Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, though no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand upon the existing knowledge. This tutorial is intended for any scientist analyzing a PK dataset with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data. Overview Data from clinical trials is frequently incomplete, particularly datasets collected during large, late phase trials, during routine clinical patient care or follow-up visits. Portions of data may be missing or inaccurate due to factors such as study site noncompliance, patient noncompliance, inappropriate sample handling, data entry errors, and analytical problems. How "problematic" data are handled can impact its interpretation, especially when data used for population pharmacokinetic (PPK) modeling contains missing or erroneous data. Prior to beginning an analysis, pharmacometricians often spend a large portion of time dealing with problematic data. During data cleaning (data quality assurance), the first step is to identify missing or problematic data. Concentration-time data and dosing records are often the primary concern, but other issues, such as missing or questionable covariate data, must also be considered. Once issues/discrepancies are identified, the next challenge is to evaluate frequency of occurrence of each type of problem and the associated reason to establish appropriate methods for handling these erroneous data. Prior studies have addressed handling of specific types of problematic data, though no set of broad recommendations spanning the various types of problematic data have been previously presented. Accepted Article This article is protected by copyright. All rights reserved Through review of published methods, simulation of data sets with known errors, and evaluation using different methods for handling these errors, this tutorial aims to provide guidance for dealing with problematic clinical (and some non-clinical) concentration vs. time, dosing, and covariate data. This tutorial is intended to be utilized by scientists analyzing pharmacokinetic data with either missing data or where apparently questionable or erroneous data is present. Although data quality assurance (QA) and control (QC) are essential to successful modeling, this tutorial assumes the dataset has already undergone appropriate QC or was assembled from locked, clean data. Basic assessments include exploratory data analysis by plotting and...

High‐dose melphalan (HDM) is part of the conditioning regimen in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT). However, individual sensitivity to melphalan varies, and many patients experience severe toxicities. Prolonged severe neutropenia is one of the most severe toxicities and contributes to potentially life‐threatening infections and failure of ASCT. Granulocyte‐colony stimulating factor (G‐CSF) is given to stimulate neutrophil proliferation after melphalan administration. The aim of this study was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model capable of predicting neutrophil kinetics in individual patients with MM undergoing ASCT with high‐dose melphalan and G‐CSF administration. The extended PK/PD model incorporated several covariates, including G‐CSF regimen, stem cell dose, hematocrit, sex, creatinine clearance, p53 fold change, and race. The resulting model explained portions of interindividual variability in melphalan exposure, therapeutic effect, and feedback regulation of G‐CSF on neutrophils, thus enabling simulation of various doses and prediction of neutropenia duration.

Background Monoclonal antibody (mAb) immune checkpoint inhibitor (ICI) therapies have dramatically impacted oncology this past decade. However, only about one-third of patients respond to treatment, and biomarkers to predict responders are lacking. Recent ICI clinical pharmacology data demonstrate high baseline drug clearance (CL 0 ) significantly associates with shorter overall survival, independent of ICI exposure, in patients receiving ICI mAb therapies. This suggests CL 0 may predict outcomes from ICI therapy, and cachectic signalling may link elevated CL 0 and poor response. Our aim was to determine if mouse models of cancer cachexia will be useful for studying these phenomena and their underlying mechanisms. Methods We evaluated pembrolizumab CL in the C26 and Lewis lung carcinoma mouse models of cancer cachexia. A single treatment of vehicle or pembrolizumab, at a dose of 2 or 10 mg/kg, was administered intravenously by tail vein injection. Pembrolizumab was quantified by an ELISA in serial plasma samples, and FcRn gene (Fcgrt) expression was assessed in liver using real-time quantitative reverse transcription PCR. Non-compartmental and mixed-effects pharmacokinetics analyses were performed. Results We observed higher pembrolizumab CL 0 and decreased Fcgrt expression in whole liver tissue from tumour-bearing vs. tumour-free mice. In multivariate analysis, presence of tumour, total murine IgG, muscle weight and Fcgrt expression were significant covariates on CL, and total murine IgG was a significant covariate on V1 and Q. Conclusions These data demonstrate increases in catabolic clearance of monoclonal antibodies observed in humans can be replicated in cachectic mice, in which Fcgrt expression is also reduced. Notably, FcRn activity is essential for proper antigen presentation and antitumour immunity, which may permit the study of cachexia's impact on FcRn-mediated clearance and efficacy of ICI therapies.

