Covariate modeling in pharmacometrics: General points for consideration.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Model-Based Dose Selection of Subcutaneous Nivolumab in Patients with Advanced Solid Tumors.

The pharmacokinetics (PK) of intravenous (i.v.) nivolumab is well characterized. A subcutaneous (s.c.) nivolumab formulation with and without recombinant human hyaluronidase PH20 enzyme is being evaluated in CheckMate 8KX (NCT03656718). A model-based analysis was conducted to characterize the PK of nivolumab s.c. and predict systemic exposures after i.v. and s.c. administration to guide dosing regimen selection for nivolumab s.c. A prior i.v. model was modified to incorporate an s.c. extravascular compartment and estimate the absorption rate constant and bioavailability of nivolumab s.c. Serum concentration-time data from 82 patients treated with nivolumab s.c. 720, 960, or 1,200 mg were pooled with existing i.v. data from multiple studies for model development. Prediction-corrected visual predictive check (pcVPC) plots assessed the model's performance. Stochastic simulations were conducted to predict exposures for i.v. and s.c. administration. The data were described by a two-compartment model with time-varying clearance, zero-order infusion into the central compartment after i.v. dosing, and first-order absorption from the extravascular compartment after s.c. dosing. The pcVPC suggested that the model adequately described the observed nivolumab s.c. data. Predicted nivolumab exposures at 1,200 mg s.c. every 4 weeks (q4w) were higher than those at the approved dose of 3 mg/kg i.v. q2w and lower than those at the highest tested safe dose of 10 mg/kg i.v. q2w. Nivolumab PK is well-characterized using the combined s.c./i.v. population PK model. The model-based analysis facilitated a comprehensive benefit-risk assessment of nivolumab s.c. and informed selection of 1,200 mg s.c. q4w for phase III evaluation.

BMS-986012, an Anti-Fucosyl-GM1 Monoclonal Antibody as Monotherapy or in Combination With Nivolumab in Relapsed/Refractory SCLC: Results From a First-in-Human Phase 1/2 Study.

Introduction: Fucosyl-GM1 is a monosialoganglioside with limited expression in healthy tissues and high expression on SCLC cells. BMS-986012 is a nonfucosylated, first-in-class, fully human immunoglobulin G1 monoclonal antibody that binds to fucosyl-GM1. Methods: CA001-030 is a phase 1/2, first-in-human study of BMS-986012 as monotherapy or in combination with nivolumab for adults with relapsed or refractory SCLC. Safety is the primary end point. Additional end points include objective response rate, duration of response, progression-free survival, pharmacokinetics, and overall survival. Results: Patients (BMS-986012 monotherapy, n = 77; BMS-986012 + nivolumab, n = 29) were predominantly of male sex (58%), 63 years old (mean), current or past tobacco users (97%), and treated previously with first-line systemic therapy (99%). The most common treatment-related adverse event was pruritus (n = 95 [90%]). Grade 4 treatment-related adverse events were reported in 2% (n = 2) of patients. The objective response rate (95% confidence interval [CI]) was higher with BMS-986012 plus nivolumab (38% [20.7%-57.7%]) than with monotherapy (4% [0.8%-11.0%]). Median (95% CI) duration of response with BMS-986012 plus nivolumab was 26.4 (4.4-not reached) months. Progression-free survival (95% CI) at 24 weeks with monotherapy and BMS-986012 plus nivolumab was 12.2% (6.0%-20.7%) and 39.3% (21.7%-56.5%), respectively. The pharmacokinetics profile of monotherapy and BMS-986012 plus nivolumab suggested dose proportionality across the tested dose range. Median overall survival (95% CI) with monotherapy and BMS-986012 plus nivolumab was 5.4 (4.0-7.3) and 18.7 (8.2-37.3) months, respectively. Conclusions: BMS-986012 in combination with nivolumab represents a well-tolerated, potential new therapy for relapsed or refractory SCLC. BMS-986012 is currently being explored in combination with carboplatin, etoposide, and nivolumab as a first-line therapy in extensive-stage SCLC (NCT04702880).

