1
|
Malek R, Sałat K, Totoson P, Karcz T, Refouvelet B, Skrzypczak-Wiercioch A, Maj M, Simakov A, Martin H, Siwek A, Szałaj N, Godyń J, Panek D, Więckowska A, Jozwiak K, Demougeot C, Kieć-Kononowicz K, Chabchoub F, Iriepa I, Marco-Contelles J, Ismaili L. Discovery of New Highly Potent Histamine H 3 Receptor Antagonists, Calcium Channel Blockers, and Acetylcholinesterase Inhibitors. ACS Chem Neurosci 2024; 15:3363-3383. [PMID: 39208251 DOI: 10.1021/acschemneuro.4c00341] [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] [Indexed: 09/04/2024] Open
Abstract
At present, one of the most promising strategies to tackle the complex challenges posed by Alzheimer's disease (AD) involves the development of novel multitarget-directed ligands (MTDLs). To this end, we designed and synthesized nine new MTDLs using a straightforward and cost-efficient one-pot Biginelli three-component reaction. Among these newly developed compounds, one particular small molecule, named 3e has emerged as a promising MTDL. This compound effectively targets critical biological factors associated with AD, including the simultaneous inhibition of cholinesterases (ChEs), selective antagonism of H3 receptors, and blocking voltage-gated calcium channels. Additionally, compound 3e exhibited remarkable neuroprotective activity against H2O2 and Aβ1-40, and effectively restored cognitive function in AD mice treated with scopolamine in the novel object recognition task, confirming that this compound could provide a novel and innovative therapeutic approach for the effective treatment of AD.
Collapse
Affiliation(s)
- Rim Malek
- Université de Franche-Comté, INSERM, UMR 1322 LINC, F-25000 Besançon, France
- Laboratory of Applied Chemistry: Heterocycles, Lipids and Polymers, Faculty of Sciences of Sfax, University of Sfax, B. P 802, Sfax 3000, Tunisia
| | - Kinga Sałat
- Department of Pharmacodynamics, Chair of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków 30-688, Poland
| | - Perle Totoson
- Université de Franche-Comté, EFS, INSERM, UMR 1098 RIGHT, F-25000 Besançon, France
| | - Tadeusz Karcz
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Bernard Refouvelet
- Université de Franche-Comté, INSERM, UMR 1322 LINC, F-25000 Besançon, France
| | - Anna Skrzypczak-Wiercioch
- University Centre of Veterinary Medicine JU-UA, University of Agriculture in Krakow, 24/28 Mickiewicz St., Kraków 30-059, Poland
| | - Maciej Maj
- Department of Biopharmacy, Medical University of Lublin, ul. W. Chodzki 4a, Lublin 20-093, Poland
| | - Alexey Simakov
- Université de Franche-Comté, EFS, INSERM, UMR 1098 RIGHT, F-25000 Besançon, France
| | - Helene Martin
- Université de Franche-Comté, EFS, INSERM, UMR 1098 RIGHT, F-25000 Besançon, France
| | - Agata Siwek
- Department of Pharmacobiology, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Natalia Szałaj
- Department of Physicochemical Drug Analysis, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Justyna Godyń
- Department of Physicochemical Drug Analysis, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Dawid Panek
- Department of Physicochemical Drug Analysis, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Anna Więckowska
- Department of Physicochemical Drug Analysis, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Krzysztof Jozwiak
- Department of Biopharmacy, Medical University of Lublin, ul. W. Chodzki 4a, Lublin 20-093, Poland
| | - Celine Demougeot
- Université de Franche-Comté, EFS, INSERM, UMR 1098 RIGHT, F-25000 Besançon, France
| | - Katarzyna Kieć-Kononowicz
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, Kraków 30-688, Poland
| | - Fakher Chabchoub
- Laboratory of Applied Chemistry: Heterocycles, Lipids and Polymers, Faculty of Sciences of Sfax, University of Sfax, B. P 802, Sfax 3000, Tunisia
| | - Isabel Iriepa
- Universidad de Alcalá. Departamento de Química Orgánica y Química Inorgánica, Alcalá de Henares, Madrid 28805, Spain
- Instituto de Investigación Química Andrés M. del Río (IQAR), Universidad de Alcalá, Alcalá de Henares, Madrid 28805, Spain, Grupo DISCOBAC, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM)
| | - José Marco-Contelles
- Laboratory of Medicinal Chemistry (IQOG, CSIC), C/ Juan de la Cierva 3, Madrid 28006, Spain
- CIBER, ISCIII, Center for Biomedical Network Research on Rare Diseases (CIBERER), Madrid 28006, Spain
| | - Lhassane Ismaili
- Université de Franche-Comté, INSERM, UMR 1322 LINC, F-25000 Besançon, France
| |
Collapse
|
2
|
Zaman SU, Pagare PP, Ma H, Hoyle RG, Zhang Y, Li J. Novel PROTAC probes targeting KDM3 degradation to eliminate colorectal cancer stem cells through inhibition of Wnt/β-catenin signaling. RSC Med Chem 2024:d4md00122b. [PMID: 39281802 PMCID: PMC11393732 DOI: 10.1039/d4md00122b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 08/20/2024] [Indexed: 09/18/2024] Open
Abstract
It has been demonstrated that the KDM3 family of histone demethylases (KDM3A and KDM3B) epigenetically control the functional properties of colorectal cancer stem cells (CSCs) through Wnt/β-catenin signaling. Meanwhile, a broad-spectrum histone demethylase inhibitor, IOX1, suppresses Wnt-induced colorectal tumorigenesis predominantly through inhibiting the enzymatic activity of KDM3. In this work, several cereblon (CRBN)-recruiting PROTACs with various linker lengths were designed and synthesized using IOX1 as a warhead to target KDM3 proteins for degradation. Two of the synthesized PROTACs demonstrated favorable degradation profile and selectivity towards KDM3A and KDM3B. Compound 4 demonstrated favorable in vitro metabolic profile in liver enzymes as well as no hERG-associated cardiotoxicity. Compound 4 also showed dramatic ability in suppressing oncogenic Wnt signaling to eliminate colorectal CSCs and inhibit tumor growth, with around 10- to 35-fold increased potency over IOX1. In summary, this study suggests that PROTACs provide a unique molecular tool for the development of novel small molecules from the IOX1 skeleton for selective degradation of KDM3 to eliminate colorectal CSCs via suppressing oncogenic Wnt signaling.
Collapse
Affiliation(s)
- Shadid U Zaman
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| | - Piyusha P Pagare
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| | - Hongguang Ma
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| | - Rosalie G Hoyle
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| | - Yan Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| | - Jiong Li
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
- Department of Oral and Craniofacial Molecular Biology, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
- Massey Cancer Center, Virginia Commonwealth University Richmond Virginia 23298-0540 USA
| |
Collapse
|
3
|
Geci R, Gadaleta D, de Lomana MG, Ortega-Vallbona R, Colombo E, Serrano-Candelas E, Paini A, Kuepfer L, Schaller S. Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans. Arch Toxicol 2024; 98:2659-2676. [PMID: 38722347 PMCID: PMC11272695 DOI: 10.1007/s00204-024-03764-9] [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: 03/12/2024] [Accepted: 04/23/2024] [Indexed: 07/26/2024]
Abstract
Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.
Collapse
Affiliation(s)
- René Geci
- esqLABS GmbH, Saterland, Germany.
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany.
| | | | - Marina García de Lomana
- Machine Learning Research, Research and Development, Pharmaceuticals, Bayer AG, Berlin, Germany
| | | | - Erika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
| | | |
Collapse
|
4
|
Li Y, Wang Z, Li Y, Du J, Gao X, Li Y, Lai L. A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans. Pharm Res 2024; 41:1369-1379. [PMID: 38918309 DOI: 10.1007/s11095-024-03725-y] [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: 10/21/2023] [Accepted: 06/02/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data. METHODS This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy. RESULTS Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy. CONCLUSION The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.
