Pub Date : 2023-11-14DOI: 10.22541/essoar.170000340.08902129/v1
Ryan Lagerquist, Imme Ebert-Uphoff, David D Turner, Jebb Q. Stewart
Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks. Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model. However, because ML-UQ is a new tool, its limitations are not yet fully appreciated. For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e. , outside the distribution of the training data. While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty. When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly. We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer. The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g. , fictitious cloud and ozone layers. We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution. We also show that when the training data are lightly perturbed – so that each basis vector of perturbation has a little variation in the training data – ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data. Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.
{"title":"Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML","authors":"Ryan Lagerquist, Imme Ebert-Uphoff, David D Turner, Jebb Q. Stewart","doi":"10.22541/essoar.170000340.08902129/v1","DOIUrl":"https://doi.org/10.22541/essoar.170000340.08902129/v1","url":null,"abstract":"Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks. Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model. However, because ML-UQ is a new tool, its limitations are not yet fully appreciated. For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e. , outside the distribution of the training data. While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty. When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly. We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer. The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g. , fictitious cloud and ozone layers. We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution. We also show that when the training data are lightly perturbed – so that each basis vector of perturbation has a little variation in the training data – ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data. Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"14 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169993986.65715321/v1
shenglan xiao, Dina Wang, Yadi Wu, Xi Huang, Qing Zhang, Dayan Wang, Yuelong Shu
Influenza constitutes a critical respiratory infection that imposes significant public health burdens. The precise influence of these pollutants on influenza activity remains unclear. This study aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide data on air pollution and the influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects amid the concentrations of six distinct air pollutants, namely PM2.5, PM10, O , CO, SO , and NO , and the incidence of ILI, Flu A, as well as Flu B. Our analysis indicated that there was generally no distinction in the effects of air pollutants on the incidence of ILI, Flu A, and Flu B, although variations existed in terms of the specific level of risk associated with each of these categories. Specifically, elevated levels of PM2.5, PM10, CO, SO , and NO were predominantly associated with an increased risk of influenza. In contrast, the effect of O concentration on influenza was bidirectional whereby it promoted influenza outbreaks at low and high levels.
{"title":"Impact of Ambient Air Pollutants on Influenza-like illness, Influenza A and Influenza B: A Nationwide Time-Series Study in China","authors":"shenglan xiao, Dina Wang, Yadi Wu, Xi Huang, Qing Zhang, Dayan Wang, Yuelong Shu","doi":"10.22541/au.169993986.65715321/v1","DOIUrl":"https://doi.org/10.22541/au.169993986.65715321/v1","url":null,"abstract":"Influenza constitutes a critical respiratory infection that imposes significant public health burdens. The precise influence of these pollutants on influenza activity remains unclear. This study aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide data on air pollution and the influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects amid the concentrations of six distinct air pollutants, namely PM2.5, PM10, O , CO, SO , and NO , and the incidence of ILI, Flu A, as well as Flu B. Our analysis indicated that there was generally no distinction in the effects of air pollutants on the incidence of ILI, Flu A, and Flu B, although variations existed in terms of the specific level of risk associated with each of these categories. Specifically, elevated levels of PM2.5, PM10, CO, SO , and NO were predominantly associated with an increased risk of influenza. In contrast, the effect of O concentration on influenza was bidirectional whereby it promoted influenza outbreaks at low and high levels.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"23 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169997327.74961038/v1
Chenwen Zhang, Yugang Shan
In recent years, China has become more closely connected with other countries, and Internet technology has developed rapidly. The new situation has put forward new requirements and challenges to the study of oral English. The disadvantages of traditional oral English teaching are gradually exposed. It is difficult for traditional teaching methods to adapt to the new situation of English learning. This paper analyzes the disadvantages of traditional oral English teaching, analyzes the significance of Internet-based oral English learning, and come up with the basic implementation strategies of independent learning, hoping to improve the efficiency of oral English learning.
