1
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Gilleran JA, Ashraf K, Delvillar M, Eck T, Fondekar R, Miller EB, Hutchinson A, Dong A, Seitova A, De Souza ML, Augeri D, Halabelian L, Siekierka J, Rotella DP, Gordon J, Childers WE, Grier MC, Staker BL, Roberge JY, Bhanot P. Structure-Activity Relationship of a Pyrrole Based Series of PfPKG Inhibitors as Anti-Malarials. J Med Chem 2024; 67:3467-3503. [PMID: 38372781 DOI: 10.1021/acs.jmedchem.3c01795] [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: 02/20/2024]
Abstract
Controlling malaria requires new drugs against Plasmodium falciparum. The P. falciparum cGMP-dependent protein kinase (PfPKG) is a validated target whose inhibitors could block multiple steps of the parasite's life cycle. We defined the structure-activity relationship (SAR) of a pyrrole series for PfPKG inhibition. Key pharmacophores were modified to enable full exploration of chemical diversity and to gain knowledge about an ideal core scaffold. In vitro potency against recombinant PfPKG and human PKG were used to determine compound selectivity for the parasite enzyme. P. berghei sporozoites and P. falciparum asexual blood stages were used to assay multistage antiparasitic activity. Cellular specificity of compounds was evaluated using transgenic parasites expressing PfPKG carrying a substituted "gatekeeper" residue. The structure of PfPKG bound to an inhibitor was solved, and modeling using this structure together with computational tools was utilized to understand SAR and establish a rational strategy for subsequent lead optimization.
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Affiliation(s)
- John A Gilleran
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Kutub Ashraf
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - Melvin Delvillar
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - Tyler Eck
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - Raheel Fondekar
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
- Rutgers School of Pharmacy, 160 Frelinghuysen Road, Piscataway, New Jersey 08854, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Ashley Hutchinson
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Aiping Dong
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Alma Seitova
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Mariana Laureano De Souza
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - David Augeri
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Levon Halabelian
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - John Siekierka
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - David P Rotella
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - John Gordon
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, Pennsylvania 19140, United States
| | - Wayne E Childers
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, Pennsylvania 19140, United States
| | - Mark C Grier
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Bart L Staker
- Seattle Structural Genomics Center for Infectious Disease, Seattle, Washington 98109, United States
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, Washington 98109, United States
| | - Jacques Y Roberge
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Purnima Bhanot
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
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2
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Poulin P, Nicolas JM, Bouzom F. A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs. J Pharm Sci 2024; 113:118-130. [PMID: 37634869 DOI: 10.1016/j.xphs.2023.08.018] [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: 07/05/2023] [Revised: 08/18/2023] [Accepted: 08/19/2023] [Indexed: 08/29/2023]
Abstract
In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.
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Affiliation(s)
- Patrick Poulin
- Consultant Patrick Poulin Inc., Québec City, Québec, Canada; School of Public Health, Université de Montréal, Montréal, Québec, Canada.
| | | | - François Bouzom
- DMPK, Development Science, UCB Pharma, Braine I'Alleud, Belgium; Current: Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA 93534, USA
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3
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Arakawa H, Nakazono Y, Matsuoka N, Hayashi M, Shirasaka Y, Hirao A, Tamai I. Induction of open-form bile canaliculus formation by hepatocytes for evaluation of biliary drug excretion. Commun Biol 2023; 6:866. [PMID: 37608051 PMCID: PMC10444810 DOI: 10.1038/s42003-023-05216-z] [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: 10/12/2022] [Accepted: 08/04/2023] [Indexed: 08/24/2023] Open
Abstract
Biliary excretion is a major drug elimination pathway that affects their efficacy and safety. The currently available in vitro sandwich-cultured hepatocyte method is cumbersome because drugs accumulate in the closed bile canalicular lumen formed between hepatocytes and their amounts cannot be mealsured directly. This study proposes a hepatocyte culture model for the rapid evaluation of drug biliary excretion using permeation assays. When hepatocytes are cultured on a permeable support coated with the cell adhesion protein claudins, an open-form bile canalicular lumen is formed at the surface of the permeable support. Upon application to the basolateral (blood) side, drugs appear on the bile canalicular side. The biliary excretion clearance of several drugs, as estimated from the obtained permeabilities, correlates well with the reported in vivo biliary excretion clearance in humans. Thus, the established model is useful for applications in the efficient evaluation of biliary excretion during drug discovery and development.
