the healthcare system has witnessed tremendous progress during the last century on 
 several fronts . several infectious 
 diseases , such as polio and smallpox , have been practically eradicated among humans . 
 despite these advances , other conditions such as cancer , coronary artery diseases , hiv , 
 and many more this led the way to new advancements in the areas of molecular genetics , 
 life sciences , biotechnology , and molecular biology . despite the fact that 99.9% of human 
 dna sequences are identical , the 0.1% variation cascades into huge differences in disease 
 susceptibility , disease progression , and response to intervention among 
 individuals . since the human genome project , efforts have been underway to adopt genomic medicine in order to : ( i ) identify specific 
 genes that are responsible for common hereditary diseases and aberrations in major 
 pathways leading to illness , ( ii ) elucidate the underlying molecular mechanism of disease , 
 ( iii ) identify potential therapeutic targets , ( iv ) design small - molecule drugs to 
 intervene in the disease processes , ( v ) predict responses to treatment , and ( vi ) predict 
 responses to drug intervention . personalized medicine is critically important and hence is increasingly favored in many 
 areas of medicine , especially in oncology due to the complexities of the disease and 
 lethality of the chemotherapeutics . a meta - analysis of 39 prospective studies from the us 
 hospitals estimated the overall incidence of serious adverse drug reactions at a rate of 
 6.7% . in this study , more than 2.2 million 
 hospitalized patients had serious adverse drug reactions and ~106,000 patients had fatal 
 adverse drug reactions , making it between the fourth and sixth leading cause of death in 
 the united states . the cost of drug - related morbidity and mortality was estimated to be 
 more than us$177 billion in the year 2000 . in 
 addition to these acute adverse drug reactions , patients receiving incompatible and 
 inordinate treatments can suffer several long - term medical and socioeconomic 
 complications . for example , relapsed cancer , secondary neoplasms , heart disease , and many 
 other chronic medical conditions are prevalent among long - term survivors of cancer . personalized treatment , when applied in clinical settings , helps to answer two important 
 questions : ( i ) for a given individual , what drug or combination of drugs should be given 
 to treat a specific disease condition ? and ( ii ) how much and when should the drug(s ) be 
 administered ? pharmacogenomics , a field that has evolved in the last decade , has been 
 highly recommended for several disease conditions toward predicting the response for a 
 planned treatment protocol on an individual basis and has been put into practice in some 
 cases . pharmacogenomics has shown great promise in predicting the treatment response for a 
 given patient and has demonstrated the ability to alleviate much of the morbidity that can 
 be associated with treatment , making it an excellent tool to address the first of the two 
 questions above . however , because the purview of pharmacogenomics is limited to genotypic 
 variation , it has limited scope to comprehensively answer the second question , which is at 
 least as important to personalized treatment . in addition to genetic variation , several other nongenetic molecular mechanisms interface 
 within the human body . the manifestation of a specific gene sequence into a final disease 
 outcome , with or without drug intervention , proceeds at various levels . first , the genes 
 are transcribed and translated into proteins which act as enzymes in numerous metabolic 
 reactions . if a specific gene is implicated in drug disposition , the gene expression also 
 affects the distribution , metabolism , and elimination of the compound . the resultant phenotypes at the bio - atomic or -molecular 
 level then exert phenotypic changes at the cellular , tissue , and organ level through their 
 influence on the disease and response pathways . variations / aberrations , not only in gene 
 sequence and expression , but in any of the steps mentioned above , will result in an 
 unexpected outcome at an organismal level . figure 1 
 enumerates the cascade of events and validated processes contributing to the variations in 
 these steps . the clinical implication of these events is the possibility of observing a 
 subgroup of patients with same genotype but unique proteome , metabolome , and cellular 
 responses , which results in completely different treatment outcomes for each individual 
 patient . the key factors , besides gene expression , responsible for such consequences 
 include  but are not limited to epigenetic factors , nonheritable functionally 
 induced ( extragenetic ) factors , stochasticity in biochemical reactions , interactions in 
 signal transduction and metabolic networks , environment , nutrition / lifestyle , organ 
 failure ( renal , hepatic ) , coadministered drugs , pregnancy , disease type , and microflora 
 dynamics . exclusive , isolated application of pharmacogenomics has therefore come under 
 scrutiny by some in the literature and has resulted in a few new clinical guidelines that 
 are broadly accepted for management of patients . the foregoing discussion substantiates the need for developing procedures to accurately 
 measure and/or predict the phenotypic outcome for a specific pharmacogenomic variant . in 
 addition , methodologies must also be developed to determine optimal dosing of drug to 
