Clinical Pharmacokinetics and Pharmacodynamics of Atezolizumab in Metastatic Urothelial Carcinoma.

Atezolizumab, a humanized immunoglobulin G1 (IgG1) monoclonal antibody targeting human programmed death-ligand 1 (PD-L1), is US Food and Drug Administration (FDA) approved in metastatic urothelial carcinoma (MUC) and is being investigated in various malignancies. This analysis based upon 906 patients from two phase I and one phase II MUC studies, is the first report of the clinical pharmacokinetics (PK) and pharmacodynamics (PD) of atezolizumab. Atezolizumab exhibited linear PK over a dose range of 1-20 mg/kg, including the labeled 1,200 mg dose. The clearance, volume of distribution, and terminal half-life estimates from population pharmacokinetic (PopPK) analysis of 0.200 L/day, 6.91 L, and 27 days, respectively, were as expected for an IgG1. Exposure-response analyses did not identify statistically significant relationships with either objective response rate or adverse events of grades 3-5 or of special interest. None of the statistically significant covariates from PopPK (body weight, gender, antitherapeutic antibody, albumin, and tumor burden) would require dose adjustment.

Simulations to Predict Clinical Trial Outcome of Bevacizumab Plus Chemotherapy vs. Chemotherapy Alone in Patients With First-Line Gastric Cancer and Elevated Plasma VEGF-A.

To simulate clinical trials to assess overall survival (OS) benefit of bevacizumab in combination with chemotherapy in selected patients with gastric cancer (GC), a modeling framework linking OS with tumor growth inhibition (TGI) metrics and baseline patient characteristics was developed. Various TGI metrics were estimated using TGI models and data from two phase III studies comparing bevacizumab plus chemotherapy vs. chemotherapy as first-line therapy in 976 GC patients. Time-to-tumor-growth (TTG) was the best TGI metric to predict OS. TTG, Eastern Cooperative Oncology Group (ECOG) score, albumin level, and Asian ethnicity were significant covariates in the final OS model. The model correctly predicted a decreased hazard ratio favorable to bevacizumab in patients with high baseline plasma VEGF-A above the median of 113.4 ng/L. Based on trial simulations, in trials enrolling patients with elevated baseline plasma VEGF-A (500 patients per arm), the expected hazard ratio was 0.82 (95% prediction interval: 0.70-0.95), independent of ethnicity.

A Tumor Growth Inhibition Model Based on M-Protein Levels in Subjects With Relapsed/Refractory Multiple Myeloma Following Single-Agent Carfilzomib Use.

Change in tumor size estimated using longitudinal tumor growth inhibition (TGI) modeling is an early predictive biomarker of clinical outcomes for multiple cancer types. We present the application of TGI modeling for subjects with multiple myeloma (MM). Longitudinal time course changes in M-protein data from relapsed and/or refractory MM subjects who received single-agent carfilzomib in phase II studies (n = 456) were fit to a TGI model. The tumor growth rate estimate was similar to that of other anti-myeloma agents, indicating that the model is robust and treatment-independent. An overall survival model was subsequently developed, which showed that early change in tumor size (ECTS) at week 4, Eastern Cooperative Oncology Group performance status (ECOG PS), hemoglobin, sex, percent bone marrow cell involvement, and number of prior regimens were significant independent predictors for overall survival (P < 0.001). ECTS based on M-protein modeling could be an early biomarker for survival in MM following exposure to single-agent carfilzomib.

Exploratory modeling and simulation to support development of motesanib in Asian patients with non-small cell lung cancer based on MONET1 study results.

The motesanib phase III MONET1 study failed to show improvement in overall survival (OS) in non-small cell lung cancer, but a subpopulation of Asian patients had a favorable outcome. We performed exploratory modeling and simulations based on MONET1 data to support further development of motesanib in Asian patients. A model-based estimate of time to tumor growth was the best of tested tumor size response metrics in a multivariate OS model (P < 0.00001) to capture treatment effect (hazard ratio, HR) in Asian patients. Significant independent prognostic factors for OS were baseline tumor size (P < 0.0001), smoking history (P < 0.0001), and ethnicity (P < 0.00001). The model successfully predicted OS distributions and HR in the full population and in Asian patients. Simulations indicated that a phase III study in 500 Asian patients would exceed 80% power to confirm superior efficacy of motesanib combination therapy (expected HR: 0.74), suggesting that motesanib combination therapy may benefit Asian patients.

Evaluation of tumor size response metrics to predict survival in oncology clinical trials.

Model-based drug development in oncology is still lagging despite a good momentum in the clinical pharmacology and pharmacometry community in the past few years. The failure rate of late-stage oncology studies is one of the highest across therapeutic areas. The modeling of the relationship between longitudinal tumor size and overall survival has been proposed to enhance learning in early clinical studies, to predict overall survival, and to simulate clinical trials. This approach has the potential to support proof of concept, early clinical decisions, and design of late-stage trials, but it is not yet widely integrated into the oncology drug development process. In this article, we review the state of these modeling efforts and discuss several key applications of these models. We conclude by suggesting a few paths forward.