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Affiliation(s)
- Hiroshi Arakawa
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Yuya Nakazono
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Natsumi Matsuoka
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Momoka Hayashi
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Yoshiyuki Shirasaka
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Atsushi Hirao
- Division of Molecular Genetics, Cancer Research Institute, Kanazawa University, Kanazawa, 920-1192, Japan
- WPI Nano Life Science Institute (WPI-Nano LSI), Kanazawa University, Kanazawa, 920-1192, Japan
| | - Ikumi Tamai
- Faculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, 920-1192, Japan.
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4
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Nagar S, Radice C, Tuohy R, Stevens R, Bennyhoff D, Korzekwa K. The Rat Continuous Intestine Model Predicts the Impact of Particle Size and Transporters on the Oral Absorption of Glyburide. Mol Pharm 2023; 20:219-231. [PMID: 36541850 DOI: 10.1021/acs.molpharmaceut.2c00597] [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: 12/24/2022]
Abstract
Oral drug absorption is known to be impacted by the physicochemical properties of drugs, properties of oral formulations, and physiological characteristics of the intestine. The goal of the present study was to develop a mathematical model to predict the impact of particle size, feeding time, and intestinal transporter activity on oral absorption. A previously published rat continuous intestine absorption model was extended for solid drug absorption. The impact of active pharmaceutical ingredient particle size was evaluated with glyburide (GLY) as a model drug. Two particle size suspensions of glyburide were prepared with average particle sizes of 42.7 and 4.1 μm. Each suspension was dosed as a single oral gavage to male Sprague Dawley rats, and concentration-time (C-t) profiles of glyburide were measured with liquid chromatography coupled with tandem mass spectrometry. A continuous rat intestine absorption model was extended to include drug dissolution and was used to predict the absorption kinetics of GLY depending on particle size. Additional literature datasets of rat GLY formulations with particle sizes ranging from 0.25 to 4.0 μm were used for model predictions. The model predicted reasonably well the absorption profiles of GLY based on varying particle size and varying feeding time. The model predicted inhibition of intestinal uptake or efflux transporters depending on the datasets. The three datasets used formulations with different excipients, which may impact the transporter activity. Model simulations indicated that the model provides a facile framework to predict the impact of transporter inhibition on drug C-t profiles. Model simulations can also be conducted to evaluate the impact of an altered intestinal lumen environment. In conclusion, the rat continuous intestine absorption model may provide a useful tool to predict the impact of varying drug formulations on rat oral absorption profiles.
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Affiliation(s)
- Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania19140, United States
| | - Casey Radice
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania19140, United States
| | - Robert Tuohy
- Pace Analytical Life Sciences LLC, Norristown, Pennsylvania19401, United States
| | - Raymond Stevens
- Particle Solutions LLC, West Chester, Pennsylvania19382, United States
| | - Dale Bennyhoff
- Particle Solutions LLC, West Chester, Pennsylvania19382, United States
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania19140, United States
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5
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Vugler A, O’Connell J, Nguyen MA, Weitz D, Leeuw T, Hickford E, Verbitsky A, Ying X, Rehberg M, Carrington B, Merriman M, Moss A, Nicholas JM, Stanley P, Wright S, Bourne T, Foricher Y, Brookings D, Horsley H, Herrmann M, Rao S, Kohlmann M, Florian P. An orally available small molecule that targets soluble TNF to deliver anti-TNF biologic-like efficacy in rheumatoid arthritis. Front Pharmacol 2022; 13:1037983. [PMID: 36467083 PMCID: PMC9709720 DOI: 10.3389/fphar.2022.1037983] [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: 09/06/2022] [Accepted: 10/21/2022] [Indexed: 07/30/2023] Open
Abstract
Tumor necrosis factor (TNF) is a pleiotropic cytokine belonging to a family of trimeric proteins with both proinflammatory and immunoregulatory functions. TNF is a key mediator in autoimmune diseases and during the last couple of decades several biologic drugs have delivered new therapeutic options for patients suffering from chronic autoimmune diseases such as rheumatoid arthritis and chronic inflammatory bowel disease. Attempts to design small molecule therapies directed to this cytokine have not led to approved products yet. Here we report the discovery and development of a potent small molecule inhibitor of TNF that was recently moved into phase 1 clinical trials. The molecule, SAR441566, stabilizes an asymmetrical form of the soluble TNF trimer, compromises downstream signaling and inhibits the functions of TNF in vitro and in vivo. With SAR441566 being studied in healthy volunteers we hope to deliver a more convenient orally bioavailable and effective treatment option for patients suffering with chronic autoimmune diseases compared to established biologic drugs targeting TNF.
