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To assess the combined role of anti-viral monoclonal antibodies (mAbs) and vaccines in reducing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission and mortality in the United States, an agent-based model was developed that accounted for social contacts, movement/travel, disease progression, and viral shedding. The model was calibrated to coronavirus disease 2019 (COVID-19) mortality between October 2020 and April 2021 (aggressive pandemic phase), and projected an extended outlook to estimate mortality during a less aggressive phase (April-August 2021). Simulated scenarios evaluated mAbs for averting infections and deaths in addition to vaccines and aggregated non-pharmaceutical interventions. Scenarios included mAbs as a treatment of COVID-19 and for passive immunity for postexposure prophylaxis (PEP) during a period when variants were susceptible to the mAbs. Rapid diagnostic testing paired with mAbs was evaluated as an early treatment-as-prevention strategy. Sensitivity analyses included increasing mAb supply and vaccine rollout. Allocation of mAbs for use only as PEP averted up to 14% more infections than vaccine alone, and targeting individuals ≥ 65 years averted up to 37% more deaths. Rapid testing for earlier diagnosis and mAb use amplified these benefits. Doubling the mAb supply further reduced infections and mortality. mAbs provided benefits even as proportion of the immunized population increased. Model projections estimated that ~ 42% of expected deaths between April and August 2021 could be averted. Assuming sensitivity to mAbs, their use as early treatment and PEP in addition to vaccines would substantially reduce SARS-CoV-2 transmission and mortality even as vaccination increases and mortality decreases. These results provide a template for informing public health policy for future pandemic preparedness.

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

Model‐informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients’ survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non‐small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan‐Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR‐positive, chemotherapy in EGFR‐positive, chemotherapy in EGFR‐negative, and gefitinib in EGFR‐negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR‐positive than in EGFR‐negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

As described in the ICH E5 guidelines, a bridging study is an additional study executed in a new geographical region or subpopulation to link or "build a bridge" from global clinical trial outcomes to the new region. The regulatory and scientific goals of a bridging study is to evaluate potential subpopulation differences while minimizing duplication of studies and meeting unmet medical needs expeditiously. Use of historical data (borrowing) from global studies is an attractive approach to meet these conflicting goals. Here, we propose a practical and relevant approach to guide the optimal borrowing rate (percent of subjects in earlier studies) and the number of subjects in the new regional bridging study. We address the limitations in global/regional exchangeability through use of a Bayesian power prior method and then optimize bridging study design with a return on investment viewpoint. The method is demonstrated using clinical data from global and Japanese trials in dapagliflozin for type 2 diabetes.

Purpose Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data. Methods Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data. Results Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response. Conclusions This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms. Electronic supplementary material The online version of this article (10.1007/s00280-019-03840-3) contains supplementary material, which is available to authorized users.

SummaryBackgroundAntiviral monoclonal antibodies (mAbs) developed for treatment of COVID-19 reduce the magnitude and duration of viral shedding and can thus potentially contribute to reducing transmission of the causative virus, severe acute respiratory coronavirus 2 (SARS-CoV-2). However, use of these mAbs in combination with a vaccine program has not been considered in public health strategic planning.MethodsWe developed an agent-based model to characterize SARS-CoV-2 transmission in the US population during an aggressive phase of the pandemic (October 2020 to April 2021), and simulated the effects on infections and mortality of combining mAbs as treatment and post-exposure prophylaxis (PEP) with a vaccine program plus non-pharmaceutical interventions. We also interrogated the impact of rapid diagnostic testing, increased mAb supply, and vaccine rollout.FindingsAllocation of mAbs as PEP or targeting those ≥65 years provided the greatest incremental benefits relative to vaccine in averting infections and deaths, by up to 17% and 41%, respectively. Rapid testing, facilitating earlier diagnosis and mAb use, amplified these benefits. The model was sensitive to mAb supply; doubling supply further reduced infections and mortality, by up to two-fold, relative to vaccine. mAbs continued to provide incremental benefits even as proportion of the vaccinated population increased.InterpretationUse of anti-viral mAbs as treatment and PEP in combination with a vaccination program would substantially reduce SARS-CoV-2 transmission and pandemic burden. These results may help guide resource allocation and patient management decisions for COVID-19 and can also be used to inform public health policy for current and future pandemic preparedness.FundingRegeneron Pharmaceuticals.

Dupilumab demonstrated efficacy with an acceptable safety profile in two randomized, double-blind, placebo-controlled, parallel-group, phase III trials in adolescents (12-17 years; LIBERTY AD ADOL) and children (6-11 years; LIBERTY AD PEDS) with atopic dermatitis (AD) treated for 16 weeks. Here, we present the pharmacokinetic profiles and exposure-response relationships of dupilumab that guided the posology in these populations. A total of 251 adolescent patients with moderate-to-severe AD were randomized to subcutaneous dupilumab monotherapy every 2 weeks (q2w) (200 mg q2w, baseline weight

Olaparib is a poly ADP-ribose polymerase inhibitor that induces synthetic lethality in tumors with deficient homologous recombination repair. Population exposure-response analyses were performed to evaluate the efficacy and safety of olaparib exposure in patients with cancer. Data from multiple phase I/II/III clinical studies from both capsule and tablet formulations were combined for efficacy (N = 410) and safety (N = 757) analyses. Exposure-progression-free survival (Cox proportional hazards model indicated that a 300 mg b.i.d. tablet was statistically superior to the 200 mg b.i.d. tablet dose (hazard ratio of 0.96), although the difference was small. Exposure-safety logistic regression models and hemoglobin models predicted similar probability of safety events or hemoglobin decrease with largely overlapping 95% confidence intervals at 300 mg b.i.d. tablet, 200 mg b.i.d. tablet, and 400 mg b.i.d. capsule. The analyses provided key assessments to support the approval of olaparib 300 mg tablet therapeutic dose in patients with ovarian and breast cancer, regardless of their breast cancer (BRCA) mutation status.

