Effect of Carbamazepine on the Pharmacokinetics of Erdafitinib in Healthy Participants

Erdafitinib, a selective and potent oral pan-FGFR inhibitor, is metabolized mainly through CYP2C9 and CYP3A4 enzymes. This phase 1, open-label, single-sequence, drug-drug interaction study evaluated the pharmacokinetics, safety, and tolerability of a single oral dose of erdafitinib alone and when co-administered with steady state oral carbamazepine, a dual inducer of CYP3A4 and CYP2C9, in 13 healthy adult participants (NCT04330248). Compared with erdafitinib administration alone, carbamazepine co-administration decreased total and free maximum plasma concentrations of erdafitinib (Cmax) by 35% (95% CI 30%-39%) and 22% (95% CI 17%-27%), respectively. The areas under the concentration-time curve over the time interval from 0 to 168 hours, to the last quantifiable data point, and to time infinity (AUC168h, AUClast, AUCinf), were markedly decreased for both total erdafitinib (56%-62%) and free erdafitinib (48%-55%). The safety profile of erdafitinib was consistent with previous clinical studies in healthy participants, with no new safety concerns when administered with or without carbamazepine. Co-administration with carbamazepine may reduce the activity of erdafitinib due to reduced exposure. Concomitant use of strong CYP3A4 inducers with erdafitinib should be avoided.

Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program

A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.

Pharmacodynamic-Mediated Drug Disposition (PDMDD) Model of Daratumumab Monotherapy in Patients with Multiple Myeloma

BACKGROUND AND OBJECTIVE

We aimed to quantify the daratumumab concentration- and CD38 dynamics-dependent pharmacokinetics using a pharmacodynamic mediated disposition model (PDMDD) in patients with multiple myeloma (MMY) following daratumumab IV or SC monotherapy. Daratumumab, a human IgG monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action, has been approved to treat patients with multiple myeloma (MM).

METHODS

In total, 7788 daratumumab plasma samples from 850 patients with diagnosis of MMY were used. The serum concentration-time data of daratumumab were analysed using nonlinear mixed-effects modeling with NONMEM^®. The PDMDD with quasi steady-state approximation (QSS) was compared to the previously developed Michaelis-Menten (MM) approximation with respect to the parameter estimates, the goodness-of-fit plots and prediction-corrected visual predictive check, as well as model-based simulations. The effect of patients' covariates on daratumumab pharmacokinetics was also investigated.

RESULTS

The QSS approximation characterized the concentration- and CD38 dynamics-dependency of daratumumab pharmacokinetics within the doses ranging from 0.1 to 24 mg/kg after IV administration and 1200 and 1800 mg after SC administration in patients with MMY, mechanistically describing the binding of daratumumab and CD38, the internalization of the daratumumab-CD38 complex and the CD38 turnover. Compared to the previously developed MM approximation, the MM approximation with the non-constant total target and dose-correction provided substantial improvement of the model fit, but it was still not as good as the QSS approximation. The effect of the previously identified covariates as well as the newly identified covariate (baseline M protein) on daratumumab pharmacokinetics was confirmed, but the magnitude of the effect was deemed not clinically relevant.

CONCLUSIONS

Accounting for the CD38 turnover and its binding capacity to daratumumab, the QSS approximation provided a mechanistic interpretation of daratumumab PK parameters and consequently well described the concentration- and CD38 dynamics-dependency of daratumumab pharmacokinetics. CLINICAL STUDIES INCLUDED IN THE ANALYSIS WERE REGISTERED WITH THE NCT NUMBER BELOW AT HTTP://WWW.

CLINICALTRIALS

GOV : MMY1002 (ClinicalTrials.gov: NCT02116569), MMY1003 (NCT02852837), MMY1004 (NCT02519452), MMY1008 (NCT03242889), GEN501 (NCT00574288), MMY2002 (NCT01985126), MMY3012 (NCT03277105).

Plasma and liver pharmacokinetics of the GalNAc-siRNA JNJ-73763989 in rAAV-HBV infected mice

