PK/PD model of indisulam and capecitabine: interaction causes excessive myelosuppression

Indisulam synergizes with melphalan to inhibit Multiple Myeloma malignancy via targeting TOP2A

Multiple myeloma (MM) is the second most prevalent hematologic malignancy which remains uncurable. Numerous drugs have been discovered to inhibit MM cells. Indisulam, an aryl sulfonamide, has a potent anti-myeloma activity in vitro and in vivo. This study aims to explore the new mechanism of indisulam and investigate its potential use in combination with melphalan. We examined DNA damage in MM cells through various methods such as western blotting (WB), immunofluorescence, and comet assay. We also identified the role of topoisomerase IIα (TOP2A) using bioinformatic analyses. The impact of indisulam on the RNA and protein levels of TOP2A was investigated through qPCR and WB. Cell proliferation and apoptosis were assessed using CCK-8 assays, Annexin V/PI assays and WB. We predicted the synergistic effect of the combination treatment based on calculations performed on a website, and further explored the effect of indisulam in combination with melphalan on MM cell lines and xenografts. RNA sequencing data and basic experiments indicated that indisulam caused DNA damage and inhibited TOP2A expression by decreasing transcription and promoting degradation via the proteasome pathway. Functional experiments revealed that silencing TOP2A inhibited cell proliferation and induced apoptosis and DNA damage. Finally, Indisulam/melphalan combination treatment demonstrated a strong synergistic anti-tumor effect compared to single-agent treatments in vitro and in vivo. These findings suggest that combination therapies incorporating indisulam and melphalan have the potential to enhance treatment outcomes for MM.

The platelet to lymphocyte ratio is a potential inflammatory marker predicting the effects of adjuvant chemotherapy in patients with stage II colorectal cancer

Background

The effects of adjuvant chemotherapy in patients with stage II colorectal cancer (CRC) has been in controversy for a long time. Our study aimed to find an effective inflammatory marker to predict the effects of chemotherapy.

Methods

Seven hundred eight stage II CRC patients in our institution were included. The subpopulation treatment effect pattern plot (STEPP) analysis was used to determine the optimal inflammatory marker and cut-off value. Propensity score matching (PSM) was performed to balance discrepancy between the chemotherapy and non-chemotherapy group. Survival analyses based on overall survival (OS) and cancer-specific survival (CSS) were performed with Kaplan-Meier methods with log-rank test and Cox proportional hazards regression. The restricted mean survival time (RMST) was used to measure treatment effect.

Results

The platelet to lymphocyte ratio (PLR) was chosen as the optimal marker with a cut-off value of 130 according to STEPP. In OS analysis, PLR was significantly associated with the effects of chemotherapy (interaction p = 0.027). In the low-PLR subgroup, the chemotherapy patients did not have a longer OS than the non-chemotherapy patients (HR: 0.983, 95% CI: 0.528–1.829). In the high-PLR subgroup, the chemotherapy patients had a significantly longer OS than the non-chemotherapy patients (HR: 0.371, 95% CI: 0.212–0.649). After PSM, PLR was still associated with the effects of chemotherapy. In CSS analysis, PLR was not significantly associated with the effects of chemotherapy (interaction p = 0.116). In the low-PLR subgroup, the chemotherapy patients did not have a longer CSS than the non-chemotherapy patients (HR: 1.016, 95% CI: 0.494–2.087). In the high-PLR subgroup, the chemotherapy patients had a longer CSS than the non-chemotherapy patients (HR: 0.371, 95% CI: 0.212–0.649). After PSM, PLR was not associated with the effects of chemotherapy.

Conclusions

PLR is an effective marker to predict the effects of chemotherapy in patients with stage II CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08521-0.

Evaluation of ABC gene polymorphisms on the pharmacokinetics and pharmacodynamics of capecitabine in colorectal patients: Implications for dosing recommendations

AIMS

The aims are to develop a population pharmacokinetic model of capecitabine (CAP) and its main metabolites after the oral administration of CAP in colorectal cancer patients with different polymorphisms of the ATP-binding cassette (ABC) gene and a population pharmacokinetic/pharmacodynamic model capable of accounting for the neutropenic effects, and to optimize the dosing strategy based on the polymorphisms of the ABC gene and/or the administration regimen as a single agent or in combination.