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Affiliation(s)
- Alexander Vugler
- Immunology Therapeutic Area, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - James O’Connell
- Discovery Sciences, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Mai Anh Nguyen
- Sanofi R&D, TMED Pharmacokinetics Dynamics and Metabolism, Frankfurt am Main, Germany
| | - Dietmar Weitz
- Sanofi R&D, Drug Metabolism and Pharmacokinetics, Frankfurt am Main, Germany
| | - Thomas Leeuw
- Sanofi R&D, Type 1/17 Immunology, Immunology & Inflammation Research TA, Frankfurt, Germany
| | - Elizabeth Hickford
- Development Science, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | | | - Xiaoyou Ying
- Sanofi R&D, Translation In vivo Models, Cambridge, MA, United States
| | - Markus Rehberg
- Sanofi R&D, Translational Disease Modelling, Frankfurt am Main, Germany
| | - Bruce Carrington
- Discovery Sciences, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Mark Merriman
- Immunology Therapeutic Area, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Andrew Moss
- Translational Medicine Immunology, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Jean-Marie Nicholas
- Development Science, Drug Metabolism and Pharmacokinetics, UCB Pharma, Braine-I’Alleud, Belgium
| | - Phil Stanley
- Immunology Therapeutic Area, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Sara Wright
- Early PV Missions, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Tim Bourne
- Milvuswood Consultancy, Penn, United Kingdom
| | - Yann Foricher
- Sanofi R&D, Integrated Drug Discovery, Medicinal Chemistry, Therapeutic Area Immunology & Inflammation, Vitry-sur-Seine, France
| | - Daniel Brookings
- Global Chemistry, Discovery Sciences, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Helen Horsley
- Global Chemistry, Discovery Sciences, PV Early Solutions, UCB Pharma, Slough, United Kingdom
| | - Matthias Herrmann
- Sanofi R&D, Type 1/17 Immunology, Immunology & Inflammation Research TA, Frankfurt, Germany
| | - Srinivas Rao
- Sanofi R&D, Translation In vivo Models, Cambridge, MA, United States
| | - Markus Kohlmann
- Sanofi R&D, Early Clinical Development, Therapeutic Area Immunology and Inflammation, Frankfurt am Main, Germany
| | - Peter Florian
- Sanofi R&D, Type 1/17 Immunology, Immunology & Inflammation Research TA, Frankfurt, Germany
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6
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Korzekwa K, Radice C, Nagar S. A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles. Clin Transl Sci 2022; 15:2035-2052. [PMID: 35588513 PMCID: PMC9372417 DOI: 10.1111/cts.13314] [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: 10/25/2021] [Revised: 04/19/2022] [Accepted: 05/08/2022] [Indexed: 12/02/2022] Open
Abstract
To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (Kp,mem), and PermQ. Kp,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in Vss and t1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.
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Affiliation(s)
- Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, PA, USA
| | - Casey Radice
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, PA, USA
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, PA, USA
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7
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Korzekwa K, Yadav J, Nagar S. Using Partition Analysis as a Facile Method to Derive Net Clearances. Clin Transl Sci 2022; 15:1867-1879. [PMID: 35579201 PMCID: PMC9372430 DOI: 10.1111/cts.13310] [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: 10/25/2021] [Revised: 04/19/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.
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Affiliation(s)
- Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University, PA 19140, Philadelphia
| | - Jaydeep Yadav
- Department of Pharmaceutical Sciences, Temple University, PA 19140, Philadelphia.,Current Address: Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Boston, MA, USA
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University, PA 19140, Philadelphia
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8
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Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. TOXICS 2022; 10:232. [PMID: 35622645 PMCID: PMC9143724 DOI: 10.3390/toxics10050232] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/24/2022] [Indexed: 02/04/2023]
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.
