Recipient ABCB1, donor and recipient CYP3A5 genotypes influence tacrolimus pharmacokinetics in liver transplant cases.

BACKGROUND: Effective immunosuppression through optimization of trough levels tacrolimus reduces post-transplant mortality rate in liver transplant cases. METHODS: Meta-analysis was carried out to evaluate how donor/recipient CYP3A5 (n = 678) and recipient ABCB1 (n = 318) genotypes influence tacrolimus pharmacokinetics till one-month of transplantation. RESULTS: The donor CYP3A5*3/*3 genotype exhibited higher concentration/dose (C/D) ratio of tacrolimus in week 1 (mean difference: 65.04, 95% CI: 15.30-114.79 ng/ml/mg/kg), week 2 (mean difference: 21.7, 95% CI: 12.6-30.9 ng/ml/mg/kg) and week 4 (mean difference: 43.28, 95% CI: 17.09 - 69.49 ng/ml/mg/kg) compared to *1/*1 and *1/*3 genotypes. The recipient CYP3A5 *3/*3 genotype did not showed significant difference in tacrolimus C/D ratio in week 1 compared to other two genotypes. However, week 2 (mean difference: 44.16, 95% CI: 3.68-84.65 ng/ml/mg/kg) and week 4 (mean difference: 43.74, 95% CI: 12.50-75.00 ng/ml/mg/kg) availability was higher in *3/*3 mutant recipients. However, the recipient ABCB1 3435 C > T polymorphism has no significant influence on tacrolimus pharmacokinetics till one month of transplant. CONCLUSIONS: The donor and recipient CYP3A5*3 polymorphism influences tacrolimus pharmacokinetics in the first month post-transplantation, whereas the association with recipient ABCB1 3435 C > T is inconclusive.

Classification and regression tree-based prediction of 6-mercaptopurine-induced leucopenia grades in children with acute lymphoblastic leukemia.

PURPOSE: The rationale of the current study was to develop 6-mercaptopurine (6-MP)-mediated hematological toxicity prediction model for acute lymphoblastic leukemia (ALL) therapeutic management. METHODS: A total of 96 children with ALL undergoing therapy with MCP-841 protocol were screened for all the ten exons of TPMT, exon 2, exon 3 and intron 2 of ITPA using bidirectional sequencing. This dataset was used to construct prediction models of leucopenia grade by constructing classification and regression trees (CART) followed by smart pruning. RESULTS: The developed CART model indicated TPMT*12 and TPMT*3C as the key determinants of toxicity. TPMT int3, int4 and int7 polymorphisms exert toxicity when co-segregated with one mutated allele of TPMT*12 or TPMT*3C or ITPA exon 3. The developed CART model exhibited 93.6% accuracy in predicting the toxicity. The area under the receiver operating characteristic curve was 0.9649. CONCLUSIONS: TPMT *3C and TPMT*12 are the key determinants of 6-MP-mediated hematological toxicity while other variants of TPMT (int3, int4 and int7) and ITPA ex2 interact synergistically with TPMT*3C or TPMT*12 variant alleles to enhance the toxicity. TPMT and ITPA variants cumulatively are excellent predictors of 6-MP-mediated toxicity.

Development of neuro-fuzzy model to explore gene-nutrient interactions modulating warfarin dose requirement.

AIM: To investigate the influence of alterations in vitamin K (K1, K2 and K3) in modulating warfarin dose requirement. PATIENTS & METHODS: Reverse phase HPLC to determine the plasma vitamin K; PCR-RFLP to detect polymorphisms; and the neuro-fuzzy model to predict warfarin dose were used. RESULTS: The developed neuro-fuzzy model showed a mean absolute error of 0.000024 mg/week. CYP2C9*2 and CYP2C9*3 mediated warfarin sensitivity was observed when vitamin K is in high and low tertiles, respectively. VKORC1-1639G>A exhibited warfarin sensitivity in all combinations. Higher vitamin K1 was observed in CYP4F2 V433M polymorphism. The requirement of warfarin is low in GGCX 8016 GG genotype compared with GA and AA genotypes. CONCLUSION: Vitamin K profile along with genetic testing ensures precision in warfarin dose optimization.

Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization.

