minireview

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14 February 2024

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16 February 2024

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Abstract
The present minireview assessed the benefits of using artificial intelligence applications in healthcare at predicting the targets of bioactive small molecules in human. Method. The tool is a tuned algorithm, with novel data in web interface. Results. We used tables to order the information of Swiss Target Prediction that allow predictions on combination of chemotherapies and probable side effects. This tool is useful to understand the molecular mechanisms underlying a given phenotype or bioactivity, to rationalize possible favorable or unfavorable side effects and to predict off-targets of known molecules as well as to clear the way for drug repurposing. Predictions are done using a ligand-based approach to compare the similarity between a query molecule and the known ligands of a large collection of protein targets. Conclusion. This study allowed us to formulate recommendations for the use of some of the chemotherapeutic agent groups in cancer patients at a low cost. It is very important and beneficial for patients that take medicine for a longer period and on whose lives depend on such treatment.
Keywords: 
Subject: Biology and Life Sciences  -   Life Sciences

Introduction

Cancer is defined as a proliferation disorder of cells that acquired the capacity of invading other tissues and sending metastasis to distant areas of the body, which together, bring about eventual death of the host [1]. This disease has come to constitute a major problem of public health. Despite the benefits of advances in research and treatment, more than six million people in the world die of the disease. It is estimated that if early prevention strategies are not implemented, there will be millions of new cases in the future, which will ultimately collapse the resources and infrastructures of health services of the countries, due to the high cost of cancer therapies. Cancer patients need an integral treatment and in most of the cases patients, families and even institutions of sanitary attention are unable to afford the high cost [2]. Hence, it is extremely important to develop preventive and low-cost management strategies to handle the problem. Table 1 and Table 2 below, respectively show the common types of cancer and cancer diagnosis based on gender:
Today, many different kinds of chemotherapy or chemo-drugs are used to treat cancer. These substances are applied either alone or in combination with other drugs or treatments. These drugs are very different in their chemical composition, as well as in the way they are prescribed and given. Moreover, their usefulness in treating certain types of cancer and their side effects vary [3].
Anticancer drug development over the past 50 years, can be grouped based on their pharmacodynamic characteristics, their chemical structure and their relationships to other drugs (Table 3).

Relevant sections

The risk of COVID-19 infection for cancer patients is reported slightly high compared with the general population. Among patients with cancer and COVID-19, severe illness and mortality is slightly high and this is associated to the combine effect of general risk factors and peculiar risk factors of cancer patients [4]. The basic nutritional management for cancer patients is administration of adequate amount of energy, protein/amino acids and micronutrients in order to prevent malnutrition coupled with suitable rehabilitation [5].
It is important to note that not all medicines and drugs used in cancer treatment work the same way. Other drugs, such as targeted therapy, hormone therapy and immunotherapy applied in cancer management, work differently. Cancer patients are highly vulnerable to SARS-CoV-2 infections due to their frequent contacts with the healthcare system, their immunocompromised state caused by cancer or its therapies, supportive medications such as steroids, and most importantly, their advanced age and comorbidities [6]. Chemotherapy drugs target cells at different phases of the cell cycle. Understanding how these drugs work could help the doctors predict which drug combinations are likely to give better results. Moreover, doctors can plan the administration frequency of each drug based on cell phase timing [7]. Cancer therapy should not be suspended or postponed under SARS-CoV-2 infections, since this will worsen the condition of the patients and may lead to immediate death in aggressive cases.
In fact, cancer cells tend to form new cells more quickly than normal cells and this makes them a better target for chemotherapy drugs. However, chemo drugs are not selective in cell destruction as they can attack both healthy and cancerous cells. This means that these drugs damage normal cells along with the cancer cells, with untold side effects.

Discussion and conclusions

The side effects of chemotherapy in cancer patients are important and determine the tolerability and continuation of therapy, and adversely affect the patients’ life quality [8]. Every administration of chemo should aim at finding a balance between killing the cancer cells and sparing the normal cells. However, the good news is that most normal cells are capable of recovering from the effects of chemo over time, a situation, which does not prevail in cancer cells, since they are mutated cells. These kind of cells are incapable to recover from the effects of chemo; hence, constituting advantage in the use of chemo to attack many types of cancer cells [9].
Advances in cancer chemotherapy has been greatly supported mainly by the progresses in studies of tumor cell biology and in the development of new anticancer drugs, studies that are fundamental in discerning drug resistance, ensure adequate biochemical modulation and prediction target tool.
The follow information describes chemotherapy target prediction, for developing new strategies with personalized cancer treatments (Table 4).

