1
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Milgram BC, Borrelli DR, Brooijmans N, Henderson JA, Hilbert BJ, Huff MR, Ito T, Jackson EL, Jonsson P, Ladd B, O’Hearn EL, Pagliarini RA, Roberts SA, Ronseaux S, Stuart DD, Wang W, Guzman-Perez A. Discovery of STX-721, a Covalent, Potent, and Highly Mutant-Selective EGFR/HER2 Exon20 Insertion Inhibitor for the Treatment of Non-Small Cell Lung Cancer. J Med Chem 2025; 68:2403-2421. [PMID: 39824516 PMCID: PMC11831596 DOI: 10.1021/acs.jmedchem.4c02377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 12/11/2024] [Accepted: 12/23/2024] [Indexed: 01/20/2025]
Abstract
After L858R and ex19del epidermal growth factor receptor (EGFR) mutations, ex20ins mutations are the third most common class of driver-mutations in non-small cell lung cancer (NSCLC). Unfortunately, first-, second-, and third-generation EGFR tyrosine kinase inhibitors (TKIs) are generally ineffective for ex20ins patients due to insufficient mutant activity and selectivity over wild-type EGFR, leading to dose-limiting toxicities. While significant advances in recent years have been made toward identifying potent EGFR ex20ins mutant inhibitors, mutant vs wild-type EGFR selectivity remains a significant challenge. STX-721 (53) is a potent, irreversible inhibitor of the majority of EGFR/HER2 ex20ins mutants and demonstrates excellent mutant vs wild-type selectivity both in vitro and in vivo. STX-721 is currently in phase 1/2 clinical trials for EGFR/HER2 ex20ins-driven NSCLC.
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Affiliation(s)
- Benjamin C. Milgram
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Deanna R. Borrelli
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Natasja Brooijmans
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Jack A. Henderson
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Brendan J. Hilbert
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Michael R. Huff
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Takahiro Ito
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Erica L. Jackson
- Scorpion
Therapeutics, South San Francisco, California 94080, United States
| | - Philip Jonsson
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Brendon Ladd
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Erin L. O’Hearn
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Raymond A. Pagliarini
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Simon A. Roberts
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Sébastien Ronseaux
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Darrin D. Stuart
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Weixue Wang
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
| | - Angel Guzman-Perez
- Scorpion
Therapeutics, 1 Winthrop
Square, Boston, Massachusetts 02110, United States
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2
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Ning M, Fang M, Shah K, Dixit V, Pade D, Musther H, Neuhoff S. A cross-species assessment of in silico prediction methods of steady-state volume of distribution using Simcyp simulators. J Pharm Sci 2025; 114:1410-1422. [PMID: 39732199 DOI: 10.1016/j.xphs.2024.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 12/18/2024] [Accepted: 12/18/2024] [Indexed: 12/30/2024]
Abstract
Predicting steady-state volume of distribution (Vss) is a key component of pharmacokinetic predictions and often guided using preclinical data. However, when bottom-up prediction from physiologically-based pharmacokinetic (PBPK) models and observed Vss misalign in preclinical species, or predicted Vss from different models varies significantly, no consensus exists for selecting models or preclinical species to improve the prediction. Through systematic analysis of Vss prediction across rat, dog, monkey, and human, using common methods, a practical strategy for predicting human Vss, with or without integration of preclinical PK information is warranted. In this analysis, we curated a dataset of 57 diverse compounds with measured physicochemical and protein binding data, together with observed Vss in these species. Using a bottom-up approach, prediction performance was consistent across species for each method. Although no method consistently outperformed others for all compound types and across species, M2 (Rodgers-Rowland method) performed marginally better for acids. Comparable compound-specific global tissue Kp scalars were needed to match observed Vss for both, human and preclinical species. Consequently, application of geometric mean values of preclinical Kp scalar to human Vss prediction improved accuracy. We propose a decision tree for human Vss prediction using PBPK methods with or without integrating preclinical PK information.
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Affiliation(s)
- Miaoran Ning
- Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Ma Fang
- Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Kushal Shah
- Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals (Europe), 86-88 Jubilee Avenue, Milton Park, Abingdon, Oxfordshire OX14 4RW, United Kingdom; Quantitative Clinical Pharmacology, Takeda Pharmaceuticals International Inc., 35 Landsdowne st, Cambridge, MA 02139, USA
| | - Vaishali Dixit
- Non-clinical development, Mersana Therapeutics, 840 Memorial Drive, Cambridge MA 02139, USA
| | - Devendra Pade
- Certara UK Ltd., Certara Predictive Technologies Division, 1 Concourse Way, Level 2-Acero, Sheffield S1 2BJ, United Kingdom; Pharmacokinetics and Drug Metabolism, Amgen Inc, 360 Binney St, Cambridge, MA 02141, USA
| | - Helen Musther
- Certara UK Ltd., Certara Predictive Technologies Division, 1 Concourse Way, Level 2-Acero, Sheffield S1 2BJ, United Kingdom
| | - Sibylle Neuhoff
- Certara UK Ltd., Certara Predictive Technologies Division, 1 Concourse Way, Level 2-Acero, Sheffield S1 2BJ, United Kingdom.
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3
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Price E, Saulnier V, Kalvass JC, Doktor S, Weinheimer M, Hassan M, Scholz S, Nijsen M, Jenkins G. AURA: Accelerating drug discovery with accuracy, utility, and rank-order assessment for data-driven decision making. J Pharm Sci 2025; 114:1186-1195. [PMID: 39706566 DOI: 10.1016/j.xphs.2024.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 12/10/2024] [Accepted: 12/10/2024] [Indexed: 12/23/2024]
Abstract
Biopharmaceutical companies generate a wealth of data, ranging from in silico physicochemical properties and machine learning models to both low and high-throughput in vitro assays and in vivo studies. To effectively harnesses this extensive data, we introduce a statistical methodology facilitated by Accuracy, Utility, and Rank Order Assessment (AURA), which combines basic statistical analyses with dynamic data visualizations to evaluate endpoint effectiveness in predicting intestinal absorption. We demonstrated that various physicochemical properties uniquely influence intestinal absorption on a project-specific basis, considering factors like intestinal efflux, passive permeability, and clearance. Projects within both the "Rule of 5" (Ro5) and beyond "Rule of 5" (bRo5) space present unique absorption challenges, emphasizing the need for tailored optimization strategies over one-size-fits-all approaches. This is corroborated by the improved accuracy of project-specific correlations over global models. The differences in correlations between and within project teams-due to their unique chemical spaces-highlight how complex and nuanced the prediction of intestinal absorption can be. Here, we implement a standardized methodology, AURA, that any organization can incorporate into their workflow to enhance early-stage drug optimization. By automating analytics, integrating diverse data types, and offering flexible visualizations, AURA enables cross-functional teams to make data-driven decisions, optimize workflows, and enhance research efficiency.
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Affiliation(s)
- Edward Price
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States.
| | - Virginia Saulnier
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - John Cory Kalvass
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Stella Doktor
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Manuel Weinheimer
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Majdi Hassan
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Spencer Scholz
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Marjoleen Nijsen
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Gary Jenkins
- Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, United States
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4
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Carroll CL, Johnson MG, Ding Y, Kang Z, Vijayan RSK, Bardenhagen JP, Fang C, Lapointe D, Li M, Liu CY, Lv X, Ma X, Pang J, Shepard HE, Suarez C, Yau AJ, Williams CC, Wu Q, Heald RA, Robinson HMR, Smith GCM, Cross JB, Do MKG, Jiang Y, Lively S, Yap TA, Giuliani V, Heffernan T, Jones P, Di Francesco ME. Discovery of ART0380, a Potent and Selective ATR Kinase Inhibitor Undergoing Phase 2 Clinical Studies for the Treatment of Advanced or Metastatic Solid Cancers. J Med Chem 2024; 67:21890-21904. [PMID: 39630604 DOI: 10.1021/acs.jmedchem.4c01595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2024]
Abstract
One of the hallmarks of cancer is high levels of DNA replication stress and defects in the DNA damage response (DDR) pathways, which are critical for maintaining genomic integrity. Ataxia telangiectasia and Rad3-related protein (ATR) is a key regulator of the DDR machinery and an attractive therapeutic target, with multiple ATR inhibitors holding significant promise in ongoing clinical studies. Herein, we describe the discovery and characterization of ART0380 (6), a potent and selective ATR inhibitor with a compelling in vitro and in vivo pharmacological profile currently undergoing Phase 2 clinical studies in patients with advanced or metastatic solid tumors as monotherapy and in combination with DNA-damaging agents (NCT04657068 and NCT05798611). ART0380 (6) has a favorable human PK profile suitable for both intermittent and continuous once-daily (QD) dosing, characterized by a dose-proportional increase in exposure and low variability.
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Affiliation(s)
- Christopher L Carroll
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Michael G Johnson
- ChemPartner Corporation, South San Francisco, California 94080, United States
| | | | - Zhijun Kang
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - R S K Vijayan
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Jennifer P Bardenhagen
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Cheng Fang
- ChemPartner Corporation, Shanghai 201203, China
| | - David Lapointe
- ChemPartner Corporation, South San Francisco, California 94080, United States
| | - Meng Li
- ChemPartner Corporation, Shanghai 201203, China
| | - Chiu-Yi Liu
- Translational Research to AdvanCe Therapeutics and Innovation in Oncology (TRACTION), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Xiaobing Lv
- ChemPartner Corporation, Shanghai 201203, China
| | - XiaoYan Ma
- Translational Research to AdvanCe Therapeutics and Innovation in Oncology (TRACTION), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Jihai Pang
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Hannah E Shepard
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Catalina Suarez
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Anne Ju Yau
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Christopher C Williams
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Qi Wu
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Robert A Heald
- Artios Pharma, The Glenn Berge Building, Babraham Research Campus, Cambridge CB223FH, U.K
| | - Helen M R Robinson
- Artios Pharma, The Glenn Berge Building, Babraham Research Campus, Cambridge CB223FH, U.K
| | - Graeme C M Smith
- Artios Pharma, The Glenn Berge Building, Babraham Research Campus, Cambridge CB223FH, U.K
| | - Jason B Cross
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Mary K Geck Do
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Yongying Jiang
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Sarah Lively
- ChemPartner Corporation, South San Francisco, California 94080, United States
| | - Timothy A Yap
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Virginia Giuliani
- Translational Research to AdvanCe Therapeutics and Innovation in Oncology (TRACTION), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Timothy Heffernan
- Translational Research to AdvanCe Therapeutics and Innovation in Oncology (TRACTION), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Philip Jones
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - M Emilia Di Francesco
- Institute for Applied Cancer Science (IACS), The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
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5
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Wu Y, Meibohm B, Zhang T, Hou X, Wang H, Sun X, Jiang M, Zhang B, Zhang W, Liu Y, Jin W, Wang F. Translational modelling to predict human pharmacokinetics and pharmacodynamics of a Bruton's tyrosine kinase-targeted protein degrader BGB-16673. Br J Pharmacol 2024; 181:4973-4987. [PMID: 39289908 DOI: 10.1111/bph.17332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 07/27/2024] [Accepted: 08/08/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND AND PURPOSE Bifunctional small molecule degraders, which link the target protein with E3 ubiquitin ligase, could lead to the efficient degradation of the target protein. BGB-16673 is a Bruton's tyrosine kinase (BTK) degrader. A translational PK/PD modelling approach was used to predict the human BTK degradation of BGB-16673 from preclinical in vitro and in vivo data. EXPERIMENTAL APPROACH A simplified mechanistic PK/PD model was used to establish the correlation between the in vitro and in vivo BTK degradation by BGB-16673 in a mouse model. Human and mouse species differences were compared using the parameters generated from in vitro human or mouse blood, and human or mouse serum spiked TMD-8 cells. Human PD was then predicted using the simplified mechanistic PK/PD model. KEY RESULTS BGB-16673 showed potent BTK degradation in mouse whole blood, human whole blood, and TMD-8 tumour cells in vitro. Furthermore, BGB-16673 showed BTK degradation in a murine TMD-8 xenograft model in vivo. The PK/PD model predicted human PD and the observed BTK degradation in clinical studies both showed robust BTK degradation in blood and tumour at clinical dose range. CONCLUSION AND IMPLICATIONS The presented simplified mechanistic model with reduced number of model parameters is practically easier to be applied to research projects compared with the full mechanistic model. It can be used as a tool to better understand the PK/PD behaviour for targeted protein degraders and increase the confidence when moving to the clinical stage.
