Translational and Clinical Pharmacology最新文献

{"title":"Brief introduction to parametric time to event model.","authors":"Hyeong-Seok Lim","doi":"10.12793/tcp.2021.29.e7","DOIUrl":"https://doi.org/10.12793/tcp.2021.29.e7","url":null,"abstract":"<p><p>This tutorial explains the basic concept of parametric time to event (TTE) models, focusing on commonly used exponential, Weibull, and log-logistic model. TTE data is commonly used as endpoint for treatment effect of a drug or prognosis of diseases. Although non-parametric Kaplan-Meier analysis has been widely used for TTE data analysis, parametric modeling analysis has its own advantages such as ease of simulation, and evaluation of continuous covariate. Accelerated failure time model is introduced as a covariate model for TTE data together with proportional hazard model. Compared to proportional hazard model, accelerated failure time model provides more intuitive results on covariate effect since it states that covariates change TTE whereas in proportional hazard model covariates affect hazard.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"29 1","pages":"1-5"},"PeriodicalIF":0.9,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/48/31/tcp-29-1.PMC8020361.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38875101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative pharmacokinetics between tenofovir disoproxil phosphate and tenofovir disoproxil fumarate in healthy subjects.","authors":"Sangmi Lee,&nbsp;Eunwoo Kim,&nbsp;Seol Ju Moon,&nbsp;Jina Jung,&nbsp;SeungHwan Lee,&nbsp;Kyung-Sang Yu","doi":"10.12793/tcp.2021.29.e4","DOIUrl":"https://doi.org/10.12793/tcp.2021.29.e4","url":null,"abstract":"<p><p>Tenofovir is the representative treatment for human immunodeficiency virus and hepatitis B virus infection. This study was conducted to assess the pharmacokinetics (PKs) and safety characteristics after a single administration of tenofovir disoproxil phosphate compared to tenofovir disoproxil fumarate in healthy male subjects. An open-label, randomized, single administration, two-treatment, two-sequence crossover study was conducted in 37 healthy volunteers. Serial blood samples were collected up to 72 hours. Non-compartmental analysis was used to calculate the PK parameters. The 90% confidence intervals (90% CIs) of the geometric mean ratio (GMR) were calculated for comparing tenofovir disoproxil phosphate to tenofovir disoproxil fumarate. Safety assessments were performed including clinical laboratory tests, adverse events, etc. during the study. The GMR and 90% CIs were 1.0514 (0.9527-1.1603) for C_max and 1.0375 (0.9516-1.1311) for AUC_last, respectively, and both fell within the conventional bioequivalence range of 0.8-1.25. Both tenofovir salt forms were tolerable. This study demonstrated that tenofovir disoproxil phosphate (292 mg) was bioequivalent to tenofovir disoproxil fumarate (300 mg).</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"29 1","pages":"45-52"},"PeriodicalIF":0.9,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/35/0e/tcp-29-45.PMC8020360.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38875105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of pharmacokinetics and safety characteristics between two olopatadine hydrochloride 5 mg tablet formulations in healthy Korean subjects.","authors":"Jae Hoon Kim,&nbsp;Minyu Lee,&nbsp;Namsick Kim,&nbsp;Tae-Young Oh,&nbsp;Seung-Kwan Nam,&nbsp;Yoon Seok Choi,&nbsp;In Sun Kwon,&nbsp;Jin Gyu Jung,&nbsp;Jang Hee Hong","doi":"10.12793/tcp.2021.29.e6","DOIUrl":"https://doi.org/10.12793/tcp.2021.29.e6","url":null,"abstract":"<p><p>Histamine acts by binding to four histamine receptors (H1 to H4), of which the H1 is known to participate in dilate blood vessels, bronchoconstriction, and pruritus. Olopatadine hydrochloride blocks the release of histamine from mast cells and it inhibits H1 receptor activation. Olopatadine hydrochloride is anti-allergic agent that is effectively used. The object of this study had conducted to compare the pharmacokinetics (PKs) and safety characteristics between olopatadine hydrochloride 5 mg (test formulation) and olopatadine hydrochloride 5 mg (reference formulation; Alerac ^®) in Korean subjects. This study had conducted an open-label, randomized, fasting condition, single-dose, 2-treatment, 2-period, 2-way crossover. Subjects received single-dosing of reference formulation or test formulation in each period and blood samples were collected over 24 hours after administration for PK analysis. A wash-out period of 7 days was placed between the doses. Plasma concentration of olopatadine were determined using liquid chromatography-tandem spectrometry mass (LC-MS/MS). A total of 32 subjects were enrolled and 28 subjects completed. There were not clinical significantly different in the safety between two treatment groups for 32 subjects who administered the study drug more than once. The geometric mean ratio of test formulation to reference formulation and its 90% confidence intervals for The peak plasma concentration (C_max) and the areas under the plasma concentration-time curve from 0 to the last concentration (AUC_last) were 1.0845 (1.0107-1.1637) and 1.0220 (1.0005-1.0439), respectively. Therefore, the test formulation was bioequivalent in PK characteristics and was equally safe as the reference formulation.