 realize a specific phenotypic outcome . traditionally , pharmaceutical companies prescribe 
 the  optimal  dosing information based on clinical trials conducted at a 
 population level of the general public and patients . the adoption of standard - dose - for - all 
 approach from drug labels and trial - and - error approach to titrate the patient to maximum 
 tolerated dose has resulted in severe toxicity in some patients and insufficient treatment 
 in others . the so - called evidence - based medicine and the adoption of treatment standards 
 based on large epidemiological studies or randomized controlled trials have significantly 
 hampered the efforts to truly personalize the medical practice . a paradigm shift from evidence - based medicine to mechanism - based 
 medicine will ensure that each patient is treated according to his / her own mechanism and 
 thus should be the impetus for expanding implementation and utilization of personalized 
 medicine . in this work , we highlight some of the limitations associated with the isolated 
 application of pharmacogenomics to personalize treatment . specific instances of 
 biomolecular processes responsible for such outcomes are analyzed in detail with respect 
 to each level of phenotype . finally , emerging tools and methodologies to augment the 
 potentials of pharmacogenomics for a comprehensive realization of personalized treatment 
 are discussed . we first provide an introduction to the concept of pharmacogenomics , its 
 prevalence , strengths , and limitations in personalizing treatment ( see pharmacogenomics 
 and gene expression ) . we will then appraise the complexities in the molecular and cellular 
 level phenotypic manifestation of gene sequences , highlighting potential processes 
 responsible for this variation . the advantages of measuring / predicting cellular responses 
 and challenges are also discussed ( see complexities in predicting molecular phenotypes ) . finally , we highlight some of the emerging technological and quantitative tools to extend 
 the scope of personalized treatment beyond pharmacogenomics ( see future directions and 
 emerging technologies ) . gene sequencing and expression profiling are excellent tools for discerning variations in 
 disease susceptibility , disease diagnosis and classification , and prognosis for a given 
 treatment . within the realm of personalized treatment , pharmacogenetics garners prime 
 attention in utilizing gene sequencing tools for clinical translation . pharmacogenetics 
 aims to provide insight into the influence of genetic variants on the molecular biology of 
 disease and response to drug intervention . the concept of genetic polymorphism in the 
 human genome forms the core of pharmacogenomics . common sources of genetic polymorphisms 
 include single - nucleotide polymorphisms ( snps ) , nucleotide repeats , deletions , insertions , 
 and recombination . pharmacogenomics possesses a great potential to propel the development 
 of new therapeutic agents and/or administer existing drugs to a targeted subgroup of the 
 patient population who display a specific genotypic trait . gene expression analysis and 
 pharmacogenomics are being considered as companion diagnostic tools ( tests recommended 
 when prescribing a specific medication ) in several cases . the first conceivable utility of gene expression variation in the disease cycle is the 
 elucidation of disease susceptibility . a case  control study , conducted to 
 demonstrate the association of nramp1 gene and susceptibility to tuberculosis , 
 estimated that the odds of developing tuberculosis is 4.07 among subjects who are 
 heterozygous for two nramp1 polymorphisms . genetic polymorphism has also been exploited in many studies to 
 diagnose and classify the existing categories of many cancers . in a landmark work on 
 molecular classification of cancer using gene expression , dna microarray technique was 
 utilized to distinguish between acute myeloid leukemia and acute lymphoblastic 
 leukemia . a study devoted to 
 characterizing diffuse large b - cell lymphoma , a common form of non - hodgkin 's 
 lymphoma , using microarray gene expression profile , revealed two molecularly distinct 
 forms of diffuse large b - cell lymphoma . these 
 two new subtypes , germinal center b - like diffuse large b - cell lymphoma and activated 
 b - like diffuse large b - cell lymphoma , are representative of different stages of b - cell 
 differentiation and predict overall prognosis . these systematic and unbiased elucidations 
 of disease subtypes , based on global gene expression profiles , not only assist clinicians 
 in choosing appropriate treatment strategies that maximize efficacy but also minimize 
 unwarranted side effects . gene expression profiling also helps to direct the prediction of 
 prognosis of the disease for a specific treatment regimen . one of the well - studied and 
 clinically adopted examples of gene expression techniques is the demonstration of the 
 relationship between her-2/neu gene and a wide variety of human cancers . amplification of her-2/neu gene or overexpression of her-2/neu protein is 
 observed in as many as 34% of the breast cancer patients . in these patients , abnormalities in her-2/neu gene and 
 protein her-2/neu gene amplification also predicts node status , tumor grade , 
 overall survival , and time to relapse in breast cancer patients . one of the classical examples of applications of pharmacogenetics is the incidence of 
 genetic polymorphism in thiopurine s - methyltransferase ( tpmt ) gene among 
 humans . 
