Population Pharmacokinetics of Tenofovir in HIV-1-Uninfected Members of Serodiscordant Couples and Effect of Dose Reporting Methods.

Antiretroviral preexposure prophylaxis (PrEP) with once-daily dosing of tenofovir and tenofovir-emtricitabine was shown to be effective for preventing HIV-1 infection in individuals who had HIV-1-seropositive partners (the Partners PrEP Study). We developed a population pharmacokinetic model for tenofovir and investigated the impacts of different dose reporting methods. Dosing information was collected as patient-reported dosing information (PRDI) from 404 subjects (corresponding to 1,280 drug concentration records) from the main trial and electronic monitoring-based adherence data collected from 211 subjects (corresponding to 327 drug concentration records) in an ancillary adherence study. Model development was conducted with NONMEM (7.2), using PRDI with a steady-state assumption or using PRDI replaced with electronic monitoring records where available. A two-compartment model with first-order absorption was the best model in both modeling approaches, with the need for an absorption lag time when electronic monitoring-based dosing records were included in the analysis. Age, body weight, and creatinine clearance were significant covariates on clearance, but only creatinine clearance was retained in the final models per stepwise selection. Sex was not a significant covariate on clearance. Tenofovir population pharmacokinetic parameter estimates and the precisions of the parameters from the two final models were comparable with the point estimates of the parameters, differing from 0% to 35%, and bootstrap confidence intervals widely overlapped. These findings indicate that PRDI was sufficient for population pharmacokinetic model development in this study, with a high level of adherence per multiple measures.

Budesonide Foam Has a Favorable Safety Profile for Inducing Remission in Mild-to-Moderate Ulcerative Proctitis or Proctosigmoiditis.

BACKGROUND: Budesonide foam, a rectally administered, second-generation corticosteroid with extensive hepatic first-pass metabolism, is efficacious for the treatment of mild-to-moderate ulcerative proctitis and ulcerative proctosigmoiditis. AIM: The aim of this study was to comprehensively assess the safety and pharmacokinetic profile of budesonide foam. METHODS: Data from five phase III studies were pooled to further evaluate safety, including an open-label study (once-daily treatment for 8 weeks), an active-comparator study (once-daily treatment for 4 weeks), and two placebo-controlled studies and an open-label extension study (twice-daily treatment for 2 weeks, then once daily for 4 weeks). Data from the placebo-controlled studies and two phase I studies (i.e., patients with mild-to-moderate ulcerative colitis and healthy volunteers) were pooled to evaluate the pharmacokinetics of budesonide foam. RESULTS: A similar percentage of patients reported adverse events in the budesonide foam and placebo groups, with the majority of adverse events being mild or moderate in intensity (93.3 vs 96.0%, respectively). Adverse events occurred in 41.4 and 36.3% of patients receiving budesonide foam and placebo, respectively. Mean morning cortisol concentrations remained within the normal range for up to 8 weeks of treatment; there were no clinically relevant effects of budesonide foam on the hypothalamic-pituitary-adrenal axis. Population pharmacokinetic analysis demonstrated low systemic exposure after budesonide foam administration. CONCLUSIONS: This integrated analysis demonstrated that budesonide foam for the induction of remission of distal ulcerative colitis is safe overall, with no clinically relevant effects on the hypothalamic-pituitary-adrenal axis.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q1 = 4.9 % and q3 = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Population pharmacokinetics of intravenous ondansetron in oncology and surgical patients aged 1-48 months.

PURPOSE: Until recently, ondansetron was approved for the prevention of nausea and vomiting only in patients older than 2 years. However, as the use of ondansetron in patients younger than 2 years had been documented, characterization of ondansetron pharmacokinetics in this younger pediatric age group was warranted. METHODS: The pharmacokinetics of intravenously administered ondansetron were evaluated in oncology and surgical patients aged 1-48 months. Pooled data from 124 patients, including 745 pharmacokinetic samples, were analyzed using nonlinear mixed-effects modeling. RESULTS: Ondansetron pharmacokinetics were described by a two-compartment model. Body-size effects on ondansetron disposition were accounted for via standard allometric relationships, normalized to 10.4 kg. A maturation process with a half-life of approximately 4 months was incorporated to describe a decrease in clearance (CL) in infants. Clearance [95% confidence interval (CI)] for a typical patient was 1.53 (1.34-1.78) L/h/kg(0.75) with an interindividual variability of 56.8%. Ondansetron CL was reduced by 31%, 53%, and 76% for the typical 6-month-, 3-month-, and 1-month-old patient, respectively. Simulations showed that an ondansetron dose of 0.1 mg/kg in children younger than 6 months produced exposure similar to a 0.15-mg/kg dose in older children. CONCLUSIONS: The population pharmacokinetic analysis of ondansetron allows for characterization of individual patients based on body weight and age. It is recommended that patients younger than 4 months receiving ondansetron be closely monitored.

