Advances in Nuclear Magnetic Resonance for Drug Discovery

Abstract

BACKGROUND: Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. OBJECTIVE/METHOD: A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR. CONCLUSION: NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets.

Figure 1

The number of new drugs approved by the U.S. Food and Drug Administration on a yearly basis. The straight line highlights the negative trend in drug approval rates.

Figure 2

High-quality NMR solution structures of Northeast Structural Genomics (NESG) consortium target proteins. A methodology for semiautomated data analysis was used to solve the eight NMR protein structures. NMR data collection was accomplished in ~1–9 days per structure and ~1–2 weeks of an expert’s time was required for semiautomated data analysis and structure calculation. For each structure, a ribbon drawing is shown on the left and a “sausage” representation of the backbone is shown on the right where the thickness reflects the precision achieved for the determination of the polypeptide backbone conformation. (Reprinted with permission from reference [64], Copyright 2005 by the National Academy of Sciences of the United States of America)

Figure 3

(a) Superposition of the CSP-guided docking with ADF filtering conformers (blue) with the original x-ray structures (yellow) for S. aureus nuclease complexed to thymidine 3′,5′-bisphosphate (PDB-ID: 1SNC) (Reprinted with permission from reference [112], Copyright 2008 by American Chemical Society). (b) Expanded view of the HADDOCK model of human NAT1 bound to its substrate PABA. Carbon, nitrogen, oxygen, sulfur, and hydrogen atoms are colored white, blue, red, yellow, and pink, respectively. The lengths of several hydrophobic contacts are provided and the hydrogen bonds to the carboxylic acid group of PABA are indicated in red. (Reprinted with permission from reference [118], Copyright 2006 by Elsevier Ltd.)

Figure 4

(a) General fragment-based scheme using a novel cyclic amidine-based aspartyl protease pharmacophore to generate BACE-1 inhibitors. (b) BACE-1 inhibitor complex. (Reprinted with permission from reference [184], Copyright 2007 by American Chemical Society)

Figure 5

(a) Analysis of the in vivo activity of 8-azaxanthine (AZA) in A. nidulans targeting urate oxidase. The PCA scores plot comparing A. nidulans inactive urate oxidase mutant (uaZ14) (), wild-type with AZA (), uaZ14 mutant with AZA (), and wild-type cells (◆). Results clearly demonstrate the selective activity (see Figure 7b) of AZA (Reprinted with permission from reference [201], Copyright 2006 by American Chemical Society). (b) Analysis of the in vivo activity of D-cycloserine (DCS) in mycobacteria targeting alanine racemase. PCA scores plot comparing wild-type (mc²155) (), inactive D-alanine racemase mutant (TAM23) (●), DCS resistant mutants (GPM14 (), GPM16 ()), restored D-alanine racemase activity mutant (TAM23 pTAMU3) () mc²155 with DCS (), and TAM23 with DCS (), GPM14 with DCS (), GPM16 with DCS (), and TAM23 pTAMU3 with DCS (). The results clearly demonstrate the active, non-selective inhibition of DCS (see Figure 7c). The secondary target of DCS is predicted to be D-alanine-D-alanine ligase (Reprinted with permission from reference [200], Copyright 2006 by American Chemical Society).