Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it
Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from ‘function-first’ to ‘target-first’ methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing ‘wet’ experiments, such that ‘what if?’ experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to ‘function-first’ or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the ‘omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.
Abbreviations
NF-κB
nuclear factor-kappa B
Ro5
rule-of-5
Introduction
As illustrated in Fig. 1, classical drug discovery (or pharmacology or chemical genetics) started with an organism displaying a phenotype where there was a need for change (e.g. a disease) and involved the assay of various drugs in vivo to identify one or more that was efficacious (and nontoxic). There was no need to discover (let alone start with) a postulated mechanism of drug action; for a successful drug, this could come later (often much later) [1-3]. This approach is thus ‘function first’, and is equivalent in terms of (chemical) genetic or genotype–phenotype mapping [4] to ‘forward’ genetics, and has lead to the discovery of many drugs that are still in use (and mainly still without detailed knowledge of their mechanisms of action). By contrast, particularly as a result of the systematic (human) genome sequencing programmes, drug discovery largely changed to an approach that was based on the ability of chemicals to bind to or inhibit chosen molecular targets at low concentrations in vitro[5]. This would then necessarily be followed by tests of efficacy in whole organisms. This approach is thus ‘target-first’, and is equivalent to ‘reverse’ genetics, and (despite some spectacular new molecules that work on selected patients, as well as the important rise of biologicals) has been rather ineffectual because the vast majority of small molecule drugs (90–95%) fail to go forward, even from the ‘first into humans’ phase, to become successful and marketable drugs; a set of phenomena known as ‘attrition’ [6-11]. This is not unexpected to systems biologists, who would see the distinction as being similar to the distinction between hypothesis-dependent and data-driven science [12, 13]. The present review aims to illustrate why this is the case, as well as what we might seek to do to improve matters. Figure 2 provides an overview of the present review, which begins by recognizing the role of robustness in biochemical networks.
A contrast between classical (‘function first’) forward chemical discovery with the more recent target-first or ‘reverse’ strategy. It is suggested that a reversion to the more classical approach through phenotypic screening is likely to prove beneficial from a systems point of view.
A ‘mind map’ [436] summarizing the present review. The map should be read starting at the 12 o'clock position and working clockwise.
The robustness of biochemical networks
Somewhat in contrast to designed and artificial network structures such as roads, railways and process plants [14], natural evolution has selected much less for cheapness and efficiency than for robustness to parameter changes (whether caused by mutation or otherwise) [15-26]. This is straightforwardly understandable in that an organism with a mutation messing up a whole pathway will soon be selected out, and so the selection pressure for robustness is very high. Typically, it is the network topologies and feedback structures themselves, rather than the exact parameter values involved, that are responsible for the robustness to parameter changes [27]. However, another way to think about this is that, by diminishing the sensitivity of individual steps to particular changes in their parameters (or to inhibitors), no individual enzyme or target or inhibitor is likely to have much effect unless it affects many other steps by itself. This is easily achieved by having enzymes obeying Henri–Michaelis–Menten kinetics operating at (or below) their Km values (Fig. 3), where a certain amount of inhibition of them (other than uncompetitive inhibition) [28] simply raises the concentration of their substrate and restores flux. (If the substrate of an enzyme has a concentration that is maintained essentially constant by regulatory mechanisms, then competitive inhibition of an enzyme that uses it in a minor pathway can be expected to be as effective in vivo as it is in the spectrophotometer.)
The kinetics of a typical enzyme obeying Henri–Michaelis–Menten kinetics; if the substrate concentration is near the Km, initial inhibition of the enzyme increases the substrate concentration that restores the local flux. The enzyme is said to have a high elasticity towards its substrate. This is common in biology. A rare [437] but highly important exception is the inhibition of 5-enolpyruvoylshikimate-3-phosphate synthase (ESPS; EC 2.5.1.19) by the herbicide glyphosate, which is uncompetitive with respect to one of the substrates, shikimate-3-phosphate [438, 439], such that the extent of inhibition is effectively increased by the raised substrate concentration.
The corollary is clear: to have a major effect on a typical biochemical network, it is necessary to modulate multiple steps simultaneously (see below), such that any drug that acts solely on a single (molecular) target is unlikely to be successful. The same is true of schemes designed to increase the fluxes in pathways of biotechnological interest [29-35]. This distributed nature of flux control, which contributes to robustness, has long been established, and indeed is proven mathematically for certain kinds of networks via the theorems of metabolic control analysis [36-42]. These show that, by normalizing appropriately, the contributions (‘control coefficients’, also known as their local sensitivities) [43] to a particular flux of all the steps in a biochemical pathway add up to 1, and thus most individual steps are likely to have only a small contribution.
Polypharmacology as a desirable goal
If we are to design drugs that overcome this robustness, we need either to find individual molecules that hit a useful set of multiple targets [44-46] (for an example from neuropharmacology, see [47-51]) or use cocktails of drugs [24, 52-54], each of which hits mainly an individual target. The former is known as polypharmacology [44, 45, 55-67] or multi-target drug discovery [68, 69] and the recognition that we need to attack multiple targets in pharmacology is reflected in names such as ‘systems pharmacology’ [6, 70-88] or ‘systems medicine’ [89-93]. The use of cocktails is of course commonplace in diseases such as cancer and HIV-AIDS [94].
One issue is that finding a good subset of even a small number of targets from a large number of possible targets is a combinatorial optimization problem [95]. All combinations of n drugs specific for n targets gives 2n possibilities [54], whereas finding the best combination of even just three or four drugs or targets out of 1000 gives 166 million or 41 billion combinations, respectively, resulting in numbers that are too large for typical experimental analyses (but easily accessible computationally; see below).
Polypharmacology in pharmacogenomics and personalized medicine
An important recognition, if not that recent in origin [96], is that every patient is different and thus their response to drugs will also be different [97-102]. As neatly phrased by Henney [103], quoting an 18th Century physician (Caleb Parry), ‘It is much more important to know what kind of patient has a disease than to know what kind of disease a patient has’. The essential combinatorial argument is straightforward [104]: if we define for any character, such as the fasting low-density lipoprotein-cholesterol level, the ‘normal range’ to be the middle 95 percentiles, then any individual has a probability of 0.95 of being ‘normal’ (for that character). (This is conventional but thereby ignores systematic errors or biases [105].) The probability of being normal for two (independent) characters is thus 0.952 and, for n independent characters, is 0.95n. This drops below 1% when n = 90, and there are of course thousands of characters. What is probably more unexpected, therefore, is not that individuals are different but that they display any similarity of response at all (in part, this presumably reflects the evolution and selection for robustness described above, and the fact that many characters are not of course entirely independent.)
From the point of view of polypharmacology, a drug that interacts usefully with n targets can more easily afford to ‘lose’ one of them (e.g. as a result of an inactivating single nucleotide polymorphism or other mutation) if n is large, whereas a drug that has only one target may provide a very strong variation in response between individuals. Assuming that adverse drug reactions are taken into account, a drug with multiple useful targets is thus likely to show significantly less variation in the response across populations. Drugs do of course require transporters to reach to their sites of action (see below) and this concept should also be included as part of the relevant polypharmacological analysis of multiple ‘targets’ (i.e. preferred macromolecules with which the drug is intended to interact).
How target-specific are the presently available marketed drugs?
The argument that one should seek to hit multiple targets begs the question of which proteins do successful (and thus marketed) drugs actually bind to, given that many of them were in fact isolated on the basis of their ability to bind to a specific and isolated molecular target? What takes place in real cells, tissues and organisms, however, is very different: individual drugs [44, 46, 55, 57, 61, 63, 64, 66, 67, 106-145], and even intermediary metabolites [146-149], are now seen to bind to a great many more entities than just the single ‘target’ via which they were typically discovered. Drugs on average bind to six targets [150], whereas ligands in some classes typically bind to many more [44, 114]. This ‘drug promiscuity’ [151] can be accounted for in terms of the comparatively limited number of protein motifs used in evolution [152], which are often related to each other [145, 153], as well as the fact that only a small number of biophysical forces determine binding; together, these make complete specificity generally implausible in small molecules and, as a consequence, bioactivity in one species is often enriched in other species [154, 155]. A typical example of promiscuity is outlined below.
