individual drug response
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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 ◽  
...  

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.


2020 ◽  
Vol 111 (10) ◽  
pp. 3780-3792
Author(s):  
Esther Hee ◽  
Meng Kang Wong ◽  
Sheng Hui Tan ◽  
Zhang’E Choo ◽  
Chik Hong Kuick ◽  
...  

2020 ◽  
Vol 20 ◽  
pp. 128-139
Author(s):  
Hao Cui ◽  
Hanqing Kong ◽  
Fuhui Peng ◽  
Chunjing Wang ◽  
Dandan Zhang ◽  
...  

Biomedicines ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 34 ◽  
Author(s):  
Candace R. Lewis ◽  
Katrin H. Preller ◽  
B. Blair Braden ◽  
Cory Riecken ◽  
Franz X. Vollenweider

Psilocybin is the psychoactive compound of mushrooms in the psilocybe species. Psilocybin directly affects a number of serotonin receptors, with highest affinity for the serotonin 2A receptor (5HT-2Ar). Generally, the effects of psilocybin, and its active metabolite psilocin, are well established and include a range of cognitive, emotional, and perceptual perturbations. Despite the generality of these effects, there is a high degree of inter-individual variability in subjective psilocybin experiences that are not well understood. Others have shown brain morphology metrics derived from magnetic resonance imaging (MRI) can predict individual drug response. Due to high expression of serotonin 2A receptors (5HT-2Ar) in the cingulate cortex, and its prior associations with psilocybin, we investigate if cortical thickness of this structure predicts the psilocybin experience in healthy adults. We hypothesized that greater cingulate thickness would predict higher subjective ratings in sub-scales of the Five-Dimensional Altered State of Consciousness (5D-ASC) with high emotionality in healthy participants (n = 55) who received oral psilocybin (either low dose: 0.160 mg/kg or high dose: 0.215 mg/kg). After controlling for sex, age, and using false discovery rate (FDR) correction, we found the rostral anterior cingulate predicted all four emotional sub-scales, whereas the caudal and posterior cingulate did not. How classic psychedelic compounds induce such large inter-individual variability in subjective states has been a long-standing question in serotonergic research. These results extend the traditional set and setting hypothesis of the psychedelic experience to include brain structure metrics.


2020 ◽  
Vol 14 ◽  
pp. 117822342098037
Author(s):  
Mariko Asaoka ◽  
Shipra Gandhi ◽  
Takashi Ishikawa ◽  
Kazuaki Takabe

Neoadjuvant chemotherapy (NAC) had been developed as a systematic approach before definitive surgery for the treatment of locally advanced or inoperable breast cancer such as inflammatory breast cancer in the past. In addition to its impact on surgery, the neoadjuvant setting has a benefit of providing the opportunity to monitor the individual drug response. Currently, the subject of NAC has expanded to include patients with early-stage, operable breast cancer because it is revealed that the achievement of a pathologic complete response (pCR) is associated with excellent long-term outcomes, especially in patients with aggressive phenotype breast cancer. In addition, this approach provides the unique opportunity to escalate adjuvant therapy in those with residual disease after NAC. Neoadjuvant chemotherapy in breast cancer is a rapidly evolving topic with tremendous interest in ongoing clinical trials. Here, we review the improvements and further challenges in the NAC setting in translational breast cancer research.


Author(s):  
Bede P. Busby ◽  
Eliatan Niktab ◽  
Christina A. Roberts ◽  
Jeffrey P. Sheridan ◽  
Namal V. Coorey ◽  
...  

Abstract Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug–gene and gene–gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.


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