Selumetinib (AZD6244, ARRAY-142886) is a mitogen-activated protein kinase kinase inhibitor that has been tested for treatment of non-small cell lung cancer (NSCLC). Selumetinib (75 mg twice daily) plus docetaxel in patients with advanced NSCLC has been assessed in phase 2 (SELECT-2) and phase 3 (SELECT-1) clinical trials. The objective of the current analysis was to investigate the exposure-response relationship of selumetinib in these 2 clinical trials, based on the development of a population pharmacokinetic (PopPK) model for selumetinib and its active metabolite, N-desmethyl selumetinib, in patients with NSCLC. A PopPK model using data from seven phase 1 studies was first developed and served as prior information for the development of the patient PopPK model. The pharmacokinetics (PK) of selumetinib and N-desmethyl selumetinib were modeled simultaneously. A two-compartment model with zero-first order absorption and first-order elimination reasonably described the selumetinib PK. The N-desmethyl metabolite of selumetinib was described by a one-compartment model with first-order elimination. The final PK parameter estimates were similar between patients with NSCLC and patients in the phase 1 population. Selumetinib apparent clearance and central volume of distribution were 11.9 L/h and 32.1 L, respectively, in patients. Individual selumetinib exposure metrics were estimated to investigate the correlation between exposure and efficacy/safety endpoints observed in NSCLC studies. There was no significant difference in progression-free survival (the primary endpoint) among the different quartiles of exposure. Similarly, no significant correlation was observed between selumetinib exposure and other secondary efficacy or safety endpoints. The conclusions are in accordance with the reported clinical findings.

ATM inhibitors, such as 7, have demonstrated the antitumor potential of ATM inhibition when combined with DNA double-strand break-inducing agents in mouse xenograft models. However, the properties of 7 result in a relatively high predicted clinically efficacious dose. In an attempt to minimize attrition during clinical development, we sought to identify ATM inhibitors with a low predicted clinical dose (50 mg) and focused on strategies to increase both ATM potency and predicted human pharmacokinetic half-life (predominantly through the increase of volume of distribution). These efforts resulted in the discovery of 64 (AZD0156), an exceptionally potent and selective inhibitor of ATM based on an imidazo[4,5- c]quinolin-2-one core. 64 has good preclinical phamacokinetics, a low predicted clinical dose, and a high maximum absorbable dose. 64 has been shown to potentiate the efficacy of the approved drugs irinotecan and olaparib in disease relevant mouse models and is currently undergoing clinical evaluation with these agents.

Modeling the relationship between drug concentrations and heart rate corrected QT interval (QTc) change from baseline (C-∆QTc), based on Phase I single ascending dose (SAD) or multiple ascending dose (MAD) studies, has been proposed as an alternative to thorough QT studies (TQT), in assessing drug-induced QT prolongation risk. The present analysis used clinical SAD, MAD and TQT study data of an experimental compound, AZD5672, to evaluate the performance of: (i) three computational platforms (linear mixed-effects modeling implemented via PROC MIXED in SAS, as well as in R using LME4 package and linear quantile mixed models (LQMM) implemented via LQMM package; (ii) different model structures with and without treatment- or time-specific intercepts; and (iii) three methods for calculating the confidence interval (CI) of QTc prolongation (analytical and bootstrap methods with fixed or varied geometric mean concentrations). We show that treatment- and time-specific intercepts may need to be included into C-∆QTc modeling through PROC MIXED or LME4, regardless of their statistical significance. With the intersection union test (IUT) in the TQT study as a reference for comparison, inclusion of these intercepts increased the feasibility for C-∆QTc modelling of SAD or MAD to reach the same conclusion as the IUT analysis based on TQT study. Compared to PROC MIXED or LME4, the LQMM method is less dependent on inclusion of treatment- or time-specific intercepts, and the bootstrap CI calculation methods provided higher likelihood for C-∆QTc modeling of SAD and MAD studies to reach the same conclusion as the IUT based on the TQT study.

