• Genomics & Informatics
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• 제19권1호
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• pp.10.1-10.7
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• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• BMB Reports
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• 제43권12호
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• pp.836-841
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• 2010

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = $2.90{\times}10^{-4}$) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.

• 약학회지
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• 제18권1호
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• pp.49-58
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• 1974

Tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimization. Aluminum flufenamate was the drug chosen and microcrystalline cellulose nad starch were the binder and disintegrant, respectivley. The effect of the binder and disintegrant concentration on tablet hardness, friability, volume, in vitro release rate, and urinary excretion rate of drug in human subjects was recorded. Since a reasonably rapid release rate of drug is generally an important objective in the design of solid dosage form, optimization of this parameter was employed in studying the applicability of constrained optimization to a pharmaceutical product design problem. In addition to finding optimal sitivity analysis studies to such problems was also illustratd. It would appear that prediction of the in vivo t$_{50%}$ response from a knowledge of the incitro t$_{50%}$ response can be made fairly accurately for the tablet system used in this study.

• Laboraroty Animal Research
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• 제34권4호
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• pp.248-256
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• 2018

O-2-$^{18}F$-fluoroethyl-l-tyrosine ($[^{18}F]FET$) has been widely used for glioblastomas (GBM) in clinical practice, although evaluation of its applicability in non-clinical research is still lacking. The objective of this study was to examine the value of $[^{18}F]FET$ for treatment evaluation and prognosis prediction of anti-angiogenic drug in an orthotopic mouse model of GBM. Human U87MG cells were implanted into nude mice and then bevacizumab, a representative anti-angiogenic drug, was administered. We monitored the effect of anti-angiogenic agents using multiple imaging modalities, including bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT). Among these imaging methods analyzed, only $[^{18}F]FET$ uptake showed a statistically significant decrease in the treatment group compared to the control group (P=0.02 and P=0.03 at 5 and 20 mg/kg, respectively). This indicates that $[^{18}F]FET$ PET is a sensitive method to monitor the response of GBM bearing mice to anti-angiogenic drug. Moreover, $[^{18}F]FET$ uptake was confirmed to be a significant parameter for predicting the prognosis of anti-angiogenic drug (P=0.041 and P=0.007, on Days 7 and 12, respectively, on Pearson's correlation; P=0.048 and P=0.030, on Days 7 and 12, respectively, on Cox regression analysis). However, results of BLI or MRI were not significantly associated with survival time. In conclusion, this study suggests that $[^{18}F]FET$ PET imaging is a pertinent imaging modality for sensitive monitoring and accurate prediction of treatment response to anti-angiogenic agents in an orthotopic model of GBM.

• 한국응용약물학회:학술대회논문집
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• 한국응용약물학회 2007년도 Proceedings of The Convention
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• pp.11-15
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• 2007

Metabolomics is an emerging technology which makes it possible to evaluate change of biological system in response to the physiological, environmental alterations. It has advantages in the simplicity and sensitivity to analyze metabolites since the researcher can use cutting edge instrument, such as mass spectrometry and simple sample preparation method compared to genomics or proteomics. Nowadays this technology has been tried in pharmaceutical area to investigate toxicity and efficacy of drug candidates and drugs in preclinical test. The metabolomic applications on the pharmaceutics for early prediction on toxicity and efficacy are described in this presentation. The multivariate analysis to get metabolic fingerprinting and its relations with the physiological changes are investigated with several drugs. Feasibility of metabolomic application for pharmaceutical area would be suggested from those researches.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• 한국임상약학회지
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• 제18권2호
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• pp.84-96
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• 2008

In the recent, pharmacokinetic (PK)/pharmacodynamic (PD) modeling has appeared as a critical path tools in new drug development to optimize drug efficacy and safety. PK/PD modeling is the mathematical approaches of the relationships between PK and PD. This approach in new drug development can be estimated inaccessible PK and PD parameters, evaluated competing hypothesis, and predicted the response under new conditions. Additionally, PK/PD modeling provides the information about systemic conditions for understanding the pharmacology and biology. These advantages of PK/PD model development are to provide the early decision-making information in new drug development process, and to improve the prediction power for the success of clinical trials. The purpose of this review article is to summarize the PK/PD modeling process, and to provide the theoretical and practical information about widely used PK/PD models. This review also provides model schemes and the differential equations for the development of PK/PD model.

