Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401).

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (Ctrough )-driven efficacy requiring Ctrough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.

Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients' characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once-daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib  PKs was best described by a two-compartment model with a delayed zero-order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross-validation, and allowed to derive a composite predictive risk score from a set of 75 patients' baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients' characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed.

Results from a First-in-Human Phase I Study of Siremadlin (HDM201) in Patients with Advanced Wild-Type TP53 Solid Tumors and Acute Leukemia.

PURPOSE: This phase I, dose-escalation study investigated the recommended dose for expansion (RDE) of siremadlin, a p53-MDM2 inhibitor, in patients with wild-type TP53 advanced solid or hematologic cancers. PATIENTS AND METHODS: Initial dosing regimens were: 1A (day 1; 21-day cycle; dose 12.5-350 mg) and 2A (days 1-14; 28-day cycle; dose 1-20 mg). Alternative regimens included 1B (days 1 and 8; 28-day cycle) and 2C (days 1-7; 28-day cycle). The primary endpoint was incidence of dose-limiting toxicities (DLT) during cycle 1. RESULTS: Overall, 115 patients with solid tumors and 93 with hematologic malignancies received treatment. DLTs occurred in 8/92 patients with solid tumors and 10/53 patients with hematologic malignancies. In solid tumors, an RDE of 120 mg was defined in 1B. In hematologic tumors, RDEs were defined in 1A: 250 mg, 1B: 120 mg, and 2C: 45 mg. More patients with hematologic malignancies compared with solid tumors experienced grade 3/4 treatment-related adverse events (71% vs. 45%), most commonly resulting from myelosuppression. These were more frequent and severe in patients with hematologic malignancies; 22 patients exhibited tumor lysis syndrome. Overall response rates at the RDEs were 10.3% [95% confidence interval (CI), 2.2-27.4] in solid tumors and 4.2% (95% CI, 0.1-21.1), 20% (95% CI, 4.3-48.1), and 22.2% (95% CI, 8.6-42.3) in acute myeloid leukemia (AML) in 1B, 1A, and 2C, respectively. CONCLUSIONS: A common safety profile was identified and preliminary activity was noted, particularly in AML. Comprehensive investigation of dosing regimens yielded recommended doses/regimens for future combination studies.

A framework to guide dose & regimen strategy for clinical drug development.

Optimizing new drug therapies remains a challenge for clinical development, despite the use of ever more sophisticated quantitative methodologies. Although conceptually simple, the idea of finding the right treatment at the right dose for the right patient to ensure an appropriate balance of risks and benefits is challenging and requires a multidisciplinary approach. In this paper, we present a framework developed as a tool for organizing knowledge and facilitating collaboration in development teams.

Pharmacokinetic-pharmacodynamic guided optimisation of dose and schedule of CGM097, an HDM2 inhibitor, in preclinical and clinical studies.

BACKGROUND: CGM097 inhibits the p53-HDM2 interaction leading to downstream p53 activation. Preclinical in vivo studies support clinical exploration while providing preliminary evidence for dosing regimens. This first-in-human phase I study aimed at assessing the safety, MTD, PK/PD and preliminary antitumor activity of CGM097 in advanced solid tumour patients (NCT01760525). METHODS: Fifty-one patients received oral treatment with CGM097 10-400 mg 3qw (n = 31) or 300-700 mg 3qw 2 weeks on/1 week off (n = 20). Choice of dose regimen was guided by PD biomarkers, and quantitative models describing the effect of CGM097 on circulating platelet and PD kinetics. RESULTS: No dose-limiting toxicities were reported in any regimens. The most common treatment-related grade 3/4 AEs were haematologic events. PK/PD models well described the time course of platelet and serum GDF-15 changes, providing a tool to predict response to CGM097 for dose-limiting thrombocytopenia and GDF-15 biomarker. The disease control rate was 39%, including one partial response and 19 patients in stable disease. Twenty patients had a cumulative treatment duration of >16 weeks, with eight patients on treatment for >32 weeks. The MTD was not determined. CONCLUSIONS: Despite delayed-onset thrombocytopenia frequently observed, the tolerability of CGM097 appears manageable. This study provided insights on dosing optimisation for next-generation HDM2 inhibitors. TRANSLATIONAL RELEVANCE: Haematologic toxicity with delayed thrombocytopenia is a well-known on-target effect of HDM2 inhibitors. Here we have developed a PK/PD guided approach to optimise the dose and schedule of CGM097, a novel HDM2 inhibitor, using exposure, platelets and GDF-15, a known p53 downstream target to predict patients at higher risk to develop thrombocytopenia. While CGM097 had shown limited activity, with disease control rate of 39% and only one patient in partial response, the preliminary data from the first-in-human escalation study together with the PK/PD modeling provide important insights on how to optimize dosing of next generation HDM2 inhibitors to mitigate hematologic toxicity.

