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Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development
Drug Discovery Today: Technologies
Vol. 3, No. 2 2006
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Drug development
Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development Jan van der Greef1,2, Aram Adourian1, Pieter Muntendam1,*, Robert N. McBurney1 1 2
BG Medicine, Inc., Waltham, MA 02451, USA TNO Systems Biology, Netherlands Organization for Applied Scientific Research, Zeist, Netherlands
Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical transla-
Section Editors: Bart Ellenbroek – Radboud University, Nijmegen, The Netherlands Twan Ederveen – N.V. Organon, Oss, The Netherlands
tion is of paramount importance in optimizing the pharmaceutical value chain. Here, we advance the case for a molecular systems approach to crossing the preclinical-to-clinical translational chasm and for metabolomic analysis of readily accessible bodyfluids as a key technology in translational activities.
Introduction The continued commercial success of the pharmaceutical industry as a whole depends on reversing the trends in several leading indicators of its productivity. These indicators include: the recent year-over-year decreases in new product launches; increases in product development time; withdrawals of blockbuster drugs, and a nonlinear increase over time in research and development expenditures [1]. The bleak outlook has even led regulators to initiate activities, such as the Critical Path Initiative by US FDA [2] and the Innovative Medicines *Corresponding author: P. Muntendam ([email protected]) 1740-6749/$ ß 2006 Published by Elsevier Ltd.
DOI: 10.1016/j.ddtec.2006.05.003
Initiative by EU [3], designed to improve the productivity of the pharmaceutical value chain, focusing on predictability and efficiency from laboratory to marketable products. Despite enormous technological advances, only 8% of the new drug development projects entering the clinical phase yield a marketable product [4]. The translation of preclinical development projects into clinical development successes is appallingly bad. Here, we focus on new opportunities for a molecular systems approach to improve the productivity of the pharmaceutical value chain and on the specific role and advantages of metabolomics in translational activities within the value chain.
Crossing the translational chasm via a molecular systems approach The key to crossing the translational chasm (Fig. 1), in either direction, is uniformity of information. Historically, preclinical studies have focused on biochemical, physiological or histological outcome measures whereas clinical studies have relied on high-level outcome measures, such as disease rating 205
Drug Discovery Today: Technologies | Drug development
Vol. 3, No. 2 2006
Figure 1. A diagram of the pharmaceutical value chain which indicates the biomarkers and the information that can be gained from, or applied to, various stages in the chain. The transition of a drug development project from preclinical studies to clinical studies represents a key, and often troublesome, stage in the drug development process. Similarly challenging is the useful communication of clinical findings back to preclinical scientists who seek to improve upon the efficacy or safety profiles of first-generation drugs in a class. The gap between preclinical studies and clinical trials is referred to as the ‘translational chasm’ (cf. [23]).
scales or largely symptomatic adverse event assessments. Preclinical and clinical datasets are two very different types of information representing the different languages of preclinical drug discoverers and clinical drug developers. A molecular systems approach [5] is the ‘babel-fish’ [6] that can facilitate effective communication between drug discoverers and drug developers because it can yield uniform biochemical information about disease processes (molecular systems pathology) and drug action (molecular systems pharmacology) in both preclinical and clinical phases of the value chain. Fig. 1 depicts the pharmaceutical value chain and the fruits, in terms of biomarkers and information, that can be generated by applying the tools of molecular systems approach.
Translational studies rely upon molecular systems analysis of bodyfluids Whereas molecular systems analysis of an animal disease model or of animal responses to drug treatment can involve bioanalytical profiling of readily accessible bodyfluid and body tissue samples, clinical studies in volunteers or patients rarely yield tissue samples. Therefore, bodyfluids, such as blood plasma or serum and urine, will be the sample types most commonly available for translational activities. This sample type limitation means that not all the classes of molecular information associated with molecular systems pathology/pharmacology (Fig. 2) will routinely be incorporated into the translational datasets. Table 1 presents a comparison of technologies that comprise a molecular systems approach and their applicability to translational studies. The analysis of bodyfluids, such as plasma and urine, permits molecular phenotyping primarily by proteomics/ 206
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peptidomics and metabolomics. Although proteomics is a powerful bioanalytical approach, proteins involved in similar functions in animals and humans often have amino acid sequence differences that can hinder comparative analyses, especially if the genome sequence for the relevant animal has not been completed. By contrast, metabolomics provides the ultimate in molecular phenotyping, with the attractive feature that metabolites have identical structures in different mammalian species. In Fig. 2, the yellow background highlights the role of metabolomics in translational studies.
