Assessing trends in cytokine-CYP drug interaction and relevance to drug dosing.

The regulation of drug-metabolizing enzymes and transporters by cytokines has been extensively studied, in vitro and in clinic. Cytokine-mediated suppression of CYPs or drug transporters may increase or decrease the systemic clearance of drug substrates that are primarily cleared via these pathways; neutralization of cytokines by therapeutic proteins may thereby alter systemic exposures of such drug substrates. The FDA recommends evaluating such clinical drug interactions during clinical development and has provided labeling recommendations for therapeutic proteins. To determine the clinical relevance of these drug interactions to dose adjustments, trends in steady-state exposures (AUCss) of CYP-sensitive substrates co-administered with cytokine modulators as reported in the UW DIDB were extracted and examined for each of the CYPs. Co-administration of CYP3A (midazolam/simvastatin), CYP2C19 (omeprazole), or CYP1A2 (caffeine/tizanidine) substrates with anti-IL-6 and with anti-IL-23 therapeutics led to changes in systemic exposures of CYP substrates ranging from ~ -58% to ~35%; no significant trends were observed for CYP2D6 (dextromethorphan) and CYP2C9 (warfarin) substrates. Although none of these changes in systemic exposures have been reported as clinically meaningful, dose adjustment of midazolam for optimal sedation in acute care settings has been reported. Simulated concentration-time profiles of midazolam under conditions of elevated cytokine levels when co-administered with tocilizumab, suggest a ~6-7 fold increase in midazolam clearance suggesting potential implications of cytokine- CYP drug interactions on dose adjustments of sensitive CYP3A substrates in acute care settings. Additionally, this article also provides a brief overview of non-clinical and clinical assessments of cytokine-CYP drug interactions, in drug discovery and development. Significance Statement Significance statement: There has been significant progress in understanding cytokine-mediated drug interactions for CYP-sensitive substrates. This article provides an overview of the progress in this field, including a trend analysis of systemic exposures of CYP-sensitive substrates co-administered with anti-IL-x therapeutics. In addition, the review also provides a perspective of current methods used to assess these drug interactions during drug development, and a focus on individualized medicine, particularly in acute care settings.

Population Pharmacokinetics and Exposure-Response Analysis for the CTLA-4 Inhibitor Tremelimumab in Metastatic NSCLC Patients in the Phase III POSEIDON Study.

Blockade of CTLA-4 by tremelimumab combined with anti-PD-L1 durvalumab and chemotherapy provided increased antitumor activity and long-term survival benefits in first-line metastatic non-small cell lung cancer (mNSCLC) in the phase III POSEIDON study. We performed population pharmacokinetic modeling for tremelimumab using data from 1,605 patients across 6 studies (including POSEIDON) in multiple tumors (lung cancer, bladder cancer, malignant mesothelioma, and other solid tumors), and identified a 2-compartment model with linear and time-varying clearance for tremelimumab. Cox proportional hazard regression models were applied to 326 patients with mNSCLC from POSEIDON to evaluate the association between exposure metrics and efficacy end points, adjusting for baseline prognostic covariates. Improved progression-free survival (PFS) and overall survival (OS) in the tremelimumab arm (in combination with durvalumab and chemotherapy) was associated with higher tremelimumab exposure (e.g., minimum concentration at 5th dose (Cmin,dose5 ) and area under the curve at 5th dose (AUCdose5 )). However, further case-matching analyses yielded hazard ratios for the comparison of tremelimumab-treated patients in the Cmin,dose5 quartile 1 (Q1) subgroup with matched chemotherapy-treated patients of 1.04 (95% confidence interval (CI): 0.76-1.44) for OS and 0.99 (95% CI: 0.72-1.36) for PFS, suggesting that the observed apparent exposure-response relationship might be confounded. No relationship between tremelimumab exposure and safety (grade ≥3 treatment-emergent adverse events [AEs], AEs of special interest, or discontinuation due to AEs) was identified. These results support the consistent benefit observed with tremelimumab 75 mg every 3 weeks for up to 5 doses in combination with durvalumab and chemotherapy in POSEIDON as first-line therapy for mNSCLC.

Population Pharmacokinetics and Exposure-Response Analysis of Tremelimumab 300 mg Single Dose Combined with Durvalumab 1500 mg Q4W (STRIDE) in Patients with Unresectable Hepatocellular Carcinoma.

