Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

Chen, Lei; Huang, Tao; Zhang, Jian; Zheng, Mingyue; Kong, Feng; Cai, Yu-Dong; Chou, Kuo‐Chen

doi:10.1155/2013/485034

Cited by 22 publications

(17 citation statements)

References 35 publications

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“…More than 80% of people in developing countries hardly have access to most basic medical procedures, drugs, and vaccines (3). On the other hand, drugs' side effects may have serious life threatening consequences (4,5). The use of complementary medicine has dramatically increased in developed and developing countries for reasons such as ease of access, lower costs, and efficacy.…”

Section: Introductionmentioning

confidence: 99%

The Relationship Between Knowledge and Attitude of Patients with Chronic Diseases Regarding Complementary Medicine

Seyyedrassoli¹,

Ghahramanian²,

Rahimlou³

2017

Iran Red Crescent Med J

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Background: Evidence shows the effectiveness of complementary medicine and its use in the treatment of chronic diseases are increasing. Objectives: This study aimed to determine the relationship between knowledge and attitude toward complementary medicine in patients with chronic disease and referred to a hospital in an urban area of Iran. Methods: This cross-sectional correlation study was conducted with 400 patients with chronic diseases referred to a hospital in an urban area of Iran. The samples were chosen using convenience sampling. The patients' knowledge and attitudes were assessed using a researcher-made questionnaire and the holistic complementary and complementary medicine questionnaire, respectively. Results: The patients' knowledge was the highest and lowest in the subscales of herbal medicine and energy therapy, respectively. Also, their attitudes mean regarding holistic health was better than attitude regarding scientific validity of complementary medicine (21.37 versus 15.88). There were statistically significant relationships between knowledge and attitude (r = 0.28, P < 0.001). While the relationship between knowledge regarding complementary medicine and attitude toward holistic health (r = 0.52, P < 0.001), its relationship with attitude toward the scientific validity of complementary medicine was reverse and negative (r = -.23, P < 0.001). Moreover, demographic variables such as the level of education, marital status, disease type, exercise habits, and information about the methods of complementary medicine had statistically significant relationships with the patients' knowledge about complementary medicine. The frequency of consumption of vegetables per week (r = -0.18, P < 0.001) and the duration of disease (r = -0.16, P = 0.002) had a statistically significant reverse relationship with knowledge regarding complementary medicine. Conclusions: Knowledge regarding herbal medicine was high in patients with chronic diseases. Given a lack of sufficient knowledge and poor attitudes about other aspects of complementary medicine, patients need more education about the significance of complementary medicine and its positive effects on their health.

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Section: Introductionmentioning

confidence: 99%

The Relationship Between Knowledge and Attitude of Patients with Chronic Diseases Regarding Complementary Medicine

Seyyedrassoli¹,

Ghahramanian²,

Rahimlou³

2017

Iran Red Crescent Med J

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“…Chemicals are linked to other chemicals according to the evidence derived from experiments, databases and the literature. This type of chemical-chemical interaction information is widely used to investigate several biological problems [ 7 , 28 – 36 ]. We downloaded a file, named “chemical_chemical.links.detailed.v4.0.tsv.gz”, from STITCH (Version 4.0), which lists large numbers of chemical-chemical interactions.…”

Section: Methodsmentioning

confidence: 99%

A computational method for the identification of candidate drugs for non-small cell lung cancer

et al. 2017

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Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

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“…Thus, several in silico models for predicting side effects have been constructed by simulating interactions between chemical substances and their key off-targets (Chen and Ung, 2001;Wang et al, 2013). Otherwise, models have been derived by a machine learning technique using the "known CPI-side effect profiles" as the training data (Chen et al, 2013;Yamanishi et al, 2012). However, these models could be improved for practical use since the known CPI data were quite limited, and many missing CPIs must be included in the reference data.…”

Section: Introductionmentioning

confidence: 99%

<i>In silico</i> systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation

et al. 2020

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In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.

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Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

Cited by 22 publications

References 35 publications

The Relationship Between Knowledge and Attitude of Patients with Chronic Diseases Regarding Complementary Medicine

The Relationship Between Knowledge and Attitude of Patients with Chronic Diseases Regarding Complementary Medicine

A computational method for the identification of candidate drugs for non-small cell lung cancer

<i>In silico</i> systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation

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