The current state of drug discovery and a potential role for NMR metabolomics

Abstract

The pharmaceutical industry has significantly contributed to improving human health. Drugs have been attributed to both increasing life expectancy and decreasing health care costs. Unfortunately, there has been a recent decline in the creativity and productivity of the pharmaceutical industry. This is a complex issue with many contributing factors resulting from the numerous mergers, increase in out-sourcing, and the heavy dependency on high-throughput screening (HTS). While a simple solution to such a complex problem is unrealistic and highly unlikely, the inclusion of metabolomics as a routine component of the drug discovery process may provide some solutions to these problems. Specifically, as the binding affinity of a chemical lead is evolved during the iterative structure-based drug design process, metabolomics can provide feedback on the selectivity and the in vivo mechanism of action. Similarly, metabolomics can be used to evaluate and validate HTS leads. In effect, metabolomics can be used to eliminate compounds with potential efficacy and side effect problems while prioritizing well-behaved leads with druglike characteristics.

Figure 1

(a) A correlation between increasing U.S. life expectancy (solid black diamonds) and the decreasing U.S. mortality rate associated with infectious disease (solid red circles) between 1930 and 2006 (adapted from Armstong et al.). (b) A correlation between the decrease in U.S. heart disease death rates and the decrease in U.S. LDL cholesterol levels (adapted from Kaufman et al.). (c) Plot of the total U.S. health care cost per capita (solid black diamonds) and the associated expenditures on pharmaceuticals (solid red circles) between 2000 and 2011. (d) Plot of the decrease in new drugs approved by the FDA (solid black diamonds) between 1996 and 2013. Trend lines are provided to simply assist in visualizing the overall data trends and are not a model fit.

Figure 2

Plot of manuscripts per year where the key word “metabolomics” was included in the topic field in the Web of Science database updated Jan 9, 2014.

Figure 3

A flowchart of protocol used for the NMR analysis of bacterial metabolomes. Reprinted with permission from ref (78). Copyright 2013 Proteomass Scientific Society.

Figure 4

Illustration of hypothetical PCA scores plot for the following scenarios (A) inactive compound, (B) active and selective inhibitor, (C) active, nonselective inhibition of target and secondary protein, and (D) active, nonselective preferential inhibition of secondary protein. Labels correspond to: wild-type cells (open circles), drug-treated wild-type cells (black solid circles), knockout mutant cells where the protein target of the drug is inactive (open triangles), and drug-treated mutant cells (black solid triangles). Abbreviations correspond to mutant cells (mut) and wild-type cells (wt). Adapted from Forgue et al. (2006).

Figure 5

(a) The PCA scores plot comparing A. nidulansuaZ14 mutant (green X’s), wild-type with AZA (solid red squares), uaZ14 mutant with AZA (solid blue circles), and wild-type cells (solid black diamonds). The clustering pattern is comparable to the hypothetical PCA scores plot depicted in Figure 4B, which is consistent with an active and selective inhibitor. Abbreviations correspond to mutant cells (mut), wild-type cells (wt), and 8-azaxanthine (AZA). Reprinted with permission from ref (95). Copyright 2006 American Chemical Society. (b) PCA scores plot comparing Mycobacterium smegmatis mc²155 (solid blue squares), TAM23 (solid black circles), GPM14 (solid red diamonds), GPM16 (solid green upright triangles), TAM23 pTAMU3 (solid gold inverted triangles), mc²155 with DCS (solid purple squares), and TAM23 with DCS (solid red circles), GPM14 with DCS (solid purple diamonds), GPM16 with DCS (solid cyan upright triangles), and TAM23 pTAMU3 with DCS (solid orange inverted triangles). The clustering pattern is comparable to the hypothetical PCA scores plot depicted in Figure 4C, which is consistent with an active and nonselective inhibitor. Abbreviations correspond to mutant cells (mut), wild-type cells (wt) and d-cycloserine (DCS). Reprinted with permission from ref (96). Copyright 2007 American Chemical Society. (c) 2D OPLS-DA scores plot demonstrating the clustering pattern for 12 antibiotics with known biological targets and three compounds of unknown in vivo activity: untreated M. smegmatis cells (solid black squares), chloramphenicol (solid cyan diamonds), ciprofloxacin (solid gold diamonds), gentamicin (solid pink diamonds), kanamycin (solid purple diamonds), rifampicin (solid red diamonds), streptomycin (solid yellow diamonds), ethambutol (solid light-green inverted triangles), ethionamide (solid cyan inverted triangles), isoniazid (solid pink inverted triangles), ampicillin (solid red upright triangles), d-cycloserine (solid upright purple triangles), vancomycin (solid upright orange triangles), amiodorone (solid purple circles), chlorprothixene (solid light-green circles), and clofazimine (solid red circles) treated M. smegmatis cells. The ellipses correspond to the 95% confidence limits from a normal distribution for each cluster. The untreated M. smegmatis cells (solid black squares) was designated the control class, and the remainder of the cells were designated as treated. The OPLS-DA used one predictive component and six orthogonal components to yield a R²X of 0.715, R²Y of 0.803, Q² of 0.671, and a CV-ANOVA p-value of 1.54 × 10^–34. Reprinted with permission from ref (97). Copyright 2012 American Chemical Society.