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Transplantation 03/2011; 91(6):e36-8. · 4.00 Impact Factor
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ABSTRACT: Enteric-coated mycophenolate sodium (EC-MPS) is widely used in renal transplantation. With a delayed absorption profile, it has not been possible to develop limited sampling strategies to estimate area under the curve (mycophenolic acid [MPA] AUC₀₋₁₂), which have limited time points and are completed in 2 hours. We developed and validated simplified strategies to estimate MPA AUC₀₋₁₂ in an Indian renal transplant population prescribed EC-MPS together with prednisolone and tacrolimus. Intensive pharmacokinetic sampling (17 samples each) was performed in 18 patients to measure MPA AUC₀₋₁₂. The profiles at 1 month were used to develop the simplified strategies and those at 5.5 months used for validation. We followed two approaches. In one, the AUC was calculated using the trapezoidal rule with fewer time points followed by an extrapolation. In the second approach, by stepwise multiple regression analysis, models with different time points were identified and linear regression analysis performed. Using the trapezoidal rule, two equations were developed with six time points and sampling to 6 or 8 hours (8hrAUC[₀₋₁₂exp]) after the EC-MPS dose. On validation, the 8hrAUC(₀₋₁₂exp) compared with total measured AUC₀₋₁₂ had a coefficient of correlation (r²) of 0.872 with a bias and precision (95% confidence interval) of 0.54% (-6.07-7.15) and 9.73% (5.37-14.09), respectively. Second, limited sampling strategies were developed with four, five, six, seven, and eight time points and completion within 2 hours, 4 hours, 6 hours, and 8 hours after the EC-MPS dose. On validation, six, seven, and eight time point equations, all with sampling to 8 hours, had an acceptable r with the total measured MPA AUC₀₋₁₂ (0.817-0.927). In the six, seven, and eight time points, the bias (95% confidence interval) was 3.00% (-4.59 to 10.59), 0.29% (-5.4 to 5.97), and -0.72% (-5.34 to 3.89) and the precision (95% confidence interval) was 10.59% (5.06-16.13), 8.33% (4.55-12.1), and 6.92% (3.94-9.90), respectively. Of the eight simplified approaches, inclusion of seven or eight time points improved the accuracy of the predicted AUC compared with the actual and can be advocated based on the priority of the user.
Therapeutic drug monitoring 03/2011; 33(2):165-70. · 2.43 Impact Factor
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ABSTRACT: We describe the pharmacokinetic profile of mycophenolic acid (MPA) in a patient receiving Mycophenolate mofetil (MMF) during her first and second renal transplantations. The MMF dose required to achieve a therapeutic range of MPA-AUC(0)(-)(12)(h) early following the second transplantation was 10 times greater than that required late following the first transplantation. Her MMF requirement then declined and continued to decrease even beyond 1 year. Intra-individual variability in MPA profiles precluded the ability to predict MMF dosing for the second transplant based on that during the first. Therapeutic drug monitoring of MMF should be continued beyond 1 year of transplantation.
Nephrology Dialysis Transplantation 10/2010; 25(10):3449-52. · 3.40 Impact Factor
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ABSTRACT: In renal transplant patients, there is an established relationship between mycophenolate area under the curve and clinical outcome. The authors have developed and validated a limited sampling strategy to estimate mycophenolic acid area under the curve to 12 hours (MPA AUC0-12) in a stable renal transplant Indian population prescribed a formulation of mycophenolate mofetil (Mofilet) along with prednisolone and tacrolimus. Intensive pharmacokinetic sampling was performed in 29 patients to measure mycophenolate concentration from trough to 12 hours postdose. Subsets of different timed concentrations against total measured 12-hour area under the curve were analyzed by linear regression. Three models were identified and linear regression analysis done. After all subset regression analysis, three, four, and five time point limited sampling strategies (LSS) were developed having correlation coefficients above 0.92. Validation of the models was performed using the jackknife method and their predictive performances were tested. After validation, the correlation coefficients for all three models were above 0.901. The five-point LSS had the best predictive performance with a bias (95% confidence interval) of 0.67% (-3.45 to 4.79) and mean precision 7.73%. In all patients except one, the five-point LSS estimation for total area under the curve was within +/- 20% of the total measured AUC0-12. Trough concentration had a significant correlation with AUC0-12 (r = 0.69). However, if dosing in routine clinical practice was adjusted based only on trough concentration, 41% of our patients would require a different dose compared with monitoring using AUC0-12. The five-point LSS uses half-hourly samples from trough to 1.5 hour postdose with an additional sample at 3 hours. Ninety-three percent of our patients had a Cmax within 1.5 hour and inclusion of all the time points up to1.5 hour gave a better estimate of AUC0-12. This model simplifies area under the curve measurement with high precision in stable adult renal transplant patients.