Cross-species molecular system pathology comparisons are crucial to success As a drug development project advances from preclinical studies to clinical trials, future project success probably depends on an affirmative answer to the question ‘Did the animal disease model that was used for crucial decision making adequately represent the human disease mechanisms?’ Metabolomic profiling of bodyfluid samples from the animal model and from patients can go a long way towards providing an answer to that question. The ideal animal model of a disease should comprehensively mimic the biochemical processes of the human disease and these biochemical processes should be reflected in the spectrum of differences in serum metabolites between diseased state and healthy state for both human and animals. Fig. 3 illustrates comparisons of serum metabolites between type 2 diabetic patients and a rat model of the disease. The upper part of Fig. 3 shows a molecular system image (MSI, a self-organizing map [7]) created from the levels of approximately 100 metabolites measured in serum samples
Vol. 3, No. 2 2006
Drug Discovery Today: Technologies | Drug development
Figure 2. The basic elements of a technology platform for molecular systems pathology/systems pharmacology (see [5] for definitions), from biological samples to raw datasets to integration of datasets and analyses to results and visualizations. The focus of this article is on the use of metabolomic analyses in activities that bridge the translational chasm. In this figure, those aspects of the technology platform related to metabolomics and translational activities are presented on a yellow background.
from 14 type 2 diabetics beside an MSI created on the same coordinates using measurements of the identical metabolites in serum samples from eight disease-model rats. The similarity of the two MSIs is striking. A reasonable conclusion from such a comparison is that the biochemical processes in the rat model are a good reflection of the biochemical mechanisms underlying the human disease. A complementary biochemical viewpoint on the adequacy of an animal disease model can be obtained by examining Correlation NetworksTM (CNs, see [8,9]) constructed from the
serum metabolite datasets from the patients and the rat model, compared to their healthy counterparts. The lower part of Fig. 3 shows CNs created from the serum metabolite datasets for the type 2 diabetics and the rat disease model. They reveal both similarities and differences in the relationships between serum metabolites, partly reinforcing support for the rat disease model but uncovering information that should be considered a caution when results obtained in this particular model are being used for stage-gate decisionmaking in drug development. www.drugdiscoverytoday.com
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Table 1. A comparison of metabolomics technologies Technology 1
Technology 2
Technology 3
Technology 4
Name of specific type of technology
NMR metabolomic concentration profiling
Mass spectrometry metabolomic concentration profiling
Stable isotope mass spectrometry metabolomic flux analysis
Integration of metabolomics with other measurements
Names of specific technologies with associated companies and company websites
Metabometrix (http://www.metabometrix.com)
Lipomics (http://www.lipomics.com) Metabolon (http://www.metabolon.com) Biocrates (http://www.biocrates.at) Phenomenome (http://www.phenomenome.com)
SiDMAP (http://www.sidmap.com)
BG Medicine (http://www.bg-medicine.com) Icoria (http://www.icoria.com)
Pros
Quantitation
Ability to profile low concentration compounds
Dynamic metabolic flux information
Broad-spectrum metabolite coverage; systems-level integration with other ‘omics’ data
Cons
Only high abundance metabolites
Chromatographic complexity, more quantitation expertise needed
Limited compatibility of isotopic labels with some sample types
Requires sophisticated data integration capabilities
Refs
[13,14]
[15–18]
[19]
[20–22]
In a study on urine biomarkers of human osteoarthritis compared to experimental osteoarthritis in guinea pigs, substantial biochemical similarities between the two datasets were revealed and potentially useful disease biomarkers could be identified [10,11]. By contrast, urinalysis of samples from both a primate model of multiple sclerosis (MS) and MS patients revealed a poor comparison of the urine metabolite datasets [12], possibly due to the lack of similarity between the biochemical processes of the animal MS model and human MS or due to the limitations of the bioanalytical technology or sample type. The key point, unrelated to the specifics of the results available to date for particular animal models, is that a molecular systems analysis, even restricted to a limited bioanalytical platform applied to a single bodyfluid, can provide an enormously valuable level of biochemical detail about the adequacy of every proposed animal model for a human disease. This information can be used to select the most appropriate animal model for the human disease as a whole, or for biochemically homogeneous sub-populations of a patient population defined by a limited number of signs and symptoms, or for different stages of the progression of the human disease. Such an approach, routinely incorporated into the pharmaceutical value chain, is a clear framework for a functional bridge from discovery to development across the translational chasm.