{"title":"Knowledge graph modeling of college students' independent learning style and application of knowledge-based reasoning","authors":"Chenwen Zhang, Yugang Shan","doi":"10.22541/au.169997327.74961038/v1","DOIUrl":"https://doi.org/10.22541/au.169997327.74961038/v1","url":null,"abstract":"In recent years, China has become more closely connected with other countries, and Internet technology has developed rapidly. The new situation has put forward new requirements and challenges to the study of oral English. The disadvantages of traditional oral English teaching are gradually exposed. It is difficult for traditional teaching methods to adapt to the new situation of English learning. This paper analyzes the disadvantages of traditional oral English teaching, analyzes the significance of Internet-based oral English learning, and come up with the basic implementation strategies of independent learning, hoping to improve the efficiency of oral English learning.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"34 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Aim The U.S. Food and Drug Administration (FDA) has granted approval for the use of cyclin-dependent kinase 4 and 6 inhibitors (CDKIs) in the management of advanced and metastatic breast cancer. We evaluated the real-world data associated with safety in younger and older adults base on Adverse Event Reporting System (FAERS) database of the FDA. Methods We conducted disproportionality analysis to detect and compare CDKI-related adverse events (AEs) among the younger and older adults. Results The data used were from 3,851, 64,731, and 8,420 case reports on abemaciclib, palbociclib, and ribociclib, respectively Disproportionality analysis revealed 170, 397, and 626 AEs of abemaciclib, palbociclib, and ribociclib, respectively, in younger adults, and 113, 475, and 557 in older adults. The numbers of system organ classes for abemaciclib, palbociclib, and ribociclib were 27 each among younger adults, and 25, 27, and 27 among older adults. We found several expected AE signals same with drug instructions, such as diarrhea, neutropenia, and thromboembolic events. Furthermore, unexpected AE signals, such as campylobacter sepsis, enterococcal endocarditis, and atrioventricular block were identified. Conclusion Our results align with clinical observations, emphasizing possible AEs associated with CDKIs. It is essential to conduct future clinical research to confirm differences in CDKIs among younger and older individuals.
{"title":"Analysis of cyclin-dependent kinase 4 and 6 inhibitor adverse events in both younger and older adults using the FDA Adverse Event Reporting System database","authors":"Qiongtong Fang, Qiongyan Fang, Fuqiang Huang, Xinrong Wu, Huibin Zhao, Jiabi Liang, Yishen Chen, Cheng Li, Meirong Zhang, Wen-ji Luo","doi":"10.22541/au.169998501.11420287/v1","DOIUrl":"https://doi.org/10.22541/au.169998501.11420287/v1","url":null,"abstract":"Abstract Aim The U.S. Food and Drug Administration (FDA) has granted approval for the use of cyclin-dependent kinase 4 and 6 inhibitors (CDKIs) in the management of advanced and metastatic breast cancer. We evaluated the real-world data associated with safety in younger and older adults base on Adverse Event Reporting System (FAERS) database of the FDA. Methods We conducted disproportionality analysis to detect and compare CDKI-related adverse events (AEs) among the younger and older adults. Results The data used were from 3,851, 64,731, and 8,420 case reports on abemaciclib, palbociclib, and ribociclib, respectively Disproportionality analysis revealed 170, 397, and 626 AEs of abemaciclib, palbociclib, and ribociclib, respectively, in younger adults, and 113, 475, and 557 in older adults. The numbers of system organ classes for abemaciclib, palbociclib, and ribociclib were 27 each among younger adults, and 25, 27, and 27 among older adults. We found several expected AE signals same with drug instructions, such as diarrhea, neutropenia, and thromboembolic events. Furthermore, unexpected AE signals, such as campylobacter sepsis, enterococcal endocarditis, and atrioventricular block were identified. Conclusion Our results align with clinical observations, emphasizing possible AEs associated with CDKIs. It is essential to conduct future clinical research to confirm differences in CDKIs among younger and older individuals.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"8 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169994075.59042911/v1
Philip Ambery, Grzegorz Zajac, Joachim Almquist, Susanne Prothon, Carol Astbury, Mary Brown, Szilard Nemes, Joselyne Nsabimana, Karl Edman, Lisa Oberg, Matti Lepisto, Goran Endro, Suman Mitra, Graham Belfield, Christina Keen, Tim Heise
