Jos H Beijnen’s research while affiliated with Netherlands Cancer Institute and other places

Purpose Palliative treatment options for HR + HER2- advanced breast cancer (ABC) patients have increased, but data is lacking about the optimal treatment sequence. We used real-world data from a comprehensive cancer center to describe applied treatment sequences and we determined treatment-related and survival outcomes. Methods Patients aged 18 years and older with HR + HER2- ABC treated with systemic treatment were included in this historic cohort study. Sequential treatment schedules, time to treatment discontinuation, time to chemotherapy, and overall survival (OS) were determined, stratified by first-line treatment. Results 202 patients were included. They received a total of 650 treatment lines (median 3; range: 1–11). 91 (45%), 25 (12%), 24 (12%), 28 (14%), 22 (11%) and 12 (6%) patients started first-line treatment with non-steroidal aromatase inhibitors (NSAI), NSAI + cyclin dependent kinase 4/6-inhibitors (CDK4/6i), fulvestrant + CDK4/6i, tamoxifen, chemotherapy and other treatment, respectively. 10, 13, and 14 different treatment regimens were given in first, second and third-line, respectively. Of the patients who started first-line NSAI monotherapy (n = 91), 3 (3%) died before receiving second-line treatment. Conclusion In this real-world cohort, we observed a wide variety of different treatment sequences applied in daily clinical practice, some of which were in discordance with the current guidelines. Fear that patients may never get around to treatment with CDK4/6i if a patient did not start with a CDK4/6i was not supported by our study results.

Purpose In exposure–response analyses of oral targeted anticancer agents, longitudinal plasma trough concentrations are often aggregated into a single value even though plasma trough concentrations can vary over time due to dose adaptations, for example. The aim of this study was to compare joint models to conventional exposure–response analyses methods with the application of alectinib as proof-of-concept. Methods Joint models combine longitudinal pharmacokinetic data and progression-free survival data to infer the dependency and association between the two datatypes. The results from the best joint model and the standard and time-dependent cox proportional hazards models were compared. To normalize the data, alectinib trough concentrations were normalized using a sigmoidal transformation to transformed trough concentrations (TTC) before entering the models. Results No statistically significant exposure–response relationship was observed in the different Cox models. In contrast, the joint model with the current value of TTC in combination with the average TTC over time did show an exposure–response relationship for alectinib. A one unit increase in the average TTC corresponded to an 11% reduction in progression (HR, 0.891; 95% confidence interval, 0.805–0.988). Conclusion Joint models are able to give insights in the association structure between plasma trough concentrations and survival outcomes that would otherwise not be possible using Cox models. Therefore, joint models should be used more often in exposure–response analyses of oral targeted anticancer agents.

Introduction Post-kala-azar dermal leishmaniasis (PKDL) arises as a dermal complication following a visceral leishmaniasis (VL) infection. Current treatment options for PKDL are unsatisfactory, and there is a knowledge gap regarding the distribution of antileishmanial compounds within human skin. The present study investigated the skin distribution of miltefosine in PKDL patients, with the aim to improve the understanding of the pharmacokinetics at the skin target site in PKDL. Methods Fifty-two PKDL patients underwent treatment with liposomal amphotericin B (20 mg/kg) plus miltefosine (allometric dosing) for 21 days. Plasma concentrations of miltefosine were measured on study days 8, 15, 22 and 30, while a punch skin biopsy was taken on day 22. A physiologically based pharmacokinetic (PBPK) model was developed to evaluate the distribution of miltefosine into the skin. Results Following the allometric weight-based dosing regimen, median miltefosine concentrations on day 22 were 43.73 µg/g (IQR: 21.94–60.65 µg/g) in skin and 33.29 µg/mL (IQR: 25.9–42.58 µg/mL) in plasma. The median individual concentration ratio of skin to plasma was 1.19 (IQR: 0.79–1.9). In 87% (45/52) of patients, skin exposure was above the suggested EC90 PK target of 10.6 mg/L associated with in vitro susceptibility. Simulations indicated that the residence time of miltefosine in the skin would be more than 2-fold longer than in plasma, estimated by a mean residence time of 604 versus 266 hours, respectively. Conclusion This study provides the first accurate measurements of miltefosine penetration into the skin, demonstrating substantial exposure and prolonged retention of miltefosine within the skin. These findings support the use of miltefosine in cutaneous manifestations of leishmaniasis. In combination with parasitological and clinical data, these results are critical for the future optimization of combination therapies with miltefosine in the treatment of PKDL.

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.