Collapse
Affiliation(s)
- Yuelin Li
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Zonghu Wang
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Yuru Li
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Jiewen Du
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Xiangrui Gao
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Yuanpeng Li
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China
| | - Lipeng Lai
- XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China.
| |
Collapse
|
5
|
Rath M, Wellnitz J, Martin HJ, Melo-Filho C, Hochuli JE, Silva GM, Beasley JM, Travis M, Sessions ZL, Popov KI, Zakharov AV, Cherkasov A, Alves V, Muratov EN, Tropsha A. Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles. J Med Chem 2024; 67:6508-6518. [PMID: 38568752 DOI: 10.1021/acs.jmedchem.3c02446] [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: 04/05/2024]
Abstract
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.
Collapse
Affiliation(s)
- Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Cleber Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Joshua E Hochuli
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guilherme Martins Silva
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jon-Michael Beasley
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maxfield Travis
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L Sessions
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Konstantin I Popov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada
| | - Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| |
Collapse
|
6
|
Zhang F, Erskine TC, McClymont EL, Moore LM, LeBaron MJ, McNett D, Marty SS. Predictions of tissue concentrations of myclobutanil, oxyfluorfen, and pronamide in rat and human after oral exposures via GastroPlus TM physiologically based pharmacokinetic modelling. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:285-307. [PMID: 38588502 DOI: 10.1080/1062936x.2024.2333878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/15/2024] [Indexed: 04/10/2024]
Abstract
Heritage agrochemicals like myclobutanil, oxyfluorfen, and pronamide, are extensively used in agriculture, with well-established studies on their animal toxicity. Yet, human toxicity assessment relies on conventional human risk assessment approaches including the utilization of animal-based ADME (Absorption, Distribution, Metabolism, and Excretion) data. In recent years, Physiologically Based Pharmacokinetic (PBPK) modelling approaches have played an increasing role in human risk assessment of many chemicals including agrochemicals. This study addresses the absence of PBPK-type data for myclobutanil, oxyfluorfen, and pronamide by generating in vitro data for key input PBPK parameters (Caco-2 permeability, rat plasma binding, rat blood to plasma ratio, and rat liver microsomal half-life), followed by generation of PBPK models for these three chemicals via the GastroPlusTM software. Incorporating these experimental input parameters into PBPK models, the prediction accuracy of plasma AUC (area under curve) was significantly improved. Validation against rat oral administration data demonstrated substantial enhancement. Steady-state plasma concentrations (Css) of pronamide aligned well with published data using measured PBPK parameters. Following validation, parent-based tissue concentrations for these agrochemicals were predicted in humans and rats after single or 30-day repeat exposure of 10 mg/kg/day. These predicted concentrations contribute valuable information for future human toxicity risk assessments of these agrochemicals.
Collapse
Affiliation(s)
- F Zhang
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - T C Erskine
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - E L McClymont
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - L M Moore
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - M J LeBaron
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - D McNett
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| | - S S Marty
- Toxicology & Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA
| |
Collapse
|
7
|
Lombardo F, Bentzien J, Berellini G, Muegge I. Prediction of Human Clearance Using In Silico Models with Reduced Bias. Mol Pharm 2024; 21:1192-1203. [PMID: 38285644 DOI: 10.1021/acs.molpharmaceut.3c00812] [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] [Indexed: 01/31/2024]
Abstract
Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.
Collapse
Affiliation(s)
- Franco Lombardo
- CmaxDMPK, LLC, Framingham , Massachusetts 01701, United States
| | - Jörg Bentzien
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Giuliano Berellini
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Ingo Muegge
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| |
Collapse
|
8
|
Di L. Quantitative Translation of Substrate Intrinsic Clearance from Recombinant CYP1A1 to Humans. AAPS J 2023; 25:98. [PMID: 37798423 DOI: 10.1208/s12248-023-00863-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
CYP1A1 is a cytochrome P450 family 1 enzyme that is mostly expressed in the extrahepatic tissues. To understand the CYP1A1 contribution to drug clearance in humans, we examined the in vitro-in vivo extrapolation (IVIVE) of intrinsic clearance (CLint) for a set of drugs that are in vitro CYP1A1 substrates. Despite being strong in vitro CYP1A1 substrates, 82% of drugs gave good IVIVE with predicted CLint within 2-3-fold of the observed values using human liver microsomes and hepatocytes, suggesting they were not in vivo CYP1A1 substrates due to the lack of extrahepatic contribution to CLint. Only three drugs (riluzole, melatonin and ramelteon) that are CYP1A2 substrates yielded significant underprediction of in vivo CLint up to 11-fold. The fold of CLint underprediction was linearly proportional to human recombinant CYP1A1 (rCYP1A1) CLint, indicating they were likely to be in vivo CYP1A1 substrates. Using these three substrates, a calibration curve can be developed to enable direct translation from in vitro rCYP1A1 CLint to in vivo extrahepatic contributions in humans. In vivo CYP1A1 substrates are planar and small, which is consistent with the structure of the active site. This is in contrast to the in vitro substrates, which include large and nonplanar molecules, suggesting rCYP1A1 is more accessible than what is in vivo. The impact of CYP1A1 on first-pass intestinal metabolism was also evaluated and shown to be minimal. This is the first study providing new insights on in vivo translation of CYP1A1 contributions to human clearance using in vitro rCYP1A1 data.
Collapse
Affiliation(s)
- Li Di
- Pharmacokinetic, Dynamics and Drug Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut, 06543, USA.
| |
Collapse
|
9
|
LaPorte M, Alverez C, Chatterley A, Kovaliov M, Carder EJ, Houghton MJ, Lim C, Miller ER, Samankumara LP, Liang M, Kerrigan K, Yue Z, Li S, Tomaino F, Wang F, Green N, Stott GM, Srivastava A, Chou TF, Wipf P, Huryn DM. Optimization of 1,2,4-Triazole-Based p97 Inhibitors for the Treatment of Cancer. ACS Med Chem Lett 2023; 14:977-985. [PMID: 37465292 PMCID: PMC10351062 DOI: 10.1021/acsmedchemlett.3c00163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/14/2023] [Indexed: 07/20/2023] Open
Abstract
The AAA+ ATPase p97 (valosin-containing protein, VCP) is a master regulator of protein homeostasis and therefore represents a novel target for cancer therapy. Starting from a known allosteric inhibitor, NMS-873, we systematically optimized this scaffold, in particular, by applying a benzene-to-acetylene isosteric replacement strategy, specific incorporation of F, and eutomer/distomer identification, which led to compounds that exhibited nanomolar biochemical and cell-based potency. In cellular pharmacodynamic assays, robust effects on biomarkers of p97 inhibition and apoptosis, including increased levels of ubiquitinated proteins, CHOP and cleaved caspase 3, were observed. Compound (R)-29 (UPCDC-30766) represents the most potent allosteric inhibitor of p97 reported to date.
Collapse
Affiliation(s)
- Matthew
G. LaPorte
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Celeste Alverez
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmaceutical Sciences, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Alexander Chatterley
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Marina Kovaliov
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Evan J. Carder
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Michael J. Houghton
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Chaemin Lim
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Eric R. Miller
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Lalith P. Samankumara
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Mary Liang
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmaceutical Sciences, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Kaylan Kerrigan
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Zhizhou Yue
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Shan Li
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Francesca Tomaino
- Leidos
Biomedical Research, Inc., Frederick, Maryland 21702, United States
| | - Feng Wang
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Neal Green
- Leidos
Biomedical Research, Inc., Frederick, Maryland 21702, United States
| | - Gordon M. Stott
- Leidos
Biomedical Research, Inc., Frederick, Maryland 21702, United States
| | - Apurva Srivastava
- Leidos
Biomedical Research, Inc., Frederick, Maryland 21702, United States
| | - Tsui-Fen Chou
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Peter Wipf
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmaceutical Sciences, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Donna M. Huryn
- University
of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmaceutical Sciences, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| |
Collapse
|
10
|
Manevski N, Umehara K, Parrott N. Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CL p) from Hepatocyte Intrinsic Clearance (CL int). Mol Pharm 2023. [PMID: 37235687 DOI: 10.1021/acs.molpharmaceut.2c01001] [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: 05/28/2023]
Abstract
Hepatocyte intrinsic clearance (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 μL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (Papp > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CLp IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CLp IVIVE. Our results indicate that the current practice of prospective CLp IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CLp IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CLp IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.
