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Background: With the current treatment options for visceral leishmaniasis (VL), recrudescence of the parasite is seen in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting patient relapse in cases of VL. This study aimed to characterize the kinetics of circulating Leishmania parasites in the blood, during and after different antileishmanial therapies, and to find predictors for clinical relapse of disease. Methods: Data from three clinical trials, in which Eastern African VL patients received various antileishmanial regimens, were combined in this study. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during, and up to six months after treatment. An integrated population pharmacokinetic-pharmacodynamic model was developed using non-linear mixed effects modelling. Results: Parasite proliferation was best described by an exponential growth model, with an in vivo parasite doubling time of 7.8 days (RSE 12%). Parasite killing by fexinidazole, liposomal amphotericin B, sodium stibogluconate, and miltefosine was best described by linear models directly relating drug concentrations to the parasite elimination rate. After treatment, parasite growth was assumed to be suppressed by the host immune system, described by an Emax model driven by the time after treatment. No predictors for the high variability in onset and magnitude of the immune response could be identified. Model-based individual predictions of blood parasite load on Day 28 and Day 56 after start of treatment were predictive for clinical relapse of disease. Conclusion: This semi-mechanistic pharmacokinetic-pharmacodynamic model adequately captured the blood parasite dynamics during and after treatment, and revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could be a useful biomarker to assess treatment efficacy of a treatment regimen in a clinical trial setting. Author summary: After primary treatment of visceral leishmaniasis (VL), relapse of disease occurs in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting disease relapse at an early stage. Leishmania kinetoplast DNA loads in blood from East African VL patients from three clinical trials, treated with five different treatment regimens, were used to develop a semi-mechanistic model to integrate in vivo parasite replication in the host, parasite clearance by different VL drug regimens, and suppression of parasite regrowth by the host immune system after treatment. This model deepened our understanding of the parasite-drug-host interaction and described the in vivo parasite growth rate in human for the first time. Moreover, the model revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could serve as a biomarker to predict long-term clinical outcome, an important new tool for optimizing future VL treatment regimens. [ABSTRACT FROM AUTHOR]

Adequate power to identify an exposure‐response relationship in a phase IIa clinical trial for pulmonary tuberculosis (TB) is important for dose selection and design of follow‐up studies. Currently, it is not known what response marker provides the pharmacokinetic‐pharmacodynamic (PK‐PD) model more power to identify an exposure‐response relationship. We simulated colony‐forming units (CFU) and time‐to‐positivity (TTP) measurements for four hypothetical drugs with different activity profiles for 14 days. The power to identify exposure‐response relationships when analyzing CFU, TTP, or combined CFU + TTP data was determined at 60 total participants, or with 25 out of 60 participants in the lowest and highest dosing groups (unbalanced design). For drugs with moderate bactericidal activity, power was low (<59%), irrespective of the data analyzed. Power was 1.9% to 29.4% higher when analyzing TTP data compared to CFU data. Combined analysis of CFU and TTP further improved the power, on average by 4.2%. For a drug with a medium‐high activity, the total sample size needed to achieve 80% power was 136 for CFU, 72 for TTP, and 68 for combined CFU + TTP data. The unbalanced design improved the power by 16% over the balanced design. In conclusion, the power to identify an exposure‐response relationship is low for TB drugs with moderate bactericidal activity or with a slow onset of activity. TTP provides the PK‐PD model with more power to identify exposure‐response relationships compared to CFU, and combined analysis or an unbalanced dosing group study design offers modest further improvement. [ABSTRACT FROM AUTHOR]

