METHOD OF DESIGNING DRUGS THROUGH REVERSE POLYMORPHISM OF TARGET GENES

Abstract: This invention relates to a method of determining the most efficacious and safe drug for a population of subjects to modulate a gene or gene product (e.g., protein or polypeptide) by determining the nucleic acid sequence of all or a portion of a gene and/or all or a portion of the encoded amino acid sequence of that gene with the lowest frequency of polymorphisms.

Description: METHOD OF DESIGNING DRUGS THROUGH REVERSE POLYMORPHISM OF TARGET GENES RA K GROUND OF THE INVENTION One of the most important problems in the pharmaceutical industry is the choice of an efficacious and safe drug target to be pursued for the expensive and time- consuming drug discovery and development process. Frequently, during the drug target discovery and validation process, many targets, all important and involved within the same disease pathway of interest (e.g. Asthma, Hypertension, etc.), are chosen for further development. All of these targets are examined for their role in the pathway, tissue specific expression, stability, half-life, approachability and other characteristics. In a later stage of the drug discovery process, about 5 - 20 targets, all functioning in one disease pathway, have been accumulated. They have fulfilled most of the above-mentioned criteria and are called "validated drug targets". It is extremely difficult to decide which one of the "validated targets" would likely to be the right one to be chosen for further drug development. At this point in drug discovery, one of the most difficult decisions must be made. If a target is chosen for further development, it will enter into the high throughput screening process. After a drug lead is detected, it will be optimized and submitted to pre-clinical and finally clinical development (see Figure 1 ). These later processes represent the major cost factor in drug development, and therefore the decision as to which target is the "best" one to be chosen has a huge impact upon the success of a pharmaceutical company. Many drugs, although shown to be effective in pre-clinical development, fail to show statistically valid efficacy in later clinical tests. Some, show reduced or no efficacy in a subset of the clinical trial population. In the worst case, the drug proves to be toxic to some individuals. At this point, termination of clinical development is likely, resulting in the loss of typically substantial invested resources. In addition, even if drugs are effective through Phase III clinical trials and submitted for New Drug Entity (NDE) approval to the Food and Drug Administration (FDA), there is no guarantee on how the drug will perform in the global market. Thus, a method of identifying the best drug target for all populations would speed the development of new drugs and avoid unforseen efficacy, side effects, and toxicity problems in individuals. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a diagram depicting the steps in the process of genomics-aided drug discovery and development Figure 2 shows the impact of some representative sequence vaπations on the processes involved in the production of a protein Figure 3 is a list of drug targets depicting known inter-individual vaπations in targets and their effect upon the efficacy of drugs and therapeutic compounds OBJECTS AND SUMMARY OF THE INVENTION One object of the invention is to provide a method of determining the nucleic acid sequence of all or a portion of an endogenous gene of a population of individuals, said nucleic acid sequence expressing the lowest single nucleotide vaπation among said population, comprising' (A) prepaπng a collection of genomic nucleic acids compπsmg all or a portion of the endogenous genes of a population of individuals; (B) determining the sequence polymorphisms for all of a poπion of the genomic nucleic acids; and (C) determining the nucleic acid sequence of all or a portion of the genomic nucleic acids which have the lowest polymorphism frequency/vaπation. Another object of the invention is to provide a method of determining the reverse amino acid polymorphism rate of an ammo acid sequence by performing the steps above followed by determining the ammo acid sequence encoded by the nucleic acid sequence of (C); and determining the reverse amino acid polymorphism rate for vaπous regions of the ammo acid sequence. Also contemplated are one or more of the steps in the above method being automated. The methods of the invention can be applied to any population group including eukaryotes (multicellular or single cellular) and prokaryotes, more preferably a vertebrate population (e g , birds, fish, reptiles, amphibians and mammals), more preferably a mammalian population, and most preferably wherein the subject is a human Another object of the invention is to identify compounds which bind to sequences (e g , amino acid or nucleic acid sequences) with a reverse polymoφhism rate Ideally, these compounds modulate the activity of the gene or protein compπsing these sequences A further object of the invention is to provide a method of ranking domains of a protein (or nucleic acid) for potentially being the target of a drug, compπsing identifying a reverse polymoφhism rate (or rate of polymoφhic frequency) of an amino acid sequence or protein compπsing said amino acid sequence (or ot a nucleic acid sequence or a gene compπsing said nucleic sequence) using the above methods It is another object of the invention to provide a computer system compπsing (A) a database of genomic hbraπes for a plurality of organisms, said hbraπes having multiple genomic and/or amino