 to date , it serves as a prototypic system for displaying the potential for the utilization 
 of a pharmacogenomics - based approach to individualized drug dosing within clinical 
 settings . tpmt is a cytosolic drug - metabolizing enzyme that plays a key role in the 
 metabolism of purine antimetabolites such as 6-mercaptopurine ( 6-mp ) and 
 azathioprine . thiopurine is an 
 immunosuppressant that is used to treat childhood acute lymphoblastic leukemia , 
 inflammatory bowel diseases , autoimmune diseases , and immunosuppression following solid 
 organ transplantation . tpmt catalyzes the s - methylation of thiopurines and 
 promotes pathways leading to inactive metabolites of methylated mercaptopurines . hence , 
 tpmt activity level is inversely proportional to the amount of active cytotoxic 
 metabolite , 6-thioguanine nucleotide ( 6-tgn ) , produced . a total of 21 genetic polymorphisms have 
 been identified in the tpmt gene which correlate with decreased tpmt activity 
 levels and hence thiopurine - induced toxicity . tpmt * 1 is the 
  wild - type  allele ; tpmt * 3a is the most common variant allele , 
 found in ~5% of caucasians , where s tpmt * 3c is the most common variant allele 
 found in east asian population with a frequency of ~2% . the presence of tpmt*3a and * 3b results in extremely low or no 
 tpmt enzyme activity , which leads to elevated levels of 6-tgn . if treated with a standard 
 dose , patients who are homozygous for these alleles will encounter life - threatening 
 myelosuppression and , in some cases , even secondary malignancies . thus , it was suggested that patients with low tpmt expression should 
 be treated with substantially lower doses of thiopurines . on the other hand , many clinical 
 studies concluded that efficacy will be compromised in patients with high tpmt activity 
 who are treated with standard dosing schedules , and therefore , treatment with higher doses 
 is recommended . in all the above examples , pharmacogenomics provides some vital information for 
 predicting treatment outcome , but they are limited to population - level variations . in some 
 sense , it is equivalent to segregating the population into a few response groups and 
 disregarding intragroup variations . to be complete and effective , following the first step 
 of  genetic personalization ,  individuals in each subgroup must be 
 characterized based on the downstream response of that genotype . it is well - known that 
 within a specific genotype , there is a distribution of phenotypes across the patient 
 population ( figure 2 ) . for example , in the case of the 
 tpmt gene , although there are only five important genotypes in the human , there 
 are as many enzyme activity levels ( the manifestation of the tpmt gene ) as there 
 are patients . no two patients with same tpmt genotype will have an identical 
 enzyme activity level . in addition , only two - thirds of the total variance in tpmt activity 
 is accounted through genotyping . recently , for 
 warfarin , an important us food and drug administration  approved drug to bear the 
 pharmacogenomics information in the label , pharmacogenomic - guided treatment has been shown 
 to have no significant difference in clinical outcome compared with the traditional 
 treatment . other studies also point to 
 declining scope for pharmacogenomics in guiding dose regimen , given the cost and effort 
 involved . complex diseases such as cancer , hiv infection , and many others are invariably treated 
 with complex treatment regimens that often involve multiple drugs . when the drugs are 
 influenced by more than one gene independently , pharmacogenomics - based approach alone may 
 not be sufficient to predict the drug response . consider a treatment involving a 
 combination of three drugs and having genetic polymorphism in each of the 
 drug - metabolizing enzymes with three different gene expression patterns ( high , 
 intermediate , and low ) . this will produce 27 ( 3 ) unique gene expression 
 profiles in a given patient population . if one or more drugs are also substrates for drug 
 transporters , where genetic polymorphism and hence three different gene expressions are 
 possible , the number surges to 81 ( 3 ) . when this is then translated into 
 phenotypes and further into cellular response , it will produce a significant variation in 
 the response . besides clinically relevant snps and their influence over treatment 
 outcomes , there are several other putative genetic polymorphisms that are yet to be 
 characterized , which may play a significant role in determining the drug 
 response . in light of more than 150,000 
 validated snps , proteins , and interactions between them , this works out to be a 
 mind - boggling diversity ! however , when phenotypes are measured , which include the drug 
 concentrations and/or cellular response , the characterization of patients may correlate 
 more closely with clinical observations . additional complexity surfaces when the findings of the gene expression profiles are 
 translated to the global population of different ethnic origin due to the inherent 
 variation in disease susceptibility , risk , incidence , and response . for example , there is 
 a significant variation observed in vincristine - induced peripheral neuropathy among 
 caucasian and african - american patients undergoing treatment for precursor b - cell acute 
 lymphoblastic leukemia . pharmacogenomic 
 studies of vincristine - metabolizing enzyme cyp3a5 revealed the polymorphic expression 
 between different races with ~70% of african - americans expressing cyp3a5 compared with 20% 
 of caucasians . dose interruptions and average 
 toxicity grades are significantly lower in african - american patients as a result of 
 elevated metabolism and clearance of vincristine . if the dosing for african patients were 
 to be determined based on studies on caucasians , these patients will receive significantly 
 lower exposure to vincristine . another important consideration to expand the scope of pharmacogenomics is related to its 
 reliance on decision making under static conditions . pharmacogenomics uses gene sequence 
 and gene expression snapshots with the assumption that deterministic evolution of 
 molecular events leads to predictable phenotypes . it considers each genetic variation as 
 an independent causal factor for the observed response . it fails to take into account 
 variation in transport limitations and the spatial heterogeneity of biochemical reactions . 
 however , human physiology is a complex , dynamical system , and often , therapeutic responses 
 are a manifestation of the interplay between many levels of physiological processes . 