A novel single-dose regimen of 300 mg tremelimumab in combination with durvalumab (STRIDE) has demonstrated a favorable benefit-risk profile in the phase 1/2 Study 22 trial (in patients with unresectable hepatocellular carcinoma, uHCC) and in the phase 3 HIMALAYA study. The current analysis evaluated the population pharmacokinetics (PopPK) of tremelimumab and durvalumab, and the exposure-response (ER) relationship for efficacy and safety of STRIDE in patients with uHCC. Previous PopPK models for tremelimumab and durvalumab were updated using data from previous studies in various cancers combined with data from Study 22 and HIMALAYA. Typical population mean parameters and associated inter- and intra-individual variability were assessed, as was the influence of covariates. Individual exposure metrics were derived from the individual empirical Bayes estimates as drivers for ER analysis related to efficacy and safety from HIMALAYA. The observed pharmacokinetics of tremelimumab in uHCC were well described by a 2-compartment model with both linear and time-dependent clearance. All identified covariates changed tremelimumab PK parameters by <25%, and thus had minimal clinical relevance; similar results were obtained from durvalumab PopPK analysis. None of tremelimumab or durvalumab exposure metrics were significantly associated with overall survival (OS), progression-free survival (PFS), or adverse events. Baseline aspartate aminotransferase and neutrophil-to-lymphocyte ratio (NLR) were associated with OS (P < .001) by the Cox proportional hazards model. No covariate was identified as a significant factor for PFS. No dose adjustment for tremelimumab or durvalumab is needed based on PopPK covariate analyses or ER analyses. Our findings support the novel STRIDE dosing regimen in patients with uHCC.

Entrectinib dose confirmation in pediatric oncology patients: pharmacokinetic considerations.

PURPOSE: Entrectinib is a central nervous system-active potent inhibitor of tropomyosin receptor kinase (TRK), with anti-tumor activity against neurotrophic NTRK gene fusion-positive tumors. This study investigates the pharmacokinetics of entrectinib and its active metabolite (M5) in pediatric patients and aims to understand whether the pediatric dose of 300 mg/m2 once daily (QD) provides an exposure that is consistent with the approved adult dose (600 mg QD). METHODS: Forty-three patients aged from birth to 22 years were administered entrectinib (250-750 mg/m2 QD) orally with food in 4-week cycles. Entrectinib formulations included capsules without acidulant (F1) and capsules with acidulant (F2B and F06). RESULTS: Although there was interpatient variability with F1, entrectinib and M5 exposures increased dose dependently. Lower systemic exposures were observed in pediatric patients receiving 400 mg/m2 QD entrectinib (F1) versus adults receiving either the same dose/formulation or the recommended flat dose of 600 mg QD (~ 300 mg/m2 for a 70 kg adult) due to suboptimal F1 performance in the pediatric study. The observed pediatric exposures following 300 mg/m2 QD entrectinib (F06) were comparable to those in adults receiving 600 mg QD. CONCLUSIONS: Overall, the F1 formulation of entrectinib was associated with lower systemic exposure in pediatric patients compared with the commercial acidulant formulation (F06). Systemic exposures achieved in pediatric patients with the F06 recommended dose (300 mg/m2) were within the known efficacious range in adults, confirming the adequacy of the recommended dose regimen with the commercial formulation.

Population Pharmacokinetics of Monalizumab in Patients With Advanced Solid Tumors.

Monalizumab is a novel, first-in-class humanized immunoglobulin G4 monoclonal antibody immune checkpoint inhibitor that targets the inhibitory CD94/NKG2A receptors. The objectives of this analysis were to develop a population pharmacokinetic (PK) model of monalizumab, evaluate the impact of clinically relevant covariates on monalizumab PK, and provide dose justification for clinical trials. We developed a monalizumab population PK model to characterize the PK properties of monalizumab in patients with advanced solid tumors or head and neck squamous cell carcinoma. Data from clinical studies D419NC00001 (NCT02671435) and IPH2201-203 (NCT02643550) were pooled for the analysis, resulting in a data set of 3066 PK samples derived from 507 subjects. The PK of monalizumab were reasonably described by a 2-compartment model with first-order elimination. Monalizumab generally exhibited linear PK over a dose range of 22.5-750 mg or 10 mg/kg every 2 weeks. The estimate of clearance was ≈0.255 L/day, and apparent volume of distribution was 6.36 L for a typical individual, consistent with previous findings for endogenous immunoglobulin Gs and other therapeutic monoclonal antibodies. Baseline albumin and body weight were identified as significant covariates of clearance; body weight, sex, and smoking status had a significant impact on volume of distribution; and none of these covariates had impact on peripheral volume of distribution. Although these covariates were identified as statistically significant, they are considered to be not clinically meaningful, as changes in monalizumab exposure were <30%. Therefore, no dose adjustments of monalizumab based on patient or disease characteristics are recommended.

Strategies for clinical dose optimization of T cell-engaging therapies in oncology.

