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The pressing need for combined genotype–phenotype analysis in clinical practice
260
News & Comment
TRENDS in Pharmacological Sciences Vol.23 No.6 June 2002
Letters
The pressing need for combined genotype–phenotype analysis in clinical practice Since the term ‘polymorphisms’ entered clinical circles, many trials have begun to elucidate the possible relationship between drugs and side-effects. An increasing number of articles on cytochrome P450 (CYP) polymorphisms attempt to correlate experimental evidence with clinical practice to provide a firm scientific basis for selecting optimal drug therapies. Thus, pharmacogenomic research, which seeks to identify genetic factors that contribute to interpatient and interdrug variations, embraces both toxicity and efficacy [1–3]. An individual’s response to a drug involves a complex combination of genetic and non-genetic factors. Genetic variants in the drug target itself, disease pathway genes or drug-metabolizing enzymes are the rule not the exception, and might all be used as predictors of drug efficacy or toxicity. Pharmacogenomics should aim to elaborate personalized medicine, whereby administration of the drug class and dosage is tailored to an individual genotype [4]. Identification of alleles that are associated with inactive or occasionally enhanced gene expression has advanced clinical research. For example, the CYP genotype, which is ‘fixed’ for life and can always be readily and safely identified, provides new opportunities for relating CYP expression with epidemiology and outcomes-based research in a way that was previously impossible with a simple phenotype based on a probe drug. This has led to a gradual shift towards genotype-based, rather than phenotype-based, approaches to population studies despite the greater costs and limitations often associated with genetic testing [5]. Genotyping assesses a person’s ‘fingerprint’ forever, and never requires replication: extra costs are therefore justified by lifetime records. Phenotyping gathers information linked to stochastic factors, which are particularly relevant in patients who receive multiple medications. When a http://tips.trends.com
patient has a lower-metabolizer phenotype, drugs that are primarily or even moderately affected by metabolic enzymes must be shunted through less common metabolic pathways. This might not greatly affect many healthy controls. However, it might be highly relevant when other pathways are simultaneously inhibited or induced by co-medication. Individual lifestyle exposures (e.g. ethanol, progestinic-estrogens, workplace pollutants and herbal extracts) might exacerbate the formation of rare and toxic metabolites. Nevertheless, mishandling of molecular biology can produce questionable results. Published articles can be meaningless if the reported results are not adequately validated [6,7]. Many clinicians randomly sequence samples. Over- or under-estimation of allele frequencies in a subpopulation during a genotype analysis might lead to misleading assessments of phenotype–genotype relationships. The PCR–RFLP (polymerase chain reaction–restriction fragment length polymorphism) analyses that are widely used in clinical laboratories are meaningless without confirmation by sequencing or ‘chip’ technologies. Although both the equipment and reagents are expensive, this is the only way to reach 98% confidence levels. Furthermore, allele results can be inappropriately combined, as, for example, when two different heterozygote CYP polymorphisms get promptly attributed to poor-metabolizer subcategories without stopping to check whether they are even on the same chromosome arm. By contrast, phenotype analyses are subject to many variables such as biomarker specificity, physical form, diet, gender, age, ethnicity, lifestyle and other environmental factors [8,9]. Combined genotype–phenotype analysis could clarify many pharmacodynamic and pharmacokinetic issues, while effectively limiting errors related to neglected variables (statistically corroborated conclusions might not be valid if important variables are omitted, particularly in meta-analysis of data from several sources). Along with extensive preliminary patient-evaluation procedures, clinical pharmacodynamic
and pharmacokinetic studies need to employ combined genotype–phenotype analysis to eliminate possible pitfalls and maximize the interpretative potential of the data [10]. Combined genotyping–phenotyping of both patients and controls should drastically reduce study dropouts thereby cutting the number of papers dedicated to the side-effects of drugs rather than drug efficacy, particularly in anticancer therapeutic protocols. Particularly in geographical areas characterized by extensive genetic mixing, the ultimate goal must be to find the right therapy (drug and dosage) for a given subpopulation at a specific moment [11]. As genomic tools are sharpened, so will be our ability to dissect disease into its component parts. New molecular anticancer therapies that target specific genetic–biochemical abnormalities should help further personalize treatment. Although much standardization and advanced bioinformatics will be needed for reproducibility and data interpretation, combined genotype–phenotype analysis could allow rapid diffusion of pharmacogenomics among clinicians. Andrea Sapone* Moreno Paolini Gian Luigi Biagi Dept of Pharmacology, Pharmacogenomic Unit, University of Bologna, via Irnerio 48, 40126 Bologna, Italy. *e-mail: [email protected] Giorgio Cantelli-Forti Dept of Preventive Medicine and Community Health, The University of Texas, Medical Branch, Galveston, TX 77555-1010, USA. Frank J. Gonzalez NIH/NCI, Laboratory of Metabolism, 9000 Rockville Pike, Bethesda, MD 20892, USA. References 1 Ginsburg, G.S. et al. (2001) Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol. 19, 491–496 2 Roses, A.D. (2001) Pharmacogenetics. Hum. Mol. Genet. 