Strategy for Extending Half-life in Drug Design and Its Significance

被引:50
作者
Gunaydin, Hakan [1 ]
Altman, Michael D. [1 ]
Ellis, J. Michael [2 ,3 ]
Fuller, Peter [2 ]
Johnson, Scott A. [1 ]
Lahue, Brian [1 ]
Lapointe, Blair [2 ]
机构
[1] Merck & Co Inc, Dept Modeling & Informat, 33 Ave Louis Pasteur, Boston, MA 02115 USA
[2] Merck & Co Inc, Dept Med Chem, 33 Ave Louis Pasteur, Boston, MA 02115 USA
[3] Celgene Corp, Med Chem, 200 Cambridge Pk Dr, Cambridge, MA 02140 USA
关键词
PK; half-life extension; dose prediction; MMP; HUMAN-SERUM-ALBUMIN; PREDICTION; CLEARANCE; BINDING; MODEL;
D O I
10.1021/acsmedchemlett.8b00018
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Preclinical optimization of compounds toward viable drug candidates requires an integrated understanding of properties that impact predictions of the clinically efficacious dose. The importance of optimizing half-life, unbound clearance, and potency and how they impact dose predictions are discussed in this letter. Modest half-life improvements for short half-life compounds can dramatically lower the efficacious dose. The relationship between dose and half-life is nonlinear when unbound clearance is kept constant, whereas the relationship between dose and unbound clearance is linear when half-life is kept constant. Due to this difference, we show that dose is more sensitive to changes in half-life than changes in unbound clearance when half-lives are shorter than 2 h. Through matched molecular pair analyses, we also show that the strategic introduction of halogens is likely to increase half-life and lower projected human dose even though increased lipophilicity does not guarantee extended half-life.
引用
收藏
页码:528 / 533
页数:11
相关论文
共 16 条
[1]   Crystallographic analysis reveals common modes of binding of medium and long-chain fatty acids to human serum albumin [J].
Bhattacharya, AA ;
Grüne, T ;
Curry, S .
JOURNAL OF MOLECULAR BIOLOGY, 2000, 303 (05) :721-732
[2]   Allometric scaling of pharmacokinetic parameters in drug discovery:: Can human CL, VSS and t1/2 be predicted from in-vivo rat data? [J].
Caldwell, GW ;
Masucci, JA ;
Yan, ZY ;
Hageman, W .
EUROPEAN JOURNAL OF DRUG METABOLISM AND PHARMACOKINETICS, 2004, 29 (02) :133-143
[3]   Prediction of Human Pharmacokinetics From Preclinical Information: Comparative Accuracy of Quantitative Prediction Approaches [J].
Hosea, Natilie A. ;
Collard, Wendy T. ;
Cole, Susan ;
Maurer, Tristan S. ;
Fang, Rick X. ;
Jones, Hannah ;
Kakar, Shefali M. ;
Nakai, Yasuhiro ;
Smith, Bill J. ;
Webster, Rob ;
Beaumont, Kevin .
JOURNAL OF CLINICAL PHARMACOLOGY, 2009, 49 (05) :513-533
[4]   Can the pharmaceutical industry reduce attrition rates? [J].
Kola, I ;
Landis, J .
NATURE REVIEWS DRUG DISCOVERY, 2004, 3 (08) :711-715
[5]  
Kramer C., 2017, J MED CHEM, DOI [10.1021/acs.jmed-chem.7b00935, DOI 10.1021/ACS.JMED-CHEM.7B00935]
[6]   Validation of Early Human Dose Prediction: A Key Metric for Compound Progression in Drug Discovery [J].
Page, Ken M. .
MOLECULAR PHARMACEUTICS, 2016, 13 (02) :609-620
[7]   Relevance of Half-Life in Drug Design [J].
Smith, Dennis A. ;
Beaumont, Kevin ;
Maurer, Tristan S. ;
Di, Li .
JOURNAL OF MEDICINAL CHEMISTRY, 2018, 61 (10) :4273-4282
[8]   Volume of Distribution in Drug Design [J].
Smith, Dennis A. ;
Beaumont, Kevin ;
Maurer, Tristan S. ;
Di, Li .
JOURNAL OF MEDICINAL CHEMISTRY, 2015, 58 (15) :5691-5698
[9]  
SUDLOW G, 1975, MOL PHARMACOL, V11, P824
[10]   Random forest: A classification and regression tool for compound classification and QSAR modeling [J].
Svetnik, V ;
Liaw, A ;
Tong, C ;
Culberson, JC ;
Sheridan, RP ;
Feuston, BP .
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2003, 43 (06) :1947-1958