in fact , the 
 increased use of pharmaceuticals in developed nations has significantly 
 contributed to an increase in life expectancy ( figure 1a ) .
lichtenberg has estimated 
 that 40% of the increase in life expectancy from 1982 to 2001 across 
 52 countries can be attributed to new drug launches .
specifically , greater than 80% of the gains in life expectancy 
 for cancer patients have been attributed to new treatments that include 
 medicines .
a separate study indicates 
 that 5060% of life expectancy gains in cancer patients are 
 directly attributed to pharmaceuticals .
similarly , the death rate from human immunodeficiency virus infection / acquired 
 immunodeficiency syndrome ( hiv / aids ) decreased 85% after the approval 
 of antiretroviral treatments in 1995 and , 
 correspondingly , aids patients now have close to normal life expectancies . a comparable outcome has been observed with a 
 25% decline in coronary heart disease and mortality between 1997 and 
 2007 , which is due , in part , to cholesterol - lowering drugs ( figure 1b ) .
a recently completed 22-year follow - up 
 study on the benefit of treating hypertension indicated that life 
 expectancy increased one day for each month of treatment .
in addition , the introduction of antibiotics 
 has largely contributed to the eight year increase in life expectancy 
 between 1944 and 1972 ( figure 1a ) .
in fact , the major causes of death in 1900 were 
 infectious diseases ( pneumonia , tuberculosis , diarrhea , and enteritis ) , but due to the beneficial impact of antibiotics , 
 by 2013 the leading causes of death can be attributed to an aging 
 population ( heart disease , cancer , stroke , and chronic lung disease ) .
clearly , there is a growing body of empirical 
 evidence demonstrating the significant contribution of pharmaceuticals 
 to increasing life expectancy .
( 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 . ) .
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 .
the costs of pharmaceuticals 
 are a significant contributor to these rising health care expenditures , 
 where pharmaceuticals expenditures account for approximately 10% of 
 the total u.s .
pharmaceuticals have actually contributed to an overall decrease 
 in the cost of health care .
simply , pharmaceutical 
 use prevents serious health - related events that lowers the use of 
 health care resources such as emergency room visits , hospital stays , 
 medical personnel time ( nurses , physicians , surgeons , etc . ) , surgery 
 facilities , and diagnostic services ( x - ray , magnetic resonance imaging 
 ( mri ) , computerized tomography ( ct ) scans , etc . ) .
in fact , a recent 
 study of the canadian health care system indicated that for each dollar 
 spent on pharmaceuticals by a male patient , a decrease of 1.48 dollars 
 was achieved in other health care resources .
similarly , the use of cholesterol lowering drugs in scandinavia 
 has led to a 34% reduction in hospital stays and a corresponding savings 
 of $ 3872 per patient .
multiple sclerosis patients with cladribine ( immunosuppressant ) resulted 
 in a similar decrease in hospital stays , emergency room visits , and 
 missed work . likewise , a randomized study 
 across 28 countries analyzed the incremental cost - effectiveness ratio 
 ( icer ) of treating patients with clopidogrel , an antiplatelet agent .
the treatment was determined to be cost - effective , 
 with an icer that ranged from $ 4833 to $ 6475 .
these studies , among 
 others , have clearly indicated that the use of pharmaceuticals is 
 a beneficial and cost - effective approach to lowering total health 
 care costs .
despite the historical benefits of drug discovery to 
 human health and well - being , there has been a disturbing decline over 
 the last decades in the creativity and output of the pharmaceutical 
 industry .
since 1996 , there has been a precipitous drop in the number of new 
 drugs approved by the fda ( figure 1d ) . also , 
 an increasing number of these new drugs are simply repositioned or  me too  .
furthermore , there has been a steady decline in the efficiency 
 of drug discovery as measured by the number of drugs per dollar of 
 research and development ( r&d ) spending . what are the underlying problems with drug discovery causing this 
 diminishing productivity ?
first and foremost , drug discovery is probably 
 the most difficult endeavor embarked upon and , as a result , a high 
 failure rate is commonly encountered .
for example , an extremely high 
 attrition rate occurs during the drug discovery process , where estimates 
 indicate that it takes upward of 45 active research projects to generate 
 one new drug application ( nda ) to the fda .
furthermore , the corresponding success rate of ndas in clinical 
 trials is only 11% .
further contributing 
 to the situation is the high cost associated with the drug discovery 
 process .
the average cost for developing a new drug has been estimated 
 to range from $ 800 million to $ 1.8 billion 
 dollars .
the high cost associated with 
 drug discovery will severely limit the number of active research projects 
 pursued by a company . unfortunately , 
 in response to these challenges , a number of serious and deleterious 
 decisions have been made that have only aggravated the situation . 

