A population pharmacokinetic-pharmacodynamic model of navtemadlin, its glucuronide metabolite (M1) and serum macrophage inhibitory cykokine-1 (MIC-1).

Navtemadlin is a potent, selective, orally available inhibitor of murine double minute 2 that restores p53 activity to induce apoptosis in TP53 wild-type malignancies. Using richly sampled pharmacokinetic (PK) and pharmacodynamic (PD) data from healthy volunteers, a population PK/PD model was developed.A population PK (PPK) model described the PK characteristics of navtemadlin and its major metabolite acyl glucuronide (M1) and quantified enterohepatic recirculation (EHR). Post hoc individual PK parameters from this model were coupled with PD data for serum macrophage inhibitory cytokine-1 (MIC-1, GDF15), a cytokine biomarker of p53 activation, to construct a population PK/PD model that described plasma concentration-driven MIC-1 excursions and enabled simulation of the extent and duration of navtemadlin PD effects.The median apparent clearance (CL/F) and apparent central volume (V2/F) of navtemadlin were 36.4 L/hr and 159 L. The typical maximum stimulatory effect (Smax) was close to the median maximum MIC-1 ratio to baseline of 7.29 in observed data.Simulation revealed a dose-dependent increase of MIC-1 with steady state attained in approximately 7 days, in a 7-day-on/21-day-off dose regimen. Elevated MIC-1 concentrations persist through 17-19 days, leaving about 9-11 PD-free days in a 28-day cycle.

Population pharmacokinetic and exposure-response analyses of ivosidenib in patients with IDH1-mutant advanced hematologic malignancies.

Ivosidenib is a once daily (q.d.), orally available, potent mutant isocitrate dehydrogenase 1 (mIDH1) inhibitor approved for treatment of patients with relapsed or refractory (R/R) acute myeloid leukemia (AML) and intensive chemotherapy ineligible AML with a susceptible IDH1 mutation. Population pharmacokinetics (PKs; N = 253), exposure-response (efficacy [n = 201] and safety [n = 253]), and concentration-corrected electrocardiogram QT interval (QTc; n = 171) analyses were performed using phase I data (100 mg twice daily and 300-1200 mg q.d.). Ivosidenib disposition was well-described by a two-compartment PK model with first-order absorption and elimination. Between-subject variability was moderate for PK parameters. Intrinsic factors did not affect ivosidenib PKs. Moderate/strong CYP3A4 inhibitors increased the area under the plasma ivosidenib concentration-time curve at steady state (AUCss ) by 60%. Efficacy responders and nonresponders had similar ivosidenib exposures. Based on AUCss , there was no apparent relationship between ivosidenib exposure and efficacy or adverse events. The plasma ivosidenib concentration-QT analysis showed a mean change in QTc using Fridericia's method (ΔQTcF) of 17.2 msec at the approved 500 mg q.d. dose. Because of the direct association between ivosidenib exposure and QTcF, patients should have their electrocardiograms and electrolytes monitored, and comedications that increase ivosidenib exposure or prolong the QT interval should be avoided. These model-based analyses quantitatively provide a framework to describe the relationship among ivosidenib dose, exposure, and clinical end points. With precautions for QTc prolongation, the exposure-response analyses support the 500 mg q.d. dose in patients with AML with a susceptible IDH1 mutation.

Phase 1 Concentration-QTc and Cardiac Safety Analysis of the MDM2 Antagonist KRT-232 in Patients With Advanced Solid Tumors, Multiple Myeloma, or Acute Myeloid Leukemia.

Cardiac safety and plasma concentration-QTc interval analyses were completed using data from 2 phase 1 studies of the selective mouse double minute chromosome 2 antagonist, KRT-232, in patients with solid tumors or multiple myeloma and acute myeloid leukemia (AML) who received KRT-232 doses of 15 to 480 mg once daily (QD; N = 130). A linear mixed-effects model related change from baseline Fridericia-corrected QT interval (ΔQTcF) to KRT-232 plasma concentrations. The final model included parameters for the intercept (with between-subject variability), KRT-232 concentration-ΔQTcF slope, and baseline QTcF effect on the intercept. Diagnostic plots indicated an adequate model fit. Mean (90% confidence interval) predicted ΔQTcF values at the maximum clinical dose (480 mg QD) were 2.04 (0.49-3.60) milliseconds for patients with solid tumors and 4.52 (2.35-6.69) milliseconds for patients with AML. Because the 90% confidence interval upper bound of the mean ΔQTcF was predicted to be below 10 milliseconds at doses up to 480 mg QD in patients with solid tumors, multiple myeloma, or AML, KRT-232 does not result in clinically meaningful QT prolongation at the doses currently under investigation in clinical trials. No significant cardiac safety concerns were identified at these doses.

Estimating the Population Health Impact of Recently Introduced Modified Risk Tobacco Products: A Comparison of Different Approaches.

