Demonstrating Bioequivalence for Two Dose Strengths of Niraparib and Abiraterone Acetate Dual-Action Tablets Versus Single Agents: Utility of Clinical Study Data Supplemented with Modeling and Simulation.

BACKGROUND AND OBJECTIVE: The combination of niraparib and abiraterone acetate (AA) plus prednisone is under investigation for the treatment of patients with metastatic castration-resistant prostate cancer (mCRPC) and metastatic castration-sensitive prostate cancer (mCSPC). Regular-strength (RS) and lower-strength (LS) dual-action tablets (DATs), comprising niraparib 100 mg/AA 500 mg and niraparib 50 mg/AA 500 mg, respectively, were developed to reduce pill burden and improve patient experience. A bioequivalence (BE)/bioavailability (BA) study was conducted under modified fasting conditions in patients with mCRPC to support approval of the DATs. METHODS: This open-label randomized BA/BE study (NCT04577833) was conducted at 14 sites in the USA and Europe. The study had a sequential design, including a 21-day screening phase, a pharmacokinetic (PK) assessment phase comprising three periods [namely (1) single-dose with up to 1-week run-in, (2) daily dose on days 1-11, and (3) daily dose on days 12-22], an extension where both niraparib and AA as single-agent combination (SAC; reference) or AA alone was continued from day 23 until discontinuation, and a 30-day follow-up phase. Patients were randomly assigned in a parallel-group design (four-sequence randomization) to receive a single oral dose of niraparib 100 mg/AA 1000 mg as a LS-DAT or SAC in period 1, and patients continued as randomized into a two-way crossover design during periods 2 and 3 where they received niraparib 200 mg/AA 1000 mg once daily as a RS-DAT or SAC. The design was powered on the basis of crossover assessment of RS-DAT versus SAC. During repeated dosing (periods 2 and 3, and extension phase), all patients also received prednisone/prednisolone 5 mg twice daily. Plasma samples were collected for measurement of niraparib and abiraterone plasma concentrations. Statistical assessment of the RS-DAT and LS-DAT versus SAC was performed on log-transformed pharmacokinetic parameters data from periods 2 and 3 (crossover) and from period 1 (parallel), respectively. Additional paired analyses and model-based bioequivalence assessments were conducted to evaluate the similarity between the LS-DAT and SAC. RESULTS: For the RS-DAT versus SAC, the 90% confidence intervals (CI) of geometric mean ratios (GMR) for maximum concentration at a steady state (Cmax,ss) and area under the plasma concentration-time curve from 0-24 h at a steady state (AUC 0-24h,ss) were respectively 99.18-106.12% and 97.91-104.31% for niraparib and 87.59-106.69 and 86.91-100.23% for abiraterone. For the LS-DAT vs SAC, the 90% CI of GMR for AUC0-72h of niraparib was 80.31-101.12% in primary analysis, the 90% CI of GMR for Cmax,ss and AUC 0-24h,ss of abiraterone was 85.41-118.34% and 86.51-121.64% respectively, and 96.4% of simulated LS-DAT versus SAC BE trials met the BE criteria for both niraparib and abiraterone. CONCLUSIONS: The RS-DAT met BE criteria (range 80%-125%) versus SAC based on 90% CI of GMR for Cmax,ss and AUC 0-24h,ss. The LS-DAT was considered BE to SAC on the basis of the niraparib component meeting the BE criteria in the primary analysis for AUC 0-72h; abiraterone meeting the BE criteria in additional paired analyses based on Cmax,ss and AUC 0-24h,ss; and the percentage of simulated LS-DAT versus SAC BE trials meeting the BE criteria for both. GOV IDENTIFIER: NCT04577833.

Teclistamab: Mechanism of action, clinical, and translational science.