Aims A multistudy analysis of cediranib, a potent, selective inhibitor of all three vascular endothelial growth factor receptors (VEGFR-1, -2 and -3), was conducted to establish population exposure-safety models for the relationship of cediranib exposure to the safety endpoints, diastolic and systolic blood pressure (DBP and SBP) and diarrhoea in cancer patients. These models were applied to predict safety outcomes for different cediranib dose regimens. Methods Models for hypertension and diarrhoea were constructed based on data from 10 Phase I and three Phase II studies comprising 631 cancer patients following cediranib once-daily oral dosing. Daily DBP and SBP were simultaneously characterized using indirect response models for predicted cediranib concentration-time courses, while daily diarrhoea events were modelled as ordered categorical variables with a proportional odds model with a Markov element for predicted average cediranib concentrations. Results For 20 mg cediranib once-daily oral administration, the mean increase in DBP and SBP was predicted to be 7 (95% CI 3-13) and 8 mmHg (95% CI 3-16), respectively, while the probability of mild diarrhoea, but not the severity, was predicted to increase over time. Severe diarrhoea was predicted to be resolved rapidly upon discontinuation of cediranib treatment. Conclusions Maximum blood pressure increase was observed within the first few days of cediranib treatment, consistent with the pharmacokinetic profile of cediranib reaching steady state in about 5 days. The probability of diarrhoea increased with cediranib concentration but was far more dependent on the status of diarrhoea predicted on the previous day.

Quantitative pharmacology (QP) applications in translational medicine, drug-development, and therapeutic use were crowd-sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial-burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model-informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.

The accuracy of physiologically based pharmacokinetic (PBPK) model prediction in children, especially those younger than 2 years old, has not been systematically evaluated. The aim of this study was to characterize the pediatric predictive performance of the PBPK approach for 10 drugs extensively metabolized by CYP1A2 (theophylline), CYP2C8 (desloratidine, montelukast), CYP2C9 (diclofenac), CYP2C19 (esomeprazole, lansoprazole), CYP2D6 (tramadol), and CYP3A4 (itraconazole, ondansetron, sufentanil). Model performance in children was evaluated by comparing simulated plasma concentration-time profiles with observed clinical results for each drug and age group. PBPK models reasonably predicted the pharmacokinetics of desloratadine, diclofenac, itraconazole, lansoprazole, montelukast, ondansetron, sufentanil, theophylline, and tramadol across all age groups. Collectively, 58 out of 67 predictions were within 2-fold and 43 out of 67 predictions within 1.5-fold of observed values. Developed PBPK models can reasonably predict exposure in children age 1 month and older for an array of predominantly CYP metabolized drugs.

Modelingsimulation (MS) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (RD), regulatory approval, and marketing of novel therapeutics. Applications of MS help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the RD process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical RD.

Naloxegol is approved for the treatment of opioid-induced constipation (OIC) in adults with chronic noncancer pain. Population exposure-response models were developed using data from a phase II study comprising 185 adults with OIC. The weekly probability of response defined as having ≥3/week spontaneous bowel movements (SBMs) and ≥1 SBM/week increase over baseline was characterized by a longitudinal mixed-effects logistic regression dose-response model, and the probability of time to discontinuation was modeled with a Weibull distribution function. The predicted probability of SBM in a given week increased with increasing naloxegol dose. The model predicted that 12.5, 25, and 37.5 mg doses would produce median response rates of 40%, 50%, and 60%, and dropout rates of 13.3%, 16.7%, and 23.3%, respectively. The large overlap of predicted difference of the response rate between placebo and the 25 or 37.5 mg doses suggested little utility of using a 37.5 mg dose in phase III studies.

Zolmitriptan is a serotonin (5-HT) 1B/1D receptor agonist effective for the treatment of migraine. This analysis aimed to develop a population pharmacokinetic (PK) model for zolmitriptan and its active metabolite in adults and adolescents and provide appropriate dosing regimens to be used in clinical trials for children 6–11 years old. The data from a single-dose clinical study of 5.0-mg zolmitriptan nasal spray (ZNS) conducted in adult and adolescent patients with migraine between migraine attacks was applied. Similar plasma concentration profiles of zolmitriptan and its metabolite, 183C91, were observed in adults and adolescents. A 1-compartment model with first-order absorption and first-order elimination reasonably described the zolmitriptan PK. With a portion of elimination of zolmitriptan being treated as the conversion from zolmitriptan to 183C91, the disposition of 183C91 was described by a 1-compartment model with first-order elimination. The estimated typical apparent volume of distribution and clearance of zolmitriptan were 136 L and 121 L/h, respectively, with 56.5% and 42.6% between-subject variability, respectively. Based on the simulation results with the final population PK model, a body weight–based dosing scheme of 5.0 and 2.5 mg ZNS in children greater than and less than 50 kg is recommended to achieve exposures similar to those of the adult and adolescent population administered 5.0 mg ZNS. The recommended doses for children to achieve exposure similar to that observed in adults given 2.5 mg ZNS are 2.5 mg (≥50 kg) and 1.0 mg (15–50 kg). These dosing regimens could be used in future clinical trials.

Naloxegol is a peripherally acting μ-opioid receptor antagonist that was developed for the treatment of opioid-induced constipation. Modeling and simulation of naloxegol efficacy and tolerability informed selection of doses for phase III studies and provided comprehensive dosage recommendations for the naloxegol US package insert.

