Population Pharmacokinetic and Covariate Analysis of Anlotinib in Patients with Malignant Tumors

doi:10.21203/rs.3.rs-2375943/v1

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Population Pharmacokinetic and Covariate Analysis of Anlotinib in Patients with Malignant Tumors

https://doi.org/10.21203/rs.3.rs-2375943/v1

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Purpose The objective of this study was to develop a population pharmacokinetic (popPK) model of anlotinib and to investigate the impact of various covariates in patients with malignant tumors.

Methods A total of 407 anlotinib plasma concentrations from 16 patients were analyzed in this study. Anlotinib was administered orally 12 or 16 mg in the single-dose phase and 12 mg once daily in the multiple-dose phase. PopPK model was established using nonlinear mixed-effects model (NONMEM) method. The potential influence of demographic and pathophysiological factors on oral anlotinib pharmacokinetic was investigated in a covariate analysis. The final model was evaluated using goodness-of-fit plots, visual predictive check, and bootstrap methods.

Results The pharmacokinetic profile of anlotinib was best described by a one-compartment model with first-order absorption and linear elimination. The population estimates of the apparent total clearance (CL/F), apparent volume of distribution (V/F) and absorption rate constant (Ka) were 8.91 L/h, 1950 L and 0.745 h^-1, respectively. Body weight was identified as a significant covariate on V/F. Patients with low body weight tended to show higher exposure to anlotinib than those with high body weight. However, these differences were not clinically significant in the simulations of the individual body weight effects. No obvious bias was found in the final model by bootstrap and VPC methods.

Conclusion This popPK model adequately described the pharmacokinetics of anlotinib in patients with malignant tumors. Anlotinib does not need any dose modifications since the effect size for the individual covariate is not considered clinically relevant with anlotinib exposure.

Population pharmacokinetic

Anlotinib

Covariate Analysis

NONMEM

Anlotinib is an oral administered multiple receptor tyrosine kinase inhibitor that targets vascular endothelial growth factor receptor (VEGFR) 2 and 3, fibroblast growth factor (FGFR)1–4, platelet-derived growth factor receptor (PDGFR) α and β, c-Kit, and Ret [1, 2]. It has high potency and broad spectrum of inhibitory actions on tumor angiogenesis and proliferation [3]. Anlotinib was first approved by the National Medical Products Administration (NMPA) of China as a third-line treatment for patients with refractory advanced non-small cell lung cancer in 2018 and a second-line for advanced alveolar soft-part sarcoma, clear-cell sarcoma, and other types of advanced soft tissue sarcoma in 2019 [4, 5]. Currently, anlotinib is undergoing several clinical trials worldwide for the treatment of various other cancer types [6–8].

The absorption, distribution, metabolism and excretion profile of anlotinib has been researched in a number of studies. Anlotinib can be rapidly absorbed following oral administration with a time to maximum plasma concentration (Tmax) of 4–11 h [9]. It has a large apparent volume of distribution (V) and a long half-life (t_1/2) of 96 ± 17 h [9, 10]. Anlotinib was mainly eliminated by extensive hepatic metabolism mediated by cytochrome P450 and primarily excreted in feces and urine [11]. Besides, anlotinib exposure showed a moderate to high inter- or intra-individual variability in previous study [9]. Thus, it is necessary to perform a popPK study of anlotinib in patients with malignant tumors to analysis individual differences and various influence factors, and to explore whether dosing regimens need to be adjusted according to the covariates.

The objectives of current study were to 1) describe the pharmacokinetics behavior of anlotinib using a population approach in patients with malignant tumors, and 2) evaluate the impact of potential influence factors such as demographic and pathophysiological covariates which might explain the variability in exposure of anlotinib.