the pharmaceutical industry has undergone an extensive round of mergers . 
 between 2000 and 2011 ,
the pharmaceutical industry has lost nearly 
 300000 jobs according to the consulting firm challenger , gray & 
 christmas .
obviously , this drastic decline in highly trained personnel 
 with extensive drug discovery experience has only contributed to the 
 continuing decrease in the discovery of new drugs . additionally , the 
 pharmaceutical industry has significantly diminished drug development 
 efforts in a number of therapeutic areas including alzheimer s 
 and parkinson s disease , antibiotics , psychiatric disorders , vaccines , and even cardiovascular diseases .
these therapeutic areas are being abandoned 
 by some companies because of the low success rate or a low return 
 on investment .
instead , the focus is on identifying the next  blockbuster  
 drug with profits in the billions of dollars per year .
this approach effectively eliminates the possibility 
 of discovering new therapies for important areas of human health . 

conversely , the concentrated effort within a few research areas has 
 inevitably led to the decrease in new drugs and the increase in the 
 number of redundant drugs .
other 
 ill - conceived managerial decisions have also contributed to the decline 
 in the productivity and creativity of the pharmaceutical industry , 
 which include outsourcing , the application of 
 metrics , and restricting project timelines . in effect , drug discovery is being treated like 
 any other manufacturing process where cost savings is driving business 
 decisions , but assembly line science does not work because the process 
 is highly interdisciplinary and unpredictable .
outsourcing simply leads to the isolation of critical components 
 of the process , but breakthroughs and problem - solving require routine 
 interactions between experts with diverse skill sets and experiences . 

similarly , metrics emphasizes the bookkeeping of meaningless data 
 to create the illusion of success , but the number of compounds synthesized , 
 assays run , or structures solved is irrelevant if a drug is not the 
 outcome .
finally , restricting research projects to a predefined timeline 
 of only two to three years almost guarantees a pattern of repetitive 
 failures . a priori predicting how long it will take any project to 
 yield a positive outcome
, an informed decision 
 based on experience and results should be used to make a decision 
 on whether to terminate or continue a project . the focus on 
 predefined timelines and
assembly line science is 
 a result of the integral role that hts have been playing in the drug 
 discovery process .
this has occurred despite the fact that hts has not contributed 
 to an increase in the number of new drug discoveries . in general
, hts does not provide information on the 
 in vivo mechanism of action , does not provide evidence of a direct 
 binding interaction with the protein target , and has a tendency to 
 identify a large number of hits with undesirable modes of action or 
 false positives . furthermore
, the false leads that routinely emerge from hts will 
 contribute to delays in a drug discovery project and may eventually 
 result in a project being terminated . in fact , poor hts outcomes may 
 also contribute to the observed failures of drug leads in the clinic .
of course , the hts community has been aggressively responding to 
 these issues by improving the quality and diversity of screening libraries , by improving the statistical analysis of hts data sets , and by combining hts with virtual screens .
unfortunately , a significant number of the problems encountered 
 in the pharmaceutical industry are self - inflicted and will require 
 a philosophical paradigm shift in how drug discovery is conducted . 
 clearly , these problems will not be solved with technology advancements 
 like structure - based drug discovery , combinatorial 
 chemistry , fragment - based screening , or metabolomics , but the adaption of metabolomics and other related approaches as 
 an integral part of a drug discovery program has the potential of 
 benefiting the drug discovery process .
metabolomics is a natural extension 
 of genomics , transcriptomics , and proteomics , but instead of monitoring 
 changes in the expression of genes or proteins , metabolomics monitors 
 changes in the concentration of the low - molecular - weight ( < 1 kda ) 
 compounds ( metabolites ) present in a cell , tissue , organ , organism , 
 or biofluid ( urine , plasma , cerebrospinal fluid , etc . ) .
the metabolome 
 is the entire collection of small molecules or metabolites within 
 a biological sample that may include amino acids , carbohydrates , cofactors , 
 fatty acids , nucleotides , and even xenobiotics that includes drugs 
 and drug - associated metabolites .
the number of metabolites for a particular 
 biological system may range from thousands to hundreds of thousands 
 of compounds .
importantly , changes in the metabolome or in a specific 
 metabolite are a direct result of changes in the biological activity 
 of an enzyme or protein .
the change in the expression level of a gene or protein 
 does not necessarily correlate with a change in protein activity .
thus , metabolomics provides a direct measure 
 of the state of the cell or biological system , where changes in the 
 metabolome capture how the system responds to environmental or genetic 
 stress .
specifically , a drug or an active chemical lead would be expected 
 to perturb the metabolome of a cell or tissue upon treatment . targeted and untargeted
a targeted metabolomics 
 approach follows changes to a specific metabolite or set of metabolites 
 based on some prior information or hypothesis .
specifically , the identified 
 or targeted metabolites are expected to respond to a drug treatment , 
 disease state , genetic modification , or some other environmental stressor . 