Innovative approaches in the design of T cell-engaging (TCE) molecules are ushering in a new wave of promising immunotherapies for the treatment of cancer. Their mechanism of action, which generates an in trans interaction to create a synthetic immune synapse, leads to complex and interconnected relationships between the exposure, efficacy, and toxicity of these drugs. Challenges thus arise when designing optimal clinical dose regimens for TCEs with narrow therapeutic windows, with a variety of dosing strategies being evaluated to mitigate key side effects such as cytokine release syndrome, neurotoxicity, and on-target off-tumor toxicities. This review evaluates the current approaches to dose optimization throughout the preclinical and clinical development of TCEs, along with perspectives for improvement of these strategies. Quantitative approaches used to aid the understanding of dose-exposure-response relationships are highlighted, along with opportunities to guide the rational design of next-generation TCE molecules, and optimize their dose regimens in patients.

In vitro and clinical investigations to determine the drug-drug interaction potential of entrectinib, a small molecule inhibitor of neurotrophic tyrosine receptor kinase (NTRK).

Background Entrectinib is a CNS-active, potent inhibitor of tyrosine receptor kinases A/B/C, ROS1 and anaplastic lymphoma kinase approved for use in patients with solid tumors. We describe the in vitro and clinical studies investigating potential entrectinib drug-drug interactions. Methods In vitro studies with human biomaterials assessed the enzymes involved in entrectinib metabolism, and whether entrectinib modulates the activity of the major cytochrome P450 (CYP) enzymes or drug transporter P-glycoprotein. Clinical studies investigated the effect of a strong CYP3A4 inhibitor (itraconazole) and inducer (rifampin) on single-dose entrectinib pharmacokinetics. The effect of entrectinib on sensitive probe substrates for CYP3A4 (midazolam) and P-glycoprotein (digoxin) were also investigated. Results Entrectinib is primarily metabolized by CYP3A4. In vitro, entrectinib is a CYP3A4/5 inhibitor (IC50 2 µM) and a weak CYP3A4 inducer. Entrectinib inhibited P-glycoprotein (IC50 1.33 µM) but is a poor substrate. In healthy subjects, itraconazole increased entrectinib Cmax and AUC by 73% and 504%, respectively, and rifampin decreased entrectinib Cmax and AUC by 56% and 77%, respectively. Single dose entrectinib did not affect midazolam AUC, although Cmax decreased by 34%. Multiple dose entrectinib increased midazolam AUC by 50% and decreased Cmax by 21%. Single dose entrectinib increased digoxin AUC and Cmax by 18% and 28%, respectively, but did not affect digoxin renal clearance. Conclusions Entrectinib is a CYP3A4 substrate and is sensitive to the effects of coadministered moderate/strong CYP3A4 inhibitors and strong inducers, and requires dose adjustment. Entrectinib is a weak inhibitor of CYP3A4 and P-glycoprotein and no dose adjustments are required with CYP3A4/P- glycoprotein substrates.Registration Number (Study 2) NCT03330990 (first posted online November 6, 2017) As studies 1 and 3 are phase 1 trials in healthy subjects, they are not required to be registered.

Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making.

BACKGROUND AND OBJECTIVE: Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. METHODS: A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. RESULTS: The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. CONCLUSIONS: The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

Predicted limited redistribution of T cells to secondary lymphoid tissue correlates with increased risk of haematological malignancies in asplenic patients.

The spleen, a secondary lymphoid tissue (SLT), has an important role in generation of adaptive immune responses. Although splenectomy remains a common procedure, recent studies reported poor prognosis and increased risk of haematological malignancies in asplenic patients. The high baseline trafficking of T lymphocytes to splenic tissue suggests splenectomy may lead to loss of blood-borne malignant immunosurveillance that is not compensated for by the remaining SLT. To date, no quantitative analysis of the impact of splenectomy on the human T cell trafficking dynamics and tissue localisation has been reported. We developed a quantitative computational model that describes organ distribution and trafficking of human lymphocytes to explore the likely impact of splenectomy on immune cell distributions. In silico splenectomy resulted in an average reduction of T cell numbers in SLT by 35% (95%CI 0.12-0.97) and a comparatively lower, 9% (95%CI 0.17-1.43), mean decrease of T cell concentration in SLT. These results suggest that the surveillance capacity of the remaining SLT insufficiently compensates for the absence of the spleen. This may, in part, explain haematological malignancy risk in asplenic patients and raises the question of whether splenectomy has a clinically meaningful impact on patient responses to immunotherapy.

Characterization of the pharmacokinetics of entrectinib and its active M5 metabolite in healthy volunteers and patients with solid tumors.

