Spontaneously Occurring Tumors in Companion Animals. Working with the National Cancer Institute. Phase I Trial Design and Methodology. Drug Development in Europe. Beverly A. Bibliografische Informationen. Figure 4c shows the percentage of samples with mutations of the 32 TargetCancer genes, their function classification, and number of targeting drugs. Its mutations significantly occur in the brain cancer GBM Among the seven drugs targeting the gene, three afatinib, erlotinib, and gefitinib were used to treat lung cancer, two cetuximab and panitumumab were used to treat colorectal cancer, and one cetuximab was used to treat head and neck cancer.
To explore the associations between the drugs and cancer types, we generated a drug-cancer network, which comprised nodes drugs and 33 cancer types and drug-cancer associations Fig.
Drug-cancer network. The red ellipse represents the cancer; the green rectangle represents the cytotoxic drug; the green diamond represents the targeted drug. The cancer abbreviations included in the Table 3. In the drug-cancer network, the degree number of cancer types of the drugs ranged from one to eleven, and the average degree was 1. The degree distribution of these drugs was strongly right-skewed, indicating that most drugs had a low degree and only a small portion of the nodes had a high degree.
The degree of the cytotoxic drugs was 2. Among the drugs, 35 belonged to the cytotoxic drugs while 70 belonged to the targeted drugs. Among the 21 drugs, 15 were cytotoxic drugs while six were targeted drugs. The most commonly used drug was doxorubicin that could be used to treat 11 cancer types, including leukemia, breast cancer, stomach cancer, lymphoma, ovarian cancer, lung cancer, sarcoma, thyroid cancer, bladder cancer, kidney cancer, and brain cancer.
Doxorubicin is a cytotoxic anthracycline antibiotic isolated from cultures of Streptomyces peucetius var. The result indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. In the drug-cancer network, the degree number of drugs of the 33 cancer types ranged from one to 40 and the average degree was 7. The degree distribution of the cancer types was not obviously right-skewed.
Among the 33 cancer types, 11 had one drug, 12 had at least two drugs and less than 10 drugs, and ten had at least ten drugs Table 3. They were leukemia number of drugs: 40 , lymphoma 28 , breast cancer 27 , lung cancer 17 , prostate cancer 15 , ovarian cancer 12 , melanoma 11 , colorectal cancer 10 , kidney cancer 10 , and stomach cancer Among the 40 drugs used to treat leukemia, 24 belonged to cytotoxic drugs while 16 drugs were the targeted drugs. Similarly, the numbers of cytotoxic drugs and targeted drugs were similar to each other for lymphoma, breast cancer, and lung cancer.
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However, for prostate cancer, melanoma, and kidney cancer, the numbers of targeted drugs were significantly higher than those of cytotoxic drugs. Besides the drug-cancer network, we generated a specific network for targeted drugs, their targets, and their indications. The network contained nodes 89 drugs, targets, and 23 cancer types and edges drug-cancer associations and drug-target associations Fig. Network of targeted drugs, targets, and cancer types. The red rectangle represents the cancer; the green rectangle represents the targeted drug, the blue rectangle represents the drug target.
In the network, drugs had two types of neighbors: drug target and drug indication cancer type. The target degree number of targets of the 89 drugs ranged from one to 18, and the average degree was 2. The cancer degree number of cancer types of the 89 drugs ranged from one to four and the average degree was 1.
Among the 89 drugs, 22 had more than two targets. The drug regorafenib had 18 targets, which was approved by FDA to treat gastrointestinal stromal tumors and metastatic colorectal cancer. Among the 89 drugs, 19 drugs could be used to treat more than one cancer types. Four drugs bevacizumab, everolimus, hydroxyurea, and recombinant interferon Alfa-2b could be used to treat four types of cancer. The degree number of drugs of targets ranged from one to seven and the average degree was 1. The EGFR epidermal growth factor receptor and KDR kinase insert domain receptor were the most popular targets and both could be targeted by seven drugs, separately.
The EGFR-related seven drugs could be used to treat six cancer types, while KDR-related drugs could be used to treat seven types of cancer. There were three common cancer types: colorectal cancer, thyroid cancer, pancreatic cancer. The degree number of drugs of cancer types ranged from one to 16 and the average degree was 5. As we discussed before, leukemia had 16 targeted drugs can be used to treat. During the analysis, we noticed that, among the 89 drugs, 70 drugs had at least one common target. Applying the common target-based approach, we discovered novel drug-cancer associations among 52 drugs and 16 cancer types.
To evaluate the novel drug-cancer associations, we utilized the clinical trial studies to see if the drug had been investigated in the corresponding cancer type. After searching using the 52 drugs and their predicted cancer types against ClinivalTrials. The later part of novel drug-cancer associations might provide valuable clues for drug repurposing. The most well-studied association was the thalidomide-lymphoma, which had clinical trial studies, including 15 Phase III clinical trial studies and one Phase IV clinical trial study.
The drug thalidomide was approved to treat multiple myeloma. Recently its combination with other drugs entered to treat the peripheral T-cell lymphoma in the Phase 4 study ClinicalTrials. FDA-approved anticancer medicines play important roles in the successful cancer treatment and novel anticancer drug development. In this study, we comprehensively collected FDA-approved anticancer drugs from to According to their action mechanisms, we groups them into two sets: cytotoxic and targeted agency. Then we performed a comprehensive analysis from the perspective of drugs, drug indications, drug targets, and their relationships.
For drugs, we summarized their historical characteristics and delivery methods. For targets, we surveyed their cellular location, functional classification, genetic patterns.