AIM: To develop more precise pharmacogenomic algorithm for prediction of safe and effective dose of warfarin. MATERIALS & METHODS: An artificial neural network (ANN) algorithm was developed by using age, gender, BMI, plasma vitamin K levels, thyroid status and ten genetic variables as the inputs and therapeutic warfarin dose as the output. Hyperbolic tangent function was used to build an ANN architecture. RESULTS: This model explained 93.5% variability in warfarin dosing and predicted warfarin dose accurately in 74.5% patients whose international normalized ratio (INR) was less than 2.0 and in 83.3% patients whose INR was more than 3.5. This algorithm reduced the out-of-range INRs (odds ratio [OR]: 0.49; 95% CI: 0.30-0.79; p = 0.003), the rate of adverse drug reactions (OR: 0.00; 95% CI: 0.00-1.21; p = 0.06) and time to reach first therapeutic INR (OR: 6.73; 95% CI: 2.17-22.31; p < 0.0001). This algorithm was found to be applicable in both euthyroid and hypothyroid status. S-warfarin/7-hydroxywarfarin ratio was found to increase in subjects with CYP2C9*2 and CYP2C9*3 justifying the warfarin sensitivity attributed to these variants. CONCLUSION: An application of ANN for warfarin dosing improves predictability and provides safe and effective dosing.

Mechanistic insights into the effect of CYP2C9*2 and CYP2C9*3 variants on the 7-hydroxylation of warfarin.

AIM: To evaluate the impact of CYP2C9*2 and CYP2C9*3 variants on binding and hydroxylation of warfarin. MATERIALS & METHODS: Multiple linear regression model of warfarin pharmacokinetics was developed from the dataset of patients (n = 199). Pymol based in silico models were developed for the genetic variants. RESULTS: CYP2C9*2 and CYP2C9*3 variants exhibited high warfarin/7-hydroxywarfarin (multiple linear regression model), dose-dependent disruption of hydrogen bonds with warfarin, dose-dependent increase in the distance between C7 of S-warfarin and Fe-O of CYP2C9, dose-dependent decrease in the glide scores (in silico). CONCLUSION: CYP2C9*2 and CYP2C9*3 variants result in disruption of hydrogen bonding interactions with warfarin and longer distance between C7 and Fe-O thus impairing warfarin 7-hydroxylation due to lower binding affinity of warfarin. Original submitted 7 May 2014; Revision submitted 30 October 2014.

Methodological issues in the development of a pharmacogenomic algorithm for warfarin dosing: comparison of two regression approaches.

AIM: To ascertain whether multiple polynomial regression (MPR) has any advantage over multiple linear regression (MLR) in developing pharmacogenomic algorithms. MATERIALS & METHODS: Two pharmacogenomic algorithms were developed based on MPR and MLR models from a warfarin pharmacogenomic data set (derivation cohort [n = 125] and validation cohort [n = 115]). RESULTS: The MPR model showed better correlation with therapeutic dose (r = 0.62 vs 0.52); better diagnostic utility in distinguishing the warfarin-sensitive and warfarin-resistant patients (area under the receiver operating characteristic curves: 0.89 vs 0.81); and lower rate of underestimation (13.9 vs 20%) compared with the MLR model. Rate of overestimation was higher in the MPR than the MLR (10 vs 6.7%) model. CONCLUSION: The MPR approach has advantages over the MLR approach in predicting accurate and safe dose.

Retrospective evidence for clinical validity of expanded genetic model in warfarin dose optimization in a South Indian population.

AIM: To optimize warfarin dose in patients at risk for thrombotic events, we have recently developed a pharmacogenomic algorithm, which explained 44.9% of the variability in warfarin dose requirements using age, gender, BMI, vitamin K intake, CYP2C9 (*2 and *3) and VKORC1 (*3, *4 and -1639 G>A) as predictors. The aim of the current study is to develop an expanded genetic model that can explain greater percentage of warfarin variability and that has clinical validity. PATIENTS & METHODS: CYP2C9*8, CYP4F2 V433M, GGCX G8016A and thyroid status were added to an expanded genetic model (n = 243). RESULTS: The expanded genetic model explained 61% of the variability in warfarin dose requirements, has a prediction accuracy of ±11 mg/week and can differentiate warfarin sensitive and warfarin resistant groups efficiently (areas under receiver operating characteristic curves: 0.93 and 0.998, respectively; p < 0.0001). Higher percentage of International Normalized Ratios in therapeutic range (52.68 ± 4.21 vs 43.80 ± 2.27; p = 0.04) and prolonged time in therapeutic range (61.74 ± 3.18 vs 47.75 ± 5.77; p = 0.03) were observed in subjects with a prediction accuracy of <1 mg/day compared with subjects with prediction accuracy >1 mg/day. In the warfarin-resistant group, primary hypothyroidism was found to induce more resistance while in the warfarin-sensitive group, hyperthyroidism was found to increase sensitivity. CONCLUSION: The expanded genetic model explains greater variability in warfarin dose requirements and it prolongs time in therapeutic range and minimizes out-of-range International Normalized Ratios. Thyroid status also influences warfarin dose adjustments.

