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Handa K, Sasaki S, Asano S, Kageyama M, Iijima T, Bender A. Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models. J Chem Inf Model 2024. [PMID: 39254593 DOI: 10.1021/acs.jcim.4c00921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.78 using high throughput percentage inhibition data [J. Med. Chem. 2013, 56(3), 781-795] which we aimed to improve upon here using a combined fingerprint and physics-based approach. To this end, we collected 225 publicly available compounds with pIC50 values against MATE1. Subsequently, on the one hand, we performed a physics-based approach using an Alpha-Fold protein structure, from which we obtained MM-GB/SA scores for those compounds. On the other hand, we built Random Forest (RF) and message passing neural network models using extended-connectivity fingerprints with radius 4 (ECFP4) and chemical structures as graphs, respectively, which also included MM-GB/SA scores as input variables. In a five-fold cross-validation with a separate test set, we found that the best predictivity for the hold-out test was observed in the RF model (including ECFP4 and MM-GB/SA data) with an ROC-AUC of 0.833 ± 0.036; while that of the MM-GB/SA regression model was 0.742. However, the MM-GB/SA model did not show a dependency of the performance on the particular chemical space being predicted. Additionally, via structural interaction fingerprint analysis, we identified interacting residues with inhibitor as identical for those with noninhibitors, including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, and Tyr306. The similar binding modes are consistent with the observed similar IC50 value inhibitor when using different substrates experimentally, and practically, this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence, we were able to build highly predictive classification models for MATE1 inhibitory activity with both ECFP4 and MM-GB/SA score as input features, which is fit-for-purpose for use in the drug discovery process.
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Affiliation(s)
- Koichi Handa
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Shunta Sasaki
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo 191-8512, Japan
| | - Satoshi Asano
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Michiharu Kageyama
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Takeshi Iijima
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
- Institutul STAR-UBB, Universitatea Babes-Bolyai, Str. Mihail Kogălniceanu nr. 1, Cluj-Napoca 400084, Romania
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Tsutsumi T, Kawabata K, Yamazaki N, Tsukigawa K, Nishi H, Tokumura A. Extracellular and intracellular productions of lysophosphatidic acids and cyclic phosphatidic acids by lysophospholipase D from exogenously added lysophosphatidylcholines to cultured NRK52E cells. Biochim Biophys Acta Mol Cell Biol Lipids 2023:159349. [PMID: 37295607 DOI: 10.1016/j.bbalip.2023.159349] [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: 05/09/2023] [Revised: 05/29/2023] [Accepted: 06/01/2023] [Indexed: 06/12/2023]
Abstract
Lysophosphatidic acid (LPA) is a bioactive lysophospholipid that is a notable biomarker of kidney injury. However, it is not clear how LPA is produced in renal cells. In this study, we explored LPA generation and its enzymatic pathway in a rat kidney-derived cell, NRK52E cells. Culturing of NRK52E cells with acyl lysophosphatidylcholine (acyl LPC), or lyso-platelet activating factor (lysoPAF, alkyl LPC) was resulted in increased extracellular level of choline, co-product with LPA by lysophospholipase D (lysoPLD). Their activities were enhanced by addition of calcium ions to the cell culture medium, but failed to be inhibited by S32826, an autotaxin (ATX)-specific inhibitor. Liquid chromatography-tandem mass spectrometric analysis revealed the small, but significant extracellular production of acyl LPA/cyclic phosphatidic acid (cPA) and alkyl LPA/cPA. The mRNA expression of glycerophosphodiesterase (GDE) 7 with lysoPLD activity was elevated in confluent NRK52E cells cultured over 3 days. GDE7 plasmid-transfection of NRK52E cells augmented both extracellular and intracellular productions of LPAs (acyl and alkyl) as well as extracellular productions of cPAs (acyl and alkyl) from exogenous LPCs (acyl and alkyl). These results suggest that intact NRK52E cells are able to produce choline and LPA/cPA from exogenous LPCs through the enzymatic action of GDE7 that is located on the plasma membranes and intracellular membranes.