Therapeutic drug monitoring 04/2010; 32(2):136-40. · 2.43 Impact Factor
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Transplantation 11/2009; 88(9):1143-5. · 4.00 Impact Factor
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ABSTRACT: To develop and validate limited sampling strategy (LSS) equations to estimate area under the curve (AUC(0-12)) in renal transplant patients.
Twenty-nine renal transplant patients (3-6 months post transplant) who were at steady state with respect to tacrolimus kinetics were included in this study. The blood samples starting with the predose (trough) and collected at fixed time points for 12 h were analysed by microparticle enzyme immunoassay. Linear regression analysis estimated the correlations of tacrolimus concentrations at different sampling time points with the total measured AUC(0-12). By applying multiple stepwise linear regression analysis, LSS equations with acceptable correlation coefficients (R(2)), bias and precision were identified. The predictive performance of these models was validated by the jackknife technique.
Three models were identified, all with R(2) > or = 0.907. Two point models included one with trough (C(0)) and 1.5 h postdose (C(1.5)), another with trough and 4 h postdose. Increasing the number of sampling time points to more than two increased R(2) marginally (0.951 to 0.990). After jackknife validation, the two sampling time point (trough and 1.5 h postdose) model accurately predicted AUC(0-12). Regression coefficient R(2) = 0.951, intraclass correlation = 0.976, bias [95% confidence interval (CI)] 0.53% (-2.63, 3.69) and precision (95% CI) 6.35% (4.36, 8.35).
The two-point LSS equation [AUC(0-12) = 19.16 + (6.75.C(0)) + (3.33.C1.5)] can be used as a predictable and accurate measure of AUC(0-12) in stable renal transplant patients prescribed prednisolone and mycophenolate.
British Journal of Clinical Pharmacology 07/2008; 66(4):467-72. · 2.96 Impact Factor
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ABSTRACT: {{Background: KDIGO guidelines endorse use of low cost generics. However, risks and benefits of generic (G) vs. innovator (I) brands of mycophenolate acid (MPA) in renal allograft recipients is unclear. Methods: Renal allograft recipients, who underwent transplantation at our center between January 2003 & June 2010, on innovator brands of Mycophenolate Mofetil / Sodium (MMF-I / MPS-I) or its generic equivalents (MMF-G1, MMF-G2 / MPS-G1) along with prednisolone and tacrolimus underwent extrapolated MPA-AUC0-12h measurements at specific intervals and adhoc, for MPA dose adjustment to achieve a target AUC of 30-60mg.h/L. The difference in the dose, AUC, dose controlled AUC (Dc-AUC = AUC/ Dose), number of tests, graft function, graft survival, rejections, infections, leucopenia, diarrhea and cost of MPA between the innovator and generic MMF/MPS users were analyzed. Results: Of the 305 renal allograft recipients (mean age =36.6±11.8 yrs
Journal of American Society of Nephrology. 22:602A+.