Aims A population pharmacokinetic (PK) model was developed for cediranib to simulate cediranib exposure for different doses, including comedication with strong uridine glucuronosyl transferase/P-glycoprotein inducers such as rifampicin, in cancer patients. Methods Plasma concentrations and covariates from 625 cancer patients after single or multiple oral cediranib administrations ranging from 0.5 to 90 mg in 19 Phase I and II studies were included in the analysis. Stepwise covariate modelling was used to develop the population PK model. The final model was used to simulate cediranib exposure in cancer patients to evaluate cediranib target coverage and the need for dose adjustment for covariates or coadministration with rifampicin. Results A two-compartment model with sequential zero- and first-order absorption and first-order elimination adequately described the cediranib concentration–time courses. Body weight and age were identified as having statistically significant impact on cediranib PK, but only

Naloxegol is a peripherally acting µ-opioid receptor antagonist approved for use as an orally administered tablet (therapeutic doses of 12.5 and 25 mg) for the treatment of opioid-induced constipation. Over a wide dose range (i.e. single supratherapeutic doses up to 1000 mg in healthy volunteers), the pharmacokinetic properties of naloxegol appear to be time- and dose-independent. Naloxegol is rapidly absorbed, with mean time to maximum plasma concentration of

Aztreonam is a monocyclic β-lactam antibiotic often used to treat infections caused by Enterobacteriaceae or Pseudomonas aeruginosa. Despite the long history of clinical use, population pharmacokinetic modeling of aztreonam in renally impaired patients is not yet available. The aims of this study were to assess the impact of renal impairment on aztreonam exposure and to evaluate dosing regimens for patients with renal impairment. A population model describing aztreonam pharmacokinetics following intravenous administration was developed using plasma concentrations from 42 healthy volunteers and renally impaired patients from 2 clinical studies. The final pharmacokinetic model was used to predict aztreonam plasma concentrations and evaluate the probability of pharmacodynamic target attainment (PTA) in patients with different levels of renal function. A 2-compartment model with first-order elimination adequately described aztreonam pharmacokinetics. The population mean estimates of aztreonam clearance, intercompartmental clearance, volume of distribution of the central compartment, and volume of distribution of the peripheral compartment were 4.93 L/h, 9.26 L/h, 7.43 L, and 6.44 L, respectively. Creatinine clearance and body weight were the most significant variables to explain patient variability in aztreonam clearance and volume of distribution, respectively. Simulations using the final pharmacokinetic model resulted in a clinical susceptibility break point of 4 and 8 mg/L, respectively, based on the clinical use of 1- and 2-g loading doses with the same or reduced maintenance dose every 8 hours for various renal deficiency patients. The population pharmacokinetic modeling and PTA estimation support adequate PTAs (>90% PTA) from the aztreonam label for dose adjustment of aztreonam in patients with moderate and severe renal impairment.

Predictive performance of physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) models of drugs predominantly eliminated through kidney in the pediatric population was evaluated. After optimization using adult clinical data, the verified PBPK models can predict 33 of 34 drug clearance within twofold of the observed values in children 1 month and older. More specifically, 10 of 11 of predicted clearance values were within 1.5-fold of those observed in children between 1 month and 2 years old. The PopPK approach also predicted 19 of 21 drug clearance within twofold of the observed values in children. In summary, our analysis demonstrated both PBPK and PopPK adult models, after verification with additional adult pharmacokinetic (PK) studies and incorporation of known ontogeny of renal filtration, could be applied for dosing regimen recommendation in children 1 month and older for renally eliminated drugs in a first-in-pediatric study.

Naloxegol is a polyethylene glycol derivative of naloxone approved in the US as a once-daily oral treatment for opioid-induced constipation (OIC) in adults with chronic noncancer pain. Population exposure-response models were constructed based on data from two phase III studies comprising 1,331 adults with noncancer pain and OIC. In order to characterize the protocol-defined naloxegol responder rate, the number of daily spontaneous bowel movements (SBMs) was characterized by a longitudinal ordinal nonlinear mixed-effects logistic regression dose-response model, and the incidence of diary entry discontinuation was described by a time-to-event model. The mean number of SBMs per week increased with increasing naloxegol dose. The predicted placebo-adjusted responder rates (90% confidence interval) were 10.4% (4.6-13.4%) and 11.1% (4.8-14.4%) for naloxegol 12.5 and 25 mg/day, respectively. Model-predicted response to naloxegol was influenced by the baseline SBM frequency and characteristics of the opioid treatment.

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents.