acid sequences, wherein said sequences are arranged to compπse at least one reference sequence (e g , wild-type sequence) and a plurality of related sequences compπsing a polymoφhιsm(s), and (B) a user interface capable of receiving data for a novel polymoφhism for compaπson and display the ranking of a portion of the nucleic acid sequence or amino acid sequence compπsing said polymoφhism based on the frequency of polvmoφhism in the region Another object of the invention is to provide a computer program product compπsing a computer-usable medium having computer-readable program code embodied thereon relating to a database including a plurality of genomic hbraπes for one or more types of organisms, said libraries having multiple genomic and/or amino acid sequences, wherein said sequences are arranged to compπse at least one reference sequence and a plurality of related sequences compπsing a polymoφhιsm(s), the computer program product further compπsing computer- readable program code for effecting the following steps withm a computing system (A) providing an interface for receiving novel polymoφhisms for compaπson with a reference sequence, (B) determining the reverse polymoφhism rate for said novel polymoφhisms; and (C) displaying the results of said determination The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descπption of preferred embodiments of the invention, as illustrated in the accompanying drawings and discussed below DETAILED DESCRIPTION OF THE INVENTION Most of the vaπations of drug efficacy in different individuals can be attπbuted to inter-individual differences in the genetic information Sequence polymoφhisms, such as single nucleotide polymoφhisms (SNPs) and insertions and deletions can change the function of genes and the proteins thereby encoded dramatically These changes impact the metabolism of drugs as well as their effect on the drug target In Figure 3, a representative list of drug targets is shown which depicts known inter-individual vaπations in targets and their effect upon the efficacy of drugs and therapeutic compounds This invention provides a method, which rapidly and effectively selects the "best validated target" from the panel of "validated targets" The term "best validated target" is defined as the target, which shows the lowest inter-individual vaπation in functional regions of the gene These genomic regions include the 5' transcπptional control sequences and translational regulating regions, coding sequences, exon-intron boundaπes, splice and sphce/branch sites, as well as 3' un-translated sequences, and other genomic fragments involved in expressed polypeptide vaπation and function Figure 2 shows the impact of some representative sequence vaπations on the processes of gene expression, pre-mRNA splicing, mRNA stability, protein translation, protein function and stability Structural regions of a protein are, for example, alpha helices, beta sheets and hehx-turn-helix motifs Functional motifs important for activity and domains important for stability and half-life are more complex and need to be defined on a protem-by-protein basis A few examples of such domains are di-nucleotide binding folds, trans-membrane domains, leucine zipper motifs, amphipathic helices, zinc fingers, cysteine (S-S) bπdges, cysteme πch domains, glycine πch domains, N- terminal amino acid sequence, and signal peptides. etc The functional regions of each target must be defined on the gene level (DNA sequence) as well as the protein level These motifs can include, but are not limited to, intracellular or extracellular targeting signals, enzymatic active sites and ligand binding domains in the instance of amino acid sequences. Polymoφhisms will not have the same impact in every one of the functional or structural domains or motifs. They will not always affect the composition or amino acid sequence of the encoded protein. Below is a ranking of polymoφhisms according to their position in the structural and functional domains and motifs as well as their deductible impact on the protein sequence: A) Polymoφhisms are termed "high impact polymoφhisms" if they are located in the following domains: splice sites, splice junctions and branch points (within introns); coding regions introducing a premature stop codon; coding regions resulting in a non-conservative amino acid exchange; the TATA consensus sequence; the polyadenylation consensus sequence; and the like. B) Polymoφhisms are termed "medium impact polymoφhisms" if they are located in the following domains: coding regions introducing a conservative amino acid substitution; coding regions changing the codon usage bias; the general promoter region, and transcriptional regulating elements; and the like. C) Polymoφhisms are termed "low impact polymoφhisms" if they are located in the following domains: coding regions without impacting amino acid code and codon usage bias; 3' untranslated regions (except polyadenylation sites); intronic regions (except splice sites and splice branch sites); and the like. Subsequently, the occurrence of polymoφhisms in any of the above functional or structural motifs is defined as the polymoφhism rate or polymoφhism frequency. Furthermore, a low occurrence of polymoφhisms is defined as the "reverse polymoφhism rate". The reverse polymoφhism rate is dependent on the nature of the polymoφhism. A high impact polymoφhism (see above) will result in a lower incidence than a medium and low impact polymoφhism, respectively. Thus, the higher the reverse polymoφhism rate, the lower the frequency of inter-individual sequence polymoφhisms will be in domains or motifs of the gene or