Collapse
Affiliation(s)
- Nenad Manevski
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
| | - Kenichi Umehara
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
| | - Neil Parrott
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, 4070 Basel, Switzerland
| |
Collapse
|
11
|
Tynelius N, Bundgaard C, Müller CE. Evaluation of a Five-Probe Metabolic Control Cocktail in Long-Term Cocultured Human Hepatocytes. J Pharm Sci 2023:S0022-3549(23)00099-0. [PMID: 36893963 DOI: 10.1016/j.xphs.2023.03.001] [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: 12/19/2022] [Revised: 03/01/2023] [Accepted: 03/01/2023] [Indexed: 03/10/2023]
Abstract
Hepatocyte cocultures like HepatoPac have become more frequently used for the assessment of the intrinsic clearance of slowly metabolised drugs during drug discovery due to a superiority in enzymatic activity over time compared to liver microsomal fractions and suspended primary hepatocytes. However, the relatively high cost and practical limitations prevent several quality control compounds to be included in studies and the activities of many important metabolic enzymes are consequently often not monitored. In this study, we have evaluated the possibility for a cocktail approach of quality control compounds in the human HepatoPac system to ensure adequate activity of the major metabolising enzymes. Five reference compounds were selected based on their known metabolic substrate profile in order to capture major CYP and non-CYP metabolic pathways in the incubation cocktail. The intrinsic clearance of the reference compounds when incubated as singlets or in a cocktail was compared and no considerable difference was observed. We show here that a cocktail approach of quality control compounds allows for easy and efficient evaluation of the metabolic competency of the hepatic coculture system over an extended incubation period.
Collapse
Affiliation(s)
- Nanna Tynelius
- Translational DMPK, H. Lundbeck A/S, Valby, 2500 Copenhagen, Denmark
| | | | - Claudia E Müller
- Translational DMPK, H. Lundbeck A/S, Valby, 2500 Copenhagen, Denmark.
| |
Collapse
|
12
|
Fagerholm U, Hellberg S, Alvarsson J, Spjuth O. In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods. Altern Lab Anim 2023; 51:39-54. [PMID: 36572567 DOI: 10.1177/02611929221148447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier (ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.
Collapse
Affiliation(s)
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Prosilico AB, Huddinge, Sweden.,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| |
Collapse
|
13
|
Ladumor MK, Storelli F, Liang X, Lai Y, Enogieru OJ, Chothe PP, Evers R, Unadkat J. Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes. CPT Pharmacometrics Syst Pharmacol 2022; 12:261-273. [PMID: 36540952 PMCID: PMC9931433 DOI: 10.1002/psp4.12901] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/07/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.
Collapse
Affiliation(s)
- Mayur K. Ladumor
- Department of PharmaceuticsUniversity of Washington School of PharmacySeattleWashingtonUSA
| | - Flavia Storelli
- Department of PharmaceuticsUniversity of Washington School of PharmacySeattleWashingtonUSA
| | - Xiaomin Liang
- Drug MetabolismGilead Sciences Inc.Foster CityCaliforniaUSA
| | - Yurong Lai
- Drug MetabolismGilead Sciences Inc.Foster CityCaliforniaUSA
| | | | - Paresh P. Chothe
- Global Drug Metabolism and PharmacokineticsTakeda Development Center USA, Inc.LexingtonMassachusettsUSA
| | - Raymond Evers
- Preclinical Sciences and Translational SafetyJanssen Research & Development, LLCSpring HousePennsylvaniaUSA
| | - Jashvant D. Unadkat
- Department of PharmaceuticsUniversity of Washington School of PharmacySeattleWashingtonUSA
| |
Collapse
|
14
|
Pagare P, Obeng S, Huang B, Marcus MM, Nicholson KL, Townsend AE, Banks ML, Zhang Y. Preclinical Characterization and Development on NAQ as a Mu Opioid Receptor Partial Agonist for Opioid Use Disorder Treatment. ACS Pharmacol Transl Sci 2022; 5:1197-1209. [PMID: 36407950 PMCID: PMC9667545 DOI: 10.1021/acsptsci.2c00178] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Indexed: 11/06/2022]
Abstract
Mu opioid receptor (MOR) selective antagonists and partial agonists have clinical utility for the treatment of opioid use disorders (OUDs). However, the development of many has suffered due to their poor pharmacokinetic properties and/or rapid metabolism. Our recent efforts to identify MOR modulators have provided 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(isoquinoline-3-carboxamido)morphinan (NAQ), a low-efficacy partial agonist, that showed sub-nanomolar binding affinity to the MOR (K i 0.6 nM) with selectivity over the delta opioid receptor (δ/μ 241) and the kappa opioid receptor (κ/μ 48). Its potent inhibition of the analgesic effect of morphine (AD50 0.46 mg/kg) and precipitation of significantly less withdrawal symptoms even at 100-fold greater dose than naloxone represents a promising molecule for further development as a novel OUD therapeutic agent. Therefore, further in vitro and in vivo characterization of its pharmacokinetics and pharmacodynamics properties was conducted to fully understand its pharmaceutical profile. NAQ showed favorable in vitro ADMET properties and no off-target binding to several classes of GPCRs, enzymes, and ion channels. Following intravenous administration, 1 mg/kg dose of NAQ showed a similar in vivo pharmacokinetic profile to naloxone; however, orally administered 10 mg/kg NAQ demonstrated significantly improved oral bioavailability over both naloxone and naltrexone. Abuse liability assessment of NAQ in rats demonstrated that NAQ functioned as a less potent reinforcer than heroin. Chronic 5 day NAQ pretreatment decreased heroin self-administration in a heroin-vs-food choice procedure similar to the clinically used MOR partial agonist buprenorphine. Taken together, these studies provide evidence supporting NAQ as a promising lead to develop novel OUD therapeutics.
Collapse
Affiliation(s)
- Piyusha
P. Pagare
- Department
of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia23298-0540, United States
| | - Samuel Obeng
- Department
of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia23298-0540, United States
| | - Boshi Huang
- Department
of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia23298-0540, United States
| | - Madison M. Marcus
- Department
of Pharmacology and Toxicology, Virginia
Commonwealth University School of Medicine, Richmond, Virginia23298-0613, United States
| | - Katherine L. Nicholson
- Department
of Pharmacology and Toxicology, Virginia
Commonwealth University School of Medicine, Richmond, Virginia23298-0613, United States
| | - Andrew E. Townsend
- Department
of Pharmacology and Toxicology, Virginia
Commonwealth University School of Medicine, Richmond, Virginia23298-0613, United States
| | - Matthew L. Banks
- Department
of Pharmacology and Toxicology, Virginia
Commonwealth University School of Medicine, Richmond, Virginia23298-0613, United States
| | - Yan Zhang
- Department
of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia23298-0540, United States
| |
Collapse
|
15
|
Liu C, van Mil J, Noorlander A, Rietjens IMCM. Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict In Vivo Nrf2 Activation by EGCG and Its Colonic Metabolites in Humans. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:14015-14031. [PMID: 36262111 DOI: 10.1021/acs.jafc.2c04811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
(-)-Epigallocatechin gallate (EGCG) is prone to microbial metabolism when reaching the colon. This study aimed to develop a human physiologically based kinetic (PBK) model for EGCG, with sub-models for its colonic metabolites gallic acid and pyrogallol. Results show that the developed PBK model could adequately predict in vivo time-dependent blood concentrations of EGCG after either the single or repeated oral administration of EGCG under both fasting and non-fasting conditions. The predicted in vivo blood Cmax of EGCG indicates that the Nrf2 activation is limited, while concentrations of its metabolites in the intestinal tract may reach levels that are higher than that of EGCG and also high enough to activate Nrf2 gene transcription. Taken together, combining in vitro data with a human PBK model allowed the prediction of a dose-response curve for EGCG-induced Nrf2-mediated gene expression in humans and provided insights into the contribution of gut microbial metabolites to this effect.