An example: statins
Although low-density lipoprotein-cholesterol is widely regarded as a major determinant of cardiovascular diseases as a result of its appearance in atherosclerotic plaques, its correlation with disease when in its normal range is poor [115, 156]. Nonetheless, subsequent to the discovery of a ligand (later marketed as lovastatin) from Aspergillus terreus that would inhibit HMG-CoA reductase, and thus lower cholesterol, a great swathe of ‘statins’ have been marketed, and the epidemiological evidence that they can prolong life is good. It is again widely assumed that this is because they lower cholesterol, whereas this is neither logical, nor (as stated) true. Although there is a highly unfortunate tendency to lump all such molecules as ‘statins’ (presumably because they were discovered via their ability to inhibit HMG-CoA reductase), expression profiling studies straightforwardly show that they have no such unitary mode of action [157]. The resolution of the paradox [158] is uncomplicated [62]. All statins consist of a substructure that mimics hydroxymethylglutarate (and not, incidentally, its CoA derivative), termed the ‘front end’, bound to a wide variety of other structures (the ‘back ends’). In most cases, it is likely the ‘back end’ that accounts for most of the biological activity, and mainly because such molecules are anti-inflammatory. In a previous review [62], more than 50 literature citations up to June 2008 were provided. More recent examples are now available [159-165]. A similar tale can be told for ‘glitazones’ [62].
Drug biophysics and the rule-of-5
Lipophilicity is widely seen as an important concept in drug discovery, albeit that there is no doubt that drug promiscuity tends to increase with lipophilicity [107, 119, 122, 124, 126, 127, 144, 150, 166-172]. In an extremely influential review [173] and later reprint [174], Chris Lipinski and colleagues, when seeking to minimize the number of drugs that failed for reasons of pharmacodynamics and pharmacokinetics, proposed four rules (known as the ‘rule-of-5’ or Ro5 because each rule contains elements that are multiples of 5). They predicted that poor absorption or permeation into cells for a molecule is more likely when the number of hydrogen-bond donors > 5, the number of hydrogen-bond acceptors > 10, the relative molecular mass > 500 and the calculated log P (cLog P) > 5. This last in particular is a measure of lipophilicity, and those who design chemical libraries will always seek molecules that obey the Ro5, including through experimental measurements of the partition coefficient log P[175-177] and/or the distribution coefficient log D[178]. Note, however, that natural product-based drugs (still a major source of leads and indeed marketed drugs; see below) very rarely, if ever, obey the Ro5 and, indeed, even some synthetic drugs have very large molecular weights [169]; for example, navitoclax dihydrochloride [179], a Bcl-2 inhibitor, has seven ring systems and a relative molecular mass of 1047.5. Indeed, there is an increasing recognition [154, 155, 166, 180-186] that over-reliance on Ro5 compliance would lose many desirable drugs, including known ‘blockbusters’.
Designing chemical libraries: the role of natural products in drug discovery
Originally, of course, all drugs were natural products, and even now natural products (or chemical moieties derived therefrom) continue to contribute to many useful and profit-making drugs. Notwithstanding, many drug companies have abandoned them. This makes little sense [187] because they represent an exceptionally rich resource that occupies a distinct chemical space [188-204], and they continue to provide approximately half of all useful drugs [205-212]. The ability to detect novel and previously cryptic natural products, whether via pheromone activity [213] and co-culture [214, 215], pharmacognosy [216], proteomics [217] and metabolomics [218], or via (meta)genomics [219] and genome mining [220], will increase greatly the utility of natural products in drug discovery. Their common role as iron chelators [221-224] makes them of special interest [26, 62, 225].
One reason given for the otherwise very odd loss of interest in natural products is that their high fraction of stereocentres often makes them difficult to manipulate chemically. Probably a more pertinent reason is that their failure to obey Lipinski's rules has led to the perception that they do not easily permeate cells. The facts of permeation speak otherwise, not least because, if they are active against intracellular targets as most are (and, in humans, are active orally, and thus must cross at least the gut epithelium), they must cross membranes easily enough. There remains a question as to how (Fig. 4).
Two means by which pharmaceutical drugs can cross cellular (and intracellular) membranes, namely via ‘free’ diffusion or via one or more carriers (A). In a first assumed method (B), they can do so by ‘dissolving’ in any phospholipid bilayer portions of the cell membrane. Alternatively (C), they can hitchhike on one of the many hundreds of natural (genome-encoded) carrier molecules.
The role of drug transporters
… what is certain today is that most molecules of physiological or pharmacological significance are transported into and out of cells by proteins rather than by a ‘passive’ solubility into the lipid bilayer and diffusion through it … [226]
Notwithstanding the above quotation (dating from 1999), it is widely assumed that drugs cross membranes according to their lipophilicity, via what little [227] phospholipid bilayer sections of biological membranes may be uninfluenced by proteins (Fig. 4A). Actually, the evidence for this mode of transport is essentially non-existent (and, in truth, it is hard to acquire directly). There is an alternative view that we have reviewed extensively [151, 228-231], for which there is abundant evidence, as well as a number of recent reviews (e.g. from 2012 alone: [84, 232-267]); this is that transbilayer transport in vivo is negligible, and drugs cross biomembranes by hitchhiking on genetically encoded solute transporters that are normally involved in the intermediary metabolism of the host. In humans, there are more than 1000 of these [231], and a number of online databases exist [151, 250].
The evidence cited above comes in various flavours, although the most pertinent for our purposes are the many clear experimental examples that show precisely which genetically-encoded transporters are used to transport specific drugs. This is especially easily achieved, and can be made quantitative, when the drugs themselves are toxic (or can be added at toxic concentrations), as in yeast [268] and trypanosomes [269-272]. It is important that the assays are at least semi-quantitative because binary (qualitative yes/no) assays that look for resistance when carriers are deleted may miss them. To emphasize once more, this is because multiple carriers can often transport each drug, and so the loss of just one is not normally going to confer ‘complete’ resistance. It probably underpins the widespread belief in ‘passive’ diffusion across membranes because ‘passive’ is often used erroneously as a synonym for ‘transporters that we do not happen to know about and that are in fact important’ [151, 231].
A flipside of this is illustrated by examples where there is clear evidence that the expression (profiles) of a subset of transporters substantially determines the efficacy of the drug in question. Gemcitabine, the best drug against pancreatic cancer, provides an excellent example because the drug is only efficacious when a suitable nucleoside transporter is well expressed in the target tissue [233, 245, 249, 252, 273-288].
Drug transporters: ‘barriers’, tissue and interspecies differences
As well as the historical change in an understanding of the mode of action of narcotics (‘general anaesthetics’), which went from entirely lipid-only views to one where the protein targets were identified and recognized [151, 231], there are at least three contrafactuals that those who believe in lipid-transport-only theories need to explain: (a) the fact that most drugs do not diffuse across the blood–brain barrier (and others) where the lipids are not significantly different [151, 231]; (b) the substantially varying tissue distributions [289-296]; and (c) the very large species differences in cellular drug uptake [297-299]. By contrast, the transporter-only view recognizes the possibility of varying degrees of tissue/individual/species enzyme distribution [289, 291, 293, 296, 300-305] and specificity [151], and their requirement for effecting transport provides a simple explanation for all these phenomena. In other words, the primacy of the need for transporters to effect drug transport into any cell at meaningful rates means that we need to seek to understand which drugs use which transporters. As noted above, if a drug can hitchhike on half a dozen transporters, a knockout of only one will tend to show little phenotypic effect, and thus careful quantitative methods may be necessary to discriminate which transporters are involved; in such cases, therefore, although the knowledge of the multiple transporters is interesting, it may not be that important to the function of getting drugs to intracellular targets.
Overall, this recognition of the importance of drug transporters shows that the problem of understanding how drugs get into cells is not so much a problem of biophysics, but rather a problem of quantitative systems biology. What is meant by this is outlined below.