10, 2261–2267 3 Adam, G.I. (2001) The development of pharmacogenomic models to predict drug response. Curr. Opin. Drug Discov. Devel. 4, 296–300 4 Adam, G.I. et al. (2000) Pharmacogenomics to predict drug response. Pharmacogenomics 1, 5–14 5 Baba, Y. (2001) Development of novel biomedicine based on genome science. Eur. J. Pharmacol. Sci. 13, 3–4
0165-6147/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S0165-6147(02)02047-3
News & Comment
6 Lesko, L.J. et al. (2001) Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu. Rev. Pharmacol. Toxicol. 41, 347–366 7 Mehr, I.J. (2000) Preparing for the revolution – pharmacogenomics and the clinical lab. Pharmacogenomics 1, 1–4 8 Kricka, L.J. (2001) Microchips, microarrays, biochips and nanochips: personal laboratories for the 21st century. Clin. Chim. Acta 307, 219–223 9 Clarke, P.A. et al. (2001) Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential (2). Biochem. Pharmacol. 62, 1311–1336 10 Winkelmann, B.R. et al. (2001) Rationale and design of the LURIC study – a resource for functional genomics, pharmacogenomics and long-term prognosis of cardiovascular disease. Pharmacogenomics 2, S1–S73 11 McLeod, H.L. et al. (2001) Pharmacogenomics: unlocking the human genome for better drug therapy. Annu. Rev. Pharmacol. Toxicol. 41, 101–121
Can ‘real-lab’ supersede ‘virtualligand’ promises? Molecular modellers seek to develop software that produces data in silico to identify the desirable output of molecular structures derived from protein ligand targeting. Combinatorial synthesis (combichem) can then be coupled to highthroughput screening (HTS) to select efficiently those molecules that display promise as therapeutic drugs [1]. In silico prophesies for sieving out losers
A seemingly optimal library-identified candidate designer-molecule that targets a particular protein therapeutic site might, as a result of obligate chirality of the molecule, necessitate a difficult synthesis of such an identified pharmacophore. Ideally, this pharmacophore will target only one receptor active-site of the identified ligand or enzyme in vivo to avoid side-effects. Pursuit of such a selective pharmacophore from the start of the identification process can increase significantly the probability of predicting whether the agent is a winner or loser: such winner–loser outcome prediction is the basis for the financial success of a marketable therapeutic agent. Although such a therapeutic lead compound must have survived a sieving-out of unpromising virtuals in silico, chirality and regiospecific http://tips.trends.com
TRENDS in Pharmacological Sciences Vol.23 No.6 June 2002
problems in the more complex biomolecules under examination can readily undermine the drug lead pipeline. Moreover, the recognition that only one enantiomer is likely to be pharmacologically active might mean a successful designer pharmacophore is unlikely to be acceptable to agencies (because of doubts about marketable formulations). In addition, generic forms of such therapeutic drugs might display micro-heterogenicity of enzyme protein components where a few changes in amino acid residues are not readily discernible even by extremely sensitive bioassays [2], as distinct from enzyme isoform specificity [3]. Bioavailability as a result of genomics and proteomics?
Many therapeutic agents are selfadministered orally in gelatin capsule form to manifest ‘slow release’, which achieves a chosen desirable uniformity in the plasma concentration of the drug according to accepted pharmacokinetic predictability of the drug. Nevertheless, gut microflora can cleave glycosides (e.g. bioconversion of the soya glycones, genistin and daidzin, to the aglycone forms, genistein and daidzein), and further metabolism by idiosyncratic bacteria can render individuals tolerant to particular therapeutic regimes: pharmacogenetic explanations might need to be determined for each individual as specified by their genomic–proteomic profile! It follows that the trend is towards individually designed medication, although such medication might need to be more realistically provided as ‘form and dose’ according to the usual considerations of age, weight and gender [i.e. a common form of the drug (e.g. pill or liquid) and common dose levels might be required to make such medication practical]. More often this will be biomonitored using non-invasive techniques, such as measurement of blood flow and blood temperature, and drug blood level smart monitoring by light (i.e. at present, oxygen levels in the blood can be monitored by detecting the colour of the blood by shining a light onto the skin and measuring the reflected light; thus, it is possible that detectors that could, for example, be clipped onto the patient’s finger could be developed to monitor the blood concentration of drugs).
