Geographical Variability in Paromomycin Pharmacokinetics Does Not Explain Efficacy Differences between Eastern African and Indian Visceral Leishmaniasis Patients.

INTRODUCTION: Intramuscular paromomycin monotherapy to treat visceral leishmaniasis (VL) has been shown to be effective for Indian patients, while a similar regimen resulted in lower efficacy in Eastern Africa, which could be related to differences in paromomycin pharmacokinetics. METHODS: Pharmacokinetic data were available from two randomized controlled trials in VL patients from Eastern Africa and India. African patients received intramuscular paromomycin monotherapy (20 mg/kg for 21 days) or combination therapy (15 mg/kg for 17 days) with sodium stibogluconate. Indian patients received paromomycin monotherapy (15 mg/kg for 21 days). A population pharmacokinetic model was developed for paromomycin in Eastern African and Indian VL patients. RESULTS: Seventy-four African patients (388 observations) and 528 Indian patients (1321 observations) were included in this pharmacokinetic analysis. A one-compartment model with first-order kinetics of absorption and elimination best described paromomycin in plasma. Bioavailability (relative standard error) was 1.17 (5.18%) times higher in Kenyan and Sudanese patients, and 2.46 (24.5%) times higher in Ethiopian patients, compared with Indian patients. Ethiopian patients had an approximately fourfold slower absorption rate constant of 0.446 h-1 (18.2%). Area under the plasma concentration-time curve for 24 h at steady-state (AUCτ,SS) for 15 mg/kg/day (median [interquartile range]) was higher in Kenya and Sudan (172.7 µg·h/mL [145.9-214.3]) and Ethiopia (230.1 µg·h/mL [146.3-591.2]) compared with India (97.26 µg·h/mL [80.83-123.4]). CONCLUSION: The developed model provides detailed insight into the pharmacokinetic differences among Eastern African countries and India, however the resulting differences in paromomycin exposure do not seem to explain the geographical differences in paromomycin efficacy in the treatment of VL patients.

Improving data reliability using a non-compliance detection method versus using pharmacokinetic criteria.

Data from clinical trials present numerous problems for the data analyst. These include non-compliance with the prescribed dosing regimen and inaccurate recollection of dosing history by patients as well as mistakes in recording data. Several methods have been proposed to address these issues. One such technique by Lu et al. (Selecting reliable pharmacokinetic data for explanatory analyses of clinical trials in the presence of possible noncompliance. J. Pharmacokinet. Pharmacodyn. 28:343-362 (2001)) identifies occasions in pharmacokinetic (PK) data where the preceding dosing history is likely to be unreliable. We used this method, implemented in the software program NONMEM (beta) VI, to clean a dataset containing indinavir (IDV) plasma concentrations from HIV-1 infected patients. The data was also cleaned by inspection in Microsoft Excel using clinical PK criteria. A one-compartment model with first order absorption and elimination was fit to both sets of cleaned data. IDV population PK parameters obtained from these analyses were similar to those reported previously. It is established that IDV nephrotoxicity is related to high IDV exposure. However, no relationships were found between any PK parameters and nephrotoxicity in the "compliance cleaned" dataset. In the "PK cleaned" dataset, the oral clearance and apparent volume were lower by 9.1% and 6.6%, respectively in patients with any type of nephrotoxicity and the maximum IDV concentration (C(max)) was 12.1% higher. In patients suffering from nephrolithiasis in particular, C(max) was 15.5% higher. Accordingly, the use of the non-compliance detection method did not improve the reliability of our dataset over the usual method of applying clinical criteria. In fact, analyses on the compliance-cleaned dataset missed some exposure-toxicity relationships. Thus, automated methods must be tested rigorously with 'real life' datasets, used with caution, and always in conjunction with clinical reasoning to avoid overlooking a signal in noisy data.