Multiple myeloma (MM) remains incurable despite improvements in treatment options. B-cell maturation antigen (BCMA) is predominantly expressed in B-lineage cells and represents a promising new target for MM. Teclistamab (TECVAYLITM ) is the first T-cell redirecting bispecific antibody approved for patients with MM. Targeting both CD3 receptor complex on T cells and BCMA on myeloma cells, teclistamab leads to T-cell activation and subsequent lysis of BCMA+ cells. The recommended dose of teclistamab is 1.5 mg/kg subcutaneous weekly after two step-up doses of 0.06 and 0.3 mg/kg, which was selected after review of safety, efficacy, pharmacokinetic, and pharmacodynamic data. Exposure-response analyses of efficacy and safety data were also used to confirm the teclistamab dose. Teclistamab resulted in a high rate of deep and durable responses (63% overall response, 45.5% complete response or better, with 22 months median duration of response) in patients with triple-exposed relapsed/refractory MM. Common adverse reactions included cytokine release syndrome, hematologic abnormalities, and infections. Teclistamab is currently being investigated as monotherapy as well as combination therapy across different MM indications.

Challenging the Norm: A Multidisciplinary Perspective on Intravenous to Subcutaneous Bridging Strategies for Biologics.

The transition from intravenous (i.v.) to subcutaneous (s.c.) administration of biologics is a critical strategy in drug development aimed at improving patient convenience, compliance, and therapeutic outcomes. Focusing on the increasing role of model-informed drug development (MIDD) in the acceleration of this transition, an in-depth overview of the essential clinical pharmacology, and regulatory considerations for successful i.v. to s.c. bridging for biologics after the i.v. formulation has been approved are presented. Considerations encompass multiple aspects beginning with adequate pharmacokinetic (PK) and pharmacodynamic (i.e., exposure-response) evaluations which play a vital role in establishing comparability between the i.v. and s.c. routes of administrations. Selected key recommendations and points to consider include: (i) PK characterization of the s.c. formulation, supported by the increasing preclinical understanding of the s.c. absorption, and robust PK study design and analyses in humans; (ii) a thorough characterization of the exposure-response profiles including important metrics of exposure for both efficacy and safety; (iii) comparability studies designed to meet regulatory considerations and support approval of the s.c. formulation, including noninferiority studies with PK and/or efficacy and safety as primary end points; and (iv) comprehensive safety package addressing assessments of immunogenicity and patients' safety profile with the new route of administration. Recommendations for successful bridging strategies are evolving and MIDD approaches have been used successfully to accelerate the transition to s.c. dosing, ultimately leading to improved patient experiences, adherence, and clinical outcomes.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology.

The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition. This review dives into the burgeoning world of AI and machine learning (ML), showcasing different applications of AI in clinical pharmacology and the impact of successful AI/ML implementation on drug development and/or regulatory decisions. This review also highlights recommendations for areas of opportunity in clinical pharmacology, including data analysis (e.g., handling large data sets, screening to identify important covariates, and optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, and training). Realizing the benefits of AI in drug development and understanding its value will lead to the successful integration of AI tools in our clinical pharmacology and pharmacometrics armamentarium.

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities.

INTRODUCTION: Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility. AREAS COVERED: In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models. EXPERT OPINION: Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

Population Pharmacokinetics and Exposure-Response with Teclistamab in Patients With Relapsed/Refractory Multiple Myeloma: Results From MajesTEC-1.