Reverse translation: enabling successful secondgeneration drug discovery A further challenge encountered in the pharmaceutical value chain involves crossing the translational chasm in 208
www.drugdiscoverytoday.com
the reverse direction to enable the discovery of secondgeneration drugs with improved efficacy or safety characteristics relative to a first-in-class drug. Historically, very little useful information relating to efficacy or safety issues, in the language that drug discoverers understand, is passed back to the preclinical phase from clinical trials or post-marketing studies. Outcome measures or adverse event reports rarely provide any information upon which to base in vitro or in vivo assays for undesirable effects of a drug candidate. Whereas efficacy is assumed to be related to the targeted biochemical action of the drug, it is generally not known whether an undesirable effect results from an on-target or an off-target mechanism of drug action. Additionally, if the undesirable effect does indeed result from an off-target mechanism, no biochemical information is usually available. A molecular systems approach to clinical efficacy and safety of a first-in-class drug can surmount this challenge, dramatically facilitating the process of second-generation drug discovery. A step-by-step molecular systems approach to second-generation drug discovery, built around metabolomics in available bodyfluids, is depicted in Fig. 4 and described in its legend. The essence of this approach is the use of system response profiles (SRPs) comprising biomarker sets and correlation networks as the common language between clinical and preclinical phases of the value chain. Comparison of biomarker signatures related to efficacy and safety can reveal the likelihood that an undesired effect results from an on-target action of the first-generation drug. Such information might be a key determinant in decision-making about the viability of a second-generation drug discovery program based on the mechanism of action
Vol. 3, No. 2 2006
Drug Discovery Today: Technologies | Drug development
Figure 3. Comparisons of the molecular system images (MSIs) and correlation networks (CNs) for serum metabolite biomarkers from type 2 diabetic (T2D) patients (left-side of figure) and from a rat model of the disease (right-side of figure). For the MSIs, each of the 144 hexagons (or ‘tiles’) contributing to the image reflects the average abundance level of a particular metabolite in the serum samples from human T2D patients or from the rat disease model relative to the level of the particular metabolites in serum samples from control human subjects or normal rats (red tile, metabolite higher in abundance in samples from disease state relative to control; blue tile, metabolite lower in abundance in samples from disease state relative to control; orange, yellow and green tiles, intermediate relative values as in a color spectrum; light green, no difference between disease and control). Across the MSIs, there is considerable agreement between the human and rat datasets. Agreement, at this level of biochemical detail, between the serum metabolite biomarker profiles for the animal model and the human disease provides strong support for preclinical development stage-gate decisions for drug candidates for the treatment of type 2 diabetes. The CNs display a 29-member subset of the serum metabolites for both the T2D patients and the rat model for which the absolute value of the correlation coefficient for the disease-state association between pairs of the metabolites shown in the MSIs was greater than 0.8. Some strong correlations, both positive and negative, are identical for both human disease and rat model, however some differences can be seen between these networks, possibly reflecting the imperfect nature of even a good animal model of a human disease. Such detailed comparisons between human disease and animal model at the level of molecular systems pathology enable better decisions to be made concerning the advancement of drug candidates in the discovery–development pipeline based on their performance in studies which employ animal models of the human disease.
of a first-generation drug. Furthermore, the SRPs from the clinical phase can be compared to the corresponding SRPs generated from a variety of animal models to choose the animal model which best mimics the action of the first-generation drug in both efficacy and safety biochemical processes – not necessarily the animal model used for preclinical efficacy studies. Once the ‘best’ animal model has been selected and the SRP information related to the efficacy and safety of the firstgeneration drug has been incorporated into the armamentarium of the drug discoverers, the second-generation drug dis-
covery program can focus on different aspects of the SRPs as surrogates for efficacy or safety response measures for structure–activity relationship (SAR) studies.
Conclusion Metabolomic technologies have a special place in a molecular systems approach to translational activities within the pharmaceutical value chain because of the absolute identity of metabolites across species. Although ‘fit for the task’ right now, there are ample opportunities for future improvements www.drugdiscoverytoday.com
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Figure 4. A schematic concept for the translational use of metabolomics data in research to discover a second-generation drug candidate based on the efficacy and safety profile in patients treated with the a first-generation drug. Step 1: Plasma or serum metabolite profiling of blood samples, derived from patients treated with a first-generation drug vs. a placebo (Pbo) or standard of care (SoC) for the disease, yields system response profiles (SRPs, see [5]) including biomarker sets that can be statistically associated with efficacy or safety (AE: adverse event) outcome measures. A correlation network for the drug-treated state can also be created from the datasets.
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in these technologies (see Outstanding issues for metabolomic technologies). With the available and emerging bioanalytical technologies for profiling bodyfluids and the statistical and bioinformatic methods to analyze and interpret the large datasets generated by a molecular systems approach, it should be possible to minimize the number of future drug development projects that are ‘Lost in translation’. The optimal situation will be achieved when a molecular systems approach is routinely implemented from the onset of every new drug discovery program, so that comprehensive biochemical datasets for disease and drug response states are available as a common language throughout each specific drug discovery/ development project, and beyond for other projects that focus on the same or related indications or on compounds of similar structures. Nirvana!
Outstanding issues Limits of detection and dynamic ranges of particular metabolomic platforms. New platforms must be developed to target certain classes of molecules. Current understanding of metabolic pathways is insufficient to interpret fully the complexity of molecular species routinely identified from large metabolomic datasets. Bioinformatic integration of metabolomic datasets with proteomic, transcriptomic and traditional clinical chemistry datasets has substantial room for improvement. Cross-compartment relationships and systems dynamics are hardly understood.
