A Dynamic Model of Hand-and-Foot Syndrome in Patients Receiving Capecitabine

A Markov model of fibrosis development in nonalcoholic fatty liver disease predicts fibrosis progression in clinical cohorts

Disease progression in nonalcoholic steatohepatitis (NASH) is highly heterogenous and remains poorly understood. Fibrosis stage is currently the best predictor for development of end-stage liver disease and mortality. Better understanding and quantifying the impact of factors affecting NASH and fibrosis is essential to inform a clinical study design. We developed a population Markov model to describe the transition probability between fibrosis stages and mortality using a unique clinical nonalcoholic fatty liver disease cohort with serial biopsies over 3 decades. We evaluated covariate effects on all model parameters and performed clinical trial simulations to predict the fibrosis progression rate for external clinical cohorts. All parameters were estimated with good precision. Age and diagnosis of type 2 diabetes (T2D) were found to be significant predictors in the model. Increase in hepatic steatosis between visits was the most important predictor for progression of fibrosis. Fibrosis progression rate (FPR) was twofold higher for fibrosis stages 0 and 1 (F0-1) compared to fibrosis stage 2 and 3 (F2-3). A twofold increase in FPR was observed for T2D. A two-point steatosis worsening increased the FPR 11-fold. Predicted fibrosis progression was in good agreement with data from external clinical cohorts. Our fibrosis progression model shows that patient selection, particularly initial fibrosis stage distribution, can significantly impact fibrosis progression and as such the window for assessing drug efficacy in clinical trials. Our work highlights the increase in hepatic steatosis as the most important factor in increasing FPR, emphasizing the importance of well-defined lifestyle advise for reducing variability in NASH progression during clinical trials.

Can immunotherapy reinforce chemotherapy efficacy? a new perspective on colorectal cancer treatment

As one of the main threats to human life (the fourth most dangerous and prevalent cancer), colorectal cancer affects many people yearly, decreases patients' quality of life, and causes irreparable financial and social damages. In addition, this type of cancer can metastasize and involve the liver in advanced stages. However, current treatments can't completely eradicate this disease. Chemotherapy and subsequent surgery can be mentioned among the current main treatments for this disease. Chemotherapy has many side effects, and regarding the treatment of this type of tumor, chemotherapy can lead to liver damage, such as steatohepatitis, steatosis, and sinus damage. These damages can eventually lead to liver failure and loss of its functions. Therefore, it seems that other treatments can be used in addition to chemotherapy to increase its efficiency and reduce its side effects. Biological therapies and immunotherapy are one of the leading suggestions for combined treatment. Antibodies (immune checkpoint blockers) and cell therapy (DC and CAR-T cells) are among the immune system-based treatments used to treat tumors. Immunotherapy targets various aspects of the tumor that may lead to 1) the recruitment of immune cells, 2) increasing the immunogenicity of tumor cells, and 3) leading to the elimination of inhibitory mechanisms established by the tumor. Therefore, immunotherapy can be used as a complementary treatment along with chemotherapy. This review will discuss different chemotherapy and immunotherapy methods for colorectal cancer. Then we will talk about the studies that have dealt with combined treatment.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Population pharmacokinetic/pharmacodynamic joint modeling of ixazomib efficacy and safety using data from the pivotal phase III TOURMALINE‐MM1 study in multiple myeloma patients

Ixazomib is an oral proteasome inhibitor approved in combination with lenalidomide and dexamethasone for the treatment of relapsed/refractory multiple myeloma (MM). Approval in the United States, Europe, and additional countries was based on results from the phase III TOURMALINE‐MM1 (C16010) study. Here, joint population pharmacokinetic/pharmacodynamic time‐to‐event (TTE) and discrete time Markov models were developed to describe key safety (rash and diarrhea events, and platelet counts) and efficacy (myeloma protein [M‐protein] and progression‐free survival [PFS]) outcomes observed in TOURMALINE‐MM1. Models reliably described observed safety and efficacy results; prior immunomodulatory drug therapy and race were significant covariates for diarrhea and rash events, respectively, whereas M‐protein dynamics were sufficiently characterized using TTE models of relapse and dropout. Moreover, baseline M‐protein was identified as a significant covariate for observed PFS. The developed framework represents an integrated approach to describing safety and efficacy with MM therapy, enabling the simulation of prospective trials and potential alternate dosing regimens.

