A genetic algorithm-based approach for the prediction of metabolic drug-drug interactions involving CYP2C8 or CYP2B6.

BACKGROUND AND OBJECTIVES: A genetic algorithm (GA) approach was developed to predict drug-drug interactions (DDIs) caused by cytochrome P450 2C8 (CYP2C8) inhibition or cytochrome P450 2B6 (CYP2B6) inhibition or induction. Nighty-eight DDIs, obtained from published in vivo studies in healthy volunteers, have been considered using the area under the plasma drug concentration-time curve (AUC) ratios (i.e., ratios of AUC of the drug substrate administered in combination with a DDI perpetrator to AUC of the drug substrate administered alone) to describe the extent of DDI. METHODS: The following parameters were estimated in this approach: the contribution ratios (CRCYP2B6 and CRCYP2C8, i.e., the fraction of the dose metabolized via CYP2B6 or CYP2C8, respectively) and the inhibitory or inducing potency of the perpetrator drug (IRCYP2B6, IRCYP2C8 and ICCYP2B6, for inhibition of CYP2B6 and CYP2C8, and induction of CYP2B6, respectively). The workflow consisted of three main phases. First, the initial estimates of the parameters were estimated through GA. Then, the model was validated using an external validation. Finally, the parameter values were refined via a Bayesian orthogonal regression using all data. RESULTS: The AUC ratios of 5 substrates, 11 inhibitors and 19 inducers of CYP2B6, and the AUC ratios of 19 substrates and 23 inhibitors of CYP2C8 were successfully predicted by the developed methodology within 50-200% of observed values. CONCLUSIONS: The approach proposed in this work may represent a useful tool for evaluating the suitable doses of a CYP2C8 or CYP2B6 substrates co-administered with perpetrators.

General clinical and methodological considerations on the extrapolation of pharmacokinetics and optimization of study protocols for small molecules and monoclonal antibodies in children.

Pharmacometric modelling plays a key role in both the design and analysis of regulatory trials in paediatric drug development. Studies in adults provide a rich source of data to inform the paediatric investigation plans, including knowledge on drug pharmacokinetics (PK), safety and efficacy. In children, drug disposition differs widely from birth to adolescence but extrapolating adult to paediatric PK, safety and efficacy either with pharmacometric or physiologically based approaches can help design or in some cases reduce the need for clinical studies. Aspects to consider when extrapolating PK include the maturation of drug metabolizing enzyme expression, glomerular filtration, drug excretory systems, and the expression and activity of specific transporters in conjunction with other drug properties such as fraction unbound. Knowledge of these can be used to develop extrapolation tools such as allometric scaling plus maturation functions or physiologically based PK. PK/pharmacodynamic approaches and well-designed clinical trials in children are of key importance in paediatric drug development. In this white paper, state-of-the-art of current methods used for paediatric extrapolation will be discussed. This paper is part of a conect4children implementation of innovative methodologies including pharmacometric and physiologically based PK modelling in clinical trial design/paediatric drug development through dissemination of expertise and expert advice. The suggestions arising from this white paper should define a minimum set of standards in paediatric modelling and contribute to the regulatory science.

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 Quantitative Approach to the Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 2C8 Inhibition.

BACKGROUND: Ohno and Colleagues proposed an approach for predicting drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) 3A4 based on the use of the ratio of the inhibited to non-inhibited area under the plasma concentration time curve (AUC) of substrates to estimate the fraction of the dose metabolized via CYP3A4 (contribution ratio, CR) and the in vivo inhibitory potency of a perpetrator (inhibition ratio, IR). This study evaluated the performance of this approach on DDIs mediated by CYP2C8 inhibitors. RESEARCH DESIGN AND METHODS: Initial estimates of CR and IR of CYP2C8 substrates and inhibitors were calculated for 33 DDI in vivo studies. The approach was externally validated with 17 additional studies. Bayesian orthogonal regression was used to refine the estimates of the parameters. Assessment of prediction success was conducted by plotting observed versus predicted AUC ratios. RESULTS: Final estimates of CRs and IRs were obtained for 19 CYP2C8 substrates and 23 inhibitors, respectively. The method demonstrated good predictive capacity, with only two values outside of the prespecified limits. CONCLUSIONS: The approach may help to adapt dose regimens for CYP2C8 substrates when given in combination with CYP2C8 inhibitors and to map the potential DDIs of new molecular entities.