METHODS

Forty-eight patients diagnosed with colorectal cancer were included, with 432 plasma levels of CAP, 5'-desoxi-5-fluorouridine (5'-DFUR) and 5-fluorouracil (5-FU), and 370 neutrophil observations. Capecitabine doses ranged from 1250 to 2500 mg/m² /24 h. Plasma measurements of CAP, 5'-DFUR and 5-FU were obtained at 1, 2 and 3 hours post administration. Neutrophil levels were measured between day 15 and day 24 post administration.

RESULTS

The pharmacokinetic model incorporates oxaliplatin as a covariate on absorption lag time, rs6720173 (ABCG5 gene) on clearance of 5'-DFUR (182% increase for mutated rs6720173) and rs2271862 (ABCA2 gene) on clearance of 5-FU (184% increase for mutated rs2271862). System- (Circ₀ = 3.54 × 10⁹ cells/mL, MTT = 204 hours and γ = 6.0 × 10^-2 ) and drug-related (slope [SLP] = 3.1 × 10^-2 mL/mg). Co-administration of oxaliplatin resulted in a 2.84-fold increase in SLP. The predicted exposure thresholds to G3/4 neutropenia in combination and monotherapy were 26 and 70 mg·h/L, respectively.

CONCLUSIONS

The population pharmacokinetic/pharmacodynamic model characterized the time course of capecitabine and its metabolites in plasma. Dose recommendations of capecitabine in patients with mutated and wild allele for single nucleotide polymorphisms rs2271862 of ≤3000 and ≤2400 mg/m² /24 h in monotherapy and ≤1750 and ≤600 mg/m² /24 h in combination with oxaliplatin, respectively, have been proposed.

Prediction of exposure-driven myelotoxicity of continuous infusion 5-fluorouracil by a semi-physiological pharmacokinetic-pharmacodynamic model in gastrointestinal cancer patients

Purpose

To describe 5-fluorouracil (5FU) pharmacokinetics, myelotoxicity and respective covariates using a simultaneous nonlinear mixed effect modelling approach.

Methods

Thirty patients with gastrointestinal cancer received 5FU 650 or 1000 mg/m²/day as 5-day continuous venous infusion (14 of whom also received cisplatin 20 mg/m²/day). 5FU and 5-fluoro-5,6-dihydrouracil (5FUH2) plasma concentrations were described by a pharmacokinetic model using NONMEM. Absolute leukocyte counts were described by a semi-mechanistic myelosuppression model. Covariate relationships were evaluated to explain the possible sources of variability in 5FU pharmacokinetics and pharmacodynamics.

Results

Total clearance of 5FU correlated with body surface area (BSA). Population estimate for total clearance was 249 L/h. Clearances of 5FU and 5FUH2 fractionally changed by 77%/m² difference from the median BSA. 5FU central and peripheral volumes of distribution were 5.56 L and 28.5 L, respectively. Estimated 5FUH2 clearance and volume of distribution were 121 L/h and 96.7 L, respectively. Baseline leukocyte count of 6.86 × 10⁹/L, as well as mean leukocyte transit time of 281 h accounting for time delay between proliferating and circulating cells, was estimated. The relationship between 5FU plasma concentrations and absolute leukocyte count was found to be linear. A higher degree of myelosuppression was attributed to combination therapy (slope = 2.82 L/mg) with cisplatin as compared to 5FU monotherapy (slope = 1.17 L/mg).

Conclusions

BSA should be taken into account for predicting 5FU exposure. Myelosuppression was influenced by 5FU exposure and concomitant administration of cisplatin.

Electronic supplementary material

The online version of this article (10.1007/s00280-019-04028-5) contains supplementary material, which is available to authorized users.