the metabolites 
 or metabolic pathways affected by these external stress factors are 
 unknown .
the goal of untargeted metabolomics is to monitor the entirety 
 of the metabolome in order to identify the affected metabolites and 
 pathways . in either case , the metabolome extracted from cells or biofluids 
 are compared before and after the addition of the stress factor ( drug 
 treatment , genetic mutation , disease , etc . ) .
a variety of analytical 
 techniques are routinely used for metabolomics , but mass spectrometry 
 ( ms ) and nuclear magnetic resonance ( nmr ) 
 spectroscopy are the two most commonly 
 employed methods .
the application of nmr to metabolomics has been 
 extensively reviewed , and only critical issues will be highlighted .
metabolomics 
 is a valuable tool of chemical biology and systems biology , and its 
 application has been rapidly expanding ( figure 2 ) .
the ease of use and general utility has contributed to the continually 
 increasing number of metabolomics studies .
a standard protocol for 
 analyzing bacterial cell cultures with metabolomics is illustrated 
 in figure 3 .
unfortunately , 
 the apparent simplicity of metabolomics also makes it easy to apply 
 the technique incorrectly .
untargeted metabolomics 
 consists of acquiring one - dimensional ( 1d ) h nmr spectra 
 for a set of cell lysates or biofluid samples that are then analyzed 
 using standard multivariate statistical analysis techniques .
the 1d h nmr spectra provides a 
 fingerprint of the state of the metabolome , where principal component 
 analysis ( pca ) , partial - least - squares ( pls ) , and orthogonal projection 
 to latent structures discriminant analysis ( opls - da ) determine if 
 the metabolomes differentiate between the multiple classes ( e.g. , 
 drug treated vs untreated cells ) .
pls and opls - da s - plots and loading 
 plots are then used to identify the spectral features ( i.e. , chemical 
 shifts and associated metabolites ) that primarily contribute to these 
 observed class differentiations .
incorrect sample preparation and 
 collection procedures may lead to biologically irrelevant changes 
 to the metabolome .
inappropriate data 
 processing and analysis protocols may bias the metabolomics data set , 
 causing an erroneous interpretation .
the lack of 
 model validation may imply a class distinction that does not really 
 exist .
similarly , class separations in 
 a scores plot may be identified that are not statistically justified .
correspondingly , a current challenge in the field of metabolomics 
 is optimizing and standardizing protocols .
 
 was included in the topic field in the web of science database updated 
 jan 9 , 2014 .
a flowchart of protocol 
 used for the nmr analysis of bacterial 
 metabolomes . reprinted with permission from ref ( 78 ) .
copyright 2013 proteomass 
 scientific society . unlike genomics or proteomics 
 samples , metabolomics samples are 
 not static and will change with time and from the handling and processing 
 procedures .
verifying that the observed 
 changes in a metabolomics sample are biologically relevant , as opposed 
 to artifacts of sample preparation , is critical to a successful metabolomics 
 study .
thus , multiple replicates of cell samples are grown , harvested , 
 and lysed under identical conditions as is practically possible and 
 as quickly as possible .
importantly , processing of samples should 
 be completely randomized because , at a minimum , a time - bias will be 
 imprinted on the samples if each class was processed sequentially . 
 similarly , each step of the process should be performed by the same 
 individual .
again , variations in individual techniques may bias the 
 outcome if each class was processed by different researchers . despite 
 these efforts , significant within class variability will occur because 
 of the intrinsic nature of biological samples .
thus , it is also critical 
 to prepare enough replicates per class in order to obtain statistical 
 relevance to any observed class discrimination . typically , a minimum 
 of 610 replicates of cell lysates is required , but the sample 
 size increases significantly for a clinical study .
it is also critical to quantify the statistical significance 
 of the cluster separations in the resulting scores plot . acquiring 
 and processing the nmr spectra for
each cell lysate can 
 also induce unintended perturbations in the metabolomics data set . 