protein which are important for function and structure. For example, if there is no polymoφhism found in a functional domain which is defined or known to be of importance to the functionality of the gene or protein, the reverse polymoφhism rate is defined to be infinite. The "best validated target" is a target, which will have a high reverse polymoφhism rate in domains or motifs which are important for drug target functionality and stability. Thus, a target which displays a high reverse polymoφhism rate is a target which shows the lowest inter-individual and inter-ethnic variation in domains and motifs important for drug efficacy, and the target which when modulated will demonstrate consistent efficacy among different ethnic groups. Most of the variations of drug efficacy in different individuals can be attributed to inter-individual differences in the genetic information. Sequence polymoφhisms, such as single nucleotide polymoφhisms (SNPs) and insertions and deletions can change the functionality of genes and proteins dramatically. These changes affect the activity and interaction of drugs as well as their interaction with the drug target. Identifying gene sequences with the lowest variation provides the target for drug design which is most likely to be efficacious and produce uniform reactivity in multicultural populations. There are basically eight steps in the process of determining the reverse polymoφhism rate of a nucleic acid sequence and the reverse amino acid polymoφhism rate of the encoded polypeptide. These are as follows: 1. Genomic DNA Collection Step one involves establishing a collection of genomic DNAs (nucleic acid sequences of the genes) of a reference population, which is a representation of major ethnic groups or species groups (e.g., murine variation). The lowest detectable polymoφhism frequency is linked to the number of individuals tested. Therefore, as the number of individuals screened increases, the sensitivity of the method similarly increases. Establishing a collection of genomic DNAs, typically in the form of a database, can be performed as would be known to the skilled artisan. 2. Sequence Analysis Once the DNAs are collected, their sequences can be analyzed. Either the full genetic information (entire gene) or important functional parts of the gene are studied. For example, a portion of a known gene with a previously characterized and validated drug target can be analyzed in each individual of the reference population. The nucleic acid analysis can be performed by enzymatic sequencing, chemical sequencing, sequencing by hybridization, DNA sequence mismatch scanning methods, nucleic acid heteroduplex analysis, polymoφhism screening methods such as hybridization to oligonucleotide arrays, or any other method which yields the nucleotide sequence of the gene. Such sequencing methods would be known and available to the skilled artisan. See, for example. DNA SEQUENCING PROTOCOLS (Hugh G. Griffin and Annette M. Griffin eds., Humana Press, 1993) and S. B. Primrose, PRINCIPLES OF GENOME ANALYSIS: A GUIDE TO MAPPING AND SEQUENCING DNA FROM DIFFERENT ORGANISMS, (2nd ed., Blackwell Science Inc., 1998); HIGH-SPEED IMAGING AND SEQUENCE ANALYSIS (Alan M. Frank and James S. Walton eds., Society of Photo-optical instrumentation Engineers, 1999); and CURRENT PROTOCOLS IN MOLECULAR BIOLOGY ON CD-ROM (Frederick M. Ausebel et al., editors, John Wiley & Sons, 1993). Once a nucleic acid is sequenced, sequence reading hardware can be used to assemble the sequence fragments (using programs such as PC-GENE or DNA- STAR), remove vector sequences, and confirm the sequence. Then sequence patterns within the nucleic acid can be determined. Pattern matching programs can be used to identify other nucleic acid sequences that share similarity. As the wealth of information grows on DNA sequences, elements such as promoters (see, e.g., Ohler, 2000, Genome Res. 10(4): 539-42), enhancers, transcription factors (see, e.g., Roulet et al., 2000, J. Mol. Biol. , ,297(4): 833-48), and the like can be more readily identified using various computer programs, alone or in conjunction with experimental data. See also, van Helden et al., 2000, "Discovering regulatory elements in non-coding sequences by analysis of spaced dyads," Nuc. Acids Res. 28(8): 1808-18; Scherf et al., 2000, "Highly specific localization of promoter regions in large genomic sequences by Promoterlnspector: A novel context analysis approach," J. Mol. Riol. 297(3): 599-606; Sutcliffe et al., 2000 "TOGA: An automated parsing technology for analyzing expression of nearly all genes," Proc. Natl. Acad. Sci. TISA 97(5): 1976-81; Kel-Margoulis et al, 2000," COMPEL: a database on composite regulatory elements providing combinatorial transcriptional regulation," Nuc. Acids Res. 28(1): 311-5; SEQUENCE ANALYSIS PRIMER (Michael Gribskov and John Devereux eds., W. H. Freeman Co., 1992); BIOLOGICAL SEQUENCE ANALYSIS: PROBABILISTIC MODELS OF PROTEINS AND NUCLEIC ACIDS (Richard Durbin ed., Cambridge University Press 1999); Tieng et al., HIGH PERFORMANCE COMPUTATIONAL METHODS FOR BIOLOGICAL SEQUENCE ANALYSIS (Kluwer Academic Publishing, 1996); ICRF HANDBOOK OF GENOME ANALYSIS (Nigel K. Spurr et al., eds. Blackwell Science Inc., 1998); "Computer Analysis of Sequence Data," in METHODS IN MOLECULAR BIOLOGY vol. 25 (Annette M. Griffin and Hugh G. Griffin eds., 1994); BIOINFORMATICS: A PRACTICAL GUIDE TO THE ANALYSIS OF GENES AND PROTEINS (Andreas Baxevanis et al., eds., John Wiley & Sons, 1998); and Pierre Baldi et al., BIOINFORMATICS: THE MACHINE LEARNING APPROACH — ADAPTIVE COMPUTATION AND MACHINE LEARNING (MIT Press, 1998) to