Handling of data below the lower limit of quantification (LLOQ), below the limit of quantification (BLOQ) in population pharmacokinetic (PopPK) analyses is important for reducing bias and imprecision in parameter estimation. We aimed to evaluate whether using the concentration data below the LLOQ has superior performance over several established methods. The performance of this approach ("All data") was evaluated and compared to other methods: "Discard," "LLOQ/2," and "LIKE" (likelihood-based). An analytical and residual error model was constructed on the basis of in-house analytical method validations and analyses from literature, with additional included variability to account for model misspecification. Simulation analyses were performed for various levels of BLOQ, several structural PopPK models, and additional influences. Performance was evaluated by relative root mean squared error (RMSE), and run success for the various BLOQ approaches. Performance was also evaluated for a real PopPK data set. For all PopPK models and levels of censoring, RMSE values were lowest using "All data." Performance of the "LIKE" method was better than the "LLOQ/2" or "Discard" method. Differences between all methods were small at the lowest level of BLOQ censoring. "LIKE" method resulted in low successful minimization (<50%) and covariance step success (<30%), although estimates were obtained in most runs (∼90%). For the real PK data set (7.4% BLOQ), similar parameter estimates were obtained using all methods. Incorporation of BLOQ concentrations showed superior performance in terms of bias and precision over established BLOQ methods, and shown to be feasible in a real PopPK analysis.

As pazopanib plasma trough concentrations are correlated with treatment outcome, we explored whether single nucleotide polymorphisms in the elimination pathway of pazopanib affect systemic pazopanib concentrations.

The incidence of neutropenia in metastatic castration-resistant prostate cancer (mCRPC) patients treated with docetaxel has been reported to be lower compared to patients with other solid tumors treated with a similar dose. It is suggested that this is due to increased clearance of docetaxel in mCRPC patients, resulting in decreased exposure. The aims of this study were to (1) determine if exposure in mCRPC patients is lower vs patients with other solid tumors by conducting a meta-analysis, (2) evaluate the incidence of neutropenia in patients with mCRPC vs other solid tumors in a clinical cohort, and (3) discuss potential clinical consequences. A meta-analysis was conducted of studies which reported areas under the plasma concentration-time curves (AUCs) of docetaxel and variability. In addition, grade 3/4 neutropenia was evaluated using logistic regression in a cohort of patients treated with docetaxel. The meta-analysis included 36 cohorts from 26 trials (n = 1150 patients), and showed that patients with mCRPC had a significantly lower mean AUC vs patients with other solid tumors (fold change [95% confidence interval (CI)]: 1.8 [1.5-2.2]), with corresponding AUCs of 1.82 and 3.30 mg∙h/L, respectively. Logistic regression, including 812 patient, demonstrated that patients with mCRPC had a 2.2-fold lower odds of developing grade 3/4 neutropenia compared to patients with other solid tumors (odds ratio [95%CI]: 0.46 [0.31-0.90]). These findings indicate that mCRPC patients have a lower risk of experiencing severe neutropenia, possibly attributable to lower systemic exposure to docetaxel.

Trastuzumab is associated with cardiotoxicity, manifesting as a decrease of the left-ventricular ejection fraction (LVEF). Administration of anthracyclines prior to trastuzumab increases risk of cardiotoxicity. High-sensitive troponin T and N-terminal-pro-brain natriuretic peptide (NT-proBNP) are molecular markers that may allow earlier detection of drug-induced cardiotoxicity. In this analysis we aimed to quantify the kinetics and exposure-response relationships of LVEF, troponin T and NT-proBNP measurements, in patients receiving anthracycline and trastuzumab. Repeated measurements of LVEF, troponin T and NT-proBNP and dosing records of anthracyclines and trastuzumab were available from a previously published clinical trial. This trial included 206 evaluable patients with early breast cancer. Exposure to anthracycline and trastuzumab was simulated based on available dosing records and by using a kinetic-pharmacodynamic (K-PD) and a fixed pharmacokinetic (PK) model from literature, respectively. The change from baseline troponin T was described with a direct effect model, affected by simulated anthracycline concentrations, representing myocyte damage. The relationship between trastuzumab and LVEF was described by an indirect effect compartment model. The EC50 for LVEF decline was significantly affected by the maximum troponin T concentration after anthracycline treatment, explaining 15.1% of inter-individual variability. In this cohort, NT-proBNP changes could not be demonstrated to be related to anthracycline or trastuzumab treatment. Pharmacodynamic models for troponin T and LVEF were successfully developed, identifying maximum troponin T concentration after anthracycline treatment as a significant determinant for trastuzumab-induced LVEF decline. These models can help identify patients at risk of drug-induced cardiotoxicity and optimize cardiac monitoring strategies.