Future Directions

We used machine with build models to cancer drug target prediction. Then, new chemo-protective drugs are developed and specialists contribute to the solution of unmet needs to elucidate epidemiologic and pathophysiologic aspects, as potential tools for detection and monitoring of cancer dysfunction. This study allowed us to formulate recommendations for the use of some of the chemotherapeutic agent groups in cancer patients, which made it possible to lower the cost of treatment. It is very important for patients that take medicine for a longer period and on whose lives depend on it.

Author Contributions

DCGa,b,c,d,e, NOBb,c,d,e, AVPb,c,d,e, MOHb,c,d,e, DEOGb,c,d,e, HJOb,c,d,e, GBJ. [a] Contributed to the conception and design of the work. [b] Contributed to the collection, analysis, or interpretation of data. [c] Critically revised the manuscript for important intellectual content. [d] Drafted manuscript. [e] Gave final approval.

Funding

This article received no kind of economical support.

Ethics Statements

Not applicable

Authors' information

All participating authors are qualified medical science researched recognized by the Health Ministry of Mexico.

Acknowledgements

We thank Dr. Cyril Ndidi Nwoye Nnamezie, an expert translator and native English speaker, a researcher in Medical Science and a physician by profession. The authors express their profound gratitude to the National Institute of Pediatrics [NIP] for the support in the publication of this article issued on the Program A022. The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Our special thanks for the essential support provided by the Swiss National Science Foundation for the scientific developments and the SIB Swiss Institute of Bioinformatics for the creation and maintenance of the Web tools. The SIB Molecular Modelling Group has developed the Swiss Drug Design project. Swiss target prediction is operated by the Molecular Modelling Group of the SIB Swiss Institute of Bioinformatics and the University of Lausanne.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. Pain control: A guide for people with cancer and their families. [Control del dolor: Guía para las personas con cáncer y sus familias] National Cancer Institutte. USA. pp 70. https://www.cancer.gov › espanol › control-dolor.
  2. Ross JA, Severson RK, Pollock BH, et al. Childhood cancer in the Unites States. Cancer. 1996; 77: 201-207. [CrossRef]
  3. Calderón GD, Juárez OH, Guevara ZA, Juárez JA, Segura AL, Barragán MG, Hernández GE. Comparison Between types of cancer chemotherapies used in a private and a government-based hospital in Mexico. Proc West Pharmacol Soc. 2009; 52: 26-29.
  4. Noriyuki Katsumata. Clinical Guidelines for Cancer Chemotherapy under COVID-19 in the World. Gan To Kagaku Ryoho. 2021; 48(8): 992-995.
  5. Takashi Higashiguchi. Nutritional Management in Cancer Chemotherapy Based on Pathophysiology of Sarcopenia. Gan To Kagaku Ryoho. 2020; 47(11): 1552-1556.