Effects of cyclophosphamide related genetic variants on clinical outcomes of adult hematopoietic cell transplant patients.

PURPOSE: Genetic variants may influence the pharmacokinetics and pharmacodynamics (PKPD) of cyclophosphamide (CY). CY plays a critical role in conditioning chemotherapy for hematopoietic cell transplantation (HCT), but its use is limited by toxicity. We explored the effect of genetic variants, potentially affecting PKPD of CY, and outcomes after HCT. METHODS: This observational pharmacogenomic study included 85 adults with hematologic malignancies who received reduced intensity conditioning with CY, fludarabine, and total body irradiation. We collected recipient DNA prior to HCT and evaluated 97 candidate variants in 66 genes and 3 metabolism phenotypes potentially involved in PKPD pathways of CY. In multivariable analysis we investigated the association between the genotypes and four clinical outcomes: Day 180 non-relapse mortality (NRM) and day 180 overall survival (OS), acute graft-versus-host-disease (aGVHD) grades 2-4, and engraftment. p values were not adjusted for multiple testing. RESULTS: The median recipient age was 63 years (range 21-75). Acute myeloid leukemia was the most common diagnosis (34%; n = 29). In multivariable analysis adjusted for exposure to phosphoramide mustard, the final active metabolite of CY, we identified 6 variants in 6 genes associated with at least one of the clinical outcomes. An ABCC4 variant (rs9561778) was associated with poor Day 180 NRM (p < 0.01), MUTYH variant (rs3219484) with higher Day 180 NRM and aGVHD (both p < 0.01), and SYNE1 variant (rs4331993) with better Day 180 OS and engraftment (both p ≤ 0.01). CONCLUSION: The present study suggests that genetic variants influencing the PKPD of CY may help identify patients at risk for inferior outcomes after HCT using CY-based reduced-intensity conditioning.

Population pharmacokinetic and exposure-response analyses of elotuzumab plus pomalidomide and dexamethasone for relapsed and refractory multiple myeloma.

PURPOSE: Elotuzumab plus pomalidomide/dexamethasone (E-Pd) demonstrated efficacy and safety in relapsed and refractory multiple myeloma (RRMM). The clinical pharmacology of elotuzumab [± lenalidomide/dexamethasone (Ld)] was characterized previously. These analyses describe elotuzumab population pharmacokinetics (PPK), the effect of Pd, and assess elotuzumab exposure-response relationships for efficacy and safety in patients with RRMM. METHODS: A previously established PPK model was updated with E-Pd data from the phase 2 ELOQUENT-3 study (NCT02654132). The dataset included 8180 serum concentrations from 440 patients with RRMM from 5 clinical trials. Elotuzumab PK parameter estimates were used to generate individual daily time-varying average concentrations (daily Cavg) for multi-variable time-to-event exposure-response analyses of progression-free survival (PFS) and time to the first occurrence of grade 3 + adverse events (AEs) in RRMM. RESULTS: Elotuzumab PK were well-described by a two-compartment model with parallel linear and Michaelis-Menten elimination from the central compartment (Vmax) and non-renewable target-mediated elimination from the peripheral compartment (Kint). Co-administration with Pd resulted in a 19% and 51% decrease in elotuzumab linear clearance and Kint, respectively, versus Ld; steady-state exposures were similar. Vmax increased with increasing serum M-protein. Hazard ratios (95% confidence intervals) for daily Cavg were 0.9983 (0.9969-0.9997) and 0.9981 (0.9964-0.9998) for PFS and grade 3 + AEs, respectively. CONCLUSIONS: The PPK model adequately described the data and was appropriate for determining exposures for exposure-response analyses. There were no clinically relevant differences in elotuzumab exposures between Pd and Ld backbones. In ELOQUENT-3, increasing elotuzumab daily Cavg prolonged PFS without increasing grade 3 + AEs.

Nivolumab exposure-response analysis for adjuvant treatment of melanoma supporting a change in posology.