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Affiliation(s)
- Yue Wu
- Department of DMPK-BA, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Taichang Zhang
- Department of DMPK-BA, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Xinfeng Hou
- Department of Translational Science, BeiGene (Beijing) Co., Ltd., Beijing, China
- Migrasome Therapeutics Co. Ltd., Beijing, China
| | - Haitao Wang
- Department of Translational Science, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Xiaona Sun
- Department of Discovery Biology, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Ming Jiang
- Department of Discovery Biology, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Bo Zhang
- Department of Molecular Science, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Wenjing Zhang
- Department of Translational Science, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Ye Liu
- Department of Molecular Science, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Wei Jin
- Department of Translational Science, BeiGene (Beijing) Co., Ltd., Beijing, China
| | - Fan Wang
- Department of DMPK-BA, BeiGene (Beijing) Co., Ltd., Beijing, China
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6
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Himstedt A, Rapp H, Stopfer P, Lotz R, Scheuerer S, Arnhold T, Sauer A, Borghardt JM. Beyond CL and V SS: A comprehensive approach to human pharmacokinetic predictions. Drug Discov Today 2024; 29:104238. [PMID: 39521329 DOI: 10.1016/j.drudis.2024.104238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 10/31/2024] [Accepted: 11/05/2024] [Indexed: 11/16/2024]
Abstract
This article presents a comprehensive examination of processes related to the prediction of human pharmacokinetics (PK), a crucial task of clinical drug candidate selection. By systematically incorporating in vitro absorption, distribution, metabolism and excretion (ADME) and in vivo PK data with expert judgement, the study achieves high-quality human PK predictions for 40 orally administered compounds from Boehringer Ingelheim's new chemical entity (NCE) portfolio. Overall, the article provides a detailed evaluation of and guidance for a structured process to predict full concentration-time profiles beyond single-parameter predictions, using state-of-the-art methodologies. Furthermore, it discusses future challenges and improvements, and aims to provide valuable insights for scientists working in drug metabolism and PK (DMPK) or PK/pharmacodynamics (PK/PD) modelling.
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Affiliation(s)
- Anneke Himstedt
- Global Research DMPK, Global Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Hermann Rapp
- Global Research DMPK, Global Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Peter Stopfer
- Clinical Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Ralf Lotz
- Nonclinical Pharmacokinetics, Global Nonclinical Safety and DMPK, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Stefan Scheuerer
- Nonclinical Pharmacokinetics, Global Nonclinical Safety and DMPK, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thomas Arnhold
- Clinical Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Achim Sauer
- Global Research DMPK, Global Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
| | - Jens Markus Borghardt
- Global Research DMPK, Global Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
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7
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Kwon JH, Han JY, Kim M, Kim SK, Lee DK, Kim MG. Prediction of human pharmacokinetic parameters incorporating SMILES information. Arch Pharm Res 2024; 47:914-923. [PMID: 39589671 DOI: 10.1007/s12272-024-01520-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 11/16/2024] [Indexed: 11/27/2024]
Abstract
This study aimed to develop a model incorporating natural language processing analysis for the simplified molecular-input line-entry system (SMILES) to predict clearance (CL) and volume of distribution at steady state (Vd,ss) in humans. The construction of CL and Vd,ss prediction models involved data from 435 to 439 compounds, respectively. In machine learning, features such as animal pharmacokinetic data, in vitro experimental data, molecular descriptors, and SMILES were utilized, with XGBoost employed as the algorithm. The ChemBERTa model was used to analyze substance SMILES, and the last hidden layer embedding of ChemBERTa was examined as a feature. The model was evaluated using geometric mean fold error (GMFE), r2, root mean squared error (RMSE), and accuracy within 2- and 3-fold error. The model demonstrated optimal performance for CL prediction when incorporating animal pharmacokinetic data, in vitro experimental data, and SMILES as features, yielding a GMFE of 1.768, an r2 of 0.528, an RMSE of 0.788, with accuracies within 2-fold and 3-fold error reaching 75.8% and 81.8%, respectively. The model's performance in Vd,ss prediction was optimized by leveraging animal pharmacokinetic data and in vitro experimental data as features, yielding a GMFE of 1.401, an r2 of 0.902, an RMSE of 0.413, with accuracies within 2-fold and 3-fold error reaching 93.8% and 100%, respectively. This study has developed a highly predictive model for CL and Vd,ss. Specifically, incorporating SMILES information into the model has predictive power for CL.
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Affiliation(s)
- Jae-Hee Kwon
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Ja-Young Han
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Minjung Kim
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Seong Kyung Kim
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Dong-Kyu Lee
- College of Pharmacy, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Myeong Gyu Kim
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea.
- College of Pharmacy, Ewha Womans University, Seoul, 03760, Republic of Korea.
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8
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Heimbach T, Musuamba Tshinanu F, Raines K, Borges L, Kijima S, Malamatari M, Moody R, Veerasingham S, Seo P, Turner D, Fang L, Stillhart C, Bransford P, Ren X, Patel N, Sperry D, Chen H, Rostami-Hodjegan A, Lukacova V, Sun D, Nguefack JF, Carducci T, Grimstein M, Pepin X, Jamei M, Stamatopoulos K, Li M, Sanghavi M, Tannergren C, Mandula H, Zhao Z, Ju TR, Wagner C, Arora S, Wang M, Rullo G, Mitra A, Kollipara S, Chirumamilla SK, Polli JE, Mackie C. PBBM Considerations for Base Models, Model Validation, and Application Steps: Workshop Summary Report. Mol Pharm 2024; 21:5353-5372. [PMID: 39348508 PMCID: PMC11539057 DOI: 10.1021/acs.molpharmaceut.4c00758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 10/02/2024]
Abstract
The proceedings from the 30th August 2023 (Day 2) of the workshop "Physiologically Based Biopharmaceutics Models (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives" are provided herein. Day 2 covered PBBM case studies from six regulatory authorities which provided considerations for model verification, validation, and application based on the context of use (COU) of the model. PBBM case studies to define critical material attribute (CMA) specification settings, such as active pharmaceutical ingredient (API) particle size distributions (PSDs) were shared. PBBM case studies to define critical quality attributes (CQAs) such as the dissolution specification setting or to define the bioequivalence safe space were also discussed. Examples of PBBM using the credibility assessment framework, COU and model risk assessment, as well as scientific learnings from PBBM case studies are provided. Breakout session discussions highlighted current trends and barriers to application of PBBMs including: (a) PBBM credibility assessment framework and level of validation, (b) use of disposition parameters in PBBM and points to consider when iv data are not available, (c) conducting virtual bioequivalence trials and dealing with variability, (d) model acceptance criteria, and (e) application of PBBMs for establishing safe space and failure edges.
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Affiliation(s)
- Tycho Heimbach
- Pharmaceutical
Sciences and Clinical Supply, Merck &
Co., Inc., Rahway, New Jersey 07065, United States
| | - Flora Musuamba Tshinanu
- Belgian
Federal Agency for Medicines and Health Products, Galileelaan 5/03, Brussels 1210, Belgium
| | - Kimberly Raines
- Office
of
Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research
(CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United
States
| | - Luiza Borges
- ANVISA, SIA Trecho 5 − Guará, Brasília, DF 71205-050, Brazil
| | - Shinichi Kijima
- Office of
New Drug V, Pharmaceuticals and Medical
Devices Agency (PMDA), Tokyo 100-0013, Japan
| | - Maria Malamatari
- Medicines
& Healthcare Products Regulatory Agency, 10 S Colonnade, London SW1W 9SZ, U.K.
| | - Rebecca Moody
- Office
of
Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research
(CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United
States
| | - Shereeni Veerasingham
- Pharmaceutical
Drugs Directorate (PDD), Health Canada, 1600 Scott St, Ottawa, Ontario K1A 0K9, Canada
| | - Paul Seo
- Office of
Clinical Pharmacology (OCP), Office of Translational Sciences (OTS),
Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United States
| | - David Turner
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
| | - Lanyan Fang
- Division
of Quantitative Methods and Modeling (DQMM), Office of Research and
Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation
and Research (CDER), Food and Drug Administration
(FDA), Silver Spring, Maryland 20903-1058, United States
| | - Cordula Stillhart
- Pharmaceutical
R&D, F. Hoffmann-La Roche Ltd., Basel CH-4070, Switzerland
| | - Philip Bransford
- Data and
Computational Sciences, Vertex Pharmaceuticals,
Inc., Boston, Massachusetts 02210, United States
| | - Xiaojun Ren
- PK Sciences/Translational
Medicine, BioMedical Research, Novartis, One Health Plaza, East Hanover, New Jersey 07936, United States
| | - Nikunjkumar Patel
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
| | - David Sperry
- Eli Lilly
and Company, Lilly Corporate
Center, Indianapolis, Indiana 46285, United States
| | - Hansong Chen
- Office
of
Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research
(CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United
States
| | - Amin Rostami-Hodjegan
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
- Centre
for Applied Pharmacokinetic Research, University
of Manchester, Stopford Building, Oxford Road, Manchester M139PT, U.K.
