Rifapentine Population Pharmacokinetics and Dosing Recommendations for Latent Tuberculosis Infection.

Rationale: Rifapentine has been investigated at various doses, frequencies, and dosing algorithms, but clarity on the optimal dosing approach is lacking.Objectives: To characterize rifapentine population pharmacokinetics, including autoinduction, and determine optimal dosing strategies for short-course rifapentine-based regimens for latent tuberculosis infection.Methods: Rifapentine pharmacokinetic studies were identified though a systematic review of literature. Individual plasma concentrations were pooled, and nonlinear mixed-effects modeling was performed. A subset of data was reserved for external validation. Simulations were performed under various dosing conditions, including current weight-based methods; and alternative methods driven by identified covariates.Measurements and Main Results: We identified nine clinical studies with a total of 863 participants with pharmacokinetic data (n = 4,301 plasma samples). Rifapentine population pharmacokinetics were described successfully with a one-compartment distribution model. Autoinduction of clearance was driven by rifapentine plasma concentrations. The maximum effect was a 72% increase in clearance and was reached after 21 days. Drug bioavailability decreased by 27% with HIV infection, decreased by 28% with fasting, and increased by 49% with a high-fat meal. Body weight was not a clinically relevant predictor of clearance. Pharmacokinetic simulations showed that current weight-based dosing leads to lower exposures in individuals with low weight, which can be overcome with flat dosing. In HIV-positive patients, 30% higher doses are required to match drug exposure in HIV-negative patients.Conclusions: Weight-based dosing of rifapentine should be removed from clinical guidelines, and higher doses for HIV-positive patients should be considered to provide equivalent efficacy.

Effect of inotuzumab ozogamicin on the QT interval in patients with haematologic malignancies using QTc-concentration modelling.

AIM: The aim of this study was to characterize the effect of inotuzumab ozogamicin on QT interval in patients with B-cell malignancies. METHODS: Data were pooled from three clinical studies including 250 patients (n = 2743) who received inotuzumab ozogamicin monotherapy. Patients with relapsed/refractory acute lymphoblastic leukaemia (NCT01564784 and NCT01363297) received 1.8 mg m-2 per cycle in divided doses (mean Cmax 371 ng ml-1 ; considered therapeutic) and patients with relapsed/refractory non-Hodgkin lymphoma (NCT00868608) received 1.8 mg m-2 per cycle as a single dose (mean Cmax 569 ng ml-1 ; considered supratherapeutic). Triplicate 12-lead electrocardiograms were performed at baseline and predefined time points postdose with paired pharmacokinetic collections. The exposure-response relationship between corrected QT interval (QTc: QT interval corrected using population-specific formula [QTcS] or QT interval corrected using Fridericia's formula [QTcF]) and inotuzumab ozogamicin concentration was characterized using a linear mixed-effects model, and simulations were performed using the final validated model. Full model development involved testing for covariates that may account for part of the identified variability. RESULTS: QTc intervals had a small but positive correlation with inotuzumab ozogamicin concentration. Based on 1000 simulations, median (upper 95% CI) QTcS and QTcF changes from baseline were <10 ms at both therapeutic (2.70 ms [5.40 ms] and 2.53 ms [4.92 ms], respectively) and supratherapeutic (4.14 ms [8.28 ms] and 3.87 ms [7.54 ms], respectively) concentrations. CONCLUSIONS: Inotuzumab ozogamicin (1.8 mg m-2 per cycle) is not predicted to pose a clinically significant safety risk for QT prolongation in patients with acute lymphoblastic leukaemia or non-Hodgkin lymphoma.

Disease progression modeling of the North Star Ambulatory Assessment for Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder caused by decreased or absent dystrophin gene leading to progressive muscle degeneration and weakness in young boys. Disease progression models for the North Star Ambulatory Assessment (NSAA), a functional measurement widely used to assess outcomes in clinical trials, were developed using a longitudinal population modeling approach. The relationship between NSAA total score over time, loss of ambulation, and potential covariates that may influence disease progression were evaluated. Data included individual participant observations from an internal placebo-controlled phase II clinical trial and from the external natural history database for male patients with DMD obtained through the Cooperative International Neuromuscular Research Group (CINRG). A modified indirect response model for NSAA joined to a loss of ambulation (LOA) time-to-event model described the data well. Age was used as the independent variable because ambulatory function is known to vary with age. The NSAA and LOA models were linked using the dissipation rate constant parameter from the NSAA model by including the parameter as a covariate on the hazard equation for LOA. No covariates were identified. The model was then used as a simulation tool to explore various clinical trial design scenarios. This model contributes to the quantitative understanding of disease progression in DMD and may guide model-informed drug development decisions for ongoing and future DMD clinical trials.

Evaluation of the absolute oral bioavailability of the anaplastic lymphoma kinase/c-ROS oncogene 1 kinase inhibitor lorlatinib in healthy participants.