INTRODUCTION: Various approaches have been used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP). AIMS AND METHODS: We aimed to compare and contrast aspects of models considering effects on mortality that were known to experts attending a meeting on models in 2018. RESULTS: Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the "null scenario" or joint smoking and MRTP habits in an "alternative scenario". The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between scenarios. Estimating effects on population health depends on frequency of use of the MRTP and smoking, and the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized. CONCLUSIONS: Despite methodological differences, most modellers have assumed the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, to be very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction. IMPLICATIONS: There is a need to estimate the population health impact of introducing modified risk nicotine-containing products for smokers unwilling or unable to quit. This paper reviews a variety of modeling methodologies proposed to do this, and discusses the implications of the different approaches. It should assist modelers in refining and improving their models, and help toward providing authorities with more reliable estimates.

Exposure-Response-Based Product Profile-Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer.

The aims of this work were to characterize ipatasertib exposure-response (E-R) relationships in a phase II study and to quantitatively assess benefit-risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration-resistant prostate cancer. Logistic regression and Cox proportional-hazards models characterized E-R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E-R models. Despite a steeper E-R relationship when accounting for dose modifications, similar dose-response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit-risk balance than other doses (200-500 mg daily), was selected for further development in metastatic castration-resistant prostate cancer.

Population Modeling of Modified Risk Tobacco Products Accounting for Smoking Reduction and Gradual Transitions of Relative Risk.

INTRODUCTION: As suggested by the Food and Drug Administration (FDA) Modified Risk Tobacco Product (MRTP) Applications Draft Guidance, we developed a statistical model based on public data to explore the effect on population mortality of an MRTP resulting in reduced conventional cigarette smoking. Many cigarette smokers who try an MRTP persist as dual users while smoking fewer conventional cigarettes per day (CPD). Lower-CPD smokers have lower mortality risk based on large cohort studies. However, with little data on the effect of smoking reduction on mortality, predictive modeling is needed. METHODS: We generalize prior assumptions of gradual, exponential decay of Excess Risk (ER) of death, relative to never-smokers, after quitting or reducing CPD. The same age-dependent slopes are applied to all transitions, including initiation to conventional cigarettes and to a second product (MRTP). A Monte Carlo simulation model generates random individual product use histories, including CPD, to project cumulative deaths through 2060 in a population with versus without the MRTP. Transitions are modeled to and from dual use, which affects CPD and cigarette quit rates, and to MRTP use only. RESULTS: Results in a hypothetical scenario showed high sensitivity of long-run mortality to CPD reduction levels and moderate sensitivity to ER transition rates. CONCLUSIONS: Models to project population effects of an MRTP should account for possible mortality effects of reduced smoking among dual users. In addition, studies should follow dual-user CPD histories and quit rates over long time periods to clarify long-term usage patterns and thereby improve health impact projections. IMPLICATIONS: We simulated mortality effects of a hypothetical MRTP accounting for cigarette smoking reduction by smokers who add MRTP use. Data on relative mortality risk versus CPD suggest that this reduction may have a substantial effect on mortality rates, unaccounted for in other models. This effect is weighed with additional hypothetical effects in an example.

Population pharmacokinetic analysis of axitinib in healthy volunteers.

AIMS: Axitinib is a potent and selective second generation inhibitor of vascular endothelial growth factor receptors 1, 2 and 3 approved for second line treatment of advanced renal cell carcinoma. The objectives of this analysis were to assess plasma pharmacokinetics and identify covariates that may explain variability in axitinib disposition following single dose administration in healthy volunteers. METHODS: Plasma concentration-time data from 337 healthy volunteers in 10 phase I studies were analyzed, using non-linear mixed effects modelling (nonmem) to estimate population pharmacokinetic parameters and evaluate relationships between parameters and food, formulation, demographic factors, measures of renal and hepatic function and metabolic genotypes (UGT1A1*28 and CYP2C19). RESULTS: A two compartment structural model with first order absorption and lag time best described axitinib pharmacokinetics. Population estimates for systemic clearance (CL), central volume of distribution (Vc ), absorption rate constant (ka ) and absolute bioavailability (F) were 17.0 l h(-1) , 45.3 l, 0.523 h(-1) and 46.5%, respectively. With axitinib Form IV, ka and F increased in the fasted state by 207% and 33.8%, respectively. For Form XLI (marketed formulation), F was 15% lower compared with Form IV. CL was not significantly influenced by any of the covariates studied. Body weight significantly affected Vc , but the effect was within the estimated interindividual variability for Vc . CONCLUSIONS: The analysis established a model that adequately characterizes axitinib pharmacokinetics in healthy volunteers. Vc was found to increase with body weight. However, no change in plasma exposures is expected with change in body weight; hence no dose adjustment is warranted.

Axitinib in metastatic renal cell carcinoma: results of a pharmacokinetic and pharmacodynamic analysis.