Dose schedule optimization and the pharmacokinetic driver of neutropenia

Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an in vivo experimental system. Specifically, we find total AUC and Cmax are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia in vivo following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.

Pharmacokinetics and exposure-effect relationships of capecitabine in elderly patients with breast or colorectal cancer

PURPOSE

The aims of the present study were (1) to investigate the impact of great age on pharmacokinetics of capecitabine and its metabolites and (2) to evaluate the exposure-effect relationship of capecitabine in elderly patients.

METHODS

Data collected from 20 elderly patients (75-92 years old) with breast or colorectal cancer who received oral capecitabine were analyzed. In order to study the old age effect on pharmacokinetics, data collected from two phase I studies involving 40 younger adults (<75 years old) with metastatic cancer who received oral capecitabine were added in the database. The population pharmacokinetic analysis was based on a four-compartment model describing the sequence of capecitabine and three of its metabolites.

RESULTS

The absorption rate constant was found lower in the oldest patient group (≥75 years) compared with the youngest group, and the constant rate elimination of the 5-fluorouracil metabolite was found decreased over time (i.e., after 2 consecutive weeks of capecitabine administration). This time effect was not found different between the two age groups. In elderly patients, the exposure-safety analysis showed, from the second cycle of chemotherapy, significantly higher median exposures of capecitabine and its metabolites (5'-deoxy-5-fluorocytidine, 5'-deoxy-5-fluorouridine and 5-fluorouracil) in patients who experienced hand-foot syndrome compared with patients who did not.

CONCLUSION

This study puts forward new arguments for the treatment of elderly cancer patients who could benefit from capecitabine chemotherapy with acceptable toxicity.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia

AIMS

Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia.

METHODS

A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework.

RESULTS

Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 μg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time.

CONCLUSIONS

The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

Population pharmacokinetic modelling to assess clinical drug-drug interaction between AZD7325 and midazolam

WHAT IS KNOWN AND OBJECTIVE

AZD7325 is a selective gamma-amino-butyric acid (GABAA )α2, 3 receptor modulator. The aims of this analysis were to develop population pharmacokinetic (PPK) models of AZD7325 and midazolam and to assess the induction effect of AZD7325 on CYP3A4 with midazolam as a substrate.

METHODS

Drug-drug interaction data of AZD7325 and midazolam from 24 healthy subjects were available for model development. PPK models were developed in a sequential manner using NONMEM. Both AZD7325 and midazolam pharmacokinetics were described by two-compartment models, and a transit compartment absorption model and a first-order absorption model were applied for the absorption of AZD7325 and midazolam, respectively. The induction of CYP3A by AZD7325 was described by a transit enzyme model, where the elimination of midazolam was proportionally linked to the enzyme amount. Simulations were performed to predict dosing regimens to account for the induction of CYP3A4.

RESULTS AND DISCUSSION

The population estimates for AZD7325 clearance, intercompartmental clearance, central and peripheral volume were 36, 29·2 L/h, 169 and 392 L, respectively, with interindividual variability (IIV) of 35% and 24% for clearance and central volume. Midazolam clearance, intercompartmental clearance, central and peripheral volume were estimated to be 62·7, 34·7 L/h, 133 and 146 L, respectively, with 43% IIV for clearance. The estimated mean transit time for induction of the CYP3A4 enzyme was 197 h, with 57% IIV.

WHAT IS NEW AND CONCLUSION

The PPK models developed adequately described the clinical observation of AZD7325-mediated CYP3A4 enzyme induction with midazolam as a probe. The model could provide basis for the rational dosing of AZD7325 in clinical practice.

A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination

Purpose

In clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models.

Methods

This paper presents a new pharmacokinetic–pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects.

Results

The model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules.

Conclusions

A good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-013-2208-8) contains supplementary material, which is available to authorized users.