Pemetrexed is used for the treatment for non-small cell lung cancer and mesothelioma. Patients with renal impairment are withheld treatment with this drug as it is unknown what dose is well tolerated in this population.

Childhood leukaemia is the most common type of cancer in children and represents among 25% of the diagnoses in children <15 years old. Childhood survival rates have significantly improved within the last 40 years due to a rapid advancement in therapeutic interventions. However, in high-risk groups, survival rates remain poor. Pharmacokinetic (PK) data of cancer medications in children are limited and thus current dosing regimens are based on studies with small sample sizes. In adults, large variability in PK is observed and dose individualisation (plasma concentration guided dosing) has been associated with improved clinical outcomes; whether this is true for children is still unknown. This provides an opportunity to explore this strategy in children to potentially reduce toxicities and ensure optimal dosing. This paper will provide a protocol to systematically review studies that have used dose individualisation of drugs used in the treatment of childhood leukaemias.

Chemotherapy-induced toxicities frequently occur in non-small cell lung cancer (NSCLC) patients treated with platinum-based chemotherapy. Low skeletal muscle mass (SMM) has been associated with a higher incidence of toxicities for several types of cancers and cytostatics. The aim of this study was to evaluate the association between skeletal muscle measures and chemotherapy-induced toxicity in a large cohort of NSCLC patients.

Over 75 kinase inhibitors (KIs) have been approved for the treatment of various cancers. KIs are orally administrated but mostly lack pediatric age-appropriate dosage forms or instructions for dose manipulation. This is highly problematic for clinical practice in pediatric oncology, as flexible oral formulations are essential to individually set dosages and to adjust it to a child's swallowability. Most KIs are poorly soluble, categorized in Biopharmaceutics Classification System (BCS) class II or IV, and improperly manipulating the KI formulation can alter pharmacokinetics and jeopardize KI drug safety and efficacy. Therefore, the goals of this review were to provide practical recommendations for manipulating the formulation of the 15 most frequently used KIs in pediatric oncology (i.e., bosutinib, cabozantinib, cobimetinib, crizotinib, dabrafenib, dasatinib, entrectinib, imatinib, larotrectinib, nilotinib, ponatinib, ruxolitinib, selumetinib, sunitinib and trametinib) based on available literature studies and fundamental drug characteristics and to establish a decision tool that supports decisions regarding formulation manipulation of solid oral dosages of KIs that have been or will be licensed (for adult and/or pediatric cancers) but are not included in this review.

Olaparib, niraparib, rucaparib, and talazoparib are poly (ADP-ribose) polymerase (PARP) inhibitors approved for the treatment of ovarian, breast, pancreatic, and/or prostate cancer. Poly (ADP-ribose) polymerase inhibitors are potent inhibitors of the PARP enzymes with comparable half-maximal inhibitory concentrations in the nanomolar range. Olaparib and rucaparib are orally dosed twice a day, extensively metabolized by cytochrome P450 enzymes, and inhibitors of several enzymes and drug transporters with a high risk for drug-drug interactions. Niraparib and talazoparib are orally dosed once a day with a lower risk for niraparib and a minimal risk for talazoparib to cause drug-drug interactions. All four PARP inhibitors show moderate-to-high interindividual variability in plasma exposure. Higher exposure is associated with an increase in toxicity, mostly hematological toxicity. For talazoparib, exposure-efficacy relationships have been described, but for olaparib, niraparib, and rucaparib this relationship remains inconclusive. Further studies are required to investigate exposure-response relationships to improve dosing of PARP inhibitors, in which therapeutic drug monitoring could play an important role. In this review, we give an overview of the pharmacokinetic properties of the four PARP inhibitors, including considerations for patients with renal dysfunction or hepatic impairment, the effect of food, and drug-drug interactions. Furthermore, we focus on the pharmacodynamics and summarize the available exposure-efficacy and exposure-toxicity relationships.

Rationale: Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PK) modelling approaches are widely accepted in non-radiopharmaceutical drug development and research, while there is no major role for these approaches in radiopharmaceutical development yet. In this review, a literature search was performed to specify different research purposes and questions that have previously been answered using both PBPK and population PK modelling for radiopharmaceuticals. Methods: The literature search was performed using the databases PubMed and Embase. Wide search terms included radiopharmaceutical, tracer, radioactivity, physiologically based pharmacokinetic model, PBPK, population pharmacokinetic model and nonlinear mixed-effects model. Results: Eight articles and twenty articles were included for this review based on this literature search for population PK modelling and PBPK modelling, respectively. Included population PK analyses showed to have an added value to develop predictive models for a population and to describe individual variability sources. Main purposes of PBPK models appeared related to optimizing treatment (planning), or more specifically: to find the optimal combination of peptide amount and radioactivity, to optimize treatment planning by reducing the number of measurements, to individualize treatment, to get insights in differences between pre-therapeutic and therapeutic scans or to understand inter-patient differences. Other main research subjects were regarding radiopharmaceutical comparisons, selecting ligands based on their peptide characteristics and gaining a better understanding of drug-drug interactions. Conclusions: The use of PK modelling approaches in radiopharmaceutical research remains scarce, but can be expanded to obtain a better understanding of PK and whole-body distribution of radiopharmaceuticals in general. PK modelling of radiopharmaceuticals has great potential for the nearby future and could contribute to the evolving research of radiopharmaceuticals.