References 1 Taylor, D. (2003) Fewer new drugs from the pharmaceutical industry. BMJ 326, 408–409 2 US Department of Health and Human Services (2004) Innovation or Stagnation, Challenge and Opportunity on the Critical Path to New Medicinal Products (http://www.fda.gov/oc/initiatives/criticalpath/ whitepaper.html) 3 European Commission (2005) Innovative Medicines Initiative (IMI). Strategic Research Agenda (http://europa.eu.int/comm/research/fp6/ index_en.cfm?p=1_innomed)
Drug Discovery Today: Technologies | Drug development
4 Bain & Co., (2003) Rebuilding Big Pharma’s Business Model. Bain & Co. 5 Van der Greef, J. and McBurney, R.N. (2005) Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat. Rev. Drug Discov. 4, 961–967 6 Adams, D. (1979) The Hitchhikers Guide to the Galaxy. Pan Macmillan 7 Kohonen, T. (2001) Self-Organizing Maps (3rd edn), Springer 8 Steuer, R. et al. (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 9 Oresic, M. et al. (2004) Phenotype characterisation using integrated gene transcript, protein and metabolite profiling. Appl. Bioinform. 3, 205–217 10 Lamers, R-J.A.N. et al. (2003) Identification of disease- and nutrient-related metabolic fingerprints in osteoarthritic guinea pigs. J. Nutr. 133, 1776–1780 11 Lamers, R-J.A.N. et al. (2005) Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis Cartilage 13, 762–768 12 ‘t Hart, B.A. et al. (2003) 1-H NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urine of MS patients and non-human primates with MS-like disease. J. Neurol. Sci. 212, 21–30 13 Lindon, J.C. et al. (2003) Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol. 187, 137–146 14 Lindon, J.C. et al. (2005) The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6, 691–699 15 Morris, M. and Watkins, S.M. (2005) Focused metabolomic profiling in the drug development process: advances from lipid profiling. Curr. Opin. Chem. Biol. 9, 407–412 16 Beecher, C.W.W. and Tripp, R. (2004) Metabolomics – applications in drug discovery and development. Business Brief. Pharmatech. 17 Weinberger, K. and Graber, A. (2005) Using comprehensive metabolomics to identify novel biomarkers. Screening Trends Drug Discov. 6, 42–45 18 Gray, G. and Heath, D. (2005) A global reorganization of the metabolome in arabidopsis during cold acclimation is revealed by metabolic fingerprinting. Physiologia Plantarum. 124, 236–248 19 Boros, L.G. et al. (2004) Use of metabolic pathway flux information in targeted cancer drug design. Drug Discov. Today: Ther. Strategies 4, 435–443 20 van der Greef, J. et al. (2003) The role of metabolomics in systems biology. In Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (Harrigan, G.G. and Goodacre, R., eds), pp. 171–198, Kluwer Academic Publishers 21 Davidov, E. et al. (2004) Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model. OMICS 8, 267–288 22 Johnson, P.C. and Higgins, A.J. (2004) Precise phenotypic anchoring for drug target identification, validation and biomarker discovery using an advanced systems biology approach. Drug Discov. World Summer 55–62 23 Moore, G.A. (1999) Crossing the Chasm (revised edn), Harper Collins
Step 2: The biomarker sets associated with efficacy or safety criteria from the patient study in Step 1 are compared to determine whether the drug-induced biochemical mechanisms underlying adverse events are likely to be the same as those responsible for the efficacy of the drug. In the case where the overlap of the biomarkers sets associated with efficacy and safety is small (e.g. Case 2), there are good prospects for a second-generation drug candidate being discovered with improved efficacy-to-safety characteristics. The correlation network for the drug-treated state can be evaluated to determine the metabolite components and sub-networks related to the safety of the first-generation drug. Step 3: A study similar to that outlined in Step 1 is undertaken with the first-generation drug in various animal models of the disease for which that drug is indicated. SRPs for the drug-treated state for the various animal models are compared with the SRPs from the drug-treated patients to select the ‘best’ animal model to use in second-generation drug discovery activities. Such information would already be available if a molecular systems pathology/systems pharmacology approach had been applied to drug discovery and development activities for the first-generation drug. Step 4: With knowledge of the aspects of the drug response SRPs in the ‘best’ animal model that are associated with efficacy or safety for this class of drugs in patients, illustrated here using the correlation network, it will be possible to undertake in vivo structure activity relationship (SAR) studies with candidate compounds and select a second-generation drug candidate. Different aspects of the correlation network can be used as indicators of the probable efficacy or safety of candidate compounds. For example, the simulated changes shown in the correlation network for the three hypothetical compounds would be consistent with the choice of the right-side compound as the second-generation drug candidate, because the sub-network associated with the safety criterion is absent from the network generated by this compound.