 furthermore , human physiology is complicated by homeostatic feedback loops , molecular 
 cross talk , and bypass mechanisms that can lead to unexpected therapeutic responses . these 
 events might confound many physiological processes including , but not limited to , drug 
 metabolism and disposition , drug transport , cellular targets and signaling pathways , and 
 cellular response pathways ( e.g. , apoptosis , cell cycle control ) . thus , one must remain circumspect about the isolated assessment of 
 pharmacogenomics ( or any other upstream biomarker ) as a stand - alone personalizing tool . the scope for controlled clinical trials , a gold standard accepted by the us food and drug 
 administration for validating efficacy and safety of a pharmacogenomic tests , to validate 
 and adopt to the clinical practice are also limited as the resulting number of groups make 
 such studies an expensive and time - consuming exercise . to this end , pharmacogenomics has 
 not realized its fullest potential in some of the drug  disease applications that 
 were deemed as a classical case for pharmacogenomics - based personalization . it therefore emerges that a more 
 comprehensive approach to  personalization ,  encompassing and integrating many 
 dimensions and levels of human physiology , is needed to portray a complete picture of 
 ongoing drug  disease dynamics . from the foregoing discussion , it is clear that gene sequencing and gene expression 
 profiling play key roles in identifying patient subgroups , but individual patients are yet 
 to be characterized on the phenotypic distribution . there are several levels of 
 phenotypes , and a specific one depends on the objective at hand . in this work , we classify 
 phenotypes into two broad categories : ( i ) molecular phenotypes , an immediate effect of a 
 specific gene sequence , and ( ii ) cellular phenotypes , the influence of molecular 
 phenotypes on various cell populations . we define molecular phenotypes as the biomolecular 
 manifestation of a gene sequence , which encompasses proteins , enzymes ( proteome ) , and 
 metabolite concentrations ( metabolic phenotype ) . if a specific drug distribution and 
 metabolism is found to be modified due to the above factors , the resulting drug 
 concentration at various parts of the body is also considered as the molecular phenotype 
 ( drug phenotype ) . molecular phenotypes interact with various cellular populations as drug 
 transporters , inhibitors , and signaling molecules to produce a cellular phenotype . variations in phenotype arise due to numerous factors , including stochasticity in gene 
 expression , transcriptional and translational noise , complexities in biochemical and 
 signal transduction networks , nonlinearity in biochemical processes , and quasi - determinism 
 in biological events . this leads to a nonbijective relationship ( a single gene producing 
 more than one phenotypic trait and a specific phenotype resulting from the expression of 
 several genes ) between genotype and phenotype . stochasticity in gene expression is one of the important factors that contributes to 
 phenotypic variations observed in isogeneic cell populations . these stochastic events are triggered by transcriptional and 
 translational fluctuation which , in turn , arises due to several factors such as random 
 activation / repression of promoter , degradation of transcriptional and protein products , 
 transcriptional and translational burst , feedback loops , etc . moreover , these gene regulatory functions fluctuate dynamically , making 
 static gene expression profiles untenable for personalized treatment . besides quantitative 
 variations in molecular contents the majority of drugs are metabolized by more than one enzyme 
 ( e.g. , 6-mp metabolized by tpmt , hgprt ( hypoxanthine - guanine phosphoribosyltransferase ) , 
 and itpa ( inosine triphosphate pyrophosphatase ) and transported / eliminated by several 
 other proteins , where gene variations and some of the above - mentioned processes are 
 inevitable . the end result is the formation of cell populations with uniquely different 
 proteomes and metabolomes from the same genomes . the resulting phenotype is not 
 comprehensible through simple deductive reasoning . as such , it is not uncommon for two 
 genetically identical persons from similar backgrounds to show significantly different 
 clinical phenotypes in response to drug intervention . treated with 6-mp encountered completely 
 different clinical outcome , both in terms of efficacy and toxicity . one of the main reasons for treatment failure and variability in cellular response , 
 despite the drug being at a therapeutically effective concentration in the plasma , is the 
 development of multidrug resistance to therapeutic medications mediated by drug 
 transporters . p - glycoprotein ( p - gp ) , which is a member of the atp - binding cassette ( abc ) 
 family of proteins , is located on the plasma membrane and serves as drug efflux 
 pumps . important members of this family include abcb1 and abcg2 ( bcrp ) . on the other hand , solute 
 carrier super family of proteins , such as oatp family ( e.g. , oatp1b1 , oatp1b3 , and 
 oatp2b1 ) , act as uptake transporters . together , 
 they have the ability to uptake / efflux structurally and functionally dissimilar cytotoxic 
 agents , thereby modulating the intracellular drug concentration . these genes display 
 genetic polymorphism in humans which has profound impact on pharmacokinetics and clinical 
 responses to many drugs . furthermore , stochastic and dynamic regulation of these genes 
 result in heterogeneous population of three different types of cells  intrinsically 
 resistant , acquired resistant , and sensitive cells . intrinsically resistant cells acquire 
 this phenotype due to a spontaneous mutation involving single or multiple random steps . 