Automatic assessment of adverse drug reaction reports with interactive visual exploration

A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual’s expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)—an ordinal logistic regression model—automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing automatic assessments by the ACA model to ADR reports labeled by ADR experts, i.e., the ground truth results from the multinomial logistic regression and the decision tree. The ACA model achieves an accuracy of 85.99%, a multiclass macro-averaged area under the curve (AUC) of 0.9572, while the multinomial logistics regression and decision tree yield 80.82%, 0.8603, and 85.39%, 0.9440, respectively, on the testing set. The new warning signal is able to assist ADR experts to quickly focus on reports of interest with our interactive visualzation tool. Reports of interest that are selected with high scores of the warning signal are analyzed in details by an ADR expert. The usefulness of the overall method is further evaluated through the interactive analysis of the data by ADR expert. Our ACA model achieves good performance and is superior to the multinomial logistics and the decision tree. The warning signal we designed allows efficient filtering of the full ADR reports down to much fewer reports showing anomalies. The usefulness of our interactive visualization is demonstrated by examples of unusual reports that are quickly identified. Our overall method could potentially improve the capability of analyzing ADR reports and reduce human labor and the chance of missing critical reports.

Risk prediction models based on hematological/body parameters for chemotherapy-induced adverse effects in Chinese colorectal cancer patients

PURPOSE

To determine risk factors and develop novel prediction models for chemotherapy-induced adverse effects (CIAEs) in Chinese colorectal cancer (CRC) patients receiving capecitabine.

METHODS

A total of 233 Chinese CRC patients receiving post-operative chemotherapy with capecitabine were randomly divided into a training set (70%) and a validation set (30%). CIAE-related hematological/body parameters were screened by univariate logistic regression. Based on a set of factors selected from LASSO (least absolute shrinkage and selection operator) logistic regression, stepwise multivariate logistic regression was applied to develop prediction models. Area under the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (HL) test were used to evaluate the discriminatory ability and the goodness of fit of each model.

RESULTS

In total, 35 variables were identified to be associated with CIAEs in univariate analysis. Developed multivariable models had AUCs (area under curve) ranging from 0.625 to 0.888 and 0.428 to 0.760 in the training and validation set, respectively. The grade ≥ 1 anemia multivariable model achieved the best discriminatory ability with AUC of 0.760 (95%CI: 0.609-0.912) and good calibration with HL P value of 0.450. Then, a nomogram was constructed to predict grade ≥ 1 anemia, which included variables of age, pre-operative hemoglobin count, and pre-operative albumin count, with C-indexes of 0.775 and 0.806 in the training and validation set, respectively.

CONCLUSIONS

This study identified valuable hematological/body parameters related to CIAEs. A nomogram based on the multivariable model including three hematological/body predictors can accurately predict grade ≥ 1 anemia, facilitating clinicians to implement personalized medicine early for Chinese CRC patients receiving post-operative chemotherapy for better safety treatment. Trial registration This study was registered as a clinical trial at www.clinicaltrials.gov (NCT03030508).

Population Pharmacokinetics of Intracellular 5-Fluorouridine 5'-Triphosphate and its Relationship with Hand-and-Foot Syndrome in Patients Treated with Capecitabine

Capecitabine is an oral pro-drug of 5-fluorouracil. Patients with solid tumours who are treated with capecitabine may develop hand-and-foot syndrome (HFS) as side effect. This might be a result of accumulation of intracellular metabolites. We characterised the pharmacokinetics (PK) of 5-fluorouridine 5'-triphosphate (FUTP) in peripheral blood mononuclear cells (PBMCs) and assessed the relationship between exposure to capecitabine or its metabolites and the development of HFS. Plasma and intracellular capecitabine PK data and ordered categorical HFS data was available. A previously developed model describing the PK of capecitabine and metabolites was extended to describe the intracellular FUTP concentrations. Subsequently, a continuous-time Markov model was developed to describe the development of HFS during treatment with capecitabine. The influences of capecitabine and metabolite concentrations on the development of HFS were evaluated. The PK of intracellular FUTP was described by an one-compartment model with first-order elimination (k_e,FUTP was 0.028 h^-1 (95% confidence interval 0.022-0.039)) where the FUTP influx rate was proportional to the 5-FU plasma concentrations. The predicted individual intracellular FUTP concentration was identified as a significant predictor for the development and severity of HFS. Simulations demonstrated a clear exposure-response relationship. The intracellular FUTP concentrations were successfully described and a significant relationship between these intracellular concentrations and the development and severity of HFS was identified. This model can be used to simulate future dosing regimens and thereby optimise treatment with capecitabine.