Study design and patient population

The popPK and covariate data analyzed in this study originated from a Phase I additional study of anlotinib, which evaluated pharmacokinetic, efficacy and safety in patients with advanced cancer confirmed pathologically and/or cytologically, with no standard therapy. Eligibility criteria included age between 18–65, Eastern Cooperative Oncology Group (ECOG) PS 0–1, and an estimated survival duration of more than 3 months. Patients administered other chemotherapy drugs halted their treatment for at least 30 days, while those who had received major surgery rested for at least 4 weeks [12]. The clinical study was conducted in accordance with the Declaration of Helsinki, International Conference on Harmonization, and Good Clinical Practice guidelines. The study protocol was reviewed and approved by the Institutional Review Board of Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, and informed consent was obtained from all patients [12].

Pharmacokinetic Sampling And Assays

A total of 16 patients (8 males and 8 females) were enrolled in the study. In the single dose phase, 13 patients administered a dose of 12 mg of anlotinib orally, while 3 received 16 mg. Ten days after the single dose, patients administered 12 mg of anlotinib once daily on a 2-weeks-on/1-week-off dosing schedule for two cycles of 21 days each. In the single-dose phase, dense blood samples were collected following administration at 0 h (pre-dose), and 1, 2, 4, 8, 11, 24, 48, 72, 120, 168, and 240 h post-dose. In the multiple-dose phase, blood samples were collected for each treatment cycle 24 h after dosing on days 1, 4, 7, 10, 14, and 18. One patient withdrew from the trial in the second cycle of multiple doses due to serious adverse events.

All anlotinib plasma concentrations were detected by a validated ultra-high performance liquid chromatography-tandem mass spectrometry method as previously described ^[13]. The lower limit of quantification (LLOQ) was 0.05 ng/mL. A total of 407 anlotinib concentrations were included for popPK analysis.

Poppk Analysis

The popPK analysis of anlotinib was conducted using nonlinear mixed-effects modeling (NONMEM) software (version 7.5, ICON Development Solutions). Covariate screening, bootstrap procedure, and visual predictive check (VPC) were performed using Perl-speaks-NONMEM (PsN, version 5.2.6, https://uupharmacometrics.github.io/PsN/). Data processing, calculation of descriptive statistics, and output visualization were performed using R (version 4.1.0, https://www.r-project.org/). The first-order conditional estimation method with interaction method was adopted for base model development and covariate testing. Model selection was based on the following: successful minimization; successful covariance estimation; objective function values (OFV); Akaike information criterion values; Bayesian information criterion values; precision of each parameter estimate; shrinkage for each interindividual and residual variability; condition numbers; visual inspection of scatter plots.

Anlotinib plasma concentration-time data was evaluated by one- or two-compartment models. The interindividual variability of the pharmacokinetic parameters was estimated using an exponential random effect of the form (Eq. 1):

$${\theta }_{i}=\theta \bullet {e}^{\eta i}$$

where θ_i represents the pharmacokinetic parameters of the ith subject, θ represents the typical value of the parameters, and η_i is defined as the random effect of the ith subject under the assumption of a normal distribution with a mean value of 0 and a variance of ω², respectively.

The residual variability was characterized by the additive model (Eq. 2), proportional model (Eq. 3), and combined model (Eq. 4):

$${C}_{ij}={PRED}_{ij}+{\epsilon }_{1}$$

$${C}_{ij}={PRED}_{ij}\times (1+{\epsilon }_{1})$$

$${C}_{ij}={PRED}_{ij}\times (1+{\epsilon }_{1})+{\epsilon }_{2}$$

where Cij represents the jth observed concentration of the ith subject, PRED_ij represents the jth predicted concentration of the ith subject, ${\epsilon }_{1}$ and ${\epsilon }_{2}$ are defined as residual random variations on the assumption of a normal distribution with a mean value of 0 and a variance of ω₁² and ω₂², respectively.

Demographic and pathophysiological covariates were evaluated on the popPK of anlotinib. The continuous covariates evaluated included age, body weight, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), γ-glutamyl transferase (γ-GGT), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), total protein (TP), albumin (ALB), globulin (GLO), creatinine (CR), and urea nitrogen (UN). Sex and ECOG were evaluated as categorical covariates. An automated stepwise covariate model (SCM) building within PsN was used to identify significant covariates ^[14]. The correlation coefficient (r) between covariates was calculated, and one covariate was selected for analysis when r > 0.5. Covariates were then tested on each pharmacokinetic parameter with statistical criteria of P < 0.05 for forward inclusion procedure and P < 0.001 for backward elimination procedure. The effects of continuous covariates were estimated using a power function (Eq. 5), and the categorical covariates were estimated using a linear function (Eq. 6).

$$P={P}_{pop}\times {\left(\frac{COV}{median}\right)}^{{\theta }_{cov}}$$

$$P={P}_{pop}\times \left(1+{\theta }_{cov}\times COV\right)$$

where, P represents the individual pharmacokinetic parameter, P_pop represents the population value of the parameter, COV represents the value of the covariate, median represents the median value of the covariate, and θ_cov represents the slope of the covariate effect.

Model Evaluation

The final popPK model was evaluated using predictive checks and bootstrap analysis. Goodness-of-fit (GOF) plots were visually inspected. Visual predictive checks (VPCs) were performed 1000 times by simulating concentrations using the final model to validate the predictive performance. The bootstrap resample technique (n = 1,000) was used to calculate the standard errors and 95% confidence intervals (CIs) around the parameter estimates to validate the reliability and stability of the final model.

Simulation

Model-based simulations were performed 1000 times to evaluate the final covariate effects on the exposure of anlotinib. Area under the curve values from day 1 to day 21 (AUC_{0 − 21d}) and C_max of each patient with different covariate values were calculated. Each change in the AUC_{0 − 21d} or C_max was compared with a typical patient. The typical patient was defined by baseline medians (for continuous covariate) and modes (for categorical covariate) of the covariates in the total analyzed patients.

3.1 Patients’ characteristics

The popPK analysis dataset was consisted of 407 plasma concentration-time points from 16 patients with malignant tumors. Patients (8 males and 8 females) had a median age of 49 (range 22–65) years and weight of 62.5 (range 47–86) kg. All anlotinib concentrations were above LLOQ except concentrations before first dose. Baseline characteristics and demographics of the patients were summarized in Table 1.

Table 1

Baseline demographics and covariates of population
Characteristics	N = 16
Continuous covariates
Age (years)	49.00 (22–65)
Body weight (kg)	62.50 (47–86)
Alanine aminotransferase (U/L)	18.00 (8–46)
Aspartate aminotransferase (U/L)	15.00 (11–49)
Lactate dehydrogenase (U/L)	160.00 (122–253)
Alkaline phosphatase (U/L)	71.00 (47–167)
γ-Glutamyl transferase (U/L)	19.00 (9-259)
Total bilirubin (µmol/L)	8.95 (4.6–24)
Direct bilirubin (µmol/L)	3.30 (2.2–7.7)
Indirect bilirubin (µmol/L)	5.65 (2.2–17.5)
Total protein (g/L)	72.05 (66.8–79.4)
Albumin (g/L)	45.50 (40.5–49.2)
Globulin (g/L)	27.70 (21.8–35.5)
Creatinine (µmol/L)	66.00 (53–98)
Urea nitrogen (mmol/L)	3.80 (2-6.7)
Categorical covariates
Male/female (number of patients)	8/8
ECOG 0/1 (number of patients)	9/7
Results are expressed as median (range)
ECOG, Eastern Cooperative Oncology Group Performance Status

Poppk Analysis

The structural popPK model of anlotinib was adequately described by a one-compartment disposition model with first-order absorption and elimination. A two-compartment model did not improve the fit, consequently a one-compartment model was selected. Interindividual variability was best described by the exponential equation, while residual variability was best characterized by a combined residual variability model consisting of both proportional and additive components.