randomizing the nmr data collection , utilizing automation , and obtaining 
 efficient solvent suppression minimizes these concerns .
specifically , 
 high - quality solvent suppression is required to avoid baseline correcting 
 the nmr spectrum . a minimal and uniform approach to processing the 
 nmr spectrum ( fourier transformation and phase correction )
is preferred 
 because baseline correction , applying a window function , zero filling , 
 and other processing steps will modify the data in a biologically 
 irrelevant manner .
proper preprocessing of the metabolomics data set , 
 which includes removing noise , normalization , 
 scaling , and aligning ( or binning ) , is also necessary for a reliable 
 pca , pls , or opls - da model and meaningful interpretation of the metabolomics 
 data .
removing noise regions eliminates the possibility that serendipitous 
 covariant noise peaks may contribute to irrelevant class separation . 

normalization corrects for the variability in total metabolite concentration 
 or nmr sensitivity due to experimental differences in the total number 
 of cells harvested , in the efficiency in extracting the metabolomes , 
 and potentially due to changes in instrument performance .
similarly , 
 scaling accounts for large differences in metabolite concentrations 
 within a given sample ( dynamic range issue ) and prevents intense peaks 
 from dominating the multivariate statistical analysis .
binning or 
 aligning the spectra corrects for small variations in peak position 
 and peak shape due to differences in sample conditions ( ph , ionic 
 strength , concentration , etc . ) and instrument variability between 
 replicates . in practice ,
multiple program packages are used for each 
 step of the nmr data processing and analysis including separate software 
 for processing of the 1d h nmr spectra , preprocessing 
 the metabolomics data set , and the multivariate statistical analysis . 

this situation clearly contributes to the lack of a standard set of 
 protocols and the significant variations between individual metabolomics 
 studies . to address this issue
, we have developed an open - source platform 
 for the complete handling of nmr metabolomics data and the generation 
 of validated multivariate statistical models ( mvapack , http://bionmr.unl.edu/mvapack.php ) .
we have also developed software to 
 analyze the statistical significance of cluster separations in pca , 
 pls , and opls - da scores plots ( pca / pls - da utilities , http://bionmr.unl.edu/pca-utils.php ) .
the first step 
 of the drug discovery process is to identify initial chemical leads 
 with reasonable affinity ( typically a kd of 10 m or less ) and novel structures ( patentable ) that have 
 druglike characteristics ( lipinski s rule of five ) and are synthetically achievable and malleable . 

this is primarily accomplished by using a high - throughput screen of 
 a chemical library composed of hundreds of thousands to millions of 
 compounds .
the chemical leads are then 
 evolved to high - affinity ligands ( kd  
 nm ) through an iterative process involving structure - based drug design , 
 traditional medicinal chemistry techniques , and multiple activity 
 assays .
this process may include cell - based assays to measure cell 
 viability when the desired activity of a chemical lead is cell death 
 ( i.e. , infectious disease and cancer ) . while this process is generally 
 very efficient and high
affinity ligands typically emerge , there is 
 a fundamental difference between a true drug lead and a tight - binding 
 ligand .
it is well - known that an increase in affinity can be achieved 
 by increasing the size and hydrophobicity of a compound , which are 
 generally detrimental to druglike characteristics .
in fact , the drift of corporate libraries toward 
 these undesirable traits has been well - documented , which inevitably led to the development of lipinski s 
 rule of five .
furthermore , compounds 
 with poor physiochemical properties also tend to provide misleading 
 biological activity . for this class of compounds ,
the inhibition of 
 a protein target likely occurs through undesirable mechanisms such 
 as micelle formation or induced protein aggregation or precipitation .
correspondingly , the process of increasing binding affinity may 
 actually change the mode of action of a lead candidate in an undesirable 
 direction .
besides improving efficacy , increasing the binding 
 affinity of chemical leads is also intended to improve selectivity . 
 reasonably , the more a particularly compound has been optimized to 
 bind to the exact sequence and structural characteristics of a specific 
 active site , the less likely it will bind to other proteins .
of course , 
 a secondary assay using a panel of functional homologues with high 
 sequence and structure similarity to the therapeutic target is routinely 
 used to evaluate compound selectivity .
the lack of efficacy 
 and issues with toxicity , which include off - target activity , are the 
 primary reasons potential drugs fail in the clinic .
correspondingly , evaluating issues related to bioavailability 
 ( absorption , distribution ) , in vivo activity , in vivo selectivity , 
 compound stability ( metabolism , excretion ) , and toxicity are an integral , 
 but extremely challenging component of the drug discovery process . 