name a few. 3. Sequence Polymoφhism Analysis Once the nucleic acids are sequenced, then the sequence polymoφhisms among the variants of the gene can be ascertained. Determining the variation between the nucleic acid sequences also can be performed prior to analysis of the regulatory components of a nucleic acid or gene (c/s-regulatory domains, open reading frame, intron and exon determination, etc.). The variants identified can include single nucleotide polymoφhisms (SNPs) or nucleotide insertions or deletions. Each subject's gene or genomic region is analyzed and either heterozygote or homozygote nucleotide changes relative to a reference sequence and any deviations from the reference sequence are recorded. At this time, the incidence of a particular polymoφhism can also be determined in a particular population of individuals if the sample of subjects is great enough. Population samplings are typically at least about 20 subjects (although fewer subjects can comprise a population sampling, but is not preferred) or more subjects, preferably about 50 or more subjects, or more preferably about 100 or more subjects, and most preferably about 100 to about 1000 or more subjects. 4. Polymoφhism Grouping Once all the polymoφhism variations (e.g., insertions, deletions, SNPs, etc.) from a particular sampling of subjects for a particular gene or fragment thereof are determined, the polymoφhisms can be grouped based on the functional regions of the gene in which the polymoφhism is located. Functional regions at the level of the nucleic acid (e.g., ribonucleic acid or deoxyribonucleic acid) include 5' untranslated regions (i.e., regulatory response elements, transcriptional factor binding sites, translational factor binding sites, TATA box, translational start site, ribosomal binding site, and the like), exons, introns, exon-intron boundaries, splice sites, splice- branch sites, 3' un-translated region (polyadenylation site, translational stop codon, regulatory factor response elements, attenuation sites etc.), enhancers, promoters, and silencers. 5. Determination of Reverse Polymoφhism Rate for the Nucleic Acid The reverse polymoφhism rate for the gene or nucleic acid can then be ascertained based in part on the functional and/or structural grouping in which each polymoφhism falls. Each of the identified and putative functional domains of the gene is assigned a reverse polymoφhism rate, which reflects the relative impact of the polymoφhism on functionality of the gene. The impact of the polymoφhism will depend on the location in the functional regions of the nucleic acid as described in Step 4. If the polymoφhisms destroys the functional region of the nucleic acid (e.g., destroys a splice acceptor site), it will be designated as high impact. Alternatively, if the polymoφhism only perturbs the functionality of the gene (e.g., increases or decreases the activity of the gene as compared to the activity observed for the wild-type or reference gene), then the polymoφhism will be designated a medium impact polymoφhism. If no impact is observed due to the presence of the polymoφhism in the gene as compared to the wild-type or reference gene, then the polymoφhism will be designated a low impact polymoφhism. As descπbed, high impact polymoφhisms typically are infrequent Thus, the identified polymoφhisms will be determined to fall into one of those three categoπes, and the reverse polymoφhism rate for each functional region is determined Once determined, domains with the reverse polymoφhism rates will then be used as targets for drug regulation, which will allow drug companies to produce drugs with a lower incidence of drug response vaπabihty among a population of subjects, if the drug response (e g , adverse drug response) would previously have been linked to a polymoφhism in that specific gene In preferred embodiments, the impact of the polymoφhism may be determined by performing cell based assays using the methods descπbed in, for example, WO 99/52926, wherein a cell compπsing and expressing the reference sequence is compared to a cell compπsing and expressing the sequence with a polymoφhism The impact of the polymoφhism to the cell can then be assessed biochemically, visually, via the use of microarray technology (see for example the microarray analysis descπbed in WO 99/52926), and the like to expeπmenlally categonze the polymoφhism in that portion of the amino acid or nucleic acid sequence as high impact, medium impact or low impact 6 Determination of the Open Reading Frame and Ammo Acid Sequence Encoded Thereby Once the gene or nucleic acid fragment thereof is sequenced, computer algoπthms can be used to ascertain the best open reading frames (ORFs), which may encode a protein or polypeptide A program can be used to quickly ascertain the protein sequence associated with that particular ORF Such programs will take into account or perform a codon bias analysis to locate coding regions and weight matπx or consensus sequence methods to locate signals such as promoters, translation initiation regions, RNA processing sites, and terminators m the region of likely coding frames As is apparent, such methods are readily available to the artisan of ordinary skill See for example, SEQUENCE ANALYSIS PRIMER (Michael Gπbskov and John Devereux eds , W H Freeman Co , 1992), BIOLOGICAL SEQUENCE ANALYSIS. PROBABILISTIC MODELS OF PROTEINS AND NUCLEIC ACIDS (Richard Durbm ed., Cambπdge University Press 1999). 1998), BIOINFORMATICS A PRACTICAL GUIDE TO THE ANALYSIS OF GENES AND PROTEINS (Andreas Baxevanis et al., eds., John Wiley & Sons, 1998). 