Evaluation of patient-reported severity of hand-foot syndrome under capecitabine using a Markov modeling approach

Purpose

The inclusion of the patient’s perspective has become increasingly important when reporting adverse events and may assist in management of toxicity. The relationship between drug exposure and toxicity can be quantified by combining Markov elements with pharmacometric models. A minimal continuous-time Markov model (mCTMM) was applied to patient-reported outcomes using hand–foot syndrome (HFS) induced by capecitabine anti-cancer therapy as an example.

Methods

Patient-reported HFS grades over time of 150 patients from two observational studies treated with oral capecitabine were analyzed using a mCTMM approach. Grading of HFS severity was based on the Common Terminology Criteria for Adverse Events. The model was evaluated by visual predictive checks (VPC). Furthermore, a simulation study of the probability of HFS severity over time was performed in which the standard dosing regimen and dose adjustments according to HFS severity were investigated.

Results

The VPC of the developed dose–toxicity model indicated an accurate description of HFS severity over time. Individual absolute daily dose was found to be a predictor for HFS. The simulation study demonstrated a reduction of severe HFS using the recommended dose adjustment strategy.

Conclusion

A minimal continuous-time Markov model was developed based on patient-reported severity of hand–foot syndrome under capecitabine. Thus, a modeling framework for patient-reported outcomes was created which may assist in the optimization of dosage regimens and adjustment strategies aiming at minimizing symptom burden during anti-cancer drug therapy.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04128-7) contains supplementary material, which is available to authorized users.

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.

SNPs in the COX-2/PGES/EP signaling pathway are associated with risk of severe capecitabine-induced hand-foot syndrome

PURPOSE

Capecitabine is a widely used 5-fluorouracil oral prodrug. Hand-foot syndrome (HFS), one of the most common adverse events of capecitabine, impacts patients' quality of life seriously. The pathogenesis of HFS remains unclear but was usually considered as a type of inflammation conducted by cyclooxygenase-2 (COX-2). The COX-2/PGES/EP signaling pathway plays an important role in the inflammatory reaction. We hypothesized that the single nucleotide polymorphisms (SNPs) in this pathway may be associated with the risk of HFS induced by capecitabine.

PATIENTS AND METHODS

Using DNA from blood samples of 225 patients, we genotyped 19 SNPs in 6 core genes (COX-2, PGES, EP1, EP2, EP3, and EP4). Common Terminology Criteria for Adverse Events version 3.0 was used to grade hand-foot syndrome. We used logistic regression analysis to evaluate the correlations between genotype variants and occurrence of HFS. The cumulative incidence of HFS was assessed by Kaplan-Meier analysis.

RESULTS

Among the 225 participants, 58.6% (132/225) patients developed into HFS, including 41.3% (93/225) grade 1 HFS, 10.2% (23/225) grade 2 HFS and 7.1% (16/225) grade 3 HFS. Multivariate logistic regression analysis showed the AG/GG genotype of rs3810255 to be associated with a significantly higher risk of grade 2/3 HFS, while the AG/AA genotype of rs17131450 to be associated with a significantly lower risk of grade 2/3 HFS (OR = 3.646, P = 0.011; and OR = 0.266, P = 0.036; respectively).

CONCLUSION

Our study showed that rs3810255 AG/GG genotypes and rs17131450 GG genotypes to be associated with high risk of capecitabine-induced HFS.

Assessment of Demographic and Socio-Behavioral Factors on Adherence to HIV Pre-Exposure Prophylaxis Using a Markov Modeling Approach

Purpose: Adherence is important for the effectiveness of human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP). The objective of the current work is to assess the impact of multiple demographic and socio-behavioral factors on the adherence to tenofovir-based PrEP among HIV serodiscordant couples in East Africa using Markov mixed-effects modeling approach.