 acquired resistant phenotypes are initially sensitive to drugs but eventually develop drug 
 resistance , also through random processes . recently , even sensitive cells have been shown 
 to acquire the resistant phenotype from other resistant populations through the exchange 
 of p - gp via microparticles and tunneling nanotubes . the evolution of these cellular fractions during treatment has the 
 greatest impact on clinical outcomes . besides these genetic and epigenetic factors , drug 
 resistance may also be conferred by microenvironment characterized by poor vasculature , 
 spatial heterogeneity with regions of hypoxia and acidity , and transport 
 limitations . in these cases , despite 
 abundant drug in plasma , the actual targeted site of action will lack the desired 
 concentration of drug . given these complex factors , sophisticated models incorporating 
 probabilistic nature of these nongenetic , random events will greatly augment the 
 predictions based on gene expression . the final phase of drug  disease cycle involves the drug or its metabolites 
 interfering with the normal functions of one or more cell types and producing either 
 desired outcomes ( efficacy ) or undesired outcomes ( side effects ) . the majority of these cell responses are governed by 
 small molecules , which are the cumulative outcome of all of the processes outlined in the 
 previous sections ( gene sequence , drug dosing , and other biochemical processes ) . at the 
 molecular level , activation or repression of gene and enzyme activity may not be 
 translated into accumulation or depletion of its corresponding metabolites due to 
 multiplicity of metabolic pathway network and robustness of metabolite profiles . in 
 addition to these molecular contents , cellular functions are also affected by stochastic 
 gene regulation . due to the complex and interacting nature of these regulatory networks , 
 gene products from a specific network ultimately , this results in the disruption of the normal cellular 
 functions and produces unintended consequences . developments in single - cell measurements and various lineage - tracing techniques have 
 revealed a wealth of knowledge in attributing the sources of nongenetic cell - to - cell 
 variations . even genetically identical cells in the same environment have shown varied 
 response and sensitivity to drugs . feinerman 
 et al . demonstrated how intraclonal 
 differences in signaling protein levels in t cells produced distribution in response among 
 individual cells , which ultimately leads to diverse biological functions and interferes 
 with antigen discrimination during t - cell activation . using 10,000 cells from each of 15 
 different cell lines , gascoigne and taylor 
 analyzed cellular response to three classes of antimitotic drugs . these cells , besides 
 inter  cell line variability , exhibited a significant intra  cell line 
 variation . indeed , these variations are not genetically predetermined but are driven by 
 variations in the signaling network stability as even the sister cells were shown to have 
 faced different fates . studied nongenetic cell - to - cell variability in response to trail - induced ( tnf - related 
 apoptosis - inducing ligand ) apoptosis . they have shown the existence of significant 
 differences in timing and death probability in that some cells died within 45  min 
 of exposure , whereas others needed as much as 812  h. apart from genetic and 
 epigenetic sources , stochastic fluctuations in biochemical reactions arising from low copy 
 number , differences in cell cycle phase , and natural divergence of protein levels were 
 cited as the determinant factors of time to death . dynamic studies on negative feedback 
 loops between tumor suppressor p53 and oncogene mdm2 , with genetically identical cells in 
 uniform environment subjected to gamma irradiation , revealed interesting features on 
 cell - to - cell variability . significant 
 cell - to - cell variations were observed in the amplitude of the oscillations that was 
 attributed to the production rates of proteins . in addition , due to this variation in 
 protein production , even sister cells lost correlation to each other within 11  h of 
 cell division . recent studies show that despite intrinsic noise in molecular network , phenotypic 
 cell - to - cell variability is rendered by deterministic processes , often through 
 uncharacterized molecular regulatory mechanisms . microenvironment plays a significant role in determining the 
 ultimate outcome to any pathophysiological stimuli . in vivo experiments in mice 
 seeded with invasive or proliferative melanoma cell types in addition , proliferative cell types were predominant in the outer rim of the tumor 
 confirming the role of microenvironment in regulating the switch . studies have also 
 illustrated the presence of small numbers of slow - cycling melanoma cells ( jarid1b+ ) , 
 within the main population of aggressive cells , evading chemotherapy and resulting in the 
 selection of jarid1b+ cells . interestingly , 
 jarid1b expression is dynamically and temporarily regulated , thereby negating the utility 
 of gene expression profiling . this has a profound impact on treatment planning ; a 
 snapshot of gene expression reveals a specific cell type but the emergence of 
 heterogeneity renders the treatment regimen largely ineffective . microenvironment also 
 promotes genetic instability among cancer cells , specifically through deletion and 
 transversions . despite the foregoing 
 discussion on cell - to - cell variability , the inherent robustness in metabolic networks 
 drive the physiological state to very few distinct modes , which results in multimodal 
 distribution in response space . robust 
 computational approaches are available to incorporate these multiple sources of 
 stochasticity and heterogeneity , which enable prediction of population 
 behavior . the relationship between cellular heterogeneity and unpredictable clinical response is 
 less obvious but extremely critical . for example , in cancer cells , protein expression 
 outliers allow some cells to fall outside the drug 's range of efficacy , enabling 
 those cells to survive ongoing treatments . 