Adagrasib (Krazati™) is the second FDA-approved specific KRASG12C inhibitor for non-small cell lung cancer (NSCLC) patients harboring this mutation. The impact of the drug efflux transporters ABCB1 and ABCG2, and the drug-metabolizing enzymes CYP3A and carboxylesterase 1 (CES1) on the pharmacokinetics of oral adagrasib were studied using genetically modified mouse models. Adagrasib was potently transported by human ABCB1 and modestly by mouse Abcg2 in vitro. In Abcb1a/b-/- and Abcb1a/b;Abcg2-/- mice, the brain-to-plasma ratios were enhanced by 33- and 55-fold, respectively, compared to wild-type mice, whereas ratios in Abcg2-/- mice remained unchanged. The influence of ABC transporters was completely reversed by coadministration of the dual ABCB1/ABCG2 inhibitor elacridar, increasing the brain penetration in wild-type mice by 41-fold while no signs of acute CNS toxicity were observed. Tumor ABCB1 overexpression may thus confer adagrasib resistance. Whereas the ABC transporters did not affect adagrasib plasma exposure, CYP3A and Ces1 strongly impacted its apparent oral availability. The plasma AUC0-8 h was significantly enhanced by 2.3-fold in Cyp3a-/- compared to wild-type mice, and subsequently 4.3-fold reduced in transgenic CYP3A4 mice, indicating substantial CYP3A-mediated metabolism. Adagrasib plasma exposure was strongly reduced in Ces1-/- compared to wild-type mice, but tissue exposure was slightly increased, suggesting that adagrasib binds to plasma Ces1c in mice and is perhaps metabolized by Ces1. This binding could complicate interpretation of mouse studies, especially since humans lack circulating CES1 enzyme(s). Our results may be useful to further optimize the clinical safety and efficacy of adagrasib, and give more insight into potential drug-drug interactions risks.

We have successfully developed and validated a bioanalytical assay using liquid chromatography tandem mass spectrometry to simultaneously quantify the first approved KRASG12C inhibitor sotorasib and its major circulating metabolite (M24) in various mouse matrices. M24 was synthesized in-house via low-pH hydrolysis. We utilized a fast and efficient protein precipitation method in a 96-well plate format to extract both analytes from biological matrices. Erlotinib was selected as the internal standard in this assay. Gradient elution using methanol and 0.1 % formic acid in water (v/v) was applied on an Acquity UPLC BEH C18 column to separate all analytes. Sotorasib, M24, and erlotinib were detected with a triple quadrupole mass spectrometer in positive electrospray ionization in multiple reaction monitoring mode. During the validation and sample quantification, a linear calibration range was observed for both sotorasib and M24 in a range of 4 - 4000 nM and 1 - 1000 nM, respectively. The %bias and %CV (both intra- and inter-day) for all tested levels in all investigated matrices were lower than 15 % as required by the guidelines. Sotorasib had a rather short room temperature stability in mouse plasma for up to 8 h compared to M24 which was stable up to 16 h at room temperature. This method has been successfully applied to measure sotorasib and M24 in several mouse matrices from three different mouse strains. We can conclude that the plasma exposure of sotorasib in mice is limited via human CYP3A4- and mouse Cyp3a-mediated metabolism of sotorasib into M24.

Background & Purpose Heroin (diacetylmorphine; diamorphine) is a highly addictive opioid prodrug. Heroin prescription is possible in some countries for chronic, treatment-refractory opioid-dependent patients and as a potent analgesic for specific indications. We aimed to study the pharmacokinetic interactions of heroin and its main pharmacodynamically active metabolites, 6-monoacetylmorphine (6-MAM) and morphine, with the multidrug efflux transporters P-glycoprotein/ABCB1 and BCRP/ABCG2 using wild-type, Abcb1a/1b and Abcb1a/1b;Abcg2 knockout mice. Methods & Results Upon subcutaneous (s.c.) heroin administration, its blood levels decreased quickly, making it challenging to detect heroin even shortly after dosing. 6-MAM was the predominant active metabolite present in blood and most tissues. At 10 and 30 min after heroin administration, 6-MAM and morphine brain accumulation were increased about 2-fold when mouse (m)Abcb1a/1b and mAbcg2 were ablated. Fifteen minutes after direct s.c. administration of an equimolar dose of 6-MAM, we observed good intrinsic brain penetration of 6-MAM in wild-type mice. Still, mAbcb1 limited brain accumulation of 6-MAM and morphine without affecting their blood exposure, and possibly mediated their direct intestinal excretion. A minor contribution of mAbcg2 to these effects could not be excluded. Conclusions We show that mAbcb1a/1b can limit 6-MAM and morphine brain exposure. Pharmacodynamic behavioral/postural observations, while non-quantitative, supported moderately increased brain levels of 6-MAM and morphine in the knockout mouse strains. Variation in ABCB1 activity due to genetic polymorphisms or environmental factors (e.g., drug interactions) might affect 6-MAM/morphine exposure in individuals, but only to a limited extent.