The need for quantitative biochemical network models
It is a commonplace in engineering that, if one aims to understand the system being designed, especially if it is complex, then it is necessary to have a parallel mathematical or computational in silico model of the artefact of interest. This has long been recognized in a few areas of biology (e.g. neurophysiology) [306, 307], although only more recently are we beginning to see human biochemical and physiological (and especially metabolic) network models of the type that we require [92, 308-319], both for the entire organism or for elements such as the liver [320], a liver cell [321] or a macrophage [322, 323]. The development of these is best performed using crowd-sourcing or community-based methods [319, 324-326]. The great utility of such reconstructed networks [327-330] lies in areas such as: testing whether the model is accurate, in the sense that it reflects (or can be made to reflect) known experimental facts; analyzing the model to understand which parts of the system contribute most to some desired properties of interest; hypothesis generation and testing, allowing rapid analysis of the effects of manipulating experimental conditions in the model without having to perform complex and costly experiments (or to restrict the number that are performed); and testing what changes in the model would improve the consistency of its behaviour; along with experimental observations.
They also provide the necessary ground substance for inferencing modes of action of compounds with unknown or off-target effects (see below).
The metabolite-likeness of successful pharmaceutical drugs
Because we know the structures of successful, marketed drugs, it is possible to develop concepts such as drug-likeness [331-333] that capture the properties possessed by successfully marketed drugs. However, armed with the widely available metabolomics data indicating the metabolites that cells, tissues or body fluids typically possess [334-337], it is possible to investigate whether (because we consider that they must hitchhike on carriers used in intermediary metabolism) successful (i.e. marketed) drugs are more similar to human metabolites than to say the Ro5-compliant molecules typically found in drug discovery libraries. When such studies are performed, the answer is that most synthetic compounds in chemical databases are not metabolite-like [338], whereas successful drugs are indeed commonly metabolite-like [339-341]. This adds weight to the view that those seeking to discover new drugs should consider the metabolite-likeness of their molecules early in the discovery process, along with the question of which transporters they are likely to use. It also leads to the obvious recognition [84] that it is important to incorporate into human metabolic network models the reaction steps that cover the metabolism of candidate and marketed pharmaceuticals (including their absorption, distribution and excretion).
Frequency encoding as part of biochemical signalling
Assays are an important part of the drug discovery process, although a simple binding or inhibition assay of a specific target (whether isolated or even when within a cell) does not clarify whether the inhibition serves any useful function. A particularly clear and interesting example comes from signalling pathways in which the signal is not based on amplitude (i.e. that might reasonably reflect an inhibition) but on frequency (that almost certainly will not, at least not directly). The transcription factor nuclear factor-kappa B (NF-κB) provides a good example.
Because a collection of nominally similar cells or unicellular organisms is not even close to being identical (thermodynamically, an ‘ensemble’), for fundamental statistical reasons [104], there is the question of how to correlate macroscopic measurements of metabolic or signalling molecules with phentotypic effects. In cases such as when the phenotype is the ability to replicate or divide, which is necessarily a single-cell property, one simply cannot make such as correlation, even in principle [342-344], and sometimes the variability of the expression profiles between single, axenic microbial cells of even single proteins is huge [345, 346].
Another specific case in which we cannot expect to relate the properties of collections of cells to a phenotype of interest is when they are not in a steady-state, and especially when they oscillate. This is exactly what happens in the NF-κB system. What we found, on comparing the behaviour of a mathematical model of the system [347, 348] (Fig. 5) with the behaviour of individual cells determined microscopically [349, 350], was that there is indeed a substantial oscillation in the distribution of NF-κB between the nucleus and the cytoplasm, and that this dynamic behaviour (rather than say a ‘static’ concentration of the NF-κB) can be related to changes in gene expression controlled by the transcription factor. More simply, macroscopic snapshots of the NF-κB concentration provide no information on the dynamics (and their heterogeneity) [351], and it is the dynamics that is important: the protein signal is frequency-encoded [352, 353]. This phenomenon appears to be widespread, and also applies, for example, to p53-Mdm2 [354-359], ERK [360], Stat/Smad [361] and elsewhere [362, 363]. Such studies indicate the need to study their interaction (and effects on biology) at as high a level of organization as possible, and certainly not solely by focussing on individual molecules. Analysis of cells (often called high-content screening) [364-381] is a start, although we need to return to ‘phenotypic’ screening at the level of the differentiated organism.
The behaviour of a model of the NF-κB pathway. At time zero, after a 2000-s period of pre-equilibration in silico, NF-κB is ‘added’ at a concentration of 0.1 μm. For more details, see Ihekwaba et al. [347].
Phenotypic screening
Thus, we come full circle to the distinction made in Fig 1. If we wish to discover new drugs that work effectively at the level of the organism, we need to move towards initial analyses that are conducted in differentiated organisms [382-394]. For financial and ethical reasons, this mainly means model organisms, with candidates including Saccharomyces cerevisiae[394, 395], Caenorhabditis elegans[154, 396-402], Drosophila melanogaster[403-405] and Danio rero (zebrafish) [405-410]. (Because of the numbers of organisms involved, fragment-based discovery methods [172, 411-422] are preferable.) This will find us the effects, under circumstances where transporters are not a major issue, and will assess toxicity at once. What this will not necessarily clarify is the modes of action of the drugs; for this, appropriate analyses are needed, many of which can now be performed on a genome-wide scale [268, 269, 423, 424]. An important additional strategy is based on the use of inferencing methods.
Inferencing (parameters from measurement of variables)
In a typical biochemical network, the parameters are the topology of the network, the starting (or fixed) concentrations of enzymes, their kinetic properties (e.g. Km and Vmax) and the starting or ‘fixed’ concentrations of metabolites and effectors. pH and time are also usually treated as honorary parameters. The variables of the system are then the changes in metabolite concentrations or fluxes that occur when one of the parameters is changed (e.g. by adding a substrate or effector to the system). The issue (Fig. 6) is how to identify which parameters have changed by measurement of changes in the variable alone (i.e. what effectors do is modify some of the parameters). The welcome answer is that they can [139, 425-435], although many of these problems are quite under-determined, and the numerical methods do not yet scale well. However, what this tells us is that the availability of candidate networks, together with series of ‘omics’ measurements of variables, does indeed allow the possibility of inferring the modes or molecular sites of action of polypharmacological agents when added to whole cells or organisms.
A typical inferencing problem. Left: a (simple) network for which the properties are understood, with metabolite C being an inhibitor of enzyme E5. We then add an effector with an unknown mode of action, whose observable effects on the variables are to raise C and lower F. Does the effector stimulate E2, inhibit E4, inhibit E5, stimulate E6, or all or none of these? Simple inspection of the qualitative network makes it hard to decide, although quantitative (Bayesian types of) inferencing methods can point to the sites of action of the effectors based on a knowledge of the starting network and the two sets of variables alone [428].
Concluding remarks
The present review has sought to identify a number of areas where we might beneficially look again at how useful medicines are discovered:
recognizing that the solution to failed target-first approaches that lead to attrition involves adopting function-first approaches
recognizing that this follows in part from the fact that very few diseases (and no complex ones) have a unitary cause, and thus poly-pharmacology approaches are required
recognizing the need for quantitative biochemical models that we can interrogate in silico and then validate
recognizing the major role of drug transporters in getting drugs to their sites of action (and stopping their accumulation at toxic levels)
recognizing that this involves a radical re-evaluation of the utility of the Ro5 as commonly used
recognizing that most transporters evolved and were selected to transport natural, endogenous metabolites, and that successful drugs are structurally ‘like’ metabolites
recognizing that this invites a major consideration of the benefits of natural products in drug discovery
recognizing that phenotypic screening is important, although establishing mechanisms and modes of action requires genome-wide analyses coupled with sophisticated inferencing methods.
Taking all these together will once again set us more securely on a path to successful drug discovery.
References
1Grunberg E & Schnitzer RJ (1952) Studies on the activity of hydrazine derivatives of isonicotinic acid in the experimental tuberculosis of mice. Q Bull Sea View Hosp13, 3–11.
