Comparing Circulating Tumor Cell Counts with Dynamic Tumor Size Changes as Predictor of Overall Survival: A Quantitative Modeling Framework.

PURPOSE: Quantitative relationships between treatment-induced changes in tumor size and circulating tumor cell (CTC) counts, and their links to overall survival (OS), are lacking. We present a population modeling framework identifying and quantifying such relationships, based on longitudinal data collected in patients with metastatic colorectal cancer (mCRC) to evaluate the value of tumor size and CTC counts as predictors of OS. EXPERIMENTAL DESIGN: A pharmacometric approach (i.e., population pharmacodynamic modeling) was used to characterize the changes in tumor size and CTC count and evaluate them as predictors of OS in 451 patients with mCRC treated with chemotherapy and targeted therapy in a prospectively randomized phase III study (CAIRO2). RESULTS: A tumor size model of tumor quiescence and drug resistance was used to characterize the tumor size time-course, and was, in addition to the total normalized dose (i.e., of all administered drugs) in a given cycle, related to the CTC counts through a negative binomial model (CTC model). Tumor size changes did not contribute additional predictive value when the mean CTC count was a predictor of OS. Treatment reduced the typical mean count from 1.43 to 0.477 (HR = 3.94). The modeling framework was applied to explore whether dose modifications (increased and reduced) would result in a CTC count below 1/7.5 mL after 1 to 2 weeks of treatment. CONCLUSIONS: Time-varying CTC counts can be useful for early predicting OS in patients with mCRC, and may therefore have potential for model-based treatment individualization. Although tumor size was connected to CTC, its link to OS was weaker.

Tumor Time-Course Predicts Overall Survival in Non-Small Cell Lung Cancer Patients Treated with Atezolizumab: Dependency on Follow-Up Time.

The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model-derived variables were explored as predictors of OS in 88 patients with non-small cell lung cancer treated with atezolizumab. To investigate the impact of follow-up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time-course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor-time course-OS relationship could be identified based on early phase I data.

A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab-Treated non-small Cell Lung Cancer Patients.

To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial. Following exploratory analyses, two plasma biomarkers were chosen for further study and correlation with change in tumor size (the sum of the longest diameter) was assessed in a pharmacokinetic/pharmacodynamic (PK/PD) tumor modeling framework. When longitudinal kinetics of biomarkers and tumor size were modeled, tumor shrinkage was found to significantly correlate with area under the curve (AUC), baseline factors (metastatic sites, liver metastases, and smoking status), and relative change in interleukin (IL)-18 level from baseline at day 21 (RCFBIL -18,d21 ). Although AUC was a major predictor of tumor shrinkage, the effect was estimated to dissipate with an average half-life of 80 days, whereas RCFBIL -18,d21 seemed relevant to the duration of the response.

The risk of febrile neutropenia in breast cancer patients following adjuvant chemotherapy is predicted by the time course of interleukin-6 and C-reactive protein by modelling.

AIMS: Early identification of patients with febrile neutropenia (FN) is desirable for initiation of preventive treatment, such as with antibiotics. In this study, the time courses of two inflammation biomarkers, interleukin (IL)-6 and C-reactive protein (CRP), following adjuvant chemotherapy of breast cancer, were characterized. The potential to predict development of FN by IL-6 and CRP, and other model-derived and clinical variables, was explored. METHODS: The IL-6 and CRP time courses in cycles 1 and 4 of breast cancer treatment were described by turnover models where the probability for an elevated production following initiation of chemotherapy was estimated. Parametric time-to-event models were developed to describe FN occurrence to assess: (i) predictors available before chemotherapy is initiated; (ii) predictors available before FN occurs; and (iii) predictors available when FN occurs. RESULTS: The IL-6 and CRP time courses were successfully characterized with peak IL-6 typically occurring 2 days prior to CRP peak. Of all evaluated variables the CRP time course was most closely associated with the occurrence of FN. Since the CRP peak typically occurred at the time of FN diagnosis it will, however, have limited value for identifying the need for preventive treatment. The time course of IL-6 was the predictor that could best forecast FN events. Of the variables available at baseline, age was the best, although in comparison a relatively weak, predictor. CONCLUSIONS: The developed models add quantitative knowledge about IL-6 and CRP and their relationship to the development of FN. The study suggests that IL-6 may have potential as a clinical predictor of FN if monitored during myelosuppressive chemotherapy.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

PURPOSE: To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. METHODS: Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. RESULTS: The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. CONCLUSIONS: Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Risk factors for development of cholestatic drug-induced liver injury: inhibition of hepatic basolateral bile acid transporters multidrug resistance-associated proteins 3 and 4.

Impaired hepatic bile acid export may contribute to development of cholestatic drug-induced liver injury (DILI). The multidrug resistance-associated proteins (MRP) 3 and 4 are postulated to be compensatory hepatic basolateral bile acid efflux transporters when biliary excretion by the bile salt export pump (BSEP) is impaired. BSEP inhibition is a risk factor for cholestatic DILI. This study aimed to characterize the relationship between MRP3, MRP4, and BSEP inhibition and cholestatic potential of drugs. The inhibitory effect of 88 drugs (100 µM) on MRP3- and MRP4-mediated substrate transport was measured in membrane vesicles. Drugs selected for investigation included 50 BSEP non-inhibitors (24 non-cholestatic; 26 cholestatic) and 38 BSEP inhibitors (16 non-cholestatic; 22 cholestatic). MRP4 inhibition was associated with an increased risk of cholestatic potential among BSEP non-inhibitors. In this group, for each 1% increase in MRP4 inhibition, the odds of the drug being cholestatic increased by 3.1%. Using an inhibition cutoff of 21%, which predicted a 50% chance of cholestasis, 62% of cholestatic drugs inhibited MRP4 (P < 0.05); in contrast, only 17% of non-cholestatic drugs were MRP4 inhibitors. Among BSEP inhibitors, MRP4 inhibition did not provide additional predictive value of cholestatic potential; almost all BSEP inhibitors were also MRP4 inhibitors. Inclusion of pharmacokinetic predictor variables (e.g., maximal unbound concentration in plasma) in addition to percent MRP4 inhibition in logistic regression models did not improve cholestasis prediction. Association of cholestasis with percent MRP3 inhibition was not statistically significant, regardless of BSEP-inhibition status. Inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI.