Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia

Integrative population pharmacokinetic/pharmacodynamic analysis of nemonoxacin capsule in Chinese patients with community-acquired pneumonia

Introduction: Nemonoxacin is an innovative quinolone antibiotic for treatment of community-acquired pneumonia (CAP). As more data are available from clinical studies, it is necessary to perform an integrative pharmacokinetic/pharmacodynamic (PK/PD) analysis to support and justify the optimal dosing regimen of nemonoxacin in clinical practice. Methods and Results: We developed a population PK model using non-linear mixed effect model based on the data of 195 Chinese subjects receiving nemonoxacin in phase I to III clinical trials. The base model was a standard two-compartment PK model defined by clearance (12 L/h) and central volume of distribution (86 L). Covariates included creatinine clearance (CL_cr), body weight (BW), sex, disease status and food. Compared to the subject with BW 60 kg, C_max and A U C 0 - 24 ,   ss reduced by 24% and 19% in the subject with BW 80 kg, respectively. Compared to the subject with CL_cr 150 ml/min, A U C 0 - 24 ,   ss and T_1/2 increased by 28% and 24%, respectively in the subject with CL_cr 30 ml/min. Compared to the fasted status, T_max of nemonoxacin increased by 1.2 h in the subject with fed status. Effects of sex and disease status on PK parameters were small (change of PK parameters ≤19%). AUC_0-24/MIC and %T > MIC were identified as the optimal PK/PD indices for predicting clinical efficacy. The AUC_0-24/MIC target was 63.3, 97.8, and 115.7 against Streptococcus pneumoniae, Staphylococcus aureus, and Haemophilus influenzae, respectively. The %T > MIC target was 7.96% against Klebsiella pneumoniae. Monte Carlo simulation showed that treatment with nemonoxacin 500 mg q24 h could attain a PK/PD cutoff value higher than the MIC₉₀ against S. pneumoniae and S. aureus. The corresponding cumulative fraction of response (CFR) was greater than 93%, while nemonoxacin 750 mg q24 h would provide higher PK/PD cutoff value against Haemophilus parainfluenzae, and higher CFR (83%) than 500 mg q24 h. Conclusion: Integrative PK/PD analysis justifies the reliable clinical and microbiological efficacy of nemonoxacin 500 mg q24 h in treating CAP caused by S. pneumoniae, S. aureus, and K. pneumoniae, irrespective of patient sex, mild renal impairment, empty stomach or not. However, nemonoxacin 750 mg q24 h would provide better efficacy than 500 mg q24 h for the CAP caused by H. parainfluenzae in terms of CFR.

Optimization of G-CSF dosing schedule in patients treated with eribulin: a modeling approach

BACKGROUND

Granulocyte colony-stimulating factors (G-CSF) are commonly given to limit chemotherapy-induced neutropenia, but, in case of weekly chemotherapy such as eribulin, their administration schedules remain empirical.

OBJECTIVES

This pharmacokinetic/pharmacodynamic (PK/PD) study was conducted to establish the effect of different G-CSF regimens on neutropenia's incidence for patients treated by eribulin, to propose an optimal G-CSF dosing schedule.

METHODS

A population PK/PD model was developed to describe absolute neutrophil counts' (ANC) time course in 87 cancer patients receiving eribulin. The structural model considered ANC dynamics, neutropenic effect of eribulin and stimulating effect of G-CSF. Final model estimates were used to calculate neutropenia's incidence following different G-CSF dosing schedules for 1000 virtual subjects.

RESULTS

The final model successfully described most of the ANC time course for all patients. Simulations showed that a single G-CSF administration 48 h after each eribulin injection reduced the risk of severe neutropenia from 29.7 to 5.2%. Five days of G-CSF only after the second eribulin injection or no G-CSF administration induces similar incidence of neutropenia.

CONCLUSION

Simulations showed a single G-CSF administration 48 h after the end of each eribulin injection seems to be the optimal schedule to reduce eribulin-induced neutropenia. However, the new administration scheme should be tested in real life to evaluate its pertinence.

TRIAL REGISTRATION

Eudract 2015-001753-32, 2015/01/26.

Biomarker-Guided Individualization of Antibiotic Therapy

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

Population Pharmacokinetic-Pharmacodynamic Modeling and Covariate Analyses of Neutropenia and Thrombocytopenia in Patients With Solid Tumors Treated With Lurbinectedin

Lurbinectedin is a selective inhibitor of oncogenic transcription. Reversible myelosuppression is its most relevant toxicity. Pharmacokinetic-pharmacodynamic analyses were conducted to characterize the time course of absolute neutrophil count and platelet count recovery and to detect and quantify the effect of relevant covariates in patients with advanced solid tumors treated with lurbinectedin. Absolute neutrophil count, platelet count, and lurbinectedin total plasma concentration were assessed in 244 patients treated with lurbinectedin with varied dosing schedules and doses. A reference extended semimechanistic pharmacokinetic-pharmacodynamic model of myelosuppression was used. Granulocyte colony-stimulating factor (G-CSF) administration was modeled as a dichotomous covariate, and platelet transfusions were included as a bolus dose into the last compartment of the model, representing the central circulation. Final models were suitable to describe the time course of absolute neutrophil count and platelet count recovery. A lurbinectedin dose of 3.2 mg/m² every 3 weeks can be administered without primary prophylaxis with G-CSF. G-CSF followed by ≤2 dose reductions of 20%, if needed, gradually reduced grade 4 neutropenia from cycle 3 onward. BSA-based dosing reduced the incidence of grade ≥ 3 thrombocytopenia. One-week dose delays because of low absolute neutrophil count occurred in 3.5% of patients, thus supporting every-3-week administration. CYP3A inhibitors produced absolute 11.0% and 6.2% increases in grade ≥ 3 neutropenia and thrombocytopenia, respectively. Neutropenia and thrombocytopenia after lurbinectedin administration to cancer patients are noncumulative, reversible, short lasting, and clinically manageable with secondary prophylaxis of G-CSF or platelet transfusion and, if needed, dose reductions.

Effect of renal function on neutrophil decreases following eribulin administration

BACKGROUND

Eribulin therapy has recently attracted attention from various viewpoints, including quality of life, and is considered a standard therapy for inoperable or recurrent breast cancer. Although a reduction in renal function reportedly decreases total eribulin clearance, its association with dose-limiting toxicity and the reduction of neutrophils remain unclear.

AIM

This study was aimed at analyzing the association between decreased renal function prior to eribulin administration and the occurrence of neutrophil reduction and time to treatment failure in patients with breast cancer.

METHODS AND RESULTS

We retrospectively assessed patients with breast cancer, who underwent eribulin therapy between July 2011 and March 2018. Multivariate analysis revealed creatinine clearance <70 mL/min and serum albumin levels <3.9 mg/dL as predictive factors for neutrophil reduction. Even on increasing the relative dose intensity by these factors, no difference in time to treatment failure was observed, suggesting that treatment efficacy is potentially unaffected.

CONCLUSIONS

For continuous eribulin therapy, eribulin may need to be administered to individual patients in accordance with renal function and albumin levels before treatment initiation.

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

Impact of granulocyte colony-stimulating factor on FOLFIRINOX-induced neutropenia prevention: A population pharmacokinetic/pharmacodynamic approach

AIMS

Granulocyte colony-stimulating factor (G-CSF) is frequently prescribed to prevent chemotherapy-induced neutropenia, but the administration schedule remains empirical in case of bimonthly chemotherapy such as FOLFIRINOX regimen. This pharmacokinetic/pharmacodynamic (PK/PD) study was performed to determine the effect of different G-CSF regimens on the incidence and duration of neutropenia following FOLFIRINOX administration in order to propose an optimal G-CSF dosing schedule.

METHODS

A population PK/PD model was developed to describe individual neutrophil time course from absolute neutrophil counts (ANC) obtained in 40 advanced cancer patients receiving FOLFIRINOX regimen. The structural model considered ANC dynamics, neutropenic effect of cytotoxics and the stimulating effect of G-CSF on neutrophils. Final model estimates were used to simulate different G-CSF dosing schedules for 1000 virtual subjects. The incidence and duration of neutropenia were then calculated for different G-CSF dosing schedules.