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We further applied network methodology to investigate their relationships. Its application to discover novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development. Nat Rev Drug Discov. Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol Oncol. Drug repositioning: identifying and developing new uses for existing drugs.
Cancer drug discovery by repurposing: teaching new tricks to old dogs. Trends Pharmacol Sci. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol Res. Kinch MS. An analysis of FDA-approved drugs for oncology. Drug Discov Today. Blagosklonny MV. Analysis of FDA approved anticancer drugs reveals the future of cancer therapy. Cell Cycle. Functional differentiation of cytotoxic cancer drugs and targeted cancer therapeutics.
Reg Toxicol Pharmacol. Adverse reactions to targeted and non-targeted chemotherapeutic drugs with emphasis on hypersensitivity responses and the invasive metastatic switch. Cancer Metastasis Rev. A tale of two approaches: complementary mechanisms of cytotoxic and targeted therapy resistance may inform next-generation cancer treatments.
Tseng HH, He B. Molecular markers as therapeutic targets in lung cancer. Chin J Ccancer. Arrell DK, Terzic A. The optimization process must consider all of the properties of the filter at the same time. Fig 2 summarizes the main points of the ISE-based modeling process. More details on the utility of ISE for extracting the best sets of descriptors, as well as the best ranges, from a certain set of descriptors can be found in our previously reported studies [ 20 , 30 ].
The ISE algorithm was applied to construct an in silico prediction system for detecting natural products with potential anticancer activity. This study was based on a set of anticancer drugs labeled as active chemicals and 2, natural products labeled as inactive phytochemicals. From previous projects, we learned that predictive models for virtual screening purposes should cover the same range of properties as those possessed by the objects in the screened database.
In light of that, we selected, as the inactive set, chemicals with the same "property range" as the chemicals in the screened database. As well, in order to make sure that our active set of chemicals would not be biased by having similar structures, we checked the structural diversity within the anticancer drugs and the 2, natural products and found that both databases were highly diverse.
Fig 4 presents distribution plots of the Lipinski and Oprea physicochemical properties of the set of anticancer drugs. Table 1 presents three of the filters as an example.
The Matthews correlation coefficients MCCs of the different filters are very close, but they differ in their true positive percentage and true negative percentage. Filter number 1, presented in Table 1 , has a MCC of 0. The filter is composed of ranges of four descriptors. Each molecule that fall within these ranges is considered active; while molecules having as least one descriptor that fall outside the range is considered inactive.
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It is worth stating that we presumed that most of the screened natural products were inactive, and thus, this classification is considered a false positive, although we are aware that some of those natural products were active and were correctly classified by our proposed prediction model. The Matthews correlation coefficients MCCs , the true positive TP percentages, the true negative TN percentages, and the descriptors' ranges are shown. The composition of the output list of best discriminative filters was analyzed.
Table 2 lists the most redundant descriptors of the 29 filters used to produce the anticancer indexing model. The third column reports how many more times each descriptor was redundant rather than random. The efficiency of the anticancer activity-indexing model, which was produced by the 29 range-based filters, is displayed in Fig 6.
Figs 7 and 8 show the enrichment plot and the receiver operating characteristic ROC plot of the suggested anticancer bioactivity-indexing model, respectively. The enrichment plot Fig 7 illustrates how the anticancer drug candidates could be predicted if natural products are ranked according to their scores as predicted by the ISE-based model, rather than based on random selection. An enrichment plot where the ISE-based model overlaid with the perfect model at the one percent highest fraction indicates the high prioritization power of the constructed model.
Fig 9 shows twelve natural products that were highly indexed as potential anticancer drug candidates by our ISE-based anticancer indexing model. Searching the scientific literature revealed that few of those molecules Neoechinulin[ 38 ] , Colchicine[ 39 ], and Piperolactam[ 40 ] have already been experimentally screened for their anticancer activity and found active.
A highly efficient and robust model for indexing natural products for their anticancer bioactivity has been built using the ISE algorithm. We believe that the use of such an in silico model to screen large databases of natural products could undoubtedly save time and costs and aid in detecting novel natural-based anticancer drug candidates.
We have disclosed some highly indexed phytochemicals that could serve as potential anticancer drug candidates.
A literature search shows that few of those molecules have already been experimentally screened for their anti-cancerous activity and found active. The other phytochemicals await evaluation for their anti-cancerous activity in wet lab. As well, this study provides important insights into discriminative properties of natural products having anti-cancerous activity.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. Introduction Cancer is one of the primary global diseases that cause morbidity and mortality in millions of people worldwide [ 1 ].
Methods To construct the predictive model, we used a set of anticancer drugs to constitute the active domain all anticancer drugs are presented in SMILES format followed by their common names in the supporting information S1 Table. Download: PPT. Fig 1. Diversity within anticancer drugs A, left side and diversity within natural products database B, right side. Fig 2. Flowcharts for the modeling process 2a , and the ISE engine 2b.
Results and discussion The ISE algorithm was applied to construct an in silico prediction system for detecting natural products with potential anticancer activity. Fig 3. Fig 4. Violation distribution of anticancer drugs to Lipinski rule of 5 for drug-likeness left side and Oprea rule for lead-likeness right side. Table 1. Three filters out of the 29 filters used for producing the anticancer indexing model. Fig 5. Redundancy of descriptors in the 29 filters used to produce the anticancer indexing model.
Fig 6. Fig 7. Enrichment plot of the anticancer potential activity-indexing model of natural products. Fig 8. A receiver operating characteristic ROC curve showing the performance of the anticancer bioactivity-indexing model. Fig 9. Twelve of the natural products that are scored highly as potential anticancer drug candidates according to our ISE-based anticancer indexing model.