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Affiliation(s)
- Xiaoqing Chang
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, 109 T.W. Alexander Drive, Durham, NC 27709, USA;
| | - David G. Allen
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Shannon Bell
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Paul C. Brown
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Lauren Browning
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Patricia Ceger
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Jeffery Gearhart
- The Henry M. Jackson Foundation, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Pertti J. Hakkinen
- National Library of Medicine, National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA;
| | - Shruti V. Kabadi
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, 5001 Campus Drive, HFS-275, College Park, MD 20740, USA;
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA;
| | - Annie Lumen
- U.S. Food and Drug Administration, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA;
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, Division of Toxicology and Risk Assessment, 5 Research Place, Rockville, MD 20850, USA;
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy;
| | - Heather A. Pangburn
- Air Force Research Laboratory, 711 Human Performance Wing, 2729 R Street, Area B, Building 837, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Elijah J. Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA;
| | - Emily N. Reinke
- U.S. Army Public Health Center, 8252 Blackhawk Rd., Aberdeen Proving Ground, MD 21010, USA;
| | - Alexandre J. S. Ribeiro
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Nisha Sipes
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Lisa M. Sweeney
- UES, Inc., 4401 Dayton-Xenia Road, Beavercreek, OH 45432, Assigned to Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Ronald Wange
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Barbara A. Wetmore
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, Office of the Associate Director for Science, 1600 Clifton Road, S102-2, Atlanta, GA 30333, USA
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9
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Ng TSC, Hu H, Kronister S, Lee C, Li R, Gerosa L, Stopka SA, Burgenske DM, Khurana I, Regan MS, Vallabhaneni S, Putta N, Scott E, Matvey D, Giobbie-Hurder A, Kohler RH, Sarkaria JN, Parangi S, Sorger PK, Agar NYR, Jacene HA, Sullivan RJ, Buchbinder E, Mikula H, Weissleder R, Miller MA. Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy. SCIENCE ADVANCES 2022; 8:eabl6339. [PMID: 35486732 PMCID: PMC9054019 DOI: 10.1126/sciadv.abl6339] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non-small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.
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Affiliation(s)
- Thomas S. C. Ng
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Huiyu Hu
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Stefan Kronister
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Institute of Applied Synthetic Chemistry, Technische Universität Wien, Vienna, Austria
| | - Chanseo Lee
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Ran Li
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sylwia A. Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Ishaan Khurana
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Michael S. Regan
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sreeram Vallabhaneni
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Niharika Putta
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Ella Scott
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Dylan Matvey
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Anita Giobbie-Hurder
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Rainer H. Kohler
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Jann N. Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Sareh Parangi
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Nathalie Y. R. Agar
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Heather A. Jacene
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ryan J. Sullivan
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Hannes Mikula
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Institute of Applied Synthetic Chemistry, Technische Universität Wien, Vienna, Austria
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Miles A. Miller
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Corresponding author.
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10
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Radice C, Korzekwa K, Nagar S. Predicting impact of food and feeding time on oral absorption of drugs with a novel rat continuous intestinal absorption model. Drug Metab Dispos 2022; 50:750-761. [PMID: 35339986 DOI: 10.1124/dmd.122.000831] [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: 01/14/2022] [Accepted: 03/17/2022] [Indexed: 11/22/2022] Open
Abstract
Intricacies in intestinal physiology, drug properties, and food effects should be incorporated into models to predict complex oral drug absorption. A previously published human continuous intestinal absorption model based on the convection-diffusion equation was modified specifically for the male Sprague-Dawley rat in this report. Species-specific physiological conditions along intestinal length 'x' - experimental velocity and pH under fasted and fed conditions, were measured and incorporated into the intestinal absorption model. Concentration- time (C-t) profiles were measured upon a single IV and PO dose for three drugs, amlodipine (AML), digoxin (DIG), and glyburide (GLY). Absorption profiles were predicted and compared with experimentally collected data under three feeding conditions: 12-hr fasted rats were provided food at two specific times after oral drug dose (1 hr and 2 hr for AML and GLY, 0.5 hr and 1 hr for DIG), or were provided food for the entire study. IV versus PO C-t profiles suggested absorption even at later times, and informed design of appropriate mathematical input functions based on experimental feeding times. With this model, AML, DIG and GLY oral C-t profiles for all feeding groups were generally well predicted, with exposure overlap coefficients (EOC) in the range of 0.80 - 0.97. Efflux transport for DIG and uptake and efflux transport for GLY were included, modeling uptake transporter inhibition in the presence of food. Results indicate that the continuous intestinal rat model incorporates complex physiological processes and feeding times relative to drug dose, into a simple framework to provide accurate prediction of oral absorption. Significance Statement A novel rat continuous intestinal model predicts drug absorption with respect to time and intestinal length. Feeding time relative to dose was modeled as a key effect. Experimental fasted/fed intestinal pH and velocity, efflux and uptake transporter expression along intestinal length, and uptake transporter inhibition in the presence of food, were modeled. The model uses the pharmacokinetic profiles of three model drugs and provides a novel framework to study food effects on absorption.