7. Group Amino Acid Polymoφhisms Once the polymoφhisms are determined, they can be grouped into functional and structural domains and motifs of the protein or amino acid sequence. Structural domains include, but are not limited to, alpha helices, beta sheets, helix-turn-helix, cysteine-rich domains, glycine-rich domains, hydrophobic domains, hydrophillic domains, trans-membrane domains, disulfide bridges, and the like. Functional domains include, but are not limited to, the active site for a ligand binding to the protein or polypeptide fragment thereof, ligand binding folds, amphipathic helices, zinc fingers, leucine zippers, dimerization domains, proteolytic cleavage sites, enzymatic active sites, transmembrane domains, intracellular and extracellular targeting motifs and signals (e.g., nuclear targeting motifs which target a protein to the nucleus of a cell) and all other domains important for protein function in general or in specifically-investigated situations, and the like. Amino acid polymoφhisms, after being grouped, can further be weighed or considered according to the type of polymoφhism. For instance, conservative amino acid substitutions (e.g., Arginine to Lysine or Alanine to Valine) may not induce a structural or functional change in the protein, such that the binding of a drug to that region would not be perturbed. This weighing will be determined by whether the amino acid polymoφhism is identified in a structural and/or functional domain and/or in a conserved motif is of high, medium or low impact. The impact of a particular amino acid polymoφhism to the structure or function of a protein or amino acid sequence can be identified several ways. Computational chemistry (e.g., programs directed to enzymatic activity cites or protein folding) will be useful in determining whether the structure of a protein or polypeptide has been perturbed due to the change. Ideally, X-ray crystallography studies would further establish the change to the protein containing the polymoφhism as compared to the reference or wild-type protein. Alternatively, the protein with the polymoφhism can be assessed in competition assays to determine whether it competes functionally (e.g., with binding, etc.) with the protein. Additionally, cell assays m which the gene expressing the native protein has been functionally knocked out and replaced with a gene encoding a protein compπsing the ammo acid polymoφhism can be assessed for impact on cell function using, for example, microarray technology. Such cell assays can then be used and studied, for example by microarrays or proteomics, to determine whether the presence of the polymoφhism functionally impacts the activity of the protein. Methods for performing such competition assays, cell based assays, microarray analysis and proteomic analysis would be known to the artisan of ordinary skill. These assays can similarly be utilized to determine the functional impact of a nucleic acid polymoφhism on the activity and/or functionality of mRNAs and proteins produced from the DNA for categoπzing the impact of the polymoφhism on gene or nucleic acid functionality in Step 5 Methods for performing such assays are known. For example, for competition assays see, e.g., Ed Harlow et al., ANTIBODIES: A LABORATORY MANUAL (Cold Spπng Harbor Laboratory, 1988) and USING ANTIBODIES: A LABORATORY MANUAL (Cold Spπng Harbor Laboratory, 1998); for assessing the activity of the protein or nucleic acid in a cell, the procedures as set forth in WO 99/52926 entitled "Regulated Target Expression for Screening can be used; for microarray analysis of the impact of a particular polymoφhism on the activity/functionality of a gene, the mRNA transcπbed by the gene or the protein encoded by the procedures using microarray technology can be performed generally as descπbed by Stoughton et al., in U S. Patent No. 5,965,352 entitled "Methods for identifying pathways of drug action"; and for proteomic analysis of a cell, the methods descπbed by e g , Michael J. Dunn, FROM GENOME TO PROTEOME: ADVANCES IN THE PRACTICE AND APPLICATION OF PROTEOMICS (Verlagsgesellschaft, 2000); Suhai, GENOMICS AND PROTEOMICS: FUNCTIONAL AND COMPUTATIONAL ASPECTS (Plenum Publishing, 2000); Pennington et al., PROTEOMICS: FROM PROTEIN SEQUENCE TO FUNCTION (Spπnger Verlag, 2000); Humphery-Smith et al., J. Protein Chem 16(5): 537-44 (1997); and P. Jolles et al., PROTEOMICS IN FUNCTIONAL GENOMICS. PROTEIN STRUCTURE AND ANALYSIS (Birkhauser, 2000) can be used. Proteomic techniques include: multiplex high throughput screens using two hybπd analysis with yeast, fluorescence resonance energy transfer (FRET), extrapolation of the proteins expressed by a cell or tissue based on RNA arrays, proteomic chip arrays, two dimensional gel electrophoresis for characterizing proteome profiles of up to about 10,000 proteins and high resolution mass spectometry (HRMS). 