Predicting Cavg of CFTR Modulators Using Sparse Sampling: Evaluating Single and Multiple Time Points with Machine Learning Approaches

Predicting Cavg of CFTR Modulators Using Sparse Sampling: Evaluating Single and Multiple Time Points with Machine Learning Approaches | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 June 2025 V1 Latest version Share on Predicting Cavg of CFTR Modulators Using Sparse Sampling: Evaluating Single and Multiple Time Points with Machine Learning Approaches Authors : Saly Abouelenein 0000-0003-1740-6392 , Ashritha Chalamalla , Ed Acosta 0000-0002-6466-7723 , Kathleen Ramos , and Jennifer Guimbellot [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174973419.93224560/v1 218 views 127 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Cystic fibrosis (CF) is caused by genetic variants in the CFTR gene, which causes a defective chloride channel and impaired chloride and water transport. CFTR modulators, including elexacaftor, tezacaftor, and ivacaftor (ETI), improved clinical outcomes by correcting the underlying defect in the CFTR protein. Pharmacokinetic parameters such the average concentration (Cavg) is useful for optimizing ETI therapy, but clinical research that requires intensive blood sampling is a significant barrier. This study evaluated the feasibility of using one or more timepoints to predict Cavg. Methods: Using data from an intensive ETI concentration dataset, we tested different approaches to predict Cavg from one or more samples. The parameters R2, bias, and precision were used to assess predictive performance. Results: For single timepoints, the 6-hour sample yielded the highest predictive accuracy for elexacaftor (R² = 0.91) and ivacaftor (R² = 0.92), while the 8-hour sample was optimal for tezacaftor (R² = 0.87). For two timepoints, the 2 and 6 hour combination yielded the best prediction for elexacaftor and ivacaftor (R² = 0.97), while the 0 and 8 hour combination was optimal for tezacaftor (R² = 0.93). The 0+6/8 hour combination provided reasonable balance across all measures for prediction. Multiple other single and double timepoints provided similar results, with modest reductions in precision and bias. Conclusion: Sparse sampling strategies can accurately estimate Cavg, reducing the need for more frequent sampling in clinical research. The findings support the use of fewer blood samples for future studies investigating clinical utility of modulator quantitation. Supplementary Material File (prediction_article_2025-06_finsal.docx) Download 194.83 KB Information & Authors Information Version history V1 Version 1 12 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Saly Abouelenein 0000-0003-1740-6392 University of Arkansas for Medical Sciences View all articles by this author Ashritha Chalamalla Arkansas Children's Research Institute View all articles by this author Ed Acosta 0000-0002-6466-7723 University of Alabama at Birmingham Health System View all articles by this author Kathleen Ramos University of Washington School of Medicine View all articles by this author Jennifer Guimbellot [email protected] University of Arkansas for Medical Sciences View all articles by this author Metrics & Citations Metrics Article Usage 218 views 127 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Saly Abouelenein, Ashritha Chalamalla, Ed Acosta, et al. Predicting Cavg of CFTR Modulators Using Sparse Sampling: Evaluating Single and Multiple Time Points with Machine Learning Approaches. Authorea . 12 June 2025. DOI: https://doi.org/10.22541/au.174973419.93224560/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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