Population pharmacokinetics and covariate analysis of Sym004, an antibody mixture against the epidermal growth factor receptor, in subjects with metastatic colorectal cancer and other solid tumors Abstract Sym004 is an equimolar mixture of two monoclonal antibodies, futuximab and modotuximab, which non-competitively block the epidermal growth factor receptor (EGFR). Sym004 has been clinically tested for treatment of solid tumors. The present work characterizes the non-linear pharmacokinetics (PK) of Sym004 and its constituent antibodies and investigates two types of covariate models for interpreting the interindividual variability of Sym004 exposure. Sym004 serum concentration data from 330 cancer patients participating in four Phase 1 and 2 trials (n = 247 metastatic colorectal cancer, n = 87 various types advanced solid tumors) were pooled for non-linear mixed effects modeling. Dose regimens of 0.4–18 mg/kg Sym004 dosed by i.v. infusion weekly or every 2nd week were explored. The PK profiles for futuximab and modotuximab were parallel, and the parameter values for their population PK models were similar. The PK of Sym004 using the sum of the serum concentrations of futuximab and modotuximab was well captured by a 2-compartment model with parallel linear and saturable, Michaelis–Menten-type elimination. The full covariate model including all plausible covariates included in a single step showed no impact on Sym004 exposure of age, Asian race, renal and hepatic function, tumor type and previous anti-EGFR treatments. The reduced covariate model contained statistically and potentially clinically significant influences of body weight, albumin, sex and baseline tumor size. Population PK modeling and covariate analysis of Sym004 were feasible using the sum of the serum concentrations of the two constituent antibodies. Full and reduced covariate models provided insights into which covariates may be clinically relevant for dose modifications and thus may need further exploration.

Cardiac risk assessment based on early Phase I data and PK-QTc analysis is concordant with the outcome of thorough QTc trials: an assessment based on eleven drug candidates Abstract Cardiac safety assessment is a key regulatory requirement for almost all new drugs. Until recently, one evaluation aspect was via a specifically designated, expensive, and resource intensive thorough QTc study, and a by-time-point analysis using an intersection–union test (IUT). ICH E14 Q&A (R3) (http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf) allows for analysis of the PK-QTc relationship using early Phase I data to assess QTc liability. In this paper, we compared the cardiac risk assessment based on the early Phase I analysis with that from a thorough QTc study across eleven drug candidate programs, and demonstrate that the conclusions are largely the same. The early Phase I analysis is based upon a linear mixed effect model with known covariance structure (Dosne et al. in Stat Med 36(24):3844–3857, 2017). The treatment effect was evaluated at the supratherapeutic Cmax as observed in the thorough QTc study using a non-parametric bootstrap analysis to generate 90% confidence intervals for the treatment effect, and implementation of the standardized methodology in R and SAS software yielded consistent results. The risk assessment based on the concentration–response analysis on the early Phase I data was concordant with that based on the standard analysis of the thorough QTc study for nine out of the eleven drug candidates. This retrospective analysis is consistent with and supportive of the conclusion of a previous prospective analysis by Darpo et al. (Clin Pharmacol Ther 97(4):326–335, 2015) to evaluate whether C-QTc analysis can detect QTc effects in a small study with healthy subjects.

Impact of Phase 1 study design on estimation of QT interval prolongation risk using exposure–response analysis Abstract The International Council for Harmonisation (ICH) guidelines have been revised allowing for modeling of concentration-QT (C-QT) data from Phase I dose-escalation studies to be used as primary analysis for QT prolongation risk assessment of new drugs. This work compares three commonly used Phase I dose-escalation study designs regarding their efficiency to accurately identify drug effects on QT interval through C-QT modeling. Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. Clinical trial simulations were performed for each design and across different scenarios (e.g. different magnitudes of drug effect, QT variability), assuming a pre-specified linear mixed effect (LME) model for the relationship between drug concentration and change from baseline QT (ΔQT). Analyses suggest no systematic bias in either the predictions of placebo-adjusted ΔQT (ΔΔQT) or the LME model parameter estimates across all evaluated designs. Additionally, false negative rates remained similar and adequately controlled across all evaluated designs. However, compared to the crossover designs, the parallel design had significantly less power to correctly exclude a clinically significant QT effect, especially in the presence of substantial intercept inter-individual variability. In such cases, parallel design is associated with increased uncertainty around ΔΔQT prediction, mainly attributed to the uncertainty around the estimation of the treatment-specific intercept in the model. Throughout all the evaluated scenarios, the crossover design with interleaving cohorts had consistently the best performance characteristics. The results from this investigation will further facilitate informed decision-making during Phase I study design and the interpretation of the associated C-QT modeling output.

Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM Abstract Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.