</p><p><strong>Trial registration: </strong>Clinical Research Information Service Identifier: KCT0005943.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"29 1","pages":"65-72"},"PeriodicalIF":0.9,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/46/e5/tcp-29-65.PMC8020358.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38874567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacogenomic information from CPIC and DPWG guidelines and its application on drug labels.","authors":"Deok Yong Yoon,&nbsp;Soyoung Lee,&nbsp;Mu Seong Ban,&nbsp;In-Jin Jang,&nbsp;SeungHwan Lee","doi":"10.12793/tcp.2020.28.e18","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e18","url":null,"abstract":"<p><p>There are several hurdles to overcome before implementing pharmacogenomics (PGx) in precision medicine. One of the hurdles is unawareness of PGx by clinicians due to insufficient pharmacogenomic information on drug labels. Therefore, it might be important to implement PGx that reflects pharmacogenomic information on drug labels, standard of prescription for clinicians. This study aimed to evaluate the level at which PGx was being used in clinical practice by comparing the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines and drug labels of the US Food and Drug Administration (FDA) and the Korea Ministry of Food and Drug Safety (MFDS). Two PGx guidelines and drugs labels were scrutinized, and the concordance of the pharmacogenomic information between guidelines and drug labels was confirmed. The concordance of the label between FDA and MFDS was analyzed. In FDA labels, the number of concordant drug with guidelines was 24, while 13 drugs were concordant with MFDS labels. The number of drugs categorized as contraindication, change dose, and biomarker testing required was 7, 12 and 12 for the FDA and 8, 5 and 4 for the MFDS, respectively. The pharmacogenomic information of 9 drugs approved by both FDA and MFDS was identical. In conclusion, pharmacogenomic information on clinical implementation guidelines was limited on both FDA and MFDS labels because of various reasons including the characteristics of the guidelines and the drug labels. Therefore, more effort from pharmaceutical companies, academia and regulatory affairs needs to be made to implement pharmacogenomic information on drug labels.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 4","pages":"189-198"},"PeriodicalIF":0.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/15/28/tcp-28-189.PMC7781807.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38805068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting human pharmacokinetics from preclinical data: volume of distribution.","authors":"Dong-Seok Yim, Suein Choi","doi":"10.12793/tcp.2020.28.e19","DOIUrl":"10.12793/tcp.2020.28.e19","url":null,"abstract":"<p><p>This tutorial introduces background and methods to predict the human volume of distribution (V_d) of drugs using <i>in vitro</i> and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: <i>V_d</i> = <i>V_p</i> + ∑ <i>_T</i> (<i>V_T</i> × <i>k_tp</i> ). In this equation, V_p (plasma volume) and V_T (tissue volume) are known physiological values, and k_tp (tissue plasma partition coefficient) is experimentally measured. Here, the k_tp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human V_d has been the efforts to find a better function giving a more accurate k_tp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human V_d. Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental V_d parameters (e.g., V_c, V_p, and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict V_d, there is no consensus on method choice. When the discrepancy between PBPK-predicted V_d and allometry-predicted V_d is huge, physiological plausibility of all input and output data (e.g., r²-value of the allometric curve) may be reviewed for careful decision making.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 4","pages":"169-174"},"PeriodicalIF":0.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a8/9d/tcp-28-169.PMC7781809.