BACKGROUND: Teclistamab, a B-cell maturation antigen × CD3 bispecific antibody, is approved in patients with relapsed/refractory multiple myeloma (RRMM) who have previously received an immunomodulatory agent, a proteasome inhibitor, and an anti-CD38 antibody. OBJECTIVE: We report the population pharmacokinetics of teclistamab administered intravenously and subcutaneously (SC) and exposure-response relationships from the phase I/II, first-in-human, open-label, multicenter MajesTEC-1 study. METHODS: Phase I of MajesTEC-1 consisted of dose escalation and expansion at the recommended phase II dose (RP2D; 1.5 mg/kg SC weekly, preceded by step-up doses of 0.06 and 0.3 mg/kg); phase II investigated the efficacy of teclistamab RP2D in patients with RRMM. Population pharmacokinetics and the impact of covariates on teclistamab systemic exposure were assessed using a 2-compartment model with first-order absorption for SC and parallel time-independent and time-dependent elimination pathways. Exposure-response analyses were conducted, including overall response rate (ORR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and the incidence of grade ≥ 3 anemia, neutropenia, lymphopenia, leukopenia, thrombocytopenia, and infection. RESULTS: In total, 4840 measurable serum concentration samples from 338 pharmacokinetics-evaluable patients who received teclistamab were analyzed. The typical population value of time-independent and time-dependent clearance were 0.449 L/day and 0.547 L/day, respectively. The time-dependent clearance decreased rapidly to < 10% after 8 weeks of teclistamab treatment. Patients who discontinue teclistamab after the 13th dose are expected to have a 50% reduction from Cmax in teclistamab concentration at a median (5th to 95th percentile) time of 15 days (7-33 days) after Tmax and a 97% reduction from Cmax in teclistamab concentration at a median time of 69 days (32-163 days) after Tmax. Body weight, multiple myeloma type (immunoglobulin G vs non-immunoglobulin G), and International Staging System (ISS) stage (II vs I and III vs I) were statistically significant covariates on teclistamab pharmacokinetics; however, these covariates had no clinically relevant effect on the efficacy of teclistamab at the RP2D. Across all doses, ORR approached a plateau at the concentration range associated with RP2D, and in patients who received the RP2D, a flat exposure-response curve was observed. No apparent relationship was observed between DoR, PFS, OS, and the incidence of grade ≥3 adverse events across the predicted exposure quartiles. CONCLUSION: Body weight, myeloma type, and ISS stage impacted systemic teclistamab exposure without any clinically relevant effect on efficacy. The exposure-response analyses for ORR showed a positive trend with increasing teclistamab systemic exposure, with a plateau at the RP2D, and there was no apparent exposure-response trend for safety or other efficacy endpoints. These analyses support the RP2D of teclistamab in patients with RRMM. CLINICAL TRIAL REGISTRATION: NCT03145181 (phase I, 09 May 2017); NCT04557098 (phase II, 21 September 2020).

Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis.

Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.

Development of Therapeutic Proteins for a New Subcutaneous Route of Administration After the Establishment of Intravenous Dosages: A Systematic Review.

Therapeutic proteins may first be developed as intravenous (i.v.) therapies with new subcutaneous (s.c.) dosage forms being subsequently developed to provide an alternative route of administration. As of August 2022, there have been 9 therapeutic proteins which were developed as a new s.c. dosage form after the approval of the corresponding i.v. product. This article provides a systematic review of prior experiences in the i.v. to s.c. switch development programs. We describe what types of clinical studies were conducted to support the i.v. to s.c. switch for these nine therapeutic proteins. Publicly available scientific advice from health authorities is summarized, particularly regarding recommendations on overall development strategy, dose selection, immunogenicity assessment, and indication extrapolation. The clinical data from these i.v. to s.c. development programs demonstrate that: (1) when switching from i.v. dosing to s.c. dosing, trough drug concentration (Ctrough ) from s.c. dosing should not be inferior to i.v. dosing with average drug concentration (Cavg ; equivalent to AUC, area under the curve after correcting for dosing intervals between i.v. and s.c. administration) being matched or non-inferior to i.v. dosing; and (2) with appropriate s.c. dose regimens, treatment with s.c. therapeutic proteins can generally achieve similar efficacy and safety as the corresponding i.v. products, suggesting that the much higher maximum concentration (Cmax ) after i.v. infusion as compared with that from s.c. injection is often not relevant to the treatment effect.

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.

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.

Ibrutinib does not have clinically relevant interactions with oral contraceptives or substrates of CYP3A and CYP2B6.

Ibrutinib may inhibit intestinal CYP3A4 and induce CYP2B6 and/or CYP3A. Secondary to potential induction, ibrutinib may reduce the exposure and effectiveness of oral contraceptives (OCs). This phase I study evaluated the effect of ibrutinib on the pharmacokinetics of the CYP2B6 substrate bupropion, CYP3A substrate midazolam, and OCs ethinylestradiol (EE) and levonorgestrel (LN). Female patients (N = 22) with B-cell malignancies received single doses of EE/LN (30/150 µg) and bupropion/midazolam (75/2 mg) during a pretreatment phase on days 1 and 3, respectively (before starting ibrutinib on day 8), and again after ibrutinib 560 mg/day for ≥ 2 weeks. Intestinal CYP3A inhibition was assessed on day 8 (single-dose ibrutinib plus single-dose midazolam). Systemic induction was assessed at steady-state on days 22 (EE/LN plus ibrutinib) and 24 (bupropion/midazolam plus ibrutinib). The geometric mean ratios (GMRs; test/reference) for maximum plasma concentration (Cmax ) and area under the plasma concentration-time curve (AUC) were derived using linear mixed-effects models (90% confidence interval within 80%-125% indicated no interaction). On day 8, the GMR for midazolam exposure with ibrutinib coadministration was ≤ 20% lower than the reference, indicating lack of intestinal CYP3A4 inhibition. At ibrutinib steady-state, the Cmax and AUC of EE were 33% higher than the reference, which was not considered clinically relevant. No substantial changes were noted for LN, midazolam, or bupropion. No unexpected safety findings were observed. A single dose of ibrutinib did not inhibit intestinal CYP3A4, and repeated administration did not induce CYP3A4/2B6, as assessed using EE, LN, midazolam, and bupropion.