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Vol. 3, No. 2 2006
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Drug development
Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development Jan van der Greef1,2, Aram Adourian1, Pieter Muntendam1,*, Robert N. McBurney1 1 2
BG Medicine, Inc., Waltham, MA 02451, USA TNO Systems Biology, Netherlands Organization for Applied Scientific Research, Zeist, Netherlands
Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical transla-
Section Editors: Bart Ellenbroek – Radboud University, Nijmegen, The Netherlands Twan Ederveen – N.V. Organon, Oss, The Netherlands
tion is of paramount importance in optimizing the pharmaceutical value chain. Here, we advance the case for a molecular systems approach to crossing the preclinical-to-clinical translational chasm and for metabolomic analysis of readily accessible bodyfluids as a key technology in translational activities.
Introduction The continued commercial success of the pharmaceutical industry as a whole depends on reversing the trends in several leading indicators of its productivity. These indicators include: the recent year-over-year decreases in new product launches; increases in product development time; withdrawals of blockbuster drugs, and a nonlinear increase over time in research and development expenditures [1]. The bleak outlook has even led regulators to initiate activities, such as the Critical Path Initiative by US FDA [2] and the Innovative Medicines *Corresponding author: P. Muntendam ([email protected]) 1740-6749/$ ß 2006 Published by Elsevier Ltd.
DOI: 10.1016/j.ddtec.2006.05.003
Initiative by EU [3], designed to improve the productivity of the pharmaceutical value chain, focusing on predictability and efficiency from laboratory to marketable products. Despite enormous technological advances, only 8% of the new drug development projects entering the clinical phase yield a marketable product [4]. The translation of preclinical development projects into clinical development successes is appallingly bad. Here, we focus on new opportunities for a molecular systems approach to improve the productivity of the pharmaceutical value chain and on the specific role and advantages of metabolomics in translational activities within the value chain.
Crossing the translational chasm via a molecular systems approach The key to crossing the translational chasm (Fig. 1), in either direction, is uniformity of information. Historically, preclinical studies have focused on biochemical, physiological or histological outcome measures whereas clinical studies have relied on high-level outcome measures, such as disease rating 205
Drug Discovery Today: Technologies | Drug development
Vol. 3, No. 2 2006
Figure 1. A diagram of the pharmaceutical value chain which indicates the biomarkers and the information that can be gained from, or applied to, various stages in the chain. The transition of a drug development project from preclinical studies to clinical studies represents a key, and often troublesome, stage in the drug development process. Similarly challenging is the useful communication of clinical findings back to preclinical scientists who seek to improve upon the efficacy or safety profiles of first-generation drugs in a class. The gap between preclinical studies and clinical trials is referred to as the ‘translational chasm’ (cf. [23]).
scales or largely symptomatic adverse event assessments. Preclinical and clinical datasets are two very different types of information representing the different languages of preclinical drug discoverers and clinical drug developers. A molecular systems approach [5] is the ‘babel-fish’ [6] that can facilitate effective communication between drug discoverers and drug developers because it can yield uniform biochemical information about disease processes (molecular systems pathology) and drug action (molecular systems pharmacology) in both preclinical and clinical phases of the value chain. Fig. 1 depicts the pharmaceutical value chain and the fruits, in terms of biomarkers and information, that can be generated by applying the tools of molecular systems approach.
Translational studies rely upon molecular systems analysis of bodyfluids Whereas molecular systems analysis of an animal disease model or of animal responses to drug treatment can involve bioanalytical profiling of readily accessible bodyfluid and body tissue samples, clinical studies in volunteers or patients rarely yield tissue samples. Therefore, bodyfluids, such as blood plasma or serum and urine, will be the sample types most commonly available for translational activities. This sample type limitation means that not all the classes of molecular information associated with molecular systems pathology/pharmacology (Fig. 2) will routinely be incorporated into the translational datasets. Table 1 presents a comparison of technologies that comprise a molecular systems approach and their applicability to translational studies. The analysis of bodyfluids, such as plasma and urine, permits molecular phenotyping primarily by proteomics/ 206
www.drugdiscoverytoday.com
peptidomics and metabolomics. Although proteomics is a powerful bioanalytical approach, proteins involved in similar functions in animals and humans often have amino acid sequence differences that can hinder comparative analyses, especially if the genome sequence for the relevant animal has not been completed. By contrast, metabolomics provides the ultimate in molecular phenotyping, with the attractive feature that metabolites have identical structures in different mammalian species. In Fig. 2, the yellow background highlights the role of metabolomics in translational studies.