Mechanism-based pharmacodynamic modeling

Pharmacodynamic modeling is based on a quantitative integration of pharmacokinetics, pharmacological systems, and (patho-) physiological processes for understanding the intensity and time-course of drug effects on the body. Application of such models to the analysis of meaningful experimental data allows for the quantification and prediction of drug-system interactions for both therapeutic and adverse drug responses. In this chapter, commonly used mechanistic pharmacodynamic models are presented with respect to their important features, operable equations, and signature profiles. In addition, literature examples showcasing the utility of these models to adverse drug events are highlighted. Common model types that are covered include simple direct effects, biophase distribution, indirect effects, signal transduction, and irreversible effects.

Two-stage model-based design of cancer phase I dose escalation trials: evaluation using the phase I program of barasertib (AZD1152)

Introduction Modeling and simulation of pharmacokinetics and pharmacodynamics has previously been shown to be potentially useful in designing Phase I programs of novel anti-cancer agents that show hematological toxicity. In this analysis, a two-stage model-based trial design was evaluated retrospectively using data from the Phase I program with the aurora kinase inhibitor barasertib. Methods Data from two Phase I trials and four regimens were used (n = 79). Using barasertib-hydroxy QPA plasma concentrations and neutrophil count data from only study 1A, a PKPD model was developed and subsequently used to predict the MTD and a safe starting dose for the other trials. Results The PKPD model based on data from the first study adequately described the time course of neutrophil count fluctuation. The two-stage model-based design provided safe starting doses for subsequent phase I trials for barasertib. Predicted safe starting dose levels were higher than those used in two subsequent trials, but lower than used in the other trial. Discussion The two-stage approach could have been applied safely to define starting doses for alternative dosing strategies with barasertib. The limited improvement in efficiency for the phase I program of barasertib may have been due to the fact that starting doses for the studied phase I trials were already nearly optimal. Conclusion Application of the two-stage model-based trial design in Phase I programs with novel anti-cancer drugs that cause haematological toxicity is feasible, safe, and may lead to a reduction in the number of patient treated at sub-therapeutic dose-levels.

Pharmacokinetics, pharmacodynamics and the pharmacokinetic/ pharmacodynamic relationship of zolpidem in healthy subjects

Zolpidem is one of the most frequently prescribed hypnotics, as it is a very short-acting compound with relatively few side effects. Zolpidem's short duration of action is partly related to its short elimination half-life, but the associations between plasma levels and pharmacodynamic (PD) effects are not precisely known. In this study, the concentration-effect relationships for zolpidem were modelled. Zolpidem (10 mg) was administered in a double-blind, randomised, placebo-controlled trial to determine PD and pharmacokinetics (PK) in 14 healthy volunteers. Zolpidem was absorbed and eliminated quickly, with a median T(max) of 0.78 h (range: 0.33-2.50) and t(1/2) of 2.2 h. Zolpidem reduced saccadic peak velocity (SPV), adaptive tracking performance, electroencephalogram (EEG) alpha power and visual analogue scale (VAS) alertness score and increased body sway, EEG beta power and VAS 'feeling high'. Short- and long-term memory was not affected. Central nervous system effects normalised more rapidly than the decrease of plasma concentrations. For most effects, zolpidem's short duration of action could be adequately described by both a sigmoid E(max) model and a transit tolerance model. For SPV and EEG alpha power, the tolerance model seemed less suitable. These PK/PD models have different implications for the mechanism underlying zolpidem's short duration of action. A sigmoid E(max) model (which is based on ligand binding theory) would imply a threshold value for the drug's effective concentrations. A transit tolerance model (in which a hypothetical factor builds up with time that antagonises the effects of the parent compound) is compatible with a rapid reversible desensitisation of GABAergic subunits.

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.

Preclinical pharmacokinetic/pharmacodynamic models to predict synergistic effects of co-administered anti-cancer agents

PURPOSE

Pharmacokinetic/pharmacodynamic (PK/PD) models have been shown to be useful in predicting tumor growth rates in mouse xenografts. We applied novel PK/PD models to the published anticancer combination therapies of tumor growth inhibition to simulate synergistic changes in tumor growth rates. The parameters from the PK/PD model were further used to estimate clinical doses of the combination.