8. Determine the Reverse Amino Acid Polymoφhism Rate Once it is determined whether an amino acid polymoφhism does or does not induce a functional or structural alteration to a protein or polypeptide, the amino acid polymoφhism can be categorized into low, medium or high impact polymoφhisms. The level of impact to structure/function of a protein or polypeptide in addition to the rate at which the polymoφhism occurs in a population of subjects allows one to calculate the reverse amino acid polymoφhism rate. Typically, a low amino acid polymoφhism frequency is associated with domains in protein associated with a high impact polymoφhism. Once a domain in the protein is identified which is associated with a low amino acid polymoφhism rate (e.g., low frequency of amino acid variation), this protein or portion thereof can be used as bait for compounds which may regulate the function of that protein by binding to that domain on the protein. For both steps 5 and 8, prioritization for puφoses of drug screening of amino acid sequences or nucleic acid sequences is based on their reverse polymoφhism rate. The reverse polymoφhism rate is determined based on analysis of the incidence and impact of a particular polymoφhism. From this information, it can be inferred whether a particular amino acid sequence or nucleic acid sequence has a reverse polymoφhism rate, thus making it a suitable target for drug targeting. In addition to these steps, the amino acid sequences and nucleic acids sequences identified with a preferred reverse polymoφhism rate can then be further assessed with regard to the biological relevance of the gene and the protein it encodes, the pharmaceutical tractability of the gene or the protein it encodes (e.g., cellular location and access of a drug to the target), and even the financial, research and experimental ability of targeting said gene or protein encoded thereby. Preferably, as new genetic and protein elements (motifs, conserved domains, signals, etc.) are characterized in both proteins and genes, the information thus acquired can be added to the databases containing information on regions of reverse polymoφhism rates in nucleic acids and polypeptides, this further accumulation of data will allow for additional inferences on these newly identified motifs, etc. For instance, whether the novel polymoφhism can be inferred as a low, medium or high impact polymoφhism. Such self-momtoπng by a system can be performed using, for example, the bioinformatics techniques descπbed in International PCT Application WO 00/15847 entitled "Genomic Knowledge Discovery " As will be apparent to the artisan of ordinary skill, the methods claimed can be organized into computer programs which produce displays providing the relative frequency of polymoφhisms of a particular region of a sequence (e g , amino acid or nucleic acid sequence) These programs would then be part of a computer system to enable human users to enter additional polymoφhism data, provide new inferences from which to ascertain additional polymoφhic frequency levels for a particular region of sequence, and introduce new genomic databases of information based on novel genes (the proteins encoded thereby) for a plurality of organisms The population of individuals can be any population of organisms, smgle- celled prokaryotes or eukaryotes, multicellular eukaryotes, invertebrate or vertebrate species (e g , fish, reptiles and avians), or mammalian species (e g , canines, felines, bovines, avians, ovines, porcines, muπnes, capπnes, equmes, pπmates, rodents, agπcultural animals, etc ), including humans Further, this method can be used with a population of homologous genes from one or more different species For example, a population of genes can compnse endogenous homologues, or regions of endogenous homologues, from rodents and humans Drugs can then be designed for testing in rodents, or for veteπnary use, as well as human use This invention thus provides a novel method to rank "validated drug targets" according to their universal drugabi ty (e g , ability to enter the cell of an organism and bind to its target regardless of, for example, intraspecies polymoφhic vaπation) Thus, a decision can be made very early in the drug discovery and development process, rather than in the later stages, such as pre-clinical and clinical testing. By evaluating the effect of a drug on the target sequence, this novel process will result in time savings, πsk reduction and optimization of drug development efforts Thus, instead of just searching for a few polymoφhisms m genes which affect a particular function, the methods of this invention examine and quantify the overall effect of polymoφhisms on drug action and efficacy, which are important for therapeutic use. The novel concept governing this process is to determine polymoφhisms, i.e., the reverse polymoφhism rate, ahead of the drug discovery process. Previously, the search for polymoφhisms was triggered when a drug showed unexpectedly reduced efficacy in sub-populations during clinical development. The novelty of this invention is that it offers for the first time, a tool for ranking targets due to their importance and expected efficacy on a global market, i.e., for all major ethnic groups. This ranking can be done at an early stage, prior to clinical testing, to reduce the expense and the adverse effects encountered during such trials. Figure 3 depicts the importance of adding a novel validation method to the Drug Validation process. By determining the "reverse polymoφhism rate" of the drug target's gene and corresponding protein sequence, the "best validated target" can be picked which will show the least genetic variation in functional domains important for drug action and will show consistent efficacy among different ethnic groups (i.e., on the global market). Genes and gene products identified according to the methods of this invention can then be used in the design of therapeutic and diagnostic agents. For example, nucleic acid sequences and amino acid sequences identified in accordance with these methods as important targets for modulation of metabolic activity thereby can serve as targets for the screening and development of natural or synthetic chemical compounds, which have utility as therapeutic drugs for the treatment of disorders resulting from variations of such metabolic activity. As an example, a compound capable of binding to the encoded polypeptide