After forward selection and backward elimination processes using SCM, the results of covariates analysis showed that age, sex, ECOG, ALT, AST, LDH, ALP, γ-GGT, TBIL, DBIL, IBIL, TP, ALB, GLO, CR and UN had no statistically significant impact on anlotinib pharmacokinetic parameters (CL/F, V/F or Ka). However, in the final model, body weight was found to be a statistically significant covariate that influenced V/F of anlotinib.

The OFV and conditional number of the final popPK model were 1239.9 and 73.71, respectively. The typical value of CL/F, V/F, and Ka were 8.91 L/h (9.5% IIV), 1950 L (6.3 IIV), and 0.745 h^− 1 (16.9% IIV), respectively. The proportional residual was 0.173 with an additive residual of 0.842 ng/mL. The final estimated popPK parameters of anlotinib are summarized in Table 2.

Table 2

Parameter estimates of anlotinib final popPK model
Parameter (Unit)	Estimate (RSE %) [Shrinkage %]	Bootstrap (median [95% CI])
Fixed effects
CL/F (L/h)	8.91 (9.5)	8.959 [7.426, 10.622]
V/F (L)	1950 (6.3)	1953.965 [1727.647, 2211.361]
WT on V/F	1.52 (24.8)	1.502 [0.723, 2.400]
Ka (h^− 1)	0.745 (16.9)	0.739 [0.535, 1.022]
Random effects
IIV_CL/F (CV %)	36.1 (17.4) [0.1]	34.2 [21.2, 46.7]
IIV_V/F (CV %)	23 (16.3) [2.9]	20.8 [12.3, 28.2]
IIV_ka (CV %)	61.4 (21.2) [15.9]	58.3 [32.8, 86.3]
Proportional residual variability (CV %)	0.173 (14.3) [5.1]	0.172 [0.130, 0.214]
Additive residual variability (SD; ng/mL)	0.842 (33.5) [5.1]	0.852 [0.448, 1.269]
RSE, relative standard error; CI, confidence interval; CL/F, apparent clearance; V/F, apparent volume; WT, weight; ka, first-order absorption rate constant; IIV, inter-individual variability; CV, coefficient of variation; SD, standard deviation.

The final model included the following covariate relationships (Eqs. 7–9):

$$TVCL (L/h)=8.91$$

$$TVV \left(L\right)=1950\times {\left(\frac{WTKG}{62.5}\right)}^{1.52}$$

$$TVKa \left({\text{h}}^{-1}\right)=0.745$$

Model Evaluation

The diagnostic GOF plots for the final model of anlotinib were shown in Fig. 1, which consisted of observed concentration (DV) versus individual predicted concentration (IPRED), observed concentration (DV) versus population predicted concentration (PRED), conditional weighted residuals (CWRES) versus population prediction concentration (PRED), and conditional weighted residuals (CWRES) versus time after dose (TAD). The scatterplots of DV versus IPRED and DV versus PRED presented a relatively small under-prediction of the higher plasma concentrations of anlotinib. Most of dots in the plots of CWRES versus PRED or TAD revealed a homogenous distribution of around 0, distributed between − 1 and + 2.

The results of the 1,000 bootstrap analyses were summarized in Table 2. Typical parameter values of the final model were similar to the bootstrap estimated median values and fell within the 95% CI (2.5th percentile and 97.5th percentile) of the corresponding parameters. Comparison of simulated values and observed data illustrated that the final model can capture the pharmacokinetic characteristics of anlotinib.

The VPC plot of the final model was shown in Fig. 2. Most of observed concentrations fell within the range bounded by the 5th and 95th percentiles of the simulated concentrations (95% confidence interval), which indicated that the simulated concentrations were consistent with the observed concentrations.