Predictions of Systemic, Intracellular, and Lung Concentrations of Azithromycin With Different Dosing Regimens Used in COVID-19 Clinical Trials.

Azithromycin (AZ), a broad-spectrum macrolide antibiotic, is being investigated in patients with coronavirus disease 2019 (COVID-19). A population pharmacokinetic model was implemented to predict lung, intracellular poly/mononuclear cell (peripheral blood monocyte (PBM)/polymorphonuclear leukocyte (PML)), and alveolar macrophage (AM) concentrations using published data and compared against preclinical effective concentration 90% (EC90 ) for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The final model described the data reported in eight publications adequately. Consistent with its known properties, concentrations were higher in AM and PBM/PML, followed by lung tissue, and lowest systemically. Simulated PBM/PML concentrations exceeded EC90 following the first dose and for ~ 14 days following 500 mg q.d. for 3 days or 500 mg q.d. for 1 day/250 mg q.d. on days 2-5, 10 days following a single 1,000 mg dose, and for > 20 days with 500 mg q.d. for 10 days. AM concentrations exceeded the 90% inhibitory concentration for > 20 days for all regimens. These data will better inform optimization of dosing regimens for AZ clinical trials.

Efficacy and Safety Exposure-Response Relationships of Apalutamide in Patients with Nonmetastatic Castration-Resistant Prostate Cancer.

PURPOSE: To evaluate the relationship between exposure of apalutamide and its active metabolite, N-desmethyl-apalutamide, and selected clinical efficacy and safety parameters in men with high-risk nonmetastatic castration-resistant prostate cancer. PATIENTS AND METHODS: An exploratory exposure-response analysis was undertaken using data from the 1,207 patients (806 apalutamide and 401 placebo) enrolled in the SPARTAN study, including those who had undergone dose reductions and dose interruptions. Univariate and multivariate Cox regression models evaluated the relationships between apalutamide and N-desmethyl-apalutamide exposure, expressed as area under the concentration-time curve at steady state, and metastasis-free survival (MFS). Univariate and multivariate logistic regression models assessed the relationship between apalutamide and N-desmethyl-apalutamide exposure and common treatment-emergent adverse events including fatigue, fall, skin rash, weight loss, and arthralgia. RESULTS: A total of 21% of patients in the apalutamide arm experienced dose reductions diminishing the average daily dose to 209 mg instead of 240 mg. Within the relatively narrow exposure range, no statistically significant relationship was found between MFS and apalutamide and N-desmethyl-apalutamide exposure. Within apalutamide-treated subjects, skin rash and weight loss had a statistically significant association with higher apalutamide exposure. CONCLUSIONS: The use of apalutamide at the recommended dose of 240 mg once daily provided a similar delay in metastases across the SPARTAN patient population, regardless of exposure. The exploratory exposure-safety analysis supports dose reductions in patients experiencing adverse events.

Pharmacokinetic Drug-Drug Interaction of Apalutamide, Part 2: Investigating Interaction Potential Using a Physiologically Based Pharmacokinetic Model.