First‐in‐human dose predictions are primarily based on no‐observed‐adverse‐effect levels in animal studies. Predictions from these animal models are only as effective as their ability to predict human results. To narrow the gap between human and animals, researchers have, among other things, focused on the replacement of animal cytochrome P450 (CYP) enzymes with their human counterparts (called humanization), especially in mice. Whereas research in humanized mice is extensive, the emphasis has been particularly on qualitative rather than quantitative predictions. Because the CYP3A4 enzyme is most involved in the metabolism of clinically used drugs, most benefit was expected from CYP3A4 models. There are several applications of these mouse models regarding in vivo CYP3A4 functionality, one of which might be their capacity to help improve first‐in‐human (FIH) dose predictions for CYP3A4‐metabolized drugs. To evaluate whether human‐CYP3A4‐transgenic mouse models are better predictors of human exposure compared to the wild‐type mouse model, we performed a meta‐analysis comparing both mouse models in their ability to accurately predict human exposure of small‐molecule drugs metabolized by CYP3A4. Results showed that, in general, the human‐CYP3A4‐transgenic mouse model had similar accuracy in the prediction of human exposure compared to the wild‐type mouse model, suggesting that there is limited added value in humanization of the mouse Cyp3a enzymes if the primary aim is to acquire more accurate FIH dose predictions. Despite the results of this meta‐analysis, corrections for interspecies differences through extension of human‐CYP3A4‐transgenic mouse models with pharmacokinetic modeling approaches seems a promising contribution to more accurate quantitative predictions of human pharmacokinetics. [ABSTRACT FROM AUTHOR]

Limited information is available concerning infant exposure and safety when breastfed by mothers receiving chemotherapy. Whereas defining distribution to breast milk is important to infer drug exposure, infant pharmacokinetics also determine to what extent the infant will be exposed to potential toxic effects. We aimed to assess the impact of chemotherapy containing breast milk on infants by predicting systemic and local (intestinal) exposure of paclitaxel and doxorubicin in infants through breast milk using a physiologically‐based pharmacokinetic (PBPK) approach. Whole‐body PBPK models of i.v. paclitaxel and doxorubicin were extended from the literature, with an oral absorption component to enable predictions in infants receiving paclitaxel or doxorubicin‐containing breast milk. For safety considerations, worst‐case scenarios were explored. Finally, paclitaxel and doxorubicin exposures in plasma and intestinal tissue of infants following feeding of breast milk from paclitaxel‐ or doxorubicin‐treated mothers were simulated and breast milk discarding strategies were evaluated. The upper 95th percentile of the predicted peak concentrations in peripheral venous blood were 3.48 and 0.74 nM (0.4%–1.7% and 0.1%–1.8% of on‐treatment) for paclitaxel and doxorubicin, respectively. Intestinal exposure reached peak concentrations of 1.0 and 140 μM for paclitaxel and doxorubicin, respectively. Discarding breast milk for the first 3 days after maternal chemotherapy administration reduced systemic and tissue exposures even further, to over 90% and 80% for paclitaxel and doxorubicin, respectively. PBPK simulations of chemotherapy exposure in infants after breastfeeding with chemotherapy containing breast milk suggest that particularly local gastrointestinal adverse events should be monitored, whereas systemic adverse events are not expected. [ABSTRACT FROM AUTHOR]

Aims: Carboplatin is generally dosed based on a modified Calvert formula, in which the Cockcroft–Gault‐based creatinine clearance (CRCL) is used as proxy for the glomerular filtration rate (GFR). The Cockcroft–Gault formula (CG) overpredicts CRCL in patients with an aberrant body composition. The CT‐enhanced estimate of RenAl FuncTion (CRAFT) was developed to compensate for this overprediction. We aimed to evaluate whether carboplatin clearance is better predicted by CRCL based on the CRAFT compared to the CG. Methods: Data of four previously conducted trials was used. The CRAFT was divided by serum creatinine to derive CRCL. The difference between CRAFT‐ and CG‐based CRCL was assessed by population pharmacokinetic modelling. Furthermore, the difference in calculated carboplatin dose was assessed in a heterogeneous dataset. Results: In total, 108 patients were included in the analysis. Addition of the CRAFT‐ and CG‐based CRCL as covariate on carboplatin clearance led, respectively, to an improved model fit with a 26‐point drop in objective function value and a worsened model fit with an increase of 8 points. In 19 subjects with serum creatinine <50 μmol/L, the calculated carboplatin dose was 233 mg higher using the CG. Conclusions: Carboplatin clearance is better predicted by CRAFT vs. CG‐based CRCL. In subjects with low serum creatinine, the calculated carboplatin dose using CG exceeds the dose using CRAFT, which might explain the need for dose capping when using the CG. Therefore, the CRAFT might be an alternative for dose capping while still dosing accurately. [ABSTRACT FROM AUTHOR]