| | - Viera Lukacova
- Simulations
Plus Inc., 42505 10th Street West, Lancaster, California 93534, United States
| | - Duxin Sun
- The University
of Michigan, North Campus Research Complex
(NCRC), 1600 Huron Parkway, Ann Arbor, Michigan 48109, United States
| | - Jean-Flaubert Nguefack
- Head of
Biopharmacy Team, Montpellier, Synthetics Platform, Global CMC, Sanofi, Paris 75008, France
| | - Tessa Carducci
- Analytical
Commercialization Technology, Merck &
Co., Inc., 126 E. Lincoln
Ave, Rahway, New Jersey 07065, United States
| | - Manuela Grimstein
- Office of
Clinical Pharmacology (OCP), Office of Translational Sciences (OTS),
Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United States
| | - Xavier Pepin
- Simulations
Plus Inc., 42505 10th Street West, Lancaster, California 93534, United States
| | - Masoud Jamei
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
| | | | - Min Li
- Office of
Clinical Pharmacology (OCP), Office of Translational Sciences (OTS),
Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United States
| | - Maitri Sanghavi
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
| | - Christer Tannergren
- Biopharmaceutics
Science, New Modalities & Parenteral Product Development, Pharmaceutical
Technology & Development, Operations, AstraZeneca, Gothenburg 43183, Sweden
| | - Haritha Mandula
- Office
of
Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research
(CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United
States
| | - Zhuojun Zhao
- Office
of
Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research
(CDER), Food and Drug Administration (FDA), Silver Spring, Maryland 20903-1058, United
States
| | - Tzuchi Rob Ju
- Analytical
R&D, AbbVie Inc., 1 North Waukegan Road, North
Chicago, Illinois 60064, United States
| | - Christian Wagner
- Global
Drug Product Development, Global CMC Development, the Healthcare Business of Merck KGaA, Darmstadt 64293, Germany
| | - Sumit Arora
- Janssen
Pharmaceutica NV, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Michael Wang
- Pharmaceutical
Sciences and Clinical Supply, Merck &
Co., Inc., Rahway, New Jersey 07065, United States
| | - Gregory Rullo
- Regulatory
CMC, AstraZeneca, 1 Medimmune Way, Gaithersburg, Maryland 20878, United States
| | - Amitava Mitra
- Clinical
Pharmacology, Kura Oncology Inc, Boston, Massachusetts 02210, United States
| | - Sivacharan Kollipara
- Biopharmaceutics
Group, Global Clinical Management, Integrated Product Development
Organization (IPDO), Dr. Reddy’s
Laboratories Ltd., Bachupally,
Medchal Malkajgiri District, Hyderabad, 500 090 Telangana, India
| | - Siri Kalyan Chirumamilla
- Certara
Predictive
Technologies, Level 2-Acero, Simcyp Ltd, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom
| | - James E. Polli
- School
of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States
| | - Claire Mackie
- Janssen
Pharmaceutica NV, Turnhoutseweg 30, 2340 Beerse, Belgium
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9
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Tajiri A, Matsumoto S, Maeda S, Soga T, Kagiyama K, Ikeda H, Fukasawa K, Miyata A, Kamimura H. Prediction of human serum concentration-time profiles of therapeutic monoclonal antibodies using common marmosets ( Callithrix jacchus): initial assessment with canakinumab, adalimumab, and bevacizumab. Xenobiotica 2024; 54:648-657. [PMID: 38977390 DOI: 10.1080/00498254.2024.2371921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/10/2024]
Abstract
Cynomolgus monkeys and human FcRn transgenic mice are generally used for pharmacokinetic predictions of therapeutic monoclonal antibodies (mAbs). In the present study, the application of the common marmoset, a small nonhuman primate, as a potential animal model for prediction was evaluated for the first time.Canakinumab, adalimumab, and bevacizumab, which exhibited linear pharmacokinetics in humans, were selected as the model compounds. Marmoset pharmacokinetic data were reportedly available only for canakinumab, and those for adalimumab and bevacizumab were acquired in-house.Four pharmacokinetic parameters for a two-compartment model (i.e. clearance and volume of distribution in the central and peripheral compartments) in marmosets were extrapolated to the values in humans with allometric scaling using the average exponents of the three mAbs. As a result, the observed human serum concentration-time curves of the three mAbs following intravenous administration and those of canakinumab and adalimumab following subcutaneous injections (with an assumed absorption rate constant and bioavailability) were reasonably predicted.Although further prediction studies using a sufficient number of other mAbs are necessary to evaluate the versatility of this model, the findings indicate that marmosets can be an alternative to preceding animals for human pharmacokinetic predictions of therapeutic mAbs.
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Affiliation(s)
- Ayaka Tajiri
- Drug Discovery Department, R&D Division, Meiji Seika Pharma Co., Ltd, Tokyo, Japan
| | - Shogo Matsumoto
- Drug Discovery Department, R&D Division, Meiji Seika Pharma Co., Ltd, Tokyo, Japan
| | - Satoshi Maeda
- Yaotsu Breeding Center, CLEA Japan, Inc., Gifu, Japan
| | - Takuma Soga
- Yaotsu Breeding Center, CLEA Japan, Inc., Gifu, Japan
| | | | - Hiroshi Ikeda
- Tokyo Animal and Diet Department, CLEA Japan, Inc., Tokyo, Japan
| | | | - Atsunori Miyata
- Drug Discovery Department, R&D Division, Meiji Seika Pharma Co., Ltd, Tokyo, Japan
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10
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Lautz LS, Dorne JLCM, Punt A. Application of partition coefficient methods to predict tissue:plasma affinities in common farm animals: Influence of ionisation state. Toxicol Lett 2024; 398:140-149. [PMID: 38925423 DOI: 10.1016/j.toxlet.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/17/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024]
Abstract
Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.
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Affiliation(s)
- L S Lautz
- Wageningen Food Safety Research, Akkermaalsbos 2, Wageningen, WB 6708, the Netherlands.
| | - J-L C M Dorne
- European Food Safety Authority, Via Carlo Magno 1A, Parma 43126, Italy
| | - A Punt
- Wageningen Food Safety Research, Akkermaalsbos 2, Wageningen, WB 6708, the Netherlands
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11
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Stokke C, Gnesin S, Tran-Gia J, Cicone F, Holm S, Cremonesi M, Blakkisrud J, Wendler T, Gillings N, Herrmann K, Mottaghy FM, Gear J. EANM guidance document: dosimetry for first-in-human studies and early phase clinical trials. Eur J Nucl Med Mol Imaging 2024; 51:1268-1286. [PMID: 38366197 PMCID: PMC10957710 DOI: 10.1007/s00259-024-06640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/04/2024] [Indexed: 02/18/2024]
Abstract
The numbers of diagnostic and therapeutic nuclear medicine agents under investigation are rapidly increasing. Both novel emitters and novel carrier molecules require careful selection of measurement procedures. This document provides guidance relevant to dosimetry for first-in human and early phase clinical trials of such novel agents. The guideline includes a short introduction to different emitters and carrier molecules, followed by recommendations on the methods for activity measurement, pharmacokinetic analyses, as well as absorbed dose calculations and uncertainty analyses. The optimal use of preclinical information and studies involving diagnostic analogues is discussed. Good practice reporting is emphasised, and relevant dosimetry parameters and method descriptions to be included are listed. Three examples of first-in-human dosimetry studies, both for diagnostic tracers and radionuclide therapies, are given.
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Affiliation(s)
- Caroline Stokke
- Department of Diagnostic Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
- Department of Physics, University of Oslo, Oslo, Norway.
| | - Silvano Gnesin
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Francesco Cicone
- Nuclear Medicine Unit, Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Søren Holm
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Marta Cremonesi
- Department of Medical Imaging and Radiation Sciences, European Institute of Oncology, IRCCS, Milan, Italy
| | - Johan Blakkisrud
- Department of Diagnostic Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Thomas Wendler
- Computer-Aided Medical Procedures and Augmented Reality, Technische Universität München, Munich, Germany
- Clinical Computational Medical Imaging Research, Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany
| | - Nic Gillings
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen, and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
- National Center for Tumor Diseases (NCT), NCT West, Heidelberg, Germany
| | - Felix M Mottaghy
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jonathan Gear
- Joint Department of Physics, Royal Marsden NHSFT & Institute of Cancer Research, Sutton, UK
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12
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Liang X, Koleske ML, Yang J, Lai Y. Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example. AAPS J 2024; 26:13. [PMID: 38182946 DOI: 10.1208/s12248-023-00882-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/07/2023] [Indexed: 01/07/2024] Open
Abstract
To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.
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Affiliation(s)
- Xiaomin Liang
- Drug Metabolism, Gilead Sciences Inc., 333 Lakeside Dr., Foster City, California, 94404, USA
| | - Megan L Koleske
- Drug Metabolism, Gilead Sciences Inc., 333 Lakeside Dr., Foster City, California, 94404, USA
| | - Jesse Yang
- Drug Metabolism, Gilead Sciences Inc., 333 Lakeside Dr., Foster City, California, 94404, USA
| | - Yurong Lai
- Drug Metabolism, Gilead Sciences Inc., 333 Lakeside Dr., Foster City, California, 94404, USA.
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13
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Reali F, Fochesato A, Kaddi C, Visintainer R, Watson S, Levi M, Dartois V, Azer K, Marchetti L. A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis. Front Pharmacol 2024; 14:1272091. [PMID: 38239195 PMCID: PMC10794428 DOI: 10.3389/fphar.2023.1272091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/04/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.
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Affiliation(s)
- Federico Reali
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Anna Fochesato
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Italy
| | - Chanchala Kaddi
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Roberto Visintainer
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Shayne Watson
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Micha Levi
- Gates Medical Research Institute, Cambridge, MD, United States
| | | | - Karim Azer
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Luca Marchetti
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
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14
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Freeman DB, Hopkins TD, Mikochik PJ, Vacca JP, Gao H, Naylor-Olsen A, Rudra S, Li H, Pop MS, Villagomez RA, Lee C, Li H, Zhou M, Saffran DC, Rioux N, Hood TR, Day MAL, McKeown MR, Lin CY, Bischofberger N, Trotter BW. Discovery of KB-0742, a Potent, Selective, Orally Bioavailable Small Molecule Inhibitor of CDK9 for MYC-Dependent Cancers. J Med Chem 2023; 66:15629-15647. [PMID: 37967851 PMCID: PMC10726352 DOI: 10.1021/acs.jmedchem.3c01233] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023]
Abstract
Transcriptional deregulation is a hallmark of many cancers and is exemplified by genomic amplifications of the MYC family of oncogenes, which occur in at least 20% of all solid tumors in adults. Targeting of transcriptional cofactors and the transcriptional cyclin-dependent kinase (CDK9) has emerged as a therapeutic strategy to interdict deregulated transcriptional activity including oncogenic MYC. Here, we report the structural optimization of a small molecule microarray hit, prioritizing maintenance of CDK9 selectivity while improving on-target potency and overall physicochemical and pharmacokinetic (PK) properties. This led to the discovery of the potent, selective, orally bioavailable CDK9 inhibitor 28 (KB-0742). Compound 28 exhibits in vivo antitumor activity in mouse xenograft models and a projected human PK profile anticipated to enable efficacious oral dosing. Notably, 28 is currently being investigated in a phase 1/2 dose escalation and expansion clinical trial in patients with relapsed or refractory solid tumors.
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Affiliation(s)
- David B. Freeman
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Tamara D. Hopkins
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Peter J. Mikochik
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Joseph P. Vacca
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Hua Gao
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Adel Naylor-Olsen
- Naylor
Olsen Consulting, LLC, 3369 Saddle Wood Court, Lansdale, Pennsylvania 19446, United States
| | - Sonali Rudra
- TCG
Lifesciences Private Limited, Block BN, Plot 7, Salt-lake Electronics Complex, Sector V, Kolkata 700091, West Bengal, India
| | - Huixu Li
- WuXi
AppTec (Tianjin) Co., Ltd., 168 NanHai Road, 10th Avenue, TEDA, Tianjin 300457, P. R. China
| | - Marius S. Pop
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Rosa A. Villagomez
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Christina Lee
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Heng Li
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Minyun Zhou
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Douglas C. Saffran
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Nathalie Rioux
- Certara
Strategic Consulting, 100 Overlook Center, Suite 101, Princeton, New Jersey 08540, United States
| | - Tressa R. Hood
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Melinda A. L. Day
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Michael R. McKeown
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Charles Y. Lin
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - Norbert Bischofberger
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
| | - B. Wesley Trotter
- Kronos
Bio, Inc., 301 Binney
Street, 2nd Floor East, Cambridge, Massachusetts 02142, United States
- Kronos
Bio, Inc., 1300 So. El
Camino Real Suite 400, San Mateo, California 94402, United States
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15
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Lignet F, Friese-Hamim M, Jaehrling F, El Bawab S, Rohdich F. Preclinical Pharmacokinetics and Translational Pharmacokinetic/Pharmacodynamic Modeling of M8891, a Potent and Reversible Inhibitor of Methionine Aminopeptidase 2. Pharm Res 2023; 40:3011-3023. [PMID: 37798538 PMCID: PMC10746753 DOI: 10.1007/s11095-023-03611-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION M8891 is a selective and reversible inhibitor of methionine aminopeptidase 2 (MetAP2). We describe translational research to define the target pharmacokinetics (PK) of M8891 and associated pharmacodynamic (PD) levels, which were used to support efficacious dose selection in humans. METHODS In vitro and in vivo PK characteristics were investigated in animal species, and data integrated using in vitro-in vivo correlation and allometric methods to predict the clearance, volume of distribution, and absorption parameters of M8891 in humans. In parallel, inhibition of MetAP2 activity by M8891 was studied in renal cancer xenografts in mice by measuring accumulation of Met-EF1α, a substrate of MetAP2. The corresponding PD effect was described by a turnover and effect compartment model. This model was used to simulate PD at the M8891 dose showing in vivo efficacy, i.e. significant tumor growth inhibition. Simulations of M8891 PK and associated PD in humans were conducted by integrating predicted human PK parameters into the preclinical PK/PD model. RESULTS The target minimum PD level associated with efficacy was determined to be 125 µg Met-EF1α per mg protein. Integrating predicted human PK parameters into the preclinical PK/PD model defined a minimal M8891 concentration at steady-state (Ctrough) of 1500 ng/mL (3.9 µM) in humans as being required to produce the corresponding minimum target Met-EF1a level (125 µg per mg protein). CONCLUSION The defined target PK and PD levels supported the design of the clinical Phase Ia dose escalation study of M8891 (NCT03138538) and selection of the recommended Phase II dose.
