Bioinformatics approaches in personalised pharmacotherapy
Date
2019-06-27
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Abstract
Kogutavate terviseandmete hulk kasvab kiiresti. Tänu neile andmetele on meditsiinilise ravi pakkumisel võimalik senisest enam arvesse võtta individuaalseid bioloogilisi andmeid. See doktoritöö käsitleb mitmeid personaalses meditsiinis esinevaid probleeme ja näitab, et ravi individualiseerimiseks kasutatavad andmed tulevad väga erinevatest allikatest.
Inimestevahelised erinevused teevad ravimite metabolismi ennustamise keerukaks, siiski on ravi käigus kogutavad kontsentratsioonimõõtmised ravimiefekti hindamisel heaks allikaks. Me arendasime välja täppisdoseerimise tööriista, mis võimaldab vankomütsiini ravil vastsündinutele määrata ravi tõhustavat personaalseid doose kasutades selleks nende endi ravi käigus kogutud kontsentratsioone.
Suurema osa ravimiteraapiate puhul ei ole võimalik pidevalt ravimi kontsentratsioone koguda. Nende ülejäänud ravimite puhul on heaks informatsiooniallikaks geneetika. Paljude ravimimetabolismiga seotud geneetiliste variantide mõju on piisav, et tingida muutuseid ravi läbiviimisel. Me uurisime geneetika ja ravimite kõrvalmõjude omavahelisi seoseid kasutades rahvastikupõhist lähenemist. See toetus Eesti Geenivaramu geeniandmetele ja teistele laiapõhjalistele terviseandmete registritele. Me leidsime ja valideerisime seose, et CTNNA3 geenis olev geenivariant tõstab oksikaamide ravil olevate inimeste jaoks kõrvalmõjude sagedust.
Arvutuslik geneetika toetub kvantitatiivsetele meetoditele, millest kõige levinum on ülegenoomne assotsiatsiooni analüüs (GWAS). Sagedasti kasutatav GWASi järelsamm on aega nõudev GWASist ilmnenud p-väärtuste visuaalne hindamine teiste samas genoomi piirkonnas olevate geneetiliste variantide kontekstis. Selle sammu automatiseerimiseks arendasime me kaks tööriista, Manhattan Harvester ja Cropper, mis võimaldavad automaatselt huvipakkuvaid piirkondi tuvastada ja nende headust hinnata.
The amount of collected health data is growing fast. Insights from these data allow using biological patient specifics to improve therapy management with further individualization. This thesis addresses problems in multiple sub-fields of personalised medicine and aims to illustrate that data for precision medicine emerges from different sources. Drug metabolism is difficult to predict because individual biological differences. Fortunately, drug concentrations are a good proxy for drug effect. To address the growing need for tools that allow on-line therapy adjustment based on individual concentrations we have developed and externally evaluated a precision dosing tool that allows individualised dosing of vancomycin in neonates. Other than drugs used in therapeutic drug monitoring, most pharmacotherapies can not rely on continuous concentration measurements but for such drugs genetics provides a valuable source of information for individualization. Effects of many genetic variants in drug metabolism pathways are often large enough to require changes in drug prescriptions or schedules. We have applied a population-based approach in testing relations between drug related adverse effects and genomic loci, and found and validated a novel variant in CTNNA3 gene that increases adverse drug effects in patients with oxicam prescriptions. This was done by leveraging the data in Estonian Genome Center and linking these to nation-wide electronic health data registries. Computational genetics relies on quantitative methods for which the most common is the genome-wide association analysis (GWAS). A common GWAS downstream step involves time-consuming visual assessment of the association study p-values in context with other variants in genomic vicinity. In order to streamline this step, we developed, Manhattan Harvester and Cropper, that allow for automated detection of peak areas and assign scores by emulating human evaluators.
The amount of collected health data is growing fast. Insights from these data allow using biological patient specifics to improve therapy management with further individualization. This thesis addresses problems in multiple sub-fields of personalised medicine and aims to illustrate that data for precision medicine emerges from different sources. Drug metabolism is difficult to predict because individual biological differences. Fortunately, drug concentrations are a good proxy for drug effect. To address the growing need for tools that allow on-line therapy adjustment based on individual concentrations we have developed and externally evaluated a precision dosing tool that allows individualised dosing of vancomycin in neonates. Other than drugs used in therapeutic drug monitoring, most pharmacotherapies can not rely on continuous concentration measurements but for such drugs genetics provides a valuable source of information for individualization. Effects of many genetic variants in drug metabolism pathways are often large enough to require changes in drug prescriptions or schedules. We have applied a population-based approach in testing relations between drug related adverse effects and genomic loci, and found and validated a novel variant in CTNNA3 gene that increases adverse drug effects in patients with oxicam prescriptions. This was done by leveraging the data in Estonian Genome Center and linking these to nation-wide electronic health data registries. Computational genetics relies on quantitative methods for which the most common is the genome-wide association analysis (GWAS). A common GWAS downstream step involves time-consuming visual assessment of the association study p-values in context with other variants in genomic vicinity. In order to streamline this step, we developed, Manhattan Harvester and Cropper, that allow for automated detection of peak areas and assign scores by emulating human evaluators.
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Keywords
bioinformaatika, geneetilised assotsiatsiooniuuringud, farmakoteraapia, farmakogeneetika, personaalmeditsiin