Collapse
Affiliation(s)
- Chen Liu
- Tea Refining and Innovation Key Laboratory of Sichuan Province, College of Horticulture, Sichuan Agricultural University, Chengdu611130, Sichuan, China
- Division of Toxicology, Wageningen University and Research, WageningenNL 6703 HE, the Netherlands
| | - Jolijn van Mil
- Division of Toxicology, Wageningen University and Research, WageningenNL 6703 HE, the Netherlands
| | - Annelies Noorlander
- Division of Toxicology, Wageningen University and Research, WageningenNL 6703 HE, the Netherlands
| | - Ivonne M C M Rietjens
- Division of Toxicology, Wageningen University and Research, WageningenNL 6703 HE, the Netherlands
| |
Collapse
|
16
|
Esposito S, Orsatti L, Pucci V. Subcutaneous Catabolism of Peptide Therapeutics: Bioanalytical Approaches and ADME Considerations. Xenobiotica 2022; 52:828-839. [PMID: 36039395 DOI: 10.1080/00498254.2022.2119180] [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: 10/14/2022]
Abstract
Many peptide drugs such as insulin and glucagon-like peptide (GLP-1) analogues are successfully administered subcutaneously (SC). Following SC injection, peptides may undergo catabolism in the SC compartment before entering systemic circulation, which could compromise their bioavailability and in turn affect their efficacy.This review will discuss how both technology and strategy have evolved over the past years to further elucidate peptide SC catabolism.Modern bioanalytical technologies (particularly liquid chromatography-high-resolution mass spectrometry) and bioinformatics platforms for data mining has prompted the development of in silico, in vitro and in vivo tools for characterizing peptide SC catabolism to rapidly address proteolytic liabilities and, ultimately, guide the design of peptides with improved SC bioavailability.More predictive models able to recapitulate the interplay between SC catabolism and other factors driving SC absorption are highly desirable to improve in vitro/in vivo correlations.We envision the routine incorporation of in vitro and in vivo SC catabolism studies in ADME screening funnels to develop more effective peptide drugs for SC delivery.
Collapse
|
17
|
Langthaler K, Jones CR, Christensen RB, Eneberg E, Brodin B, Bundgaard C. Characterization of intravenous pharmacokinetics in Göttingen minipig and clearance prediction using established in vitro to in vivo extrapolation methodologies. Xenobiotica 2022; 52:591-607. [PMID: 36000364 DOI: 10.1080/00498254.2022.2115425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
1. The use of the Göttingen minipig as an animal model for drug safety testing and prediction of human pharmacokinetics (PK) continues to gain momentum in pharmaceutical research and development. The aim of this study was to evaluate in vitro to in vivo extrapolation (IVIVE) methodologies for prediction of hepatic, metabolic clearance (CLhep,met) in Göttingen minipig, using a comprehensive set of compounds.2. In vivo clearance was determined in Göttingen minipig by intravenous cassette dosing and hepatocyte intrinsic clearance, plasma protein binding and non-specific incubation binding were determined in vitro. Prediction of CLhep,met was performed by IVIVE using conventional and adapted formats of the well-stirred liver model.3. The best prediction of in vivo CLhep,met from scaled in vitro kinetic data was achieved using an empirical correction factor based on a 'regression offset' of the IVIV relationship.4. In summary, these results expand the in vitro and in vivo PK knowledge in Göttingen minipig. We show regression corrected IVIVE provides superior prediction of in vivo CLhep,met in minipig offering a practical, unified scaling approach to address systematic under-predictions. Finally, we propose a reference set for researchers to establish their own 'lab-specific' regression correction for IVIVE in minipig.
Collapse
Affiliation(s)
- K Langthaler
- Translational DMPK, H. Lundbeck A/S, Copenhagen, Denmark.,CNS Drug Delivery and Barrier Modelling, University of Copenhagen, Copenhagen, Denmark
| | - C R Jones
- Translational DMPK, H. Lundbeck A/S, Copenhagen, Denmark
| | | | - E Eneberg
- Translational DMPK, H. Lundbeck A/S, Copenhagen, Denmark
| | - B Brodin
- CNS Drug Delivery and Barrier Modelling, University of Copenhagen, Copenhagen, Denmark
| | - C Bundgaard
- Translational DMPK, H. Lundbeck A/S, Copenhagen, Denmark
| |
Collapse
|
18
|
Kamble SH, Berthold EC, Kanumuri SRR, King TI, Kuntz MA, León F, Mottinelli M, McMahon LR, McCurdy CR, Sharma A. Metabolism of Speciociliatine, an Overlooked Kratom Alkaloid for its Potential Pharmacological Effects. AAPS J 2022; 24:86. [PMID: 35854066 PMCID: PMC9932950 DOI: 10.1208/s12248-022-00736-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/05/2022] [Indexed: 01/21/2023] Open
Abstract
Speciociliatine, a diastereomer of mitragynine, is an indole-based alkaloid found in kratom (Mitragyna speciosa). Kratom has been widely used for the mitigation of pain and opioid dependence, as a mood enhancer, and/or as an energy booster. Speciociliatine is a partial µ-opioid agonist with a 3-fold higher binding affinity than mitragynine. Speciociliatine has been found to be a major circulating alkaloid in humans following oral administration of a kratom product. In this report, we have characterized the metabolism of speciociliatine in human and preclinical species (mouse, rat, dog, and cynomolgus monkey) liver microsomes and hepatocytes. Speciociliatine metabolized rapidly in monkey, rat, and mouse hepatocytes (in vitro half-life was 6.6 ± 0.2, 8.3 ± 1.1, 11.2 ± 0.7 min, respectively), while a slower metabolism was observed in human and dog hepatocytes (91.7 ± 12.8 and > 120 min, respectively). Speciociliatine underwent extensive metabolism, primarily through monooxidation and O-demethylation metabolic pathways in liver microsomes and hepatocytes across species. No human-specific or disproportionate metabolites of speciociliatine were found in human liver microsomes. The metabolism of speciociliatine was predominantly mediated by CYP3A4 with minor contributions by CYP2D6.
Collapse
Affiliation(s)
- Shyam H. Kamble
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, USA
| | - Erin C. Berthold
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Siva Rama Raju Kanumuri
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, USA
| | - Tamara I. King
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, USA
| | - Michelle A. Kuntz
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, USA
| | - Francisco León
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Marco Mottinelli
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | | | - Christopher R. McCurdy
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, USA,Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL, USA,Corresponding Author Abhisheak Sharma, M. Pharm., Ph.D., UF CTSI, Translational Drug Development Core, Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA. , Phone: 352-294-8690, Christopher R. McCurdy, Ph.D., FAAPS, UF CTSI, Translational Drug Development Core, Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA. , Phone: 352-294-8691
| | - Abhisheak Sharma
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, 32610, USA. .,Translational Drug Development Core, Clinical and Translational Sciences Institute, University of Florida, Gainesville, FL, 32610, USA.
| |
Collapse
|
19
|
The impact of reference data selection for the prediction accuracy of intrinsic hepatic metabolic clearance. J Pharm Sci 2022; 111:2645-2649. [DOI: 10.1016/j.xphs.2022.06.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/29/2022] [Accepted: 06/29/2022] [Indexed: 11/22/2022]
|
20
|
Obrezanova O, Martinsson A, Whitehead T, Mahmoud S, Bender A, Miljković F, Grabowski P, Irwin B, Oprisiu I, Conduit G, Segall M, Smith GF, Williamson B, Winiwarter S, Greene N. Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Mol Pharm 2022; 19:1488-1504. [PMID: 35412314 DOI: 10.1021/acs.molpharmaceut.2c00027] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.
Collapse
Affiliation(s)
- Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Anton Martinsson
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Tom Whitehead
- Intellegens Ltd., Eagle Labs, Cambridge CB4 3AZ, U.K
| | - Samar Mahmoud
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Andreas Bender
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K.,Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Filip Miljković
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Piotr Grabowski
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Ben Irwin
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Ioana Oprisiu
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | | | - Matthew Segall
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Graham F Smith
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Beth Williamson
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge CB10 1XL, U.K
| | - Susanne Winiwarter
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceutical R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Nigel Greene
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
| |
Collapse
|
21
|
Cytochrome P450 isoforms contribution, plasma protein binding, toxicokinetics of enniatin A in rats and in vivo clearance prediction in humans. Food Chem Toxicol 2022; 164:112988. [PMID: 35398446 DOI: 10.1016/j.fct.2022.112988] [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: 08/19/2021] [Revised: 03/28/2022] [Accepted: 04/02/2022] [Indexed: 11/21/2022]
Abstract
Emerging mycotoxins, such as enniatin A (ENNA), are becoming a worldwide concern owing to their presence in different types of food and feed. However, comprehensive toxicokinetic data that links intake, exposure and toxicological effects of ENNA has not been elucidated yet. Therefore, the present study investigated the in vitro (rat and human) and in vivo (rat) toxicokinetic properties of ENNA. Towards this, an easily applicable and sensitive bioanalytical method was developed and validated for the estimation of ENNA in rat plasma. ENNA exhibited high plasma protein binding (99%), high hepatic clearance and mainly underwent metabolism via CYP3A4 (74%). The in-house predicted hepatic clearance (54 mL/min/kg) and observed in vivo rat clearance (55 mL/min/kg) were comparable. The predicted in vivo human hepatic clearance was 18 mL/min/kg. ENNA underwent slow absorption (Tmax = 4 h) and rapid elimination following oral administration to rats. The absolute oral bioavailability was 47%. The toxicokinetic findings for ENNA from this study will help in designing and interpreting toxicological studies in rats. Besides, these findings could be used in physiologically based toxicokinetic (PBTK) model development for exposure predictions and risk assessment for ENNA in humans.
