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      Developing a mechanistic understanding of the nonlinear pharmacokinetics of letermovir and prospective drug interaction with everolimus using physiological‐based pharmacokinetic modeling

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          Abstract

          Letermovir is approved for use in cytomegalovirus‐seropositive hematopoietic stem cell transplant recipients and is investigated in other transplant settings. Nonlinear pharmacokinetics (PKs) were observed in clinical studies after intravenous and oral dosing across a wide dose range, including the efficacious doses of 240 and 480 mg. A physiologically‐based PK (PBPK) model for letermovir was built to develop a plausible explanation for the nonlinear PKs observed in clinical studies. In vitro studies suggested that letermovir elimination and distribution are mediated by saturable uridine glucuronosyltransferases (UGT)‐metabolism and by saturable hepatic uptake via organic anion‐transporting polypeptides (OATP) 1B. A sensitivity analysis of parameters describing the metabolism and distribution mechanisms indicated that the greater than dose‐proportional increase in letermovir exposure is best described by a saturable OATP1B‐mediated transport. This PBPK model was further used to evaluate the drug interaction potential between letermovir and everolimus, an immunosuppressant that may be co‐administered with letermovir depending on regions. Because letermovir inhibits cytochrome P450 (CYP) 3A and everolimus is a known CYP3A substrate, an interaction when concomitantly administered is anticipated. The drug–drug interaction simulation confirmed that letermovir will likely increase everolimus are under the curve by 2.5‐fold, consistent with the moderate increase in exposure observed with midazolam in the clinic. The output highlights the importance of drug monitoring, which is common clinical practice for everolimus to maintain safe and efficacious drug concentrations in the targeted patient population when concomitantly administered with letermovir.

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          Mechanistic approaches to volume of distribution predictions: understanding the processes.

          To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes. Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle. The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively. Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.
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            Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver.

            Intracellular concentrations of drugs and metabolites are often important determinants of efficacy, toxicity, and drug interactions. Hepatic drug distribution can be affected by many factors, including physicochemical properties, uptake/efflux transporters, protein binding, organelle sequestration, and metabolism. This white paper highlights determinants of hepatocyte drug/metabolite concentrations and provides an update on model systems, methods, and modeling/simulation approaches used to quantitatively assess hepatocellular concentrations of molecules. The critical scientific gaps and future research directions in this field are discussed.
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              A Mechanistic Framework for In Vitro–In Vivo Extrapolation of Liver Membrane Transporters: Prediction of Drug–Drug Interaction Between Rosuvastatin and Cyclosporine

              Background and Objectives The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator®. Methods In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model. Results The simulated area under the plasma concentration–time curve (AUC), maximum concentration (C max) and the time to reach C max (t max) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers. Conclusion The results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design. Electronic supplementary material The online version of this article (doi:10.1007/s40262-013-0097-y) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                karsten_menzel@merck.com
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                21 April 2023
                June 2023
                : 16
                : 6 ( doiID: 10.1111/cts.v16.6 )
                : 1039-1048
                Affiliations
                [ 1 ] Merck & Co., Inc. Kenilworth New Jersey USA
                [ 2 ] Kymera Therapeutics Watertown Massachusetts USA
                [ 3 ] Simulations Plus Lancaster California USA
                [ 4 ] FDA Silver Spring Maryland USA
                [ 5 ] MSD K.K. Tokyo Japan
                Author notes
                [*] [* ] Correspondence

                Karsten Menzel, ADME & DT, Merck & Co., Inc., West Point, PA 19486, USA.

                Email: karsten_menzel@ 123456merck.com

                Article
                CTS13509 CTS-2022-0342
                10.1111/cts.13509
                10264951
                37085998
                06f438db-9570-475e-8ae8-cd45f3e37f02
                © 2023 Merck Sharp and Dohme LLC. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 11 February 2023
                : 18 November 2022
                : 17 February 2023
                Page count
                Figures: 3, Tables: 2, Pages: 10, Words: 5825
                Categories
                Article
                Research
                Articles
                Custom metadata
                2.0
                June 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:14.06.2023

                Medicine
                Medicine

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