this critical information is a primary outcome of clinical trials , 
 but of course this is a costly and highly inefficient means of identifying 
 problematic drug leads .
ideally , serious problems would be identified 
 and eliminated prior to initiating a clinical trial .
in essence , as the affinity of 
 a chemical lead is enhanced it is 
 also critical to validate and maintain a desirable in vivo mode of 
 action .
are there any off - target effects or toxicity 
 issues ? because the metabolome captures the state of the cell and 
 is a direct measure of protein activity , any observed changes in the 
 metabolome as a result of a drug treatment would provide information 
 on the drug s activity and selectivity . in this manner ,
untreated 
 wild - type cells represent a negative control and a knockout mutant 
 cell line , where the protein target has been genetically inactivated 
 to provide a positive control .
the difference between these two samples 
 represents the activity of the protein target as measured by the observed 
 changes in the metabolome .
this difference is observed with a pca , 
 pls , or opls - da scores plot generated from a set of 1d h nmr spectra .
simply , each 1d h nmr spectrum is reduced 
 to a single point in the principal component ( pc)-space of a scores 
 plot .
similar spectra , and correspondingly similar metabolomes , will 
 cluster together in a scores plot .
conversely , nmr spectra obtained 
 from distinctly different metabolomes will form separate clusters 
 in a scores plot .
the in vivo activity of a chemical lead can 
 then be ascertained 
 by treating both the wild - type and mutant cells with the lead compound 
 and interpreting the resulting clustering patterns in a pca , pls , 
 or opls - da scores plot ( figure 4 ) .
a chemical 
 lead that is inactive in vivo would have no impact on the metabolome 
 of either the wild - type or mutant cells .
the separation in the scores 
 plot would simply reflect the difference in the activity of the target 
 protein .
so , the treated and untreated wild - type cells will cluster 
 together and separate from the treated and untreated mutant cells 
 in the scores plot ( figure 4a ) .
a chemical 
 lead with the desired in vivo activity and selectivity , only inhibiting 
 the identified protein target , would perturb the metabolome of the 
 wild - type cells in a manner similar to the mutant cells .
thus , wild - type 
 cells treated with the chemical lead would cluster together with both 
 the treated and untreated mutant cells in the scores plot ( figure 4b ) .
the untreated wild - type cells would still form 
 a separate cluster because the protein target is still active .
the 
 metabolome of the mutant cells is not affected by treatment with the 
 chemical lead because the protein target is already inactivated and 
 the chemical lead does not inhibit a secondary protein .
conversely , 
 if the chemical lead was not selective , then the treated wild - type 
 and mutant cells would form a third cluster separate from the untreated 
 wild - type and mutant clusters ( figure 4c ) . 

changes in the metabolome from the treated cells would result from 
 inactivating multiple proteins , but the untreated mutant cells would 
 only have the target protein inactivated . finally ,
if the chemical 
 lead actually inhibits the wrong protein target in vivo , then four 
 separate clusters would be observed in a scores plot ( figure 4d ) .
effectively , a different set of active or inactive 
 proteins is present in the four different cell cultures .
the treated 
 wild - type cells would have the wrong protein inactivated , whereas 
 the treated mutant cells would have both the original target protein 
 and the wrong protein inactivated .
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 .
importantly , the results of the metabolomics study would 
 be used 
 to prioritize chemical leads and focus effort .
compounds that maintained 
 the desired in vivo activity and selectivity in the metabolomics experiments 
 while providing a route to improve binding affinity would be identified 
 as chemical leads and proceed to more advanced animal and absorption , 
 distribution , metabolism , and excretion ( adme ) studies .
conversely , 
 compounds that lack in vivo activity in the metabolomics study are 
 likely to experience bioavailability issues and compounds that are 
 nonselective in the metabolomics study are likely to encounter toxicity 
 issues .
compounds that unexpectedly target the wrong 
 protein in the metabolomics study should simply be abandoned .
importantly , 
 the cluster analysis can be supplemented with an in - depth and exhaustive 
 analysis of metabolite concentration changes ( figure 3 ) .
changes to a specific set of metabolite should be correlated 
 with each of the clusters observed in the scores plot .
s - plots from 
 pls and opls - da are routinely used to identify the metabolites primarily 
 contributing to the observed class differentiation in the scores plot . 