Collapse
|
22
|
Preiss LC, Lauschke VM, Georgi K, Petersson C. Multi-Well Array Culture of Primary Human Hepatocyte Spheroids for Clearance Extrapolation of Slowly Metabolized Compounds. AAPS J 2022; 24:41. [PMID: 35277751 DOI: 10.1208/s12248-022-00689-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/04/2022] [Indexed: 12/13/2022] Open
Abstract
Accurate prediction of human pharmacokinetics using in vitro tools is an important task during drug development. Albeit, currently used in vitro systems for clearance extrapolation such as microsomes and primary human hepatocytes in suspension culture show reproducible turnover, the utility of these systems is limited by a rapid decline of activity of drug metabolizing enzymes. In this study, a multi-well array culture of primary human hepatocyte spheroids was compared to suspension and single spheroid cultures from the same donor. Multi-well spheroids remained viable and functional over the incubation time of 3 days, showing physiological excretion of albumin and α-AGP. Their metabolic activity was similar compared to suspension and single spheroid cultures. This physiological activity, the high cell concentration, and the prolonged incubation time resulted in significant turnover of all tested low clearance compounds (n = 8). In stark contrast, only one or none of the compounds showed significant turnover when single spheroid or suspension cultures were used. Using multi-well spheroids and a regression offset approach (log(CLint) = 1.1 × + 0.85), clearance was predicted within 3-fold for 93% (13/14) of the tested compounds. Thus, multi-well spheroids represent a novel and valuable addition to the ADME in vitro tool kit for the determination of low clearance and overall clearance prediction. Graphical Abstract.
Collapse
Affiliation(s)
- Lena C Preiss
- Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany.,Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.,Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Katrin Georgi
- Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Carl Petersson
- Department of Drug Metabolism and Pharmacokinetics (DMPK), The Healthcare Business of Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany.
| |
Collapse
|
23
|
Fagerholm U, Hellberg S, Alvarsson J, Arvidsson McShane S, Spjuth O. In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models. Xenobiotica 2021; 51:1366-1371. [PMID: 34845977 DOI: 10.1080/00498254.2021.2011471] [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: 10/19/2022]
Abstract
Volume of distribution at steady state (Vss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human Vss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed Vss.The mean prediction error and % with <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed Vss within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with <2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise Vss in drug discovery applications.
Collapse
Affiliation(s)
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Staffan Arvidsson McShane
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| |
Collapse
|
24
|
Sohlenius-Sternbeck AK, Terelius Y. Evaluation of ADMET Predictor TM in early discovery DMPK project work. Drug Metab Dispos 2021; 50:95-104. [PMID: 34750195 DOI: 10.1124/dmd.121.000552] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/05/2021] [Indexed: 11/22/2022] Open
Abstract
A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLM) and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff) and P-gp substrate identification (P-gp substrate) in the software ADMET PredictorTM (AP) from Simulations Plus. The dataset consisted of a total of 4794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R2=0.79), guiding the synthesis of new compounds and for prioritzing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability and permeability by using artificial neural network (ANN) models in the optional ModelerTM module of AP. Predictions of the test sets were performed with both the global and the local models and the R2 value for linear regression for predicted versus measured HLM CLint based on logarithmic data was 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and lab specific assay conditions for parameters which require biological assay systems. Significance Statement AP is useful early in projects for predicting and categorizing LogD, metabolic stability and permeability, to guide the synthesis of new compounds and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can give improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.
Collapse
|
25
|
Fagerholm U, Spjuth O, Hellberg S. Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma. Xenobiotica 2021; 51:1095-1100. [PMID: 34346291 DOI: 10.1080/00498254.2021.1964044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Variability of the unbound fraction in plasma (fu) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro fu-estimates between labs for a range of different drugs, and to develop and validate an in silico fu-prediction method and compare the results to the lab variability.A new in silico method with prediction accuracy (Q2) of 0.69 for log fu was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min fu for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds.Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of fu-estimations and improving safety of drug candidates entering the clinical phase.
Collapse
Affiliation(s)
| | - Ola Spjuth
- Prosilico AB, Huddinge, Sweden.,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | |
Collapse
|
26
|
Sonker AK, Bhateria M, Karsauliya K, Singh SP. Investigating the glucuronidation and sulfation pathways contribution and disposition kinetics of Bisphenol S and its metabolites using LC-MS/MS-based nonenzymatic hydrolysis method. CHEMOSPHERE 2021; 273:129624. [PMID: 33515962 DOI: 10.1016/j.chemosphere.2021.129624] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 06/12/2023]
Abstract
Despite showing serious health consequences and widespread exposure, the toxicokinetic information required to evaluate the health risks of BPS is insufficient. Thus, we aim to describe the comprehensive toxicokinetics of BPS and its glucuronide (BPS-G) and sulfate (BPS-S) metabolites in rats. Simultaneous quantification of BPS and its metabolites (authentic standards) was accomplished using UPLC-MS/MS method. BPS displayed rapid absorption, extensive metabolism and fast elimination after oral administration. Following intravenous administration, BPS exhibited CL (8.8 L/h/kg) higher than the rat hepatic blood flow rate suggesting the likelihood of extrahepatic clearance. The CL value differed from those reported previously (sheep and piglets) and the probable reason could be attributed to dose- and/or interspecies differences. BPS was extensively metabolized and excreted primarily through urine as BPS-G (∼56%). BPS and BPS-S exhibited a high protein binding capacity in comparison to BPS-G. In in vitro metabolic stability study, BPS was predominantly metabolized through glucuronidation. The predicted in vivo hepatic clearance of BPS suggested it to be a high and intermediate clearance chemical in rats and humans, respectively. The significant interspecies difference observed in the clearance of BPS between rats and humans indicated that toxicokinetics of BPS should be considered for health risk assessment in humans.
Collapse
Affiliation(s)
- Ashish Kumar Sonker
- Toxicokinetics Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Analytical Chemistry Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India
| | - Manisha Bhateria
- Toxicokinetics Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India; Analytical Chemistry Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India
| | - Kajal Karsauliya
- Toxicokinetics Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India; Analytical Chemistry Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India
| | - Sheelendra Pratap Singh
- Toxicokinetics Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Analytical Chemistry Laboratory & Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Lucknow, India.
| |
Collapse
|
27
|
Fagerholm U, Hellberg S, Spjuth O. Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology. Molecules 2021; 26:molecules26092572. [PMID: 33925103 PMCID: PMC8124353 DOI: 10.3390/molecules26092572] [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: 03/22/2021] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022] Open
Abstract
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.
Collapse
Affiliation(s)
- Urban Fagerholm
- Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden; (S.H.); (O.S.)
- Correspondence: ; Tel.: +46-70-1731302
| | - Sven Hellberg
- Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden; (S.H.); (O.S.)
| | - Ola Spjuth
- Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden; (S.H.); (O.S.)