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38805065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioequivalence data analysis.","authors":"Gowooni Park,&nbsp;Hyungsub Kim,&nbsp;Kyun-Seop Bae","doi":"10.12793/tcp.2020.28.e20","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e20","url":null,"abstract":"<p><p>SAS^® is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS^® for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS^®. The main SAS^® procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are \"sasLM\" and \"nlme\" respectively. For fixed effects only or balanced data, the SAS^® PROC GLM and R \"sasLM\" provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS^® PROC MIXED and R \"nlme\" are better for providing estimates without bias. The SAS^® and R scripts are provided for convenience.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 4","pages":"175-180"},"PeriodicalIF":0.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/50/14/tcp-28-175.PMC7781810.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38805066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the pharmacokinetics and food effects of a novel formulation tamsulosin 0.4 mg capsule compared with a 0.2 mg capsule in healthy male volunteers.","authors":"Mu Seong Ban,&nbsp;Yu Kyong Kim,&nbsp;Byungwook Kim,&nbsp;Jina Jung,&nbsp;Yong-Il Kim,&nbsp;Jaeseong Oh,&nbsp;Kyung-Sang Yu","doi":"10.12793/tcp.2020.28.e17","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e17","url":null,"abstract":"<p><p>Tamsulosin, an alpha-1 adrenoreceptor antagonist, has been used as a primary option for medical treatment of benign prostate hyperplasia. An open-label, single-dose, randomized, three-treatment, three-period, three sequence crossover study was conducted to evaluate the pharmacokinetics (PKs) of 0.2 and 0.4 mg tamsulosin hydrochloride (HCl) in the fed versus the fasted state. Subjects were randomly assigned to three sequences and received one of the following treatments at each period: tamsulosin HCl 0.2 or 0.4 mg in the fed state with a high-fat meal, or tamsulosin HCl 0.4 mg in the fasted state. Blood samples for the PK analysis were collected at pre-dose and up to 48 h post-dose. The PK parameters were calculated by a non-compartmental method. The geometric mean ratio (GMR) and its 90% confidence intervals (CIs) of the plasma maximum concentration (C_max) and area under concentration curve from time zero to last measurable concentration (AUC_last) were calculated. Twenty-two subjects completed the study. The systemic exposure of tamsulosin 0.4 mg decreased approximately 9% in the fed state compared to the fasted state, and the time to reach peak concentration was slightly delayed in the fed state. The dose normalized GMR and its 90% CIs of C_max and AUC_last for 0.2 and 0.4 mg tamsulosin in the fed state were within 0.8 and 1.25 range. Systemic exposure of tamsulosin was decreased in the fed condition compared to the fasted condition. Linear PK profiles were observed between 0.2 and 0.4 mg tamsulosin in the fed state.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov Identifier: NCT02529800.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 4","pages":"181-188"},"PeriodicalIF":0.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/08/b6/tcp-28-181.PMC7781808.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38805067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic medical records-based comparison of glycemic control efficacy between sulfonylureas and dipeptidyl peptidase-4 inhibitors added on to metformin monotherapy in patients with type 2 diabetes.","authors":"Suhrin Lee,&nbsp;SeungHwan Lee,&nbsp;In-Jin Jang,&nbsp;Kyung-Sang Yu,&nbsp;Su-Jin Rhee","doi":"10.12793/tcp.2020.28.e21","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e21","url":null,"abstract":"<p><p>Sulfonylurea (SU) and dipeptidyl peptidase-4 (DPP-4) inhibitors are most common secondary agents that are added to metformin monotherapy. Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice and real-world data could help appropriate therapeutic information. Therefore, this study aims to compare the glycemic effectiveness of SU and DPP-4 inhibitors, which are added to metformin monotherapy in real clinical practice using electronic medical record (EMR) data. EMR data of type 2 diabetes patients treated at Seoul National University Hospital from December 2002 to December 2012 were retrieved and analyzed. The patients were divided into three groups: patients who maintained metformin monotherapy (M), and patients who added SU (MS) or DPP-4 inhibitors (MD) to metformin monotherapy. The mean change in HbA1c level, the proportion of patients achieving the HbA1c target < 7.0%, proportion of patients with treatment failure, and probability of treatment failure occurrence and changes in prescription were evaluated to compare glycemic control efficacy between SU and DPP-4 inhibitors. The MS showed significantly greater reduction in the Hb1Ac level than MD. The proportion of patients achieving HbA1c < 7.0% is higher in MD, whereas the proportion of patients with treatment failure was greater in MS. The probability of the treatment failure and probability of changes in the prescription were lower in MD than MS with hazard ratio of 0.499 and 0.579, respectively. In conclusion, this real-world study suggested that DPP-4 inhibitors are expected to show more durable glycemic control efficacy than SU in long-term use.