Prediction of the drug-drug interaction potential of the α1-acid glycoprotein bound, CYP3A4/CYP2C9 metabolized oncology drug, erdafitinib.

Erdafitinib is a potent oral pan-fibroblast growth factor receptor inhibitor being developed as oncology drug for patients with alterations in the fibroblast growth factor receptor pathway. Erdafitinib binds preferentially to α1-acid glycoprotein (AGP) and is primarily metabolized by cytochrome P450 (CYP) 2C9 and 3A4. This article describes a physiologically based pharmacokinetic (PBPK) model for erdafitinib to assess the drug-drug interaction (DDI) potential of CYP3A4 and CYP2C9 inhibitors and CYP3A4/CYP2C9 inducers on erdafitinib pharmacokinetics (PK) in patients with cancer exhibiting higher AGP levels and in populations with different CYP2C9 genotypes. Erdafitinib's DDI potential as a perpetrator for transporter inhibition and for time-dependent inhibition and/or induction of CYP3A was also evaluated. The PBPK model incorporated input parameters from various in vitro and clinical PK studies, and the model was verified using a clinical DDI study with itraconazole and fluconazole. Erdafitinib clearance in the PBPK model consisted of multiple pathways (CYP2C9/3A4, renal, intestinal; additional hepatic clearance), making the compound less susceptible to DDIs. In poor-metabolizing CYP2C9 populations carrying the CYP2C9*3/*3 genotype, simulations shown clinically relevant increase in erdafitinib plasma concentrations. Simulated luminal and enterocyte concentration showed potential risk of P-glycoprotein inhibition with erdafitinib in the first 5 h after dosing, and simulations showed this interaction can be avoided by staggering erdafitinib and digoxin dosing. Other than a simulated ~ 60% exposure reduction with strong CYP3A/2C inducers such as rifampicin, other DDI liabilities were minimal and considered not clinically relevant.

Clopidogrel, a CYP2C8 inhibitor, causes a clinically relevant increase in the systemic exposure to the active metabolite of selexipag in healthy subjects.

AIMS: Selexipag is a prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension. Cytochrome P450 (CYP) 2C8 is involved in the metabolism of selexipag and its active metabolite, ACT-333679. This study evaluated the interaction of selexipag and clopidogrel, a CYP2C8 inhibitor. METHODS: The study had a 2-treatment, 1-sequence, crossover design. Pharmacokinetics (PK) and CYP2C8 genotype were assessed in healthy male subjects administered selexipag (200 µg twice daily [b.i.d.]) alone or with clopidogrel (300 mg single dose or 75 mg once daily [o.d.]). PK modelling and simulation were conducted to support dosing recommendations. RESULTS: Clopidogrel had a comparatively small effect on selexipag (<1.5-fold difference in any PK variable). For ACT-333679, the major contributor to the drug effect, the area under the plasma concentration-time curve during a dose interval and the maximum plasma concentration increased 2.25-fold (90% confidence interval [CI] 2.06, 2.46) and 1.69-fold (90% CI 1.55, 1.84), respectively with clopidogrel 300 mg and 2.70-fold (90% CI 2.45, 2.96) and 1.90-fold (90% CI 1.72, 2.11), respectively with clopidogrel 75 mg. The effect of clopidogrel on selexipag and ACT-333679 exposure was comparable for all identified CYP2C8 genotypes. PK simulations predicted comparable exposure to ACT-333679 following selexipag 400 µg b.i.d., 400 µg o.d. in combination with clopidogrel 75 mg o.d and 200 µg b.i.d. with clopidogrel 75 mg o.d. CONCLUSION: Results suggest that ACT-333679 exposure can be maintained within the therapeutic range by reducing selexipag dosing frequency to o.d. or dose to half, when selexipag is coadministered with clopidogrel.

Population pharmacokinetics of trabectedin in adolescent patients with cancer.