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden
| |
Collapse
|
28
|
Wegler C, Matsson P, Krogstad V, Urdzik J, Christensen H, Andersson TB, Artursson P. Influence of Proteome Profiles and Intracellular Drug Exposure on Differences in CYP Activity in Donor-Matched Human Liver Microsomes and Hepatocytes. Mol Pharm 2021; 18:1792-1805. [PMID: 33739838 PMCID: PMC8041379 DOI: 10.1021/acs.molpharmaceut.1c00053] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/07/2023]
Abstract
Human liver microsomes (HLM) and human hepatocytes (HH) are important in vitro systems for studies of intrinsic drug clearance (CLint) in the liver. However, the CLint values are often in disagreement for these two systems. Here, we investigated these differences in a side-by-side comparison of drug metabolism in HLM and HH prepared from 15 matched donors. Protein expression and intracellular unbound drug concentration (Kpuu) effects on the CLint were investigated for five prototypical probe substrates (bupropion-CYP2B6, diclofenac-CYP2C9, omeprazole-CYP2C19, bufuralol-CYP2D6, and midazolam-CYP3A4). The samples were donor-matched to compensate for inter-individual variability but still showed systematic differences in CLint. Global proteomics analysis outlined differences in HLM from HH and homogenates of human liver (HL), indicating variable enrichment of ER-localized cytochrome P450 (CYP) enzymes in the HLM preparation. This suggests that the HLM may not equally and accurately capture metabolic capacity for all CYPs. Scaling CLint with CYP amounts and Kpuu could only partly explain the discordance in absolute values of CLint for the five substrates. Nevertheless, scaling with CYP amounts improved the agreement in rank order for the majority of the substrates. Other factors, such as contribution of additional enzymes and variability in the proportions of active and inactive CYP enzymes in HLM and HH, may have to be considered to avoid the use of empirical scaling factors for prediction of drug metabolism.
Collapse
Affiliation(s)
- Christine Wegler
- Department
of Pharmacy, Uppsala University, 752 37 Uppsala, Sweden
- DMPK,
Research and Early Development Cardiovascular, Renal and Metabolism,
BioPharmaceuticals R&D, AstraZeneca, 431 50 Gothenburg, Sweden
| | - Pär Matsson
- Department
of Pharmacy, Uppsala University, 752 37 Uppsala, Sweden
| | - Veronica Krogstad
- Department
of Pharmaceutical Biosciences, School of Pharmacy, University of Oslo, 0315 Oslo, Norway
| | - Jozef Urdzik
- Department
of Surgical Sciences, Uppsala University, 751 85 Uppsala, Sweden
| | - Hege Christensen
- Department
of Pharmaceutical Biosciences, School of Pharmacy, University of Oslo, 0315 Oslo, Norway
| | - Tommy B. Andersson
- DMPK,
Research and Early Development Cardiovascular, Renal and Metabolism,
BioPharmaceuticals R&D, AstraZeneca, 431 50 Gothenburg, Sweden
| | - Per Artursson
- Department
of Pharmacy and Science for Life Laboratory, Uppsala University, 752 37 Uppsala, Sweden
| |
Collapse
|
29
|
Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans. Mol Divers 2021; 25:1261-1270. [PMID: 33569705 PMCID: PMC8342319 DOI: 10.1007/s11030-021-10186-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/18/2021] [Indexed: 11/05/2022]
Abstract
Abstract Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings. Graphical abstract ![]()
Supplementary informations The online version of this article (10.1007/s11030-021-10186-7) contains supplementary material, which is available to authorized users.
Collapse
|
30
|
|
31
|
Jones RS, Chang JH, Flores M, Brecht E. Evaluation of a Competitive Equilibrium Dialysis Approach for Assessing the Impact of Protein Binding on Clearance Predictions. J Pharm Sci 2021; 110:536-542. [PMID: 32941852 DOI: 10.1016/j.xphs.2020.09.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/04/2020] [Accepted: 09/09/2020] [Indexed: 12/17/2022]
Abstract
Fraction unbound (fu) is an important consideration when characterizing the ADME properties of drug candidates. For highly bound compounds, there can be low confidence in quantifying fu introducing uncertainty in certain parameter estimations. Specifically, predictions of clearance (CL) rely on accurate fu values measured in plasma (fu,p) and microsomes (fu,mic) to scale in vitro intrinsic CL to in vivo CL. However, determining the ratio of fu,p/fu,mic may circumvent the need to measure discrete binding values. The purpose of this study was to evaluate a plasma-to-microsome competitive equilibrium dialysis (cED) method to determine fu,p/fu,mic ratio (fuR) for nine physiochemically-distinct compounds, and to investigate the impact of altering microsomal concentrations on fuR. The values of fuR were comparable to ratios calculated from discretely measured fu,p and fu,mic values. Furthermore, increasing microsomal concentrations increased fuR for basic and neutral compounds. When using fuR values, there was a good in vitro-in vivo correlation (IVIVC) (≤3-fold observed in vivo CL). These results suggest that the cED method used to determine fuR may be an appropriate, alternative IVIVC approach. Application of cED may extend beyond IVIVC of CL to evaluate other parameters such as species differences in protein binding and free tissue to plasma ratios.
Collapse
Affiliation(s)
- Robert S Jones
- Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080.
| | - Jae H Chang
- Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080
| | - Mauricio Flores
- Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080
| | - Elliot Brecht
- Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080
| |
Collapse
|
32
|
Kanebratt KP, Janefeldt A, Vilén L, Vildhede A, Samuelsson K, Milton L, Björkbom A, Persson M, Leandersson C, Andersson TB, Hilgendorf C. Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies. J Pharm Sci 2020; 110:422-431. [PMID: 33122050 DOI: 10.1016/j.xphs.2020.10.043] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CLint) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CLint within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.
Collapse
Affiliation(s)
- Kajsa P Kanebratt
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden.
| | - Annika Janefeldt
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Liisa Vilén
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Anna Vildhede
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Kristin Samuelsson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Lucas Milton
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Anders Björkbom
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Marie Persson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Carina Leandersson
- Physical & Analytical Chemistry, Research and Early Development Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Tommy B Andersson
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| | - Constanze Hilgendorf
- DMPK, Research and Early Development Cardiovascular Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
| |
Collapse
|
33
|
Docci L, Klammers F, Ekiciler A, Molitor B, Umehara K, Walter I, Krähenbühl S, Parrott N, Fowler S. In Vitro to In Vivo Extrapolation of Metabolic Clearance for UGT Substrates Using Short-Term Suspension and Long-Term Co-cultured Human Hepatocytes. AAPS JOURNAL 2020; 22:131. [DOI: 10.1208/s12248-020-00482-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/10/2020] [Indexed: 01/08/2023]
|
34
|
Pike A, Williamson B, Harlfinger S, Martin S, McGinnity DF. Optimising proteolysis-targeting chimeras (PROTACs) for oral drug delivery: a drug metabolism and pharmacokinetics perspective. Drug Discov Today 2020; 25:1793-1800. [DOI: 10.1016/j.drudis.2020.07.013] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/07/2020] [Accepted: 07/14/2020] [Indexed: 12/28/2022]
|
35
|
Chan TS, Scaringella YS, Raymond K, Taub ME. Evaluation of Erythromycin as a Tool to Assess CYP3A Contribution of Low Clearance Compounds in a Long-Term Hepatocyte Culture. Drug Metab Dispos 2020; 48:690-697. [PMID: 32503882 DOI: 10.1124/dmd.120.090951] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/29/2020] [Indexed: 12/16/2022] Open
Abstract
Long-term hepatocyte culture systems such as HepatoPac are well suited to evaluate the metabolic turnover of low clearance (CL) drugs because of their sustained metabolic capacity and longer-term viability. Erythromycin (ERY), a moderate, mechanism-based inhibitor of CYP3A, was evaluated as a tool in the HepatoPac model to assess contribution of CYP3A to the clearance of drug candidates. ERY inhibited CYP3A activity by 58% and 80% at 3 and 10 μM, respectively, for up to 72 hours. At 30 µM, ERY inhibited midazolam hydroxylation by >85% for the entire 144-hour duration of the incubation. Alprazolam CLint was inhibited 58% by 3 μM of ERY, 75% by 15 μM of ERY, 89% by 30 μM of ERY, and 94% by 60 μM of ERY. ERY (30 μM) did not markedly affect CLint of substrates for several other major cytochrome P450 isoforms evaluated and did not markedly inhibit uridine diphosphoglucuronosyl transferase (UGT) isoforms 1A1, 1A3, 1A4, 1A6, 1A9, 2B7, or 2B15 as assessed using recombinant UGTs. ERY only mildly increased CYP3A4 gene expression by 2.1-fold (14% of rifampicin induction) at 120 µM, indicating that at effective concentrations for inhibition of CYP3A activity (30-60 µM), arylhydrocarbon receptor, constitutive androstane receptor, and pregnane-X-receptor activation are not likely to markedly increase levels of other drug-metabolizing enzymes or transporters. ERY at concentrations up to 60 µM was not toxic for up to 6 days of incubation. Use of ERY to selectively inhibit CYP3A in high-functioning, long-term hepatocyte models such as HepatoPac can be a valuable strategy to evaluate the contribution of CYP3A metabolism to the overall clearance of slowly metabolized drug candidates. SIGNIFICANCE STATEMENT: This work describes the use of erythromycin as a selective inhibitor of CYP3A to assess the contribution of CYP3A in the metabolism of compounds using long-term hepatocyte cultures.