Cross-species molecular system pathology comparisons are crucial to success As a drug development project advances from preclinical studies to clinical trials, future project success probably depends on an affirmative answer to the question ‘Did the animal disease model that was used for crucial decision making adequately represent the human disease mechanisms?’ Metabolomic profiling of bodyfluid samples from the animal model and from patients can go a long way towards providing an answer to that question. The ideal animal model of a disease should comprehensively mimic the biochemical processes of the human disease and these biochemical processes should be reflected in the spectrum of differences in serum metabolites between diseased state and healthy state for both human and animals. Fig. 3 illustrates comparisons of serum metabolites between type 2 diabetic patients and a rat model of the disease. The upper part of Fig. 3 shows a molecular system image (MSI, a self-organizing map [7]) created from the levels of approximately 100 metabolites measured in serum samples
Vol. 3, No. 2 2006
Drug Discovery Today: Technologies | Drug development
Figure 2. The basic elements of a technology platform for molecular systems pathology/systems pharmacology (see [5] for definitions), from biological samples to raw datasets to integration of datasets and analyses to results and visualizations. The focus of this article is on the use of metabolomic analyses in activities that bridge the translational chasm. In this figure, those aspects of the technology platform related to metabolomics and translational activities are presented on a yellow background.
from 14 type 2 diabetics beside an MSI created on the same coordinates using measurements of the identical metabolites in serum samples from eight disease-model rats. The similarity of the two MSIs is striking. A reasonable conclusion from such a comparison is that the biochemical processes in the rat model are a good reflection of the biochemical mechanisms underlying the human disease. A complementary biochemical viewpoint on the adequacy of an animal disease model can be obtained by examining Correlation NetworksTM (CNs, see [8,9]) constructed from the
serum metabolite datasets from the patients and the rat model, compared to their healthy counterparts. The lower part of Fig. 3 shows CNs created from the serum metabolite datasets for the type 2 diabetics and the rat disease model. They reveal both similarities and differences in the relationships between serum metabolites, partly reinforcing support for the rat disease model but uncovering information that should be considered a caution when results obtained in this particular model are being used for stage-gate decisionmaking in drug development. www.drugdiscoverytoday.com
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Table 1. A comparison of metabolomics technologies Technology 1
Technology 2
Technology 3
Technology 4
Name of specific type of technology
NMR metabolomic concentration profiling
Mass spectrometry metabolomic concentration profiling
Stable isotope mass spectrometry metabolomic flux analysis
Integration of metabolomics with other measurements
Names of specific technologies with associated companies and company websites
Metabometrix (http://www.metabometrix.com)
Lipomics (http://www.lipomics.com) Metabolon (http://www.metabolon.com) Biocrates (http://www.biocrates.at) Phenomenome (http://www.phenomenome.com)
SiDMAP (http://www.sidmap.com)
BG Medicine (http://www.bg-medicine.com) Icoria (http://www.icoria.com)
Pros
Quantitation
Ability to profile low concentration compounds
Dynamic metabolic flux information
Broad-spectrum metabolite coverage; systems-level integration with other ‘omics’ data
Cons
Only high abundance metabolites
Chromatographic complexity, more quantitation expertise needed
Limited compatibility of isotopic labels with some sample types
Requires sophisticated data integration capabilities
Refs
[13,14]
[15–18]
[19]
[20–22]
In a study on urine biomarkers of human osteoarthritis compared to experimental osteoarthritis in guinea pigs, substantial biochemical similarities between the two datasets were revealed and potentially useful disease biomarkers could be identified [10,11]. By contrast, urinalysis of samples from both a primate model of multiple sclerosis (MS) and MS patients revealed a poor comparison of the urine metabolite datasets [12], possibly due to the lack of similarity between the biochemical processes of the animal MS model and human MS or due to the limitations of the bioanalytical technology or sample type. The key point, unrelated to the specifics of the results available to date for particular animal models, is that a molecular systems analysis, even restricted to a limited bioanalytical platform applied to a single bodyfluid, can provide an enormously valuable level of biochemical detail about the adequacy of every proposed animal model for a human disease. This information can be used to select the most appropriate animal model for the human disease as a whole, or for biochemically homogeneous sub-populations of a patient population defined by a limited number of signs and symptoms, or for different stages of the progression of the human disease. Such an approach, routinely incorporated into the pharmaceutical value chain, is a clear framework for a functional bridge from discovery to development across the translational chasm.