261
No relaxation without virtual representation?
‘Real-lab’ experiments (wet biochemistry) were judged by some authors as obsolescent in the age of super-computers linked worldwide, but in practice the de jure conclusion has yielded to a de facto position mainly because of our relative ignorance of protein conformational adoptions despite minimum-energybased predictions. Thus, we need to grow real crystals for experimental laboratory X-ray crystallography (atomic juxtapositioning yield the binding site composition of amino acids in proteins and enzymes down to ~1.5 Å resolution). Pharmacophores will be best identified by a symbiosis of ‘real-lab’ and ‘virtual’ approaches! Alan Wiseman Biochemistry Group, School of Biomedical & Life Sciences, University of Surrey, Guildford, UK GU2 7XH. Len Woods* Woodside Consulting, 14a The Spinney, Camberley, Surrey, UK GU15 1HH. *e-mail: [email protected] References 1 DePalma, A. (2001) A winning combination? Chemistry & Industry 24, 799–800 2 Wiseman, A. et al. (2002) Are food and environmental toxicants ‘overdetected’ by bioassay? Trends Biotechnol. 20, 13–15 3 Lewis, D.F.V. (2001) Guide to Cytochromes P450 Structure and Function, Taylor & Francis
Book and Software Reviews TiPS Book and Software Reviews are written by experts in the field and provide a personal assessment of both the positive and the negative aspects of the publication. If you are interested in reviewing a recently published book or would like to suggest a book to be reviewed, please write with details of the publication to:
Trends in Pharmacological Sciences, Elsevier Science London, 84 Theobald’s Road, London, UK WC1X 8RR (fax: +44 20 7611 4470 or e-mail: [email protected])
0165-6147/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S0165-6147(02)02026-6
News & Comment
TRENDS in Pharmacological Sciences Vol.23 No.6 June 2002
Letters
The pressing need for combined genotype–phenotype analysis in clinical practice Since the term ‘polymorphisms’ entered clinical circles, many trials have begun to elucidate the possible relationship between drugs and side-effects. An increasing number of articles on cytochrome P450 (CYP) polymorphisms attempt to correlate experimental evidence with clinical practice to provide a firm scientific basis for selecting optimal drug therapies. Thus, pharmacogenomic research, which seeks to identify genetic factors that contribute to interpatient and interdrug variations, embraces both toxicity and efficacy [1–3]. An individual’s response to a drug involves a complex combination of genetic and non-genetic factors. Genetic variants in the drug target itself, disease pathway genes or drug-metabolizing enzymes are the rule not the exception, and might all be used as predictors of drug efficacy or toxicity. Pharmacogenomics should aim to elaborate personalized medicine, whereby administration of the drug class and dosage is tailored to an individual genotype [4]. Identification of alleles that are associated with inactive or occasionally enhanced gene expression has advanced clinical research. For example, the CYP genotype, which is ‘fixed’ for life and can always be readily and safely identified, provides new opportunities for relating CYP expression with epidemiology and outcomes-based research in a way that was previously impossible with a simple phenotype based on a probe drug. This has led to a gradual shift towards genotype-based, rather than phenotype-based, approaches to population studies despite the greater costs and limitations often associated with genetic testing [5]. Genotyping assesses a person’s ‘fingerprint’ forever, and never requires replication: extra costs are therefore justified by lifetime records. Phenotyping gathers information linked to stochastic factors, which are particularly relevant in patients who receive multiple medications. When a http://tips.trends.com
patient has a lower-metabolizer phenotype, drugs that are primarily or even moderately affected by metabolic enzymes must be shunted through less common metabolic pathways. This might not greatly affect many healthy controls. However, it might be highly relevant when other pathways are simultaneously inhibited or induced by co-medication. Individual lifestyle exposures (e.g. ethanol, progestinic-estrogens, workplace pollutants and herbal extracts) might exacerbate the formation of rare and toxic metabolites. Nevertheless, mishandling of molecular biology can produce questionable results. Published articles can be meaningless if the reported results are not adequately validated [6,7]. Many clinicians randomly sequence samples. Over- or under-estimation of allele frequencies in a subpopulation during a genotype analysis might lead to misleading assessments