Translational Modeling of Anticancer Efficacy to Predict Clinical Outcomes in a First-in-Human Phase 1 Study of MDM2 Inhibitor HDM201.

We report on a retrospective model-based assessment of the predictive value of translating antitumor drug activity from in vivo experiments to a phase I clinical study in cancer patients treated with the MDM2 inhibitor, HDM201. Tumor growth inhibition models were developed describing the longitudinal tumor size data in human-derived osteosarcoma xenograft rats and in 96 solid tumor patients under different HDM201 treatment schedules. The model structure describing both datasets captures the delayed drug effect on tumor growth via a series of signal transduction compartments, including a resistance component. The models assumed a drug-killing effect on both sensitive and resistant cells and parameterized to estimate two tumor static plasma drug concentrations for sensitive (TSCS) and resistant cells (TSCR). No change of TSCS and TSCR with schedule was observed, implying that antitumor activity for HDM201 is independent of treatment schedule. Preclinical and clinical model-derived TSCR were comparable (48 ng/mL vs. 74 ng/mL) and demonstrating TSCR as a translatable metric for antitumor activity in clinic. Schedule independency was further substantiated from modeling of clinical serum growth differentiation factor-15 (GDF-15) as a downstream marker of p53 pathway activation. Equivalent cumulative induction of GDF-15 was achieved across schedules when normalized to an equivalent total dose. These findings allow for evaluation of optimal dosing schedules by maximizing the total dose per treatment cycle while mitigating safety risk with periods of drug holiday. This approach helped guide a phase I dose escalation study in the selection of an optimal dose and schedule for HDM201.

Asciminib in Chronic Myeloid Leukemia after ABL Kinase Inhibitor Failure.

BACKGROUND: Asciminib is an allosteric inhibitor that binds a myristoyl site of the BCR-ABL1 protein, locking BCR-ABL1 into an inactive conformation through a mechanism distinct from those for all other ABL kinase inhibitors. Asciminib targets both native and mutated BCR-ABL1, including the gatekeeper T315I mutant. The safety and antileukemic activity of asciminib in patients with Philadelphia chromosome-positive leukemia are unknown. METHODS: In this phase 1, dose-escalation study, we enrolled 141 patients with chronic-phase and 9 with accelerated-phase chronic myeloid leukemia (CML) who had resistance to or unacceptable side effects from at least two previous ATP-competitive tyrosine kinase inhibitors (TKIs). The primary objective was to determine the maximum tolerated dose or the recommended dose (or both) of asciminib. Asciminib was administered once or twice daily (at doses of 10 to 200 mg). The median follow-up was 14 months. RESULTS: Patients were heavily pretreated; 70% (105 of 150 patients) had received at least three TKIs. The maximum tolerated dose of asciminib was not reached. Among patients with chronic-phase CML, 34 (92%) with a hematologic relapse had a complete hematologic response; 31 (54%) without a complete cytogenetic response at baseline had a complete cytogenetic response. A major molecular response was achieved or maintained by 12 months in 48% of patients who could be evaluated, including 8 of 14 (57%) deemed to have resistance to or unacceptable side effects from ponatinib. A major molecular response was achieved or maintained by 12 months in 5 patients (28%) with a T315I mutation at baseline. Clinical responses were durable; a major molecular response was maintained in 40 of 44 patients. Dose-limiting toxic effects included asymptomatic elevations in the lipase level and clinical pancreatitis. Common adverse events included fatigue, headache, arthralgia, hypertension, and thrombocytopenia. CONCLUSIONS: Asciminib was active in heavily pretreated patients with CML who had resistance to or unacceptable side effects from TKIs, including patients in whom ponatinib had failed and those with a T315I mutation. (Funded by Novartis Pharmaceuticals; ClinicalTrials.gov number, NCT02081378.).

Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy.

Metronomic chemotherapy is usually associated with better tolerance than conventional chemotherapy, and encouraging response rates have been reported in various settings. However, clinical development of metronomic chemotherapy has been hampered by a number of limitations, including the vagueness of its definition and the resulting empiricism in protocol design. In this study, we developed a pharmacokinetic/pharmacodynamic mathematical model that identifies in silico the most effective administration schedule for gemcitabine monotherapy. This model is based upon four biological assumptions regarding the mechanisms of action of metronomic chemotherapy, resulting in a set of 6 minimally parameterized differential equations. Simulations identified daily 0.5-1 mg/kg gemcitabine as an optimal protocol to maximize antitumor efficacy. Both metronomic protocols (0.5 and 1 mg/kg/day for 28 days) were evaluated in chemoresistant neuroblastoma-bearing mice and compared with the standard MTD protocol (100 mg/kg once a week for 4 weeks). Systemic exposure to gemcitabine was 14 times lower in the metronomic groups compared with the standard group. Despite this, metronomic gemcitabine significantly inhibited tumor angiogenesis and reduced tumor perfusion and inflammation in vivo, while standard gemcitabine did not. Furthermore, metronomic gemcitabine yielded a 40%-50% decrease in tumor mass at the end of treatment as compared with control mice (P = 0.002; ANOVA on ranks with Dunn test), while standard gemcitabine failed to significantly reduce tumor growth. Stable disease was maintained in the metronomic groups for up to 2 months after treatment completion (67%-72% reduction in tumor growth at study conclusion, P < 0.001; ANOVA on ranks with Dunn test). Collectively, our results confirmed the superiority of metronomic protocols in chemoresistant tumors in vivoCancer Res; 77(17); 4723-33. ©2017 AACR.

Modeling therapeutic response to radioiodine in metastatic thyroid cancer: a proof-of-concept study for individualized medicine.

PURPOSE: Radioiodine therapy (RAI) has traditionally been used as treatment for metastatic thyroid cancer, based on its ability to concentrate iodine. Propositions to maximize tumor response with minimizing toxicity, must recognize the infinite possibilities of empirical tests. Therefore, an approach of this study was to build a mathematical model describing tumor growth with the kinetics of thyroglobulin (Tg) concentrations over time, following RAI for metastatic thyroid cancer. EXPERIMENTAL DESIGN: Data from 50 patients with metastatic papillary thyroid carcinoma treated within eight French institutions, followed over 3 years after initial RAI treatments, were included in the model. A semi-mechanistic mathematical model that describes the tumor growth under RAI treatment was designed. RESULTS: Our model was able to separate patients who responded to RAI from those who did not, concordant with the physicians' determination of therapeutic response. The estimated tumor doubling-time (Td was found to be the most informative parameter for the distinction between responders and non-responders. The model was also able to reclassify particular patients in early treatment stages. CONCLUSIONS: The results of the model present classification criteria that could indicate whether patients will respond or not to RAI treatment, and provide the opportunity to perform personalized management plans.

Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HµREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HµREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.

Mathematical Modeling of Cancer Immunotherapy and Its Synergy with Radiotherapy.