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Affiliation(s)
- Casey Radice
- Pharmaceutical Sciences, Temple University School of Pharmacy, United States
| | - Ken Korzekwa
- Pharmaceutical Sciences, Temple University School of Pharmacy, United States
| | - Swati Nagar
- Pharmaceutical Sciences, Temple University School of Pharmacy, United States
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11
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Danishuddin, Kumar V, Faheem M, Woo Lee K. A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges. Drug Discov Today 2021; 27:529-537. [PMID: 34592448 DOI: 10.1016/j.drudis.2021.09.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/21/2021] [Accepted: 09/22/2021] [Indexed: 11/28/2022]
Abstract
Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.
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Affiliation(s)
- Danishuddin
- Department of Bio & Medical Big Data (BK4), Division of Life Sciences, Research Institute of Natural Sciences (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea
| | - Vikas Kumar
- Department of Bio & Medical Big Data (BK4), Division of Life Sciences, Research Institute of Natural Sciences (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea
| | - Mohammad Faheem
- Department of Biotechnology, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
| | - Keun Woo Lee
- Department of Bio & Medical Big Data (BK4), Division of Life Sciences, Research Institute of Natural Sciences (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea.
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12
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A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform. Sci Rep 2021; 11:11143. [PMID: 34045592 PMCID: PMC8160209 DOI: 10.1038/s41598-021-90637-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/13/2021] [Indexed: 12/17/2022] Open
Abstract
Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate’s volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.
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13
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Abdel-Tawab M. Considerations to Be Taken When Carrying Out Medicinal Plant Research-What We Learn from an Insight into the IC 50 Values, Bioavailability and Clinical Efficacy of Exemplary Anti-Inflammatory Herbal Components. Pharmaceuticals (Basel) 2021; 14:437. [PMID: 34066427 PMCID: PMC8148151 DOI: 10.3390/ph14050437] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/11/2022] Open
Abstract
Medicinal plants represent a big reservoir for discovering new drugs against all kinds of diseases including inflammation. In spite the large number of promising anti-inflammatory plant extracts and isolated components, research on medicinal plants proves to be very difficult. Based on that background this review aims to provide a summarized insight into the hitherto known pharmacologically active concentrations, bioavailability, and clinical efficacy of boswellic acids, curcumin, quercetin and resveratrol. These examples have in common that the achieved plasma concentrations were found to be often far below the determined IC50 values in vitro. On the other hand demonstrated therapeutic effects suggest a necessity of rethinking our pharmacokinetic understanding. In this light this review discusses the value of plasma levels as pharmacokinetic surrogates in comparison to the more informative value of tissue concentrations. Furthermore the need for new methodological approaches is addressed like the application of combinatorial approaches for identifying and pharmacokinetic investigations of active multi-components. Also the physiological relevance of exemplary in vitro assays and absorption studies in cell-line based models is discussed. All these topics should be ideally considered to avoid inaccurate predictions for the efficacy of herbal components in vivo and to unlock the "black box" of herbal mixtures.
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Affiliation(s)
- Mona Abdel-Tawab
- Central Laboratory of German Pharmacists, Carl-Mannich-Str. 20, 65760 Eschborn, Germany; ; Tel.: +49-6196-937-955
- Institute of Pharmaceutical Chemistry, Johann Wolfgang Goethe University, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany
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14
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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: 3.8] [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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