Model-based drug development in oncology: what's next?

Model-based estimates of tumor growth inhibition (TGI) metrics have the potential to enhance learning in early (phase II) clinical studies. They can be used as end points and biomarkers to predict treatment effect on clinical outcome measures-e.g., overall survival (OS)-and support phase II study design, end-of-phase II decisions, and phase III planning and execution. Efforts should be made to assess models in simulating independent studies with treatments of varying mechanisms of action.

Simulations using a drug-disease modeling framework and phase II data predict phase III survival outcome in first-line non-small-cell lung cancer.

Simulations were performed for carboplatin/paclitaxel (C/P) plus motesanib or bevacizumab vs. C/P as first-line treatment for advanced non-small-cell lung cancer (NSCLC) using a published drug-disease model. With 700 patients in each arm, simulated hazard ratios for motesanib (0.87; 95% confidence interval [CI], 0.71-1.1) and bevacizumab (0.89; 95% CI, 0.73-1.1) agreed with results from the respective phase III studies but did not discriminate between failed and successful studies. The current model may require further enhancement to improve its utility for predicting phase III outcomes.

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/m(2) plus docetaxel 75 mg/m(2) to the registered capecitabine dose of 1,250 mg/m(2) plus docetaxel 75 mg/m(2). 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/m(2). Simulation showed NO16853 had little power to demonstrate noninferiority (69%). The power reached 80% with a 1,000 mg/m(2) starting dose and an increased number of PFS events. A starting dose of 1,000 mg/m(2) 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.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e19; doi:10.1038/psp.2012.20; advance online publication 26 December 2012.

Tumor growth modeling from clinical trials reveals synergistic anticancer effect of the capecitabine and docetaxel combination in metastatic breast cancer.

PURPOSE: Most of the cancer chemotherapy treatments employ drugs in combination. For combination treatments, it is relevant to assess interaction between two or more anticancer agents used in clinics. Based on clinical data and using modeling techniques, the work analyzes the pharmacodynamic interaction between capecitabine and docetaxel used in combination in metastatic breast cancer. METHODS: We developed mathematical models to describe tumor growth inhibition profile under treatment based on Phase II and Phase III clinical data of capecitabine and docetaxel in metastatic breast cancer. Model parameters were estimated by population approach with NONMEM(®) on single-agent and combination data. Simulations were performed using MATLAB. RESULTS: Capecitabine and docetaxel combination in metastatic breast cancer results in a synergistic effect as compared with the simple additive effects of single-agent treatments. Docetaxel is more efficient than capecitabine at the start of treatment but develops resistance faster. Modeling revealed no resistance of capecitabine for the combination data. CONCLUSIONS: Modeling could be a powerful tool to design the most advantageous combination regimen for capecitabine and docetaxel in metastatic breast cancer in order to increase the time before regrowth and decrease the tumor size at regrowth.

On the use of change in tumor size to predict survival in clinical oncology studies: toward a new paradigm to design and evaluate phase II studies.

Drug-independent models that link biomarker response to clinical end points are critical to support early (end of phase II) clinical decisions. In oncology, change in tumor size (a biomarker of drug effect evaluated in phase II) is linked to survival (a phase III end point) in some solid tumors. Change in tumor size can be used as a primary end point in the design and evaluation of phase II studies and in supporting go/no-go decisions and phase III study design.

Fractal volume of drug distribution: it scales proportionally to body mass.

PURPOSE: To develop the physiologically sound concept of fractal volume of drug distribution, vf, and evaluate its utility and applicability in interspecies pharmacokinetic scaling. METHODS: Estimates for vf of various drugs in different species were obtained from the relationship: vf = (v - Vpl)(Vap - Vpl)/V + Vpl where v is the total volume of the species (equivalent to its total mass assuming a uniform density Ig/mL), Vpl is the plasma volume of the species and Vap is the conventional volume of drug distribution. This equation was also used to calculate the fractal analogs of various volume terms of drug distribution (the volume of central compartment, Vc, the steady state volume of distribution, Vss, and the volume of distribution following pseudodistribution equilibrium, Vz). The calculated fractal volumes of drug distribution were correlated with body mass of different mammalian species and allometric exponents and coefficients were determined. RESULTS: The calculated values of vf for selected drugs in humans provided meaningful and physiologically sound estimates for the distribution of drugs in the human body. For all fractal volume terms utilized, the allometric exponents were found to be either one or close to unity. The estimates of the allometric coefficients were found to be in the interval (0,1). These decimal values correspond to a fixed fraction of the fractal volume term relative to body mass in each one of the species. CONCLUSIONS: Fractal volumes of drug distribution scale proportionally to mass. This confirms the theoretically expected relationship between volume and mass in mammalian species.

The Escherichia coli histone-like protein HU regulates rpoS translation.