Combining radiotherapy with immune checkpoint blockade may offer considerable therapeutic impact if the immunosuppressive nature of the tumor microenvironment (TME) can be relieved. In this study, we used mathematical models, which can illustrate the potential synergism between immune checkpoint inhibitors and radiotherapy. A discrete-time pharmacodynamic model of the combination of radiotherapy with inhibitors of the PD1-PDL1 axis and/or the CTLA4 pathway is described. This mathematical framework describes how a growing tumor first elicits and then inhibits an antitumor immune response. This antitumor immune response is described by a primary and a secondary (or memory) response. The primary immune response appears first and is inhibited by the PD1-PDL1 axis, whereas the secondary immune response happens next and is inhibited by the CTLA4 pathway. The effects of irradiation are described by a modified version of the linear-quadratic model. This modeling offers an explanation for the reported biphasic relationship between the size of a tumor and its immunogenicity, as measured by the abscopal effect (an off-target immune response). Furthermore, it explains why discontinuing immunotherapy may result in either tumor recurrence or a durably sustained response. Finally, it describes how synchronizing immunotherapy and radiotherapy can produce synergies. The ability of the model to forecast pharmacodynamic endpoints was validated retrospectively by checking that it could describe data from experimental studies, which investigated the combination of radiotherapy with immune checkpoint inhibitors. In summary, a model such as this could be further used as a simulation tool to facilitate decision making about optimal scheduling of immunotherapy with radiotherapy and perhaps other types of anticancer therapies. Cancer Res; 76(17); 4931-40. ©2016 AACR.

A phase Ia/Ib clinical trial of metronomic chemotherapy based on a mathematical model of oral vinorelbine in metastatic non-small cell lung cancer and malignant pleural mesothelioma: rationale and study protocol.

BACKGROUND: Metronomic oral vinorelbine is effective in metastatic NSCLC and malignant pleural mesothelioma, but all the studies published thus far were based upon a variety of empirical and possibly suboptimal schedules, with inconsistent results. Mathematical modelling showed by simulation that a new metronomic protocol could lead to a better safety and efficacy profile. DESIGN: This phase Ia/Ib trial was designed to confirm safety (phase Ia) and evaluate efficacy (phase Ib) of a new metronomic oral vinorelbine schedule. Patients with metastatic NSCLC or malignant pleural mesothelioma in whom standard treatments failed and who exhibited ECOG performance status 0-2 and adequate organ function will be eligible. Our mathematical PK-PD model suggested an alternative weekly D1, D2 and D4 schedule (named Vinorelbine Theoretical Protocol) with a respective dose of 60, 30 and 60 mg. Trial recruitment will be two-staged, as 12 patients are planned to participate in phase Ia to confirm safety and consolidate the calibration of the model parameters. Depending on the phase Ia results and after a favourable decision from a consultative committee, the extension phase (phase Ib) will be an efficacy study including 20 patients who will receive the Optimal Vinorelbine Theoretical Protocol. The primary endpoint is the tolerance (assessed by CTC v4.0) for the phase Ia and the objective response according to RECIST 1.1 for phase Ib. An ancillary study on circulating angiogenesis biomarkers will be a subproject of the trial. DISCUSSION: This ongoing trial is the first to prospectively test a mathematically optimized schedule in metronomic chemotherapy. As such, this trial can be considered as a proof-of-concept study demonstrating the feasibility to run a computational-driven protocol to ensure an optimal efficacy/toxicity balance in patients with cancer. TRIAL REGISTRATION: EudraCT N°: 2015-000138-31.

Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients.

The MODEL1 trial is the first model-driven phase I/II dose-escalation study of densified docetaxel plus epirubicin administration in metastatic breast cancer patients, a regimen previously known to induce unacceptable life-threatening toxicities. The primary objective was to determine the maximum tolerated dose of this densified regimen. Study of the efficacy was a secondary objective. Her2-negative, hormone-resistant metastatic breast cancer patients were treated with escalating doses of docetaxel plus epirubicin every 2 weeks for six cycles with granulocyte colony stimulating factor support. A total of 16 patients were treated with total doses ranging from 85 to 110 mg of docetaxel plus epirubicin per cycle. Dose escalation was controlled by a non-hematological toxicity model. Dose densification was guided by a model of neutrophil kinetics, able to optimize docetaxel plus epirubicin dosing with respect to pre-defined acceptable levels of hematological toxicity while ensuring maximal efficacy. The densified treatment was safe since hematological toxicity was much lower compared to previous findings, and other adverse events were consistent with those observed with this regimen. The maximal tolerated dose was 100 mg given every 2 weeks. The response rate was 45 %; median progression-free survival was 10.4 months, whereas 54.6 months of median overall survival was achieved. The optimized docetaxel plus epirubicin dosing regimen led to fewer toxicities associated with higher efficacy as compared with standard or empirical densified dosing. This study suggests that model-driven dosage adjustment can lead to improved efficacy-toxicity balance in patients with cancer when several anticancer drugs are combined.