3Mdluli K, Slayden RA, Zhu Y, Ramaswamy S, Pan X, Mead D, Crane DD, Musser JM & Barry CE III (1998) Inhibition of a Mycobacterium tuberculosis beta-ketoacyl ACP synthase by isoniazid. Science280, 1607–1610.
12Kell DB & Oliver SG (2004) Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. BioEssays26, 99–105.
16Morohashi M, Winn AE, Borisuk MT, Bolouri H, Doyle J & Kitano H (2002) Robustness as a measure of plausibility in models of biochemical networks. J Theor Biol216, 19–30.
20Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO & Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc2, 727–738.
21Kim PJ, Lee DY, Kim TY, Lee KH, Jeong H, Lee SY & Park S (2007) Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci USA104, 13638–13642.
25Wu Y, Zhang X, Yu J & Ouyang Q (2009) Identification of a topological characteristic responsible for the biological robustness of regulatory networks. PLoS Comput Biol5, e1000442.
26Kell DB (2010) Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples. Arch Toxicol577, 825–889.
28Eisenthal R & Cornish-Bowden A (1998) Prospects for antiparasitic drugs. The case of Trypanosoma brucei, the causative agent of African sleeping sickness. J Biol Chem273, 5500–5505.
30Park JH, Lee KH, Kim TY & Lee SY (2007) Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc Natl Acad Sci USA104, 7797–7802.
32Becker J, Zelder O, Häfner S, Schröder H & Wittmann C (2011) From zero to hero–design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production. Metab Eng13, 159–168.
35Lee JW, Na D, Park JM, Lee J, Choi S & Lee SY (2012) Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat Chem Biol8, 536–546.
36Kacser H & Burns JA (1973) The control of flux. In Rate Control of Biological Processes Symposium of the Society for Experimental Biology, Vol. 27 ( DD Davies, ed.), pp. 65–104. Cambridge University Press, Cambridge.
37Heinrich R & Rapoport TA (1974) A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. Eur J Biochem42, 89–95.
39Giersch C (1988) Control analysis of metabolic networks. 1. Homogeneous functions and the summation theorems for control coefficients. Eur J Biochem174, 509–513.
47Kupershmidt L, Weinreb O, Amit T, Mandel S, Bar-Am O & Youdim MB (2011) Novel molecular targets of the neuroprotective/neurorescue multimodal iron chelating drug M30 in the mouse brain. Neuroscience189, 345–358.
48Weinreb O, Amit T, Bar-Am O & Youdim MBH (2011) A novel anti-Alzheimer's disease drug, ladostigil neuroprotective, multimodal brain-selective monoamine oxidase and cholinesterase inhibitor. Int Rev Neurobiol100, 191–215.
49Kupershmidt L, Amit T, Bar-Am O, Youdim MBH & Weinreb O (2012) Neuroprotection by the multitarget iron chelator M30 on age-related alterations in mice. Mech Ageing Dev133, 267–274.
50Weinreb O, Amit T, Bar-Am O & Youdim MBH (2012) Ladostigil: a novel multimodal neuroprotective drug with cholinesterase and brain-selective monoamine oxidase inhibitory activities for Alzheimer's disease treatment. Curr Drug Targets13, 483–494.
51Pollak Y, Mechlovich D, Amit T, Bar-Am O, Manov I, Mandel SA, Weinreb O, Meyron-Holtz EG, Iancu TC & Youdim MB (2013) Effects of novel neuroprotective and neurorestorative multifunctional drugs on iron chelation and glucose metabolism. J Neural Transm120, 37–48.
53Lehár J, Krueger AS, Avery W, Heilbut AM, Johansen LM, Price ER, Rickles RJ, Short GF III, Staunton JE, Jin Xet al. (2009) Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol27, 659–666.
58Gregori-Puigjané E & Mestres J (2008) A ligand-based approach to mining the chemogenomic space of drugs. Comb Chem High Throughput Screen11, 669–676.
62Kell DB (2009) Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases. BMC Med Genomics2, 2.
64Park K, Lee S, Ahn HS & Kim D (2009) Predicting the multi-modal binding propensity of small molecules: towards an understanding of drug promiscuity. Mol BioSyst5, 844–853.
65Hu Y & Bajorath J (2010) Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model50, 2112–2118.
66Yang L, Wang KJ, Wang LS, Jegga AG, Qin SY, He G, Chen J, Xiao Y & He L (2011) Chemical-protein interactome and its application in off-target identification. Interdiscip Sci3, 22–30.
69Csermely P, Korcsmáros T, Kiss HJM, London G & Nussinov R (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery. A comprehensive review. Pharmacol Ther doi:10.1016/j.pharmthera.2013.01.016
73Allerheiligen SR (2010) Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin Pharmacol Ther88, 135–137.
75Taboureau O, Nielsen SK, Audouze K, Weinhold N, Edsgärd D, Roque FS, Kouskoumvekaki I, Bora A, Curpan R, Jensen TSet al. (2010) ChemProt: a disease chemical biology database. Nucleic Acids Res39, D367–372.
76Yang R, Niepel M, Mitchison TK & Sorger PK (2010) Dissecting variability in responses to cancer chemotherapy through systems pharmacology. Clin Pharmacol Ther88, 34–38.
77van der Graaf PH & Benson N (2011) Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm Res28, 1460–1464.
78Agoram BM & Demin O (2012) Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov Today16, 1031–1036.
79Antman E, Weiss S & Loscalzo J (2012) Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip Rev Syst Biol Med4, 367–383.
80Benson N, Cucurull-Sanchez L, Demin O, Smirnov S & van der Graaf P (2012) Reducing systems biology to practice in pharmaceutical company research; selected case studies. Adv Exp Med Biol736, 607–615.
84Rostami-Hodjegan A (2012) Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther92, 50–61.
85Winter GE, Rix U, Carlson SM, Gleixner KV, Grebien F, Gridling M, Müller AC, Breitwieser FP, Bilban M, Colinge Jet al. (2012) Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML. Nat Chem Biol8, 905–912.
86Zhao S & Iyengar R (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol52, 505–521.
87Bai JP & Abernethy DR (2013) Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annu Rev Pharmacol Toxicol53, 451–473.
88Kell DB & Goodacre R (2013) Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today, in press.
91Hood L & Flores M (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol29, 613–624.
95Kell DB (2012) Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?BioEssays34, 236–244.
96Emperor of China Huang Ti & Maoshing Ni (Translator) (1995) Neijing Suwen (The Yellow Emperor's Classic of Medicine). Shambala Publications, Boston, MA.
97Sadiq SK, Mazzeo MD, Zasada SJ, Manos S, Stoica I, Gale CV, Watson SJ, Kellam P, Brew S & Coveney PV (2008) Patient-specific simulation as a basis for clinical decision-making. Philos Transact A Math Phys Eng Sci366, 3199–3219.
98Holmes MV, Shah T, Vickery C, Smeeth L, Hingorani AD & Casas JP (2009) Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS One4, e7960.
99Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, Mitropoulos K, Del Zompo M & Patrinos GP (2010) Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice. Pharmacogenomics11, 1149–1167.
100Johnson SK, Heuck CJ, Albino AP, Qu P, Zhang Q, Barlogie B & Shaughnessy JD Jr (2011) The use of molecular-based risk stratification and pharmacogenomics for outcome prediction and personalized therapeutic management of multiple myeloma. Int J Hematol94, 321–333.
103Henney AM (2012) The promise and challenge of personalized medicine: aging populations, complex diseases, and unmet medical need. Croat Med J53, 207–210.
105Broadhurst D & Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics2, 171–196.
106Krejsa CM, Horvath D, Rogalski SL, Penzotti JE, Mao B, Barbosa F & Migeon JC (2003) Predicting ADME properties and side effects: The BioPrint approach. Curr Opin Drug Discov Devel6, 470–480.
108Fliri AF, Loging WT, Thadeio PF & Volkmann RA (2005) Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nat Chem Biol1, 389–397.
109Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen Jet al. (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res34, W219–224.
110Azzaoui K, Hamon J, Faller B, Whitebread S, Jacoby E, Bender A, Jenkins JL & Urban L (2007) Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem2, 874–880.
119Peters JU, Schnider P, Mattei P & Kansy M (2009) Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds. ChemMedChem4, 680–686.
