Mikolaj Milewski, Mikhail Murashov, Yash Kapoor, Jingtao Zhang, Wei Zhu, Maria A Cueto, Nicole Buist
{"title":"Predicting Human Subcutaneous Bioavailability of Therapeutic Monoclonal Antibodies from Systemic Clearance and Volume of Distribution.","authors":"Mikolaj Milewski, Mikhail Murashov, Yash Kapoor, Jingtao Zhang, Wei Zhu, Maria A Cueto, Nicole Buist","doi":"10.1021/acs.molpharmaceut.4c00132","DOIUrl":null,"url":null,"abstract":"<p><p>Subcutaneous delivery of monoclonal antibody therapeutics is often preferred to intravenous delivery due to better patient compliance and overall lower cost to the healthcare system. However, the systemic absorption of biologics dosed subcutaneously is often incomplete. The aim of this work was to describe a human bioavailability prediction method for monoclonal antibodies delivered subcutaneously that utilizes intravenous pharmacokinetic parameters as input. A two-compartment pharmacokinetic model featuring a parallel-competitive absorption pathway and a presystemic metabolism pathway was employed. A training data set comprised 19 monoclonal antibodies (geometric mean bioavailability of 68%), with previously reported human pharmacokinetic parameters, while a validation set included data compiled from 5 commercial drug products (geometric mean bioavailability of 69%). A single fitted absorption rate constant, paired with compound-specific estimates of presystemic metabolism rate proportional to compound-specific systemic clearance parameters, resulted in calculations of human subcutaneous bioavailability closely mimicking clinical data in the training data set with a root-mean-square error of 5.5%. Application of the same approach to the validation data set resulted in predictions characterized by 12.6% root-mean-square error. Factors that may have impacted the prediction accuracy include a limited number of validation data set compounds and an uncertainty in the absorption rate, which were subsequently discussed. The predictive method described herein provides an initial estimate of the subcutaneous bioavailability based exclusively on pharmacokinetic parameters available from intravenous dosing.</p>","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1021/acs.molpharmaceut.4c00132","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/3 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Subcutaneous delivery of monoclonal antibody therapeutics is often preferred to intravenous delivery due to better patient compliance and overall lower cost to the healthcare system. However, the systemic absorption of biologics dosed subcutaneously is often incomplete. The aim of this work was to describe a human bioavailability prediction method for monoclonal antibodies delivered subcutaneously that utilizes intravenous pharmacokinetic parameters as input. A two-compartment pharmacokinetic model featuring a parallel-competitive absorption pathway and a presystemic metabolism pathway was employed. A training data set comprised 19 monoclonal antibodies (geometric mean bioavailability of 68%), with previously reported human pharmacokinetic parameters, while a validation set included data compiled from 5 commercial drug products (geometric mean bioavailability of 69%). A single fitted absorption rate constant, paired with compound-specific estimates of presystemic metabolism rate proportional to compound-specific systemic clearance parameters, resulted in calculations of human subcutaneous bioavailability closely mimicking clinical data in the training data set with a root-mean-square error of 5.5%. Application of the same approach to the validation data set resulted in predictions characterized by 12.6% root-mean-square error. Factors that may have impacted the prediction accuracy include a limited number of validation data set compounds and an uncertainty in the absorption rate, which were subsequently discussed. The predictive method described herein provides an initial estimate of the subcutaneous bioavailability based exclusively on pharmacokinetic parameters available from intravenous dosing.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.