PURPOSE: Lorlatinib is a third-generation tyrosine kinase inhibitor currently approved for the treatment of anaplastic lymphoma kinase (ALK)-positive metastatic non-small cell lung cancer. This open-label, phase 1, randomized two-sequence, two-treatment, two-period, crossover study investigated the absolute oral bioavailability of lorlatinib in healthy participants. METHODS: Eligible participants were randomized to receive two treatments in one of two sequences: lorlatinib 100 mg single oral dose followed by lorlatinib 50 mg intravenous (IV) dose, or lorlatinib IV dose followed by lorlatinib oral dose, each with at least a 10-day washout between successive lorlatinib doses. Blood samples for pharmacokinetics were collected for up to 144 hours (h) after dosing. Validated liquid chromatographic-tandem mass spectrometry was used to determine plasma concentrations of lorlatinib and its benzoic acid metabolite PF-06895751. RESULTS: In total, 11 participants were enrolled (mean age 37.6 years, all male). The adjusted geometric mean (90% confidence interval) for the absolute oral bioavailability was 80.78% (75.73-86.16%). Using non-compartmental analysis, the estimated arithmetic mean elimination plasma half-life of lorlatinib was 25.5 and 27.0 h after the oral and IV doses, respectively. No deaths, serious adverse events (AEs), or severe AEs were reported, and most treatment-emergent AEs were mild in severity, with two events of transaminase increase of moderate severity. All treatment-emergent AEs were resolved by the end of the study. CONCLUSION: Both oral and IV lorlatinib were well-tolerated in healthy participants and oral lorlatinib is highly bioavailable after oral administration.

Clinical Pharmacology Worldwide: A Global Health Perspective.

Low- and middle-income countries (LMICs) have the highest rates of mortality and morbidity globally, but lag behind high-income countries in the number of clinical trials and trained researchers, as well as research data pertaining to their populations. Lack of local clinical pharmacology and pharmacometrics expertise, limited training opportunities, and lack of local genomic data may contribute to health inequalities and limit the application of precision medicine. Continuing to develop health care infrastructure, including well-designed clinical pharmacology training and data collection in LMICs, can help address these challenges. International collaboration aimed at improving training and infrastructure and encouraging locally driven research and clinical trials will be of benefit. This review describes several examples where clinical pharmacology expertise could be leveraged, including opportunities for pharmacogenomic expertise that could drive improved recommendations for clinical guidelines. Also described are clinical pharmacology and pharmacometrics training programs in Africa, and the personal experience of a Tanzanian researcher currently on a training sabbatical in the United States, as illustrative examples of how training in clinical pharmacology can be effectively implemented in LMICs. These training efforts will benefit from advocacy for employment opportunities and career development pathways for clinical pharmacologists that are gradually being recognized and developed in LMICs. Clinical pharmacologists have a key role to play in global health, and development of training and research infrastructure to advance this expertise in LMICs will be of tremendous benefit.

Pharmacokinetic/Pharmacodynamic Modeling to Support the Re-approval of Gemtuzumab Ozogamicin.

Gemtuzumab ozogamicin (Mylotarg; Pfizer, New York, NY) was the first antibody-drug conjugate to be approved for CD33-positive acute myeloid leukemia (AML). However, it was voluntarily withdrawn from the US market due to lack of clinical benefit in the confirmatory phase III trial. In 2012, several investigator cooperative studies using a different dosing regimen showed efficacy, but pharmacokinetic (PK) data were not collected in these trials. Through simulation of expected concentrations for new dosing regimens, PK/pharmacodynamic modeling was able to support the safety and efficacy of these regimens. Significant exposure-response relationships were found for the attainment of complete remission with and without platelet recovery, attainment of blast-free status, the time course of myelosuppression, several grade ≥ 3 hepatic adverse events, and veno-occlusive disease. Gemtuzumab ozogamicin received full approval by the US Food and Drug Administration (FDA) in September 2017 for newly diagnosed and relapsed AML in adult patients and relapsed AML in pediatric patients aged 2-17 years.

The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin.

INTRODUCTION: Pharmacokinetic outcomes of transporter-mediated drug-drug interactions (TMDDIs) are increasingly being evaluated clinically. The goal of our study was to determine the effects of selective inhibition of multidrug and toxin extrusion protein 1 (MATE1), using famotidine, on the pharmacokinetics and pharmacodynamics of metformin in healthy volunteers. METHODS: Volunteers received metformin alone or with famotidine in a crossover design. As a positive control, the longitudinal effects of famotidine on the plasma levels of creatinine (an endogenous substrate of MATE1) were quantified in parallel. Famotidine unbound concentrations in plasma reached 1 µM, thus exceeding the in vitro concentrations that inhibit MATE1 [concentration of drug producing 50 % inhibition (IC50) 0.25 µM]. Based on current regulatory guidance, these concentrations are expected to inhibit MATE1 clinically [i.e. maximum unbound plasma drug concentration (C max,u)/IC50 >0.1]. RESULTS: Consistent with MATE1 inhibition, famotidine administration significantly altered creatinine plasma and urine levels in opposing directions (p < 0.005). Interestingly, famotidine increased the estimated bioavailability of metformin [cumulative amount of unchanged drug excreted in urine from time zero to infinity (A e∞)/dose; p < 0.005] without affecting its systemic exposure [area under the plasma concentration-time curve (AUC) or maximum concentration in plasma (C max)] as a result of a counteracting increase in metformin renal clearance. Moreover, metformin-famotidine co-therapy caused a transient effect on oral glucose tolerance tests [area under the glucose plasma concentration-time curve between time zero and 0.5 h (AUCglu,0.5); p < 0.005]. CONCLUSIONS: These results suggest that famotidine may improve the bioavailability and enhance the renal clearance of metformin.