Methods: The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of HIV PrEP among heterosexual HIV serodiscordant couples in Kenya and Uganda. The uninfected partner received oral PrEP according to the “bridge to antiretroviral therapy [ART]” strategy (i.e., until the infected partner had been on ART for ≥6 months). Adherence was monitored electronically; demographic and socio-behavioral data were collected during study visits. Analyzed data reflect 12 months of follow-up per participant. A two-state, first-order, discrete time Markov model was developed with longitudinal adherence data characterized by “dose taking (1)” and “dose missing (0).” Covariate effects were linearly added in the logit domain of transition probability parameters (P01 and P10) in the model. The full covariate model was initially developed, followed by backward elimination process to reduce the model. All significant covariates reported by a prior primary statistical analysis of the same data were included in the full covariate model.

Results: The model included data from 920 participants, who were predominantly male (65%). Significant covariates associated with higher adherence were 25 years or older [odds ratio (OR) for P10, 0.61], female sex (OR for P10, 0.67), participant wanting the relationship with the partner to succeed (OR for P10, 0.79; OR for P01, 1.45), and sex with partner either with 100% or <100% condom use compared to those reported no sex (OR for P10, 0.84; OR for P01, 1.21). Significant covariates associated with lower adherence were partner on ART >6 months (OR for P01, 0.86; OR for P10, 1.34), subject in the study for >6 months (OR for P01, 0.8; OR for P10, 1.25), and problematic alcohol use (OR for P01, 0.63; OR for P10, 1.16).

Conclusion: The developed Markov model provides a mechanistic understanding of relationship between demographic, socio-behavioral covariates, and PrEP adherence, by indicating the pattern of adherence influenced by each factor over time. Such data can be used for further intervention development to promote PrEP adherence.

Modeling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib

Models were developed to characterize the relationship between afatinib exposure and diarrhea and rash/acne adverse event (AE) trajectories, and their predictive ability was assessed. Based on pooled data from seven phase II/III clinical studies including 998 patients, mixed-effects models for ordered categorical data were applied to describe daily AE severity. Clinical trial simulation aided by trial execution models was used for internal and external model evaluation. The final exposure-safety model consisted of longitudinal logistic regression models with first-order Markov elements for both AEs. Drug exposure was included as daily area under the concentration-time curve (AUC), and drug effects on the AEs were correlated. Clinical trial simulation allowed adequate prediction of maximum AE grades and AE severity time courses but overestimated the proportion of AE-dependent dose reductions and discontinuations. Both diarrhea and rash/acne were correlated with afatinib exposure. The developed modeling framework allows a prospective comparison of dosing strategies and study designs with respect to safety.

A Joint Model for Vitamin K-Dependent Clotting Factors and Anticoagulation Proteins

BACKGROUND

Warfarin acts by inhibiting the reduction of vitamin K (VK) to its active form, thereby decreasing the production of VK-dependent coagulation proteins. The aim of this research is to develop a joint model for the VK-dependent clotting factors II, VII, IX and X, and the anticoagulation proteins, proteins C and S, during warfarin initiation.

METHODS

Data from 18 patients with atrial fibrillation who had warfarin therapy initiated were available for analysis. Nine blood samples were collected from each subject at baseline, and at 1-5, 8, 15 and 29 days after warfarin initiation and assayed for factors II, VII, IX and X, and proteins C and S. Warfarin concentration-time data were not available. The coagulation proteins data were modelled in a stepwise manner using NONMEM^® Version 7.2. In the first stage, each of the coagulation proteins was modelled independently using a kinetic-pharmacodynamic model. In the subsequent step, the six kinetic-pharmacodynamic models were combined into a single joint model.

RESULTS

One patient was administered VK and was excluded from the analysis. Each kinetic-pharmacodynamic model consisted of two parts: (1) a common one-compartment pharmacokinetic model with first-order absorption and elimination for warfarin; and (2) an inhibitory E _max model linked to a turnover model for coagulation proteins. In the joint model, an unexpected pharmacodynamic lag was identified and the estimated degradation half-life of VK-dependent coagulation proteins were in agreement with previously published values. The model provided an adequate fit to the observed data.

CONCLUSION

The joint model represents the first work to quantify the influence of warfarin on all six VK-dependent coagulation proteins simultaneously. Future work will expand the model to predict the influence of exogenously administered VK on the time course of clotting factor concentrations after warfarin overdose and during perioperative warfarin reversal procedures.