BACKGROUND: Apalutamide is predominantly metabolized via cytochrome P450 (CYP) 2C8 and CYP3A4, whose contributions change due to autoinduction with repeated dosing. OBJECTIVES: We aimed to predict CYP3A4 and CYP2C8 inhibitor/inducer effects on the steady-state pharmacokinetics of apalutamide and total potency-adjusted pharmacologically active moieties, and simulated drug-drug interaction (DDI) between single-dose and repeated-dose apalutamide coadministered with known inhibitors/inducers. METHODS: We applied physiologically based pharmacokinetic modeling for our predictions, and simulated DDI between single-dose and repeated-dose apalutamide 240 mg coadministered with ketoconazole, gemfibrozil, or rifampicin. RESULTS: The estimated contribution of CYP2C8 and CYP3A4 to apalutamide metabolism is 58% and 13%, respectively, after single dosing, and 40% and 37%, respectively, at steady-state. Apalutamide exposure is predicted to increase with ketoconazole (maximum observed concentration at steady-state [Cmax,ss] 38%, area under the plasma concentration-time curve at steady-state [AUCss] 51% [pharmacologically active moieties, Cmax,ss 23%, AUCss 28%]) and gemfibrozil (Cmax,ss 32%, AUCss 44% [pharmacologically active moieties, Cmax,ss 19%, AUCss 23%]). Rifampicin exposure is predicted to decrease apalutamide (Cmax,ss 25%, AUCss 34% [pharmacologically active moieties, Cmax,ss 15%, AUCss 19%]). CONCLUSIONS: Based on our simulations, no major changes in the pharmacokinetics of apalutamide or pharmacologically active moieties are expected with strong CYP3A4/CYP2C8 inhibitors/inducers. This observation supports the existing recommendations that no dose adjustments are needed during coadministration of apalutamide and the known inhibitors or inducers of CYP2C8 or CYP3A4.

Effect of Plasma Protein Binding on the Pharmacokinetics of Erdafitinib: Results of an Integrated Cross-Study Analysis.

Erdafitinib, a potent oral fibroblast growth factor receptor inhibitor, is a low extraction ratio drug highly bound to alpha-1-acid glycoprotein (AGP) with free fraction (fu ) varying across populations. This analysis aimed to characterize the impact of plasma protein binding on erdafitinib pharmacokinetics (PK). Plasma protein-binding data (fu , AGP, albumin) and PK parameters were pooled from 6 phase 1 studies in healthy participants and 1 first-in-human study in patients with cancer. Binding kinetics were characterized using a nonlinear mixed-effects model. Mean (coefficient of variation, CV%) fu was 0.510% (39.4%) for healthy participants and 0.316% (64.0%) for patients, with a 2.1-fold higher AGP and 10% lower albumin. Linear binding of erdafitinib to AGP and albumin was observed, with >1000-fold higher binding constant for AGP than albumin (17.6 vs 0.017 µM-1 ). The fu decreased with increasing AGP in a nonlinear relationship. Despite its abundance in plasma relative to AGP, albumin contributed to <4% of the overall binding of erdafitinib (1.8% in patients; 4.0% in healthy participants). The AGP-binding constant was 68.0% lower in predose (spiked) versus postdose (ex vivo) samples. Total oral clearance was generally proportional to the fu and higher in healthy participants than in patients, consistent with the differences in AGP. Correcting for fu accounted for the majority of the relationship between oral clearance and fu as expected with a low extraction ratio drug. Characterizing free erdafitinib concentrations is critical to accounting for differences in fu and to further investigating its clinical relevance.

Population Pharmacokinetics of Apalutamide and its Active Metabolite N-Desmethyl-Apalutamide in Healthy and Castration-Resistant Prostate Cancer Subjects.