Collapse
Affiliation(s)
- Tom S Chan
- Boehringer Ingelheim Pharmaceuticals Inc., Drug Metabolism and Pharmacokinetics, Ridgefield, Connecticut
| | - Young-Sun Scaringella
- Boehringer Ingelheim Pharmaceuticals Inc., Drug Metabolism and Pharmacokinetics, Ridgefield, Connecticut
| | - Klairynne Raymond
- Boehringer Ingelheim Pharmaceuticals Inc., Drug Metabolism and Pharmacokinetics, Ridgefield, Connecticut
| | - Mitchell E Taub
- Boehringer Ingelheim Pharmaceuticals Inc., Drug Metabolism and Pharmacokinetics, Ridgefield, Connecticut
| |
Collapse
|
36
|
Morita K, Kato M, Kudo T, Ito K. In vitro-in vivo extrapolation of metabolic clearance using human liver microsomes: factors showing variability and their normalization. Xenobiotica 2020; 50:1064-1075. [PMID: 32125203 DOI: 10.1080/00498254.2020.1738592] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In vitro-in vivo extrapolation (IVIVE) using human liver microsomes has been widely used to predict metabolic clearance, but some of the factors used in the process of prediction show variability for the same compound: notably, microsomal intrinsic clearance values corrected by the unbound fraction (CLint, u), physiological parameters used for scale-up, and the source of in vivo clearance data.The purpose of this study was to assess the correlation between in vitro and in vivo CLint with a focus on factors showing variability using four cytochrome P450 (CYP)3A substrates.We surveyed in vivo clearance values in literature and also determined the microsomal CLint, u values. A scaling factor (SFdirect) was defined as in vivo CLint divided by the microsomal CLint, u, which ranged from 1190 to 2310 (mg protein per kg body weight). The application of a mean SFdirect of 1600 (mg protein per kg body weight) and further normalization by the microsomal CLint, u values of midazolam, the most commonly used substrate, resulted in improved prediction accuracy for CLint, u values from various microsomal batches.The results suggest the normalization of variability might be useful for predicting the in vivo CLint.
Collapse
Affiliation(s)
- Keiichi Morita
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan.,Translational Research Division, Chugai Pharmaceutical, Co., Ltd, Kanagawa, Japan
| | - Motohiro Kato
- Research Division, Chugai Pharmaceutical, Co., Ltd, Shizuoka, Japan
| | - Toshiyuki Kudo
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan
| | - Kiyomi Ito
- Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo, Japan
| |
Collapse
|
37
|
Louisse J, Alewijn M, Peijnenburg AA, Cnubben NH, Heringa MB, Coecke S, Punt A. Towards harmonization of test methods for in vitro hepatic clearance studies. Toxicol In Vitro 2020; 63:104722. [DOI: 10.1016/j.tiv.2019.104722] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 11/07/2019] [Accepted: 11/13/2019] [Indexed: 12/26/2022]
|
38
|
Wei J, Lu J, Chen M, Xie S, Wang T, Li X. 3D spheroids generated on carbon nanotube-functionalized fibrous scaffolds for drug metabolism and toxicity screening. Biomater Sci 2019; 8:426-437. [PMID: 31746843 DOI: 10.1039/c9bm01310e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The mechanical and electrical stimuli have a profound effect on the cellular behavior and function. In this study, a series of conductive nanofibrous scaffolds are developed by blend electrospinning of poly(styrene-co-maleic acid) (PSMA) and multiwalled-carbon nanotubes (CNTs), followed by grafting galactose as cell adhesion cues. When the mass ratios of CNTs to PSMA increase up to 5%, the alignment, Young's modulus and conductivity of fibrous scaffolds increase, whereas the average diameter, pore size and elongation at break decrease. Primary hepatocytes cultured on the scaffolds are self-assembled into 3D spheroids, which restores the hepatocyte polarity and sufficient expression of drug metabolism enzymes over an extended period of time. Among these conductive scaffolds, hepatocytes cultured on fibers containing 3% of CNTs (F3) show the highest clearance rates of model drugs, offering a better prediction of the in vivo data with a high correlation value. Moreover, the drug metabolism capability is maintained over 15 days and is more sensitive towards the inducers and inhibitors of metabolizing enzymes, demonstrating the applicability for drug-drug interaction studies. Thus, this culture system has been demonstrated as a reliable in vitro model for high-throughput screening of metabolism and toxicity in the early phases of drug development.
Collapse
Affiliation(s)
- Jiaojun Wei
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, P.R. China.
| | | | | | | | | | | |
Collapse
|
39
|
Winiwarter S, Chang G, Desai P, Menzel K, Faller B, Arimoto R, Keefer C, Broccatell F. Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets. Mol Pharm 2019; 16:4077-4085. [PMID: 31348668 DOI: 10.1021/acs.molpharmaceut.9b00525] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The fraction unbound in the incubation, fu,inc, is an important parameter to consider in the evaluation of intrinsic clearance measurements performed in vitro in hepatocytes or microsomes. Reliable estimates of fu,inc based on a compound's structure have the potential to positively impact the screening timelines in drug discovery. Previous works suggested that fu,inc is primarily driven by passive processes and can be described using physicochemical properties such as lipophilicity and the protonation state of the molecule. While models based on these principles proved predictive in relatively small datasets that included marketed drugs, their applicability domain has not been extensively explored. The work presented here from the in silico ADME discussion group (part of the International Consortium for Innovation through Quality in Pharmaceutical Development, the IQ consortium) describes the accuracy of these models in large proprietary datasets that include several thousand of compounds across chemical space. Overall, the models do well for compounds with low lipophilicity. In other words, the equations correctly predict that fu,inc is, in general, above 0.5 for compounds with a calculated logP of less than 3. When applied to lipophilic compounds, the models failed to produce quantitatively accurate predictions of fu,inc, with a high risk of underestimating binding properties. These models can, therefore, be used quantitatively for less lipophilic compounds. On the other hand, internal machine-learning models using a company's own proprietary dataset also predict compounds with higher lipophilicity reasonably well. Additionally, the data shown indicate that microsomal binding is, in general, a good proxy for hepatocyte binding.
Collapse
Affiliation(s)
- Susanne Winiwarter
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D , AstraZeneca , Gothenburg SE-43183 , Sweden
| | - George Chang
- Pfizer Inc. , Groton , Connecticut 06340 , United States
| | - Prashant Desai
- Eli Lilly and Company , Indianapolis , Indiana 46285 , United States
| | | | | | - Rieko Arimoto
- Vertex Pharmaceuticals Inc. , Boston , Massachusetts 02210 , United States
| | | | - Fabio Broccatell
- Genentech Inc. , South San Francisco , California 94080 , United States
| |
Collapse
|
40
|
Bowman CM, Benet LZ. Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties. Pharm Res 2019; 36:113. [PMID: 31152241 DOI: 10.1007/s11095-019-2645-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 05/10/2019] [Indexed: 11/28/2022]
Abstract
PURPOSE To examine the interlaboratory variability in CLint values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters. METHODS Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources. RESULTS Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CLint, in vivo values, while those with non-uniform predictions typically had lower CLint, in vivo values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions. CONCLUSIONS Caution should be taken when compiling in vitro CLint values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.
Collapse
Affiliation(s)
- Christine M Bowman
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, 94143-0912, USA
| | - Leslie Z Benet
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, 94143-0912, USA.
| |
Collapse
|
41
|
Bowman CM, Benet LZ. In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction. J Pharm Sci 2019; 108:2500-2504. [PMID: 30817922 DOI: 10.1016/j.xphs.2019.02.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 02/08/2019] [Accepted: 02/14/2019] [Indexed: 12/16/2022]
Abstract
Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.