Revisiting Dosing Regimen Using Pharmacokinetic/Pharmacodynamic Mathematical Modeling: Densification and Intensification of Combination Cancer Therapy.

Controlling effects of drugs administered in combination is particularly challenging with a densified regimen because of life-threatening hematological toxicities. We have developed a mathematical model to optimize drug dosing regimens and to redesign the dose intensification-dose escalation process, using densified cycles of combined anticancer drugs. A generic mathematical model was developed to describe the main components of the real process, including pharmacokinetics, safety and efficacy pharmacodynamics, and non-hematological toxicity risk. This model allowed for computing the distribution of the total drug amount of each drug in combination, for each escalation dose level, in order to minimize the average tumor mass for each cycle. This was achieved while complying with absolute neutrophil count clinical constraints and without exceeding a fixed risk of non-hematological dose-limiting toxicity. The innovative part of this work was the development of densifying and intensifying designs in a unified procedure. This model enabled us to determine the appropriate regimen in a pilot phase I/II study in metastatic breast patients for a 2-week-cycle treatment of docetaxel plus epirubicin doublet, and to propose a new dose-ranging process. In addition to the present application, this method can be further used to achieve optimization of any combination therapy, thus improving the efficacy versus toxicity balance of such a regimen.

Model-based assessment of erlotinib effect in vitro measured by real-time cell analysis.

Real time cell analysis (RTCA) is an impedance-based technology which tracks various living cell characteristics over time, such as their number, morphology or adhesion to the extra cellular matrix. However, there is no consensus about how RTCA data should be used to quantitatively evaluate pharmacodynamic parameters which describe drug efficacy or toxicity. The purpose of this work was to determine how RTCA data can be analyzed with mathematical modeling to explore and quantify drug effect in vitro. The pharmacokinetic-pharmacodynamic erlotinib concentration profile predicted by the model and its effect on the human epidermoïd carcinoma cell line A431 in vitro was measured through RTCA output, designated as cell index. A population approach was used to estimate model parameter values, considering a plate well as the statistical unit. The model related the cell index to the number of cells by means of a proportionality factor. Cell growth was described by an exponential model. A delay between erlotinib pharmacokinetics and cell killing was described by a transit compartment model, and the effect potency, by an E max function of erlotinib concentration. The modeling analysis performed on RTCA data distinguished drug effects in vitro on cell number from other effects likely to modify the relationship between cell index and cell number. It also revealed a time-dependent decrease of erlotinib concentration over time, described by a mono-exponential pharmacokinetic model with nonspecific binding.

Preclinical pharmacokinetics, pharmacodynamics, and efficacy of RG7116: a novel humanized, glycoengineered anti-HER3 antibody.

PURPOSE: RG7116 is a novel anti-HER3 therapeutic antibody that inhibits HER3 signalling and induces antibody-dependent cellular cytotoxicity of tumor cells due to a glycoengineered antibody Fc moiety. We investigated the efficacy and pharmacokinetic/pharmacodynamic properties of HER3 signal inhibition by RG7116 in a murine xenograft model of human head and neck cancer. METHODS: SCID-beige mice bearing FaDu cells were treated with RG7116 at a weekly dose of 0.3-10 mg/kg, and tumor growth control and modulation of selected proteins (HER3 and AKT) were examined. RESULTS: Complete tumor stasis up to Day 46 was observed at a dose >3 mg/kg, and this dose down-modulated membrane HER3 expression and inhibited HER3 and AKT phosphorylation. Systemic RG7116 exposure was greater than dose-proportional and total clearance declined with increasing dose, indicating that RG7116 elimination is target-mediated. This is consistent with the better efficacy, and the HER3 and pAKT inhibition, that was observed at doses >1 mg/kg. Tumor regrowth occurred from Day 46 onwards and was associated with HER1 and HER2 upregulation, indicating the activation of alternative HER escape pathways. Modulation of HER3 and phospho-HER3 was also demonstrated in the skin and mucosa of an RG7116-treated cynomolgus monkey, suggesting that these may be useful surrogate tissues for monitoring RG7116 activity. CONCLUSIONS: These data confirm the promising efficacy of RG7116 and highlight the value of assessing the PK behavior of the antibody and measuring target protein modulation as a marker of biological activity. Clinical development of RG7116 has now begun, and phase I trials are ongoing.

