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Draft:Pharmacogenomics annotation tools

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Pharmacogenomics annotation tools are computerized tools which parse inputted genomic data to output previously issued clinical prescribing recommendations tailored to the inputted genotypes. Examples of pharmacogenomics annotation tools are PharmCAT[1], PAnno[2], and PharmVIP[3]. For those three tools, genomic data is inputted as a Variant Call Format (VCF) file, and the output is the corresponding prescribing recommendations.

Background:

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Pharmacogenomics

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Pharmacogenomics is a field of study combining pharmacology with genomics. It seeks to tailor drug prescribing to individual patients using, in part, genotyping data from those patients.[4] Individuals may possess genetic variants (differences in their DNA) that may alter the pharmacokinetics (PK; the way that drugs are absorbed, distributed to tissues, metabolized, and excreted) or pharmacodynamics (PD; how the drug interacts with it’s receptor) of specific drugs. Alterations in pharmacokinetics or pharmacodynamics can impact the effectiveness of pharmacological interventions,[5] even in non-pharmacogenomic contexts.[6] Pharmacogenomics research has benefitted from the adoption of Next Generation Sequencing (NGS) technologies, which enable higher-throughput methods of identifying pharmacologically relevant variants.[7]

Through research into these variants, both clinicians and researchers have found that modifying the doses of drugs can be effective for treating patients carrying variants affecting their PK and PD.[5] Prescribing recommendations already exist for certain variants that have been identified through prior research into genetic predictors of drug effectiveness and side effects. [8]

Star alleles

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Clinically actionable haplotypes are referred to in the literature as star (*) alleles.[9] Haplotypes are groups of variants that are inherited together, due to being physically close on the same chromosome, reducing the chance of crossover during meiosis. Star alleles are of particular interest in pharmacogenomics due to their clinical utility. Pharmacogene diplotypes are generated from the maternal and paternal star alleles, and represent the combination of parental haplotypes.[10]

Variant call format

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Genomic data is inputted as a VCF File.[1][2] VCF Files are one type of file format used in bioinformatics. They can be generated from genomic data through separate bioinformatics software.

Annotation

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Pharmacogenomics annotation also relies on genes and variants being annotated. Annotation in the genetics context means matching DNA sequences to a corresponding gene, protein, or variant. Pharmacogenomics annotation tools do exactly that, but with an extra step - they match a specific sequence (or inferred haplotype) with a phenotype, which in turn is matched to a set of corresponding dosing or prescribing recommendations.[2][11][12]

Implementation of pharmacogenomics in clinical settings

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Initiatives have sought to formulate plans, guidelines, and recommendations for implementing pharmacogenomic testing and personalized drug prescribing into clinical settings.[8][13] Pharmacogenomics faces a number of obstacles to successful implementation into clinical practice - notably, cost to sequence, lack of knowledge/education on the subject, and lack of approachability.[5] These tools seek to address, in part, this lack of approachability through software platforms that take in genomic data and output relevant and actionable clinical recommendations.

General workflow used for pharmacogenomics annotation tools.

Use

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The general workflow for pharmacogenomics annotation tools consists of two steps:[14]

  1. Processing and allele determination
  2. Matching pharmacogenomic phenotypes to diplotypes

The first phase consists of a preprocessing step and an allele determination step. This preprocessing step removes extraneous information and downloads the corresponding human reference genome sequence. It then formats the file to the standardized format.[15] The allele determination step matches inputted genotypes to named alleles. If the inputted data is phased, the step can match diplotypes without extra computation steps. If the data is unphased, the software will go through additional steps to attempt to correctly match alleles. [16][2]

The second phase also has two steps: matching phenotypes and report generation. The phenotype matching step matches the diplotypes generated in the first phase to known pharmacogenomic phenotypes (like metabolizer status, discussed below). The report generation step compiles all of the information generated in that previous step into a comprehensive report.[14] Different tools will differ in terms of how that report is generated and presented, as shown in the table below.