BACKGROUND: Entrectinib is an oral, CNS-active, potent inhibitor of tyrosine receptor kinases A/B/C, tyrosine kinase ROS proto-oncogene 1, and anaplastic lymphoma kinase approved for use in patients with solid tumors. We describe 3 clinical studies, including one investigating the single/multiple dose pharmacokinetics of entrectinib in patients and two studies in healthy volunteers investigating the absorption/distribution/metabolism/excretion (ADME) of entrectinib, its relative bioavailability, and effect of food on pharmacokinetics. METHODS: The patient study is open-label with dose-escalation and expansion phases. Volunteers received entrectinib (100-400 mg/m2, and 600-800 mg) once daily with food in continuous 28-day cycles. In the ADME study, volunteers received a single oral dose of [14C]entrectinib 600 mg. In the third study, volunteers received single doses of entrectinib 600 mg as the research and marketed formulations in the fasted state (Part 1), and the marketed formulation in the fed and fasted states (Part 2). Entrectinib and its major active metabolite M5 were assessed in all studies. RESULTS: Entrectinib was absorbed in a dose-dependent manner with maximum concentrations at ~4 h postdose and an elimination half-life of ~20 h. Entrectinib was cleared mainly through metabolism and both entrectinib and metabolites were eliminated mainly in feces (minimal renal excretion). At steady-state, the M5-to-entrectinib AUC ratio was 0.5 (with 600 mg entrectinib research formulation in patients). The research and marketed formulations were bioequivalent and food had no relevant effect on pharmacokinetics. CONCLUSIONS: Entrectinib is well absorbed, with linear PK that is suitable for once-daily dosing, and can be taken with or without food.

PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse-Type Tenosynovial Giant Cell Tumor.

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony-stimulating factor-1 receptor. Here, we show how modeling target-mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three-fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse-type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

Therapeutically-induced stable disease in oncology early clinical trials.

RATIONALE: The RECIST guideline defines four categories of response to treatment for cancer patients according to post-baseline changes in tumor burden, hence ignoring disease history. However, if left untreated, tumors grow exponentially, implying that pretreatment changes in tumor size are key to thoroughly assess efficacy. We present a model-based approach to estimate the rates of changes in tumor mass, before and after treatment onset. METHODS: Sixty-eight patients were eligible for the analysis of tumor size data from a Phase 1 study evaluating the effect of emactuzumab. In addition to tumor size measured at baseline and every six weeks during treatment, a pre-baseline measurement was gathered for each patient. A longitudinal regression model was used to estimate the rates of tumor size change before and after treatment onset. RESULTS: The median pre-treatment tumor growth exponential rate was equal to 0.022 month-1, corresponding to a tumor size doubling time of 4 months, and the on-treatment median tumor shrinkage exponential rate was equal to 0.001 month-1. Among sixteen patients categorized as stable disease per RECIST, only five had similar slopes before and after treatment while nine actually improved. One patient in particular had a therapeutically induced stabilization of the disease. CONCLUSION: Our analysis emphasizes the importance of collecting pre-baseline scans to distinguish therapeutically induced stable disease from cases where the tumor growth is not perturbed by treatment.

From waterfall plots to spaghetti plots in early oncology clinical development.

Waterfall plots are used to describe changes in tumor size observed in clinical studies. They are frequently used to illustrate the overall drug response in oncology clinical trials because of its simple representation of results. Unfortunately, this visual display suffers a number of limitations including (1) potential misguidance by masking the time dynamics of tumor size, (2) ambiguous labelling of the y-axis, and (3) low data-to-ink ratio. We offer some alternatives to address these shortcomings and recommend moving away from waterfall plots to the benefit of plots showing the individual time profiles of sum of lesion diameters (according to RECIST). The spider plot presents the individual changes in tumor measurements over time relative to baseline tumor burden. Baseline tumor size is a well-known confounding factor of drug effect which has to be accounted for when analyzing data in early clinical trials. While spider plots are conveniently correct for baseline tumor size, they cannot be presented in isolation. Indeed, percentage change from baseline has suboptimal statistical properties (including skewed distribution) and can be overly optimistic in favor of drug efficacy. We argued that plots of raw data (referred to as spaghetti plots) should always accompany spider plots to provide an equipoised illustration of the drug effect on lesion diameters.

Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network.

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level.

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.

Prediction of the Optimal Dosing Regimen Using a Mathematical Model of Tumor Uptake for Immunocytokine-Based Cancer Immunotherapy.

Purpose: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities.Experimental Design: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL2 receptor-positive cell populations (i.e., CD8+, CD4+, NK, and B cells), which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues.Results: We created a pharmacokinetic/pharmacodynamic mathematical model that incorporates the expansion of IL2R-positive target cells at multiple dose levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL2R-positive cells.Conclusions: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anticancer immunotherapy. Clin Cancer Res; 24(14); 3325-33. ©2018 AACRSee related commentary by Ruiz-Cerdá et al., p. 3236.