Assessment of Demographic and Socio-Behavioral Factors on Adherence to HIV Pre-Exposure Prophylaxis Using a Markov Modeling Approach.

Purpose: Adherence is important for the effectiveness of human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP). The objective of the current work is to assess the impact of multiple demographic and socio-behavioral factors on the adherence to tenofovir-based PrEP among HIV serodiscordant couples in East Africa using Markov mixed-effects modeling approach. Methods: The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of HIV PrEP among heterosexual HIV serodiscordant couples in Kenya and Uganda. The uninfected partner received oral PrEP according to the "bridge to antiretroviral therapy [ART]" strategy (i.e., until the infected partner had been on ART for ≥6 months). Adherence was monitored electronically; demographic and socio-behavioral data were collected during study visits. Analyzed data reflect 12 months of follow-up per participant. A two-state, first-order, discrete time Markov model was developed with longitudinal adherence data characterized by "dose taking (1)" and "dose missing (0)." Covariate effects were linearly added in the logit domain of transition probability parameters (P01 and P10) in the model. The full covariate model was initially developed, followed by backward elimination process to reduce the model. All significant covariates reported by a prior primary statistical analysis of the same data were included in the full covariate model. Results: The model included data from 920 participants, who were predominantly male (65%). Significant covariates associated with higher adherence were 25 years or older [odds ratio (OR) for P10, 0.61], female sex (OR for P10, 0.67), participant wanting the relationship with the partner to succeed (OR for P10, 0.79; OR for P01, 1.45), and sex with partner either with 100% or <100% condom use compared to those reported no sex (OR for P10, 0.84; OR for P01, 1.21). Significant covariates associated with lower adherence were partner on ART >6 months (OR for P01, 0.86; OR for P10, 1.34), subject in the study for >6 months (OR for P01, 0.8; OR for P10, 1.25), and problematic alcohol use (OR for P01, 0.63; OR for P10, 1.16). Conclusion: The developed Markov model provides a mechanistic understanding of relationship between demographic, socio-behavioral covariates, and PrEP adherence, by indicating the pattern of adherence influenced by each factor over time. Such data can be used for further intervention development to promote PrEP adherence.

Subcutaneous Furosemide in Heart Failure: Pharmacokinetic Characteristics of a Newly Buffered Solution.

Parenteral diuretics form the cornerstone of decongestion in heart failure. However, parenteral therapy routinely requires emergency room or inpatient care. A novel buffered furosemide formulation with neutral pH was developed to offer "hospital-strength" diuresis for outpatient use, including self-administration at home. Subcutaneous infusion using a biphasic delivery profile resulted in complete bioavailability (99.65%) and equivalent diuresis when compared with intravenous administration. Subcutaneous administration of buffered furosemide was well tolerated with no evidence of any drug-induced skin reactions. Subcutaneous infusion of buffered furosemide in the outpatient setting or home may help to reduce the burden of heart failure.

Genetic algorithm guided population pharmacokinetic model development for simvastatin, concurrently or non-concurrently co-administered with amlodipine.

An automated model development was performed for simvastatin, co-administered with amlodipine concurrently or non-concurrently (i.e., 4 hours later) in 17 patients with coexisting hyperlipidemia and hypertension. The single objective hybrid genetic algorithm (SOHGA) was implemented in the NONMEM software by defining the search space for structural, statistical and covariate models. Candidate models obtained from the SOHGA runs were further assessed for biological plausibility and the precision of parameter estimates, followed by traditional backward elimination process for model refinement. The final population pharmacokinetic model shows that the elimination rate constant for simvastatin acid, the active form by hydrolysis of its lactone prodrug (i.e., simvastatin), is only 44% in the concurrent amlodipine administration group compared with the non-concurrent group. The application of SOHGA for automated model selection, combined with traditional model selection strategies, appears to save time for model development, which also can generate new hypotheses that are biologically more plausible.