JNJ-73763989 is a N-Acetylgalactosamine (GalNAc) conjugated short interfering RNA (siRNA) combination product, consisting of 2 triggers, in clinical development for chronic hepatitis B virus (HBV) infection treatment, inducing a selective degradation of all HBV mRNA transcripts. Our aim is to characterize the plasma and liver pharmacokinetics (PK) of JNJ-73763989 after intravenous (IV) and subcutaneous (SC) administration in recombinant adeno-associated (rAAV) HBV infected mice. Forty-two male rAAV-HBV infected C57Bl/6 mice received JNJ-73763989 doses of 10 mg/kg IV or 1, 3 and 10 mg/kg SC. Plasma and liver concentrations were analyzed simultaneously using nonlinear mixed-effects modeling with the NONMEM 7.4. A population PK model consisting of a two-compartment disposition model with transporter-mediated drug disposition (TMDD) including internalization to the liver compartment, linear elimination from plasma and liver, and first-order absorption following SC administration was suitable to describe both plasma and liver PK. After SC dosing, absolute bioavailability was complete and flip-flop kinetics were observed. JNJ-73763989 distributes from plasma to liver via transporter-mediated liver internalization in less than 24 hours, with sustained (>42 days) liver exposure. The saturation of transporter-mediated liver internalization was hypothesized to be due to asialoglycoprotein receptor (ASGPR) saturation. Increasing the dose decreased the relative liver uptake efficiency in mice for IV, and to a lower extent for SC administered JNJ-73763989. Lower dose levels administered SC in mice can maximize the proportion of the dose reaching the liver. Significance Statement Pharmacokinetic modeling of JNJ-73763989 liver and plasma concentration-time data in mice indicated that the proportion of JNJ-3989 reaching the liver may be increased by administering lower SC doses compared to higher IV doses. Model-based simulations can be applied to optimize the dose and regimen combination.

Exposure-response analyses of erdafitinib in patients with locally advanced or metastatic urothelial carcinoma

Background

Exposure–response analyses were conducted to explore the relationship between selected efficacy and safety endpoints and serum phosphate (PO4) concentrations, a potential biomarker of efficacy and safety, in locally advanced or metastatic urothelial carcinoma patients with FGFR alterations treated with erdafitinib.

Methods

Data from two dosing regimens of erdafitinib in a phase 2 study (NCT02365597), 6 and 8-mg/day with provision for pharmacodynamically guided titration per serum PO4 levels, were analyzed using Cox proportional hazard or logistic regression models. Efficacy endpoints were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Safety endpoints were adverse events typical for FGFR inhibitors.

Results

Exposure-efficacy analyses on 156 patients (6-mg = 68; 8-mg = 88) showed that patients with higher serum PO4 levels within the first 6 weeks showed better OS (hazard ratio 0.57 [95% CI 0.46–0.72] per mg/dL of PO4; p = 0.01), PFS (hazard ratio 0.80 [0.67–0.94] per mg/dL of PO4; p = 0.01), and ORR (odds ratio 1.38 [1.02–1.86] per mg/dL of PO4; p = 0.04). Exposure-safety analyses on 177 patients (6-mg = 78; 8-mg = 99) showed that the incidence of selected adverse events associated with on-target off-tumor effects significantly rose with higher PO4.

Conclusions

The exploratory relationship between serum PO4 levels and efficacy/safety outcomes supported the use of pharmacodynamically guided dose titration to optimize erdafitinib’s therapeutic benefit/risk ratio.

Clinical trial registration number

NCT02365597.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-021-04381-4.

A translational model-based approach to inform the choice of the dose in phase 1 oncology trials: the case study of erdafitinib

PURPOSE

Erdafitinib (JNJ-42756493, BALVERSA) is a tyrosine kinase inhibitor indicated for the treatment of advanced urothelial carcinoma. In this work, a translational model-based approach to inform the choice of the doses in phase 1 trials is illustrated.

METHODS

A pharmacokinetic (PK) model was developed to describe the time course of erdafitinib plasma concentrations in mice and rats. Data from multiple xenograft studies in mice and rats were analyzed using the Simeoni tumor growth inhibition (TGI) model. The model parameters were used to derive a range of erdafitinib exposures that might inform the choice of the doses in oncology phase 1 trials. Conversion of exposures to doses was based on preliminary PK assessments from the first-in human (FIH) study.

RESULTS

A one-compartment PK disposition model, with linear absorption and dose-dependent clearance, adequately described the PK data in both mice and rats via an allometric scaling approach. The TGI model was able to describe tumor growth dynamics, providing quantitative measurements of erdafitinib antitumor potency in mice and rats. Based on these estimates, ranges of efficacious unbound concentration were identified for erdafitinib in mice (0.642-5.364 μg/L) and rats (0.782-2.565 μg/L). Based on the FIH data, it was possible to transpose exposures into doses and doses of above 4 mg/day provided erdafitinib exposures associated with significant TGI in animals. The findings were in agreement with the results of the FIH trial, in which the first hints of clinical activities were observed at 6 mg.

CONCLUSION

The successful modeling exercise of erdafitinib preclinical data showed how translational PK-PD modeling might be a tool to help to inform the choice of the doses in FIH studies.