Nivolumab monotherapy is approved as adjuvant treatment for melanoma based on results from the pivotal CheckMate 238 trial. We present a model-based, benefit-risk assessment of nivolumab in adjuvant melanoma supporting a posology change from a weight-based to a less frequent, flat-dosing regimen. The exposure-response (E-R) relationship for efficacy was evaluated using recurrence-free survival (RFS) and distant metastasis-free survival (DMFS) end points from the CheckMate 238 trial. The E-R for safety was evaluated using data from 14 studies across a broad range of doses in several tumor types using grade 3+ adverse event (AE) and grade 2+ immune-mediated AE (IMAE) end points. Nivolumab trough exposures were not significant predictors of RFS or DMFS. Covariates significantly associated with increased risk of disease recurrence or death were programmed death ligand 1 (PD-L1; less than 5% cutoff), lower baseline lactate dehydrogenase, and higher age. Covariates associated with increased risk of distant metastasis or death were PD-L1 (less than 5% cutoff) and higher age. Higher nivolumab maximum concentration after first dose (Cmax1) was significantly associated with grade 2+ IMAEs, but not grade 3+ AEs. The risk of grade 3+ AEs was significantly lower in adjuvant versus advanced melanoma. Eastern Cooperative Oncology Group Performance Status higher than zero was associated with higher incidences of grade 2+ IMAEs and grade 3+ AEs. Female patients had significantly higher incidences of grade 2+ IMAEs than male patients. Nivolumab monotherapy in adjuvant melanoma demonstrated a relatively flat E-R relationship over the range of exposures produced by 3 mg/kg every 2 weeks and predicted a comparable benefit-risk profile to flat-dosing regimens.

Higher Fludarabine and Cyclophosphamide Exposures Lead to Worse Outcomes in Reduced-Intensity Conditioning Hematopoietic Cell Transplantation for Adult Hematologic Malignancy.

Reduced-intensity conditioning regimens using fludarabine (Flu) and cyclophosphamide (Cy) have been widely used in hematopoietic cell transplantation (HCT) recipients. The optimal exposure of these agents remains to be determined. We aimed to delineate the exposure-outcome associations of Flu and Cy separately and then both combined on HCT outcomes. This is a single-center, observational, pharmacokinetic (PK)-pharmacodynamic (PD) study of Flu and Cy in HCT recipients age ≥18 years who received Cy (50 mg/kg in a single dose), Flu (150 to 200 mg/m2 given as 5 daily doses), and total body irradiation (TBI; 200 cGy). We measured trough concentrations of 9-ß-D-arabinosyl-2-fluoradenine (F-ara-A), an active metabolite of Flu, on days -5 and -4 (F-ara-ADay-5 and F-ara-ADay-4, respectively), and measured phosphoramide mustard (PM), the final active metabolite of Cy, and estimated the area under the curve (AUC). The 89 enrolled patients had a nonrelapse mortality (NRM) of 9% (95% confidence interval [CI], 3% to 15%) at day +100 and 15% (95% CI, 7% to 22%) at day +180, and an overall survival (OS) of 73% (95% CI, 63% to 81%) at day +180. In multivariate analysis, higher PM area under the curve (AUC) for 0 to 8 hours (PM AUC0-8 hr) was an independent predictor of worse NRM (P < .01 at both day +100 and day +180) and worse day +180 OS (P < .01), but no associations were identified for F-ara-A trough levels. We observed lower day +100 NRM in those with both high F-ara-ADay-4 trough levels (≥40 ng/mL; >25th percentile) and low PM AUC0-8 hr (<34,235 hr ng/mL; <75th percentile), compared with high exposures to both agents (hazard ratio, 0.06; 95% CI, 0.01 to 0.48). No patients with low F-ara-ADay-4 (<40 ng/mL; <25th percentile) had NRM by day +100, regardless of PM AUC. The interpatient PK variability was large in F-ara-ADay-4 trough and PM AUC0-8 hr (29-fold and 5.0-fold, respectively). Flu exposure alone was not strongly associated with NRM or OS in this reduced Flu dose regimen; however, high exposure to both Flu and Cy was associated with a >16-fold higher NRM. These results warrant further investigation to optimize reduced-intensity regimens based on better PK-PD understanding and possible adaptation to predictable factors influencing drug clearance.