Background: the study aims to evaluate whether high plasma trough levels of the kinase inhibitors (K.I.s) crizotinib, alectinib, osimertinib, dabrafenib, and trametinib were associated with a higher risk of toxicity in non-small-cell lung cancer patients. Methods: In this retrospective cohort study, patients with non-small-cell lung cancer treated with the selected K.I.s were included if at least one plasma trough level at steady state (Cmin,ss) was available. Data were extracted from electronic medical records and laboratory databases. The high group for each K.I. was defined as 10% of patients with the highest first Cmin,ss. The remaining patients were placed in the non-high group. The frequency of dose-limiting toxicities (DLTs), defined as adverse events leading to dose reduction, dose interruption, or permanent discontinuation, was compared between the 2 groups. Results: A total of 542 patients were included in the different K.I. groups. A high Cmin,ss of crizotinib (n = 96), alectinib (n = 105), osimertinib (n = 227), dabrafenib (n = 52), and trametinib (n = 62) correlated with a Cmin,ss ≥490, ≥870, ≥405, ≥150, and ≥25 ng/mL, respectively. DLTs were more common in the alectinib high group than in the alectinib non-high group (64% vs. 29%, P = 0.036). Liver toxicity was observed in 4 (36%) patients in the high group and 5 (5%) patients in the non-high group (P = 0.007). For other K.I.s, no significant differences were observed in the frequency of DLTs between the high and non-high groups. Conclusions: For alectinib, high Cmin,ss was correlated with a higher risk of DLT. No differences in the frequency of DLTs were observed between the high and non-high groups for crizotinib, osimertinib, dabrafenib, and trametinib.

Background Little is known about parameters that have a relevant impact on (dis)similarities in biodistribution between various ⁶⁸Ga-labeled somatostatin analogues. Additionally, the effect of tumor burden on organ uptake remains unclear. Therefore, the aim of this study was to describe and compare organ and tumor distribution of [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE using a physiologically based pharmacokinetic (PBPK) model and to identify factors that might cause biodistribution and tumor uptake differences between both peptides. In addition, the effect of tumor burden on peptide biodistribution in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients was assessed. Methods A PBPK model was developed for [⁶⁸Ga]Ga-(HA-)DOTATATE in GEP-NET patients. Three tumor compartments were added, representing primary tumor, liver metastases and other metastases. Furthermore, reactions describing receptor binding, internalization and recycling, renal clearance and intracellular degradation were added to the model. Scan data from GEP-NET patients were used for evaluation of model predictions. Simulations with increasing tumor volumes were performed to assess the tumor sink effect. Results Data of 39 and 59 patients receiving [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, respectively, were included. Evaluations showed that the model adequately described image-based patient data and that different receptor affinities caused organ uptake dissimilarities between both peptides. Sensitivity analysis indicated that tumor blood flow and blood volume impacted tumor distribution most. Tumor sink predictions showed a decrease in spleen uptake with increasing tumor volume, which seemed clinically relevant for patients with total tumor volumes higher than ~ 550 mL. Conclusion The developed PBPK model adequately predicted tumor and organ uptake for this GEP-NET population. Relevant organ uptake differences between [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE were caused by different affinity profiles, while tumor uptake was mainly affected by tumor blood flow and blood volume. Furthermore, tumor sink predictions showed that for the majority of patients a tumor sink effect is not expected to be clinically relevant.

Bioanalytical method development and validation for the quantification of antileishmanial drugs are pivotal to support clinical trials and provide the data necessary to conduct pharmacokinetic analysis. This review provides a comprehensive overview of published validated bioanalytical assays for the quantification of antileishmanial drugs amphotericin B, miltefosine, paromomycin, pentamidine, and pentavalent antimonials in human matrices. Each antileishmanial drug was discussed regarding their applicability of the assay for leishmaniasis clinical trials as well as providing their relevance for PK studies with emphasis on the choice of matrix, calibration range, sample volume, sample preparation, choice of internal standards, separation, and detection. Given that no published bioanalytical methods included multiple antileishmanial drugs in a single assay while antileishmanial shortened combination regimens currently were under investigation, it was recommended to combine various drugs in a single bioanalytical method. Furthermore, bioanalytical method development regarding target site matrix as well as applying microsampling strategies was recommended to optimize future clinical pharmacokinetic studies in leishmaniasis.