Many oral anticancer drugs are metabolized by CYP3A. Clinical drug-drug interaction (DDI) studies often only examine the effect of strong CYP3A inhibitors and inducers. The effect of moderate or weak inhibitors or inducers can be examined using physiologically based pharmacokinetic simulations, but data from these simulations are not always available early after approval of a drug. In this review we provide recommendations for clinical practice on how to deal with DDIs of oral anticancer drugs if only data from strong CYP3A inhibitors or inducers is available. These recommendations were based on reviewed data of oral anticancer drugs primarily metabolized by CYP3A and approved for the treatment of solid tumors from January 1st, 2013 to December 31st, 2015. In addition, three drugs that were registered before the new EMA guideline was issued (i.e., everolimus, imatinib, and sunitinib), were reviewed. DDIs are often complex, but if no data is available from moderate CYP3A inhibitors/inducers, a change in exposure of 50% compared with strong inhibitors/inducers can be assumed. No a priori dose adaptations are indicated for weak inhibitors/inducers, because their interacting effect is small. In case pharmacologically active metabolites are involved, the metabolic pathway, the ratio of the parent to the metabolites, and the potency of the metabolites should be taken into account.

Allopurinol, an xanthine oxidase (XO) inhibitor, is a promising intervention that may provide neuroprotection for neonates with hypoxic-ischemic encephalopathy (HIE). Currently, a double-blind, placebo-controlled study (ALBINO, NCT03162653) is investigating the neuroprotective effect of allopurinol in HIE neonates.

Lidocaine is used to treat neonatal seizures refractory to other anticonvulsants. It is effective, but also associated with cardiac toxicity. Previous studies have reported on the pharmacokinetics of lidocaine in preterm and term neonates and proposed a dosing regimen for effective and safe lidocaine use. The objective of this study was to evaluate the previously developed pharmacokinetic models and dosing regimen. As a secondary objective, lidocaine effectiveness and safety were assessed.

To develop a semi-mechanistic model, based on glutathione depletion and predict a previously identified intra-individual reduction in busulfan clearance to aid in more precise dosing.

Azide is a highly toxic chemical agent to human being. Accidental, but also intentional exposure to azide occurs. To be able to confirm azide ingestion, we developed a method to identify and quantify azide in biological matrices. Cyanide was included in the method to evaluate suggested in vivo production of cyanide after azide ingestion. Azide in biological matrices was first derivatized by propionic anhydride to form propionyl azide. Simultaneously, cyanide was converted into hydrogen cyanide. After thermal rearrangement of propionyl azide, ethyl isocyanate was formed, separated together with hydrogen cyanide by gas chromatography (GC) and detected using a nitrogen phosphorous detector (NPD). The method was linear from 1.0-100 µg/mL for both analytes, and azide was stable in human plasma at -20°C for at least 49 days. Azide was measured in the gastric content of two cases of suspected azide ingestion (case 1:1.2 mg/mL, case 2:1.5 mg/mL). Cyanide was only identified in the gastric content of case 1 (approximately 1.4 µg/mL). Furthermore, azide was quantified in plasma (19 µg/mL), serum (24 µg/mL), cell pellet (21 µg/mL) and urine (3.0 µg/mL) of case 2. This method can be used to confirm azide and cyanide exposure, and azide concentrations can be quantified in several biological matrices.

Emicizumab is an effective new treatment option for people with hemophilia A (PwHA). The approved dosing regimens are based on body weight, without the necessity for laboratory monitoring. This assumes a clear dose-concentration-response relationship, with acceptable variability due to factors other than body weight. To investigate this assumption, a systematic review on the pharmacokinetics (PK) and associated efficacy of emicizumab in humans was conducted.

Administration of single-agent docetaxel in a weekly schedule may offer similar efficacy, with a more favorable toxicity profile, compared to a three-weekly schedule in patients with metastatic breast cancer.