Collapse
Affiliation(s)
- Christine M Bowman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143
| | - Leslie Z Benet
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143.
| |
Collapse
|
42
|
Gouliarmou V, Lostia AM, Coecke S, Bernasconi C, Bessems J, Dorne JL, Ferguson S, Testai E, Remy UG, Brian Houston J, Monshouwer M, Nong A, Pelkonen O, Morath S, Wetmore BA, Worth A, Zanelli U, Zorzoli MC, Whelan M. Establishing a systematic framework to characterise in vitro methods for human hepatic metabolic clearance. Toxicol In Vitro 2018; 53:233-244. [DOI: 10.1016/j.tiv.2018.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 07/17/2018] [Accepted: 08/08/2018] [Indexed: 12/26/2022]
|
43
|
Evaluation of Generic Methods to Predict Human Pharmacokinetics Using Physiologically Based Pharmacokinetic Model for Early Drug Discovery of Tyrosine Kinase Inhibitors. Eur J Drug Metab Pharmacokinet 2018; 44:121-132. [DOI: 10.1007/s13318-018-0496-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
44
|
Li Y, Fan Y, Su H, Wang Q, Li GF, Hu Y, Jiang J, Tan B, Qiu F. Metabolic characteristics of Tanshinone I in human liver microsomes and S9 subcellular fractions. Xenobiotica 2018; 49:152-160. [PMID: 29357726 DOI: 10.1080/00498254.2018.1432087] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tanshinone I (TSI) is a lipophilic diterpene in Salvia miltiorrhiza with versatile pharmacological activities. However, metabolic pathway of TSI in human is unknown. In this study, we determined major metabolites of TSI using a preparation of human liver microsomes (HLMs) by HPLC-UV and Q-Trap mass spectrometer. A total of 6 metabolites were detected, which indicated the presence of hydroxylation, reduction as well as glucuronidation. Selective chemical inhibition and purified cytochrome P450 (CYP450) isoform screening experiments revealed that CYP2A6 was primarily responsible for TSI Phase I metabolism. Part of generated hydroxylated TSI was glucuronidated via several glucuronosyltransferase (UGT) isoforms including UGT1A1, UGT1A3, UGT1A7, UGT1A9, as well as extrahepatic expressed isoforms UGT1A8 and UGT1A10. TSI could be reduced to a relatively unstable hydroquinone intermediate by NAD(P)H: quinone oxidoreductase 1 (NQO1), and then immediately conjugated with glucuronic acid by a panel of UGTs, especially UGT1A9, UGT1A1 and UGT1A8. Additionally, NQO1 could also reduce hydroxylated TSI to a hydroquinone intermediate, which was immediately glucuronidated by UGT1A1. The study demonstrated that hydroxylation, reduction as well as glucuronidation were the major pathways for TSI biotransformation, and six metabolites generated by CYPs, NQO1 and UGTs were found in HLMs and S9 subcellular fractions.
Collapse
Affiliation(s)
- Yue Li
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Yujuan Fan
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Huizong Su
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Qian Wang
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Guo-Fu Li
- b Center for Drug Clinical Research , Shanghai University of Traditional Chinese Medicine , Shanghai , China.,c Subei People's Hospital, Yangzhou University , Yangzhou , China
| | - Yiyang Hu
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Jian Jiang
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Bo Tan
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Furong Qiu
- a Laboratory of Clinical Pharmacokinetics , Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai , China
| |
Collapse
|
45
|
Wood FL, Houston JB, Hallifax D. Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology. Drug Metab Dispos 2017; 45:1178-1188. [DOI: 10.1124/dmd.117.077040] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 09/06/2017] [Indexed: 01/07/2023] Open
|
46
|
Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF. Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. ENVIRONMENT INTERNATIONAL 2017; 106:105-118. [PMID: 28628784 PMCID: PMC6116525 DOI: 10.1016/j.envint.2017.06.004] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 06/01/2017] [Accepted: 06/02/2017] [Indexed: 05/17/2023]
Abstract
The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with physiological parameters for a virtual population. For risk-based prioritization of chemicals, predicted bioactive equivalent doses were compared to demographic-specific inferences of exposure rates that were based on NHANES urinary analyte biomonitoring data. The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average for the total population when compared to previous methods. However, for some combinations of chemical and demographic groups the margin was reduced by as much as three quarters. This TK modeling framework allows targeted risk prioritization of chemicals for demographic groups of interest, including potentially sensitive life stages and subpopulations.
Collapse
Affiliation(s)
- Caroline L Ring
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, United States; National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Robert G Pearce
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, United States; National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Barbara A Wetmore
- ScitoVation, LLC, Research Triangle Park, NC, United States; National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, United States.
| |
Collapse
|
47
|
Yu J, Zhou Z, Tay-Sontheimer J, Levy RH, Ragueneau-Majlessi I. Intestinal Drug Interactions Mediated by OATPs: A Systematic Review of Preclinical and Clinical Findings. J Pharm Sci 2017; 106:2312-2325. [DOI: 10.1016/j.xphs.2017.04.004] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 04/05/2017] [Accepted: 04/07/2017] [Indexed: 02/07/2023]
|
48
|
Ye M, Nagar S, Korzekwa K. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding. Biopharm Drug Dispos 2017; 37:123-41. [PMID: 26531057 DOI: 10.1002/bdd.1996] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 10/12/2015] [Accepted: 10/17/2015] [Indexed: 11/07/2022]
Abstract
Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.
Collapse
Affiliation(s)
- Min Ye
- Department of Pharmaceutical Science, Temple University School of Pharmacy, Philadelphia, PA, 19140, USA
| | - Swati Nagar
- Department of Pharmaceutical Science, Temple University School of Pharmacy, Philadelphia, PA, 19140, USA
| | - Ken Korzekwa
- Department of Pharmaceutical Science, Temple University School of Pharmacy, Philadelphia, PA, 19140, USA
| |
Collapse
|
49
|
Bowman CM, Benet LZ. Hepatic Clearance Predictions from In Vitro-In Vivo Extrapolation and the Biopharmaceutics Drug Disposition Classification System. Drug Metab Dispos 2016; 44:1731-1735. [PMID: 27519549 PMCID: PMC11024986 DOI: 10.1124/dmd.116.071514] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/11/2016] [Indexed: 04/20/2024] Open
Abstract
Predicting in vivo pharmacokinetic parameters such as clearance from in vitro data is a crucial part of the drug-development process. There is a commonly cited trend that drugs that are highly protein-bound and are substrates for hepatic uptake transporters often yield the worst predictions. Given this information, 11 different data sets using human microsomes and hepatocytes were evaluated to search for trends in accuracy, extent of protein binding, and drug classification based on the Biopharmaceutics Drug Disposition Classification System (BDDCS), which makes predictions about transporter effects. As previously reported, both in vitro systems (microsomes and hepatocytes) gave a large number of inaccurate results, defined as predictions falling more than 2-fold outside of in vivo values. The weighted average of the percentage of inaccuracy was 66.5%. BDDCS class 2 drugs, which are subject to transporter effects in vivo unlike class 1 compounds, had a higher percentage of inaccurate predictions and often had slightly larger bias. However, since the weighted average of the percentage of inaccuracy was still high in both classes (81.9% for class 2 and 62.3% for class 1), it may be currently hard to use BDDCS class to predict potential accuracy. The results of this study emphasize the need for improved in vitro to in vivo extrapolation experimental methods, as using physiologically based scaling is still not accurate, and BDDCS cannot currently help predict accurate results.
Collapse
Affiliation(s)
- Christine M Bowman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California
| | - Leslie Z Benet
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California
| |
Collapse
|
50
|
Hosomi A, Hirabe M, Tokuda T, Nakamura H, Amano T, Okamoto T. Calcium effects and systemic exposure of vitamin D 3 analogues after topical treatment of active vitamin D 3 -containing ointments in rats. Eur J Pharmacol 2016; 788:98-103. [DOI: 10.1016/j.ejphar.2016.06.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 06/15/2016] [Accepted: 06/15/2016] [Indexed: 10/21/2022]
|