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Affiliation(s)
- Floriane Lignet
- Drug Discovery Technologies, the Healthcare Business of Merck KGaA, 250 Frankfurter Strasse, 64293, Darmstadt, Germany.
| | - Manja Friese-Hamim
- Research Unit Oncology, the Healthcare Business of Merck KGaA, Darmstadt, Germany
| | - Frank Jaehrling
- Research Unit Oncology, the Healthcare Business of Merck KGaA, Darmstadt, Germany
| | - Samer El Bawab
- Translational Medicine, the Healthcare Business of Merck KGaA, Darmstadt, Germany
- Translational Medicine, Servier, Suresnes, France
| | - Felix Rohdich
- Drug Discovery Technologies, the Healthcare Business of Merck KGaA, 250 Frankfurter Strasse, 64293, Darmstadt, Germany
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16
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Miyake T, Tsutsui H, Hirabayashi M, Tachibana T. Quantitative Prediction of OATP-Mediated Disposition and Biliary Clearance Using Human Liver Chimeric Mice. J Pharmacol Exp Ther 2023; 387:135-149. [PMID: 37142442 DOI: 10.1124/jpet.123.001595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/14/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Drug biliary clearance (CLbile) in vivo is among the most difficult pharmacokinetic parameters to predict accurately and quantitatively because biliary excretion is influenced by metabolic enzymes, transporters, and passive diffusion across hepatocyte membranes. The purpose of this study is to demonstrate the use of Hu-FRG mice [Fah-/-/Rag2-/-/Il2rg-/- (FRG) mice transplanted with human-derived hepatocytes] to quantitatively predict human organic anion transporting polypeptide (OATP)-mediated drug disposition and CLbile To predict OATP-mediated disposition, six OATP substrates (atorvastatin, fexofenadine, glibenclamide, pitavastatin, pravastatin, and rosuvastatin) were administered intravenously to Hu-FRG and Mu-FRG mice (FRG mice transplanted with mouse hepatocytes) with or without rifampicin as an OATP inhibitor. We calculated the hepatic intrinsic clearance (CLh,int) and the change of hepatic clearance (CLh) caused by rifampicin (CLh ratio). We compared the CLh,int of humans with that of Hu-FRG mice and the CLh ratio of humans with that of Hu-FRG and Mu-FRG mice. For predicting CLbile, 20 compounds (two cassette doses of 10 compounds) were administered intravenously to gallbladder-cannulated Hu-FRG and Mu-FRG mice. We evaluated the CLbile and investigated the correlation of human CLbile with that of Hu-FRG and Mu-FRG mice. We found good correlations between humans and Hu-FRG mice in CLh,int (100% within threefold) and CLh ratio (R2 = 0.94). Moreover, we observed a much better relationship between humans and Hu-FRG mice in CLbile (75% within threefold). Our results suggest that OATP-mediated disposition and CLbile can be predicted using Hu-FRG mice, making them a useful in vivo drug discovery tool for quantitatively predicting human liver disposition. SIGNIFICANCE STATEMENT: OATP-mediated disposition and biliary clearance of drugs are likely quantitatively predictable using Hu-FRG mice. The findings can enable the selection of better drug candidates and the development of more effective strategies for managing OATP-mediated DDIs in clinical studies.
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Affiliation(s)
- Taiji Miyake
- Pharmaceutical Science Department, Translational Research Division (T.M., T.T.) and Discovery Biologics Department, Research Division (H.T.), Chugai Pharmaceutical Co., Ltd., Shizuoka, Gotemba, Japan and Chugai Research Institute for Medical Science Inc., Shizuoka, Gotemba, Japan (M.H.)
| | - Haruka Tsutsui
- Pharmaceutical Science Department, Translational Research Division (T.M., T.T.) and Discovery Biologics Department, Research Division (H.T.), Chugai Pharmaceutical Co., Ltd., Shizuoka, Gotemba, Japan and Chugai Research Institute for Medical Science Inc., Shizuoka, Gotemba, Japan (M.H.)
| | - Manabu Hirabayashi
- Pharmaceutical Science Department, Translational Research Division (T.M., T.T.) and Discovery Biologics Department, Research Division (H.T.), Chugai Pharmaceutical Co., Ltd., Shizuoka, Gotemba, Japan and Chugai Research Institute for Medical Science Inc., Shizuoka, Gotemba, Japan (M.H.)
| | - Tatsuhiko Tachibana
- Pharmaceutical Science Department, Translational Research Division (T.M., T.T.) and Discovery Biologics Department, Research Division (H.T.), Chugai Pharmaceutical Co., Ltd., Shizuoka, Gotemba, Japan and Chugai Research Institute for Medical Science Inc., Shizuoka, Gotemba, Japan (M.H.)
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17
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De Sutter PJ, Rossignol P, Breëns L, Gasthuys E, Vermeulen A. Predicting Volume of Distribution in Neonates: Performance of Physiologically Based Pharmacokinetic Modelling. Pharmaceutics 2023; 15:2348. [PMID: 37765316 PMCID: PMC10536587 DOI: 10.3390/pharmaceutics15092348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/12/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023] Open
Abstract
The volume of distribution at steady state (Vss) in neonates is still often estimated through isometric scaling from adult values, disregarding developmental changes beyond body weight. This study aimed to compare the accuracy of two physiologically based pharmacokinetic (PBPK) Vss prediction methods in neonates (Poulin & Theil with Berezhkovskiy correction (P&T+) and Rodgers & Rowland (R&R)) with isometrical scaling. PBPK models were developed for 24 drugs using in-vitro and in-silico data. Simulations were done in Simcyp (V22) using predefined populations. Clinical data from 86 studies in neonates (including preterms) were used for comparison, and accuracy was assessed using (absolute) average fold errors ((A)AFEs). Isometric scaling resulted in underestimated Vss values in neonates (AFE: 0.61), and both PBPK methods reduced the magnitude of underprediction (AFE: 0.82-0.83). The P&T+ method demonstrated superior overall accuracy compared to isometric scaling (AAFE of 1.68 and 1.77, respectively), while the R&R method exhibited lower overall accuracy (AAFE: 2.03). Drug characteristics (LogP and ionization type) and inclusion of preterm neonates did not significantly impact the magnitude of error associated with isometric scaling or PBPK modeling. These results highlight both the limitations and the applicability of PBPK methods for the prediction of Vss in the absence of clinical data.
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18
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Tess D, Chang GC, Keefer C, Carlo A, Jones R, Di L. In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes. AAPS J 2023; 25:40. [PMID: 37052732 DOI: 10.1208/s12248-023-00800-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/03/2023] [Indexed: 04/14/2023] Open
Abstract
In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CLint) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SFlin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CLint. These SFs contain both SFlin and an exponential SF (SFβ) of fraction unbound in plasma (fu,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CLint, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CLint. This observation is different than in rats, where rat liver microsomal CLint correlates well with in vivo CLint for compounds demonstrating permeability-limited metabolism. The good CLint IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.
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Affiliation(s)
- David Tess
- Modeling and Simulation, Pfizer Worldwide Research and Development, Cambridge, MA, USA
| | - George C Chang
- Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, CT, USA
| | - Christopher Keefer
- Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, CT, USA
| | - Anthony Carlo
- Discovery Sciences, Pfizer Worldwide Research and Development, Groton, CT, USA
| | - Rhys Jones
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, La Jolla, CA, USA
| | - Li Di
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, 06340, USA.
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19
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Obrezanova O. Artificial intelligence for compound pharmacokinetics prediction. Curr Opin Struct Biol 2023; 79:102546. [PMID: 36804676 DOI: 10.1016/j.sbi.2023.102546] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/17/2023]
Abstract
Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.
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Affiliation(s)
- Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, CB4 0WJ, UK.
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20
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Alsmadi MM. The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling. Drug Metab Pers Ther 2023; 38:87-105. [PMID: 36205215 DOI: 10.1515/dmpt-2022-0130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/19/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVES Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations. METHODS The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated. RESULTS Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization. CONCLUSIONS Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.
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Affiliation(s)
- Mo'tasem M Alsmadi
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
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21
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Lignet F, Esdar C, Walter-Bausch G, Friese-Hamim M, Stinchi S, Drouin E, El Bawab S, Becker AD, Gimmi C, Sanderson MP, Rohdich F. Translational PK/PD Modeling of Tumor Growth Inhibition and Target Inhibition to Support Dose Range Selection of the LMP7 Inhibitor M3258 in Relapsed/Refractory Multiple Myeloma. J Pharmacol Exp Ther 2023; 384:163-172. [PMID: 36273822 DOI: 10.1124/jpet.122.001355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/08/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
M3258 is an orally bioavailable, potent, selective, reversible inhibitor of the large multifunctional peptidase 7 (LMP7, β5i, PSMB8) proteolytic subunit of the immunoproteasome, a component of the cellular protein degradation machinery, highly expressed in malignant hematopoietic cells including multiple myeloma. Here we describe the fit-for-purpose pharmacokinetic (PK)/pharmacodynamic (PD)/efficacy modeling of M3258 based on preclinical data from several species. The inhibition of LMP7 activity (PD) and tumor growth (efficacy) were tested in human multiple myeloma xenografts in mice. PK and efficacy data were correlated yielding a free M3258 concentration of 45 nM for half-maximal tumor growth inhibition (KC50). As M3258 only weakly inhibits LMP7 in mouse cells, both in vitro and in vivo bridging studies were performed in rats, monkeys, and dogs for translational modeling. These data indicated that the PD response in human xenograft models was closely reflected in dog PBMCs. A PK/PD model was established, predicting a free IC50 value of 9 nM for M3258 in dogs in vivo, in close agreement with in vitro measurements. In parallel, the human PK parameters of M3258 were predicted by various approaches including in vitro extrapolation and allometric scaling. Using PK/PD/efficacy simulations, the efficacious dose range and corresponding PD response in human were predicted. Taken together, these efforts supported the design of a phase Ia study of M3258 in multiple myeloma patients (NCT04075721). At the lowest tested dose level, the predicted exposure matched well with the observed exposure while the duration of LMP7 inhibition was underpredicted by the model. SIGNIFICANCE STATEMENT: M3258 is a novel inhibitor of the immunoproteasome subunit LMP7. The human PK and human efficacious dose range of M3258 were predicted using in vitro-in vivo extrapolation and allometric scaling methods together with a fit-for-purpose PK/PD and efficacy model based on data from several species. A comparison with data from the Phase Ia clinical study showed that the human PK was accurately predicted, while the extent and duration of PD response were more pronounced than estimated.
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Affiliation(s)
- Floriane Lignet
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Christina Esdar
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Gina Walter-Bausch
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Manja Friese-Hamim
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Sofia Stinchi
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Elise Drouin
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Samer El Bawab
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Andreas D Becker
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Claude Gimmi
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Michael P Sanderson
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
| | - Felix Rohdich
- The Healthcare Business of Merck KGaA, Darmstadt, Germany (F.L., C.E., G.W.-B., M.F.-H., S.S., S.E.B., A.D.B., C.G., M.P.S., F.R.) and EMD Serono, Billerica, Massachusetts (E.D.)