Background: Acalabrutinib is a selective BTK inhibitor approved for the treatment of Mantle Cell Lymphoma. Relationship between plasma exposures to acalabrutinib and ACP-5862 versus clinical efficacy and safety were evaluated in subjects with B-cell malignancies, following administration of acalabrutinib monotherapy or in combination with obinutuzumab. Methods: Acalabrutinib and ACP-5862 exposures (AUC24h,ss) were generated using population pharmacokinetic (PK) analysis. To account for the contribution of ACP-5862 to overall activity, an exposure metric (total active AUC24h,ss) accounting for relative BTK potency and protein binding was estimated. Efficacy data from the pivotal Phase 3 study in previously untreated (PU) CLL subjects (n=274), who received acalabrutinib 100 mg twice daily (BID) as monotherapy or in combination, were included. Safety data pooled across 8 studies in PU or relapsed/refractory (R/R) subjects with B-cell malignancies (n=567), who received acalabrutinib 100, 175, 200, and 250 once daily or 100 mg and 200 mg BID as monotherapy or in combination, were included. Efficacy endpoints including best overall response rate (BOR), progression -free survival (PFS) and sum of products of the greatest perpendicular diameters (SPD) of index lesions were evaluated. Safety endpoints including all AEs Grade ≥2 in severity, and Grade ≥2 events of clinical interest such as, anemia, cardiac events, diarrhea, headache, hemorrhage, hepatic events, hypertension, infections, neutropenia, and thrombocytopenia were evaluated. Kaplan-Meier survival and logistic regression analyses were conducted for key efficacy and safety endpoints. Results: Median acalabrutinib/ACP-5862/total active AUC24h,ss were similar between responders versus non-responders, and across the 4 response categories (i.e. complete response, partial response, stable disease or progressive disease). No correlation between AUC24h,ss and PFS or SPD were noted. With the exception of Grade ≥2 anemia, no significant relationship between AUC24h,ss and safety outcomes was observed. Model-based analyses revealed baseline hemoglobin levels and line of therapy as highly significant predictors of Grade ≥2 anemia (p Conclusions: No clinically meaningful correlation between PK exposures and selected efficacy and safety outcomes was observed. In pivotal studies, the 100 mg BID acalabrutinib regimen demonstrated an acceptable safety profile and robust efficacy (consistent with ≥95% BTK occupancy over a dosing interval) in PU and R/R CLL. Overall, these data support a fixed 100 mg BID acalabrutinib dose for the treatment of subjects with CLL. Citation Format: Helena Edlund, Karthick Vishwanathan, Ming Yin, Rakesh Raman, Miné de Kock, Zhongqing He, Huan Liu, Marshall Baek, Nidal Al-Huniti, Joseph Ware, Shringi Sharma. Exposure versus efficacy/safety of acalabrutinib and its pharmacologically active metabolite, ACP-5862, for the treatment of chronic lymphocytic leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3023.

Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically‐based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP‐5862 to predict potential drug–drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80‐fold vs. 5.21‐fold observed) and rifampicin (0.21‐fold vs. 0.23‐fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage.

Bruton tyrosine kinase (BTK) is a key component of B-cell receptor signalling, critical for cell proliferation. Acalabrutinib, a selective, covalent BTK inhibitor, recently received an accelerated approval in relapsed/refractory mantle cell lymphoma. This analysis characterized the population pharmacokinetics (PK) of acalabrutinib and its metabolite ACP-5862. Data were obtained from six phase I/II trials in adult patients with B-cell malignancy and seven phase I trials in healthy volunteers. Pooled concentration-time data, at dose levels ranging from 15 to 400 mg, were analysed using non-linear mixed-effects modelling. Base model parameters were scaled with body weight and normalized to 70 kg (fixed exponents: 0.75 and 1 for clearance and volumes, respectively). A full covariate approach was used to evaluate any relevant effects of dose, health group/disease status, hepatic and renal impairment, use of acid-reducing agents, race and sex. A total of 11,196 acalabrutinib and 1068 ACP-5862 concentration-time samples were available. The PK of both analytes were well described using two-compartment disposition models. Acalabrutinib absorption was characterized using sequential zero- and first-order constants and a lag time. Apparent clearance (CL/F) of acalabrutinib was 169 L/h (95% CI 159–175). Relative to the 100 mg dose group, the 15 and 400 mg dose groups showed a 1.44-fold higher and 0.77-fold lower CL/F, respectively. The clearance for ACP-5862 was 21.9 L/h (95% CI 19.5–24.0). The fraction metabolized was fixed to 0.4. The central and peripheral volumes of distribution were 33.1 L (95% CI 24.4–41.0) and 226 L (95% CI 149–305) for acalabrutinib, and 38.5 L (95% CI 31.6–49.2) and 38.4 L (95% CI 32.3–47.9) for ACP-5862. None of the investigated covariates led to clinically meaningful changes in exposure. The PK of acalabrutinib and its metabolite ACP-5862 were adequately characterized. Acalabrutinib CL/F decreased with increasing dose, but the trend was small over the 75–250 mg range. No dose adjustment was necessary for intrinsic or extrinsic covariates.

Ceftazidime is a widely used β-lactam antibiotic and almost entirely excreted via glomerular filtration in kidney. The objective of this analysis was to assess the ability of physiologically based pharmacokinetic (PBPK) model to predict ceftazidime exposure in healthy volunteers and subjects with renal impairment. A full PBPK model of ceftazidime was developed using physiochemical properties and clinical data. The total clearance of 115 mL/min and renal clearance of 100 mL/min were obtained from ceftazidime package insert. Healthy and chronic kidney disease (CKD) populations were applied for sampling of virtual subjects. The established PBPK model predicted mean plasma AUCinf were 138.5 ± 19.6, 230.7 ± 22.2, 369.3 ± 53.1 and 561.8 ± 92.4 h µg/mL in healthy, mild, moderate and severe renal impairment subjects, respectively, after administration of 1 g ceftazidime intravenous bolus dose. The predicted values were in close agreement with the weighted mean of the five reported clinical studies. The exposure was slightly under predicted in subjects with severely impaired renal function, but still within 1.5-fold range. The concentration-time profiles of ceftazidime were also well captured in healthy volunteers and subjects with renal impairment. The developed PBPK model along with systems pharmacokinetics (PK) (renal impaired populations) well predicted the ceftazidime exposure. PBPK models verified with clinical study in healthy volunteers could be potentially applied to predict PK and recommend dose adjustment for CKD patients.

