BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

200 related articles for article (PubMed ID: 37235687)

  • 1. Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CL
    Manevski N; Umehara K; Parrott N
    Mol Pharm; 2023 Jul; 20(7):3438-3459. PubMed ID: 37235687
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling.
    Andrews-Morger A; Reutlinger M; Parrott N; Olivares-Morales A
    Mol Pharm; 2023 Oct; 20(10):5052-5065. PubMed ID: 37713584
    [TBL] [Abstract][Full Text] [Related]  

  • 3. In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes.
    Tess D; Chang GC; Keefer C; Carlo A; Jones R; Di L
    AAPS J; 2023 Apr; 25(3):40. PubMed ID: 37052732
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving In Vitro-In Vivo Extrapolation of Clearance Using Rat Liver Microsomes for Highly Plasma Protein-Bound Molecules.
    Trunzer M; Teigão J; Huth F; Poller B; Desrayaud S; Rodríguez-Pérez R; Faller B
    Drug Metab Dispos; 2024 Apr; 52(5):345-354. PubMed ID: 38360916
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.
    Kosugi Y; Hosea N
    Mol Pharm; 2020 Jul; 17(7):2299-2309. PubMed ID: 32478525
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.
    Keefer CE; Chang G; Di L; Woody NA; Tess DA; Osgood SM; Kapinos B; Racich J; Carlo AA; Balesano A; Ferguson N; Orozco C; Zueva L; Luo L
    Mol Pharm; 2023 Nov; 20(11):5616-5630. PubMed ID: 37812508
    [TBL] [Abstract][Full Text] [Related]  

  • 7. In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species.
    Tess DA; Ryu S; Di L
    Pharm Res; 2022 Jul; 39(7):1615-1632. PubMed ID: 35257289
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Impact of Plasma Protein Binding in Drug Clearance Prediction: A Data Base Analysis of Published Studies and Implications for In Vitro-In Vivo Extrapolation.
    Francis LJ; Houston JB; Hallifax D
    Drug Metab Dispos; 2021 Mar; 49(3):188-201. PubMed ID: 33355201
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of total hepatic clearance by combining metabolism, transport, and permeability data in the in vitro-in vivo extrapolation methods: emphasis on an apparent fraction unbound in liver for drugs.
    Poulin P
    J Pharm Sci; 2013 Jul; 102(7):2085-95. PubMed ID: 23613473
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.
    Parrott N; Manevski N; Olivares-Morales A
    Mol Pharm; 2022 Nov; 19(11):3858-3868. PubMed ID: 36150125
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data.
    Poulin P; Haddad S
    J Pharm Sci; 2013 Sep; 102(9):3239-51. PubMed ID: 23494893
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comparison of the Predictability of Human Hepatic Clearance for Organic Anion Transporting Polypeptide Substrate Drugs Between Different In Vitro-In Vivo Extrapolation Approaches.
    Izumi S; Nozaki Y; Komori T; Takenaka O; Maeda K; Kusuhara H; Sugiyama Y
    J Pharm Sci; 2017 Sep; 106(9):2678-2687. PubMed ID: 28238896
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction.
    Umehara K; Cantrill C; Wittwer MB; Di Lenarda E; Klammers F; Ekiciler A; Parrott N; Fowler S; Ullah M
    Drug Metab Dispos; 2020 Oct; 48(10):849-860. PubMed ID: 32739889
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties.
    Bowman CM; Benet LZ
    Pharm Res; 2019 May; 36(8):113. PubMed ID: 31152241
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs.
    Jones RS; Leung C; Chang JH; Brown S; Liu N; Yan Z; Kenny JR; Broccatelli F
    Drug Metab Dispos; 2022 May; ():. PubMed ID: 35636770
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Evaluation of the Disconnect between Hepatocyte and Microsome Intrinsic Clearance and In Vitro In Vivo Extrapolation Performance.
    Williamson B; Harlfinger S; McGinnity DF
    Drug Metab Dispos; 2020 Nov; 48(11):1137-1146. PubMed ID: 32847864
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Investigating the Theoretical Basis for In Vitro-In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways.
    Benet LZ; Sodhi JK
    AAPS J; 2020 Sep; 22(5):120. PubMed ID: 32914238
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Strategies to improve the prediction accuracy of hepatic intrinsic clearance of three antidiabetic drugs: Application of the extended clearance concept and consideration of the effect of albumin on CYP2C metabolism and OATP1B-mediated hepatic uptake.
    Fujino R; Hashizume K; Aoyama S; Maeda K; Ito K; Toshimoto K; Lee W; Ninomiya SI; Sugiyama Y
    Eur J Pharm Sci; 2018 Dec; 125():181-192. PubMed ID: 30287410
    [TBL] [Abstract][Full Text] [Related]  

  • 19. IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.
    Ahmad A; Pepin X; Aarons L; Wang Y; Darwich AS; Wood JM; Tannergren C; Karlsson E; Patterson C; Thörn H; Ruston L; Mattinson A; Carlert S; Berg S; Murphy D; Engman H; Laru J; Barker R; Flanagan T; Abrahamsson B; Budhdeo S; Franek F; Moir A; Hanisch G; Pathak SM; Turner D; Jamei M; Brown J; Good D; Vaidhyanathan S; Jackson C; Nicolas O; Beilles S; Nguefack JF; Louit G; Henrion L; Ollier C; Boulu L; Xu C; Heimbach T; Ren X; Lin W; Nguyen-Trung AT; Zhang J; He H; Wu F; Bolger MB; Mullin JM; van Osdol B; Szeto K; Korjamo T; Pappinen S; Tuunainen J; Zhu W; Xia B; Daublain P; Wong S; Varma MVS; Modi S; Schäfer KJ; Schmid K; Lloyd R; Patel A; Tistaert C; Bevernage J; Nguyen MA; Lindley D; Carr R; Rostami-Hodjegan A
    Eur J Pharm Biopharm; 2020 Nov; 156():50-63. PubMed ID: 32805361
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.
    Kosugi Y; Hosea N
    Mol Pharm; 2021 Mar; 18(3):1071-1079. PubMed ID: 33512165
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.