 given sufficient time , these outliers will repopulate the full distribution of cells and 
 render the treatment inefficient or eventually completely ineffective . the transcriptional 
 switching in melanoma cells allows invasive cell types to escape the 
 proliferation - targeted chemotherapy regimen . when the invasive cell types switch to a proliferative mode , the tumor cells will regrow , 
 thus leading to refractory melanoma . chemotherapy also allows slow - cycling jarid1b+ 
 melanoma cells in the heterogeneous population to thrive in cytotoxic environments . as individual 
 patients produce different levels of proteome dispersion and cellular heterogeneity , which 
 in itself is difficult to predict , the prediction of clinical response for these 
 individuals is much more complex . more likely , these cellular phenotypes are further 
 confounded by interplay of other unknown proteins and/or mechanisms . hence , it is not so 
 trivial to predict the cellular response intuitively or use simple correlations with gene 
 expression alone . a more reliable quantitative prediction of these complex phenomena calls 
 for a sophisticated modeling framework . the literature is frequented with wide range of sources producing differing evidence for 
 correlation between genotype and their corresponding molecular phenotype and/or clinical 
 response . continuing with the 
 tpmt case , population genetic studies have shown that the major gene locus 
 which regulates tpmt activity accounts for only two - thirds of the total variance in the 
 red blood cell ( rbc ) enzyme activity . tpmt 
 enzyme activity is affected by 6-mp , chronic diseases , and other coadministered drugs like 
 diuretics , nsaids , and antihypertensives . in 
 addition , tissue - specific regulation of enzyme activity has also been reported in the 
 literature . 6-mp metabolism is not only 
 affected by variations in tpmt activity but also by other nongenetic factors . coadministered drugs like 
 allopurinol and methotrexate also affect the first - pass clearance and metabolism of 
 6-mp . hence , within a specific enzyme activity range , it is possible 
 to observe as many 6-tgn concentrations as there are patients . in a study involving 170 
 inflammatory bowel disease patients , wide variation in tpmt enzyme activity , 6-tgn 
 concentration , and treatment response patients with tpmt heterozygous had enzyme activity of 
 5.113.7  u / ml with mean 6-tgn concentration of 253.5 
 pmol/8    10 rbcs ( sd : 136.5 ) compared with 
 homozygous - w.t . of > 13.7  u / ml with 151 
 pmol/8    10 rbcs ( sd : 84.7 ) . the range for tpmt activity 
 and high sd for 6-tgn concentration signifies the level of dispersion within the same 
 genotype . in addition , there was no significant correlation existed between inflammatory 
 bowel disease questionnaire score and 6-tgn concentration ( r = 0.09 ; 
 p = 0.24 ) . patient groups having similar 6-tgn concentrations resulted in two 
 different clinical outcomes of active disease and clinical remission . in our own modeling 
 study on 6-mp metabolism using nonparametric bayesian approach ( to be submitted for 
 publication ) , 6-tgn concentration prediction based only on tpmt genotype resulted 
 in wide 95% confidence region of 23743 pmol/8    10 
 rbcs ( black region in figure 5 ) . with the measurement 
 of tpmt enzyme activity , the confidence region is narrower ( gray region : 124386 
 pmol/8    10 rbcs ) as the variability in enzyme level is 
 accounted . however , with the availability of 6-tgn measurement , the 95% confidence region 
 is much tighter ( red region : 234252 pmol/8    10 
 rbcs ) . this exemplifies that the prediction of downstream response with an upstream marker 
 will most likely yield a greater variability , which will eventually hamper the dosing 
 decision . recently , drug transporter protein abcc4 was found to be actively transporting 
 6-mp and 6-tgn from the hematopoietic cells , thereby protecting the cells in 
 tpmt - deficient patients . these findings 
 demonstrate that although one can measure tpmt activity ( molecular phenotype ) precisely , 
 there is a significant uncertainty in the level of 6-tgn ( drug phenotype ) , the compound 
 that is responsible for treatment response . in addition , when 6-tgn acts on the cellular 
 population , another level of uncertainty is added which leads to different clinical 
 outcome for similar 6-tgn concentration range . hence , phenotyping enzyme activity or even 
 6-tgn concentration may not be sufficient ; rather , the cellular response , which is the 
 ultimate response variable of interest should be regarded as the basis for dose 
 individualization . figure 4 summarizes these variations 
 observed in various clinical studies . an extensive list of different covariates and their 
 effect on variability on molecular and clinical phenotypes are included in the 
 supplementary materials . from the discussions in the previous sections , it is clear that dna sequencing and gene 
 expression profiles provide some vital information on pathophysiological and clinical 
 response for a given treatment regimen ; however , augmenting this information with 
 downstream biomolecular and cellular responses will facilitate unequivocal , quantitative 
 clinical decisions . the application of qualitative upstream information in decision making 
 may lead to uninformed conclusions for a specific individual . what is needed instead is an 
 integrative approach that takes into account different levels of potential variation in 
 the drug  disease cycle to predict the clinical outcome of interest and is adaptive 
 in nature to each individual patient . as such , it should be an ongoing process rather than 
 a  study - and - adopt  approach . in other words , after the initial detailed study 
 and accumulation of information , a basic set of information must be obtained from each new 
 patient in order to adapt the approach before making predictions on clinical outcomes . 