A genetic algorithm-based, hybrid machine learning approach to model selection.

We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.

A dosing algorithm for converting from valproate monotherapy to lamotrigine monotherapy in patients with epilepsy.

A dosing algorithm was used to guide the conversion of 77 patients with epilepsy (16 years of age) from valproate monotherapy through lamotrigine adjunctive therapy at 200mg daily to lamotrigine monotherapy at 500 mg daily in an open-label study comprising a lamotrigine escalation phase (8 weeks), a valproate withdrawal phase (6 weeks), and a lamotrigine monotherapy phase (4 weeks). The algorithm was designed to maintain stable trough concentrations of lamotrigine during valproate withdrawal to minimize seizure risk. Of the 77 patients, 11 prematurely withdrew for administrative reasons (noncompliance, consent withdrawn, loss to follow-up, protocol violation). Of the remaining 66 patients, 18 withdrew because of adverse events, and 48 completed the study. Among the 67 patients with at least one lamotrigine trough serum concentration after initiation of therapy (pharmacokinetic population), mean trough serum concentrations at the end of the lamotrigine escalation phase at a lamotrigine daily dose of 200 mg (7.9 microg/mL, SD = 3.3) did not differ significantly from values during the valproate withdrawal phase (8.7 microg/mL, SD = 3.5) and the lamotrigine monotherapy phase at a lamotrigine dose of 500 mg daily (7.2 microg/mL, SD = 3.3). A similar pattern of results was observed among the 34 patients who completed the study on lamotrigine (completer population). No serious rashes were reported, and the incidence of withdrawals because of decreased seizure control was low (n = 2, or 3%; both incidents were judged to be related to noncompliance). By employment of a four-step dosing algorithm, lamotrigine serum concentrations can be maintained at a stable level with favorable tolerability during a transition from lamotrigine 200mg daily as adjunctive therapy with valproate to lamotrigine monotherapy 500 mg daily.

A joint model for nonlinear longitudinal data with informative dropout.

Subject withdrawal from a study (also called dropout, or right censoring), is common in late phase clinical trials. A number of methods dealing with dropouts have been used in practice, the most common being "last observation carried forward" (LOCF). Many of these methods, including LOCF, can result in biased estimates of the efficacy or potency of the drug, especially in the modeling context. If the likelihood of dropout is correlated to the underlying unobserved data, the dropout is informative and should not be ignored in the modeling process. The topic of informative dropout in the context of longitudinal data has received much attention in the statistical literature, in the setting of linear and generalized linear models. We extend the approach to nonlinear models. The dropout hazard, as well as the longitudinal data, is modeled parametrically. Parameters are estimated by maximizing the approximate joint likelihood as implemented in the software NONMEM. Using data from actual clinical trials, we explore the impact of the dropout model on the ability of the joint model to predict observed longitudinal data patterns.

Statistical issues in a modeling approach to assessing bioequivalence or PK similarity with presence of sparsely sampled subjects.

Drug development at different stages may require assessment of similarity of pharmacokinetics (PK). The common approach for such assessment when the difference is drug formulation is bioequivalence (BE), which employs a hypothesis test based on the evaluation of a 90% confidence interval for the ratio of average pharmacokinetic (PK) parameters. The role of formulation effect in BE assessment is replaced by subject population in PK similarity assessment. The traditional approach for BE requires that the PK parameters, primarily AUC and Cmax, be obtained from every individual. Unfortunately in many clinical circumstances, some or even all of the individuals may be sparsely sampled, making the individual evaluation difficult. In such cases, using models, particularly population models, becomes appealing. However, conducting an appropriate statistical test based on population modeling in a form consistent, at least in principle, with traditional 90% confidence interval approach is not so straightforward as it may appear. This manuscript proposes one such approach that can be applied to sparse sampling situations. The approach aims to maintain, as much as possible, the appropriateness of the hypothesis test. It is applied to data from clinical studies to address a need in drug development for assessment of PK similarity in different populations.