of phenotype–genotype relationships. The PCR–RFLP (polymerase chain reaction–restriction fragment length polymorphism) analyses that are widely used in clinical laboratories are meaningless without confirmation by sequencing or ‘chip’ technologies. Although both the equipment and reagents are expensive, this is the only way to reach 98% confidence levels. Furthermore, allele results can be inappropriately combined, as, for example, when two different heterozygote CYP polymorphisms get promptly attributed to poor-metabolizer subcategories without stopping to check whether they are even on the same chromosome arm. By contrast, phenotype analyses are subject to many variables such as biomarker specificity, physical form, diet, gender, age, ethnicity, lifestyle and other environmental factors [8,9]. Combined genotype–phenotype analysis could clarify many pharmacodynamic and pharmacokinetic issues, while effectively limiting errors related to neglected variables (statistically corroborated conclusions might not be valid if important variables are omitted, particularly in meta-analysis of data from several sources). Along with extensive preliminary patient-evaluation procedures, clinical pharmacodynamic
and pharmacokinetic studies need to employ combined genotype–phenotype analysis to eliminate possible pitfalls and maximize the interpretative potential of the data [10]. Combined genotyping–phenotyping of both patients and controls should drastically reduce study dropouts thereby cutting the number of papers dedicated to the side-effects of drugs rather than drug efficacy, particularly in anticancer therapeutic protocols. Particularly in geographical areas characterized by extensive genetic mixing, the ultimate goal must be to find the right therapy (drug and dosage) for a given subpopulation at a specific moment [11]. As genomic tools are sharpened, so will be our ability to dissect disease into its component parts. New molecular anticancer therapies that target specific genetic–biochemical abnormalities should help further personalize treatment. Although much standardization and advanced bioinformatics will be needed for reproducibility and data interpretation, combined genotype–phenotype analysis could allow rapid diffusion of pharmacogenomics among clinicians. Andrea Sapone* Moreno Paolini Gian Luigi Biagi Dept of Pharmacology, Pharmacogenomic Unit, University of Bologna, via Irnerio 48, 40126 Bologna, Italy. *e-mail: [email protected] Giorgio Cantelli-Forti Dept of Preventive Medicine and Community Health, The University of Texas, Medical Branch, Galveston, TX 77555-1010, USA. Frank J. Gonzalez NIH/NCI, Laboratory of Metabolism, 9000 Rockville Pike, Bethesda, MD 20892, USA. References 1 Ginsburg, G.S. et al. (2001) Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol. 19, 491–496 2 Roses, A.D. (2001) Pharmacogenetics. Hum. Mol. Genet. 10, 2261–2267 3 Adam, G.I. (2001) The development of pharmacogenomic models to predict drug response. Curr. Opin. Drug Discov. Devel. 4, 296–300 4 Adam, G.I. et al. (2000) Pharmacogenomics to predict drug response. Pharmacogenomics 1, 5–14 5 Baba, Y. (2001) Development of novel biomedicine based on genome science. Eur. J. Pharmacol. Sci. 13, 3–4
0165-6147/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S0165-6147(02)02047-3
News & Comment
6 Lesko, L.J. et al. (2001) Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu. Rev. Pharmacol. Toxicol. 41, 347–366 7 Mehr, I.J. (2000) Preparing for the revolution – pharmacogenomics and the clinical lab. Pharmacogenomics 1, 1–4 8 Kricka, L.J. (2001) Microchips, microarrays, biochips and nanochips: personal laboratories for the 21st century. Clin. Chim. Acta 307, 219–223 9 Clarke, P.A. et al. (2001) Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential (2). Biochem. Pharmacol. 62, 1311–1336 10 Winkelmann, B.R. et al. (2001) Rationale and design of the LURIC study – a resource for functional genomics, pharmacogenomics and long-term prognosis of cardiovascular disease. Pharmacogenomics 2, S1–S73 11 McLeod, H.L. et al. (2001) Pharmacogenomics: unlocking the human genome for better drug therapy. Annu. Rev. Pharmacol. Toxicol. 41, 101–121
Can ‘real-lab’ supersede ‘virtualligand’ promises? Molecular modellers seek to develop software that produces data in silico to identify the desirable output of molecular structures derived from protein ligand targeting. Combinatorial synthesis (combichem) can then be coupled to highthroughput screening (HTS) to select efficiently those molecules that display promise as therapeutic drugs [1]. In silico prophesies for sieving out losers
