The blood-brain barrier (BBB) transport of oxycodone was studied in rats. Microdialysis probes were inserted into the striatum and vena jugularis. Ten animals were given a bolus dose followed by a 120-min constant rate infusion to study the steady-state concepts of oxycodone BBB equilibration. Another 10 animals were given a 60-min constant rate infusion to study the rate of equilibration across the BBB. Oxycodone-D3 was used as a calibrator for the microdialysis experiments. The samples were analyzed with a liquid chromatography-tandem mass spectrometry method and a population pharmacokinetic model was used to simultaneously fit all the data using NONMEM. A two-compartment model which allowed for a delay between the venous and arterial compartments best described the pharmacokinetics for oxycodone in blood and plasma, whereas a one-compartment model was sufficient to describe the pharmacokinetics in the brain. The BBB transport of oxycodone was parameterized as CL_in and K_p,uu. CL_in describes the clearance of oxycodone across the BBB into the brain, whereas K_p,uu describes the extent of drug equilibration across the BBB. CL_in across the BBB was estimated to 1910 μl/min · g brain. K_p,uu was estimated to 3.0, meaning that the unbound concentration of oxycodone in brain was 3 times higher than in blood, which is an indication of active influx of oxycodone at the BBB. This is the first evidence of an opioid having an unbound steady-state concentration in brain that is higher than unity, which can explain potency discrepancies between oxycodone and other opioids.

This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters·h ⁻¹ , while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments inSaccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.

Accumulating evidence suggests that increasing doses of rifampicin may shorten tuberculosis treatment. The PanACEA HIGHRIF1 trial assessed safety, pharmacokinetics, and antimycobacterial activity of rifampicin at doses up to 40 mg/kg. Eighty‐three pulmonary tuberculosis patients received 10, 20, 25, 30, 35, or 40 mg/kg rifampicin daily over 2 weeks, supplemented with standard doses of isoniazid, pyrazinamide, and ethambutol in the second week. This study aimed at characterizing rifampicin pharmacokinetics observed in HIGHRIF1 using nonlinear mixed effects modeling. The final population pharmacokinetic model included an enzyme turnover model accounting for time‐dependent elimination due to autoinduction, concentration‐dependent clearance, and dose‐dependent bioavailability. The relationship between clearance and concentration was characterized by a Michaelis–Menten relationship. The relationship between bioavailability and dose was described using an E_max relationship. The model will be key in determining exposure–response relationships for rifampicin and should be considered when designing future trials and when treating future patients with high‐dose rifampicin.

Tuberculosis remains a huge public health problem and the prolonged treatment duration obstructs effective tuberculosis control. Higher rifampicin doses have been associated with better bactericidal activity, but optimal dosing is uncertain. This analysis aimed to characterize the relationship between rifampicin plasma exposure and treatment response over 6 months in a recent study investigating the potential for treatment shortening with high-dose rifampicin. Data were analyzed from 336 patients with pulmonary tuberculosis (97 with pharmacokinetic data) treated with rifampicin doses of 10, 20, or 35 mg/kg. The response measure was time to stable sputum culture conversion (TSCC). We derived individual exposure metrics with a previously developed population pharmacokinetic model of rifampicin. TSCC was modeled using a parametric time-to-event approach, and a sequential exposure-response analysis was performed. Higher rifampicin exposures increased the probability of early culture conversion. No maximal limit of the effect was detected within the observed range. The expected proportion of patients with stable culture conversion on liquid medium at week 8 was predicted to increase from 39% (95% confidence interval, 37%–41%) to 55% (49%–61%), with the rifampicin area under the curve increasing from 20 to 175 mg/L·h (representative for 10 and 35 mg/kg, respectively). Other predictors of TSCC were baseline bacterial load, proportion of culture results unavailable, and substitution of ethambutol for either moxifloxacin or SQ109. Increasing rifampicin exposure shortened TSCC, and the effect did not plateau, indicating that doses >35 mg/kg could be yet more effective. Optimizing rifampicin dosage while preventing toxicity is a clinical priority.

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC_0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R²: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC_0–24h prediction, LASSO showed the highest performance (R²: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC_0–24h prediction using LASSO, the R² was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.