Simulation

To evaluate clinical relevance of body weight on anlotinib exposure, AUC_{0 − 21d} and C_max of patients with different weights were estimated by 1000 simulations to compare with a typical patient with baseline median weight (62.5 kg). The model predicted effects on the population exposure ratios were depicted in Fig. 3. The result showed that the covariate of body weight had little influence on anlotinib exposure, because the ratios of AUC_{0 − 21d} and C_max of anlotinib in patients weight ranged from 45 to 75 kg were all within the 80–125% range. Therefore, we considered body weight had not clinically relevance with anlotinib exposure.

This is the first popPK analysis of anlotinib in patients with malignant tumors. Compare with classical pharmacokinetics, popPK can fully describe the pharmacokinetic profile of anlotinib and assess potential covariates that may contribute to the pharmacokinetic intra- and inter-variability [13, 14]. In this study, a total of 16 patients were included for the popPK analysis. By constructing the popPK model, we illustrated the pharmacokinetic characteristics of anlotinib in patients with malignant tumors, and analyzed the possible influencing factors of demographic and pathophysiological covariates. The typical values of CL/F, V/F and ka of the final model were 8.91 L/h, 1950 L, and 0.745 h-1 (16.9% IIV), respectively. Body weight had a statistically significant effect on V/F.

Anlotinib had a large V/F (1950 L) in this popPK study, which was similar with previous published study [15]. In addition, we also found that anlotinib had a long elimination half-life, which led to a significant drug accumulation under a once daily dosing regimen. Therefore, the recommended treatment regimen for anlotinib is 12 mg administered orally once-daily in a 2-weeks-on/1-week-off dosing schedule until disease progression or unacceptable tolerability [16], for reducing adverse reactions caused by drug accumulation.

In this study, SCM [17], an automated method used to determine the relationships between parameters and patients’ covariates, was applied for covariate analysis. Compare with the general method (stepwise model building in covariate model implied with NONMEM), SCM considers covariate effects on all parameters at the same time, which is more convenient and faster. Another commonly used stepwise procedure for covariate screening is the generalized additive model (GAM) implemented in the program Xpose [18, 19]. However, it is difficult to handle time-varying covariates in GAM [17]. Therefore, SCM was selected for covariate selection in this population analysis.

The results of covariate analysis showed that body weight had a statistical impact on V/F. Patients with high body weight had higher V/F of anlotinib than those with low body weight. Since the concentration value of anlotinib was inversely related to V/F, patients with low body weight tended to show higher exposure to anlotinib than those with high body weight under the same dosing regimen. However, after comparing the drug exposure of patients with different body weights with that of typical patient with 62.5 kg (baseline median body weight) by simulations, we found that the median AUC_{0 − 21d} ratios were within the bioequivalence boundary of 0.8 to 1.25, which can be considered not clinically relevant [20, 21]. Thus, we considered that there was no clinical correlation between body weight and anlotinib exposure, and anlotinib does not need any dose modifications based on body weight.

In the above clinical correlation analysis, we used AUC_{0 − 21d} and C_max instead of AUC_ss or AUC_{0 − 24h} as an indicator for comparison. This was because anlotinib was administered in a three-week cycle of 2-weeks-on/1-week-off dosing schedule (i.e., 21 days). Moreover, anlotinib has obvious drug accumulation during administration [9], and the drug concentrations continue to rise in the two weeks of administration, and then the concentrations gradually decrease in one week of withdrawal. Therefore, AUC_{0 − 21d} and C_max ratios were more clinically significant than AUC_ss and AUC_{0 − 24h}.

There are several limitations in this study. First, the sample size of patients in this study was small, only a total of 16 patients were included. Second, we did not perform the exposure-response analysis in this study. This was because the data source of this study was obtained from a pharmacokinetic study, which included a population with different kinds of cancers. Besides, the follow-up time of the study was also very short. Therefore, it was difficult to establish a correlation between exposure and response. In the subsequent study, we will include more patients with a certain type of cancer, and perform PK-PD analysis on this population to establish the E-R relationship.