Cabozantinib exposure–response analyses of efficacy and safety in patients with advanced hepatocellular carcinoma Abstract Cabozantinib, a multi-kinase inhibitor, is approved in the United States and European Union for treatment of patients with hepatocellular carcinoma following prior sorafenib treatment. In the Phase III CELESTIAL trial, hepatocellular carcinoma patients receiving cabozantinib showed longer overall survival (OS) and progression-free survival (PFS) than those receiving placebo. The approved cabozantinib (Cabometyx®) dose is 60 mg once daily with allowable dose modifications to manage adverse events (AE). Time-to-event Cox proportional hazard exposure–response (ER) models were developed to characterize the relationship between predicted cabozantinib exposure and the likelihood of various efficacy and safety endpoints. The ER models were used to predict hazard ratios (HR) for efficacy and safety endpoints for starting doses of 60, 40, or 20 mg daily. Statistically significant relationships between cabozantinib exposure and efficacy and safety endpoints were observed. For efficacy endpoints, predicted HR were lower for OS and PFS at 40 and 60 mg relative to the 20 mg dose: HR for death (OS) are 0.84 (40 mg) and 0.70 (60 mg); HR for disease progression/death (PFS) are 0.73 (40 mg) and 0.62 (60 mg). For safety endpoints, predicted HR were lower for palmar-plantar erythrodysaesthesia (PPE), diarrhea, and hypertension at 20 or 40 mg relative to the 60 mg dose: HR for PPE are 0.31 (20 mg) and 0.66 (40 mg); HR for diarrhea are 0.61 (20 mg) and 0.86 (40 mg); HR for hypertension are 0.46 (20 mg) and 0.76 (40 mg). The rate of dose modifications was predicted to increase in patients with lower cabozantinib apparent clearance. OS and PFS showed the greatest benefit at the 60 mg dose. However, higher cabozantinib exposure was predicted to increase the likelihood of AE and subsequent dose reductions appeared to decrease these risks.

Pharmacokinetics-pharmacodynamics of sertraline as an antifungal in HIV-infected Ugandans with cryptococcal meningitis Abstract The ASTRO-CM dose-finding pilot study investigated the role of adjunctive sertraline for the treatment of HIV-associated cryptococcal meningitis in HIV-infected Ugandan patients. The present study is a post hoc pharmacokinetic-pharmacodynamic analysis of the ASTRO-CM pilot study to provide insight into sertraline exposure–response–outcome relationships. We performed a population pharmacokinetic analysis using sertraline plasma concentration data and correlated various predicted PK-PD indices with the percentage change in log10 CFU/mL from baseline. Sertraline clearance was 1.95-fold higher in patients receiving antiretroviral (ART), resulting in 49% lower drug exposure. To quantify the clinical benefit of sertraline, we estimated rates of fungal clearance from cerebrospinal fluid (CSF) of ASTRO-CM patients using Poisson model and compared the clearance rates to a historical control study (COAT) in which patients received standard Cryptococcus therapy of amphotericin B (0.7–1.0 mg/kg per day) and fluconazole (800 mg/day) without sertraline. Adjunctive sertraline significantly increased CSF fungal clearance rate compared to COAT trial and sertraline effect was dose-independent with no covariate found to affect fungal clearance including ART. Study findings suggest sertraline response could be mediated by different mechanisms than directly inhibiting the initiation of protein translation as previously suggested; this is supported by the prediction of unbound sertraline concentrations is unlikely to reach MIC concentrations in the brain. Study findings also recommend against the use of higher doses of sertraline, especially those greater than the maximum FDA-approved daily dose (200 mg/day), since they unlikely provide any additional benefits and come with greater costs and risk of adverse events.

Correction to: Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients The article Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients, written by Pierre Chelle, Cindy H. T. Yeung, Santiago Bonanad, Juan Cristóbal Morales Muñoz, Margareth C. Ozelo, Juan Eduardo Megías Vericat, Alfonso Iorio, Jeffrey Spears, Roser Mir, Andrea Edginton, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 21 May 2019 without open access.

Editorial to the themed issue on application of pharmacometrics to the development of drugs for rare diseases

Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients Abstract Fanhdi/Alphanate is a plasma derived factor VIII concentrate used for treating hemophilia A, for which there has not been any dedicated model describing its pharmacokinetics (PK). A population PK model was developed using data extracted from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project. WAPPS-Hemo provided individual PK profiles for hemophilia patients using sparse observations as provided in routine clinical care by hemophilia centers. Plasma factor activity measurements and covariate data from hemophilia A patients on Fanhdi/Alphanate were extracted from the WAPPS-Hemo database. A population PK model was developed using NONMEM and evaluated for suitability for Bayesian forecasting using prediction-corrected visual predictive check (pcVPC), cross validation, limited sampling analysis and external evaluation against a population PK model developed on rich sampling data. Plasma factor activity measurements from 92 patients from 12 centers were used to derive the model. The PK was best described by a 2-compartment model including between subject variability on clearance and central volume, fat free mass as a covariate on clearance, central and peripheral volumes, and age as covariate on clearance. Evaluations showed that the developed population PK model could predict the PK parameters of new individuals based on limited sampling analysis and cross and external evaluations with acceptable precision and bias. This study shows the feasibility of using real-world data for the development of a population PK model. Evaluation and comparison of the model for Bayesian forecasting resulted in similar results as a model developed using rich sampling data.

A quantitative systems pharmacology model of colonic motility with applications in drug development Abstract We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.