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22
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Subramanian R, Wang J, Murray B, Custodio J, Hao J, Lazerwith S, MacLennan Staiger K, Mwangi J, Sun H, Tang J, Wang K, Rhodes G, Wijaya S, Zhang H, Smith BJ. Human pharmacokinetics prediction with an in vitro- in vivo correction factor approach and in vitro drug-drug interaction profile of bictegravir, a potent integrase-strand transfer inhibitor component in approved biktarvy ® for the treatment of HIV-1 infection. Xenobiotica 2022; 52:1020-1030. [PMID: 36701274 DOI: 10.1080/00498254.2023.2169207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Bictegravir (BIC) is a potent small-molecule integrase strand-transfer inhibitor (INSTI) and a component of Biktarvy®, a single-tablet combination regimen that is currently approved for the treatment of human immunodeficiency virus type 1 (HIV-1) infection. The in vitro properties, pharmacokinetics (PK), and drug-drug interaction (DDI) profile of BIC were characterised in vitro and in vivo.BIC is a weakly acidic, ionisable, lipophilic, highly plasma protein-bound BCS class 2 molecule, which makes it difficult to predict human PK using standard methods. Its systemic plasma clearance is low, and the volume of distribution is approximately the volume of extracellular water in nonclinical species. BIC metabolism is predominantly mediated by cytochrome P450 enzyme (CYP) 3A and UDP-glucuronosyltransferase 1A1. BIC shows a low potential to perpetrate clinically meaningful DDIs via known drug metabolising enzymes or transporters.The human PK of BIC was predicted using a combination of bioavailability and volume of distribution scaled from nonclinical species and a modified in vitro-in vivo correlation (IVIVC) correction for clearance. Phase 1 studies in healthy subjects largely bore out the prediction and supported the methods used. The approach presented herein could be useful for other drug molecules where standard projections are not sufficiently accurate. .
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Affiliation(s)
| | | | | | | | - Jia Hao
- Gilead Sciences, Inc, Foster City, CA, USA
| | | | | | | | | | | | - Kelly Wang
- Gilead Sciences, Inc, Foster City, CA, USA
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23
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van der Heijden L, van Nuland M, Beijnen J, Huitema A, Dorlo T. A naïve pooled data approach for extrapolation of Phase 0 microdose trials to therapeutic dosing regimens. Clin Transl Sci 2022; 16:258-268. [PMID: 36419385 PMCID: PMC9926085 DOI: 10.1111/cts.13446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/22/2022] [Accepted: 10/07/2022] [Indexed: 11/25/2022] Open
Abstract
Microdosing is a strategy to obtain knowledge of human pharmacokinetics prior to Phase I clinical trials. The most frequently used method to extrapolate microdose (≤100 μg) pharmacokinetics to therapeutic doses is based on linear extrapolation from a noncompartmental analysis (NCA) with a two-fold acceptance criterion between pharmacokinetic metrics of the extrapolated microdose and the therapeutic dose. The major disadvantage of NCA is the assumption of linear extrapolation of NCA metrics. In this study, we used a naïve pooled data (NPD) modeling approach to extrapolate microdose pharmacokinetics to therapeutic pharmacokinetics. Gemcitabine and anastrozole were used as examples of intravenous and oral drugs, respectively. Data from microdose studies were used to build a parent-metabolite model for gemcitabine and its metabolite 2',2'-difluorodeoxyuridine (dFdU) and a model for anastrozole. The pharmacokinetic microdose models were extrapolated to therapeutic doses. Extrapolation of the microdose showed differences in pharmacokinetic shape for gemcitabine and dFdU between the simulated and observed therapeutic concentrations, whereas the observed therapeutic concentrations for anastrozole were captured by the extrapolation. This study demonstrated the possible use and feasibility of an NPD modeling approach for the evaluation and application of microdose studies in early drug development. Last, physiologically-based pharmacokinetic modeling might be an alternative for microdose extrapolation of drugs with complex pharmacokinetics such as gemcitabine.
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Affiliation(s)
- Lisa van der Heijden
- Department of Pharmacy & PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Merel van Nuland
- Department of Pharmacy & PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Jos Beijnen
- Department of Pharmacy & PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of Pharmaco‐epidemiology and Clinical Pharmacology, Faculty of Science, Department of Pharmaceutical SciencesUtrecht UniversityUtrechtThe Netherlands
| | - Alwin Huitema
- Department of Pharmacy & PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Department of Clinical PharmacyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands,Department of PharmacologyPrincess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
| | - Thomas Dorlo
- Department of Pharmacy & PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands,Division of PharmacologyAntoni van Leeuwenhoek/The Netherlands Cancer InstituteAmsterdamThe Netherlands
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24
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Matsumoto S, Uehara S, Kamimura H, Cho N, Ikeda H, Maeda S, Kagiyama K, Miyata A, Suemizu H, Fukasawa K. Selection of the candidate compound at an early stage of new drug development: retrospective pharmacokinetic and metabolic evaluations of valsartan using common marmosets. Xenobiotica 2022; 52:613-624. [PMID: 36148579 DOI: 10.1080/00498254.2022.2127131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Shogo Matsumoto
- Drug & Discovery & Management Department, R&D Division, Meiji Seika Pharma Co., Ltd., Tokyo, Japan
| | - Shotaro Uehara
- Department of Applied Research for Laboratory Animals, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Hidetaka Kamimura
- Department of Applied Research for Laboratory Animals, Central Institute for Experimental Animals, Kawasaki, Japan.,Business Promotion Department, CLEA Japan, Inc., Tokyo, Japan
| | - Naoki Cho
- Drug & Discovery & Management Department, R&D Division, Meiji Seika Pharma Co., Ltd., Tokyo, Japan
| | - Hiroshi Ikeda
- Tokyo Animal & Diet Department, CLEA Japan, Inc., Tokyo, Japan
| | - Satoshi Maeda
- Yaotsu Breeding Center, CLEA Japan, Inc., Gifu, Japan
| | | | - Atsunori Miyata
- Drug & Discovery & Management Department, R&D Division, Meiji Seika Pharma Co., Ltd., Tokyo, Japan
| | - Hiroshi Suemizu
- Department of Applied Research for Laboratory Animals, Central Institute for Experimental Animals, Kawasaki, Japan
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25
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Iwata H, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K. Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data. J Chem Inf Model 2022; 62:4057-4065. [PMID: 35993595 PMCID: PMC9472274 DOI: 10.1021/acs.jcim.2c00318] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Pharmacokinetic research plays an important role in the
development
of new drugs. Accurate predictions of human pharmacokinetic parameters
are essential for the success of clinical trials. Clearance (CL) and
volume of distribution (Vd) are important factors for evaluating pharmacokinetic
properties, and many previous studies have attempted to use computational
methods to extrapolate these values from nonclinical laboratory animal
models to human subjects. However, it is difficult to obtain sufficient,
comprehensive experimental data from these animal models, and many
studies are missing critical values. This means that studies using
nonclinical data as explanatory variables can only apply a small number
of compounds to their model training. In this study, we perform missing-value
imputation and feature selection on nonclinical data to increase the
number of training compounds and nonclinical datasets available for
these kinds of studies. We could obtain novel models for total body
clearance (CLtot) and steady-state Vd (Vdss)
(CLtot: geometric mean fold error [GMFE], 1.92; percentage
within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage
within 2-fold error, 71.1%). These accuracies were comparable to the
conventional animal scale-up models. Then, this method differs from
animal scale-up methods because it does not require animal experiments,
which continue to become more strictly regulated as time passes.
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Affiliation(s)
- Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Tatsuru Matsuo
- Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan
| | - Hideaki Mamada
- DMPK Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Takahisa Motomura
- Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mayumi Matsushita
- Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan
| | - Takeshi Fujiwara
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kazuya Maeda
- Graduate School of Pharmaceutical Sciences, Department of Molecular Pharmacokinetics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Koichi Handa
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
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Frechen S, Rostami-Hodjegan A. Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom? Pharm Res 2022; 39:1733-1748. [PMID: 35445350 PMCID: PMC9314283 DOI: 10.1007/s11095-022-03250-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 12/19/2022]
Abstract
Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.
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Affiliation(s)
- Sebastian Frechen
- Bayer AG, Pharmaceuticals, Research & Development, Systems Pharmacology & Medicine, Leverkusen, 51368, Germany.
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara UK Limited (Simcyp Division), Sheffield, UK
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Petersson C, Zhou X, Berghausen J, Cebrian D, Davies M, DeMent K, Eddershaw P, Riedmaier AE, Leblanc AF, Manveski N, Marathe P, Mavroudis PD, McDougall R, Parrott N, Reichel A, Rotter C, Tess D, Volak LP, Xiao G, Yang Z, Baker J. Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey. AAPS J 2022; 24:85. [DOI: 10.1208/s12248-022-00735-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022] Open
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Eno MS, Brubaker JD, Campbell JE, De Savi C, Guzi TJ, Williams BD, Wilson D, Wilson K, Brooijmans N, Kim J, Özen A, Perola E, Hsieh J, Brown V, Fetalvero K, Garner A, Zhang Z, Stevison F, Woessner R, Singh J, Timsit Y, Kinkema C, Medendorp C, Lee C, Albayya F, Zalutskaya A, Schalm S, Dineen TA. Discovery of BLU-945, a Reversible, Potent, and Wild-Type-Sparing Next-Generation EGFR Mutant Inhibitor for Treatment-Resistant Non-Small-Cell Lung Cancer. J Med Chem 2022; 65:9662-9677. [PMID: 35838760 PMCID: PMC9340769 DOI: 10.1021/acs.jmedchem.2c00704] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
![]()
While epidermal growth factor receptor (EGFR) tyrosine
kinase inhibitors
(TKIs) have changed the treatment landscape for EGFR mutant (L858R
and ex19del)-driven non-small-cell lung cancer (NSCLC), most patients
will eventually develop resistance to TKIs. In the case of first-
and second-generation TKIs, up to 60% of patients will develop an
EGFR T790M mutation, while third-generation irreversible TKIs, like
osimertinib, lead to C797S as the primary on-target resistance mutation.
The development of reversible inhibitors of these resistance mutants
is often hampered by poor selectivity against wild-type EGFR, resulting
in potentially dose-limiting toxicities and a sub-optimal profile
for use in combinations. BLU-945 (compound 30) is a potent,
reversible, wild-type-sparing inhibitor of EGFR+/T790M and EGFR+/T790M/C797S
resistance mutants that maintains activity against the sensitizing
mutations, especially L858R. Pre-clinical efficacy and safety studies
supported progression of BLU-945 into clinical studies, and it is
currently in phase 1/2 clinical trials for treatment-resistant EGFR-driven
NSCLC.
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Affiliation(s)
- Meredith S Eno
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Jason D Brubaker
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - John E Campbell
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Chris De Savi
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Timothy J Guzi
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Brett D Williams
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Douglas Wilson
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Kevin Wilson
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Natasja Brooijmans
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Joseph Kim
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Ayşegül Özen
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Emanuele Perola
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - John Hsieh
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Victoria Brown
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Kristina Fetalvero
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Andrew Garner
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Zhuo Zhang
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Faith Stevison
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Rich Woessner
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Jatinder Singh
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Yoav Timsit
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Caitlin Kinkema
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Clare Medendorp
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Christopher Lee
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Faris Albayya
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Alena Zalutskaya
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Stefanie Schalm
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Thomas A Dineen
- Blueprint Medicines, 45 Sidney Street, Cambridge, Massachusetts 02139, United States
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Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective. Pharm Res 2022; 39:1701-1731. [PMID: 35552967 DOI: 10.1007/s11095-022-03274-2] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/25/2022] [Indexed: 12/20/2022]
Abstract
Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.