Aims Corticosteroids are the treatment of choice for many inflammatory diseases, but often lead to adverse effects, including hyperglycemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, a novel oral, non-steroidal, selective glucocorticoid receptor modulator, versus prednisolone in 46 patients with type 2 diabetes mellitus. Methods In this randomized, double-blind, 2-way cross-over study (NCT04556760), participants received either AZD9567 72 mg and prednisolone 40 mg daily (Cohort 1); AZD9567 40 mg and prednisolone 20 mg daily (Cohort 2); or placebo and prednisolone 5 mg daily (Cohort 3). Treatment duration was 3 days with a 3-week washout between treatment periods. Glycemic control was assessed after a standardized meal and with continuous glucose monitoring. Results A significant difference between AZD9567 and prednisolone in favour of AZD9567 was observed for the change from baseline to Day 4 glucose excursions post-meal in Cohort 1 (glucose AUC0-4h -4.54%; 95% CI: -8.88, -0.01; p=0.049), but not in Cohort 2 (-5.77%; 95% CI: -20.92, 12.29; p=0.435). In Cohort 1, significant differences between AZD9567 and prednisolone were also seen for the change from baseline to Day 4 in insulin and glucagon secretion post-meal (p<0.001 and p=0.005, respectively), and change from baseline to Day 4 in GLP-1 response (p=0.022). Significant differences between AZD9567 and prednisolone for 24-hour glucose control were observed for both Cohort 1 (-1.507 mmol/L; 95% CI: -2.0820, -0.9314; p<0.001), and Cohort 2 (-1.110 mmol/L; 95% CI -1.7257, -0.4941; p<0.001). Conclusions AZD9567 significantly reduced treatment-induced hyperglycemia compared with prednisolone.
{"title":"The effect of AZD9567 versus prednisolone on glycemic control in patients with Type 2 diabetes mellitus: Results from a Phase 2a clinical trial","authors":"Philip Ambery, Grzegorz Zajac, Joachim Almquist, Susanne Prothon, Carol Astbury, Mary Brown, Szilard Nemes, Joselyne Nsabimana, Karl Edman, Lisa Oberg, Matti Lepisto, Goran Endro, Suman Mitra, Graham Belfield, Christina Keen, Tim Heise","doi":"10.22541/au.169994075.59042911/v1","DOIUrl":"https://doi.org/10.22541/au.169994075.59042911/v1","url":null,"abstract":"Aims Corticosteroids are the treatment of choice for many inflammatory diseases, but often lead to adverse effects, including hyperglycemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, a novel oral, non-steroidal, selective glucocorticoid receptor modulator, versus prednisolone in 46 patients with type 2 diabetes mellitus. Methods In this randomized, double-blind, 2-way cross-over study (NCT04556760), participants received either AZD9567 72 mg and prednisolone 40 mg daily (Cohort 1); AZD9567 40 mg and prednisolone 20 mg daily (Cohort 2); or placebo and prednisolone 5 mg daily (Cohort 3). Treatment duration was 3 days with a 3-week washout between treatment periods. Glycemic control was assessed after a standardized meal and with continuous glucose monitoring. Results A significant difference between AZD9567 and prednisolone in favour of AZD9567 was observed for the change from baseline to Day 4 glucose excursions post-meal in Cohort 1 (glucose AUC0-4h -4.54%; 95% CI: -8.88, -0.01; p=0.049), but not in Cohort 2 (-5.77%; 95% CI: -20.92, 12.29; p=0.435). In Cohort 1, significant differences between AZD9567 and prednisolone were also seen for the change from baseline to Day 4 in insulin and glucagon secretion post-meal (p<0.001 and p=0.005, respectively), and change from baseline to Day 4 in GLP-1 response (p=0.022). Significant differences between AZD9567 and prednisolone for 24-hour glucose control were observed for both Cohort 1 (-1.507 mmol/L; 95% CI: -2.0820, -0.9314; p<0.001), and Cohort 2 (-1.110 mmol/L; 95% CI -1.7257, -0.4941; p<0.001). Conclusions AZD9567 significantly reduced treatment-induced hyperglycemia compared with prednisolone.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"23 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169995548.84464946/v1
Shivaraj S, Manju M, Rissi Kumar P, Shivesh PR
Agriculture is a cornerstone of India’s economy, supporting a vast majority of its population. However, farmers grapple with selecting the right crop due to diverse soil characteristics, environmental factors, plant diseases, and the need for consistent crop monitoring. This paper presents a smart system assisting farmers in specific crop selection, integrating plant diseases and consistent monitoring as vital features. By considering comprehensive data on environmental parameters(moisture), soil characteristics (including N, P, K levels), plant diseases, and consistent crop monitoring, the system recommends the most suitable crop for each season. Moreover, it offers fertilizer suggestions aligned with optimal nutrient requirements, particularly focusing on N, P, and K levels, aiming to enhance farming efficiency and sustainability.