123Allen JA & Roth BL (2011) Strategies to discover unexpected targets for drugs active at G protein-coupled receptors. Annu Rev Pharmacol Toxicol51, 117–144.
124von Eichborn J, Murgueitio MS, Dunkel M, Koerner S, Bourne PE & Preissner R (2011) PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res39, D1060–D1066.
128Meanwell NA (2011) Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol24, 1420–1456.
129Mestres J, Seifert SA & Oprea TI (2011) Linking pharmacology to clinical reports: cyclobenzaprine and its possible association with serotonin syndrome. Clin Pharmacol Ther90, 662–665.
133Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O, Mathias SL, Kouskoumvekaki L, Sklar LA & Bologa CG (2011) Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol Inform30, 100–111.
134Pérez-Nueno VI & Ritchie DW (2011) Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening. J Chem Inf Model51, 1233–1248.
135Prado-Prado F, García-Mera X, Escobar M, Sobarzo-Sánchez E, Yañez M, Riera-Fernandez P & González-Díaz H (2011) 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. Eur J Med Chem46, 5838–5851.
137Zolk O & Fromm MF (2011) Transporter-mediated drug uptake and efflux: important determinants of adverse drug reactions. Clin Pharmacol Ther89, 798–805.
141Hawley SA, Fullerton MD, Ross FA, Schertzer JD, Chevtzoff C, Walker KJ, Peggie MW, Zibrova D, Green KA, Mustard KJet al. (2012) The ancient drug salicylate directly activates AMP-activated protein kinase. Science336, 918–922.
142Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Côté Set al. (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature486, 361–367.
143Pérez-Nueno VI & Ritchie DW (2012) Identifying and characterizing promiscuous targets: implications for virtual screening. Expert Opin Drug Discov7, 1–17.
145Xie L, Xie L, Kinnings SL & Bourne PE (2012) Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu Rev Pharmacol Toxicol52, 361–379.
147Li X, Gianoulis TA, Yip KY, Gerstein M & Snyder M (2010) Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell143, 639–650.
148Li X & Snyder M (2011) Metabolites as global regulators: a new view of protein regulation: systematic investigation of metabolite-protein interactions may help bridge the gap between genome-wide association studies and small molecule screening studies. BioEssays33, 485–489.
150Mestres J, Gregori-Puigjané E, Valverde S & Solé RV (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol BioSyst5, 1051–1057.
151Kell DB, Dobson PD, Bilsland E & Oliver SG (2013) The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today18, 218–239.
153Khersonsky O & Tawfik DS (2010) Enzyme promiscuity: evolutionary and mechanistic aspects. In Comprehensive Natural Products II Chemistry and Biology ( L Mander & H-W Lui, eds), pp. 48–90. Elsevier, Oxford.
154Burns AR, Wallace IM, Wildenhain J, Tyers M, Giaever G, Bader GD, Nislow C, Cutler SR & Roy PJ (2010) A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans. Nat Chem Biol6, 549–557.
155Wallace IM, Urbanus ML, Luciani GM, Burns AR, Han MK, Wang H, Arora K, Heisler LE, Proctor M, St Onge RPet al. (2011) Compound prioritization methods increase rates of chemical probe discovery in model organisms. Chem Biol18, 1273–1283.
159Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM Jr, Kastelein JJ, Koenig W, Libby P, Lorenzatti AJ, MacFadyen JGet al. (2008) Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med359, 2195–2207.
162Bereswill S, Munoz M, Fischer A, Plickert R, Haag LM, Otto B, Kuhl AA, Loddenkemper C, Gobel UB & Heimesaat MM (2010) Anti-inflammatory effects of resveratrol, curcumin and simvastatin in acute small intestinal inflammation. PLoS One5, e15099.
165Antonopoulos AS, Margaritis M, Lee R, Channon K & Antoniades C (2012) Statins as anti-inflammatory agents in atherogenesis: molecular mechanisms and lessons from the recent clinical trials. Curr Pharm Des18, 1519–1530.
167Whitlock GA, Fish PV, Fray MJ, Stobie A & Wakenhut F (2008) Pyridyl-phenyl ether monoamine reuptake inhibitors: impact of lipophilicity on dual SNRI pharmacology and off-target promiscuity. Bioorg Med Chem Lett18, 2896–2899.
168Price DA, Blagg J, Jones L, Greene N & Wager T (2009) Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin Drug Metab Toxicol5, 921–931.
170Gleeson MP, Hersey A, Montanari D & Overington J (2011) Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat Rev Drug Discov10, 197–208.
171Good AC, Liu J, Hirth B, Asmussen G, Xiang Y, Biemann HP, Bishop KA, Fremgen T, Fitzgerald M, Gladysheva Tet al. (2012) Implications of promiscuous Pim-1 kinase fragment inhibitor hydrophobic interactions for fragment-based drug design. J Med Chem55, 2641–2648.
173Lipinski CA, Lombardo F, Dominy BW & Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev23, 3–25.
174Lipinski CA, Lombardo F, Dominy BW & Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev46, 3–26.
175Ayouni L, Cazorla G, Chaillou D, Herbreteau B, Rudaz S, Lanteri P & Carrupt PA (2005) Fast determination of lipophilicity by HPLC. Chromatographia62, 251–255.
177Demare S, Slater B, Lacombe G, Breuzin D & Dini C (2007) Accurate automated log P-o/w measurement by gradient-flow liquid-liquid partition chromatography Part 1. Neutral compounds. J Chromatogr A1175, 16–23.
182Oprea TI, Allu TK, Fara DC, Rad RF, Ostopovici L & Bologa CG (2007) Lead-like, drug-like or ‘pub-like’: how different are they?J Comput Aided Mol Des21, 113–119.
188Feher M & Schmidt JM (2003) Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci43, 218–227.
190Sprous DG & Salemme FR (2007) A comparison of the chemical properties of drugs and FEMA/FDA notified GRAS chemical compounds used in the food industry. Food Chem Toxicol45, 1419–1427.
191Ertl P, Roggo S & Schuffenhauer A (2008) Natural product-likeness score and its application for prioritization of compound libraries. J Chem Inf Model48, 68–74.
192Ertl P & Schuffenhauer A (2008) Cheminformatics analysis of natural products: lessons from nature inspiring the design of new drugs. Prog Drug Res66, 217, 219–235.
196Renner S, van Otterlo WA, Dominguez Seoane M, Mocklinghoff S, Hofmann B, Wetzel S, Schuffenhauer A, Ertl P, Oprea TI, Steinhilber Det al. (2009) Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol5, 585–592.
199Bauer RA, Wurst JM & Tan DS (2010) Expanding the range of ‘druggable’ targets with natural product-based libraries: an academic perspective. Curr Opin Chem Biol14, 308–314.
200Clemons PA, Bodycombe NE, Carrinski HA, Wilson JA, Shamji AF, Wagner BK, Koehler AN & Schreiber SL (2010) Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc Natl Acad Sci USA107, 18787–18792.
201Faller B, Ottaviani G, Ertl P, Berellini G & Collis A (2011) Evolution of the physicochemical properties of marketed drugs: can history foretell the future?Drug Discov Today16, 976–984.
202Chen HM, Engkvist O, Blomberg N & Li J (2012) A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds. MedChemComm3, 312–321.
204López-Vallejo F, Giulianotti MA, Houghten RA & Medina-Franco JL (2012) Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today17, 718–726.
212Kingston DGI (2011) Plant-derived natural products as anticancer agents. In Cancer Management in Man: Chemotherapy, Biological Therapy, Hyperthermia and Supporting Measures ( BR Minev, ed) pp, 3–23 Springer, New York.
213Kell DB, Kaprelyants AS & Grafen A (1995) On pheromones, social behaviour and the functions of secondary metabolism in bacteria. Trends Ecol Evol10, 126–129.
214Burgess JG, Jordan EM, Bregu M, Mearns-Spragg A & Boyd KG (1999) Microbial antagonism: a neglected avenue of natural products research. J Biotechnol70, 27–32.