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Zhang Z, Fu S, Wang F, Yang C, Wang L, Yang M, Zhang W, Zhong W, Zhuang X. A PBPK Model of Ternary Cyclodextrin Complex of ST-246 Was Built to Achieve a Reasonable IV Infusion Regimen for the Treatment of Human Severe Smallpox. Front Pharmacol 2022; 13:836356. [PMID: 35370741 PMCID: PMC8966223 DOI: 10.3389/fphar.2022.836356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
ST-246 is an oral drug against pathogenic orthopoxvirus infections. An intravenous formulation is required for some critical patients. A ternary complex of ST-246/meglumine/hydroxypropyl-β-cyclodextrin with well-improved solubility was successfully developed in our institute. The aim of this study was to achieve a reasonable intravenous infusion regimen of this novel formulation by a robust PBPK model based on preclinical pharmacokinetic studies. The pharmacokinetics of ST-246 after intravenous injection at different doses in rats, dogs, and monkeys were conducted to obtain clearances. The clearance of humans was generated by using the allometric scaling approach. Tissue distribution of ST-246 was conducted in rats to obtain tissue partition coefficients (Kp). The PBPK model of the rat was first built using in vivo clearance and Kp combined with in vitro physicochemical properties, unbound fraction, and cyclodextrin effect parameters of ST-246. Then the PBPK model was transferred to a dog and monkey and validated simultaneously. Finally, pharmacokinetic profiles after IV infusion at different dosages utilizing the human PBPK model were compared to the observed oral PK profile of ST-246 at therapeutic dosage (600 mg). The mechanistic PBPK model described the animal PK behaviors of ST-246 via intravenous injection and infusion with fold errors within 1.2. It appeared that 6h-IV infusion at 5 mg/kg BID produced similar Cmax and AUC as oral administration at 600 mg. A PBPK model of ST-246 was built to achieve a reasonable regimen of IV infusion for the treatment of severe smallpox, which will facilitate the clinical translation of this novel formulation.
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Affiliation(s)
- Zhiwei Zhang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Shuang Fu
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Furun Wang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Chunmiao Yang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Lingchao Wang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Meiyan Yang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Wenpeng Zhang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Wu Zhong
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Xiaomei Zhuang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China
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31
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Summerfield SG, Yates JWT, Fairman DA. Free Drug Theory - No Longer Just a Hypothesis? Pharm Res 2022; 39:213-222. [PMID: 35112229 DOI: 10.1007/s11095-022-03172-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/19/2022] [Indexed: 12/14/2022]
Abstract
The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kpuu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.
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Affiliation(s)
- Scott G Summerfield
- UK Bioanalysis Immunogenicity and Biomarkers, GSK R&D, Stevenage, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK.
| | - James W T Yates
- Drug Metabolism and Pharmacokinetics, GSK R&D, Stevenage, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - David A Fairman
- Clinical Pharmacology Modelling and Simulation, GSK R&D, Stevenage, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
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32
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Heimbach T, Kesisoglou F, Novakovic J, Tistaert C, Mueller-Zsigmondy M, Kollipara S, Ahmed T, Mitra A, Suarez-Sharp S. Establishing the Bioequivalence Safe Space for Immediate-Release Oral Dosage Forms using Physiologically Based Biopharmaceutics Modeling (PBBM): Case Studies. J Pharm Sci 2021; 110:3896-3906. [PMID: 34551349 DOI: 10.1016/j.xphs.2021.09.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/10/2021] [Accepted: 09/10/2021] [Indexed: 02/07/2023]
Abstract
For oral drug products, in vitro dissolution is the most used surrogate of in vivo dissolution and absorption. In the context of drug product quality, safe space is defined as the boundaries of in vitro dissolution, and relevant quality attributes, within which drug product variants are expected to be bioequivalent to each other. It would be highly desirable if the safe space could be established via a direct link between available in vitro data and in vivo pharmacokinetics. In response to the challenges with establishing in vitro-in vivo correlations (IVIVC) with traditional modeling approaches, physiologically based biopharmaceutics modeling (PBBM) has been gaining increased attention. In this manuscript we report five case studies on using PBBM to establish a safe space for BCS Class 2 and 4 across different companies, including applications in an industrial setting for both internal decision making or regulatory applications. The case studies provide an opportunity to reflect on practical vs. ideal datasets for safe space development, the methodologies for incorporating dissolution data in the model and the criteria used for model validation and application. PBBM and safe space, still represent an evolving field and more examples are needed to drive development of best practices.
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Affiliation(s)
- Tycho Heimbach
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., RY80B-1403, 126 E. Lincoln Ave, Rahway 07065, NJ, USA
| | - Filippos Kesisoglou
- Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., RY80B-1403, 126 E. Lincoln Ave, Rahway 07065, NJ, USA.
| | | | | | | | - Sivacharan Kollipara
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integration Product Development Organization (IPDO), Medchal Malkajgiri District, Hyberadad 500090, Telangana, India
| | - Tausif Ahmed
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integration Product Development Organization (IPDO), Medchal Malkajgiri District, Hyberadad 500090, Telangana, India
| | - Amitava Mitra
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, Springhouse, PA, USA
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Prediction of Human Pharmacokinetic Profiles of the Antituberculosis Drug Delamanid from Nonclinical Data: Potential Therapeutic Value against Extrapulmonary Tuberculosis. Antimicrob Agents Chemother 2021; 65:e0257120. [PMID: 34097484 DOI: 10.1128/aac.02571-20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Delamanid has been studied extensively and approved for the treatment of pulmonary multidrug-resistant tuberculosis; however, its potential in the treatment of extrapulmonary tuberculosis remains unknown. We previously reported that, in rats, delamanid was broadly distributed to various tissues in addition to the lungs. In this study, we simulated human plasma concentration-time courses (pharmacokinetic profile) of delamanid, which has a unique property of metabolism by albumin, using two different approaches (steady-state concentration of plasma-mean residence time [Css-MRT] and physiologically based pharmacokinetic [PBPK] modeling). In Css-MRT, allometric scaling predicted the distribution volume at steady state based on data from mice, rats, and dogs. Total clearance was predicted by in vitro-in vivo extrapolation using a scaled albumin amount. A simulated human pharmacokinetic profile using a combination of human-predicted Css and MRT was almost identical to the observed profile after single oral administration, which suggests that the pharmacokinetic profile of delamanid could be predicted by allometric scaling from these animals and metabolic capacity in vitro. The PBPK model was constructed on the assumption that delamanid was metabolized by albumin in circulating plasma and tissues, to which the simulated pharmacokinetic profile was consistent. Moreover, the PBPK modeling approach demonstrated that the simulated concentrations of delamanid at steady state in the lung, brain, liver, and heart were higher than the in vivo effective concentration for Mycobacterium tuberculosis. These results indicate that delamanid may achieve similar concentrations in various organs to that of the lung and may have the potential to treat extrapulmonary tuberculosis.
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34
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Matsumoto S, Kamimura H, Nishiwaki M, Cho N, Kato K, Yamamoto T. Empirical and theoretical approaches for the prediction of human hepatic clearance using chimeric mice with humanised liver: the use of physiologically based scaling, a novel solution for potential overprediction due to coexisting mouse metabolism. Xenobiotica 2021; 51:983-994. [PMID: 34227923 DOI: 10.1080/00498254.2021.1950865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Chimeric mice are immunodeficient mice in which the majority of the hepatic parenchymal cells are replaced with human hepatocytes.Following intravenous administration of 24 model compounds to control and chimeric mice, human hepatic clearance (CLh) was predicted using the single-species allometric scaling (SSS) method. Predictability of the chimeric mice was better than that of the control mice.Human CLh was predicted by the physiologically based scaling (PBS) method, wherein observed CLh in chimeric mice was first converted to intrinsic CLh (CLh,int). As the liver of chimeric mice contains remaining mouse hepatocytes, CLh,int was corrected by in vitro CLh ratios of the mouse to human hepatocytes according to their hepatocyte replacement index. Further, predicted human CLh was calculated based on an assumption that CLh,int in chimeric mice normalised for their liver weight was equal to CLh,int per liver weight in humans. Consequently, better prediction performance was observed with the use of the PBS method than the SSS method.SSS method is an empirical method, and the effects of coexisting mouse metabolism cannot be avoided. However, the PBS method with in vitro CLh correction might be a potential solution and may expand the application of chimeric mice in new drug development.
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Affiliation(s)
- Shogo Matsumoto
- Meiji Seika Pharma Co., Ltd., Pharmaceutical Research Labs, Yokohama, Japan
| | - Hidetaka Kamimura
- Laboratory Animal Research Department, Central Institute for Experimental Animals, Kawasaki, Japan
| | | | - Naoki Cho
- Meiji Seika Pharma Co., Ltd., Pharmaceutical Research Labs, Yokohama, Japan
| | - Kazuhiko Kato
- Meiji Seika Pharma Co., Ltd., Pharmaceutical Research Labs, Yokohama, Japan
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35
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Alsmadi MM, Al-Daoud NM, Jaradat MM, Alzughoul SB, Abu Kwiak AD, Abu Laila SS, Abu Shameh AJ, Alhazabreh MK, Jaber SA, Abu Kassab HT. Physiologically-based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment. Biopharm Drug Dispos 2021; 42:263-284. [PMID: 33904202 DOI: 10.1002/bdd.2282] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 03/18/2021] [Accepted: 04/11/2021] [Indexed: 12/24/2022]
Abstract
Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically-based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model-predicted plasma exposures in patients with different health conditions within average 2-fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non-small cell lung cancer, moderate HIP, and severe HIP at 1-, 1.5-, and 1.8-fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.
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Affiliation(s)
- Mo'tasem M Alsmadi
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Nour M Al-Daoud
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Mays M Jaradat
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Saja B Alzughoul
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Amani D Abu Kwiak
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Salam S Abu Laila
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Ayat J Abu Shameh
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Mohammad K Alhazabreh
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Sana'a A Jaber
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Hala T Abu Kassab
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
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36
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Inatani S, Mizuno‐Yasuhira A, Kamiya M, Nishino I, Sabia HD, Endo H. Prediction of a clinically effective dose of THY1773, a novel V 1B receptor antagonist, based on preclinical data. Biopharm Drug Dispos 2021; 42:204-217. [PMID: 33734452 PMCID: PMC8252455 DOI: 10.1002/bdd.2273] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/23/2021] [Accepted: 03/09/2021] [Indexed: 01/27/2023]
Abstract
THY1773 is a novel arginine vasopressin 1B (V1B ) receptor antagonist that is under development as an oral drug for the treatment of major depressive disorder (MDD). Here we report our strategy to predict a clinically effective dose of THY1773 for MDD in the preclinical stage, and discuss the important insights gained by retrospective analysis of prediction accuracy. To predict human pharmacokinetic (PK) parameters, several extrapolation methods from animal or in vitro data to humans were investigated. The fu correction intercept method and two-species-based allometry were used to extrapolate clearance from rats and dogs to humans. The physiologically based pharmacokinetics (PBPK)/receptor occupancy (RO) model was developed by linking free plasma concentration with pituitary V1B RO by the Emax model. As a result, the predicted clinically effective dose of THY1773 associated with 50% V1B RO was low enough (10 mg/day, or at maximum 110 mg/day) to warrant entering phase 1 clinical trials. In the phase 1 single ascending dose study, TS-121 capsule (active ingredient: THY1773) showed favorable PKs for THY1773 as expected, and in the separately conducted phase 1 RO study using positron emission tomography, the observed pituitary V1B RO was comparable to our prediction. Retrospective analysis of the prediction accuracy suggested that the prediction methods considering plasma protein binding, and avoiding having to apply unknown scaling factors obtained in animals to humans, would lead to better prediction. Selecting mechanism-based methods with reasonable assumptions would be critical for the successful prediction of a clinically effective dose in the preclinical stage of drug development.
