scholarly journals Understanding individual drug response variation: Pharmacokinetic analysis of diabetes trials

2020 ◽  
Author(s):  
◽  
Marjolein Kroonen
Keyword(s):  
Drug Response ◽  
Pharmacokinetic Analysis ◽  
Individual Drug ◽  
Individual Drug Response ◽  
Response Variation

scholarly journals Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer

2020 ◽  
Vol 20 ◽  
pp. 128-139
Author(s):  
Hao Cui ◽  
Hanqing Kong ◽  
Fuhui Peng ◽  
Chunjing Wang ◽  
Dandan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Response ◽  
Omics Data ◽  
Individual Drug ◽  
Non Coding Rnas ◽  
Individual Drug Response

scholarly journals Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

2014 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Silvia der Heyde ◽  
Christian Bender ◽  
Frauke Henjes ◽  
Johanna Sonntag ◽  
Ulrike Korf ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Individual Drug ◽  
Individual Drug Response

scholarly journals Neuroblastoma patient‐derived cultures are enriched for a mesenchymal gene signature and reflect individual drug response

2020 ◽  
Vol 111 (10) ◽  
pp. 3780-3792
Author(s):  
Esther Hee ◽  
Meng Kang Wong ◽  
Sheng Hui Tan ◽  
Zhang’E Choo ◽  
Chik Hong Kuick ◽  
...  
Keyword(s):  
Drug Response ◽  
Gene Signature ◽  
Individual Drug ◽  
Individual Drug Response ◽  
Neuroblastoma Patient

scholarly journals Mechanistic interpretation of non-coding variants helps discover transcriptional regulators of drug response

2018 ◽  
Author(s):  
Xiaoman Xie ◽  
Casey Hanson ◽  
Saurabh Sinha
Keyword(s):  
Transcription Factor ◽  
Dna Binding ◽  
Drug Response ◽  
Chromatin Interaction ◽  
Regulatory Relationship ◽  
Binding Strength ◽  
Mechanistic Approach ◽  
Response Variation ◽  
Coding Variants ◽  
Reference Samples

ABSTRACTIdentification of functional non-coding variants (polymorphisms) and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples can help us functionally annotate the genome, but do not provide ready answers regarding the effects of non-coding variants. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant’s impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover important transcription factors regulating phenotypic variation among individuals. We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq and Hi-C chromatin interaction data to answer this question. Using drug sensitivity measured in lymphoblastoid cell lines as the phenotype of interest, we tested if the non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA-binding strength of a TF, and used this test to identify TFs regulating individual differences in the phenotype. Our method relies on a new method for predicting variant impact on TF-DNA binding, that uses a combination of biophysical modelling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth the evidence underlying one reported association – that of the transcription factor ELF1 with the drug doxorubicin – and identified several genes that may mediate this regulatory relationship.

Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data

2021 ◽  
Vol 13 (603) ◽  
pp. eabf3637
Author(s):  
Maaike van der Lee ◽  
William G. Allard ◽  
Rolf H. A. M. Vossen ◽  
Renée F. Baak-Pablo ◽  
Roberta Menafra ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
Drug Response ◽  
Sequencing Data ◽  
Human Embryonic Kidney Cells ◽  
Continuous Scale ◽  
Long Read ◽  
Tamoxifen Metabolism ◽  
Individual Drug Response ◽  
A Prospective Study

Pharmacogenomics is a key component of personalized medicine that promises safer and more effective drug treatment by individualizing drug choice and dose based on genetic profiles. In clinical practice, genetic biomarkers are used to categorize patients into *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. However, this approach leaves a large part of variability in drug response unexplained. Here, we present a proof-of-concept approach that uses continuous-scale (instead of categorical) assignments to predict enzyme activity. We used full CYP2D6 gene sequences obtained with long-read amplicon-based sequencing and cytochrome P450 (CYP) 2D6–mediated tamoxifen metabolism data from a prospective study of 561 patients with breast cancer to train a neural network. The model explained 79% of interindividual variability in CYP2D6 activity compared to 54% with the conventional *-allele approach, assigned enzyme activities to known alleles with previously reported effects, and predicted the activity of previously uncharacterized combinations of variants. The results were replicated in an independent cohort of tamoxifen-treated patients (model R2 adjusted = 0.66 versus *-allele R2 adjusted = 0.35) and a cohort of patients treated with the CYP2D6 substrate venlafaxine (model R2 adjusted = 0.64 versus *-allele R2 adjusted = 0.55). Human embryonic kidney cells were used to confirm the effect of five genetic variants on metabolism of the CYP2D6 substrate bufuralol in vitro. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP2D6 enzyme activity and could potentially enable more accurate prediction of individual drug response.

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