Erdafitinib's effect on serum phosphate justifies its pharmacodynamically guided dosing in patients with cancer

A population pharmacokinetic (PK)–pharmacodynamic (PD) model was developed using data from 345 patients with cancer. The population PK‐PD model evaluated the effect of erdafitinib total and free plasma concentrations on serum phosphate concentrations after once‐daily oral continuous (0.5–12 mg) and intermittent (10–12 mg for 7 days on/7 days off) dosing, and investigated the potential covariates affecting erdafitinib‐related changes in serum phosphate levels. Phosphate is used as a biomarker for erdafitinib's efficacy and safety: increases in serum phosphate were observed after dosing with erdafitinib, which were associated with fibroblast growth factor receptor target engagement via inhibition of renal fibroblast growth factor 23–mediated signaling. PK‐PD model‐based simulations were performed to assess the approved PD‐guided dosing algorithm of erdafitinib (8 mg once‐daily continuous dosing, with up‐titration to 9 mg based on phosphate levels [<5.5 mg/dl] and tolerability at 14–21 days of treatment). The serum phosphate concentrations increased after the first dose and reached near maximal level after 14 days of continuous treatment. Serum phosphate increased with erdafitinib free drug concentrations: doubling the free concentration resulted in a 1.8‐fold increase in drug‐related phosphate changes. Dose adjustment after at least 14 days of dosing was supported by achievement of >95% maximal serum phosphate concentration. The peak‐to‐trough fluctuation within a dosing interval was limited for serum phosphate concentrations (5.68–5.65 mg/dl on Day 14), supporting phosphate monitoring at any time relative to dosing. Baseline phosphate was higher in women, otherwise, none of the investigated covariate–parameter relationships were considered clinically relevant. Simulations suggest that the starting dose of 8‐mg with up‐titration to 9‐mg on Days 14–21 maximized the number of patients within the target serum phosphate concentrations (5.5–7 mg/dl) while limiting the number of treatment interruptions. The findings from the PK‐PD model provided a detailed understanding of the erdafitinib concentration‐related phosphate changes over time, which supports erdafitinib's dosing algorithm.

A Population Pharmacokinetic Model of Macitentan and Its Active Metabolite Aprocitentan in Healthy Volunteers and Patients with Pulmonary Arterial Hypertension

BACKGROUND

Macitentan and its active metabolite, aprocitentan, are non-peptide, potent, dual endothelin receptor antagonists. Macitentan is approved for the treatment of pulmonary arterial hypertension in adults, at a dose of 10 mg/day.

OBJECTIVE

The objective of this study was to develop a comprehensive population model to describe the pharmacokinetics of macitentan and aprocitentan in healthy adults and adult subjects with pulmonary arterial hypertension.

METHODS

Pharmacokinetic data of 452 subjects in nine studies, after single and repeated doses (dose range 0.2-600 mg), were pooled for a non-linear mixed-effects analysis and the assessment of covariates, i.e., body weight, age, sex, race, renal and hepatic impairment, health status (healthy volunteers vs patients with pulmonary arterial hypertension), and formulation (capsules vs tablets) on pharmacokinetic parameters.

RESULTS

The final model was an open one-compartment disposition model, with linear elimination for macitentan and linear formation and elimination for aprocitentan. A semi-mechanistic absorption model described the dose dependency and multiple peaks observed for macitentan. For a female patient with pulmonary arterial hypertension after oral administration at 10 mg, macitentan reached a maximum concentration after 9 h and, following daily dosing, reached steady state after 3 days with a twofold accumulation factor. The apparent volume of distribution was 34 L and clearance was 1.39 L/h. Aprocitentan reached maximum concentration after 51 h and steady state after 9 days, with a 12.5-fold accumulation factor. Body weight, sex, race, renal impairment, health status, and formulation were statistically significant covariates on pharmacokinetic parameters.

CONCLUSIONS

The comprehensive population pharmacokinetic model adequately described the pharmacokinetics of macitentan and aprocitentan across different dose concentrations, regimens, and formulations. Several covariates significantly influenced the pharmacokinetics of macitentan and aprocitentan, but none was considered clinically relevant.

Assessment of the effect of erdafitinib on cardiac safety: analysis of ECGs and exposure-QTc in patients with advanced or refractory solid tumors

PURPOSE

To characterize the effect of erdafitinib on electrocardiogram (ECG) parameters and the relationship between erdafitinib plasma concentrations and QTc interval changes in patients with advanced or refractory solid tumors.

METHODS

Triplicate ECGs and continuous 12-lead Holter data were collected in the dose escalation part (Part 1) of the first-in-human study, with doses ranging from 0.5 to 12 mg. Triplicate ECG monitoring continued in Parts 2-4 where 2 dose regimens selected from Part 1 were expanded in prespecified tumor types. Analyses of ECG data included central tendency analyses, identification of categorical outliers and morphological assessment. A concentration-QTc analysis was conducted using a linear mixed-effect model based on extracted time matching Holter data.