In conclusion, this popPK study demonstrated that a one-compartment model with first-order absorption and elimination was well-characterized anlotinib disposition in patients with malignant tumors. Body weight was a statistically significant covariate for V/F, but it had no clinical relevance for anlotinib exposure. There was no need to adjust the anlotinib dosing regimen according to body weight.

Acknowledgements The authors thank all the patients and their families for participating in the studies. The authors also thank professor Tianyan Zhou and Dr. Xiao Zhu for their assistance in modelling and simulating in this popPK analysis.

Author contribution

Gaoqi Xu, Liqin Zhu and Luo Fang participated in the current study conception and design. Gaoqi Xu and Dihong Yang performed the data analysis. Qi Shu, Junfeng Zhu and Haiying Ding made substantial contributions in interpretation of data. Gaoqi xu drafted the manuscript. Wenxiu Xin, Like Zhong and Luo Fang made substantial contributions in revising the manuscript critically for important intellectual content. All other authors critically revised the manuscript. All authors read and approved the final version to be published.

Funding This study was supported by the Zhejiang Provincial Medical Health Science and Technology Project under Grant No. 2022KY107, and the Hospital Pharmacy Special Project of Zhejiang Pharmaceutical Association under Grant No. 2021ZYY19.

Data availability The datasets analysed during the current study are available from the corresponding author on reasonable request.

Code availability The final model code describing the final model are available from the corresponding author on reasonable request.

Ethics approval The study was conducted in accordance with the Declaration of Helsinki, International
Conference on Harmonization, and Good Clinical Practice guidelines. Institutional review boards
approved all aspects of the study

Consent to participate Informed consent in this study was obtained from the patients.

Conflict of interest The authors declare that they have no competing interests.