METHODS

A PK/PD model was built that linked the dosing regimen of a compound to the inhibition of tumor growth in mouse xenograft models. Two subsequent PK/PD models were developed to simulate the published tumor growth profiles of combination treatments. Model I predicts the tumor growth curve assuming that the effect of two anticancer drugs, AZD7762 and irinotecan, is synergistic when given in combination. Model II predicts the tumor growth curve assuming that the effect of co-administering flavopiridol and irinotecan is maximally synergistic when dosed at an optimal interval.

RESULTS

Model I was able to account for the synergistic effects of AZD7762 following the administration of irinotecan. When Model II was applied to the antitumor activity of irinotecan and flavopiridol combination therapy, the modeling was able to reproduce the optimal dosing interval between administrations of the compounds. Furthermore, Model II was able to estimate the biologically active dose of flavopiridol recommended for phase II studies.

CONCLUSIONS

The timing of clinical combination therapy doses is often selected empirically. PK/PD models provide a theoretical structure useful in the design of the optimal clinical dose, frequency of administration and the optimal timing of administration between anticancer agents to maximize tumor suppression.

Covariate-based dose individualization of the cytotoxic drug indisulam to reduce the risk of severe myelosuppression

AIM

Chemotherapy with indisulam causes myelosuppression. This study aimed to evaluate the influence of patient-related covariates on pharmacokinetics and pharmacodynamics, to identify patients at risk for severe myelosuppression and to develop a dosing algorithm for treatment optimization.

METHODS

Pharmacokinetic and pharmacodynamic data of 412 patients were available. Non-linear mixed effects modeling was used to determine the relative risk of dose-limiting myelosuppression for various covariates (demographics, physical condition, prior treatment, comedication, CYP2C genotype and biochemistry).

RESULTS

Body surface area (BSA), race and CYP2C genotype had a significant impact on indisulam elimination (P < 0.001). Low BSA, Japanese race, variant CYP2C genotype, low baseline neutrophil and thrombocyte counts and female sex were clinically relevant risk factors of dose-limiting myelosuppression (RR > 1.1). A dosing strategy was developed to optimize treatment for patient subgroups.

CONCLUSIONS

This study has identified covariates related to an increased risk of myelosuppression after indisulam therapy. Dose individualization may contribute to treatment optimization.

A dose-escalation study of indisulam in combination with capecitabine (Xeloda) in patients with solid tumours

This dose escalation study was designed to determine the recommended dose of the multi-targeted cell cycle inhibitor indisulam in combination with capecitabine in patients with solid tumours and to evaluate the pharmacokinetics of the combination. Thirty-five patients were treated with indisulam on day 1 of each 21-day cycle. Capecitabine was administered two times daily (BID) on days 1–14. Plasma concentrations of indisulam, capecitabine and its three metabolites were determined for pharmacokinetic analysis. The main dose-limiting toxicity was myelosuppression. Hand/foot syndrome and stomatitis were the major non-haematological toxicities. The recommended dose was initially established at indisulam 700 mg m⁻² and capecitabine 1250 mg m⁻² BID. However, during cycle 2 the recommended dose was poorly tolerated in three patients. A dose of indisulam 500 mg m⁻² and capecitabine 1250 mg m⁻² BID proved to be safe at cycle 1 and 2 in nine additional patients. Indisulam pharmacokinetics during cycle 1 were consistent with pharmacokinetic data from phase I mono-therapy studies. However, exposure to indisulam was remarkably increased at cycle 2 due to a drug–drug interaction between capecitabine and indisulam. Partial response was confirmed in two patients, one with colon carcinoma and the other with pancreatic carcinoma. Seventeen patients had stable disease. Indisulam (700 mg m⁻²) in combination with capecitabine (1250 mg m⁻² BID) was well tolerated during the first cycle. A dose of indisulam 500 mg m⁻² and capecitabine 1250 mg m⁻² BID was considered safe in multiple treatment cycles. The higher incidence of toxicities observed during cycle 2 can be explained by a time-dependent pharmacokinetic drug–drug interaction.