Low skeletal muscle mass (LSMM) is increasingly recognized for its predictive value for adverse events in cancer patients. In specific, the predictive value of LSMM has been demonstrated for anti-cancer drug toxicity in a variety of cancer types and anti-cancer drugs. However, due to the limited sample size and study populations focused on a single cancer type, an overall predictive value of LSMM for anti-cancer drug toxicity remains unknown. Therefore, this review aims to provide a comprehensive overview of the predictive value of LSMM and perform a meta-analysis to analyse the overall effect. A systematic search was conducted of MEDLINE, Scopus, EMBASE, and Cochrane. Inclusion criteria were skeletal muscle mass (SMM) evaluated with computed tomography (CT) or magnetic resonance imaging (MRI), articles published in English, SMM studied in humans, SMM measurement normalized for height, and patients did not receive an intervention to treat or prevent LSMM. A meta-analysis was performed using a random-effects model and expressed in odds ratio (OR) with 95% confidence interval (CI). Heterogeneity was assessed using χ2 and I2 statistics. The search yielded 907 studies. 31 studies were included in the systematic review. Sample sizes ranged from 21 to 414 patients. The occurrence of LSMM ranged from 12.2% to 89.0%. The most frequently studied cancer types were oesophageal, renal, colorectal, breast, and head and neck cancer. Patients with LSMM had a higher risk of severe toxicity (OR 4.08; 95% CI 2.48-6.70; p < 0.001) and dose-limiting toxicity (OR 2.24; 95% CI 1.28-3.92; p < 0.001) compared to patients without LSMM. To conclude, the predictive value of LSMM for anti-cancer drug toxicity can be observed across cancer types. This information increases the need for further research into interventions that could treat LSMM as well as the possibility to adapt treatment regimens based on the presence of LSMM.

Intravenous busulfan doses are often personalized to a target plasma exposure (targeted busulfan) using an individual's busulfan clearance (BuCL). We evaluated whether BuCL could be predicted by a predose plasma panel of 841 endogenous metabolomic compounds (EMCs). In this prospective cohort of 132 hematopoietic cell transplantation (HCT) patients, all had samples collected immediately before busulfan administration (preBU) and 96 had samples collected 2 weeks before busulfan (2-week-preBU). BuCL was significantly associated with 37 EMCs after univariate linear regression analysis and controlling for false discovery (< 0.05) in the 132 preBU samples. In parallel, with preBU samples, we included all 841 EMCs in a least absolute shrinkage and selection operator-penalized regression which selected 13 EMCs as predominantly associated with BuCL. Then, we constructed a prediction model by estimating coefficients for these 13 EMCs, along with sex, using ordinary least-squares. When the resulting linear prediction model was applied to the 2-week-preBU samples, it explained 40% of the variation in BuCL (adjusted R2  = 0.40). Pathway enrichment analysis revealed 18 pathways associated with BuCL. Lysine degradation followed by steroid biosynthesis, which aligned with the univariate analysis, were the top two pathways. BuCL can be predicted before busulfan administration with a linear regression model of 13 EMCs. This pharmacometabolomics method should be prioritized over use of a busulfan test dose or pharmacogenomics to guide busulfan dosing. These results highlight the potential of pharmacometabolomics as a precision medicine tool to improve or replace pharmacokinetics to personalize busulfan doses.

Introduction MCLA-128 is a bispecific monoclonal antibody targeting the HER2 and HER3 receptors. Pharmacokinetics (PK) and pharmacodynamics (PD) of MCLA-128 have been evaluated in preclinical studies in cynomolgus monkeys and mice. The aim of this study was to characterize the PK and PD of MCLA-128 and to predict a safe starting dose and efficacious clinical dose for the First-In-Human study. Methods A PK-PD model was developed based on PK data from cynomolgus monkeys and tumor growth data from a mouse JIMT-1 xenograft model. Allometric scaling was used to scale PK parameters between species. Simulations were performed to predict the safe and efficacious clinical dose, based on AUCs, receptor occupancies and PK-PD model simulations. Results MCLA-128 PK in cynomolgus monkeys was described by a two-compartment model with parallel linear and nonlinear clearance. The xenograft tumor growth model consisted of a tumor compartment with a zero-order growth rate and a first-order dying rate, both affected by MCLA-128. Human doses of 10 to 480 mg q3wk were predicted to show a safety margin of >10-fold compared to the cynomolgus monkey AUC at the no-observed-adverse-effect-level (NOAEL). Doses of ≥360 mg resulted in predicted receptor occupancies above 99% (Cmax and Cave). These doses showed anti-tumor efficacy in the PK-PD model. Conclusions This analysis predicts that a flat dose of 10 to 480 mg q3wk is suitable as starting dose for a First-in-Human study with MCLA-128. Flat doses ≥360 mg q3wk are expected to be efficacious in human, based on receptor occupancies and PK-PD model simulations.