of a designated nucleic acid or binding to the domains of the polypeptide sequence and inhibiting its biological activity can be useful as a drug component preventing a disorder resulting from the original altered metabolic activity. Alternatively, compounds which increase the expression of the polypeptide encoded by the nucleic acid sequence or which increase the activity of the amino acid sequence can be useful therapeutically. Conventional assays and techniques can be used for screening and development of such therapeutics. For example, a method for identifying compounds which specifically bind to or inhibit selected polypeptides encoded by the novel nucleic acid sequences of this invention can simply include the steps of contacting a selected protein or gene product with a test compound to permit binding of the test compound putative to the target; and determining the amount of test compound, if any, which is bound to the target (e.g., protein). Alternatively, competition assays can be performed to determine the affinity of a particular compound for the target sequence on a protein. For instance, substrate based assays can be performed, wherein sequences identified by the methods of the instant invention are bound to a substrate (e.g., membrane, bead, gel, column, and the like). Compounds from chemical libraries, small molecule libraries, etc. can then be screened in a high throughput manner to identify which compounds bind to the sequence which has been rated as having a low incidence of polymoφhisms. Molecules which are then identified as having bound the putative sequence with a low polymoφhism incident rate can then be screened in the microorganism or organism in which it is expressed to determine whether said molecule perturbs or modulates the activity of the molecule to which it binds and thereby the cell. For example, does the compound up-regulate or down-regulate the activity of the molecule. In the case of a protein, does the binding of the compound prevent the protein from functioning in its wild-type capacity. Alternatively, does the binding of the protein increase or enhance protein function. This invention is of great value to drug developers, such as pharmaceutical companies, because it will not only enable companies to target drugs more precisely, but will lead to faster customization of targets for drug design. Because the customization is accomplished early in the drug development stage, it will reduce the expense and time necessary to develop new medications and diagnostic compounds. Further, it can result in reduced number of patients needed for clinical trials since the drug will be already designed for large diverse populations of individuals. This will also lead to reduced toxicity for small groups of individuals since variations will be accounted for in the early stages of drug design rather than testing for the effects of variations in the individuals themselves. Thus there will be fewer adverse reactions in clinical trials. While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. All references cited above are herein incoφorated by reference in their entirety. U.S. Provisional Application 60/146,003, filed July 28, 1999, is also incoφorated herein by reference in its entirety and to which this application claims benefit of priority.

Claims: What is claimed is: 1. A method of determining the nucleic acid sequence of all or a portion of an endogenous gene of a population of individuals, said nucleic acid sequence expressing the lowest single nucleotide variation among said population, comprising: (A) preparing a collection of genomic nucleic acids comprising all or a portion of the endogenous genes of a population of individuals; (B) determining the sequence polymoφhisms for all or a portion of the genomic nucleic acids; and (C) determining the nucleic acid sequence of all or a portion of the genomic nucleic acids which comprises the lowest number of polymoφhisms. 2. The method of Claim 1 further comprising the steps of: (A) determining the amino acid sequence encoded by the nucleic acid sequence of (C) above; and (B) determining the reverse amino acid polymoφhism rate for various regions of the amino acid sequence. 3. The method of Claim 2, wherein one or both steps is automated. 4. The method of Claim 1 or Claim 2, wherein one or more of the steps of the method is automated. 5. The method of Claim 1 or Claim 2, wherein the population is selected from multicellular eukaryotes. 6. The method of Claim 5, wherein the population is a multicellular eukaryote and comprises a vertebrate population. 7. The method of claim 6, wherein the vertebrate population comprises and is an avian, a reptilian, a mammalian, a fish or an amphibian population. 8. The method of claim 7, wherein the mammalian population is selected from the group consisting of one or more bovine, caprine, ovine, feline, equine, porcine, canine, murine, rodent and primate organisms. 9. The method of Claim 8, wherein the mammalian population is a primate organism comprising one or more humans. 10. The method of Claim 1 or Claim 2, wherein the polymoφhism which is determined is one or more specific polymoφhisms. 1 1. The method of Claim 1 , wherein the portion of the genomic nucleic acids comprises one or more functional regions. 12. The method of Claim 11, wherein the functional region is selected from the group consisting of a 5' untranslated region, regulatory response element, transcriptional factor binding site, translational factor binding site, DATA box, translational start site, ribosomal binding site, exon, intron, exon-intron boundary, splice site, splice-branch site, 3' un-translated region, polyadenylation site, translational stop site, regulatory factor response element, enhancer, promoter, silencer, and attenuation site. 13. The method of Claim 2, wherein the amino acid sequence comprises one or more functional or structural regions. 