  6. Antonio Passaro, Christine Bestvina, Maria Velez Velez, Marina Chiara Garassino, Edward Garon, Solange Peters. Severity of COVID-19 in patients with lung cancer: evidence and challenges. J Immunother Cancer 2021; 9(3): e002266. [CrossRef]
  7. Kuhm JG. Chemotherapy-associated hematopoietic toxicity. Am J Health-Sys Pharm. 2002; 59(4): s4-s7. [CrossRef]
  8. Iki S, Urabe A. Prevention and treatment of the side effects of cancer chemotherapy. Gan To Kagaku Ryoho. 2000; 27(11): 1635-40.
  9. Yu-Feng Lin, Jia-Jun Liu, Yu-Jen Chang, Chin-Sheng Yu, Wei Yi, Hsien-Yuan Lane, Chih-Hao Lu. Predicting Anticancer Drug Resistance Mediated by Mutations. Pharmac (Basel). 2022; 15(2) 136. [CrossRef]
Table 1. Common cancer types.
Table 1. Common cancer types.
Liver cancer Colon cancer
Breast cancer Prostate cancer
Skin cancer Lymphoma cancer
Uterine cancer Stomach cancer
Lung cancer Pancreatic cancer
Table 2. Types of cancer diagnosis based on gender in hospitalized patients.
Table 2. Types of cancer diagnosis based on gender in hospitalized patients.
Young men Young women
Diagnosis Cases Diagnosis Cases
1 Severe aplastic anemia 1 Metastatic Choriocarcinoma 1
2 Cervical lymphadenopathy 1 Ovarian Choriocarcinoma 1
3 Colon adenocarcinoma 1 Estesioneuroblastoma 1
4 Congenital cardiopathy 2 Ganglioneuroblastoma 1
5 Craneopharyngioma 1 Hepatosplenomegaly 1
6 suprarenal ganglioneuroblastoma 1 Hepatoblastoma 2
7 Germinoma 2 Hemangioma of tongue 1
8 eosinophilic granuloma 1 Hepatoblastoma 1
9 Glioblastoma 1 Histiocytosis 1
10 Hepatoblastoma 3 Acute Lynphoblastic Leukemia 30
11 Histiocytosis 3 Acute Granulocytic Leukemia 1
12 Langerhan’s cell Histiocytosis 3 Lymphoblastic Lymphoma 1
13 Leukemia acute 2 Lymphangiomalipoblastoma 1
14 Acute Lymphoblastic Leukemia 30 Burkitt’s Lymphoma 3
15 Acute megakaryocytic Leukemia 1 Hodgkin’s Lymphoma 5
16 Submaxillary Lymphadenopathy 1 Diffuse Colon Lymphoma 1
17 Burkitt`s Lymphoma 2 Lymphoblastic Lymphoma of bone 1
18 Hodgkin’s Lymphoma 9 Medulloblastoma 2
19 Diffuse large B cell Lymphoma 1 Neuroblastoma 4
20 Lymphoma 1 Right Retro-orbital Neuroblastoma 1
21 Lymphoblastic Lymphoma 1 Right femur Osteosarcoma 1
22 Arteriovenous malformation 2 lymphadenopathy Osteosarcoma 1
23 Medulloblastoma 4 Maxillary Osteosarcoma 1
24 Classic Medulloblastoma 1 Osteoblastic Osteosarcoma 5
25 Neuroblastoma 6 Osteosarcoma of the knee 1
26 Osteoblastic Osteoblastoma 1 Osteosarcoma 5
27 Androblastic Osteosarcoma 1 Aneurysmal cyst bone 1
28 Anaplastic Osteosarcoma 1 Rhabdomyosarcoma 5
29 Osteoblastic Osteosarcoma 5 Alveolar Rhabdomyosarcoma 1
30 Osteosarcoma 6 Embryonal Rhabdomyosarcoma 2
31 Astrocytoma 1 Retinoblastoma 8
32 Pancreatoblastoma 1 Ewing’s Sarcoma 12
33 Pan hypopituitarism 1 Pseudocyst cerebral 1
34 Pineoloblastoma 1 Immature Teratoma 1
35 Polyploidy 1 Cystic Teratoma 1
36 Cyst of iliac bone 1 Tumor astropolitik 1
37 Rhabdomyosarcoma 4 Central Parietal Right Tumor 1
38 Alveolar Rhabdomyosarcoma 2 Endodermic Sinus Tumor 1
39 Bilateral Retinoblastoma 3 Neuroblastic Tumor of ganglion 1