PURPOSE: To characterize the trabectedin population pharmacokinetics in children and adolescent patients with cancer and compare it with the trabectedin pharmacokinetics in adults. METHODS: Plasma concentrations from ten adolescent and three children with cancer (age range 4.0-17.0 years) treated with trabectedin at doses ranging from 1.1 to 1.7 mg/m2, administered as a 24-h continuous intravenous infusion every 3 weeks, were available for the analysis. An external model evaluation was performed to verify whether a previously developed adult population pharmacokinetic model was predictive of the pediatric plasma concentrations of trabectedin. The maximum a posteriori estimation of the individual pharmacokinetic parameters for pediatric patients was conducted, after successful completion of the external evaluation step. The relationships between pharmacokinetic parameters and body size were evaluated. RESULTS: External evaluation methods showed no major differences between the adult population and children and adolescent patients of this study. The mean ± standard deviation (SD) of the individual estimated clearance and central volume of distribution in these children/adolescent patients was 36.4 ± 16.1 L/h and 13.2 ± 6.54 L, respectively. These values were similar to the typical values reported for adult patients-37.6 L/h and 13.9 L (for females) and 16.1 L (for males). The median area under the plasma concentration versus time curve (AUC) in children/adolescent patients was 55.1 µg h/L, while in the adult population the median AUC was 61.3 µg h/L, both administered a 1.5 mg/m2 dose regimen with mean (range) BSA for adults = 1.86 (0.90-2.80) vs children/adolescent patients = 1.49 (0.66-2.54). CONCLUSIONS: The adult population pharmacokinetic model adequately described the trabectedin plasma concentrations and its variability in the pediatric population of patients involved in this assessment that mostly comprised adolescents. The trabectedin systemic exposure achieved in this population was comparable (within 12%) to the exposure obtained in adult population when the same dose, expressed in mg/m2, was administered.

Multicenter Phase I Study of Erdafitinib (JNJ-42756493), Oral Pan-Fibroblast Growth Factor Receptor Inhibitor, in Patients with Advanced or Refractory Solid Tumors.

PURPOSE: Here, we report results of the first phase I study of erdafitinib, a potent, oral pan-FGFR inhibitor. PATIENTS AND METHODS: Patients age ≥18 years with advanced solid tumors for which standard antineoplastic therapy was no longer effective were enrolled (NCT01703481). Parts 2 to 4 employed molecular screening for activating FGFR genomic alterations. In patients with such alterations, two selected doses/schedules identified during part 1 dose-escalation [9 mg once daily and 10 mg intermittently (7 days on/7 days off), as previously published (Tabernero JCO 2015;33:3401-8)] were tested. RESULTS: The study included 187 patients. The most common treatment-related adverse events were hyperphosphatemia (64%), dry mouth (42%), and asthenia (28%), generally grade 1/2 severity. All cases of hyperphosphatemia were grade 1/2 except for 1 grade 3 event. Skin, nail, and eye changes were observed in 43%, 33%, and 28% of patients, respectively (mostly grade 1/2 and reversible after temporary dosing interruption). Urothelial carcinoma and cholangiocarcinoma were most responsive to erdafitinib, with objective response rates (ORR) of 46.2% (12/26) and 27.3% (3/11), respectively, in response-evaluable patients with FGFR mutations or fusions. All patients with urothelial carcinoma and cholangiocarcinoma who responded to erdafitinib carried FGFR mutations or fusions. Median response duration was 5.6 months for urothelial carcinoma and 11.4 months for cholangiocarcinoma. ORRs in other tumor types were <10%. CONCLUSIONS: Erdafitinib shows tolerability and preliminary clinical activity in advanced solid tumors with genomic changes in the FGFR pathway, at two different dosing schedules and with particularly encouraging responses in urothelial carcinoma and cholangiocarcinoma.

Systemic Exposure of Rituximab Increased by Ibrutinib: Pharmacokinetic Results and Modeling Based on the HELIOS Trial.