215Peiris D, Dunn WB, Brown M, Kell DB, Roy I & Hedger JN (2008) Metabolite profiles of interacting mycelial fronts differ for pairings of the wood decay basidiomycete fungus, Stereum hirsutum with its competitors Coprinus micaceus and Coprinus disseminatus. Metabolomics4, 52–62.
217Bumpus SB, Evans BS, Thomas PM, Ntai I & Kelleher NL (2009) A proteomics approach to discovering natural products and their biosynthetic pathways. Nat Biotechnol27, 951–956.
218Rochfort S (2005) Metabolomics reviewed: a new ‘omics’ platform technology for systems biology and implications for natural products research. J Nat Prod68, 1813–1820.
222Perron NR & Brumaghim JL (2009) A review of the antioxidant mechanisms of polyphenol compounds related to iron binding. Cell Biochem Biophys53, 75–100.
223Perron NR, Wang HC, Deguire SN, Jenkins M, Lawson M & Brumaghim JL (2010) Kinetics of iron oxidation upon polyphenol binding. Dalton Trans39, 9982–9987.
225Funke C, Schneider SA, Berg D & Kell DB (2013) Genetics and iron in the systems biology of Parkinson's disease and some related disorders. Neurochem Int62, 637–652.
229Kell DB & Dobson PD (2009) The cellular uptake of pharmaceutical drugs is mainly carrier-mediated and is thus an issue not so much of biophysics but of systems biology. In Proc Int Beilstein Symposium on Systems Chemistry ( MG Hicks & C Kettner, eds), pp. 149–168. http://www.beilstein-institut.de/Bozen2008/Proceedings/Kell/Kell.pdf. Logos Verlag, Berlin.
230Dobson P, Lanthaler K, Oliver SG & Kell DB (2009) Implications of the dominant role of cellular transporters in drug uptake. Curr Top Med Chem9, 163–184.
231Kell DB, Dobson PD & Oliver SG (2011) Pharmaceutical drug transport: the issues and the implications that it is essentially carrier-mediated only. Drug Discov Today16, 704–714.
232Bi YA, Kimoto E, Sevidal S, Jones HM, Barton HA, Kempshall S, Whalen KM, Zhang H, Ji C, Fenner KSet al. (2012) In vitro evaluation of hepatic transporter-mediated clinical drug-drug interactions: hepatocyte model optimization and retrospective investigation. Drug Metab Dispos40, 1085–1092.
233Borbath I, Verbrugghe L, Lai R, Gigot JF, Humblet Y, Piessevaux H & Sempoux C (2012) Human equilibrative nucleoside transporter 1 (hENT1) expression is a potential predictive tool for response to gemcitabine in patients with advanced cholangiocarcinoma. Eur J Cancer48, 990–996.
235Clarke JD & Cherrington NJ (2012) Genetics or environment in drug transport: the case of organic anion transporting polypeptides and adverse drug reactions. Expert Opin Drug Metab Toxicol8, 349–360.
237Delespaux V & de Koning HP (2012) Transporters in antiparasitic drug development and resistance. In Antiparasitic and Antibacterial Drug Discovery: Trypanosomatidae ( L Flohe, O Koch & T Jäger, eds), in press. Wiley-Blackwell, London.
238Ekins S, Polli JE, Swaan PW & Wright SH (2012) Computational modeling to accelerate the identification of substrates and inhibitors for transporters that affect drug disposition. Clin Pharmacol Ther92, 661–665.
239Fardel O, Kolasa E & Le Vee M (2012) Environmental chemicals as substrates, inhibitors or inducers of drug transporters: implication for toxicokinetics, toxicity and pharmacokinetics. Expert Opin Drug Metab Toxicol8, 29–46.
241Gallegos TF, Martovetsky G, Kouznetsova V, Bush KT & Nigam SK (2012) Organic anion and cation SLC22 ‘drug’ transporter (Oat1, Oat3, and Oct1) regulation during development and maturation of the kidney proximal tubule. PLoS One7, e40796.
242Harwood MD, Neuhoff S, Carlson GL, Warhurst G & Rostami-Hodjegan A (2013) Absolute abundance and function of intestinal drug transporters: a prerequisite for fully mechanistic in vitro-in vivo extrapolation of oral drug absorption. Biopharm Drug Dispos34, 2–28.
243Iusuf D, van de Steeg E & Schinkel AH (2012) Functions of OATP1A and 1B transporters in vivo: insights from mouse models. Trends Pharmacol Sci33, 100–108.
244Karlgren M, Vildhede A, Norinder U, Wisniewski JR, Kimoto E, Lai YR, Haglund U & Artursson P (2012) Classification of inhibitors of hepatic organic anion transporting polypeptides (OATPs): influence of protein expression on drug–drug interactions. J Med Chem55, 4740–4763.
245Kobayashi H, Murakami Y, Uemura K, Sudo T, Hashimoto Y, Kondo N & Sueda T (2012) Human equilibrative nucleoside transporter 1 expression predicts survival of advanced cholangiocarcinoma patients treated with gemcitabine-based adjuvant chemotherapy after surgical resection. Ann Surg256, 288–296.
246Lai Y, Varma M, Feng B, Stephens JC, Kimoto E, El-Kattan A, Ichikawa K, Kikkawa H, Ono C, Suzuki Aet al. (2012) Impact of drug transporter pharmacogenomics on pharmacokinetic and pharmacodynamic variability – considerations for drug development. Expert Opin Drug Metab Toxicol8, 723–743.
247Li RW, Yang C, Sit AS, Lin SY, Ho EY & Leung GP (2012) Physiological and pharmacological roles of vascular nucleoside transporters. J Cardiovasc Pharmacol59, 10–15.
248Mandery K, Glaeser H & Fromm MF (2012) Interaction of innovative small molecule drugs used for cancer therapy with drug transporters. Br J Pharmacol165, 345–362.
249Morinaga S, Nakamura Y, Watanabe T, Mikayama H, Tamagawa H, Yamamoto N, Shiozawa M, Akaike M, Ohkawa S, Kameda Yet al. (2012) Immunohistochemical analysis of human equilibrative nucleoside transporter-1 (hENT1) predicts survival in resected pancreatic cancer patients treated with adjuvant gemcitabine monotherapy. Ann Surg Oncol19 (Suppl 3), 558–564.
250Morrissey KM, Wen CC, Johns SJ, Zhang L, Huang SM & Giacomini KM (2012) The UCSF-FDA TransPortal: a public drug transporter database. Clin Pharmacol Ther92, 545–546.
251Mulgaonkar A, Venitz J & Sweet DH (2012) Fluoroquinolone disposition: identification of the contribution of renal secretory and reabsorptive drug transporters. Expert Opin Drug Metab Toxicol8, 553–569.
252Murata Y, Hamada T, Kishiwada M, Ohsawa I, Mizuno S, Usui M, Sakurai H, Tabata M, Ii N, Inoue Het al. (2012) Human equilibrative nucleoside transporter 1 expression is a strong independent prognostic factor in UICC T3–T4 pancreatic cancer patients treated with preoperative gemcitabine-based chemoradiotherapy. J Hepatobiliary Pancreat Sci19, 413–425.
253Nakanishi T & Tamai I (2012) Genetic polymorphisms of OATP transporters and their impact on intestinal absorption and hepatic disposition of drugs. Drug Metab Pharmacokinet27, 106–121.
254Neuvonen PJ (2012) Towards Safer and more predictable drug treatment – reflections from studies of the first BCPT Prize Awardee. Basic Clin Pharmacol Toxicol110, 207–218.
255Obaidat A, Roth M & Hagenbuch B (2012) The expression and function of organic anion transporting polypeptides in normal tissues and in cancer. Annu Rev Pharmacol Toxicol52, 135–151.
256Roth M, Obaidat A & Hagenbuch B (2012) OATPs, OATs and OCTs: the organic anion and cation transporters of the SLCO and SLC22A gene superfamilies. Br J Pharmacol165, 1260–1287.
257Saadatmand AR, Tadjerpisheh S, Brockmoller J & Tzvetkov MV (2012) The prototypic pharmacogenetic drug debrisoquine is a substrate of the genetically polymorphic organic cation transporter OCT1. Biochem Pharmacol83, 1427–1434.