Shen G, Zheng F, Ren D, Du F et al (2018). Anlotinib: a novel multi-targeting tyrosine kinase inhibitor in clinical development. J Hematol Oncol 11(1):120. doi: 10.1186/s13045-018-0664-7.
Gao Y, Liu P, Shi R (2020). Anlotinib as a molecular targeted therapy for tumors. Oncol Lett 20(2):1001–1014. doi: 10.3892/ol.2020.11685.
Xie C, Wan X, Quan H, Zheng M et al (2018). Preclinical characterization of anlotinib, a highly potent and selective vascular endothelial growth factor receptor-2 inhibitor. Cancer Sci 109(4):1207–1219. doi: 10.1111/cas.13536.
Li S (2021). Anlotinib: A Novel Targeted Drug for Bone and Soft Tissue Sarcoma. Front Oncol 11:664853. doi: 10.3389/fonc.2021.664853.
Zhou M, Chen X, Zhang H, Xia L et al (2019). China National Medical Products Administration approval summary: anlotinib for the treatment of advanced non-small cell lung cancer after two lines of chemotherapy. Cancer Commun (Lond) 39(1):36. doi: 10.1186/s40880-019-0383-7.
Han B, Li K, Wang Q, Zhang L et al (2018). Effect of Anlotinib as a Third-Line or Further Treatment on Overall Survival of Patients With Advanced Non-Small Cell Lung Cancer: The ALTER 0303 Phase 3 Randomized Clinical Trial. JAMA Oncol 4(11):1569–1575. doi: 10.1001/jamaoncol.2018.3039.
Li D, Chi Y, Chen X, Ge M et al (2021). Anlotinib in Locally Advanced or Metastatic Medullary Thyroid Carcinoma: A Randomized, Double-Blind Phase IIB Trial. Clin Cancer Res 27(13):3567–3575. doi: 10.1158/1078-0432.CCR-20-2950.
Chi Y, Shu Y, Ba Y, Bai Y et al (2021). Anlotinib Monotherapy for Refractory Metastatic Colorectal Cancer: A Double-Blinded, Placebo-Controlled, Randomized Phase III Trial (ALTER0703). Oncologist 26(10):e1693-e1703. doi: 10.1002/onco.13857.
Sun Y, Niu W, Du F, Du C et al (2016). Safety, pharmacokinetics, and antitumor properties of anlotinib, an oral multi-target tyrosine kinase inhibitor, in patients with advanced refractory solid tumors. J Hematol Oncol 9(1):105. doi: 10.1186/s13045-016-0332-8.
Zhong CC, Chen F, Yang JL, Jia WW et al (2017). Pharmacokinetics and disposition of anlotinib, an oral tyrosine kinase inhibitor, in experimental animal species. Acta Pharmacol Sin 39(6):1048–1063. doi: 10.1038/aps.2017.199.
Liu Y, Liu L, Liu L, Wang T et al (2020). A phase I study investigation of metabolism, and disposition of [14C]-anlotinib after an oral administration in patients with advanced refractory solid tumors. Cancer Chemother Pharmacol 85(5):907–915. doi: 10.1007/s00280-020-04062-8.
Hu T, An Z, Sun Y, Wang X et al (2020). Longitudinal Pharmacometabonomics for Predicting Malignant Tumor Patient Responses to Anlotinib Therapy: Phenotype, Efficacy, and Toxicity. Front Oncol 10:548300. doi: 10.3389/fonc.2020.548300.
Sheiner LB, Ludden TM (1992). Population pharmacokinetics/dynamics. Annu Rev Pharmacol Toxicol 32:185–209. doi: 10.1146/annurev.pa.32.040192.001153.
Atkinson AJ Jr, Lalonde RL (2007). Introduction of quantitative methods in pharmacology and clinical pharmacology: a historical overview. Clin Pharmacol Ther 82(1):3–6. doi: 10.1038/sj.clpt.6100248.
Zhong CC, Chen F, Yang JL, Jia WW et al (2018). Pharmacokinetics and disposition of anlotinib, an oral tyrosine kinase inhibitor, in experimental animal species. Acta Pharmacol Sin 39(6):1048–1063. doi: 10.1038/aps.2017.199.
Syed YY (2018). Anlotinib: First Global Approval. Drugs 78(10):1057–1062. doi: 10.1007/s40265-018-0939-x.
Beal SL, Sheiner LB (1992). NONMEM Users Guides. San Francisco, CA: University of California.
Mandema JW, Verotta D, Sheiner LB (1992). Building population pharmacokinetic–pharmacodynamic models. I. Models for covariate effects. J Pharmacokinet Biopharm 20(5):511–28. doi: 10.1007/BF01061469.
Jonsson EN, Karlsson MO (1999). Xpose–an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 58(1):51–64. doi: 10.1016/s0169-2607(98)00067-4.
Keunecke A, Hoefman S, Drenth HJ, Zisowsky J et al (2020). Population pharmacokinetics of regorafenib in solid tumours: Exposure in clinical practice considering enterohepatic circulation and food intake. Br J Clin Pharmacol 86(12):2362–2376. doi: 10.1111/bcp.14334.
Keunecke A, Hoefman S, Drenth HJ, Zisowsky J et al. Population pharmacokinetics of regorafenib in solid tumours: Exposure in clinical practice considering enterohepatic circulation and food intake. Br J Clin Pharmacol 86(12):2362–2376. doi: 10.1111/bcp.14334.

No competing interests reported.

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Population Pharmacokinetic and Covariate Analysis of Anlotinib in Patients with Malignant Tumors

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Abstract

Figures

Introduction

Materials And Methods

Study design and patient population

Pharmacokinetic Sampling And Assays

Poppk Analysis

Model Evaluation

Simulation

Results

3.1 Patients’ characteristics

Poppk Analysis

Model Evaluation

Simulation

Discussion

Conclusion

Declarations

References

Additional Declarations

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