A Minimal Continuous-Time Markov Pharmacometric Model

In this work, an alternative model to discrete-time Markov model (DTMM) or standard continuous-time Markov model (CTMM) for analyzing ordered categorical data with Markov properties is presented: the minimal CTMM (mCTMM). Through a CTMM reparameterization and under the assumption that the transition rate between two consecutive states is independent on the state, the Markov property is expressed through a single parameter, the mean equilibration time, and the steady-state probabilities are described by a proportional odds (PO) model. The mCTMM performance was evaluated and compared to the PO model (ignoring Markov features) and to published Markov models using three real data examples: the four-state fatigue and hand-foot syndrome data in cancer patients initially described by DTMM and the 11-state Likert pain score data in diabetic patients previously analyzed with a count model including Markovian transition probability inflation. The mCTMM better described the data than the PO model, and adequately predicted the average number of transitions per patient and the maximum achieved scores in all examples. As expected, mCTMM could not describe the data as well as more flexible DTMM but required fewer estimated parameters. The mCTMM better fitted Likert data than the count model. The mCTMM enables to explore the effect of potential predictive factors such as drug exposure and covariates, on ordered categorical data, while accounting for Markov features, in cases where DTMM and/or standard CTMM is not applicable or conveniently implemented, e.g., non-uniform time intervals between observations or large number of categories.

A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology

Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose-response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose-response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM^® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.

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.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.

IMPLICATIONS FOR PRACTICE

The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

Germline TYMS genotype is highly predictive in patients with metastatic gastrointestinal malignancies receiving capecitabine-based chemotherapy

PURPOSE

This work was initiated to extend data on the effect of pharmacogenetics and chemotherapy pharmacokinetics (PK) on clinical outcome in patients with gastrointestinal malignancies.

METHODS

We assessed 44 gene polymorphisms in 16 genes (TYMS, MTHFR, GSTP1, GSTM1, GSTT1, DPYD, XRCC1, XRCC3, XPD, ERCC1, RECQ1, RAD54L, ABCB1, ABCC2, ABCG2 and UGT2B7) in 64 patients with metastatic colorectal cancer (CRC) receiving capecitabine/oxaliplatin and 76 patients with advanced gastroesophageal cancer (GEC) receiving epirubicin/cisplatin/capecitabine, respectively. Plasma concentrations of anticancer drugs were measured for up to 24 h, and results were submitted to population PK analysis. We calculated the association between gene polymorphisms, chemotherapy exposure, tumor response, progression-free survival (PFS), overall survival (OS) and chemotherapy-related toxicity using appropriate statistical tests.

RESULTS

Patients with a low clearance of 5FU were at increased risk of neutropenia (P < 0.05) and hand-foot syndrome (P = 0.002). DPYD T85C, T1896C and A2846T mutant variants were associated with diarrhea (P < 0.05) and HFS (P < 0.02), and IVS14+1G>A additionally with diarrhea (P < 0.001). The TYMS 2R/3G, 3C/3G or 3G/3G promoter variants were associated with worse PFS in the CRC (HR = 2.0, P < 0.01) and GEC group (HR = 5.4, P < 0.001) and worse OS in the GEC group (HR = 4.7, P < 0.001). The GSTP1 A313G mutant variant was associated with a higher PFS (HR = 0.55, P = 0.001) and OS (HR = 0.60, P = 0.002) in the CRC group.

CONCLUSIONS

Germline polymorphisms of DPYD, TYMS and GSTP1 have a significant effect on toxicity and clinical outcome in patients receiving capecitabine-based chemotherapy for advanced colorectal or gastroesophageal cancer. These data should further be validated in prospective clinical studies.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib-Treated Patients With GIST

A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand–foot syndrome (HFS)). Longitudinal pharmacokinetic–pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.