258Salomon JJ & Ehrhardt C (2012) Organic cation transporters in the blood-air barrier: expression and implications for pulmonary drug delivery. Ther Deliv3, 735–747.
260Sprowl JA, Mikkelsen TS, Giovinazzo H & Sparreboom A (2012) Contribution of tumoral and host solute carriers to clinical drug response. Drug Resist Updat15, 5–20.
264Vadlapudi AD, Vadlapatla RK & Mitra AK (2012) Sodium dependent multivitamin transporter (SMVT): a potential target for drug delivery. Curr Drug Targets13, 994–1003.
265Varma MVS, Lai Y, Feng B, Litchfield J, Goosen TC & Bergman A (2012) Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions. Pharm Res29, 2860–2873.
266Yoshida K, Maeda K & Sugiyama Y (2012) Transporter-mediated drug-drug interactions involving OATP substrates: predictions based on in vitro inhibition studies. Clin Pharmacol Ther91, 1053–1064.
267Zamek-Gliszczynski MJ, Hoffmaster KA, Tweedie DJ, Giacomini KM & Hillgren KM (2012) Highlights from the international transporter consortium second workshop. Clin Pharmacol Ther92, 553–556.
268Lanthaler K, Bilsland E, Dobson P, Moss HJ, Pir P, Kell DB & Oliver SG (2011) Genome-wide assessment of the carriers involved in the cellular uptake of drugs: a model system in yeast. BMC Biol9, 70.
269Alsford S, Turner DJ, Obado SO, Sanchez-Flores A, Glover L, Berriman M, Hertz-Fowler C & Horn D (2011) High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome. Genome Res21, 915–924.
270Baker N, Alsford S & Horn D (2011) Genome-wide RNAi screens in African trypanosomes identify the nifurtimox activator NTR and the eflornithine transporter AAT6. Mol Biochem Parasitol176, 55–57.
271Schumann Burkard G, Jutzi P & Roditi I (2011) Genome-wide RNAi screens in bloodstream form trypanosomes identify drug transporters. Mol Biochem Parasitol175, 91–94.
272Alsford S, Eckert S, Baker N, Glover L, Sanchez-Flores A, Leung KF, Turner DJ, Field MC, Berriman M & Horn D (2012) High-throughput decoding of antitrypanosomal drug efficacy and resistance. Nature482, 232–236.
273Spratlin J, Sangha R, Glubrecht D, Dabbagh L, Young JD, Dumontet C, Cass C, Lai R & Mackey JR (2004) The absence of human equilibrative nucleoside transporter 1 is associated with reduced survival in patients with gemcitabine-treated pancreas adenocarcinoma. Clin Cancer Res10, 6956–6961.
274Giovannetti E, Del Tacca M, Mey V, Funel N, Nannizzi S, Ricci S, Orlandini C, Boggi U, Campani D, Del Chiaro Met al. (2006) Transcription analysis of human equilibrative nucleoside transporter-1 predicts survival in pancreas cancer patients treated with gemcitabine. Cancer Res66, 3928–3935.
275Marce S, Molina-Arcas M, Villamor N, Casado FJ, Campo E, Pastor-Anglada M & Colomer D (2006) Expression of human equilibrative nucleoside transporter 1 (hENT1) and its correlation with gemcitabine uptake and cytotoxicity in mantle cell lymphoma. Haematologica91, 895–902.
276Mori R, Ishikawa T, Ichikawa Y, Taniguchi K, Matsuyama R, Ueda M, Fujii Y, Endo I, Togo S, Danenberg PVet al. (2007) Human equilibrative nucleoside transporter 1 is associated with the chemosensitivity of gemcitabine in human pancreatic adenocarcinoma and biliary tract carcinoma cells. Oncol Rep17, 1201–1205.
277Nakano Y, Tanno S, Koizumi K, Nishikawa T, Nakamura K, Minoguchi M, Izawa T, Mizukami Y, Okumura T & Kohgo Y (2007) Gemcitabine chemoresistance and molecular markers associated with gemcitabine transport and metabolism in human pancreatic cancer cells. Br J Cancer96, 457–463.
278Oguri T, Achiwa H, Muramatsu H, Ozasa H, Sato S, Shimizu S, Yamazaki H, Eimoto T & Ueda R (2007) The absence of human equilibrative nucleoside transporter 1 expression predicts nonresponse to gemcitabine-containing chemotherapy in non-small cell lung cancer. Cancer Lett256, 112–119.
279Pérez-Torras S, García-Manteiga J, Mercadé E, Casado FJ, Carbó N, Pastor-Anglada M & Mazo A (2008) Adenoviral-mediated overexpression of human equilibrative nucleoside transporter 1 (hENT1) enhances gemcitabine response in human pancreatic cancer. Biochem Pharmacol76, 322–329.
281Maréchal R, Mackey JR, Lai R, Demetter P, Peeters M, Polus M, Cass CE, Young J, Salmon I, Devière Jet al. (2009) Human equilibrative nucleoside transporter 1 and human concentrative nucleoside transporter 3 predict survival after adjuvant gemcitabine therapy in resected pancreatic adenocarcinoma. Clin Cancer Res15, 2913–2919.
282Santini D, Schiavon G, Vincenzi B, Cass CE, Vasile E, Manazza AD, Catalano V, Baldi GG, Lai R, Rizzo Set al. (2011) Human equilibrative nucleoside transporter 1 (hENT1) levels predict response to gemcitabine in patients with biliary tract cancer (BTC). Curr Cancer Drug Targets11, 123–129.
283Hagmann W, Jesnowski R & Lohr JM (2010) Interdependence of gemcitabine treatment, transporter expression, and resistance in human pancreatic carcinoma cells. Neoplasia12, 740–747.
284Matsumura N, Nakamura Y, Kohjimoto Y, Inagaki T, Nanpo Y, Yasuoka H, Ohashi Y & Hara I (2010) The prognostic significance of human equilibrative nucleoside transporter 1 expression in patients with metastatic bladder cancer treated with gemcitabine-cisplatin-based combination chemotherapy. BJU Int108, E110–116.
285Tanaka M, Javle M, Dong X, Eng C, Abbruzzese JL & Li D (2010) Gemcitabine metabolic and transporter gene polymorphisms are associated with drug toxicity and efficacy in patients with locally advanced pancreatic cancer. Cancer116, 5325–5335.
287Wang H, Word BR & Lyn-Cook BD (2011) Enhanced efficacy of gemcitabine by indole-3-carbinol in pancreatic cell lines: the role of human equilibrative nucleoside transporter 1. Anticancer Res31, 3171–3180.
288Fisher SB, Fisher KE, Patel SH, Lim MG, Kooby DA, El-Rayes BF, Staley CA III, Adsay NV, Farris AB III & Maithel SK (2013) Excision repair cross-complementing gene-1, ribonucleotide reductase subunit M1, ribonucleotide reductase subunit M2, and human equilibrative nucleoside transporter-1 expression and prognostic value in biliary tract malignancy. Cancer119, 454–462.
289Sai Y (2005) Biochemical and molecular pharmacological aspects of transporters as determinants of drug disposition. Drug Metab Pharmacokinet20, 91–99.
290Wu CY & Benet LZ (2005) Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res22, 11–23.
292Pacanowski MA, Hopley CW & Aquilante CL (2008) Interindividual variability in oral antidiabetic drug disposition and response: the role of drug transporter polymorphisms. Expert Opin Drug Metab Toxicol4, 529–544.
293Watanabe T, Kusuhara H & Sugiyama Y (2010) Application of physiologically based pharmacokinetic modeling and clearance concept to drugs showing transporter-mediated distribution and clearance in humans. J Pharmacokinet Pharmacodyn37, 575–590.
294Pagliarusco S, Martinucci S, Bordini E, Miraglia L, Cufari D, Ferrari L & Pellegatti M (2011) Tissue distribution and characterization of drug-related material in rats and dogs after repeated oral administration of casopitant. Drug Metab Dispos39, 283–293.