INTRODUCTION: In the HELIOS trial, bendamustine/rituximab (BR) plus ibrutinib (BR-I) improved disease outcomes versus BR plus placebo in previously treated chronic lymphocytic leukemia/small lymphocytic lymphoma. Here, we describe the pharmacokinetic (PK) observations, along with modeling to further explore the interaction between ibrutinib and rituximab. METHODS: 578 subjects were randomized to ibrutinib or placebo with BR (6 cycles). Ibrutinib PK samples and tumor measurements were obtained from all subjects; a subset was evaluated for bendamustine and rituximab PK. Population rituximab PK was assessed using nonlinear mixed-effects modeling. RESULTS: Dose-normalized plasma concentration-time bendamustine data were comparable between the arms. Systemic rituximab exposure was higher with BR-I versus BR; mean trough serum concentrations were 2- to 3-fold higher in the first three cycles and 1.2- to 1.7-fold higher subsequently. No relevant safety differences were observed. In the modeling, including treatment arm as a categorical covariate and tumor burden as a continuous time-varying covariate on overall rituximab clearance significantly improved fitting of the data. CONCLUSIONS: BR-I led to higher dose-normalized systemic rituximab exposure versus BR and more rapid steady-state achievement. The modeling data suggest that rituximab disposition is, at least in part, target mediated. Determining the clinical significance of these findings requires further assessments. TRIAL REGISTRATION: This study is registered at https://clinicaltrials.gov/ct2/show/NCT01611090 .

Population Pharmacokinetic Analysis of Decitabine in Pediatric Patients With Acute Myeloid Leukemia.

Dacogen, the formulated product of the pharmaceutically active agent decitabine (5 aza-2'-deoxycytidine), is approved for treatment of myelodysplastic syndromes (MDSs) and acute myeloid leukemia (AML). The current analysis was performed to characterize the pharmacokinetics of decitabine in pediatric patients with AML and evaluate their consistency with the PK in adult patients. A population pharmacokinetic model was developed by pooling decitabine concentration-time data from 5 adult (AML and MDS) and 2 pediatric (AML) studies. A total of 840 concentration-time data points obtained from 71 adults and 28 pediatric subjects (1 to 16 years old) were available for analysis. A 2-compartment linear pharmacokinetic (PK) model with allometric scaling using body surface area accounting for body size adequately described the PK of decitabine. After accounting for body size, decitabine pharmacokinetics were not affected by age, sex, race, dosing regimen, renal function (creatinine clearance), bilirubin, or disease type (AML or MDS) and all PK parameters (including clearance, steady-state volume of distribution, maximum concentration, time to reach maximal concentration, and terminal half-life) were comparable between adult and pediatric patients. Simulated concentration-time profiles using the final population PK model suggested that decitabine exposure at steady state was similar in adults and pediatrics for a 20 mg/m2 decitabine dose administered as a 1-hour infusion once daily. The current analysis suggests that decitabine PK is similar in pediatric AML patients and a combined adult AML and MDS population.

Hepatic safety analysis of trabectedin: results of a pharmacokinetic study with trabectedin in patients with hepatic impairment and experience from a phase 3 clinical trial.

Purpose Trabectedin is metabolized by the liver and has been associated with transient, noncumulative transaminase elevation. Two recent studies further characterize hepatic tolerability with trabectedin therapy: a phase 1 pharmacokinetic study (Study #1004; NCT01273493) in patients with advanced malignancies and hepatic impairment (HI), and a phase 3 study (Study #3007; NCT01343277) of trabectedin vs. dacarbazine in patients with advanced sarcomas and normal hepatic function. Methods In Study #1004, patients received a single 3-h intravenous (IV) infusion of trabectedin: control group, trabectedin 1.3 mg/m2; HI group (baseline total bilirubin >1.5 and ≤3× upper limit of normal [ULN]; AST and ALT ≤2.5× ULN), trabectedin 0.58 or 0.9 mg/m2. In Study #3007, the trabectedin group received 1.5 mg/m2 by 24-h IV infusion every 3 weeks until disease progression or unacceptable toxicity. Results In Study #1004, dose-normalized trabectedin exposure was higher in HI patients (n = 6) versus controls (n = 9) (geometric mean ratios [90% CI] AUClast: 1.97 [1.20; 3.22]). In Study #3007, following trabectedin administration, 90% of patients had elevated ALT (32% grade 3-4) and 84% had elevated AST (17% grade 3-4). Transaminase elevations were transient and noncumulative. Progression-free survival was similar in patients with grade 3-4 hepatotoxicity (n = 109) versus grade 0-2 hepatotoxicity (n = 231) (median [95% CI]: 4.63 [4.01, 5.85] months versus 3.55 [2.73, 4.63] months; P = 0.545, HR = 0.91 [0.68-1.23]). Conclusion Trabectedin treatment of patients with HI results in higher plasma exposures. Hepatotoxicity in patients with normal liver function can be effectively addressed through dose reductions and delays.