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Affiliation(s)
- Shoko Inatani
- Drug Metabolism and PharmacokineticsDrug Safety and Pharmacokinetics LaboratoriesResearch HeadquartersTaisho Pharmaceutical Co., Ltd.SaitamaJapan
| | - Akiko Mizuno‐Yasuhira
- Drug Metabolism and PharmacokineticsDrug Safety and Pharmacokinetics LaboratoriesResearch HeadquartersTaisho Pharmaceutical Co., Ltd.SaitamaJapan
| | - Makoto Kamiya
- Development HeadquartersTaisho Pharmaceutical Co., Ltd.TokyoJapan
- Drug DevelopmentTaisho Pharmaceutical R&D Inc.NJUSA
| | - Izumi Nishino
- Development HeadquartersTaisho Pharmaceutical Co., Ltd.TokyoJapan
| | | | - Hiromi Endo
- Drug Metabolism and PharmacokineticsDrug Safety and Pharmacokinetics LaboratoriesResearch HeadquartersTaisho Pharmaceutical Co., Ltd.SaitamaJapan
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37
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Murad N, Pasikanti KK, Madej BD, Minnich A, McComas JM, Crouch S, Polli JW, Weber AD. Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds. Drug Metab Dispos 2021; 49:169-178. [PMID: 33239335 PMCID: PMC7841422 DOI: 10.1124/dmd.120.000202] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 11/03/2020] [Indexed: 12/22/2022] Open
Abstract
Volume of distribution at steady state (VD,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict VD,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human VD,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict VD,ss directly and to predict input parameters required for mechanistic and empirical VD,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on VD,ss predictions was also investigated. In predicting VD,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog VD,ss demonstrated comparable performance (62%-71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted VD,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic VD,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the VD,ss but was limited in estimating the compounds with low VD,ss SIGNIFICANCE STATEMENT: This work advances the in silico prediction of VD,ss directly from structure and with the aid of in vitro data. Rigorous and comprehensive evaluation of various methods using a large set of clinical compounds (n = 956) is presented. The scale of techniques evaluated is far beyond any previously presented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing VD,ss prediction methodologies.
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Affiliation(s)
- Neha Murad
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Kishore K Pasikanti
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Benjamin D Madej
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Amanda Minnich
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Juliet M McComas
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Sabrinia Crouch
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Joseph W Polli
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
| | - Andrew D Weber
- GlaxoSmithKline, Collegeville, Pennsylvania (N.M., K.K.P., J.M.M., S.C., J.W.P., A.D.W.); Lawrence Livermore National Laboratory, Livermore, California (A.M.); Frederick National Laboratory for Cancer Research, Frederick, Maryland (B.D.M.); and Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, San Francisco, California (N.M., K.K.P., B.D.M., A.M., J.M.M., S.C., J.W.P., A.D.W.)
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38
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Matsumoto S, Uehara S, Kamimura H, Ikeda H, Maeda S, Hattori M, Nishiwaki M, Kato K, Yamazaki H. Human total clearance values and volumes of distribution of typical human cytochrome P450 2C9/19 substrates predicted by single-species allometric scaling using pharmacokinetic data sets from common marmosets genotyped for P450 2C19. Xenobiotica 2021; 51:479-493. [PMID: 33455494 DOI: 10.1080/00498254.2020.1871113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Common marmosets (Callithrix jacchus) are small non-human primates that genetically lack cytochrome P450 2C9 (CYP2C9). Polymorphic marmoset CYP2C19 compensates by mediating oxidations of typical human CYP2C9/19 substrates.Twenty-four probe substrates were intravenously administered in combinations to marmosets assigned to extensive or poor metaboliser (PM) groups by CYP2C19 genotyping. Eliminations from plasma of cilomilast, phenytoin, repaglinide, tolbutamide, and S-warfarin in the CYP2C19 PM group were significantly slow; these drugs are known substrates of human CYP2C8/9/19.Human total clearance values and volumes of distribution of the 24 test compounds were extrapolated using single-species allometric scaling with experimental data from marmosets and found to be mostly comparable with the reported values.Human total clearance values and volumes of distribution of 15 of the 24 test compounds similarly extrapolated using reported data sets from cynomolgus or rhesus monkeys were comparable to the present predicted results, especially to those based on data from PM marmosets.These results suggest that single-species allometric scaling using marmosets, being small, has advantages over multiple-species-based allometry and could be applicable for pharmacokinetic predictions at the discovery stage of drug development.
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Affiliation(s)
- Shogo Matsumoto
- Pharmaceutical Research Labs., Meiji Seika Pharma Co., Ltd., Yokohama, Japan
| | - Shotaro Uehara
- Central Institute for Experimental Animals, Kawasaki, Japan.,Pharmaceutical University, Machida, Tokyo, Japan
| | - Hidetaka Kamimura
- Central Institute for Experimental Animals, Kawasaki, Japan.,Business Promotion Dept., CLEA Japan, Inc., Tokyo, Japan
| | - Hiroshi Ikeda
- Tokyo Animal & Diet Dept., CLEA Japan, Inc., Tokyo, Japan
| | - Satoshi Maeda
- Yaotsu Breeding Center, CLEA Japan, Inc., Gifu, Japan
| | | | - Megumi Nishiwaki
- Fuji Technical Service Center, CLEA Japan, Inc.., Shizuoka, Japan
| | - Kazuhiko Kato
- Pharmaceutical Research Labs., Meiji Seika Pharma Co., Ltd., Yokohama, Japan
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Mathew S, Tess D, Burchett W, Chang G, Woody N, Keefer C, Orozco C, Lin J, Jordan S, Yamazaki S, Jones R, Di L. Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods. J Pharm Sci 2020; 110:1799-1823. [PMID: 33338491 DOI: 10.1016/j.xphs.2020.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/17/2020] [Accepted: 12/03/2020] [Indexed: 10/22/2022]
Abstract
Volume of distribution at steady state (Vss) is an important pharmacokinetic parameter of a drug candidate. In this study, Vss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (fu,t) method with adipose and muscle also provided high Vss prediction accuracy. Overall, the high performing methods for human Vss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The fu,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful Vss prediction involves strategic integration of in silico, in vitro and in vivo approaches.
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Affiliation(s)
- Shibin Mathew
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA
| | - David Tess
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA
| | - Woodrow Burchett
- Early Clinical Development, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - George Chang
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Nathaniel Woody
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Christopher Keefer
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Christine Orozco
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Jian Lin
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Samantha Jordan
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Shinji Yamazaki
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA 92121, USA
| | - Rhys Jones
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA 92121, USA
| | - Li Di
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA.
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40
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Yim DS, Choi S. Predicting human pharmacokinetics from preclinical data: volume of distribution. Transl Clin Pharmacol 2020; 28:169-174. [PMID: 33425799 PMCID: PMC7781809 DOI: 10.12793/tcp.2020.28.e19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 11/21/2022] Open
Abstract
This tutorial introduces background and methods to predict the human volume of distribution (Vd) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑ T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp 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 Vd has been the efforts to find a better function giving a more accurate ktp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd. 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 Vd parameters (e.g., Vc, Vp, and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd 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.
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Affiliation(s)
- Dong-Seok Yim
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Suein Choi
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
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41
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Lombardo F, Bentzien J, Berellini G, Muegge I. In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters. J Pharm Sci 2020; 110:500-509. [PMID: 32891631 DOI: 10.1016/j.xphs.2020.08.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/27/2020] [Accepted: 08/27/2020] [Indexed: 12/15/2022]
Abstract
A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions.
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Affiliation(s)
- Franco Lombardo
- Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA.
| | - Jörg Bentzien
- Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA
| | - Giuliano Berellini
- Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA
| | - Ingo Muegge
- Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA
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42
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van Nuland M, Rosing H, Huitema ADR, Beijnen JH. Predictive Value of Microdose Pharmacokinetics. Clin Pharmacokinet 2020; 58:1221-1236. [PMID: 31030372 DOI: 10.1007/s40262-019-00769-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Phase 0 microdose trials are exploratory studies to early assess human pharmacokinetics of new chemical entities, while limiting drug exposure and risks for participants. The microdose concept is based on the assumption that microdose pharmacokinetics can be extrapolated to pharmacokinetics of a therapeutic dose. However, it is unknown whether microdose pharmacokinetics are actually indicative of the pharmacokinetics at therapeutic dose. The aim of this review is to investigate the predictive value of microdose pharmacokinetics and to identify drug characteristics that may influence the scalability of these parameters. The predictive value of microdose pharmacokinetics was determined for 46 compounds and showed adequate predictability for 28 of 41 orally administered drugs (68%) and 15 of 16 intravenously administered drugs (94%). Microdose pharmacokinetics were considered predictive if the mean observed values of the microdose and the therapeutic dose were within twofold. Nonlinearity may be caused by saturation of enzyme and transporter systems, such as intestinal and hepatic efflux and uptake transporters. The high degree of success regarding linear pharmacokinetics shows that phase 0 microdose trials can be used as an early human model for determination of drug pharmacokinetics.
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Affiliation(s)
- Merel van Nuland
- Department of Pharmacy and Pharmacology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Louwesweg 6, 1066 EC, Amsterdam, The Netherlands. .,Division of Pharmacology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Hilde Rosing
- Department of Pharmacy and Pharmacology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Louwesweg 6, 1066 EC, Amsterdam, The Netherlands
| | - Alwin D R Huitema
- Department of Pharmacy and Pharmacology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Louwesweg 6, 1066 EC, Amsterdam, The Netherlands.,Division of Pharmacology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jos H Beijnen
- Department of Pharmacy and Pharmacology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Louwesweg 6, 1066 EC, Amsterdam, The Netherlands.,Division of Pharmacology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
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43
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Davies M, Jones RDO, Grime K, Jansson-Löfmark R, Fretland AJ, Winiwarter S, Morgan P, McGinnity DF. Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades. Trends Pharmacol Sci 2020; 41:390-408. [PMID: 32359836 DOI: 10.1016/j.tips.2020.03.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 03/20/2020] [Accepted: 03/25/2020] [Indexed: 01/15/2023]
Abstract
During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (Cmax) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.
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Affiliation(s)
- Michael Davies
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK.
| | - Rhys D O Jones
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ken Grime
- DMPK, Research and Early Development, Respiratory, Inflammation and Autoimmune, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Rasmus Jansson-Löfmark
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Adrian J Fretland
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Boston, MA, USA
| | - Susanne Winiwarter
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Paul Morgan
- Mechanistic Safety and ADME Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Dermot F McGinnity
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
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44
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Yau E, Olivares-Morales A, Gertz M, Parrott N, Darwich AS, Aarons L, Ogungbenro K. Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution. AAPS JOURNAL 2020; 22:41. [PMID: 32016678 DOI: 10.1208/s12248-020-0418-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/07/2020] [Indexed: 12/14/2022]
Abstract
In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.
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Affiliation(s)
- Estelle Yau
- Centre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.,Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Andrés Olivares-Morales
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Michael Gertz
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Neil Parrott
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Adam S Darwich
- Centre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.,Logistics and Informatics in Health Care, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leon Aarons
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Kayode Ogungbenro
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
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45
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Patel D, Yang W, Lipert M, Wu T. Application and Impact of Human Dose Projection from Discovery to Early Drug Development. AAPS PharmSciTech 2020; 21:44. [PMID: 31897807 DOI: 10.1208/s12249-019-1598-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 12/01/2019] [Indexed: 12/31/2022] Open
Abstract
The application and impact of human dose projection (HDP) has been well recognized in the late drug development phase, with increasing appreciation earlier during discovery and early development. This commentary describes the perspective of pharmaceutical scientists on the evolving application and impact of HDP at various phases from discovery to early development, including lead generation, lead optimization, lead up to candidate nomination, and early drug development. The underlying fundamental concepts and key input parameters for HDP are briefly discussed. A broad overview of phase-specific tools and approaches commonly utilized for human dose projection in the pharmaceutical industry is provided. A discussion of phase-appropriate implementation strategies, associated limitations/assumptions and continuous refinement for HDP from discovery to early development is presented. The authors describe the phase-specific applications of human dose projection to facilitate key assessments and relative impact on decision points.