{"title":"Smart Fields: Enhancing Agriculture with Machine Learning","authors":"Shivaraj S, Manju M, Rissi Kumar P, Shivesh PR","doi":"10.22541/au.169995548.84464946/v1","DOIUrl":"https://doi.org/10.22541/au.169995548.84464946/v1","url":null,"abstract":"Agriculture is a cornerstone of India’s economy, supporting a vast majority of its population. However, farmers grapple with selecting the right crop due to diverse soil characteristics, environmental factors, plant diseases, and the need for consistent crop monitoring. This paper presents a smart system assisting farmers in specific crop selection, integrating plant diseases and consistent monitoring as vital features. By considering comprehensive data on environmental parameters(moisture), soil characteristics (including N, P, K levels), plant diseases, and consistent crop monitoring, the system recommends the most suitable crop for each season. Moreover, it offers fertilizer suggestions aligned with optimal nutrient requirements, particularly focusing on N, P, and K levels, aiming to enhance farming efficiency and sustainability.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"15 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169994410.04755017/v1
Yalan Peng, Zuming Lin, Lili Zhu, Shiqing Han, Sha-Hua Huang, Ran Hong
Valbenazine (Ingrezza), a potent and highly selective inhibitor of vesicular monoamine transporter type 2 (VMAT2) through the active metabolite hydrotetrabenazine (HTBZ), has been approved for the treatment of tardive dyskinesia and, very recently, for chorea, which is associated with Huntington’s disease. Despite numerous synthetic efforts dedicated to the synthesis of HTBZ, the industrial preparation of valbenazine uses dihydroisoquinoline as a starting material and the chiral resolution of racemic HTBZ derived from ketone reduction. Herein, we present a practical synthesis of HTBZ and valbenazine featuring a highly stereoselective 1,3-dipolar cycloaddition and enzymatic kinetic resolution. The cascade process includes cycloaddition, N˗O bond cleavage, and lactamization, which proved to be operationally facile. The allure of the enzymatic resolution developed in this work offers a rapid access toward affording tetrahydroi-soquinoline (THIQ)-fused piperidine to access key frameworks in the production of medically significant compounds, such as yohimbine and reserpine.