RESULTS

Central tendency, categorical outlier, and ECG morphologic analyses from 187 patients revealed no clinically significant effect of erdafitinib on heart rate, atrioventricular conduction or cardiac depolarization (PR and QRS), and no effect on cardiac repolarization (QTc). Concentration-QTc analysis from 62 patients indicated that the slopes of relationship between total and free erdafitinib plasma concentrations and QTcI (mean exponent of 0.395) were estimated as - 0.00269 ms/(ng/mL) and - 1.138 ms/(ng/mL), respectively. The predicted change in QTcI at the observed geometric mean of total and free concentration at the highest therapeutic erdafitinib dose (9 mg daily) was < 10 ms at the upper bound of the two-sided 90% confidence interval.

CONCLUSIONS

ECG data and the concentration-QTc relationships demonstrate that erdafitinib does not prolong QTc interval and has no effects on cardiac repolarization or other ECG parameters. Clinical trial registration numbers NCT01703481, EudraCT: 2012-000697-34.

Erdafitinib (ERDA; JNJ-42756493), a pan-fibroblast growth factor receptor (FGFR) inhibitor, in patients (pts) with metastatic or unresectable urothelial carcinoma (mUC) and FGFR alterations (FGFRa): Phase 2 continuous versus intermittent dosing

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Background: Although immune checkpoint inhibitors (ICI) have improved outcomes in some pts with platinum-resistant mUC, many pts (eg, pts with TCGA luminal 1 tumors, many of whom are FGFRa) may not benefit. ERDA, a pan-FGFR (1-4) inhibitor, demonstrated promising phase 1 activity: 11 partial responses among 24 FGFRa mUC pts. We report efficacy and safety of ERDA in the ongoing global open-label phase 2 study BLC2001 (NCT02365597). Methods: Pts had measurable mUC with specific FGFR2/ FGFR3 mutations or translocations per central lab Janssen assay, ECOG 0-2, and were chemorefractory (progressed during/following ≥ 1 line of prior systemic chemo or ≤ 12 mos of [neo]adjuvant chemo). Cisplatin-ineligible, chemo-naïve pts, and prior ICI treatment were allowed. Pts were randomized 1:1 to 28-d cycles of oral 6 mg/d continuous dosing (6 C) or 10 mg/d intermittent 7 d on/7 d off dosing (10 I) ERDA; the dose was further uptitrated if no significant treatment-related adverse events (TRAEs) were observed. The primary end point was ORR. Results: 78 pts received 6 C and 33 pts received 10 I (10 I cohort stopped early) ERDA. 31 pts in 6 C arm were further uptitrated. Across arms, 50% had ≥ 2 prior lines of therapy; 93% were chemorefractory. Confirmed ORRs (RECIST 1.1) were 35% and 24%, and disease control rates (CR+PR+SD) were 74% and 73% in the 6 C and 10 I arms, respectively. Adverse events (AEs) were manageable, and there were no treatment-related deaths (Table). Treatment is ongoing in 10 pts. Conclusions: ERDA (6 C or 10 I) has promising efficacy and tolerability in pts with FGFRa mUC. Based on these results and ERDA pharmacometric modeling, dosing was optimized at 8 mg/d (continuous), and this cohort is ongoing. Phase 3 study is planned. Clinical trial information: NCT02365597. [Table: see text]

An automated sampling importance resampling procedure for estimating parameter uncertainty

Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.

Model averaging for robust assessment of QT prolongation by concentration-response analysis

Assessing the QT prolongation potential of a drug is typically done based on pivotal safety studies called thorough QT studies. Model-based estimation of the drug-induced QT prolongation at the estimated mean maximum drug concentration could increase efficiency over the currently used intersection-union test. However, robustness against model misspecification needs to be guaranteed in pivotal settings. The objective of this work was to develop an efficient, fully prespecified model-based inference method for thorough QT studies, which controls the type I error and provides satisfactory test power. This is achieved by model averaging: The proposed estimator of the concentration-response relationship is a weighted average of a parametric (linear) and a nonparametric (monotonic I-splines) estimator, with weights based on mean integrated square error. The desired properties of the method were confirmed in an extensive simulation study, which demonstrated that the proposed method controlled the type I error adequately, and that its power was higher than the power of the nonparametric method alone. The method can be extended from thorough QT studies to the analysis of QT data from pooled phase I studies.

dOFV distributions: a new diagnostic for the adequacy of parameter uncertainty in nonlinear mixed-effects models applied to the bootstrap

Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20–200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific “effective” sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used.

Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling

Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.