Limitations

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Like any bioinformatics tools or methods, the quality of the output from these tools depends largely on the quality and type of the inputted data. Low quality data can result from repetitive sequences, as NGS technologies still struggle with identifying repetetive sequences. This in turn leads to low accuracy in genes involved in those regions, such as UGT1A1.[17] This would result in lower accuracy of the output report.[2][11]

As with the input data, output quality depends on genes and variants being annotated correctly and comprehensively, which in turn depends on research previously conducted examining those variants and their effects on response to drugs.[2][11] Generalizability to different populations also depends on the amount and quality of pharmacogenomics research conducted on those populations. Historical underrepresentation has resulted and continues to result in a lack of data in genomics.[18] As a result of this lack of data, historically underrepresented populations are the least likely to see benefit from personalized treaments.[19]

In addition, while tools may be able to parse non-SNP variants, VCF files generally do not incorporate copy number or structural variants.[2] As such, that information will not be translated into drug response phenotypes or prescribing recommendations. Structural variation has been estimated to account for approximately 22% of pharmacogenomic variability.[20] Excluding structural variation (and the accompanying 22% of pharmacogenomic variability) would therefore result in potentially inaccurate recommendations.

Comparison

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PharmCAT, PAnno, and PharmVIP are pharmacogenomics annotation tools designed to analyze sequenced or genotyped genomic data (in VCF or BAM format) to predict individual drug responses, based on genetic profiles. The key difference between these tools lies in their respective use cases:

  • PharmCAT is primarily used for detecting clinically relevant pharmacogenomic (PGx) variants based on CPIC recommendations,[8] among other sources. It relies on pre-annotated clinically significant variants and doesn't have the ability to analyze de novo variants.[1][11]
  • PAnno functions as a general annotation tool for research purposes. It can predict phenotypes of certain drugs based on toxicity, dosage, efficacy and metabolism.[2] It also relies on sources such as CPIC.
  • PharmVIP provides diplotype classification and drug-response recommendations similar to the above tools. It is unique in its HLA gene prediction module, which aims to predict alleles in HLA genes, outputting relevant information on adverse drug reactions.[21][12] It also has a separate module that aims to predict the effects of novel variants in known PGx genes.[12]

Functional & methodological comparison between different tools

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PAnno

(Pharmacogenomics Annotation Tool)

PharmVIP

(Pharmacogenomic Variant Interpretation & Prediction)

PharmCAT

(Pharmacogenomics Clinical Annotation Tool)

Main Function General PGx variant annotation Prediction & interpretation of PGx variants Clinical drug dosing recommendations from inputted variants
Databases of Recommendations PharmGKB[22], CPIC[8], PharmVar[23], dbSNP PharmGKB, CPIC, PharmVar, ClinVar[24] PharmGKB, CPIC, DPWG[13]
Star Allele Analyses Cytochrome P450 (CYP450) genes CYP450 genes, DPYD, TPMT CYP450 genes, DME, drug transporters, drug targets/receptors
Drug-Gene Interaction Analysis Yes Yes Yes
Clinical Decision Support Dosing recommendations from databases Dosing recommendations from databases, and novel variant prediction Dosing recommendations from databases
Input file type VCF VCF (BAM for HLA module) VCF
Functional Prediction of Novel Variants No Predicts HLA alleles, predicts effects of novel variants[3] No
Type of report HTML annotation report Annotation report and/or predictive report HTML annotation report
Focus Area Research & general PGx annotation Predicting variant impact & PGx annotation Clinical PGx annotation, drug dosing
Use-case Researchers analyzing PGx variants Clinicians & researchers predicting drug response (particularly regarding HLA) Clinicians & researchers analyzing PGx variants

Applications

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Pharmacogenomics Annotation tools such as PharmCAT, PAnno, and PharmVIP are used to analyze and annotate pharmacogenes by helping to predict drug metabolism, efficacy and potential adverse effects, in research and clinical applications. The annotation tools are used in:

  1. Variant identification by detecting changes in genome sequence such as SNPs and indels.[11]
  2. Converting raw genomic data into functional phenotype by stratifying the genotype-to-phenotype characteristics into low, medium, or high function, or for metabolism-related genes, into poor, intermediate or rapid metabolizers.[2]
  3. Analyzing the interaction between drugs and genes.
  4. Assisting in clinical interpretation by recommending drug dosage, assessing impact of drug metabolism for medications such as warfarin or codeine.[22][2]

Pharmacogene annotations have several advantages. They allow physicians to select the right drug and recommend the right dosage amount to reduce adverse reactions. This can reduce toxicity, improve drug efficacy and eliminate or reduce trial and error prescriptions.[25]

Future Directions

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Multi-Omics

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Multiomics or Multi-Omics seeks to analyze the genome, proteome, transcriptome, metabolome, microbiome, and others, in a concerted effort. Applying multi-omics to medicine has the potential to enhance personalized care.[26] As a concept (and a discipline) within genomics, pharmacogenomics annotation would invariably play a role in a multi-omics approach to personalized medicine, alongside transcriptomics, proteomics, microbiomics, and other-omics annotation. As with pharmacogenomics, multi-omics presents challenges, both technological and social, to clinicians and investigators.[27]

Clinical implementation through pre-emptive testing

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Pharmacogenomics remains uncommon in clinical practice.[5][28] Implementation in the clinic has been discussed, yet faces significant hurdles in order to be widespread.[29] Pre-emptive testing for pharmacologically-relevant variants has been proposed as a means to achieve more widespread adoption of pharmacogenomics in practice.[30] Pre-emptive testing is available at specific hospital sites participating in trials evaluating its clinical utility.[31] As with any other genomic data, clinical pre-emptively collected data would require annotation in order to be clinically useful.