Mathematical modeling of efficacy and safety for anticancer drugs clinical development.

INTRODUCTION: Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development. Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures. Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

Current mathematical models for cancer drug discovery.

INTRODUCTION: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND AND OBJECTIVES: Recent analysis revealed strong associations between prostate-specific antigen (PSA) dynamics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) and supported PSA dynamics as bridging surrogacy endpoints for clinical benefit from treatment with abiraterone acetate plus prednisone. This analysis aimed to investigate the abiraterone exposure-PSA dynamics relationship in mCRPC. METHODS: Abiraterone pharmacokinetics-PSA models were constructed using data from the COU-AA-301 (chemotherapy-pretreated) and COU-AA-302 (chemotherapy-naïve) trials comparing abiraterone acetate 1000 mg/day plus prednisone 5 mg twice daily with prednisone alone in mCRPC. The drug effect-PSA dynamics relationship was modeled as a function of selected pharmacokinetic measures. The influences of baseline demographic variables, laboratory values, and disease status on PSA dynamics were assessed. RESULTS: A tumor growth inhibition model best described PSA dynamics post-treatment with abiraterone acetate. Abiraterone acetate treatment in chemotherapy-pretreated and chemotherapy-naïve patients increased the PSA decay rate (k dec) to the same extent (1.28-fold, 95 % confidence interval [CI] 0.58-1.98; and 0.93-fold, 95 % CI 0.6-1.27, respectively). Lower baseline lactate dehydrogenase and higher baseline testosterone significantly increased k dec. Findings from our analysis suggest a maximum-effect relationship between abiraterone trough concentration and PSA dynamics in both patient populations. The majority of patients had a steady-state trough concentration greater than the estimated half maximal effective concentration. CONCLUSION: The model appropriately described the exposure-response relationship between abiraterone and PSA dynamics in chemotherapy-pretreated and chemotherapy-naïve patients following oral administration of abiraterone acetate.

Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis.

The mathematical modeling of tumor xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic-pharmacodynamic (PK-PD) models encompassing drug, biomarker and tumor-size data. This paper investigates the reciprocal consistency of three types of models: drug-to-tumor, such as established drug-driven tumor growth inhibition (TGI) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to-tumor, e.g. the more recent biomarker-driven TGI models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to-tumor models with a drug-to-tumor TGI model. Using the Simeoni TGI model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the drug-to-tumor potency of a new candidate drug based only on biomarker response is discussed.

Correlation between Prostate-Specific Antigen Kinetics and Overall Survival in Abiraterone Acetate-Treated Castration-Resistant Prostate Cancer Patients.

PURPOSE: We constructed a biomarker-survival modeling framework to explore the relationship between prostate-specific antigen (PSA) kinetics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients following oral administration of 1,000 mg/day of abiraterone acetate (AA). EXPERIMENTAL DESIGN: The PSA-survival modeling framework was based on data from two phase III studies, COU-AA-301 (chemotherapy pretreated, n = 1,184) and COU-AA-302 (chemotherapy naïve, n = 1,081), and included a mixed-effects tumor growth inhibition model and a Cox proportional hazards survival model. RESULTS: The effect of AA on PSA kinetics was significant (P < 0.0001) and comparable between the chemotherapy-naïve and -pretreated patients. PSA kinetics [e.g., PSA nadir, PSA response rate (≥30%, 50%, and 90%), time to PSA progression, PSA doubling time (PSADT)] were highly associated with OS in both populations. The model-based posttreatment PSADT had the strongest association with OS (HR ∼0.9 in both populations). The models could accurately predict survival outcomes. After adjusting for PSA kinetic endpoints, the treatment effect of AA on survival was no longer statistically significant in both studies, and the Prentice criteria of surrogacy were met for the PSA kinetic endpoints. A strong correlation was also observed between PSA and radiographic progression-free survival. CONCLUSIONS: The analysis revealed a consistent treatment effect of AA on PSA kinetics and strong associations between PSA kinetics and OS in chemotherapy-pretreated and -naïve patients, thereby providing a rationale to consider PSA kinetics as surrogacy endpoints to indicate clinical benefit in AA-treated patients with mCRPC regardless of chemotherapy treatment.