Background: Prediction of [177Lu]Lu-HA-DOTATATE kidney and tumor uptake based on diagnostic [68Ga]Ga-HA-DOTATATE imaging would be a crucial step for precision dosing of [177Lu]Lu-HA-DOTATATE. In this study, the population pharmacokinetic (PK) differences between [177Lu]Lu-HA-DOTATATE and [68Ga]Ga-HA-DOTATATE were assessed and subsequently [177Lu]Lu-HA-DOTATATE was predicted based on [68Ga]Ga-HA-DOTATATE imaging. Methods: A semi-physiological nonlinear mixed-effects model was developed for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE, including six compartments (representing blood, spleen, kidney, tumor lesions, other somatostatin receptor expressing organs and a lumped rest compartment). Model parameters were fixed based on a previously developed physiologically based pharmacokinetic model for [68Ga]Ga-HA-DOTATATE. For [177Lu]Lu-HA-DOTATATE, PK parameters were based on literature values or estimated based on scan data (four time points post-injection) from nine patients. Finally, individual [177Lu]Lu-HA-DOTATATE uptake into tumors and kidneys was predicted based on individual [68Ga]Ga-HA-DOTATATE scan data using Bayesian estimates. Predictions were evaluated compared to observed data using a relative prediction error (RPE) for both area under the curve (AUC) and absorbed dose. Lastly, to assess the predictive value of diagnostic imaging to predict therapeutic exposure, individual prediction RPEs (using Bayesian estimation) were compared to those from population predictions (using the population model). Results: Population uptake rate parameters for spleen, kidney and tumors differed by a 0.29-fold (15% relative standard error (RSE)), 0.49-fold (15% RSE) and 1.43-fold (14% RSE), respectively, for [177Lu]Lu-HA-DOTATATE compared to [68Ga]Ga-HA-DOTATATE. Model predictions adequately described observed data in kidney and tumors for both peptides (based on visual inspection of goodness-of-fit plots). Individual predictions of tumor uptake were better (RPE AUC –40 to 28%) compared to kidney predictions (RPE AUC –53 to 41%). Absorbed dose predictions were less predictive for both tumor and kidneys (RPE tumor and kidney –51 to 44% and –58 to 82%, respectively). For most patients, [177Lu]Lu-HA-DOTATATE tumor accumulation predictions based on individual PK parameters estimated from diagnostic imaging outperformed predictions based on population parameters. Conclusion: Our semi-physiological PK model indicated clear differences in PK parameters for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE. Diagnostic images provided additional information to individually predict [177Lu]Lu-HA-DOTATATE tumor uptake compared to using a population approach. In addition, individual predictions indicated that many aspects, apart from PK differences, play a part in predicting [177Lu]Lu-HA-DOTATATE distribution. [ABSTRACT FROM AUTHOR]

Studies to evaluate and optimize [177Lu]Lu‐PSMA treatment focus primarily on individual patient data. A population pharmacokinetic (PK) dosimetry model was developed to explore the potential of using imaging data as input for population PK models and to characterize variability in organ and tumor uptake of [177Lu]Lu‐PSMA‐617 in patients with low volume metastatic prostate cancer. Simulations were performed to identify the effect of dose adjustments on absorbed doses in salivary glands and tumors. A six‐compartment population PK model was developed, consisting of blood, salivary gland, kidneys, liver, tumor, and a lumped compartment representing other tissue (compartment 1–6, respectively), based on data from 10 patients who received [177Lu]Lu‐PSMA‐617 (2 cycles, ~ 3 and ~ 6 GBq). Data consisted of radioactivity levels (decay corrected) in blood and tissues (9 blood samples and 5 single photon emission computed tomography/computed tomography scans). Observations in all compartments were adequately captured by individual model predictions. Uptake into salivary glands was saturable with an estimated maximum binding capacity (Bmax) of 40.4 MBq (relative standard error 12.3%) with interindividual variability (IIV) of 59.3% (percent coefficient of variation [CV%]). IIV on other PK parameters was relatively minor. Tumor volume was included as a structural effect on the tumor uptake rate constant (k15), where a two‐fold increase in tumor volume resulted in a 1.63‐fold increase in k15. In addition, interoccasion variability on k15 improved the model fit (43.5% [CV%]). Simulations showed a reduced absorbed dose per unit administered activity for salivary glands after increasing radioactivity dosing from 3 to 6 GBq (0.685 Gy/GBq vs. 0.421 Gy/GBq, respectively). All in all, population PK modeling could help to improve future radioligand therapy research. [ABSTRACT FROM AUTHOR]