Population Pharmacokinetics of Ipilimumab in Combination With Nivolumab in Patients With Advanced Solid Tumors.

Ipilimumab is a fully human monoclonal antibody approved for the treatment of melanoma as monotherapy and for the treatment of melanoma, renal cell carcinoma, and colorectal cancer in combination with nivolumab. Ipilimumab time-varying clearance (CL) was assessed by a population pharmacokinetics (PPK) model developed using statistically significant covariates identified in a previous PPK analysis plus additional covariates. Data from 3,411 patients who received ipilimumab 0.3-10 mg/kg alone or in combination with nivolumab in 16 clinical trials were analyzed. Ipilimumab CL decreased over time; the change in CL was greater in patients treated with nivolumab combination than ipilimumab alone and in responders vs. nonresponders. Time-varying covariates including body weight, lactate dehydrogenase, albumin, and performance status were evaluated on change in ipilimumab CL. In addition, ipilimumab CL was similar across different tumor types, nivolumab dosing regimens, and lines of therapy. These data suggest an association of ipilimumab CL with disease severity.

Population Pharmacokinetics of Nivolumab in Combination With Ipilimumab in Patients With Advanced Malignancies.

Nivolumab is a fully human monoclonal antibody that inhibits programmed cell death-1 activation. To assess covariate effects on nivolumab clearance (CL), a population pharmacokinetics model was developed using data from 6,468 patients with colorectal cancer, hepatocellular carcinoma, melanoma, non-small cell lung cancer, renal cell carcinoma, or small cell lung cancer who received nivolumab as monotherapy or in combination with ipilimumab or chemotherapy across 25 clinical studies. Nivolumab CL was similar across the tumor types examined; CL was higher for ipilimumab 1 mg/kg every 6 weeks (by 17%) and 3 mg/kg every 3 weeks (by 29%) vs. nivolumab monotherapy. Nivolumab CL over time was partially explained by time-varying covariates. A greater decrease in nivolumab time-varying CL was associated with increased albumin and body weight and a responder status. Our findings support the observed association between nivolumab CL and disease severity.

Gemcitabine and metabolite pharmacokinetics in advanced NSCLC patients after bronchial artery infusion and intravenous infusion.

PURPOSE: We investigated the safety, pharmacokinetics, and efficacy of gemcitabine administered via bronchial artery infusion (BAI) and IV infusion in advanced NSCLC patients. METHODS: Patients were eligible if they had received at least two prior cytotoxic chemotherapy regimens. Gemcitabine was administered via BAI as 600 mg/m2 on day one of cycle one, followed by IV as 1000 mg/m2 on day eight of cycle one, and IV on days one and eight of all subsequent cycles. Pharmacokinetics for gemcitabine and dFdU metabolite in plasma, and dFdCTP active metabolite in peripheral blood mononuclear cells (PBMC) were evaluated. Intensive pharmacokinetic sampling was performed after BAI and IV infusions during cycle one. RESULTS: Three male patients (age range 59-68 years) were evaluated. All patients responded with stable disease or better. One PR was observed after cycle three, and the remaining had SD. Cmax (mean ± SD) following BAI for gemcitabine, dFdCTP, and dFdU were 7.71 ± 0.13, 66.5 ± 40.6, and 38 ± 6.27 µM and following IV infusion, 17 ± 2.36, 50.8 ± 3.61, and 83.2 ± 12.3 µM, respectively. The AUCinf (mean ± SD) following BAI for gemcitabine, dFdCTP, and dFdU were 6.89 ± 1.2, 791.1 ± 551.2, and 829.9 ± 217.8 µM h and following IV infusion, 12.5 ± 3.13, 584 ± 86.6, and 1394.64 ± 682.2 µM h, respectively. The AUC and Cmax of dFdCTP after BAI were higher than IV. The median OS was 6.27 months. No grade 3 or 4 toxicity was observed. The most common side effects were all grade ≤ 2 involving nausea, vomiting, rigor, thrombocytopenia, and anemia. CONCLUSIONS: Systemic exposure to dFdCTP was higher after BAI than IV in two out of three patients.