Reverse translation: enabling successful secondgeneration drug discovery A further challenge encountered in the pharmaceutical value chain involves crossing the translational chasm in 208
www.drugdiscoverytoday.com
the reverse direction to enable the discovery of secondgeneration drugs with improved efficacy or safety characteristics relative to a first-in-class drug. Historically, very little useful information relating to efficacy or safety issues, in the language that drug discoverers understand, is passed back to the preclinical phase from clinical trials or post-marketing studies. Outcome measures or adverse event reports rarely provide any information upon which to base in vitro or in vivo assays for undesirable effects of a drug candidate. Whereas efficacy is assumed to be related to the targeted biochemical action of the drug, it is generally not known whether an undesirable effect results from an on-target or an off-target mechanism of drug action. Additionally, if the undesirable effect does indeed result from an off-target mechanism, no biochemical information is usually available. A molecular systems approach to clinical efficacy and safety of a first-in-class drug can surmount this challenge, dramatically facilitating the process of second-generation drug discovery. A step-by-step molecular systems approach to second-generation drug discovery, built around metabolomics in available bodyfluids, is depicted in Fig. 4 and described in its legend. The essence of this approach is the use of system response profiles (SRPs) comprising biomarker sets and correlation networks as the common language between clinical and preclinical phases of the value chain. Comparison of biomarker signatures related to efficacy and safety can reveal the likelihood that an undesired effect results from an on-target action of the first-generation drug. Such information might be a key determinant in decision-making about the viability of a second-generation drug discovery program based on the mechanism of action
Vol. 3, No. 2 2006
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Figure 3. Comparisons of the molecular system images (MSIs) and correlation networks (CNs) for serum metabolite biomarkers from type 2 diabetic (T2D) patients (left-side of figure) and from a rat model of the disease (right-side of figure). For the MSIs, each of the 144 hexagons (or ‘tiles’) contributing to the image reflects the average abundance level of a particular metabolite in the serum samples from human T2D patients or from the rat disease model relative to the level of the particular metabolites in serum samples from control human subjects or normal rats (red tile, metabolite higher in abundance in samples from disease state relative to control; blue tile, metabolite lower in abundance in samples from disease state relative to control; orange, yellow and green tiles, intermediate relative values as in a color spectrum; light green, no difference between disease and control). Across the MSIs, there is considerable agreement between the human and rat datasets. Agreement, at this level of biochemical detail, between the serum metabolite biomarker profiles for the animal model and the human disease provides strong support for preclinical development stage-gate decisions for drug candidates for the treatment of type 2 diabetes. The CNs display a 29-member subset of the serum metabolites for both the T2D patients and the rat model for which the absolute value of the correlation coefficient for the disease-state association between pairs of the metabolites shown in the MSIs was greater than 0.8. Some strong correlations, both positive and negative, are identical for both human disease and rat model, however some differences can be seen between these networks, possibly reflecting the imperfect nature of even a good animal model of a human disease. Such detailed comparisons between human disease and animal model at the level of molecular systems pathology enable better decisions to be made concerning the advancement of drug candidates in the discovery–development pipeline based on their performance in studies which employ animal models of the human disease.
of a first-generation drug. Furthermore, the SRPs from the clinical phase can be compared to the corresponding SRPs generated from a variety of animal models to choose the animal model which best mimics the action of the first-generation drug in both efficacy and safety biochemical processes – not necessarily the animal model used for preclinical efficacy studies. Once the ‘best’ animal model has been selected and the SRP information related to the efficacy and safety of the firstgeneration drug has been incorporated into the armamentarium of the drug discoverers, the second-generation drug dis-
covery program can focus on different aspects of the SRPs as surrogates for efficacy or safety response measures for structure–activity relationship (SAR) studies.
Conclusion Metabolomic technologies have a special place in a molecular systems approach to translational activities within the pharmaceutical value chain because of the absolute identity of metabolites across species. Although ‘fit for the task’ right now, there are ample opportunities for future improvements www.drugdiscoverytoday.com
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Figure 4. A schematic concept for the translational use of metabolomics data in research to discover a second-generation drug candidate based on the efficacy and safety profile in patients treated with the a first-generation drug. Step 1: Plasma or serum metabolite profiling of blood samples, derived from patients treated with a first-generation drug vs. a placebo (Pbo) or standard of care (SoC) for the disease, yields system response profiles (SRPs, see [5]) including biomarker sets that can be statistically associated with efficacy or safety (AE: adverse event) outcome measures. A correlation network for the drug-treated state can also be created from the datasets.
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in these technologies (see Outstanding issues for metabolomic technologies). With the available and emerging bioanalytical technologies for profiling bodyfluids and the statistical and bioinformatic methods to analyze and interpret the large datasets generated by a molecular systems approach, it should be possible to minimize the number of future drug development projects that are ‘Lost in translation’. The optimal situation will be achieved when a molecular systems approach is routinely implemented from the onset of every new drug discovery program, so that comprehensive biochemical datasets for disease and drug response states are available as a common language throughout each specific drug discovery/ development project, and beyond for other projects that focus on the same or related indications or on compounds of similar structures. Nirvana!
Outstanding issues Limits of detection and dynamic ranges of particular metabolomic platforms. New platforms must be developed to target certain classes of molecules. Current understanding of metabolic pathways is insufficient to interpret fully the complexity of molecular species routinely identified from large metabolomic datasets. Bioinformatic integration of metabolomic datasets with proteomic, transcriptomic and traditional clinical chemistry datasets has substantial room for improvement. Cross-compartment relationships and systems dynamics are hardly understood.
