A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups.
Phenotype Prediction Scores (PPS) might be powerful tools to predict traits or the efficacy of treatments based on combinations of Single-Nucleotide Polymorphism (SNPs) in large samples. We developed a novel method to produce PPS models for small samples sizes. The set of SNPs is first filtered on t...
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doaj-5d4b124e26a24aaa9aceb8875bc0971e2021-03-18T05:31:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024682810.1371/journal.pone.0246828A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups.Daniël HöhleKim van RooijJos BloemersJames G PfausFrits MichielsPaddy JanssenEric ClaassenAdriaan TuitenPhenotype Prediction Scores (PPS) might be powerful tools to predict traits or the efficacy of treatments based on combinations of Single-Nucleotide Polymorphism (SNPs) in large samples. We developed a novel method to produce PPS models for small samples sizes. The set of SNPs is first filtered on those known to be relevant in biological pathways involved in a clinical condition, and then further filtered repeatedly in a survival strategy to select stabile positive/negative risk alleles. This method is applied on Female Sexual Interest/Arousal Disorder (FSIAD), for which two subtypes has been proposed: 1) a relatively insensitive excitatory system in the brain for sexual cues, and 2) a dysfunctional activation of brain mechanisms for sexual inhibition. A double-blind, randomized, placebo-controlled cross-over experiment was conducted on 129 women with FSIAD. The women received three different on-demand drug-combination treatments during 3 two-week periods: testosterone (0.5 mg) + sildenafil (50 mg), testosterone (0.5 mg) + buspirone (10 mg), or matching placebos. The resulted PPS were independently validated on patient-level and group-level. The AUC scores for T+S of the derivation set was 0.867 (95% CI = 0.796-0.939; p<0.001) and was 0.890 (95% CI = 0.778-1.000; p<0.001) on the validation set. For T+B the AUC of the derivation set was 0.957 (95% CI = 0.921-0.992; p<0.001) and 0.869 (95% CI = 0.746-0.992; p<0.001) for the validation set. Both formulas could reliably predict for each drug who benefit from the on-demand drugs and could therefore be useful in clinical practice.https://doi.org/10.1371/journal.pone.0246828 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniël Höhle Kim van Rooij Jos Bloemers James G Pfaus Frits Michiels Paddy Janssen Eric Claassen Adriaan Tuiten |
spellingShingle |
Daniël Höhle Kim van Rooij Jos Bloemers James G Pfaus Frits Michiels Paddy Janssen Eric Claassen Adriaan Tuiten A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. PLoS ONE |
author_facet |
Daniël Höhle Kim van Rooij Jos Bloemers James G Pfaus Frits Michiels Paddy Janssen Eric Claassen Adriaan Tuiten |
author_sort |
Daniël Höhle |
title |
A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
title_short |
A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
title_full |
A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
title_fullStr |
A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
title_full_unstemmed |
A survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
title_sort |
survival of the fittest strategy for the selection of genotypes by which drug responders and non-responders can be predicted in small groups. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2021-01-01 |
description |
Phenotype Prediction Scores (PPS) might be powerful tools to predict traits or the efficacy of treatments based on combinations of Single-Nucleotide Polymorphism (SNPs) in large samples. We developed a novel method to produce PPS models for small samples sizes. The set of SNPs is first filtered on those known to be relevant in biological pathways involved in a clinical condition, and then further filtered repeatedly in a survival strategy to select stabile positive/negative risk alleles. This method is applied on Female Sexual Interest/Arousal Disorder (FSIAD), for which two subtypes has been proposed: 1) a relatively insensitive excitatory system in the brain for sexual cues, and 2) a dysfunctional activation of brain mechanisms for sexual inhibition. A double-blind, randomized, placebo-controlled cross-over experiment was conducted on 129 women with FSIAD. The women received three different on-demand drug-combination treatments during 3 two-week periods: testosterone (0.5 mg) + sildenafil (50 mg), testosterone (0.5 mg) + buspirone (10 mg), or matching placebos. The resulted PPS were independently validated on patient-level and group-level. The AUC scores for T+S of the derivation set was 0.867 (95% CI = 0.796-0.939; p<0.001) and was 0.890 (95% CI = 0.778-1.000; p<0.001) on the validation set. For T+B the AUC of the derivation set was 0.957 (95% CI = 0.921-0.992; p<0.001) and 0.869 (95% CI = 0.746-0.992; p<0.001) for the validation set. Both formulas could reliably predict for each drug who benefit from the on-demand drugs and could therefore be useful in clinical practice. |
url |
https://doi.org/10.1371/journal.pone.0246828 |
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