Simulations to Assess Phase II Noninferiority Trials of Different Doses of Capecitabine in Combination With Docetaxel for Metastatic Breast Cancer

A phase II trial in metastatic breast cancer (MBC) (NO16853) failed to show noninferiority (progression-free survival, PFS) of capecitabine 825 mg/m² plus docetaxel 75 mg/m² to the registered capecitabine dose of 1,250 mg/m² plus docetaxel 75 mg/m². We developed a modeling framework based on NO16853 and the pivotal phase III MBC study, SO14999, to characterize the link between capecitabine dose, tumor growth, PFS, and survival to simulate response to a range of capecitabine doses and determine a minimum capecitabine dose noninferior to 1,250 mg/m². Simulation showed NO16853 had little power to demonstrate noninferiority (69%). The power reached 80% with a 1,000 mg/m² starting dose and an increased number of PFS events. A starting dose of 1,000 mg/m² could be established as noninferior in terms of efficacy to the registered dose in the second-line MBC setting, with a potentially improved safety, in line with medical practice.

Empirical Bayes estimation of random effects of a mixed-effects proportional odds Markov model for ordinal data

The objective of this work was to investigate the factors influencing the quality of empirical Bayes estimates (EBEs) of individual random effects of a mixed-effects Markov model for ordered categorical data. It was motivated by an attempt to develop a model-based dose adaptation tool for clinical use in colorectal cancer patients receiving capecitabine, which induces severe hand-and-foot syndrome (HFS) toxicity in more than a half of the patients. This simulation-based study employed a published mixed-effects model for HFS. The quality of EBEs was assessed in terms of accuracy and precision, as well as shrinkage. Three optimization algorithms were compared: simplex, quasi-Newton and adaptive random search. The investigated factors were amount of data per patient, distribution of categories within patients, magnitude of the inter-individual variability, and values of the effect model parameters. The main factors affecting the quality of EBEs were the values of parameters governing the dose-response relationship and the within-subject distribution of categories. For the chosen HFS toxicity model, the accuracy and precision of EBEs were rather low, and therefore the feasibility of their use for individual model-based dose adaptation seemed limited.

First-time-in-human study with GSK1018921, a selective GlyT1 inhibitor: relationship between exposure and dizziness

The pharmacokinetics (PK), safety, and tolerability of GSK1018921, a glycine transporter 1 (GlyT-1) inhibitor, were assessed in this first-time-in-human (FTIH) study. Single oral doses ranging from 0.5 to 280 mg and placebo were administered to 25 healthy subjects in a five-period, two-cohort, crossover study. GSK1018921 showed dose-proportional PK with a terminal half-life of ~17 h. The subjects reported dizziness with a dose-dependent frequency of 22-88% at doses of 70-280 mg. The time course of the dizziness paralleled the PK of the drug, with peak response at 2 h after the dose, consistent with time to maximum plasma concentration (T(max)). The dizziness was resolved by 10-12 h in all subjects. A Markov-chain logistic regression model was implemented in NONMEM to determine the probability of developing dizziness as a function of the plasma concentration of the compound. Frequency, onset (<1 h), and offset (4 h) were well described by the model. Exposure resulting in 80% receptor occupancy is predicted to be well tolerated.

Modelling the genesis and treatment of cancer: the potential role of physiologically based pharmacodynamics

Physiologically based modelling of pharmacodynamics/toxicodynamics requires an a priori knowledge on the underlying mechanisms causing toxicity or causing the disease. In the context of cancer, the objective of the expert meeting was to discuss the molecular understanding of the disease, modelling approaches used so far to describe the process, preclinical models of cancer treatment and to evaluate modelling approaches developed based on improved knowledge. Molecular events in cancerogenesis can be detected using 'omics' technology, a tool applied in experimental carcinogenesis, but also for diagnostics and prognosis. The molecular understanding forms the basis for new drugs, for example targeting protein kinases specifically expressed in cancer. At present, empirical preclinical models of tumour growth are in great use as the development of physiological models is cost and resource intensive. Although a major challenge in PKPD modelling in oncology patients is the complexity of the system, based in part on preclinical models, successful models have been constructed describing the mechanism of action and providing a tool to establish levels of biomarker associated with efficacy and assisting in defining biologically effective dose range selection for first dose in man. To follow the concentration in the tumour compartment enables to link kinetics and dynamics. In order to obtain a reliable model of tumour growth dynamics and drug effects, specific aspects of the modelling of the concentration-effect relationship in cancer treatment that need to be accounted for include: the physiological/circadian rhythms of the cell cycle; the treatment with combinations and the need to optimally choose appropriate combinations of the multiple agents to study; and the schedule dependence of the response in the clinical situation.