A seemingly optimal library-identified candidate designer-molecule that targets a particular protein therapeutic site might, as a result of obligate chirality of the molecule, necessitate a difficult synthesis of such an identified pharmacophore. Ideally, this pharmacophore will target only one receptor active-site of the identified ligand or enzyme in vivo to avoid side-effects. Pursuit of such a selective pharmacophore from the start of the identification process can increase significantly the probability of predicting whether the agent is a winner or loser: such winner–loser outcome prediction is the basis for the financial success of a marketable therapeutic agent. Although such a therapeutic lead compound must have survived a sieving-out of unpromising virtuals in silico, chirality and regiospecific http://tips.trends.com
TRENDS in Pharmacological Sciences Vol.23 No.6 June 2002
problems in the more complex biomolecules under examination can readily undermine the drug lead pipeline. Moreover, the recognition that only one enantiomer is likely to be pharmacologically active might mean a successful designer pharmacophore is unlikely to be acceptable to agencies (because of doubts about marketable formulations). In addition, generic forms of such therapeutic drugs might display micro-heterogenicity of enzyme protein components where a few changes in amino acid residues are not readily discernible even by extremely sensitive bioassays [2], as distinct from enzyme isoform specificity [3]. Bioavailability as a result of genomics and proteomics?
Many therapeutic agents are selfadministered orally in gelatin capsule form to manifest ‘slow release’, which achieves a chosen desirable uniformity in the plasma concentration of the drug according to accepted pharmacokinetic predictability of the drug. Nevertheless, gut microflora can cleave glycosides (e.g. bioconversion of the soya glycones, genistin and daidzin, to the aglycone forms, genistein and daidzein), and further metabolism by idiosyncratic bacteria can render individuals tolerant to particular therapeutic regimes: pharmacogenetic explanations might need to be determined for each individual as specified by their genomic–proteomic profile! It follows that the trend is towards individually designed medication, although such medication might need to be more realistically provided as ‘form and dose’ according to the usual considerations of age, weight and gender [i.e. a common form of the drug (e.g. pill or liquid) and common dose levels might be required to make such medication practical]. More often this will be biomonitored using non-invasive techniques, such as measurement of blood flow and blood temperature, and drug blood level smart monitoring by light (i.e. at present, oxygen levels in the blood can be monitored by detecting the colour of the blood by shining a light onto the skin and measuring the reflected light; thus, it is possible that detectors that could, for example, be clipped onto the patient’s finger could be developed to monitor the blood concentration of drugs).
261
No relaxation without virtual representation?
‘Real-lab’ experiments (wet biochemistry) were judged by some authors as obsolescent in the age of super-computers linked worldwide, but in practice the de jure conclusion has yielded to a de facto position mainly because of our relative ignorance of protein conformational adoptions despite minimum-energybased predictions. Thus, we need to grow real crystals for experimental laboratory X-ray crystallography (atomic juxtapositioning yield the binding site composition of amino acids in proteins and enzymes down to ~1.5 Å resolution). Pharmacophores will be best identified by a symbiosis of ‘real-lab’ and ‘virtual’ approaches! Alan Wiseman Biochemistry Group, School of Biomedical & Life Sciences, University of Surrey, Guildford, UK GU2 7XH. Len Woods* Woodside Consulting, 14a The Spinney, Camberley, Surrey, UK GU15 1HH. *e-mail: [email protected] References 1 DePalma, A. (2001) A winning combination? Chemistry & Industry 24, 799–800 2 Wiseman, A. et al. (2002) Are food and environmental toxicants ‘overdetected’ by bioassay? Trends Biotechnol. 20, 13–15 3 Lewis, D.F.V. (2001) Guide to Cytochromes P450 Structure and Function, Taylor & Francis
Book and Software Reviews TiPS Book and Software Reviews are written by experts in the field and provide a personal assessment of both the positive and the negative aspects of the publication. If you are interested in reviewing a recently published book or would like to suggest a book to be reviewed, please write with details of the publication to:
Trends in Pharmacological Sciences, Elsevier Science London, 84 Theobald’s Road, London, UK WC1X 8RR (fax: +44 20 7611 4470 or e-mail: [email protected])
0165-6147/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S0165-6147(02)02026-6