BACKGROUND: Apalutamide is a next-generation androgen receptor inhibitor approved for treatment of subjects with high-risk, non-metastatic, castration-resistant prostate cancer (NM-CRPC). OBJECTIVE: The objective of this study was to characterize the population pharmacokinetics of apalutamide and its metabolite N-desmethyl-apalutamide in healthy male and castration-resistant prostate cancer subjects. METHODS: Plasma concentration data for apalutamide and N-desmethyl-apalutamide from 1092 subjects (seven clinical studies) receiving oral apalutamide (30-480 mg) once daily were pooled for a population pharmacokinetic analysis using a non-linear mixed-effect modelling approach. The impact of clinically relevant covariates was also assessed. RESULTS: Apalutamide absorption was rapid, and the apparent steady-state volume of distribution was large (276 L), reflecting a wide body distribution. Apalutamide was eliminated slowly, with its apparent clearance increasing from 1.31 L/h after the first dose to 2.04 L/h at steady state. No evidence of time-dependent disposition was observed for N-desmethyl-apalutamide, which was also widely distributed and slowly cleared (1.5 L/h). After 4 weeks of treatment, more than 95% of steady-state exposure of apalutamide and N-desmethyl-apalutamide was reached. At a dose of apalutamide 240 mg/day, apalutamide and N-desmethyl-apalutamide exposure exhibited 5.3- and 85.2-fold accumulation in plasma, respectively. Inter-individual variability in apalutamide apparent clearance is low (< 20%). Among the covariates evaluated, apalutamide and N-desmethyl-apalutamide exposure were statistically associated only with health status, body weight, and albumin concentration, and the effect was low (< 25%). CONCLUSIONS: A population pharmacokinetic modelling approach was successfully applied to describe the pharmacokinetics of apalutamide and N-desmethyl-apalutamide. No clinically relevant covariates were identified as predictors of apalutamide and N-desmethyl-apalutamide pharmacokinetics.

Effect of Fluconazole and Itraconazole on the Pharmacokinetics of Erdafitinib in Healthy Adults: A Randomized, Open-Label, Drug-Drug Interaction Study.

BACKGROUND AND OBJECTIVES: Erdafitinib, an oral selective pan-fibroblast growth factor receptor (FGFR) kinase inhibitor, is primarily metabolized by cytochrome P450 (CYP) 2C9 and 3A4. The aim of this phase 1 study was to assess the pharmacokinetics and safety of erdafitinib in healthy participants when coadministered with fluconazole (moderate CYP2C9 and CYP3A inhibitor), and itraconazole (a strong CYP3A4 and P-glycoprotein inhibitor). The effect of CYP2C9 genotype variants (*1/*1, *1/*2, *1/*3) on the pharmacokinetics of erdafitinib was also investigated. METHODS: In this open-label, parallel-group, single-center study, eligible healthy adults were randomized by CYP2C9 genotype to receive Treatment A (single oral dose of erdafitinib 4 mg) on day 1, Treatment B (fluconazole 400 mg/day orally) on days 1-11, or Treatment C (itraconazole 200 mg/day orally) on days 1-11. Healthy adults randomized to Treatment B and C received a single oral 4-mg dose of erdafitinib on day 5. The pharmacokinetic parameters, including mean maximum plasma concentration (Cmax), area under the curve (AUC) from time 0 to 168 h (AUC168h), AUC from time 0 to the last quantifiable concentration (AUClast), and AUC from time 0 to infinity (AUC∞) were calculated from individual plasma concentration-time data using standard non-compartmental methods. RESULTS: Coadministration of erdafitinib with fluconazole increased Cmax of erdafitinib by approximately 21%, AUC168h by 38%, AUClast by 49%, and AUC∞ by 48% while coadministration with itraconazole resulted in no change in erdafitinib Cmax and increased AUC168h by 20%, AUClast by 33% and AUC∞ by 34%. Erdafitinib exposure was comparable between participants with CYP2C9 *1/*2 or *1/*3 and with wild-type CYP2C9 genotype. The ratio of total amount of erdafitinib excreted in the urine (inhibited to non-inhibited) was 1.09, the ratio of total amount of excreted metabolite M6 was 1.21, and the ratio of the metabolite to parent ratio in the urine was 1.11, when coadministration of erdafitinib with itraconazole was compared with single-dose erdafitinib. Treatment-emergent adverse events (TEAEs) were generally Grade 1 or 2 in severity; the most commonly reported TEAE was headache. No safety concerns were identified with single-dose erdafitinib when administered alone and in combination with fluconazole or itraconazole in healthy adults. CONCLUSION: Coadministration of fluconazole or itraconazole or other moderate/strong CYP2C9 or CYP3A4 inhibitors may increase exposure to erdafitinib in healthy adults and thus may warrant erdafitinib dose reduction or use of alternative concomitant medications with no or minimal CYP2C9 or CYP3A4 inhibition potential. TRIAL REGISTRATION: ClinicalTrials.gov identifier number: NCT03135106.