Olaparib is a first-in-class potent oral poly(ADP-ribose) polymerase inhibitor. The aims of this analysis were to establish an integrated population pharmacokinetic (PK) model of olaparib in patients with solid tumors and to bridge the PK of olaparib between capsule and tablet formulations. The population PK model was developed using plasma concentration data from 659 patients in 11 phase I, II, and III studies of olaparib tablets/capsules monotherapy. Relative bioavailability between the tablet and capsule formulations was estimated and the relative exposure between olaparib tablet and capsule therapeutic doses was further assessed. The concentration–time profile was described using a two-compartment model with sequential zero- and first-order absorption and first-order elimination for both capsules and tablets with different absorption parameters. Multiple-dose clearance compared with single-dose clearance was reduced by approximately 15% (auto-inhibition). Disease severity had an impact on olaparib clearance, and tablet strength had an impact on Ka. The olaparib geometric mean area under the curve (AUC) and maximal concentration (Cmax) following a single 300 mg tablet were 42.1 μg h/mL and 5.8 μg/mL, respectively, and the steady-state geometric mean AUC and Cmax following a 300 mg tablet twice daily were 49.0 μg h/mL and 7.7 μg/mL, respectively. The relative exposure (AUC) of the 300 mg tablet formulation is 13% higher than the 400 mg capsule formulation. This analysis bridged the olaparib capsule and tablet formulation PK and provided key assessment to support the approval of the olaparib tablet formulation in patients with ovarian cancer, regardless of their BRCA mutation status.

The manufacturer-recommended aztreonam dosing for patients with creatinine clearance values of 90% probability of target attainment up to MICs of 4 or 8 mg/liter, respectively. Thrice-weekly dosing should generally be avoided, except in nonsevere infections with MICs of ≤0.5 mg/liter.

Danvatirsen is a Generation 2.5 antisense oligonucleotide under clinical development. Population PK modelling was conducted using data from 3 available danvatirsen Phase I/II studies in oncology patients to investigate the impact of flat dosing on exposure compared to ideal body weight-based dosing. A total of 126 patients who received danvatirsen doses ranging from 1 to 4 mg/kg as monotherapy or in combination with durvalumab, most at 3 mg/kg (n = 70), was used in the danvatirsen population PK analysis. A 2-compartment model with linear elimination described the data well. Covariate analysis revealed ideal body weight was not a significant covariate on the PK of danvatirsen; nor was age, sex or race. The model-based simulation suggested that steady state weekly AUC and Cmax were very similar between 3 mg/kg and 200 mg flat dosing (geometric mean of AUC: 62.5 vs. 63.4 mg h/L and Cmax: 26.2 vs. 26.5 mg/L for two dose groups) with slightly less overall between-subject variability in the flat dosing regimen. The switch to flat dosing was approved by multiple regulatory agencies, including FDA, EMA, PMDA and ANSM. Several ongoing studies have been evaluating flat dosing. Interim analysis from an ongoing study (D5660C00016, NCT03421353) has shown the observed steady state concentration from 200 mg flat dose is in agreement with the model predictions. The population PK model could be further utilized in subsequent exposure-response efficacy and safety modelling.

Efficacy of ceftaroline fosamil, the prodrug of the active metabolite ceftaroline, was demonstrated in a phase 3 study of hospitalized Asian patients with Pneumonia Outcomes Research Team (PORT) risk class III-IV community-acquired pneumonia (NCT01371838). The objectives of the current analysis were to expand an existing ceftaroline and ceftaroline fosamil population pharmacokinetic (PK) model with data from this phase 3 study and a phase 1 study (NCT01458743) assessing ceftaroline PK in healthy Chinese volunteers and to evaluate the probability of PK/pharmacodynamic (PK/PD) target attainment (PTA) in Asian patients with community-acquired pneumonia (CAP) treated with ceftaroline fosamil. The ceftaroline plasma concentration-time course was simulated for 5000 Asian patients with CAP for different renal function subgroups using the final model. PTA was calculated for Streptococcus pneumoniae, Staphylococcus aureus, and non-extended-spectrum β-lactamase-producing Enterobacteriaceae. PTA was also evaluated for ceftaroline MIC90 values of isolates collected from Asia-Pacific surveillance studies (2012-2014) and for EUCAST and FDA/CLSI ceftaroline susceptibility break points. The final model reasonably described the ceftaroline PK. Race was not found to be a significant covariate impacting ceftaroline PK, suggesting similar ceftaroline PK in Asian and Western populations when corrected for body weight. High PTAs (90%-100%) were predicted for Asian patients with CAP treated with ceftaroline fosamil, covering MIC90 values of target CAP pathogens from the region. Similarly, >90% PTAs were predicted at EUCAST and FDA/CLSI clinical break points for these pathogens. These results support the use of the ceftaroline fosamil dosing regimens approved in Europe and the United States in Asian patients with PORT III-IV CAP.