Association of aberrations in one-carbon metabolism with molecular phenotype and grade of breast cancer.

We have earlier demonstrated the role of aberrant one-carbon metabolism in the etiology of breast cancer. In the current study, we examine the clinical utility of these factors in predicting the subtype of breast cancer and as indicators of disease progression. Polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) and PCR-amplified fragment length polymorphism (AFLP) approaches were used for genetic analysis. Plasma folate and homocysteine were measured using Axsym folate kit and reverse phase HPLC, respectively. Multiple linear regression models were used to test the predictability of disease progression. Luminal A subtype was associated with late age of onset, higher body mass index and lack of family history of breast cancer. Thymidylate synthase (TYMS) 5'-UTR 28 bp tandem repeat (OR: 2.09, 95% CI: 1.05-4.16) and methylene tetrahydrofolate reductase (MTHFR) C677T (OR: 4.10, 95% CI: 1.40-11.95) were strongly associated with Luminal B. Reduced folate carrier (RFC1) G80A (OR: 2.92, 95% CI: 1.22-6.97) and methionine synthase (MTR) A2756G (OR: 4.71, 95% CI: 1.66-13.31) polymorphisms were associated with LuminA-HH subtype while MTHFR C677T showed association with HER-enriched (OR: 30.41, 95% CI: 6.47-142.91). Cytosolic serine hydroxymethyltransferase (cSHMT) conferred protection against basal-like breast cancer (OR: 0.47, 95% CI: 0.22-0.98). HER-enriched and basal-like subtypes showed positive association with familial breast cancer and inverse association with plasma folate. Hyperhomocysteinemia was observed in Luminal B and basal-like subtypes. Multiple linear regression models of aberrant one-carbon metabolism were found to be moderate predictors of breast cancer grade (area under the receiver operating characteristic curve, C = 0.72, 95% CI: 0.58-0.87, P = 0.008). To conclude, aberrations in one-carbon metabolism predict the subtype of breast cancer and disease progression.

Modulatory effect of plasma folate and polymorphisms in one-carbon metabolism on catecholamine methyltransferase (COMT) H108L associated oxidative DNA damage and breast cancer risk.

The present study was aimed to investigate the modulatory role of plasma folate and eight putatively functional polymorphisms of one-carbon metabolism on catecholamine methyltransferase (COMT)-mediated oxidative DNA damage and breast cancer risk. Plasma folate and 8-oxo-2'-deoxyguanosine (8-oxodG) were estimated by commercially available kits, while polymorphisms were screened by PCR-RFLP and PCR-AFLP methods. COMT H108L polymorphism showed independent association with breast cancer (OR: 1.73, 95% CI: 1.31-2.30). No significant interaction was observed between folate status and COMT genotype. Multifactor dimensionality reduction (MDR) analysis gave evidence for the significant epistatic (gene-gene) interactions (p<0.0001) of COMT H108L with reduced folate carrier 1 (RFC1) G80A, thymidylate synthase (TYMS) 5'-UTR 3R2R, TYMS 3'-UTR ins6/de16. Increased plasma 8-oxodG were observed in cases compared to controls (mean +/- SE: 5.59 +/- 0.60 vs. 3.50 +/- 0.40 ng/ml, p<0.004). Plasma folate deficiency alone was not a significant predictor of 8-oxodG elevation. The genotype combinations namely, RFC1 G80A/methionine synthase reductase (MTRR) A66G, RFC1 G80A/SHMT C1420T/TYMS 3R2R and serine hydroxymethyltransferase (SHMT) C1420T/TYMS 3R2R/methionine synthase (MTR) A2756G/COMT H108L were strong predictors of 8-oxodG elevation in the order of risk. To conclude, the current study provides substantial evidence for a cross talk between one-carbon metabolism and COMT catalysis that might influence oxidative DNA damage and breast cancer risk.