similarly , loadings plots provide information on the relative contribution 
 to the class differentiation which can be used to infer metabolite 
 concentration changes .
in addition , c - labeled metabolites 
 combined with two - dimensional ( 2d ) h  c heteronuclear single quantum coherence ( hsqc ) experiments can be 
 used to identify and quantify metabolite concentration changes .
finally , 
 this information can be combined together to generate a metabolic 
 network to capture the global changes to the metabolome .
employing 
 metabolomics as part of a drug discovery project is very appealing 
 because of its relative simplicity , universal application , and high 
 information content .
for instance , while obtaining a knockout mutant 
 may not always be practical ( i.e. , the therapeutic target may be essential ) , 
 there are other equally valid approaches to obtaining a positive control . 

the activity of the target protein can be manipulated using rna interference 
 ( rnai ) , a known drug , monoclonal antibody , or any other means of modulating 
 the activity of a protein .
the approach can be applied to tissues , 
 organs , organisms , or a variety of biofluids .
similarly , metabolomics 
 is also extremely valuable even if the in vivo target of the chemical 
 leads is unknown .
the successful application of metabolomics to a 
 drug discovery project is simply dependent on identifying the appropriate 
 set of classes to compare ( e.g. , wild - type vs mutant , healthy vs disease , 
 drug treated vs untreated , etc . ) .
the following are a few illustrated 
 examples of using metabolomics to assist drug discovery .
8-azaxanthine 
 ( aza ) is an inhibitor of urate oxidase and was shown to inhibit new 
 hyphal growth in aspergillus nidulans .
the corresponding metabolomics study 
 demonstrated that the drug was a selective in vivo inhibitor ( figure 5a ) .
the metabolome 
 of an a. nidulansuaz14 mutant that eliminated urate oxidase was shown to be distinct from 
 the metabolome from wild - type mycelia . treating both
the wild - type 
 and uaz14 mutant mycelia with aza resulted in a pca 
 scores plot with a clustering pattern consistent with a selective 
 and active inhibitor and a potential antifungal ( figure 4b ) .
d - cycloserine ( dcs ) is a second - line treatment 
 for tuberculosis that has been used for over 50 years despite the 
 lack of an in vivo mechanism of action .
dcs has been shown to inhibit 
 both alanine racemase ( alr ) and d - alanine - d - alanine 
 ligase ( ddl ) in vitro . a metabolomics study was conducted to determine 
 if alanine racemase was the lethal target of dcs using mycobacterium smegmatis mc155 as a model 
 system .
the results indicated that dcs 
 is a promiscuous inhibitor and that alr is not the lethal target of 
 dcs ( figure 5b ) . treating both the wild - type 
 mc155 cells and
an alr null mutant ( tam23 ) 
 with dcs resulted in a pca scores plot with a clustering pattern consistent 
 with a nonselective inhibitor ( figure 4c ) . 

in fact , dcs is well known to have serious side effects that include 
 seizures and mental disorders , presumably due to off - target activity 
 ( agonist against n - methyl - d - aspartate ( nmda ) 
 receptors ) .
this is clearly consistent with the metabolomics results 
 and indicative of potential problems with nonselective inhibitors : 
 the likelihood of side effects or toxicity issues .
again , this demonstrates 
 the value of incorporating metabolomics in a drug discovery effort 
 in order to identify and eliminate such problems prior to clinical 
 trials .
these types of comparative metabolomics analysis can also 
 be used to identify a potential in vivo mode of action to identify 
 novel inhibitors .
the tuberculosis antimicrobial acquisition and coordinating 
 facility ( taacf ) screened a large chemical library against m. tuberculosis using cell - based assays .
taacf only 
 reported mic50 values , where the mechanism of action was 
 unknown .
a metabolomics study was conducted to infer an in vivo mode 
 of action for three highly active compounds identified by taacf . instead of comparing the metabolomes between 
 wild - type and mutant cell lines as before , this study compared the 
 activity of a collection of known antibiotics with defined cellular 
 targets against the taacf unknowns .
simply , multiple drugs inhibiting 
 the same cellular target would be expected to induce a similar change 
 in the metabolome .
drugs that target cell wall synthesis , mycolic 
 acid biosynthesis , or transcription , translation , or dna supercoiling 
 formed separate and distinct clusters in the opls - da scores plot . 