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46
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Charman SA, Andreu A, Barker H, Blundell S, Campbell A, Campbell M, Chen G, Chiu FCK, Crighton E, Katneni K, Morizzi J, Patil R, Pham T, Ryan E, Saunders J, Shackleford DM, White KL, Almond L, Dickins M, Smith DA, Moehrle JJ, Burrows JN, Abla N. An in vitro toolbox to accelerate anti-malarial drug discovery and development. Malar J 2020; 19:1. [PMID: 31898492 PMCID: PMC6941357 DOI: 10.1186/s12936-019-3075-5] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 12/14/2019] [Indexed: 01/08/2023] Open
Abstract
Background Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles. Methods Ionization constants were determined by potentiometric titration and partition coefficients were measured using a shake-flask method. Solubility was assessed in biorelevant media and permeability coefficients and efflux ratios were determined using Caco-2 cell monolayers. Binding to plasma and media proteins was measured using either ultracentrifugation or rapid equilibrium dialysis. Metabolic stability and cytochrome P450 inhibition were assessed using human liver microsomes. Sample analysis was conducted by LC–MS/MS. Results Both solubility and fraction unbound decreased, and permeability and unbound intrinsic clearance increased, with increasing Log D7.4. In general, development compounds were somewhat more lipophilic than legacy drugs. For many compounds, permeability and protein binding were challenging to assess and both required the use of experimental conditions that minimized the impact of non-specific binding. Intrinsic clearance in human liver microsomes was varied across the data set and several compounds exhibited no measurable substrate loss under the conditions used. Inhibition of cytochrome P450 enzymes was minimal for most compounds. Conclusions This is the first data set to describe in vitro properties for 45 legacy and development anti-malarial drugs. The studies identified several practical methodological issues common to many of the more lipophilic compounds and highlighted areas which require more work to customize experimental conditions for compounds being designed to meet the new target product profiles. The dataset will be a valuable tool for malaria researchers aiming to develop PBPK models for the prediction of human PK properties and/or drug–drug interactions. Furthermore, generation of this comprehensive data set within a single laboratory allows direct comparison of properties across a large dataset and evaluation of changing property trends that have occurred over time with changing target product and candidate profiles.
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Affiliation(s)
- Susan A Charman
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia.
| | - Alice Andreu
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Helena Barker
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Scott Blundell
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Anna Campbell
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Michael Campbell
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Gong Chen
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Francis C K Chiu
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Elly Crighton
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Kasiram Katneni
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Julia Morizzi
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Rahul Patil
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Thao Pham
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Eileen Ryan
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Jessica Saunders
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - David M Shackleford
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Karen L White
- Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Lisa Almond
- Certara UK Limited, Simcyp Division, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Maurice Dickins
- Certara UK Limited, Simcyp Division, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | | | - Joerg J Moehrle
- Medicines for Malaria Venture, PO Box 1826, 20 Route de Pré-Bois, CH-1215, Geneva 15, Switzerland
| | - Jeremy N Burrows
- Medicines for Malaria Venture, PO Box 1826, 20 Route de Pré-Bois, CH-1215, Geneva 15, Switzerland
| | - Nada Abla
- Medicines for Malaria Venture, PO Box 1826, 20 Route de Pré-Bois, CH-1215, Geneva 15, Switzerland
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Wang X, Liu Q, Zhong W, Yang L, Yang J, Covaci A, Zhu L. Estimating renal and hepatic clearance rates of organophosphate esters in humans: Impacts of intrinsic metabolism and binding affinity with plasma proteins. ENVIRONMENT INTERNATIONAL 2020; 134:105321. [PMID: 31783242 DOI: 10.1016/j.envint.2019.105321] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/29/2019] [Accepted: 11/11/2019] [Indexed: 05/25/2023]
Abstract
The renal and hepatic clearance rates of organophosphate esters (OPEs) in humans were estimated. Six OPEs and their corresponding diester metabolites (mOPEs) were quantified respectively in 30 paired human plasma and urine samples collected in Hengshui, Hebei province, China. The renal clearance rate (CLrenal) of triphenyl phosphate (TPHP), tris(chloroethyl) phosphate (TCEP) and tris(1,3-dichloro-isopropyl) phosphate (TDCIPP) was estimated to be 68.9, 50.9 and 33.3 mL/kg/day, respectively, while it was not calculated for other three OPEs due to the low detection frequencies in human samples. To estimate the clearance rates of the target OPEs, hepatic clearance rates (CLh) of OPEs were extrapolated from their in vitro intrinsic clearance data in human liver microsomes (CLHLM). The calculated CLh values of TCEP and TDCIPP were comparable to their CLrenal, indicating that the in vitro extrapolation method was suitable for estimating the clearance rates of OPEs. The higher binding affinity of TDCIPP with plasma proteins could reduce its renal clearance. The estimated half-lives of Cl-OPEs in human were longer than those of the aryl- and alkyl-OPEs. This study provided a feasible in vitro method to predict the clearance and half-lives of OPEs in human, which is significant for their accurate health risk assessment.
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Affiliation(s)
- Xiaolei Wang
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China
| | - Qing Liu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China
| | - Wenjue Zhong
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China
| | - Liping Yang
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China
| | - Jing Yang
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk-Antwerp, Belgium
| | - Lingyan Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, PR China.
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Matsumura N, Hayashi S, Akiyama Y, Ono A, Funaki S, Tamura N, Kimoto T, Jiko M, Haruna Y, Sarashina A, Ishida M, Nishiyama K, Fushimi M, Kojima Y, Yoneda K, Nakanishi M, Kim S, Fujita T, Sugano K. Prediction Characteristics of Oral Absorption Simulation Software Evaluated Using Structurally Diverse Low-Solubility Drugs. J Pharm Sci 2019; 109:1403-1416. [PMID: 31863733 DOI: 10.1016/j.xphs.2019.12.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 02/06/2023]
Abstract
The purpose of the present study was to characterize current biopharmaceutics modeling and simulation software regarding the prediction of the fraction of a dose absorbed (Fa) in humans. As commercial software products, GastroPlus™ and Simcyp® were used. In addition, the gastrointestinal unified theoretical framework, a simple and publicly accessible model, was used as a benchmark. The Fa prediction characteristics for a total of 96 clinical Fa data of 27 model drugs were systematically evaluated using the default settings of each software product. The molecular weight, dissociation constant, octanol-water partition coefficient, solubility in biorelevant media, dose, and particle size of model drugs were used as input data. Although the same input parameters were used, GastroPlus™, Simcyp®, and the gastrointestinal unified theoretical framework showed different Fa prediction characteristics depending on the rate-limiting steps of oral drug absorption. The results of the present study would be of great help for the overall progression of physiologically based absorption models.
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Affiliation(s)
- Naoya Matsumura
- Minase Research Institute, Ono Pharmaceutical Co., Ltd., 3-1-1, Sakurai, Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan.
| | - Shun Hayashi
- Preclinical Research Unit, Sumitomo Dainippon Pharma Co., Ltd., 3-1-98 Kasugadenaka, Konohana-ku, Osaka 554-0022, Japan
| | - Yoshiyuki Akiyama
- Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Asami Ono
- Laboratory for Chemistry, Manufacturing and Control Pharmaceuticals Research Center, Asahi Kasei Pharma Corporation, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan
| | - Satoko Funaki
- Drug Metabolism & Pharmacokinetics, Research Laboratory for Development, Shionogi & Co., Ltd., 3-1-1, Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Naomi Tamura
- Drug Metabolism & Pharmacokinetics, Research Laboratory for Development, Shionogi & Co., Ltd., 3-1-1, Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Takahiro Kimoto
- Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Maiko Jiko
- Medical Analysis Research Department, Towa Pharmaceutical Co., Ltd., 134 Chudoji Minami-machi, Shimogyo-ku, Kyoto 600-8813, Japan
| | - Yuka Haruna
- Medical Analysis Research Department, Towa Pharmaceutical Co., Ltd., 134 Chudoji Minami-machi, Shimogyo-ku, Kyoto 600-8813, Japan
| | - Akiko Sarashina
- Clinical PK/PD Department, Nippon Boehringer Ingelheim Co., Ltd., 6-7-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Masahiro Ishida
- Clinical PK/PD Department, Nippon Boehringer Ingelheim Co., Ltd., 6-7-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Kotaro Nishiyama
- Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., 6-7-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Masahiro Fushimi
- Biological Research Department, Sawai Pharmaceutical Co., Ltd., 5-2-30, Miyahara, Yodogawa-ku, Osaka 532-0003, Japan
| | - Yukiko Kojima
- Biological Research Department, Sawai Pharmaceutical Co., Ltd., 5-2-30, Miyahara, Yodogawa-ku, Osaka 532-0003, Japan
| | - Kazuhiro Yoneda
- Minase Research Institute, Ono Pharmaceutical Co., Ltd., 3-1-1, Sakurai, Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan
| | - Misato Nakanishi
- Minase Research Institute, Ono Pharmaceutical Co., Ltd., 3-1-1, Sakurai, Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan
| | - Soonih Kim
- Minase Research Institute, Ono Pharmaceutical Co., Ltd., 3-1-1, Sakurai, Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan
| | - Takuya Fujita
- Laboratory of Molecular Pharmacokinetics, College of Pharmaceutical Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan
| | - Kiyohiko Sugano
- Molecular Pharmaceutics Laboratory, College of Pharmaceutical Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan
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49
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Berellini G, Lombardo F. An Accurate In Vitro Prediction of Human VD ss Based on the Øie-Tozer Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset. Drug Metab Dispos 2019; 47:1380-1387. [PMID: 31578209 DOI: 10.1124/dmd.119.088914] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 09/27/2019] [Indexed: 02/13/2025] Open
Abstract
We present a model for volume of distribution at steady state (VDss) prediction, via fraction unbound in tissues, from the Øie-equation as an extension of our and other authors' previous work. It is based on easily determined or computed physicochemical descriptors such as logD7.4 and fi (7.4) (cationic fraction ionized at pH 7.4) in addition to fraction unbound in plasma (fup). We had collected, as part of other work, an extensive dataset of VDss and fup values and used the descriptors above, gathered from the literature, for a preliminary assessment of the robustness of the method applied to 191 different compounds belonging to different charge classes and scaffolds. After this step, we addressed the use of easily computed physicochemical descriptors and experimentally derived fup on the same data set and compare the results between the two approaches and against the Øie-Tozer equation using in vivo data. This approach positions itself between fully computational models and scaling methods based on in vivo animal models or in vitro Kp (tissue:plasma) data utilizing model tissues. We consider it a useful and orthogonal complement to the two very diverse approaches mentioned yet requiring minimal in vitro experimental work. It offers a relatively inexpensive, rapid, intuitive, and simple way to predict VDss in humans, at a relatively early stage of the drug discovery. SIGNIFICANCE STATEMENT: This method allows the prediction of volume of distribution at steady state for small molecules in humans without the use of animal PK data because it utilizes only in vitro data. It is therefore amenable to use at early stages, simple, intuitive, animal-sparing, and quite accurate, and it may serve scaling efforts well. Furthermore, utilizing the same dataset, we show that the performance of a model using computed pKa and logD7.4, still using experimental fraction unbound in plasma, compares well with the model using experimentally derived values.
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Affiliation(s)
| | - Franco Lombardo
- Drug Metabolism and Bioanalysis Group, Alkermes, Waltham, Massachusetts
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50
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Holt K, Ye M, Nagar S, Korzekwa K. Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions. Drug Metab Dispos 2019; 47:1050-1060. [PMID: 31324699 PMCID: PMC6750188 DOI: 10.1124/dmd.119.087973] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 07/09/2019] [Indexed: 11/22/2022] Open
Abstract
Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.
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Affiliation(s)
- Kimberly Holt
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania
| | - Min Ye
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania
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