References

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  1. ^ a b c "Home". PharmCAT. Retrieved 2025-02-21.
  2. ^ a b c d e f g h i j Liu, Yaqing; Lin, Zipeng; Chen, Qingwang; Chen, Qiaochu; Sang, Leqing; Wang, Yunjin; Shi, Leming; Guo, Li; Yu, Ying (2023-01-26). "PAnno: A pharmacogenomics annotation tool for clinical genomic testing". Frontiers in Pharmacology. 14. doi:10.3389/fphar.2023.1008330. ISSN 1663-9812. PMC 9909284. PMID 36778023.
  3. ^ a b "PharmVIP". pharmvip.nbt.or.th. Retrieved 2025-02-21.
  4. ^ Whirl-Carrillo, M; McDonagh, E M; Hebert, J M; Gong, L; Sangkuhl, K; Thorn, C F; Altman, R B; Klein, T E (2012). "Pharmacogenomics Knowledge for Personalized Medicine". Clinical Pharmacology & Therapeutics. 92 (4): 414–417. doi:10.1038/clpt.2012.96. ISSN 1532-6535. PMC 3660037. PMID 22992668.
  5. ^ a b c d Relling, Mary V.; Evans, William E. (October 2015). "Pharmacogenomics in the clinic". Nature. 526 (7573): 343–350. Bibcode:2015Natur.526..343R. doi:10.1038/nature15817. ISSN 1476-4687. PMC 4711261. PMID 26469045.
  6. ^ van den Anker, John; Reed, Michael D.; Allegaert, Karel; Kearns, Gregory L. (2018). "Developmental Changes in Pharmacokinetics and Pharmacodynamics". The Journal of Clinical Pharmacology. 58 (S10): S10 – S25. doi:10.1002/jcph.1284. ISSN 1552-4604. PMID 30248190.
  7. ^ Rabbani, Bahareh; Nakaoka, Hirofumi; Akhondzadeh, Shahin; Tekin, Mustafa; Mahdieh, Nejat (2016-05-24). "Next generation sequencing: implications in personalized medicine and pharmacogenomics". Molecular BioSystems. 12 (6): 1818–1830. doi:10.1039/C6MB00115G. ISSN 1742-2051. PMID 27066891.
  8. ^ a b c d "CPIC". 2025-01-16. Retrieved 2025-02-21.
  9. ^ Robarge, J D; Li, L; Desta, Z; Nguyen, A; Flockhart, D A (2007). "The Star-Allele Nomenclature: Retooling for Translational Genomics". Clinical Pharmacology & Therapeutics. 82 (3): 244–248. doi:10.1038/sj.clpt.6100284. ISSN 1532-6535. PMID 17700589.
  10. ^ Twesigomwe, David; Wright, Galen E. B.; Drögemöller, Britt I.; da Rocha, Jorge; Lombard, Zané; Hazelhurst, Scott (2020-08-03). "A systematic comparison of pharmacogene star allele calling bioinformatics algorithms: a focus on CYP2D6 genotyping". npj Genomic Medicine. 5 (1): 30. doi:10.1038/s41525-020-0135-2. ISSN 2056-7944. PMC 7398905. PMID 32789024.
  11. ^ a b c d e Sangkuhl, Katrin; Whirl-Carrillo, Michelle; Whaley, Ryan M.; Woon, Mark; Lavertu, Adam; Altman, Russ B.; Carter, Lester; Verma, Anurag; Ritchie, Marylyn D.; Klein, Teri E. (2020). "Pharmacogenomics Clinical Annotation Tool (PharmCAT)". Clinical Pharmacology & Therapeutics. 107 (1): 203–210. doi:10.1002/cpt.1568. ISSN 1532-6535. PMC 6977333. PMID 31306493.
  12. ^ a b c Piriyapongsa, Jittima; Sukritha, Chanathip; Kaewprommal, Pavita; Intarat, Chalermpong; Triparn, Kwankom; Phornsiricharoenphant, Krittin; Chaosrikul, Chadapohn; Shaw, Philip J.; Chantratita, Wasun; Mahasirimongkol, Surakameth; Tongsima, Sissades (November 2021). "PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation". Journal of Personalized Medicine. 11 (11): 1230. doi:10.3390/jpm11111230. ISSN 2075-4426. PMC 8618518. PMID 34834582.
  13. ^ a b "Pharmacogenetics | KNMP". www.knmp.nl (in Dutch). Retrieved 2025-03-01.
  14. ^ a b "How It Works". PharmCAT. Retrieved 2025-02-21.
  15. ^ Tan, Adrian; Abecasis, Gonçalo R.; Kang, Hyun Min (2015-07-01). "Unified representation of genetic variants". Bioinformatics. 31 (13): 2202–2204. doi:10.1093/bioinformatics/btv112. ISSN 1367-4803. PMC 4481842. PMID 25701572.