Reporting, Visualization, and Modeling of Immunogenicity Data to Assess Its Impact on Pharmacokinetics, Efficacy, and Safety of Monoclonal Antibodies.

The rapidly increasing number of therapeutic biologics in development has led to a growing recognition of the need for improvements in immunogenicity assessment. Published data are often inadequate to assess the impact of an antidrug antibody (ADA) on pharmacokinetics, safety, and efficacy, and enable a fully informed decision about patient management in the event of ADA development. The recent introduction of detailed regulatory guidance for industry should help address many past inadequacies in immunogenicity assessment. Nonetheless, careful analysis of gathered data and clear reporting of results are critical to a full understanding of the clinical relevance of ADAs, but have not been widely considered in published literature to date. Here, we review visualization and modeling of immunogenicity data. We present several relatively simple visualization techniques that can provide preliminary information about the kinetics and magnitude of ADA responses, and their impact on pharmacokinetics and clinical endpoints for a given therapeutic protein. We focus on individual sample- and patient-level data, which can be used to build a picture of any trends, thereby guiding analysis of the overall study population. We also discuss methods for modeling ADA data to investigate the impact of immunogenicity on pharmacokinetics, efficacy, and safety.

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors.

Immuno-oncology works through activation of the patient's immune system against cancer, with several advantages over other treatment approaches, including cytotoxic agents and molecular-targeted therapies. The most notable feature of immuno-oncology treatments is the nature of the patient responses achieved, which can be more durable and sustained than with other modalities. Increased understanding of immune system complexity has provided a number of opportunities to advance several strategies for the development of immuno-oncology therapies. This review outlines the clinical pharmacology characteristics and development challenges for the 6 approved immunomodulatory monoclonal antibodies that target 2 immune checkpoint pathways: ipilimumab (an anti-cytotoxic T-lymphocyte antigen-4 antibody) and, more recently, nivolumab and pembrolizumab (both anti-programmed death-1 antibodies) and atezolizumab, avelumab, and durvalumab (all anti-programmed death ligand-1 antibodies). These agents have revealed much about the clinical pharmacology features of immune checkpoint inhibitors as a class, as well as the pharmacometric approaches used to support their clinical development and regulatory approval. The development experiences with these pioneering immuno-oncology agents are likely to serve as useful guides in the discovery, progression, and approval of future drugs or combination of drugs in this class. This review includes summaries of the pharmacokinetics and exposure-response of the immune checkpoint inhibitors approved to date, as well as an overview of some quantitative systems pharmacology approaches. The ability of immuno-oncology to meet its full potential will depend on overcoming development challenges, including the need for clear strategies to determine optimal dose and scheduling for monotherapy as well as combination approaches.

Personalized fludarabine dosing to reduce nonrelapse mortality in hematopoietic stem-cell transplant recipients receiving reduced intensity conditioning.

Patients undergoing hematopoietic cell transplantation (HCT) with reduced intensity conditioning (RIC) commonly receive fludarabine. Higher exposure of F-ara-A, the active component of fludarabine, has been associated with a greater risk of nonrelapse mortality (NRM). We sought to develop a model for fludarabine dosing in adult HCT recipients that would allow for precise dose targeting and predict adverse clinical outcomes. We developed a pharmacokinetic model from 87 adults undergoing allogeneic RIC HCT that predicts F-ara-A population clearance (Clpop) accounting for ideal body weight and renal function. We then applied the developed model to an independent cohort of 240 patients to identify whether model predictions were associated with NRM and acute graft versus host disease (GVHD). Renal mechanisms accounted for 35.6% of total F-ara-A Clpop. In the independent cohort, the hazard ratio of NRM at day 100 was significantly higher in patients with predicted F-ara-A clearance (Clpred) <8.50 L/h (P < 0.01) and area under the curve (AUCpred) >6.00 µg × h/mL (P = 0.01). A lower Clpred was also associated with more NRM at month 6 (P = 0.01) and trended toward significance at 12 months (P = 0.05). In multivariate analysis, low fludarabine clearance trended toward higher risk of acute GVHD (P = 0.05). We developed a model that predicts an individual's systemic F-ara-A exposure accounting for kidney function and weight. This model may provide guidance in dosing especially in overweight individuals and those with altered kidney function.