{"title":"Practical Synthesis of Valbenazine via 1,3-Dipolar Cycloaddition","authors":"Yalan Peng, Zuming Lin, Lili Zhu, Shiqing Han, Sha-Hua Huang, Ran Hong","doi":"10.22541/au.169994410.04755017/v1","DOIUrl":"https://doi.org/10.22541/au.169994410.04755017/v1","url":null,"abstract":"Valbenazine (Ingrezza), a potent and highly selective inhibitor of vesicular monoamine transporter type 2 (VMAT2) through the active metabolite hydrotetrabenazine (HTBZ), has been approved for the treatment of tardive dyskinesia and, very recently, for chorea, which is associated with Huntington’s disease. Despite numerous synthetic efforts dedicated to the synthesis of HTBZ, the industrial preparation of valbenazine uses dihydroisoquinoline as a starting material and the chiral resolution of racemic HTBZ derived from ketone reduction. Herein, we present a practical synthesis of HTBZ and valbenazine featuring a highly stereoselective 1,3-dipolar cycloaddition and enzymatic kinetic resolution. The cascade process includes cycloaddition, N˗O bond cleavage, and lactamization, which proved to be operationally facile. The allure of the enzymatic resolution developed in this work offers a rapid access toward affording tetrahydroi-soquinoline (THIQ)-fused piperidine to access key frameworks in the production of medically significant compounds, such as yohimbine and reserpine.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"17 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.22541/au.169996506.63522482/v1
Dhrita Khatri, Ben Felmingham, Claire Moore, Smaro Lazarakis, Tayla Stenta, Lane Collier, David Elliott, David Metz, Rachel Conyers
Tacrolimus, a calcineurin inhibitor, is an effective immunosuppressant for solid organ transplants (SOT). However, its narrow therapeutic index and high variability in pharmacokinetics can lead to inefficacy, toxicities, and suboptimal outcomes. Genotyping for CYP3A5 gene prior to SOT can identify individuals at risk of high or low tacrolimus levels and guide first-dose dosing. Genotype-guided Bayesian dosing uses population pharmacokinetic data and individual patient characteristics to accurately predict the tacrolimus dose required to achieve a target concentration. This can help achieve target tacrolimus concentrations sooner and maintain them within range, reducing risk of organ rejection or tacrolimus toxicity. This review aims to assess the benefits of genotype-guided Bayesian dosing for tacrolimus and its ability to accurately predict tacrolimus dosing, leading to increased maintenance of therapeutic drug exposure in these individuals. This systematic review identified three studies that incorporated genotyping and Bayesian informed methods to predict tacrolimus dosing in the paediatric population post SOT. The studies included 369 kidney, 231 heart, 246 liver and 16 lung transplant individuals. The review found that combination of clinical, demographic, and genetic data has a significant influence on tacrolimus clearance. Combining these parameters allowed the prediction of first dose tacrolimus post SOT and ongoing therapeutic tacrolimus dosing to optimally maintain target tacrolimus levels. In conclusion, personalised tacrolimus dosing models in paediatric SOT can be developed using clinical, demographic, and genetic data to predict first dose and ongoing adjustments to meet therapeutic tacrolimus targets and reduce the risk of under- and over- exposure.
{"title":"Genotype Informed Bayesian Dosing of Tacrolimus in Paediatric Solid Organ Transplant Individuals","authors":"Dhrita Khatri, Ben Felmingham, Claire Moore, Smaro Lazarakis, Tayla Stenta, Lane Collier, David Elliott, David Metz, Rachel Conyers","doi":"10.22541/au.169996506.63522482/v1","DOIUrl":"https://doi.org/10.22541/au.169996506.63522482/v1","url":null,"abstract":"Tacrolimus, a calcineurin inhibitor, is an effective immunosuppressant for solid organ transplants (SOT). However, its narrow therapeutic index and high variability in pharmacokinetics can lead to inefficacy, toxicities, and suboptimal outcomes. Genotyping for CYP3A5 gene prior to SOT can identify individuals at risk of high or low tacrolimus levels and guide first-dose dosing. Genotype-guided Bayesian dosing uses population pharmacokinetic data and individual patient characteristics to accurately predict the tacrolimus dose required to achieve a target concentration. This can help achieve target tacrolimus concentrations sooner and maintain them within range, reducing risk of organ rejection or tacrolimus toxicity. This review aims to assess the benefits of genotype-guided Bayesian dosing for tacrolimus and its ability to accurately predict tacrolimus dosing, leading to increased maintenance of therapeutic drug exposure in these individuals. This systematic review identified three studies that incorporated genotyping and Bayesian informed methods to predict tacrolimus dosing in the paediatric population post SOT. The studies included 369 kidney, 231 heart, 246 liver and 16 lung transplant individuals. The review found that combination of clinical, demographic, and genetic data has a significant influence on tacrolimus clearance. Combining these parameters allowed the prediction of first dose tacrolimus post SOT and ongoing therapeutic tacrolimus dosing to optimally maintain target tacrolimus levels. In conclusion, personalised tacrolimus dosing models in paediatric SOT can be developed using clinical, demographic, and genetic data to predict first dose and ongoing adjustments to meet therapeutic tacrolimus targets and reduce the risk of under- and over- exposure.","PeriodicalId":487619,"journal":{"name":"Authorea (Authorea)","volume":"51 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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