References 1 Taylor, D. (2003) Fewer new drugs from the pharmaceutical industry. BMJ 326, 408–409 2 US Department of Health and Human Services (2004) Innovation or Stagnation, Challenge and Opportunity on the Critical Path to New Medicinal Products (http://www.fda.gov/oc/initiatives/criticalpath/ whitepaper.html) 3 European Commission (2005) Innovative Medicines Initiative (IMI). Strategic Research Agenda (http://europa.eu.int/comm/research/fp6/ index_en.cfm?p=1_innomed)
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4 Bain & Co., (2003) Rebuilding Big Pharma’s Business Model. Bain & Co. 5 Van der Greef, J. and McBurney, R.N. (2005) Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat. Rev. Drug Discov. 4, 961–967 6 Adams, D. (1979) The Hitchhikers Guide to the Galaxy. Pan Macmillan 7 Kohonen, T. (2001) Self-Organizing Maps (3rd edn), Springer 8 Steuer, R. et al. (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 9 Oresic, M. et al. (2004) Phenotype characterisation using integrated gene transcript, protein and metabolite profiling. Appl. Bioinform. 3, 205–217 10 Lamers, R-J.A.N. et al. (2003) Identification of disease- and nutrient-related metabolic fingerprints in osteoarthritic guinea pigs. J. Nutr. 133, 1776–1780 11 Lamers, R-J.A.N. et al. (2005) Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis Cartilage 13, 762–768 12 ‘t Hart, B.A. et al. (2003) 1-H NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urine of MS patients and non-human primates with MS-like disease. J. Neurol. Sci. 212, 21–30 13 Lindon, J.C. et al. (2003) Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol. 187, 137–146 14 Lindon, J.C. et al. (2005) The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6, 691–699 15 Morris, M. and Watkins, S.M. (2005) Focused metabolomic profiling in the drug development process: advances from lipid profiling. Curr. Opin. Chem. Biol. 9, 407–412 16 Beecher, C.W.W. and Tripp, R. (2004) Metabolomics – applications in drug discovery and development. Business Brief. Pharmatech. 17 Weinberger, K. and Graber, A. (2005) Using comprehensive metabolomics to identify novel biomarkers. Screening Trends Drug Discov. 6, 42–45 18 Gray, G. and Heath, D. (2005) A global reorganization of the metabolome in arabidopsis during cold acclimation is revealed by metabolic fingerprinting. Physiologia Plantarum. 124, 236–248 19 Boros, L.G. et al. (2004) Use of metabolic pathway flux information in targeted cancer drug design. Drug Discov. Today: Ther. Strategies 4, 435–443 20 van der Greef, J. et al. (2003) The role of metabolomics in systems biology. In Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (Harrigan, G.G. and Goodacre, R., eds), pp. 171–198, Kluwer Academic Publishers 21 Davidov, E. et al. (2004) Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model. OMICS 8, 267–288 22 Johnson, P.C. and Higgins, A.J. (2004) Precise phenotypic anchoring for drug target identification, validation and biomarker discovery using an advanced systems biology approach. Drug Discov. World Summer 55–62 23 Moore, G.A. (1999) Crossing the Chasm (revised edn), Harper Collins
Step 2: The biomarker sets associated with efficacy or safety criteria from the patient study in Step 1 are compared to determine whether the drug-induced biochemical mechanisms underlying adverse events are likely to be the same as those responsible for the efficacy of the drug. In the case where the overlap of the biomarkers sets associated with efficacy and safety is small (e.g. Case 2), there are good prospects for a second-generation drug candidate being discovered with improved efficacy-to-safety characteristics. The correlation network for the drug-treated state can be evaluated to determine the metabolite components and sub-networks related to the safety of the first-generation drug. Step 3: A study similar to that outlined in Step 1 is undertaken with the first-generation drug in various animal models of the disease for which that drug is indicated. SRPs for the drug-treated state for the various animal models are compared with the SRPs from the drug-treated patients to select the ‘best’ animal model to use in second-generation drug discovery activities. Such information would already be available if a molecular systems pathology/systems pharmacology approach had been applied to drug discovery and development activities for the first-generation drug. Step 4: With knowledge of the aspects of the drug response SRPs in the ‘best’ animal model that are associated with efficacy or safety for this class of drugs in patients, illustrated here using the correlation network, it will be possible to undertake in vivo structure activity relationship (SAR) studies with candidate compounds and select a second-generation drug candidate. Different aspects of the correlation network can be used as indicators of the probable efficacy or safety of candidate compounds. For example, the simulated changes shown in the correlation network for the three hypothetical compounds would be consistent with the choice of the right-side compound as the second-generation drug candidate, because the sub-network associated with the safety criterion is absent from the network generated by this compound.
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