Differentially expressed gene transcripts using RNA sequencing from the blood of immunosuppressed kidney allograft recipients.

We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because it's not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. DEGs were determined in 32 adult kidney recipients, without clinical acute rejection (AR), treated with antibody induction, calcineurin inhibitor, mycophenolate, with and without steroids. Blood was obtained pre-transplant (baseline), week 1, months 3 and 6 post-transplant. PBMCs were isolated, RNA extracted and gene expression measured using RNAseq. Principal components (PCs) were computed using a surrogate variable approach. DEGs post-transplant were identified by controlling false discovery rate (FDR) at < 0.01 with at least a 2 fold change in expression from pre-transplant. The top 5 DEGs with higher levels of transcripts in blood at week 1 were TOMM40L, TMEM205, OLFM4, MMP8, and OSBPL9 compared to baseline. The top 5 DEGs with lower levels at week 1 post-transplant were IL7R, KLRC3, CD3E, CD3D, and KLRC2 (Striking Image) compared to baseline. The top pathways from genes with lower levels at 1 week post-transplant compared to baseline, were T cell receptor signaling and iCOS-iCOSL signaling while the top pathways from genes with higher levels than baseline were axonal guidance signaling and LXR/RXR activation. Gene expression signatures at month 3 were similar to week 1. DEGs at 6 months post-transplant create a different gene signature than week 1 or month 3 post-transplant. RNAseq analysis identified more DEGs with lower than higher levels in blood compared to baseline at week 1 and month 3. The number of DEGs decreased with time post-transplant. Further investigations to determine the specific lymphocyte(s) responsible for differential gene expression may be important in selecting and personalizing immune suppressant drugs and may lead to targeted therapies.

Angiotensin Converting Enzyme Inhibitors (ACEI) and doxorubicin pharmacokinetics in women receiving adjuvant breast cancer treatment.

PURPOSE: Doxorubicin (DOX) chemotherapy can cause cardiac complications. Angiotensin converting enzyme inhibitors (ACEI) may protect against these complications. We performed a pharmacokinetics (PK) study to determine whether DOX levels are altered in the presence of ACEI. METHODS: In this randomized, cross-over, single-blinded drug-drug interaction study, 19 women with breast cancer prescribed DOX and cyclophosphamide every 14 days received one cycle of DOX chemotherapy with ACEI enalapril 10 mg daily and another cycle of DOX with placebo. Blood samples for DOX and doxorubicinol were drawn at baseline, 0.5, 1.0, 2.0, 4.0, 24.0 and 48.0 hours after infusion with and without ACEI enalapril. Correlative laboratories were also obtained. PK data was analyzed using non compartmental methods and DOX and doxorubicinol area under the curve (AUC) 0 to infinity, Cmax and half-life were estimated. Paired t-tests were used to determine whether DOX and its metabolite were altered with the use of enalapril (P < 0.05). RESULTS: 17 women (median age 45 years) received 60 mg/m2 DOX every two weeks for four cycles. Mean (SD) AUC0- ∞ for DOX and doxorubicinol with enalapril exposure was 1185.56 (44.64) hr*ng/ml and 1040 (80.6) hr*ng/ml, respectively. AUC0- ∞ for DOX and doxobubicinol without enalapril was 1167.73 (45.26) hr*ng/ml and 1056.32 (92.03) hr*ng/ml, respectively. There is no interaction between DOX and enalapril. Enalapril was tolerated (33% grade 1 dizziness). CONCLUSION: ACEI, enalapril, does not appear to alter the PK of DOX. Ongoing efforts to determine the effectiveness of ACEI as a cardioprotective agent in women receiving DOX chemotherapy should be continued.