40 Retinoblastoma 7 Germinal Ovary Tumor 1
41 Orbit Retinoblastoma 1 Mixed Germinal Tumor 3
42 Ewing’s Sarcoma 2 Frontoparietal Tumor 1
43 Axial Ewing’s Sarcoma 1 Neuroectodermic Tumor 1
44 Synovial Sarcoma 1 Tumor of ovary 1
45 Granulocytic Sarcoma 1 Mixed ovarian germinal tumor 1
46 Myeloid Sarcoma 1 Polycystic tumor 2
47 Hemophagocytic Syndrome 1 Wilms Tumor 8
48 Schualuma malignant 1 Submandibular Tumor 1
49 Endodermic Sinus Tumor 1 Total 131
50 Testicular Endodermic Sinus Tumor 1
51 Mediastinal Tumor 1
52 Wilms Tumor 4
53 Fibrous Tumor 2
54 Germinal Tumor 1
55 Neuroectodermal Mesenteric Tumor 1
56 Bone Tumor 1
57 Left Testicular Tumor 1
58 Right Testicular Tumor 1
59 Neuroectodermal Tumor 1
60 Retro-orbital Tumor 1
Total 142
Table 3. Drugs used cancer management.
Table 3. Drugs used cancer management.
Doxorubicin Capecitabine Methotrexate 5-fluorouracil
Epirubicin Eribulin Pralatrexate 6-Mercaptopurine
Cyclophosphamide Entansime Valrubicin Azacitidine
Paclitaxel Abraxane Omacetaxine Bleomycin
Cisplatin Ixabepilone Mitoxantrone Cladribine
Altretamine Mechlorethamine Nelarabine Dactinomycin
Clofarabine Dacarbazine Trabectedin Daunorubicin
Docetaxel Abemaciclib Mitotane Etoposide
Carboplatin Asparaginase Vincristine Floxuridine
Gemcitabine Arsenic trioxide Liposomal doxorubicin Fludarabine
Cytarabine Ifosfamide Streptozocin Idarubicin
Carmustine Temozolomide Thioguanine Irinotecan
Bendamustine Melphalan Pemetrexed Mitomycin-C
Busulfan Oxaliplatin Pentostatin Topotecan
Cabazitaxel Ixabepilone Romidepsin Pegaspargase
Teniposide Vinorelbine Omacetaxine Procarbazine
Vinblastine Mitotane Vorinostat Tirinacil
Chlorambucil Thiotepa Trifluridine Hydroxyurea
Decitabine Lomustine Nab-paclitaxel Prednisone
Table 4. Target prediction in human
Table 4. Target prediction in human
Drugs for cancer Target prediction in human
1. Fludarabine Writer
20%
Family A G protein-coupled receptor
20%
Enzyme
13.3%
Other cytosolic protein
13.3%
Oxidoreductase
6.7%
Kinase
6.7%
Protease
6.7%
Unclassified protein
6.7%
Hydrolase
6.7%
2. Floxuridine Enzyme
53.3%
Transferase
13.3%
Lyase
13.3%
Family A G protein-coupled receptor
6.7%
Hydrolase
6.7%
Eraser
6.7%
3. Cytarabine Enzyme
20%
Family A G protein-coupled receptor
20%
Transferase
13.3%
Unclassified protein
13.3%
Hydrolase
6.7%
Other cytosolic protein
6.7%
Protease
6.7%
Oxidoreductase
6.7%
Kinase
6.7%
4. Altretamine Family A G protein-coupled receptor
66.7%
Other cytosolic protein
13.3%
Cytochrome P450
6.7%
Ligand-gated ion channel
6.7%
Enzyme
6.7%
5. Bendamustine Eraser
40%
Phosphodiesterase
26.7%
Family A G protein-coupled receptor
13.3%
Oxidoreductase
6.7%
Electrochemical transporter
6.7%
Nuclear receptor
6.7%
6. Busulfan
Protease
40%
Ligand-gated ion channel
20%
Family A G protein-coupled receptor
6.7%
Unclassified protein
6.7%
Electrochemical transporter
6.7%
Adhesion
6.7%
Hydrolase
6.7%
Enzyme
6.7%
7. Carmustine
Enzyme
46.7%
Hydrolase
13.3%
Oxidoreductase
6.7%
Other cytosolic protein
6.7%
Family C G protein-coupled
6.7%
Protease
6.7%
Aminoacyltransferase
6.7%