the taacf unknowns clustered together with the cell wall inhibitors 
 implying a similar mechanism of action .
a subsequent literature search 
 verified that the taacf unknowns have been previously shown to disrupt 
 bacterial membranes .
the metabolomics analysis 
 can verify that the in vivo mechanism of action is maintained as the 
 binding affinity is improved .
( 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 ) .
( b ) pca scores plot comparing mycobacterium smegmatis mc155 ( solid 
 blue squares ) , tam23 ( solid black circles ) , gpm14 ( solid red diamonds ) , 
 gpm16 ( solid green upright triangles ) , tam23 ptamu3 ( solid gold inverted 
 triangles ) , mc155 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 ) .
( 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 rx of 0.715 , ry of 0.803 , q of 
 0.671 , and a cv - anova p - value of 1.54  10 .
metabolomics 
 is a valuable and versatile technique that is making 
 important contributions to systems biology , personalized medicine , 
 biomarker discovery , toxicology , and disease diagnostics .
similarly , 
 the inclusion of metabolomics as a routine component of the drug discovery 
 process may improve the efficiency and success rate of drug discovery . 

given the current challenges facing the pharmaceutical industry and 
 the high reliance on high - throughput screens for lead identification , 
 metabolomics fills an important need .
metabolomics is useful for verifying 
 a desirable in vivo mechanism of action for hts chemical leads and 
 for further validating and verifying the mode of action is maintained 
 as the binding affinity is iteratively increased . in this manner , 
 metabolomics can be used to identify truly novel leads with a unique 
 cellular target and bioactivity .
metabolomics is also useful for determining 
 if a chemical lead is a selective in vivo inhibitor .
this is a unique 
 asset of metabolomics that provides a simple mechanism to remove chemical 
 leads with potential side effects or toxicity issues . for a drug discovery 
 effort to effectively benefit from adapting metabolomics
incorrect sample preparation and collection 
 procedures , inappropriate data processing and analysis protocols , 
 incorrect application of statistical techniques , and the lack of model 
 validation are all common occurrences that result in erroneous outcomes . 
 nevertheless , these issues are aggressively being addressed by the 
 field , which has led to the development and adaptation of standards 
 by the metabolomics standards initiative ( msi , http://msi-workgroups.sourceforge.net/ ) and the coordination of standards 
 in metabolomics ( cosmos , http://cosmos-fp7.eu/ ) .
nmr metabolomics is also limited to systems 
 where a drug treatment modulates the metabolome in a direct response 
 to the drug s biological activity .
for instance , drugs designed 
 to prevent plaque formation in alzheimer s patients by physically 
 inhibiting -amyloid aggregation may not perturb the metabolome 
 because the drug does not affect protein activities .
furthermore , 
 the metabolomes from both a healthy control and the disease state 
 needs to be readily accessible from a cell culture , tissue , organ , 
 or biofluid for a comparative analysis .
this is relatively straightforward 
 to achieve for drugs targeting any disease with well - established cell 
 lines ( various cancers , neuronal cells , pathogenic bacteria , viruses , 
 etc . ) .
the expanding collection of disease - specific human stem cell 
 lines also significantly increases the number of diseases amenable 
 to analysis by metabolomics .
animal models may also be used , but this 
 would dramatically increase the cost while decreasing throughput . 

alternatively , a knock - down mutant cell line can be generated for 
 a specific therapeutic target , where the comparison would simply be 
 between wild - type and mutant cell lines .
in essence , the availability 
 of a cell - based or target - specific hts assay implies that it is also 
 possible to conduct a metabolomics study .
 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 . 
Benefits of Drug Discovery
Drug Discovery Challenges
Metabolomics
Conclusions