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1.
J Phys Chem B ; 117(10): 3040-6, 2013 Mar 14.
Article in English | MEDLINE | ID: mdl-23421391

ABSTRACT

Dynamic nuclear polarization (DNP) of amorphous and crystalline ortho-terphenyl (OTP) in the absence of glass forming agents is presented in order to gauge the feasibility of applying DNP to pharmaceutical solid-state nuclear magnetic resonance experiments and to study the effect of intermolecular structure, or lack thereof, on the DNP enhancement. By way of (1)H-(13)C cross-polarization, we obtained a DNP enhancement (ε) of 58 for 95% deuterated OTP in the amorphous state using the biradical bis-TEMPO terephthalate (bTtereph) and ε of 36 in the crystalline state. Measurements of the (1)H T1 and electron paramagnetic resonance experiments showed the crystallization process led to phase separation of the polarization agent, creating an inhomogeneous distribution of radicals within the sample. Consequently, the effective radical concentration was decreased in the bulk OTP phase, and long-range (1)H-(1)H spin diffusion was the main polarization propagation mechanism. Preliminary DNP experiments with the glass-forming anti-inflammation drug, indomethacin, showed promising results, and further studies are underway to prepare DNP samples using pharmaceutical techniques.


Subject(s)
Terphenyl Compounds/chemistry , Crystallization , Electron Spin Resonance Spectroscopy , Magnetic Resonance Spectroscopy
2.
Curr Opin Drug Discov Devel ; 5(3): 400-6, 2002 May.
Article in English | MEDLINE | ID: mdl-12058615

ABSTRACT

In the past decade, the pharmaceutical industry has realized the increasing significance of impacting the early phase hit-to-lead development in the drug discovery process. In particular, knowledge-based approaches emerged and evolved to address a multitude of issues such as absorption, distribution, metabolism and excretion (ADME), potency, toxicity and overall drugability. Each of these approaches seeks to bring together all relevant pieces of information and create a knowledge-oriented process to deploy such information in drug discovery. This review focuses on work relating to drugability, which aims at obtaining hits (or leads) that have enhanced likelihoods of leading to successful clinical candidates by medicinal chemistry efforts. The period covered in this review is from 1997 (since the publication of Lipinski's rule of 5) to March 2002.


Subject(s)
Artificial Intelligence , Drug Design , Animals , Databases, Factual/trends , Humans
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