• Toxicological Research
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• 제35권4호
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• pp.331-341
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• 2019

Cancer is one of the common causes of death with a high degree of mortality, worldwide. In many types of cancers, if not all, sex-biased disparities have been observed. In these cancers, an individual's sex has been shown to be one of the crucial factors underlying the incidence and mortality of cancer. Accumulating evidence suggests that differentially expressed genes and proteins may contribute to sex-biased differences in male and female cancers. Therefore, identification of these molecular differences is important for early diagnosis of cancer, prediction of cancer prognosis, and determination of response to specific therapies. In the present review, we summarize the differentially expressed genes and proteins in several cancers including bladder, colorectal, liver, lung, and nonsmall cell lung cancers as well as renal clear cell carcinoma, and head and neck squamous cell carcinoma. The sex-biased molecular differences were identified via proteomics, genomics, and big data analysis. The identified molecules represent potential candidates as sex-specific cancer biomarkers. Our study provides molecular insights into the impact of sex on cancers, suggesting strategies for sex-biased therapy against certain types of cancers.

• Asian Pacific Journal of Cancer Prevention
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• 제15권7호
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• pp.3261-3265
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• 2014

Background: To evaluate serum VEGF-A levels in squamous cell carcinoma of head and neck (SCCHN) patients and relationships with response to therapy. Materials and Methods: Serum VEGF-A levels in patients (n=72) treated with radiotherapy (RT) or radio-chemotherapy (RCT) and controls (n=40) were measured by ELISA. Results: Serum VEGF-A levels of the SCCHN cases were significantly higher (p=0.001) than in healthy controls, and in patients with positive as compared to negative lymph node status (p=0.004). Similarly, patients with advanced stage (Stage III-IV) disease had more greatly elevated levels of serum VEGF-A level than their early stage (Stage I-II) counterparts (p=0.001). In contrast, there was no significant difference (p=0.57) in serum level of VEGF-A in patients with advanced T-stage (T3-4) as compared to early stage (T1-2). Similarly, patients with distant metastasis had no significant (p=0.067) elevation in serum VEGF-A level as compared to non-metastatic disease. However, the non-responder patients had significantly higher serum VEGF-A level as compared to responders (p=0.001). Conclusions: Our results suggest that the serum VEGF-A level may be a useful biomarker for the prediction of response to therapy in SCCHN.

• Asian Pacific Journal of Cancer Prevention
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• 제17권8호
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• pp.3971-3977
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• 2016

Background: Breast cancer, the commonest cancer among women in the world, ranks top in India with an incidence rate of 1,45,000 new cases and mortality rate of 70,000 women every year. Chemotherapy outcome for breast cancer is hampered due to poor response and irreversible dose-dependent cardiotoxicity which is determined by genetic variations in drug metabolizing enzymes and transporters. Pregnane X receptor (PXR), a member of the nuclear receptor superfamily, induces expression of drug metabolizing enzymes (DMEs) and transporters leading to regulation of xenobiotic metabolism. Materials and Methods: A genomic region spanning PXR 3' UTR was amplified and sequenced using genomic DNA isolated from 96 South Indian breast cancer patients. Genetic variants observed in our study subjects were queried in miRSNP to establish SNPs that alter miRNA binding sites in PXR 3' UTR. In addition, enrichment analysis was carried out to understand the network of miRNAs and PXR in drug metabolism using DIANA miRpath and miRwalk pathway prediction tools. Results: In this study, we identified SNPs rs3732359, rs3732360, rs1054190, rs1054191 and rs6438550 in the PXR 3; UTR region. The SNPs rs3732360, rs1054190 and rs1054191 were located in the binding site of miR-500a-3p, miR-532-3p and miR-374a-3p resulting in the altered PXR level due to the deregulation of post-transcriptional control and this leads to poor treatment response and toxicity. Conclusions: Genetic variants identified in PXR 3' UTR and their effects on PXR levels through post-transcriptional regulation provide a genetic basis for interindividual variability in treatment response and toxicity associated with chemotherapy.