295Pellegatti M & Pagliarusco S (2011) Drug and metabolite concentrations in tissues in relationship to tissue adverse findings: a review. Expert Opin Drug Metab Toxicol7, 137–146.
296Sreedharan S, Stephansson O, Schiöth HB & Fredriksson R (2011) Long evolutionary conservation and considerable tissue specificity of several atypical solute carrier transporters. Gene478, 11–18.
297Ho RH, Tirona RG, Leake BF, Glaeser H, Lee W, Lemke CJ, Wang Y & Kim RB (2006) Drug and bile acid transporters in rosuvastatin hepatic uptake: function, expression, and pharmacogenetics. Gastroenterology130, 1793–1806.
299Lai Y (2009) Identification of interspecies difference in hepatobiliary transporters to improve extrapolation of human biliary secretion. Expert Opin Drug Metab Toxicol5, 1175–1187.
300Anderle P, Sengstag T, Mutch DM, Rumbo M, Praz V, Mansourian R, Delorenzi M, Williamson G & Roberts MA (2005) Changes in the transcriptional profile of transporters in the intestine along the anterior-posterior and crypt-villus axes. BMC Genomics6, 69.
304Burckhardt G & Burckhardt BC (2011) In vitro and in vivo evidence of the importance of organic anion transporters (OATs) in drug therapy. Handb Exp Pharmacol201, 29–104.
306Hodgkin AL & Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol117, 500–544.
310Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srvivas R & Palsson BØ (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA104, 1777–1782.
311Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O & Goryanin I (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol3, 135.
312Clapworthy G, Viceconti M, Coveney PV & Kohl P (2008) The virtual physiological human: building a framework for computational biomedicine I. Editorial. Philos Transact A Math Phys Eng Sci366, 2975–2978.
319Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, Haraldsdottír H, Mo ML, Rolfsson O, Stobbe MDet al. (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol, doi:10.1038/nbt.2488.
321Gille C, Bölling C, Hoppe A, Bulik S, Hoffmann S, Hübner K, Karlstädt A, Ganeshan R, König M, Rother Ket al. (2010) HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol6, 411.
322Bordbar A, Lewis NE, Schellenberger J, Palsson BØ & Jamshidi N (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol6, 422.
323Bordbar A, Mo ML, Nakayasu ES, Schrimpe-Rutledge AC, Kim YM, Metz TO, Jones MB, Frank BC, Smith RD, Peterson SNet al. (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol8, 558.
324Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Blüthgen N, Borger S, Costenoble R, Heinemann Met al. (2008) A consensus yeast metabolic network obtained from a community approach to systems biology. Nat Biotechnol26, 1155–1160.
326Thiele I, Hyduke DR, Steeb B, Fankam G, Allen DK, Bazzani S, Charusanti P, Chen FC, Fleming RM, Hsiung CAet al. (2011) A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella typhimurium LT2. BMC Syst Biol5, 8.
327Kell DB & Knowles JD (2006) The role of modeling in systems biology. In System Modeling in Cellular Biology: From Concepts to Nuts and Bolts ( Z Szallasi, J Stelling & V Periwal, eds), pp. 3–18. MIT Press, Cambridge.
329Bordbar A & Palsson BØ (2012) Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med271, 131–141.
334Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, Francis-McIntyre S, Begley P, Carroll K, Broadhurst D, Tseng Aet al. (2009) Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst134, 1322–1332.
335Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra Set al. (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res37, D603–610.
336Nobata C, Dobson P, Iqbal SA, Mendes P, Tsujii J, Kell DB & Ananiadou S (2011) Mining metabolites: extracting the yeast metabolome from the literature. Metabolomics7, 94–101.
338Gupta S & Aires-de-Sousa J (2007) Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness. Mol Divers11, 23–36.
339Adams JC, Keiser MJ, Basuino L, Chambers HF, Lee DS, Wiest OG & Babbitt PC (2009) A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Comput Biol5, e1000474.
340Dobson PD, Patel Y & Kell DB (2009) ‘Metabolite-likeness’ as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today14, 31–40.
341Peironcely JE, Reijmers T, Coulier L, Bender A & Hankemeier T (2011) Understanding and classifying metabolite space and metabolite-likeness. PLoS One6, e28966.
343Davey HM & Kell DB (1996) Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analysis. Microbiol Rev60, 641–696.
344Kell DB, Kaprelyants AS, Weichart DH, Harwood CL & Barer MR (1998) Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie Van Leeuwenhoek73, 169–187.
345Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK & Weissman JS (2003) Global analysis of protein expression in yeast. Nature425, 737–741.
347Ihekwaba AEC, Broomhead DS, Grimley R, Benson N & Kell DB (2004) Sensitivity analysis of parameters controlling oscillatory signalling in the NF-κB pathway: the roles of IKK and IκBα. Syst Biol1, 93–103.
348Ihekwaba AEC, Broomhead DS, Grimley R, Benson N, White MRH & Kell DB (2005) Synergistic control of oscillations in the NF-κB signalling pathway. Syst Biol152, 153–160.
349Nelson DE, Ihekwaba AEC, Elliott M, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG, Edwards SWet al. (2004) Oscillations in NF-κB signalling control the dynamics of gene expression. Science306, 704–708.
350Ashall L, Horton CA, Nelson DE, Paszek P, Ryan S, Sillitoe K, Harper CV, Spiller DG, Unitt JF, Broomhead DSet al. (2009) Pulsatile stimulation determines timing and specificity of NFkappa-B-dependent transcription. Science324, 242–246.
351Paszek P, Ryan S, Ashall L, Sillitoe K, Harper CV, Spiller DG, Rand DA & White MRH (2010) Population robustness arising from cellular heterogeneity. Proc Natl Acad Sci USA107, 11644–11649.
352Nelson DE, See V, Nelson G & White MR (2004) Oscillations in transcription factor dynamics: a new way to control gene expression. Biochem Soc Trans32, 1090–1092.
353Kell DB (2006) Metabolomics, modelling and machine learning in systems biology: towards an understanding of the languages of cells. The 2005 Theodor Bücher lecture. FEBS J273, 873–894.
354Lahav G, Rosenfeld N, Sigal A, Geva-Zatorsky N, Levine AJ, Elowitz MB & Alon U (2004) Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet36, 147–150.
355Geva-Zatorsky N, Rosenfeld N, Itzkovitz S, Milo R, Sigal A, Dekel E, Yarnitzky T, Liron Y, Polak P, Lahav Get al. (2006) Oscillations and variability in the p53 system. Mol Syst Biol2, 2006.0033.
357Abou-Jaoude W, Ouattara DA & Kaufman M (2009) From structure to dynamics: frequency tuning in the p53-Mdm2 network I. Logical approach. J Theor Biol258, 561–577.
358Ouattara DA, Abou-Jaoude W & Kaufman M (2010) From structure to dynamics: frequency tuning in the p53-Mdm2 network. II Differential and stochastic approaches. J Theor Biol264, 1177–1189.
359Geva-Zatorsky N, Dekel E, Batchelor E, Lahav G & Alon U (2010) Fourier analysis and systems identification of the p53 feedback loop. Proc Natl Acad Sci USA107, 13550–13555.
361Yoshiura S, Ohtsuka T, Takenaka Y, Nagahara H, Yoshikawa K & Kageyama R (2007) Ultradian oscillations of Stat, Smad, and Hes1 expression in response to serum. Proc Natl Acad Sci USA104, 11292–11297.
369Perlman ZE, Mitchison TJ & Mayer TU (2005) High-content screening and profiling of drug activity in an automated centrosome-duplication assay. ChemBioChem6, 145–151.
371Giuliano KA, Cheung WS, Curran DP, Day BW, Kassick AJ, Lazo JS, Nelson SG, Shin Y & Taylor DL (2005) Systems cell biology knowledge created from high content screening. Assay Drug Dev Technol3, 501–514.
372Grånäs C, Lundholt BK, Heydorn A, Linde V, Pedersen HC, Krog-Jensen C, Rosenkilde MM & Pagliaro L (2005) High content screening for G protein-coupled receptors using cell-based protein translocation assays. Comb Chem High Throughput Screen8, 301–309.