Phosphodiesterase
6.7%
8. Chlorambucil
Family A G protein-coupled receptor
20%
Electrochemical transporter
20%
Oxidoreductase
13.3%
Enzyme
13.3%
Nuclear receptor
6.7%
Secreted protein
6.7%
Protease
6.7%
Unclassified protein
6.7%
Fatty acid binding protein family
6.7%
9. Dacarbazine Family A G protein-coupled receptor
26.7%
Kinase
20%
Lyase
20%
Protease
6.7%
Oxidoreductase
6.7%
Writer
6.7%
Phosphodiesterase
6.7%
Transcription factor
6.7%
10. Ifosfamide Protease
40%
Enzyme
13.3%
Electrochemical transporter
13.3%
Cytochrome P450
13.3%
Family C G protein-coupled receptor
6.7%
Family A G protein-coupled receptor
6.7%
Unclassified protein
6.7%
11. Lomustine Enzyme
20%
Family A G protein-coupled receptor
13.3%
Oxidoreductase
13.3%
Electrochemical transporter
13.3%
Lyase
13.3%
Cytochrome P450
13.3%
Reader
6.7%
Family C G protein-coupled receptor
6.7%
12.
Mechlorethamine
Family A G protein-coupled receptor
20%
Lyase
20%
Kinase
13.3%
Oxidoreductase
13.3%
Protease
6.7%
Hydrolase
6.7%
Enzyme
6.7%
Other cytosolic protein
6.7%
Ligand-gated ion channel
6.7%
13. Melphalan Lyase
26.7%
Enzyme
20%
Hydrolase
13.3%
Electrochemical transporter
6.7%
Calcium channel auxiliary subunit alpha2delta family
6.7%
Other cytosolic protein
6.7%
Kinase
6.7%
Membrane receptor
6.7%
Protease
6.7%
14. Temozolomide Eraser
26.7%
Lyase
26.7%
Kinase
20%
Enzyme
6.7%
Family A G protein-coupled receptor
6.7%
Phosphodiesterase
6.7%
Protease
6.7%
15. Thiotepa Oxidoreductase
28.6%
Family A G protein-coupled receptor
21.4%
Transcription factor
14.3%
Eraser
14.3%
Ligand-gated ion channel
7.1%
Hydrolase
7.1%
Transferase
7.1%
16. Trabectedin Lyase
33.3%
Kinase
20%
Protease
20%
Enzyme
20%
Other ion channel
6.7%
17. Streptozocin Enzyme
33.3%
Family A G protein-coupled receptor
20%
Protease
13.3%
Lyase
13.3%
Hydrolase
6.7%
Transferase
6.7%1
Other cytosolic protein
6.7%
18. Hydroxyurea Lyase 100%
19. Nelarabine Lyase
26.7%
Family A G protein-coupled receptor
20%
Enzyme
13.3%
Other cytosolic protein
13.3%
Unclassified protein
6.7%
Hydrolase
6.7%
Protease
6.7%
Electrochemical transporter
6.7%
20. Pemetrexed Enzyme
26.7%
Electrochemical transporter
13.3%
Membrane receptor
13.3%
Lyase
13.3%
Transferase
6.7%
Oxidoreductase
6.7%
Ligase
6.7%
Hydrolase
6.7%
Unclassified protein
6.7%
21. Thioguanine Lyase
33.3%
Kinase
26.7%
Oxidoreductase
6.7%
Enzyme
6.7%
Hydrolase
6.7%
Voltage-gated ion cannel
6.7%
Family A G protein-coupled receptor
6.7%
Electrochemical transporter
6.7%
22. Trifluridine Enzyme
53.3%
Transferase
20%
Family A G protein-coupled receptor
13.3%
Hydrolase
6.7%
Electrochemical transporter
6.7%
23. Daunorubicin Family A G protein-coupled receptor
26.7%
Kinase
20%
Eraser
20%
Protease
6.7%
Isomerase
6.7%
Other cytosolic protein
6.7%
Unclassified protein
6.7%
Enzyme
6.7%
24. Idarubicin Kinase
20%
Enzyme
20%
Family A G protein-coupled receptor
13.3%
Protease
6.7%
Isomerase
6.7%
Other cytosolic protein
6.7%
Transcription factor
6.7%
Voltage-gated ion cannel 6.7% Eraser
6.7%
Transferase
6.7%
25. Valrubicin Kinase
26.7%
Eraser
20%