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 4","pages":"199-207"},"PeriodicalIF":0.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/14/73/tcp-28-199.PMC7781806.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38805069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to dynamical systems analysis in quantitative systems pharmacology: basic concepts and applications.","authors":"Dongwoo Chae","doi":"10.12793/tcp.2020.28.e12","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e12","url":null,"abstract":"Quantitative systems pharmacology (QSP) can be regarded as a hybrid of pharmacometrics and systems biology. Here, we introduce the basic concepts related to dynamical systems theory that are fundamental to the analysis of systems biology models. Determination of the fixed points and their local stabilities constitute the most important step. Illustration of a phase portrait further helps investigate multistability and bifurcation behavior. As a motivating example, we examine a cell circuit model that deals with tissue inflammation and fibrosis. We show how increasing the severity and duration of inflammatory stimuli divert the system trajectories towards pathological fibrosis. Simulations that involve different parameter values offer important insights into the potential bifurcations and the development of efficient therapeutic strategies. We expect that this tutorial serves as a good starting point for pharmacometricians striving to widen their scope to QSP and physiologically-oriented modeling.","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 3","pages":"109-125"},"PeriodicalIF":0.9,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/36/8c/tcp-28-109.PMC7533163.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38495211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioequivalence of the pharmacokinetics between tofacitinib aspartate and tofacitinib citrate in healthy subjects.","authors":"Wonsuk Shin,&nbsp;A-Young Yang,&nbsp;Hyeonji Yun,&nbsp;Doo-Yeoun Cho,&nbsp;Kyung Hee Park,&nbsp;Hyunju Shin,&nbsp;Anhye Kim","doi":"10.12793/tcp.2020.28.e13","DOIUrl":"https://doi.org/10.12793/tcp.2020.28.e13","url":null,"abstract":"<p><p>Tofacitinib is an oral disease-modifying anti-rheumatic drug to selectively inhibit Janus kinases. Tofacitinib is a representative small molecule inhibitor that is used to treat many diseases including rheumatoid arthritis and various autoimmune conditions. Unlike biological agents, tofacitinib has several advantages, including the ability to be administered orally and a short half-life. This study aimed to evaluate the bioequivalence of the pharmacokinetics (PK) between tofacitinib aspartate 7.13 mg (test formulation) and tofacitinib citrate 8.08 mg (reference formulation; Xeljanz®) in healthy subjects. A randomized, open-label, single-dose, 2-sequence, 2-period, 2-treatment crossover trial was conducted in 41 healthy volunteers. A total of 5 mg of tofacitinib as the test or the reference formulation was administered, and serial blood samples were collected up to 14 hours after dosing for PK analyses. The plasma concentration of tofacitinib was determined by ultra-performance liquid chromatography-tandem mass spectrometry. A non-compartmental analysis was used to estimate the PK parameters. A total of 35 subjects completed the study and the study drug was well-tolerated. The mean maximum concentration (C_max) and area under the concentration-time curve from time zero to the time of the last quantifiable concentration (AUC_last) for the test formulation were 52.67 ng/mL and 133.86 ng∙h/mL, respectively, and 50.61 ng/mL and 133.49 h∙ng/mL for the reference formulation, respectively. The geometric mean ratios (90% confidence intervals) of the C_max and AUC_last between the 2 formulations were 1.041 (0.944-1.148) and 1.003 (0.968-1.039), respectively. Tofacitinib aspartate exhibited bioequivalent PK profiles to those of the reference formulation.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov Identifier: NCT04278391.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":"28 3","pages":"160-167"},"PeriodicalIF":0.9,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/fa/tcp-28-160.PMC7533161.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38495215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}