Axitinib is a potent and selective inhibitor of vascular endothelial growth factor receptors 1, 2, and 3, approved for second-line therapy for advanced renal cell carcinoma (RCC). Axitinib population pharmacokinetic and pharmacokinetic/pharmacodynamic relationships were evaluated. Using nonlinear mixed effects modeling with pooled data from 383 healthy volunteers, 181 patients with metastatic RCC, and 26 patients with other solid tumors in 17 trials, the disposition of axitinib was best described by a 2-compartment model with first-order absorption and a lag time, with estimated mean systemic clearance (CL) of 14.6 L/h and central volume of distribution (V(c)) of 47.3 L. Of 12 covariates tested, age over 60 years and Japanese ethnicity were associated with decreased CL, whereas V(c) increased with body weight. However, the magnitude of predicted changes in exposure based on these covariates does not warrant dose adjustments. Multivariate Cox proportional hazard regression and logistic regression analyses showed that higher exposure and diastolic blood pressure were independently associated with longer progression-free and overall survivals and higher probability of partial response in metastatic RCC patients. These findings support axitinib dose titration to increase plasma exposure in patients who tolerate axitinib, and also demonstrate diastolic blood pressure as a potential marker of efficacy.

Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis.

PURPOSE: In this pharmacokinetic/pharmacodynamic meta-analysis, we investigated relationships between clinical endpoints and sunitinib exposure in patients with advanced solid tumors, including patients with gastrointestinal stromal tumor (GIST) and metastatic renal cell carcinoma (mRCC). METHODS: Pharmacodynamic data were available for 639 patients of whom 443 had pharmacokinetic data. Sunitinib doses ranged from 25 to 150 mg QD or QOD. Models to express endpoint values and/or changes from baseline by the highest-correlating exposure measures were developed in S-PLUS or NONMEM using fixed- and mixed-effects modeling. RESULTS: Tentative relationships were identified between (1) steady-state AUC of total drug (sunitinib + its active metabolite SU12662) and time to tumor progression (TTP), overall survival (OS), with AUC significantly associated with longer TTP and OS in patients with GIST and mRCC, and incidence, but not severity, of fatigue; (2) steady-state AUC of sunitinib and response probability, with AUC significantly associated with objective response in patients with mRCC and stable disease in patients with both mRCC and GIST (with no such correlations in patients with solid tumors); (3) dose and tumor size reductions; (4) total drug concentration and diastolic blood pressure (DBP), with a typical patient on sunitinib 50 mg QD (the recommended dose) predicted to experience a maximum DBP increase of 8 mmHg; and (5) cumulative AUC of total drug and absolute neutrophil count (ANC), with ANC reductions occurring predominantly after one treatment cycle. CONCLUSIONS: The results of this meta-analysis indicate that increased exposure to sunitinib is associated with improved clinical outcomes (longer TTP, longer OS, greater chance of antitumor response), as well as some increased risk of adverse effects. A sunitinib 50-mg starting dose seems reasonable, providing clinical benefit with acceptably low risk of adverse events.

A pharmacokinetic-pharmacodynamic model to optimize the phase IIa development program of maraviroc.

OBJECTIVES: To use a viral dynamics model to compare the effectiveness of in vivo viral inhibition of several doses of maraviroc (MVC;UK-427,857) and to use a modeling approach to support design decisions for a monotherapy study using various dosing regimens of maraviroc given with and without food. DESIGN: The pharmacokinetic-pharmacodynamic model was developed using clinical data from a first monotherapy study (study A4001007). This was a randomized, double-blind, placebo-controlled, multicenter study of maraviroc in 44 asymptomatic HIV-1-infected patients. Patients received maraviroc under food restrictions at 25 mg once daily or 50, 100, or 300 mg twice daily, or placebo for 10 days. METHODS: Antiviral responses were assessed by measuring plasma HIV-1 RNA levels during screening, during randomization, at baseline, and daily during the 10 days of treatment and at days 11 to 15, 19, 22, 25, and 40. An integrated pharmacokinetic-pharmacodynamic model was developed using the mixed effects modeling approach with patients' pharmacokinetic profiles on the last day of treatment, HIV-1 RNA levels over time, and the individual viral susceptibility. The parameters derived from the viral dynamic model were used to calculate average viral inhibition fraction, decay rate of actively infected cells, and basic reproductive ratio for each treatment group. Monte Carlo simulation was then used to determine the distribution of viral load change across simulated patients over time for each regimen to be studied in another monotherapy study, A4001015. RESULTS: The decline rate in the 300 mg twice daily group was comparable to that induced by potent protease inhibitor monotherapy, but was significantly slower than that in patients receiving combination therapy including both protease inhibitor and reverse transcriptase inhibitors. The efficacy of inhibition in vivo was estimated to range from 0.15 to 0.38 for the 25 mg once daily dose group and from 0.88 to 0.96 for the 300 mg twice daily dose group. CONCLUSIONS: The model has aided the analysis and interpretation of the clinical data. The use of a model-based approach for selecting doses can accelerate drug development by replacing some arms or trials with simulations.

Combining drug-disease and economic modelling to inform drug development decisions.

Drug-disease models and clinical trial simulations are increasingly being used to support trial design and treatment optimization decisions. Less widely recognized is their potential for guiding strategic development decisions. These decisions require economic valuation of the potential product profile of efficacy, safety, ease of use, and so on. This review presents a disguised case study that uses drug-disease modelling to generate a probabilistic forecast of a drug's profile. This allows a quantitative analysis of whether to pursue a once-a-day regimen for an antiretroviral being tested twice-a-day in Phase II trials, and if so, at what dose and for which market segments.