Family A G protein-coupled receptor
13.3%
Protease
13.3%
Isomerase
6.7%
Unclassified protein
6.7%
Other cytosolic protein
6.7%
Transcription factor
6.7%
26. Mitomycin C Eraser
26.7%
Lyase
26.7%
Enzyme
13.3%
Kinase
13.3%
Family A G protein-coupled receptor
13.3%
Cytochrome P450
6.7%
27. Irinotecan Family A G protein-coupled receptor
20%
Electrochemical transporter
20%
Eraser
20%
Hydrolase
6.7%
Protease
6.7%
Isomerase
6.7%
Other nuclear protein
6.7%
Cytochrome P450
6.7%
Transcription factor
6.7%
28. Topotecan Electrochemical transporter
20%
Family A G protein-coupled receptor
20%
Eraser
20%
Isomerase
6.7%
Cytochrome P450
6.7%
Transcription factor
6.7%
Other nuclear protein
6.7%
Protease
6.7%
Hydrolase
6.7%
29. Teniposide Protease
33.3%
Cytochrome P450
20%
Family A G protein-coupled receptor
13.3%
Oxidoreductase
13.3%
Nuclear receptor
6.7%
Electrochemical transporter
6.7%
Enzyme
6.7%
30. Cabazitaxel Family A G protein-coupled receptor
40%
Kinase
13.3%
Family B G protein-coupled receptor
6.7%
Cytochrome P450
6.7%
Structural protein
6.7%
Primary active transporter
6.7%
Protease
6.7%
Other cytosolic protein
6.7%
Phosphodiesterase
6.7%
31. Vinblastine Kinase
40%
Protease
20%
Family A G protein-coupled receptor
13.3%
Cytochrome P450
13.3%
Primary active transporter
6.7%
Other cytosolic protein
6.7%
32. Vinorelbine Protease
26.7%
Cytochrome P450
13.3%
Kinase
13.3%
Family A G protein-coupled receptor
13.3%
Other ion channel
13.3%
Primary active transporter
6.7%
Phosphodiesterase
6.7%
Enzyme
6.7%
33. Ixabepilone Kinase
33.3%
Enzyme
26.7%
Other cytosolic protein
20%
Oxidoreductase
6.7%
Ligand-gated ion channel
6.7%
Nuclear receptor
6.7%
34. Mitotane Family A G protein-coupled receptor
26.7%
Electrochemical transporter
20%
Enzyme
13.3%
Voltage-gated ion channel
6.7%
Nuclear receptor
6.7%
Kinase
6.7%
Transcription factor
6.7%
Unclassified protein
6.7%
Ligand-gated ion channel
6.7%
35. Omacetaxine Kinase
73.3%
Protease
13.3%
Enzyme
6.7%
Family A G protein-coupled receptor
6.7%
36. Romidepsin Eraser
66.7%
Family A G protein-coupled receptor
13.3%
Phosphodiesterase
6.7%
Enzyme
6.7%
Protease
6.7%
37. Vorinostat Eraser
86.7%
Protease
6.7%
Ligand-gated ion channel
6.7%
38. Procarbazine Kinase
73.3%
Family A G protein-coupled receptor
13.3%
Cytochrome P450
6.7%
Nuclear receptor
6.7%
39. Nab-paclitaxel Family A G protein-coupled receptor
26.7%
Kinase
26.7%
Protease
13.3%
Structural protein
6.7%
Primary active transporter
6.7%
Family B G protein-coupled receptor
6.7%
Cytochrome P450
6.7%
Phosphodiesterase
6.7%
40. Docetaxel Kinase
33.3%
Family A G protein-coupled receptor
26.7%
Family B G protein-coupled receptor
6.7%
Primary active transporter
6.7%
Structural protein
6.7%
Cytochrome P450
6.7%
Phosphodiesterase
6.7%
Protease
6.7%
41. Paclitaxel Family A G protein-coupled receptor
26.7%
Kinase
26.7%
Protease
13.3%
Structural protein
6.7%
Primary active transporter
6.7%
Family B G protein-coupled receptor
6.7%
Cytochrome P450
6.7%
Phosphodiesterase
6.7%
42. Cyclophosphamide Protease
33.3%
Enzyme
20%