RESULTS

The final model successfully described the myelosuppressive effect induced by the 3 cytotoxics for all patients. Simulations showed that pegfilgrastim administration reduced the risk of severe neutropenia by 22.9% for subjects with low ANC at the start of chemotherapy. Median duration in this group was also shortened by 3.1 days when compared to absence of G-CSF. Delayed G-CSF administration was responsible for higher incidence and longer duration of neutropenia compared to absence of administration.

CONCLUSION

The PK/PD model well described our population's ANC data. Simulations showed that pegylated-G-CSF administration 24 hours after the end of chemotherapy seems to be the optimal schedule to reduce FOLFIRINOX-induced neutropenia. We also underline the potential negative effect of G-CSF maladministration.

Quantification of the pharmacokinetic-toxicodynamic relationship of oral docetaxel co-administered with ritonavir

Introduction Oral formulations of docetaxel have successfully been developed as an alternative for intravenous administration. Co-administration with the enzyme inhibitor ritonavir boosts the docetaxel plasma exposure. In dose-escalation trials, the maximum tolerated doses for two different dosing regimens were established and dose-limiting toxicities (DLTs) were recorded. The aim of current analysis was to develop a pharmacokinetic (PK)-toxicodynamic (TOX) model to quantify the relationship between docetaxel plasma exposure and DLTs. Methods A total of 85 patients was included in the current analysis, 18 patients showed a DLT in the four-week observation period. A PK-TOX model was developed and simulations based on the PK-TOX model were performed. Results The final PK-TOX model was characterized by an effect compartment representing the toxic effect of docetaxel, which was linked to the probability of developing a DLT. Simulations of once-weekly, once-daily 60 mg and once-weekly, twice-daily 30 mg followed by 20 mg of oral docetaxel suggested that 14% and 34% of patients, respectively, would have a probability >25% to develop a DLT in a four-week period. Conclusions A PK-TOX model was successfully developed. This model can be used to evaluate the probability of developing a DLT following treatment with oral docetaxel and ritonavir in different dosing regimens.

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii

OBJECTIVES

Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.

METHODS

A. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters.

RESULTS

Monotherapies resulted in regrowth to ~10¹⁰ cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC₅₀ (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 10⁸ cfu/mL starting inoculum to a point of 10⁰ cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be 'loaded' with 80.5% and 42.2% of the daily dose, respectively).

CONCLUSION

This study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the 'traditional' indices of antibiotic action.

Eribulin mesylate use as third-line therapy in patients with metastatic breast cancer (VESPRY): a prospective, multicentre, observational study

Background

In real-world practice, eribulin mesylate provides significant survival benefit, with a manageable safety profile in heavily pretreated patients with metastatic breast cancer (MBC).

Methods

In this prospective, open-label, multicentre, observational study we evaluated the effectiveness and tolerability of eribulin as third-line treatment in a homogeneous population. The primary endpoints were the safety profile and response in metastatic sites; secondary endpoints included the response in different subtypes, overall response rate (ORR), progression-free survival (PFS) and overall survival (OS).

Results

From 2013 to 2016, 118 women were treated in 21 Sicilian institutions; the median age was 58 years (range 29-79), with 69% of patients under 65. The median cycles of eribulin were 5.5 (range 1-26). The most common adverse event was neutropenia (9.3%, 3 cases of grade 3, 4 of grade 4); only 1 case of QT prolongation was reported. Eribulin was effective in controlling metastatic disease in all sites, and it achieved the highest ORR in brain (16%) and liver (14.9%). Median OS was 31.8 months (95% CI 27.9-34.4) and median PFS 5.5 months (95% CI 4.2-6.6). PFS was 5.2 months (95% CI 2.8-8.4) in patients with triple-negative subtype. Median PFS was longer in patients over 65 years (6.1 months, 95% CI 4.4-8.3). In patients who had visceral metastases PFS was 5.5 months (95% CI 95% 3.5-6.6) and OS 33.9 months (95% CI 29.8-40.8).

Conclusions

Eribulin as third-line treatment shows an acceptable safety profile and a substantial antitumour activity in the treatment of MBC, even in elderly patients and in those with visceral disease.