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Affiliation(s)
- Toshihiko Tsutsumi
- Pharmaceutics, Graduate School of Clinical Pharmacy, Kyushu University of Health and Welfare, 1714-1 Yoshino-machi, Nobeoka, Miyazaki 882-8508, Japan.
| | - Kohei Kawabata
- Faculty of Pharmacy, Yasuda Women's University, Hiroshima, Japan
| | - Naoshi Yamazaki
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Kenji Tsukigawa
- Faculty of Pharmaceutical Sciences, Sojo University, Kumamoto, Japan
| | - Hiroyuki Nishi
- Faculty of Pharmacy, Yasuda Women's University, Hiroshima, Japan
| | - Akira Tokumura
- Faculty of Pharmacy, Yasuda Women's University, Hiroshima, Japan; Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
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Rodrigues AD. Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate
Pan‐Transporter
Inhibition Signatures. Clin Pharmacol Ther 2022; 113:986-1002. [PMID: 35869864 DOI: 10.1002/cpt.2713] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/14/2022] [Indexed: 11/11/2022]
Abstract
Solute carrier (SLC) transporters present as the loci of important drug-drug interactions (DDIs). Therefore, sponsors generate in vitro half-maximal inhibitory concentration (IC50 ) data and apply regulatory agency-guided "static" methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false-positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (Cmax,u )/IC50 ratio ≥ 0.02), investigators have attempted to deploy plasma- and urine-based SLC biomarkers in phase I studies to de-risk DDI and obviate the need for drug probe-based studies. In this regard, it was possible to generate in-house in vitro SLC IC50 data for various clinically (biomarker)-qualified perpetrator drugs, under standard assay conditions, and then estimate "% inhibition" for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUCinhibitor /AUCreference ) and % decrease in renal clearance (ΔCLrenal )). After such a "calibration" exercise, it was determined that only compounds with high R values (> 1.5) and Cmax,u /IC50 ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCLrenal > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan-SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.
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Affiliation(s)
- A. David Rodrigues
- Pharmacokinetics & Drug Metabolism, Medicine Design, Worldwide Research & Development, Pfizer Inc Groton CT USA
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Functional coupling of organic anion transporter OAT10 (SLC22A13) and monocarboxylate transporter MCT1 (SLC16A1) influencing the transport function of OAT10. J Pharmacol Sci 2022; 150:41-48. [DOI: 10.1016/j.jphs.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/11/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022] Open
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Krishnan S, Ramsden D, Ferguson D, Stahl SH, Wang J, McGinnity DF, Hariparsad N. Challenges and Opportunities for Improved Drug-Drug Interaction Predictions for Renal OCT2 and MATE1/2-K Transporters. Clin Pharmacol Ther 2022; 112:562-572. [PMID: 35598119 DOI: 10.1002/cpt.2666] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/13/2022] [Indexed: 11/08/2022]
Abstract
Transporters contribute to renal elimination of drugs; therefore drug disposition can be impacted if transporters are inhibited by comedicant drugs. Regulatory agencies have provided guidelines to assess potential drug-drug interaction (DDI) risk for renal organic cation transporter 2 (OCT2) and multidrug and toxin extrusion 1 and 2-K (MATE1/2-K) transporters. Despite this, there are challenges with translating in vitro data using currently available tools to obtain a quantitative assessment of DDI risk in the clinic. Given the high number of drugs and new molecular entities showing in vitro inhibition toward OCT2 and/or MATE1/2-K and the lack of translation to clinically significant effects, it is reasonable to question whether the current in vitro assay design and modeling practice has led to unnecessary clinical evaluation. The aim of this review is to assess and discuss available in vitro and clinical data along with prediction models intended to provide clinical context of risk, including static models proposed by regulatory agencies and physiologically-based pharmacokinetic models, in order to identify best practices and areas of future opportunity. This analysis highlights that different in vitro assay designs, including substrate and cell systems used, strongly influence the derived concentration of drug producing 50% inhibition values and contribute to high variability observed across laboratories. Furthermore, the lack of sensitive index substrates coupled with specific inhibitors for individual transporters necessitates the use of complex models to evaluate clinical DDI risk.
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Affiliation(s)
- Srinivasan Krishnan
- Drug Metabolism and Pharmacokinetics, Oncology Research & Development, AstraZeneca, Boston, Massachusetts, USA
| | - Diane Ramsden
- Drug Metabolism and Pharmacokinetics, Oncology Research & Development, AstraZeneca, Boston, Massachusetts, USA
| | - Douglas Ferguson
- Drug Metabolism and Pharmacokinetics, Oncology Research & Development, AstraZeneca, Boston, Massachusetts, USA
| | - Simone H Stahl
- Cardiovascular, Renal, and Metabolism Safety, Clinical Pharmacology and Safety Sciences, Research & Development, AstraZeneca, Cambridge, UK
| | - Joanne Wang
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
| | - Dermot F McGinnity
- Drug Metabolism and Pharmacokinetics, Oncology Research & Development, AstraZeneca, Cambridge, UK
| | - Niresh Hariparsad
- Drug Metabolism and Pharmacokinetics, Oncology Research & Development, AstraZeneca, Boston, Massachusetts, USA
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