Metronomics chemotherapy: time for computational decision support.

Over the last decade, metronomic chemotherapy has been increasingly considered as an attractive strategy for treating cancer in a variety of settings. Beside pharmaco-economic considerations making metronomics a unique opportunity in low- or middle-income countries, revisiting dosing schedules using continuous low doses of cytotoxics should theoretically permit to reduce the incidence of treatment-related toxicities, while offering unexpected novel mechanisms of actions such as antiangiogenic or immuno-stimulating properties. Consequently, a number of clinical trials sought to evaluate to what extent switching to metronomic schedules could actually impact indeed on the efficacy/toxicity balance of a variety of anticancer drugs in both adults and pediatric oncology. Vinorelbine is a vinca-alcaloïd that remains the backbone of several regimens to treat patients with metastatic breast cancer or non-small cell lung cancer. Additionally, vinorelbine is widely used to treat a variety of solid tumors in children such as rhabdomyosarcomas and acute leukemia. The recent approval of an oral formulation of vinorelbine has open the way to developing alternative metronomic schedules with this drug. Consequently, a number of clinical trials investigating on metronomic vinorelbine have been performed over the last few years, with seemingly inconsistent results to date. Of note, all the studies published thus far were based upon empirical determination of the metronomic schedule, both in terms of doses, drug-free intervals and repartition of the administrations throughout time. Because the very concept of «low, repeated doses with little or no drug-free interval¼ covers numerous possible combinations, determining the optimal protocol using traditional under-powered empirical design looks like an unreachable goal. In this context, mathematical modeling offers invaluable in silico tools to help determining the optimal metronomic schedule among a variety of possibilities. This review covers the latest clinical trials investigating on metronomic vinorelbine and proposes alternative strategies for developing computational decision support to make metronomics a scientific-grounded strategy, rather than an empirical practice at the bedside. In particular, mathematical simulations using an original pharmacokinetics/pharmacodynamics constraint models provide clues for exploring new paths in the way metronomic vinorelbine could be scheduled in patients with lung cancer.

Translational PK/PD modeling for cardiovascular safety assessment of drug candidates: Methods and examples in drug development.

INTRODUCTION: Cardiovascular toxicity is a significant cause of candidate failure in drug development. Pharmacokinetic/pharmacodynamic (PK/PD) modeling may reduce attrition by improving the understanding of the relationship between drug exposure and changes in cardiovascular endpoints. Diverse examples are discussed that elucidate how modeling can facilitate the interpretation of cardiovascular safety data in animals and enable quantitative translation of preclinical findings to man. METHODS: Twelve compounds under development in diverse therapeutic areas were tested in cardiovascular safety studies in the telemetered beagle dog and cynomolgus monkey. Drug-induced changes observed in different cardiovascular endpoints (QRS complex and QTc interval of the ECG, heart rate, blood pressure, and myocardial contractility) were described by means of PK/PD modeling. A range of direct and indirect effect models were employed to characterize the plasma concentration-cardiovascular effect relationship for each compound. RESULTS: For every drug candidate the proposed PK/PD models appropriately described the cardiovascular effects observed in dog and monkey. Two of the compounds subsequently reached clinical development and cardiovascular data were generated in first-in-human clinical trials. For one drug candidate, a threshold model was used to describe QTc prolongation in the monkey and man. Blood pressure changes induced by the second compound were linked to plasma exposure in dog and human via an indirect response model. In both cases it was found that translational modeling accurately predicted the human response observed during clinical development. DISCUSSION: In this article, a range of PK/PD models are discussed that successfully described cardiovascular safety findings in the preclinical setting. Where clinical data were available, it was found that translational modeling enabled the accurate prediction of outcomes in man and facilitated the description of the therapeutic index. PK/PD modeling is thus demonstrated as a powerful tool to aid in the quantitative cardiovascular safety assessment of drug candidates and the optimization of early clinical study protocols.