Aims Naloxegol, a polyethylene glycol conjugated derivative of the opioid antagonist naloxone, is in clinical development for treatment of opioid-induced constipation (OIC). The aim of the study was to develop a population pharmacokinetic model describing the concentration vs. time profile of orally administered naloxegol, and determine the impact of pre-specified demographic and clinical factors and concomitant medication on population estimates of apparent clearance (CL/F) and apparent central compartment volume of distribution (Vc/F). Methods Analysis included 12 844 naloxegol plasma concentrations obtained from 1247 healthy subjects, patients with non-OIC and patients with OIC in 14 phase 1, 2b and 3 clinical studies. Pharmacokinetic analysis used the non-linear mixed effects modelling program. Goodness of fit plots and posterior predictive checks were conducted to confirm concordance with observed data. Results The final model was a two compartment disposition model with dual absorptions, comprising one first order absorption (ka1 4.56 h−1) and one more complex absorption with a transit compartment (ktr 2.78 h−1). Mean (SE) parameter estimates for CL/F and Vc/F, the parameters assessed for covariate effects, were 115 (3.41) l h–1 and 160 (27.4) l, respectively. Inter-individual variability was 48% and 51%, respectively. Phase of study, gender, race, concomitant strong or moderate CYP3A4 inhibitors, strong CYP3A4 inducers, P-glycoprotein inhibitors or inducers, naloxegol formulation, baseline creatinine clearance and baseline opioid dose had a significant effect on at least one pharmacokinetic parameter. Simulations indicated concomitant strong CYP3A4 inhibitors or inducers had relevant effects on naloxegol exposure. Conclusions Administration of strong CYP3A4 inhibitors or inducers had a clinically relevant influence on naloxegol pharmacokinetics.

A population model describing quetiapine pharmacokinetics (PK) in Western and Chinese patients following oral administration of immediate-release (IR) and extended-release (XR) formulations was developed using plasma concentrations in 127 patients from 5 studies with quetiapine IR and/or XR in Western patients and 1 study with quetiapine XR in Chinese patients. A 1-compartmental model with first-order absorption and first-order elimination adequately described the quetiapine PK. The typical apparent volume of distribution and elimination rate constant of quetiapine were 574 L and 0.12 h(-) (1) , respectively. The estimated population absorption rate constants were 1.46 and 0.10 h(-1) for quetiapine IR and XR, respectively. Covariate analysis revealed that race was not a significant covariate influencing the PK of quetiapine. Simulation conducted with the final quetiapine population PK model predicted that the administration of a 200-mg twice-daily dose of quetiapine IR in Chinese patients would achieve a steady-state AUC (AUCss ) ± standard deviation of 3087 ± 1480 ng · h/mL, which is in close agreement with the reported value (3538 ± 1728 ng · h/mL). The model also predicted that once-daily administration of 300 mg quetiapine IR or XR would achieve similar exposure in terms of AUCss in Chinese patients.

The purpose of this work was to develop a consolidated set of guiding principles for reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (where population PK frequently serves as preparatory analysis to exposure–response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider two main purposes of population PK reports (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified two main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results, and (2) a scientifically literate, but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with six questions that need to be addressed throughout the report. We recommend eight sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step towards industrialization of the field of pharmacometrics such that non-technical audience also understands the role of pharmacometrics analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including PKPD and simulation reports. Electronic supplementary material The online version of this article (doi:10.1007/s10928-015-9417-1) contains supplementary material, which is available to authorized users.

AZD3241 is a potent and selective myeloperoxidase inhibitor potentially for the treatment of a number of neurodegenerative disorders, including multiple system atrophy (MSA). The objectives of this work were to develop a population pharmacokinetic (PopPK) model for AZD3241 and to investigate the correlation between AZD3241 exposure and myeloperoxidase inhibition. The PopPK model was developed using AZD3241 data from one phase 1 study in healthy subjects and one phase 2 study in patients with MSA. A one-compartment model incorporating a gallbladder compartment for enterohepatic circulation, sequential zero-first order absorption, and first-order elimination adequately described the AZD3241 concentration profiles. The apparent clearance and central volume of distribution were 63.1 L/h (interindividual variability: 34.8%) and 121.9 L (interindividual variability: 44.0%), respectively. The enterohepatic circulation model reasonably captured the second peak of AZD3241, and high-fat food increased the absorption rate by 69%. A linear regression model was applied to describe the relationship between AZD3241 exposure and percentage change from baseline in myeloperoxidase-specific activity. The developed PopPK model was consistent with known pharmacokinetic characteristics of AZD3241. This model can be used to estimate AZD3241 exposure in patients with MSA and could be applied to future pharmacokinetic-pharmacodynamic analyses of AZD3241 in clinical development.

Gefitinib is a selective inhibitor of epidermal growth factor receptor (EGFR) tyrosine kinase and is used for the treatment of non-small-cell lung cancer (NSCLC) with activating EGFR mutations. Gefitinib is metabolized by CYP2D6 and CYP3A4. This analysis compared the systemic exposure of gefitinib after oral administration in CYP2D6 ultrarapid metabolizers (UM) vs extensive metabolizers (EM). Physiologically based pharmacokinetic (PBPK) modeling was conducted using a population-based simulator. The model was calibrated using itraconazole-gefitinib clinical drug-drug interaction data and validated with gefitinib data in CYP2D6 EM vs poor metabolizers (PM). System components of the PBPK model were evaluated using published clinical metoprolol pharmacokinetic data in CYP2D6 UM, EM, and PM. Our PBPK model predicted a gefitinib geometric least-squares mean area under the plasma concentration-time curve (AUC) from time 0 to 264 hours (AUC(0-264) ) ratio in the presence vs absence of itraconazole of 1.85, similar to the ratio of 1.78 from clinical study data. Predicted and observed metoprolol geometric least-squares mean AUC(0-24) ratios in UM vs EM were also similar (0.46 and 0.55, respectively), suggesting that system components related to CYP2D6 in the PBPK model were properly established. In addition, the PBPK model also captured gefitinib pharmacokinetic profiles in CYP2D6 polymorphic populations. The final PBPK model predicted a decrease in gefitinib AUC of ∼39% in CYP2D6 UM vs EM. Such changes in exposure will have limited impact as the reduced exposure is still above gefitinib's in vitro IC90 for EGFR activating mutations in NSCLC patients.