Electrochemical transporter
13.3%
Cytochrome P450
13.3%
Family A G protein-coupled receptor
6.7%
Family C G protein-coupled receptor
6.7%
Unclassified protein
6.7%
43. Doxorubicin Kinase
20%
Eraser
20%
Protease
13.3%
Family A G protein-coupled receptor
13.3%
Isomerase
6.7%
Other cytosolic protein
6.7%
Unclassified protein
6.7%
Enzyme
6.7%
Transcription factor
6.7%
44. Epirubicin Kinase
20%
Eraser
20%
Family A G protein-coupled receptor
13.3%
Protease
13.3%
Isomerase
6.7%
Other cytosolic protein
6.7%
Unclassified protein
6.7%
Enzyme
6.7%
Transcription factor
6.7%
45. Gemcitabine Lyase
33.3%
Enzyme
20%
Transferase
13.3%
Family A G protein-coupled receptor
13.3%
Oxidoreductase
13.3%
Hydrolase
6.7%
46. Carboplatin Enzyme
45.5%
Phosphatase
18.2%
Family A G protein-coupled receptor
18.2%
Transferase
9.1%
Oxidoreductase
9.1%
47. Abemaciclib Kinase
26.7%
Protease
26.7%
Other cytosolic protein
13.3%
Enzyme
6.7%
Voltage-gated ion channel
6.7%
Membrane receptor
6.7%
Phosphodiesterase
6.7%
Family A G protein-coupled receptor
6.7%
48. Abraxane Family A G protein-coupled receptor
26.7%
Kinase
26.7%
Protease
13.3%
Structural protein
6.7%
Primary active transporter
6.7%
Family B G protein-coupled receptor
6.7%
Cytochrome P450
6.7%
Phosphodiesterase
6.7%
49. Capecitabine Kinase
33.3%
Unclassified protein
20%
Enzyme
20%
Electrochemical transporter
13.3%
Family A G protein-coupled receptor
13.3%
50. Eribulin Protease
33.3%
Family A G protein-coupled receptor 26.7% Phosphatase
13.3%
Cytochrome P450
6.7%
Enzyme
6.7%
Secreted protein
6.7%
Phosphodiesterase
6.7%
51. L-Asparagine Family C G protein-coupled receptor
33.3%
Ligand-gated ion channel
33.3%
Electrochemical transporter
20%
Family A G protein-coupled receptor
6.7%
Transferase
6.7%
52. Methotrexate Lyase
20%
Eraser
20%
Electrochemical transporter
13.3%
Membrane receptor
13.3%
Enzyme
13.3%
Oxidoreductase
6.7%
Transferase
6.7%
Ligase
6.7%
53. Pralatrexate Lyase
26.7%
Electrochemical transporter
13.3%
Membrane receptor
13.3%
Enzyme
13.3%
Oxidoreductase
6.7%
Ligase
6.7%
Transferase
6.7%
Protease
6.7%
Kinase
6.7%
54. Vincristine Protease
26.7%
Kinase
26.7%
Family A G protein-coupled receptor
20%
Cytochrome P450
13.3%
Primary active transporter
6.7%
Enzyme
6.7%
55. Mitoxantrone Kinase
33.3%
Family A G protein-coupled receptor
26.7%
Electrochemical transporter
20%
Isomerase
6.7%
Voltage-gated ion channel
6.7%
Enzyme
6.7%
56. Etoposide Protease
26.7%
Electrochemical transporter
20%
Cytochrome P450
20%
Family A G protein-coupled receptor
13.3%
Nuclear receptor
6.7%
Phosphodiesterase
6.7%
Oxidoreductase
6.7%
57. Azacitidine Enzyme
33.3%
Lyase
26.7%
Other cytosolic protein
13.3%
Hydrolase
6.7%
Family A G protein-coupled receptor
6.7%
Kinase
6.7%
Eraser
6.7%
58. 5-Fluorouracil Enzyme
26.7%
Ligand-gated ion channel
26.7%
Lyase
13.3%
Hydrolase
13.3%
Eraser
6.7%
Family A G protein-coupled receptor
6.7%
Transferase
6.7%
59. Mercaptopurine Lyase
46.7%
Kinase
26.7%
Enzyme
13.3%
Oxidoreductase
6.7%
Protease
6.7%
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