Preclinical optimization of MDM2 antagonist scheduling for cancer treatment by using a model-based approach.

PURPOSE: Antitumor clinical activity has been demonstrated for the MDM2 antagonist RG7112, but patient tolerability for the necessary daily dosing was poor. Here, utilizing RG7388, a second-generation nutlin with superior selectivity and potency, we determine the feasibility of intermittent dosing to guide the selection of initial phase I scheduling regimens. EXPERIMENTAL DESIGN: A pharmacokinetic-pharmacodynamic (PKPD) model was developed on the basis of preclinical data to determine alternative dosing schedule requirements for optimal RG7388-induced antitumor activity. This PKPD model was used to investigate the pharmacokinetics of RG7388 linked to the time-course of the antitumor effect in an osteosarcoma xenograft model in mice. These data were used to prospectively predict intermittent and continuous dosing regimens, resulting in tumor stasis in the same model system. RESULTS: RG7388-induced apoptosis was delayed relative to drug exposure with continuous treatment not required. In initial efficacy testing, daily dosing at 30 mg/kg and twice a week dosing at 50 mg/kg of RG7388 were statistically equivalent in our tumor model. In addition, weekly dosing of 50 mg/kg was equivalent to 10 mg/kg given daily. The implementation of modeling and simulation on these data suggested several possible intermittent clinical dosing schedules. Further preclinical analyses confirmed these schedules as viable options. CONCLUSION: Besides chronic administration, antitumor activity can be achieved with intermittent schedules of RG7388, as predicted through modeling and simulation. These alternative regimens may potentially ameliorate tolerability issues seen with chronic administration of RG7112, while providing clinical benefit. Thus, both weekly (qw) and daily for five days (5 d on/23 off, qd) schedules were selected for RG7388 clinical testing.

Physiologically based pharmacokinetic modeling of CYP3A4 induction by rifampicin in human: influence of time between substrate and inducer administration.

The induction of cytochrome P450 enzymes (CYPs) is an important source of drug-drug interaction (DDI) and can result in pronounced changes in pharmacokinetics (PK). Rifampicin (RIF) is a potent inducer of CYP3A4 and also acts as a competitive inhibitor which can partially mask the induction. The objective of this study was to determine a clinical DDI study design for RIF resulting in maximum CYP3A4 induction. A physiologically based pharmacokinetic (PBPK) model was developed to project the dynamics and magnitude of CYP3A4 induction in vivo from in vitro data generated with primary human hepatocytes. The interaction model included both inductive and inhibitory effects of RIF on CYP3A4 in gut and liver and accounting for the observed RIF auto-induction. The model has been verified for 4 CYP3A4 substrates: midazolam, triazolam, alfentanil and nifedipine using plasma concentration data from 20 clinical study designs with intravenous (n=7) and oral (n=13) administrations. Finally, the influence of the time between RIF and substrate administration was explored for the interaction between midazolam and RIF. The model integrating in vitro induction parameters correctly predicted intravenous induction but underestimated oral induction with 30% of simulated concentrations more than 2-fold higher than of observed data. The use of a 1.6-fold higher value for the maximum induction effect (Emax) improved significantly the accuracy and precision of oral induction with 82% of simulated concentrations and all predicted PK parameters within 2-fold of observed data. Our simulations suggested that a concomitant administration of RIF and midazolam resulted in significant competitive inhibition limited to intestinal enzyme. Accordingly, a maximum induction effect could be achieved with a RIF pretreatment of 600 mg/day during 5 days and a substrate administration at least 2 h after the last RIF dose. A period of 2 weeks after RIF removal was found sufficient to allow return to baseline levels of enzyme.