AIMS: A multistudy analysis of cediranib, a potent, selective inhibitor of all three vascular endothelial growth factor receptors (VEGFR‐1, ‐2 and ‐3), was conducted to establish population exposure–safety models for the relationship of cediranib exposure to the safety endpoints, diastolic and systolic blood pressure (DBP and SBP) and diarrhoea in cancer patients. These models were applied to predict safety outcomes for different cediranib dose regimens. METHODS: Models for hypertension and diarrhoea were constructed based on data from 10 Phase I and three Phase II studies comprising 631 cancer patients following cediranib once‐daily oral dosing. Daily DBP and SBP were simultaneously characterized using indirect response models for predicted cediranib concentration–time courses, while daily diarrhoea events were modelled as ordered categorical variables with a proportional odds model with a Markov element for predicted average cediranib concentrations. RESULTS: For 20 mg cediranib once‐daily oral administration, the mean increase in DBP and SBP was predicted to be 7 (95% CI 3–13) and 8 mmHg (95% CI 3–16), respectively, while the probability of mild diarrhoea, but not the severity, was predicted to increase over time. Severe diarrhoea was predicted to be resolved rapidly upon discontinuation of cediranib treatment. CONCLUSIONS: Maximum blood pressure increase was observed within the first few days of cediranib treatment, consistent with the pharmacokinetic profile of cediranib reaching steady state in about 5 days. The probability of diarrhoea increased with cediranib concentration but was far more dependent on the status of diarrhoea predicted on the previous day.

Understanding the influence of ethnicity on drug exposure is key to patient safety and could minimize repetitive clinical studies. This analysis aimed to evaluate the ability of physiologically-based pharmacokinetic modelling to predict exposure of CYP2C19 substrates (lansoprazole, (es)citalopram, voriconazole) across Caucasian and East Asian populations. CYP2C19 abundance levels in Japanese and Chinese populations have been re-assessed based on clinical evidence. Model performance in each population was evaluated by predicted-over-observed AUC ratios and comparison of observed data with simulated plasma concentration profiles. Exposures in 84.4% (76 out of 90) of the clinical studies were predicted within 1.5-fold of observed values. The reported concentration-time profiles were well-captured within the 90% prediction intervals. With specified CYP2C19 phenotype, PBPK modelling is capable to predict systemic exposure of drugs largely metabolized by CYP2C19 in different ethnic populations. This study demonstrated PBPK modelling can be applied to assess genotype-dependent exposure difference across ethnicities.

Objectives: Dynamics of tumor size have long been used for the clinical diagnosis, staging, prognosis and treatment of non-small cell lung cancer (NSCLC). The aim of this work was to develop a joint statistical framework to interrogate the longitudinal tumor burden, the appearance of new lesions, and changes in therapy following progression in a population of NSCLC patients treated with an EGFR inhibitor gefinitib to predict, given early results, time to progression (PFS) and overall survival at both population and individual levels. Methods: The model builds on a previously developed joint model for tumor size and survival. The tumor size model was extended to estimate the probability of a new lesion. For the survival submodel, two additional biomarkers were evaluated: the derivative of the sum of longest diameters (SLD) and the probability of new lesions. The model was fitted in a Bayesian framework using STAN. Model was developed from 434 EGFR+, NSCLC patients treated with gefitinib or carboplatin+paclitaxel (‘IPASS’, NCT00322452). Predictive performance was evaluated on a cohort of 102 EGFR+ NSCLC patients (‘IFUM’, NCT01203917). Results: Overall, the model fit well to the observed data and the predictive performance was strong in both within- and out-of-sample evaluations. Surprisingly, SLD derivative and new-lesion association parameters appear to negatively impact model performance, indicating further improvement of these submodels might be necessary. The model recapitulates response evaluation criteria in solid tumors (RECIST) outcomes with nearly 90% accuracy. When comparing progression-specific hazards, it appears the drug effect is mediated by tumor size dynamics rather than new lesion incidence. Conclusion: This Bayesian joint model of tumor size and survival accurately recapitulates the RECIST-based outcomes and generates well calibrated predictions of survival. It offers insights regarding the relative predictive value of the components of RESICT in NSCLC. Citation Format: James Dunyak, Jacqueline Buros-Novik, Eric Novik, Hong Yan, Nidal Al-Huniti. Prediction of survival based on tumor size dynamics and new lesions in EGFR mutation-positive non-small cell lung cancer patients treated with gefitinib or carboplatin and paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1674.