Cross-talk between one-carbon metabolism and xenobiotic metabolism: implications on oxidative DNA damage and susceptibility to breast cancer.

The aim of this case-control study is to explore the role of aberrations in xenobiotic metabolism in inducing oxidative DNA damage and altering the susceptibility to breast cancer. Cytochrome P4501A1 (CYP1A1) m1 (OR: 1.41, 95% CI 1.08-1.84), CYP1A1 m4 (OR: 5.13, 95% CI 2.68-9.81), Catecholamine-O-methyl transferase (COMT) H108L (OR: 1.49, 95% CI 1.16-1.92), and glutathione S-transferase (GST) T1 null (OR: 1.68, 95% CI 1.09-2.59) variants showed association with breast cancer risk. Reduced folate carrier 1 (RFC1) 80A/CYP1A1 m1/CYP1A1 m4 and RFC1 80A/thymidylate synthase (TYMS) 5'-UTR 2R/methionine synthase (MTR) 2756G/COMT 108L genetic combinations were found to inflate breast cancer risk under the conditions of low dietary folate (345 ± 110 vs. 379 ± 139 µg/day) and low plasma folate (6.81 ± 1.25 vs. 7.09 ± 1.26 ng/ml) by increasing plasma 8-oxo-2'-deoxyguanosine (8-oxodG). This increase in 8-oxodG is attributed to low methionine (49.38 ± 23.74 vs. 53.90 ± 23.85 µmol/l); low glutathione (378 ± 242 vs. 501 ± 126 µmol/l) and GSTT1 null variant; and hypermethylation of CpG island of extracellular-superoxide dismutase (EC-SOD) (92.78 ± 11.49 vs. 80.45 ± 9.86%), which impair O-methylation of catechol estrogens to methoxy estrogens, conjugation of glutathione to semiquinones/quinones and free radical scavenging respectively. Our results suggest cross-talk between one-carbon metabolism and xenobiotic metabolism influencing oxidative DNA damage and susceptibility to breast cancer.

Epistatic interactions between loci of one-carbon metabolism modulate susceptibility to breast cancer.

In view of growing body of evidence substantiating the role of aberrations in one-carbon metabolism in the pathophysiology of breast cancer and lack of studies on gene-gene interactions, we investigated the role of dietary micronutrients and eight functional polymorphisms of one-carbon metabolism in modulating the breast cancer risk in 244 case-control pairs of Indian women and explored possible gene-gene interactions using Multifactor dimensionality reduction analysis (MDR). Dietary micronutrient status was assessed using the validated Food Frequency Questionnaire. Genotyping was done for glutamate carboxypeptidase II (GCPII) C1561T, reduced folate carrier (RFC)1 G80A, cytosolic serine hydroxymethyltransferase (cSHMT) C1420T, thymidylate synthase (TYMS) 5'-UTR tandem repeat, TYMS 3'-UTR ins6/del6, methylenetetrahydrofolate reductase (MTHFR) C677T, methyltetrahydrofolate-homocysteine methyltransferase (MTR) A2756G, methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR) A66G polymorphisms by using the PCR-RFLP/AFLP methods. Low dietary folate intake (P < 0.001), RFC1 G80A (OR: 1.38, 95% CI 1.06-1.81) and MTHFR C677T (OR: 1.74 (1.11-2.73) were independently associated with the breast cancer risk whereas cSHMT C1420T conferred protection (OR: 0.72, 95% CI 0.55-0.94). MDR analysis demonstrated a significant tri-variate interaction among RFC1 80, MTHFR 677 and TYMS 5'-UTR loci (P (trend) < 0.02) with high-risk genotype combination showing inflated risk for breast cancer (OR 4.65, 95% CI 1.77-12.24). To conclude, dietary as well as genetic factors were found to influence susceptibility to breast cancer. Further, the current study highlighted the importance of multi-loci analyses over the single-locus analysis towards establishing the epistatic interactions between loci of one-carbon metabolism modulate susceptibility to the breast cancer.