  16. ^ "Named Allele Matcher 101". PharmCAT. Retrieved 2025-02-21.
  17. ^ Mantere, Tuomo; Kersten, Simone; Hoischen, Alexander (2019-05-07). "Long-Read Sequencing Emerging in Medical Genetics". Frontiers in Genetics. 10: 426. doi:10.3389/fgene.2019.00426. ISSN 1664-8021. PMC 6514244. PMID 31134132.
  18. ^ Corpas, Manuel; Pius, Mkpouto; Poburennaya, Marie; Guio, Heinner; Dwek, Miriam; Nagaraj, Shivashankar; Lopez-Correa, Catalina; Popejoy, Alice; Fatumo, Segun (2025-01-08). "Bridging genomics' greatest challenge: The diversity gap". Cell Genomics. 5 (1). doi:10.1016/j.xgen.2024.100724. ISSN 2666-979X. PMID 39694036.
  19. ^ Khoury, Muin J.; Bowen, Scott; Dotson, W. David; Drzymalla, Emily; Green, Ridgely F.; Goldstein, Robert; Kolor, Katherine; Liburd, Leandris C.; Sperling, Laurence S.; Bunnell, Rebecca (2022-08-01). "Health equity in the implementation of genomics and precision medicine: A public health imperative". Genetics in Medicine. 24 (8): 1630–1639. doi:10.1016/j.gim.2022.04.009. ISSN 1098-3600. PMC 9378460. PMID 35482015.
  20. ^ Tremmel, Roman; Zhou, Yitian; Schwab, Matthias; Lauschke, Volker M. (2023-09-08). "Structural variation of the coding and non-coding human pharmacogenome". npj Genomic Medicine. 8 (1): 1–11. doi:10.1038/s41525-023-00371-y. ISSN 2056-7944.
  21. ^ "PharmVIP". pharmvip.nbt.or.th. Retrieved 2025-03-03.
  22. ^ a b "PharmGKB". PharmGKB. Retrieved 2025-02-22.
  23. ^ "PharmVar". www.pharmvar.org. Retrieved 2025-03-01.
  24. ^ ClinVar. "ClinVar". www.ncbi.nlm.nih.gov. Archived from the original on 2025-02-22. Retrieved 2025-03-01.
  25. ^ Abbasi, Jennifer (2016-10-18). "Getting Pharmacogenomics Into the Clinic". JAMA. 316 (15): 1533–1535. doi:10.1001/jama.2016.12103. ISSN 0098-7484. PMID 27653422.
  26. ^ Zhan, Chaoying; Tang, Tong; Wu, Erman; Zhang, Yuxin; He, Mengqiao; Wu, Rongrong; Bi, Cheng; Wang, Jiao; Zhang, Yingbo; Shen, Bairong (2023-10-30). "From multi-omics approaches to personalized medicine in myocardial infarction". Frontiers in Cardiovascular Medicine. 10. doi:10.3389/fcvm.2023.1250340. ISSN 2297-055X. PMC 10642346. PMID 37965091.
  27. ^ Hasin, Yehudit; Seldin, Marcus; Lusis, Aldons (2017-05-05). "Multi-omics approaches to disease". Genome Biology. 18 (1): 83. doi:10.1186/s13059-017-1215-1. ISSN 1474-760X. PMC 5418815. PMID 28476144.
  28. ^ Pirmohamed, Munir (June 2023). "Pharmacogenomics: current status and future perspectives". Nature Reviews Genetics. 24 (6): 350–362. doi:10.1038/s41576-022-00572-8. ISSN 1471-0064.
  29. ^ Turner, Richard M; Newman, William G; Bramon, Elvira; McNamee, Christine J; Wong, Wai Lup; Misbah, Siraj; Hill, Sue; Caulfield, Mark; Pirmohamed, Munir (2020-11-01). "Pharmacogenomics in the Uk National Health Service: Opportunities and Challenges". Pharmacogenomics. 21 (17): 1237–1246. doi:10.2217/pgs-2020-0091. ISSN 1462-2416. PMID 33118435.
  30. ^ Hoffman, James M.; Haidar, Cyrine E.; Wilkinson, Mark R.; Crews, Kristine R.; Baker, Donald K.; Kornegay, Nancy M.; Yang, Wenjian; Pui, Ching-Hon; Reiss, Ulrike M.; Gaur, Aditya H.; Howard, Scott C.; Evans, William E.; Broeckel, Ulrich; Relling, Mary V. (2014). "PG4KDS: A model for the clinical implementation of pre-emptive pharmacogenetics". American Journal of Medical Genetics Part C: Seminars in Medical Genetics. 166 (1): 45–55. doi:10.1002/ajmg.c.31391. ISSN 1552-4876.
  31. ^ "PG4KDS: Clinical Implementation of Pharmacogenetics". St. Jude Care & Treatment. Retrieved 2025-03-07.
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