14. The method of Claim 13, wherein the functional or structural region is selected from the group consisting of alpha helices, beta sheet and helix-turn-helix motifs, di-nucleotide binding folds, cysteine-rich domain, glycine-rich domain, intracellular signaling motif, extracellular signaling motif, dimerization domain, proteolytic cleavage site, enzymatic active site, trans-membrane domains, leucine zipper motives, amphipathic helices, zinc fingers, and cystine (S-S) bridges. 15. A method of determining the nucleic acid sequence of an endogenous gene of a population of individuals, said nucleic acid sequence expressing the lowest single nucleotide variation among said population, comprising: (A) preparing a collection of genomic nucleic acids comprising the endogenous genes of a population of subjects; (B) analyzing all or a portion of the genomic nucleic acids for sequence information; (C) determining the sequence polymoφhisms for all or a portion of the genomic nucleic acids; (D) grouping the polymoφhisms into functional regions of the genomic nucleic acids; and (E) determining the nucleic acid sequence of all or a portion of the genomic nucleic acids which comprises the lowest frequency of variation. 16. The method of Claim 13 further comprising the steps of: (A) determining the amino acid sequence encoded by the nucleic acid sequence of (E); and (B) determining the reverse amino acid polymoφhism rate for various regions of the amino acid sequence. 17. The method of Claim 15 or Claim 16, wherein the population is selected from multicellular eukaryotes. 18. A method of screening compounds to identify drugs that target a region of an amino acid sequence with a reverse polymoφhism rate comprising the steps of: (A) preparing a collection of genomic nucleic acids comprising the endogenous genes of a population of subjects; (B) analyzing all or a portion of the genomic nucleic acids for sequence information; (C) determining the sequence polymoφhisms for all or a portion of the genomic nucleic acids; (D) grouping the polymoφhisms into functional regions of the genomic nucleic acids; (E) determining the nucleic acid sequence of all or a portion of the genomic nucleic acids which comprises the lowest number of polymoφhisms; (F) determining the amino acid sequence encoded by the nucleic acid sequence of (E); (G) determining the region(s) in the amino acid sequence possessing a low frequency of polymoφhisms for various regions of the amino acid sequence; and (H) identifying a compound which binds to a domain in said amino acid sequence which possesses said low polymoφhism frequency. 19. The method of Claim 18, wherein steps (B) to (H) are automated. 20. The method of claim 18 further comprising the step of determining whether said compound modulates the activity of a protein comprising the amino acid sequence to which the compound binds. 21. A compound identified by the method of Claim 18. 22. The method of Claim 18, wherein at least one or more steps is automated. 23. A method of ranking domains of a protein as potential drug targets comprising identifying a reverse polymoφhism rate of an amino acid sequence or protein comprising said amino acid sequence by the method of Claim 2. 24. A computer system comprising: (A) a database of genomic libraries for a plurality of organisms, said libraries having multiple genomic and/or amino acid sequences, wherein said sequences are arranged to comprise at least one reference sequence and a plurality of related sequences comprising a polymoφhism(s); and (B) a user interface capable of receiving data for a novel polymoφhism for comparison and display, wherein the display ranks the nucleic acid sequence or amino acid sequence comprising said polymoφhism by impact to ascertain a reverse polymoφhism rate. 25. The computer system of Claim 24, wherein the organism is a prokaryote or a eukaryote. 26. The computer system of Claim 24, wherein the organism is a mammal. 27. A computer program product comprising a computer-usable medium having computer-readable program code embodied thereon relating to a database including a plurality of genomic libraries for one or more types of organisms, said libraries having multiple genomic and/or amino acid sequences, wherein said sequences are arranged to comprise at least one reference sequence and a plurality of related sequences comprising a polymoφhism(s), the computer program product comprising computer-readable program code for effecting the following steps within a computing system: (A) providing an interface for receiving novel polymoφhisms for comparison with a reference sequence; (B) determining the reverse polymoφhism rate for said novel polymoφhisms; and (C) displaying the results of said determination. 28. The computer program product of claim 27, wherein the reverse polymoφhism rate is determined for an amino acid sequence or a nucleic acid sequence.

Inventors: KELLY CHRISTOPHER T, LEIGHTON JEFF, SMITH DOUGLAS, THOMANN HANS-ULRICH, KELLY, CHRISTOPHER, T., LEIGHTON, JEFF, SMITH, DOUGLAS, THOMANN, HANS-ULRICH
Applicants: GENOME THERAPEUTICS CORP, KELLY CHRISTOPHER T, LEIGHTON JEFF, SMITH DOUGLAS, THOMANN HANS ULRICH, GENOME THERAPEUTICS CORPORATION, KELLY, CHRISTOPHER, T., LEIGHTON, JEFF, SMITH, DOUGLAS, THOMANN, HANS-ULRICH
Application year: 2000
Main classification: C12P19
IPC codes: C12Q1, G06F19