 given the dynamic nature of physiological responses , this naturally warrants the 
 application of dynamic modeling and in silico approaches at a suitably 
 sophisticated level . it is not only proactive in predicting the clinical response but also 
 alleviates the need for continuous / frequent monitoring , which will be prohibitive from 
 physiological , logistical , and economical points of view . in recent times , there has been increasing recognition of the utility and practice of 
 quantitative tools in medical applications . at the same time , the tremendous increase in life 
 science research over the last several decades has resulted in a system , which publishes 
 thousands of relevant articles every year . clearly unaided individual clinicians and 
 healthcare practitioners are unable to process all these articles and incorporate into 
 practice those advances that will have the greatest clinical impact . as such , there is a 
 tremendous opportunity to develop a systematic approach to embedding scientific advances 
 in clinical decision support tools . although these tools have been largely statistical , 
 the time is now opportune to expand this quantitative approach to include mathematical 
 models and systems theoretic tools that embed scientific advances toward maximizing 
 clinical impact . mathematical models , suitably empowered by systems theoretic methodology , 
 derive their strength from their potential to quantitatively evaluate known or conjectured 
 mechanisms of medical cure . although engineering and mathematical personnel can provide 
 skillful use of quantitative tools , success of such endeavors is contingent on utilizing 
 the judgment of experienced medical personnel . for such a closely integrated effort , 
 collaboration must occur among medical and engineering researchers over an extended period 
 in a clinical setting . a recent report by the national academy of engineering and the 
 institute of medicine elaborates on how a partnership between healthcare professionals and 
 engineers could change the face of the 21st century healthcare system . a detailed road map was also laid to harness the power 
 of systems engineering tools , information technology , and compliment knowledge across 
 scientific disciplines to achieve what was termed the six institute of medicine 
 quality aims  of the healthcare system that included safety , effectiveness , patient 
 centeredness , timeliness , efficiency , and equitability . although gene expression information by itself is not ideal , systems biologists have 
 developed methodologies to predict phenotypic outcomes for a specific gene expression 
 pattern through the simulation of metabolic networks . these metabolic networks aid in 
 linking pharmacogenomic variants , such as snps , to pathophysiological ( phenotypic ) 
 outcomes . through in silico models of these metabolic networks , the effects of 
 sequence variations , alterations in specific components , and resulting biochemical 
 reaction kinetics can be analyzed in the context of the rest of the reactions in the 
 entire network . application of such an 
 approach has been demonstrated for human rbcs using a large - scale metabolic network , in 
 which in silico models predicted pathophysiological outcomes for two established 
 snps associated with two key enzymes . as expected , however , when evaluated in the 
 context of other simultaneously altered enzyme kinetics within the whole network , the 
 model predicted overall cell behavior and eventually the clinical outcome . augmenting this approach with other omics data will 
 uniquely identify the flux modes and further enhance the predictive power . cybernetic 
 models ,  which provides a framework to accommodate gene expression information and 
 enzyme regulation and predict the system - level , dynamic metabolic profile and overall 
 cellular outcome . the potential clinical utilities of these metabolic models are 
 evident from their ability to identify functionally interrelated sets of reactions and 
 metabolites that are causally related to diverse pathophysiological conditions including 
 xenobiotic metabolism and biomarker identification . the power of such approaches lies in the automatic integration of 
 patient - specific information , both genotypic and phenotypic , which leads to the prediction 
 of entire metabolic flux profiles and overall cellular outcomes . these metabolic and 
 cellular functions can readily be associated with observed efficacy and/or toxicity 
 through statistical and mathematical tools , which will help in clinical decision 
 making . recent advances in genome - scale computational models are also expected to provide key 
 insights into how complex phenotypes evolve as a function of gene variants and molecular 
 interactions . genome - scale metabolic models are large - scale extensions to traditional 
 metabolic networks to analyze the entire cell in the light of available data and 
 computational methods . mathematical representation of physiochemical , environmental , and 
 regulatory constraints and computational solutions enable the identification of feasible 
 and infeasible metabolic behavior , leading to reliable predictions even when comprehensive 
 data is not available . using a computational 
 model that accounted for all annotated gene functions , karr et al . have provided an understanding of several biological 
 processes that were not feasible earlier through experimental techniques . in addition , the 
 model has accurately predicted molecular pathologies of single gene disruption phenotypes . the reconstruction of tissue - specific , genome - scale metabolic models such as the ones 
 describing human liver metabolism , cancer cell metabolism , along with an increased 
 availability of extensive patient - specific  omics  data to refine these 
 models , bodes well for advancing these approaches for personalized treatment . often , it is not feasible to obtain objective , quantitative cellular response frequently 
 as in the case of neuropathic pain , cancer progression etc . , or the drug is so toxic to 
 some patients that we can not afford to titrate the drug dose . in these cases , measuring 
 covariates that are closely connected ( on drug  disease cycle ) to the clinical 
 response may provide acceptable surrogate information for clinical decision making . for 
 example recently , a new concept of 
 personalized treatment based on metabolomics phenotype has been proposed by nicholson 