Pharmacokinetic-Pharmacodynamic Model of Neutropenia in Patients With Myeloma Receiving High-Dose Melphalan for Autologous Stem Cell Transplant

High-dose melphalan (HDM) is part of the conditioning regimen in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT). However, individual sensitivity to melphalan varies, and many patients experience severe toxicities. Prolonged severe neutropenia is one of the most severe toxicities and contributes to potentially life-threatening infections and failure of ASCT. Granulocyte-colony stimulating factor (G-CSF) is given to stimulate neutrophil proliferation after melphalan administration. The aim of this study was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model capable of predicting neutrophil kinetics in individual patients with MM undergoing ASCT with high-dose melphalan and G-CSF administration. The extended PK/PD model incorporated several covariates, including G-CSF regimen, stem cell dose, hematocrit, sex, creatinine clearance, p53 fold change, and race. The resulting model explained portions of interindividual variability in melphalan exposure, therapeutic effect, and feedback regulation of G-CSF on neutrophils, thus enabling simulation of various doses and prediction of neutropenia duration.

Pharmacodynamic analysis of eribulin safety in breast cancer patients using real-world postmarketing surveillance data

Postmarketing surveillance is useful to collect safety data in real-world clinical settings. In this study, we applied postmarketing real-world data on a mechanistic model analysis for neutropenic profiles of eribulin in patients with recurrent or metastatic breast cancer. Demographic and safety data were collected using an active surveillance method from eribulin-treated recurrent or metastatic breast cancer patients. Changes in neutrophil counts over time were analyzed using a mechanistic pharmacodynamic model. Pathophysiological factors that might affect the severity of neutropenia were investigated, and neutropenic patterns were simulated for different treatment schedules. Clinical and laboratory data were collected from 401 patients (5199 neutrophil count measurements) who had not received granulocyte colony-stimulating factor and were eligible for pharmacodynamic analysis. The estimated mean parameters were as follows: mean transit time = 104.5 h, neutrophil proliferation rate constant = 0.0377 h-1 , neutrophil elimination rate constant = 0.0295 h-1 , and linear coefficient of drug effect = 0.0413 mL/ng. Low serum albumin levels and low baseline neutrophil counts were associated with severe neutropenia. The probability of grade ≥3 neutropenia was predicted to be 69%, 27%, and 27% for patients on standard, biweekly, and triweekly treatment scenarios, respectively, based on virtual simulations using the developed pharmacodynamic model. In conclusion, this is the first application of postmarketing surveillance data to a model-based safety analysis. This analysis of safety data reflecting authentic clinical settings will provide useful information on the safe use and potential risk factors of eribulin.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid

AIMS

Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration.

METHODS

A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration.

RESULTS

A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h(-1) , 9.37 l, 6.41 l, 13.7 l h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%).

CONCLUSION

The identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.

Pharmacodynamic modeling of adverse effects of anti-cancer drug treatment

Purpose

Adverse effects related to anti-cancer drug treatment influence patient’s quality of life, have an impact on the realized dosing regimen, and can hamper response to treatment. Quantitative models that relate drug exposure to the dynamics of adverse effects have been developed and proven to be very instrumental to optimize dosing schedules. The aims of this review were (i) to provide a perspective of how adverse effects of anti-cancer drugs are modeled and (ii) to report several model structures of adverse effect models that describe relationships between drug concentrations and toxicities.

Methods

Various quantitative pharmacodynamic models that model adverse effects of anti-cancer drug treatment were reviewed.

Results

Quantitative models describing relationships between drug exposure and myelosuppression, cardiotoxicity, and graded adverse effects like fatigue, hand-foot syndrome (HFS), rash, and diarrhea have been presented for different anti-cancer agents, including their clinical applicability.

Conclusions

Mathematical modeling of adverse effects proved to be a helpful tool to improve clinical management and support decision-making (especially in establishment of the optimal dosing regimen) in drug development. The reported models can be used as templates for modeling a variety of anti-cancer-induced adverse effects to further optimize therapy.

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662

AIMS

Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice.

METHODS

One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens.

RESULTS

Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy.

