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1.
J Biomol Screen ; 21(6): 608-19, 2016 Jul.
Article in English | MEDLINE | ID: mdl-26969322

ABSTRACT

The primary objective of early drug discovery is to associate druggable target space with a desired phenotype. The inability to efficiently associate these often leads to failure early in the drug discovery process. In this proof-of-concept study, the most tractable starting points for drug discovery within the NF-κB pathway model system were identified by integrating affinity selection-mass spectrometry (AS-MS) with functional cellular assays. The AS-MS platform Automated Ligand Identification System (ALIS) was used to rapidly screen 15 NF-κB proteins in parallel against large-compound libraries. ALIS identified 382 target-selective compounds binding to 14 of the 15 proteins. Without any chemical optimization, 22 of the 382 target-selective compounds exhibited a cellular phenotype consistent with the respective target associated in ALIS. Further studies on structurally related compounds distinguished two chemical series that exhibited a preliminary structure-activity relationship and confirmed target-driven cellular activity to NF-κB1/p105 and TRAF5, respectively. These two series represent new drug discovery opportunities for chemical optimization. The results described herein demonstrate the power of combining ALIS with cell functional assays in a high-throughput, target-based approach to determine the most tractable drug discovery opportunities within a pathway.


Subject(s)
Drug Discovery , High-Throughput Screening Assays/methods , NF-kappa B/antagonists & inhibitors , Structure-Activity Relationship , Ligands , Mass Spectrometry/methods , NF-kappa B/chemistry , Protein Binding , Signal Transduction/drug effects , TNF Receptor-Associated Factor 5/antagonists & inhibitors , TNF Receptor-Associated Factor 5/chemistry , Transcription Factor RelA/antagonists & inhibitors , Transcription Factor RelA/chemistry
2.
Curr Drug Targets Infect Disord ; 3(4): 295-309, 2003 Dec.
Article in English | MEDLINE | ID: mdl-14754431

ABSTRACT

A global effort has been undertaken to control human immunodeficiency virus (HIV) though the development of vaccines and pharmacologics. Current FDA approved pharmacological inhibitors target two of the three viral enzymes critical to replication and maturation of infectious viral particles: reverse transcriptase (RT) and protease (Pr). Although combination therapies targeting RT and Pr have significantly reduced AIDS related morbidity and mortality, resistance to individual inhibitors is a growing concern. Currently, there are six protease inhibitors in clinical use. These inhibitors target the active site of protease using peptidomimetic transition state analogs based on natural substrates. However, treatment failures arise as a lack of compliance due to HIV-inhibitor pharmacokinetics, toxicity, and tolerance. This allows reduced HIV-inhibitor pressure, increased viral replication, and the emergence of drug resistant mutations. Continued use of protease inhibitors in the face of incomplete viral suppression may result in HIV-1 escape mutants not only being resistant to the protease inhibitor used, but to all clinically available protease inhibitors. Thus, new broad-based protease inhibitors are needed to control the emerging multi-drug, cross-resistant HIV-1. Moreover, given the emergence of cross-resistant HIV-1, there is a need to target novel protease structural sites to reduce the risk of multi-drug cross-resistance. In this review, we discuss the resistance to protease inhibitors and the rationale for new strategies towards drug design for suppressing protease activity. We focus on the structure and function relationship and the influence that drug resistance mutants exert on the evolution of HIV-1 protease.


Subject(s)
Drug Design , HIV Infections/drug therapy , HIV Protease Inhibitors/pharmacology , HIV-1/physiology , Amino Acid Sequence , Drug Resistance, Multiple, Viral/genetics , HIV Infections/virology , HIV Protease Inhibitors/chemistry , HIV-1/enzymology , HIV-1/genetics , Humans , Models, Molecular , Molecular Sequence Data , Structure-Activity Relationship , Virus Replication/drug effects
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