 et al . and termed as pharmacometabonomics . metabonomics , a special case of 
 metabolomics , studies the systematic variation in the metabolic profiles due to external 
 stimuli such as genetic modification , biological stimulus , and xenobiotic intervention . pharmacometabonomics , at the intersection of pharmacology and metabonomics , was defined as 
  the prediction of the outcome , efficacy , or toxicity of a drug or xenobiotic 
 intervention in an individual based on a mathematical model of a preintervention 
 metabolite signature . pharmacometabonomics aims to study the global metabolic 
 fingerprint in predose biofluids and characteristic change in the metabolic profiles due 
 to drug dosing in the postdose biofluids . these two vital pieces of information can then 
 be correlated to the clinical responses using chemometric tools . the key metabolites 
 identified from this exercise are then mapped onto the relevant metabolic networks through 
 various databases to reveal functional relationships in disease pathways . this approach , 
 if designed carefully , promises to provide an unbiased and hypothesis - free analysis of the 
 metabolic profile , which may help to identify unexpected biomarker 
 combinations . these endogenous metabolites 
 will eventually aid in identifying patient subgroups that may be cured and/or are 
 susceptible to side effects before commencing the treatment . a recent review article 
 provides an extensive discussion of several preclinical and clinical applications of this 
 new emerging area . recent advancements in single - cell measurement and manipulation technologies allow 
 multiscale and multiparametric measurement of molecular contents , thereby enabling 
 observation of molecular events at single - cell level . elucidation of system - wide interaction of molecular and signaling 
 events and comparison with the response of pathological cells under the influence of a 
 therapeutic drug provide new quantitative mechanistic insights . when combined with mathematical models , this information provides 
 further insight into underlying mechanisms and important parameters that are unable to be 
 extracted from experimental techniques . extension of these single - cell models , accounting 
 for deterministic and stochastic population heterogeneity , aid in prediction of overall 
 physiological outcomes and emergence of multimodal cellular populations with distinct 
 phenotypic features . apart from these detailed , global mechanistic approaches , several 
 classes of semimechanistic models have been developed for various in vivo 
 processes over the last few decades . these types of models are adequate to describe a 
 macrolevel phenomenon within the exhaustive overall process and predict cellular outcome 
 for treatment intervention . however , rigorous efforts for individualization of these 
 models and effective clinical translation are lacking . integrated quantitative approaches that combine genotypic , molecular profiling , and 
 clinical data have shown promise in predicting causal relationships between specific 
 genotypic / molecular signatures and biological and/or clinical outcomes . unlike traditional statistical approaches , which are 
 often restricted to correlational studies , these approaches develop causative models , 
 purely driven by data and prior knowledge , that establishes the dependence structure among 
 various interacting biomolecular entities and have the ability to provide causal 
 influence . interactomics modeling has also shown promise to evaluate quantitative 
 interactions at macromolecular level ( dna , rna , proteins , and other molecules ) and aid in 
 understanding how local and global molecular network dynamics affect overall cellular 
 properties and ultimately lead to human diseases . although some of these approaches have largely been in use for drug 
 target discovery , or applied for other simpler organisms , it is now time to gain insights 
 from them and extend to personalized treatment based on an individual patient 's 
 omics signature . the quantitative approaches discussed here are admittedly in their infancy and require 
 extensive validation in carefully designed , prospective clinical studies . the key 
 conundrum in this regard would involve translating these fast burgeoning scientific 
 findings emerging from various fronts . admittedly , it is difficult to verify each one of 
 these findings in controlled clinical trials . an interesting option would be to integrate 
 the detailed , mechanistic network models with pbpk - ivive ( pbpk : physiologically based 
 pk ; ivive : in vitro , in vivo extrapolation ) approach in which adme 
 ( absorption , distribution , metabolism , and elimination ) is managed by physiologically 
 based pharmacokinetics - in vitro , in vivo extrapolation , whereas kinetic 
 insights for physiologically based pharmacokinetics are governed by genetic and other 
 omics data through metabolic network models . eventually , this approach could 
 serve as a screener to select candidates for controlled clinical trials and design them . in addition , several other constraints surface while translating such approaches in 
 routine clinical practice . efforts must also be diverted to effective communication of these techniques to 
 clinicians and ensure ease of use and interpretation . social , ethical , and privacy - related 
 risks of such personalized approaches are not insignificant . genetic minorities must be 
 protected from discrimination by drug developers , insurance companies , and employers . 
 unfortunately , besides these quantitative approaches , there are not many alternatives 
 available to solve current challenges in treating critical diseases . however , we believe 
 that a concerted and collaborative effort by various stakeholders and experts will pave 
 the way for effective implementation of holistic personalized medicine in clinical 
 settings .
despite recent advancements in  omic  technologies , personalized medicine has 
 not realized its fullest potential due to isolated and incomplete application of gene 
 expression tools . 
 in many instances , pharmacogenomics is being interchangeably used for 
 personalized medicine , when actually it is one of the many facets of personalized 
 medicine . 
 herein , we highlight key issues that are hampering the advancement of 
 personalized medicine and highlight emerging predictive tools that can serve as a decision 
 support mechanism for physicians to personalize treatments .