Population pharmacokinetic model of ibrutinib, a Bruton tyrosine kinase inhibitor, in patients with B cell malignancies.

PURPOSE: Ibrutinib is an oral Bruton's tyrosine kinase inhibitor, recently approved for the treatment of mantle cell lymphoma (MCL) and chronic lymphocytic leukemia (CLL) patients with at least one prior therapy. We developed a population pharmacokinetic (PK) model for ibrutinib in patients. METHODS: Ibrutinib PK data (3,477 observations/245 patients) were available from the following clinical studies: (1) A phase I dose-escalation study in recurrent B cell malignancies (dose levels of 1.25-12.5 mg/kg/day and fixed dose of 560 mg/day); (2) a phase II study in MCL (fixed dose level of 560 mg/day); (3) a phase Ib/II dose-finding study in CLL (fixed dose levels of 420 and 840 mg/day). Different compartmental PK models were explored using nonlinear mixed effects modeling. RESULTS: A two-compartment PK model with sequential zero-first-order absorption and first-order elimination was able to characterize the PK of ibrutinib. The compound was rapidly absorbed, had a high oral plasma clearance (approximately 1,000 L/h) and a high apparent volume of distribution at steady state (approximately 10,000 L). PK parameters were not dependent on dose, study, or clinical indication. The fasting state was characterized by a 67 % relative bioavailability compared with the meal conditions used in the trials and administration after a high-fat meal. Body weight and coadministration of antacids marginally increased volume of distribution and duration of absorption, respectively. CONCLUSIONS: The proposed population PK model was able to describe the plasma concentration-time profiles of ibrutinib across various trials. The linear model indicated that the compound's PK was dose independent and time independent.

Population pharmacokinetic analysis of abiraterone in chemotherapy-naïve and docetaxel-treated patients with metastatic castration-resistant prostate cancer.

BACKGROUND AND OBJECTIVES: Abiraterone acetate, an androgen biosynthesis inhibitor, prolongs survival in men with metastatic castration-resistant prostate cancer (mCRPC) in the pre- and post-chemotherapy setting as demonstrated by the pivotal phase III studies COU-AA-301 and COU-AA-302. We performed population pharmacokinetic analyses to estimate pharmacokinetic parameters after oral administration of 1,000 mg/day of abiraterone acetate in patients with mCRPC, with or without prior chemotherapy, and after a single 1,000 mg dose in healthy volunteers. The study objectives were to determine consistency between patient populations and to characterize factors that may influence abiraterone pharmacokinetics. METHODS: Studies in this analysis included COU-AA-302 (chemotherapy naïve); COU-AA-301 and COU-AA-006 (chemotherapy pretreated); and COU-AA-008, COU-AA-009, and COU-AA-014 (healthy subjects). A total of 4,627 plasma concentrations from 359 subjects (62 healthy volunteers, 297 patients) were analyzed using non-linear mixed-effects modeling. RESULTS: An Erlang-type absorption model with first-order elimination and three-transit compartments following sequential zero- and first-order processes was used to characterize abiraterone pharmacokinetics. Absorption-related parameters were affected by food intake. Abiraterone pharmacokinetics were characterized by an extensive apparent clearance, which was lower in patients with mCRPC (1,550 L/h) versus healthy subjects (2,240 L/h), and by large apparent central (5,620 L) and peripheral (17,400 L) volumes of distribution. Abiraterone pharmacokinetics were similar in chemotherapy-pretreated and -naïve patients and were characterized by a high between- and within-subject variability [e.g., between-subject coefficient of variation (CV%) for relative bioavailability for the modified fasting state was 61.1% and the CV% for within-subject variability was 71.3%]. The fat content of food taken with abiraterone acetate affected the bioavailability of abiraterone. No factors beyond food intake and health status (healthy vs. mCRPC) impacted abiraterone pharmacokinetics. CONCLUSIONS: Based on the pharmacokinetics model, the recommended 1,000 mg/day of abiraterone acetate resulted in similar abiraterone exposure for patients with mCRPC regardless of prior chemotherapy. The fat content of food affected relative bioavailability of abiraterone, though the extent of this effect is dependent on health status.

Modeling of human tumor xenografts and dose rationale in oncology.

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts.

PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.