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Although myeloablative fludarabine/busulfan (FluBu4) has been widely adopted in clinical practice, considerable interpatient variability exists in systemic busulfan exposure (AUC) when using body weight or body surface area based-dosing, leading to decreased efficacy (i.e. relapse) or increased toxicity (i.e., mucositis, veno-occlusive disease). This well-defined dose-exposure-outcome relationship has led to the widespread clinical implementation of therapeutic drug monitoring (TDM). However, individualized TDM can be time and labor intensive as well as potentially biased due to the lack of incorporation of any previously established PK data (Bayesian prior). In contrast, Bayesian maximum a posteriori (MAP) PK models consider the Bayesian prior and individualized TDM to generate a revised probability distribution (Bayes conditional posterior) to more accurately and rapidly estimate the AUC with reduced bias. There are a paucity of data comparing busulfan AUCs using individualized PK versus MAP-Bayesian-based models in adults although these more sophisticated approaches may assist in optimizing dosing of busulfan in this vulnerable population. This was a retrospective, single-center study of patients who received FluBu4 with busulfan TDM between January 1999 and September 2019. 109 patients diagnosed with a hematologic malignancy who received either sequential (n=46) or concurrent (n=63) FluBu4 were analyzed. The median age was 48 (range: 18-66), and were Hispanic (n=38), White (n=46) or African-American (n=14). TDM was performed either after a test dose of 0.8 mg/kg (n=52) or after the first dose (3.2 mg/kg) of busulfan administered during the preparative regimen (n=71), with dosing was based on actual or adjusted body weight. For PK analysis, plasma busulfan concentrations were analyzed via gas chromatography with mass selective detection. Individualized PK data were generated using WinNonlin while the MAP-Bayesian approach utilized the Bayesian prior developed from McCune et al (Clin Cancer Res, 2014). An AUC of 4800 µM˖min/24 hours was targeted based on previous literature. Based on individualized PK data, total recommended busulfan doses ranged from 9.3-21.3 mg/kg (-27.1% to +66.7% compared to FDA labeled dose of 12.8 mg/kg). When first-dose busulfan PK was compared between busulfan given sequentially versus concurrently with fludarabine there was a trend towards a higher AUC with concomitant administration (4651 vs. 4988 µM˖min; p=0.13). A strong correlation between the AUC generated from both the individualized PK an MAP-Bayesian models was observed with both the test dose (R2=0.91) and first dose (R2=0.86) of busulfan. Using the MAP Bayesian model, AUC predictions were on average higher (mean AUC 5069 versus 4886 µM˖min, p<0.0001) compared to the patient-specific individualized PK estimates. Figure 1 shows the Bland Altman plots for comparison of the individualized AUC vs. MAP-Bayesian estimates for test dose and first dose. Our individualized busulfan PK approach generated relatively similar AUC values compared to MAP-Bayesian estimates, although the higher AUC generated via MAP-Bayesian predictions may allow for lower doses of busulfan to be administered thereby potentially reducing toxicity while maintaining efficacy. Further, use of the MAP-Bayesian method may allow for more rapid dose optimization and a decreased number of serum concentrations. Further prospective studies including more patients are warranted to confirm these findings. Figure 1 Disclosures Calip: Flatiron Health: Current Employment. Patel:Janssen: Consultancy; Amgen: Consultancy; Celgene: Consultancy.

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