CONCLUSIONS

A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Clinical Weighting of Drug-Drug Interactions in Hospitalized Elderly

Adverse drug reactions impact on patient health, effectiveness of pharmacological therapy and increased health care costs. This investigation intended to detect the most critical drug-drug interactions in hospitalized elderly patients, weighting clinical risk. We conducted a cross-sectional study between January and April 2014; all patients 70 years or older, hospitalized for >24 hr and prescribed at least one medication were included in the study. Drug-drug interactions were estimated by combining Stockley's, Hansten and Tatro drug interactions. Drug-drug interactions were weighted using a risk-analysis method based on failure modes, effects and criticality analysis. We calculated a criticality index for each drug involved in the drug-drug interactions based on the severity of the interaction mechanism, the frequency the drug was involved in drug-drug interactions and the risk of drug-drug interactions in patients with impaired renal function. The average number of drugs consumed in the hospital was 6 ± 2.69, involving 160 active ingredients. The most frequent were as follows: Furosemide, followed by Enalapril. Of drug-drug interactions, 2% were classified as contraindicated, 14% advised against and 83% advised caution during the hospital stay. Thirty-four drug-drug interactions were assessed, of which 23 were pharmacodynamic drug-drug interactions and 12 were pharmacokinetic drug-drug interactions (1 was both). The clinical risk calculated for each drug-drug interaction included heparins + non-steroidal anti-inflammatory drugs (NSAIDs) or Digoxin + Calcium Gluconate, cases which are pharmacodynamic drug-drug interactions with agonist effect and clinical risk of bleeding, one of the most common clinical risks in the hospital. An index of clinical risk for drug-drug interactions can be calculated based on severity by the interaction mechanism, the frequency that the drug is involved in drug-drug interactions and the risk of drug-drug interactions in an elderly patient with impaired renal function.

Towards integrative systems pharmacology models in oncology drug development

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin

Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin

Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

The impact of structural uncertainty on cost-effectiveness models for adjuvant endocrine breast cancer treatments: the need for disease-specific model standardization and improved guidance

INTRODUCTION

Structural uncertainty relates to differences in model structure and parameterization. For many published health economic analyses in oncology, substantial differences in model structure exist, leading to differences in analysis outcomes and potentially impacting decision-making processes. The objectives of this analysis were (1) to identify differences in model structure and parameterization for cost-effectiveness analyses (CEAs) comparing tamoxifen and anastrazole for adjuvant breast cancer (ABC) treatment; and (2) to quantify the impact of these differences on analysis outcome metrics.

METHODS

The analysis consisted of four steps: (1) review of the literature for identification of eligible CEAs; (2) definition and implementation of a base model structure, which included the core structural components for all identified CEAs; (3) definition and implementation of changes or additions in the base model structure or parameterization; and (4) quantification of the impact of changes in model structure or parameterizations on the analysis outcome metrics life-years gained (LYG), incremental costs (IC) and the incremental cost-effectiveness ratio (ICER).

RESULTS

Eleven CEA analyses comparing anastrazole and tamoxifen as ABC treatment were identified. The base model consisted of the following health states: (1) on treatment; (2) off treatment; (3) local recurrence; (4) metastatic disease; (5) death due to breast cancer; and (6) death due to other causes. The base model estimates of anastrazole versus tamoxifen for the LYG, IC and ICER were 0.263 years, €3,647 and €13,868/LYG, respectively. In the published models that were evaluated, differences in model structure included the addition of different recurrence health states, and associated transition rates were identified. Differences in parameterization were related to the incidences of recurrence, local recurrence to metastatic disease, and metastatic disease to death. The separate impact of these model components on the LYG ranged from 0.207 to 0.356 years, while incremental costs ranged from €3,490 to €3,714 and ICERs ranged from €9,804/LYG to €17,966/LYG. When we re-analyzed the published CEAs in our framework by including their respective model properties, the LYG ranged from 0.207 to 0.383 years, IC ranged from €3,556 to €3,731 and ICERs ranged from €9,683/LYG to €17,570/LYG.

CONCLUSION

Differences in model structure and parameterization lead to substantial differences in analysis outcome metrics. This analysis supports the need for more guidance regarding structural uncertainty and the use of standardized disease-specific models for health economic analyses of adjuvant endocrine breast cancer therapies. The developed approach in the current analysis could potentially serve as a template for further evaluations of structural uncertainty and development of disease-specific models.