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Gopal Basu,
Vellaichamy M Annapandian,
Binu S Matthew,
Kuppusamy Saravanakumar,
Anjali Mohapatra,
Vinoi G David,
Madhivanan Sundaram,
Santosh Varughese, Denise H Fleming,
Veerasamy Tamilarasi,
Chakko K Jacob,
George T John
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ABSTRACT: Background: Weight based dose of mycophenolate has been recommended for renal transplantation. Aim: To determine the relationship between the outcome events and weight adjusted mycophenolate (MPA) dose and drug exposure (AUC) among renal allograft recipients. Patients & methods: Renal allograft recipients, who underwent transplantation at Christian Medical College Vellore, between January 2003 and December 2009, and received mycophenolate mofetil (MMF) /sodium (MPS) along with prednisolone and tacrolimus, were studied. Extrapolated MPA-AUC0-12h were measured after transplant ( between D5-10, at third month, sixth month, one year, later than one year and ad-hoc)by HPLC and the MMF/MPS dose were adjusted to maintain it between 30-60mg.h/L. The cumulative drug exposure and MPA dose and dose controlled AUC (Dc-AUC = MPA-AUC0-12h/Dosemg/Kg) were calculated up to the day of outcome events (rejections, infections, leucopenia or diarrhea) by applying trapezoidal rule, for the event-cases and respective controls. The difference of the mean cumulative drug exposure and dose between the cases and controls was analyzed. Results: Of the 235 renal allograft recipients (mean age 36.6±12.1 years; M:F=3:1) 90.6% received renal allografts from living related donors (with HLA AB ≥2 Ag match – 66.8%) and 9.4% from deceased donors. Most patients received induction therapy (74.5%)(predominantly basiliximab) and prednisolone with tacrolimus (83.0%) and MMF (51.1%) or MPS (48.9%). The recipients were followed up for a mean of 24.9±13.5 months. The mean serum creatinine (mg/dl) was 1.26±0.50 at 1 month, 1.21±0.32 at 6 months, 1.24±0.38 at 1 year, 1.27±0.41 at 2 years and 1.35±0.1 at 5 years post transplant. Patients switched from MPA to Azathioprine (9.4%) at a median of 4.9 (2.6-38.3) months after transplantation predominantly due to financial reasons (6.8%) and diarrhea (2.6%). At the first week, three months, six months, one year and later than one year respectively, the mean dose were (38.6±8.1, 39.4±13.7, 29.3±9.5, 24.9±8.7 and 24.1±10.1 mg/kg), the mean MPA-AUC0-12h were (38.1±16.5, 59.0±28.1, 56.9±23.9, 52.7±18.3 and 50.3±18.2 mg.h/L). Overall, patients taking MPS achieved significantly higher Dc-AUC than MMF (2.0±1.0 vs. 1.7±1.1 Kg.h/L: p<0.001). The cumulative exposure (MPA-AUC0-12h) till the event was significantly lower among the rejecters (16.5%) compared to the non rejecters (37.5±17.7 vs. 44.5±8.2 mg.h/L: p=0.027), but not the cumulative dose or Dc-AUC. This difference persisted even for early rejections (<3 months: 9.4%) and after controlling for the effect of induction. The cumulative MPA dose and exposure were not significantly different between patients who developed urinary tract infections (20.0%), CMV disease (8.5%), Herpes infections (5.5%), BK virus nephropathy (2.1%), systemic mycoses (2.6%) or leucopenia (21.7%) and their respective controls. However, among patients who developed diarrhea (11.1%) higher cumulative exposure (51.4±22.2 vs. 48.3±6.5 mg.h/L: p=0.494) with a significantly lower cumulative dose (31.8±8.3 vs. 35.7±4.8 mg/kg: p=0.043) was observed. Conclusion: Among Indian renal allograft recipients, despite use of similar weight based dose of MPA salts, patients who achieved lower MPAAUC0-12h (even within therapeutic range of 30-60mg.h/L) were at a higher risk of suffering rejections. Therapeutic drug monitoring of MPA during the early post transplant period is helpful in reducing rejection rates among Indian allograft recipients.
Transplantation. 90(s):255+.