Escherichia coli HU protein is a major component of the bacterial nucleoid. HU stabilizes higher order nucleoprotein complexes and belongs to a family of DNA architectural proteins. Here, we report that HU is required for efficient expression of the sigma S subunit of RNA polymerase. This rpoS-encoded alternative sigmaS factor induces a number of genes implicated in cell survival in stationary phase and in multiple stress resistance. By analysis of rpoS-lacZ fusions and by pulse-chase experiments, we show that the efficiency of rpoS translation is reduced in cells lacking HU, whereas neither rpoS transcription nor protein stability is affected by HU. Gel mobility shift assays show that HU is able to bind specifically an RNA fragment containing the translational initiation region of rpoS mRNA 1000-fold more strongly than double-stranded DNA. Together with the in vivo data, this finding strongly suggests that, by binding to rpoS mRNA, HU directly stimulates rpoS translation. We demonstrate here that HU, an abundant DNA-binding, histone-like protein, is able specifically to recognize an RNA molecule and therefore play a role in post-transcriptional regulation.

Information tools for exploratory data analysis in population pharmacokinetics.

For a group of individuals, population pharmacokinetic studies describe the interindividual variability through a statistical distribution. These studies conducted during the drug development serve as a useful marker of the safety of the drug, provide information that might be decisive for future experiments and, in a clinical context, help establish guidelines for optimal use in each patient. As complementary tools to the existing statistical and graphical techniques for population pharmacokinetic data analysis, indexes derived from information theory were used to select the most appropriate modelfor the statistical distribution, to detect atypical individuals, and to screen influential covariates. The rationale for using these indexes is shown using simulated and real data.

A stochastic model describes the heterogeneous pharmacokinetics of cyclosporin.

The pharmacokinetics of cyclosporin (CsA) are unusual because of several heterogeneous features which include the presence of more than one conformer, considerable accumulation in erythrocytes and lipoproteins, extensive plasma protein binding, distribution into deep tissues, biliary secretion and hepatic clearance involving a large number of metabolites. In this study, a stochastic compartmental model was developed to describe the heterogeneous elimination kinetics of CsA. This new approach relies on a probabilistic transfer model with a gamma distributed probability intensity coefficient for drug elimination. For comparative purposes both the stochastic model and compartmental deterministic models were fitted to real post infusion data from patients receiving CsA as a 2-hr intravenous infusion. The criteria for selecting the best model showed that the stochastic model, although simpler than the compartmental deterministic models, is more flexible and gives a better fit to the kinetic data of CsA than the compartmental deterministic models. The stochastic model with a random rate intensity coefficient adequately describes the heterogeneous pharmacokinetics of CsA.

Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming.

In enterobacteria like Salmonella, biogenesis of cell surface flagella needed for motility is dependent upon the master operon flhDC at the apex of the flagellar gene hierarchy. The operon products FlhD and FlhC act together in a FlhD(2)C(2 )heterotetramer to induce flagellar gene transcription, while FlhD also represses cell septation. The flhDC operon is pivotal to differentiation into elongated hyperflagellated swarm cells that undergo multicellular migration, most strikingly in Proteus. We set out to establish the mechanism of action of the FlhD(2)C(2) multimer. In Proteus swarm cell extracts, all the FlhC was assembled into the FlhD(2)C(2 )transcription activator, but FlhD additionally formed approximately equimolar amounts of a FlhD(2) homodimer. Both FlhD and FlhC subunits homodimerised in vivo and in vitro, suggesting that self-interactions stabilise the FlhD(2)C(2 )complex. The FlhC and FlhD subunit proteins were separately expressed and purified, and the FlhD(2)C(2)heterotetramer was reconstituted in vitro. Purified FlhC bound specifically and cooperatively to the promoter region of the flhDC-regulated flhB flagellar gene in the absence of FlhD. Purified FlhD was unable to bind this target DNA, but binding by the FlhD(2)C(2)complex was approximately tenfold greater than the FlhC subunit alone, suggesting that FlhD potentiated the FlhC/DNA interaction. In support of this possibility, pre-incubation of FlhC with FlhD reduced the apparent dissociation constant, K(D), for the FlhC/DNA complex from 100 nM to 13 nM. Furthermore, in competition assays, FlhD substantially increased the specificity of DNA recognition by FlhC, and also stabilised the resultant labile protein/DNA complex, prolonging its half-life from around two minutes to more than 40 minutes. FlhD(2)C(2)is therefore an atypical prokaryotic transcription activator in which interaction of the FlhC subunit with DNA target sequences is enhanced by the coexpressed helper subunit FlhD.

Rapid turnover of FlhD and FlhC, the flagellar regulon transcriptional activator proteins, during Proteus swarming.

The enterobacterial flhDC master operon activates expression of the flagellar biogenesis gene hierarchy and also represses cell division. During Proteus mirabilis differentiation into elongated hyperflagellated swarm cells, flhDC transcription is strongly but transiently increased. We show that concentration of the FlhD and FlhC proteins is also tightly controlled at the posttranslational level. This is achieved by protein degradation, which is most severe after differentiation when the half-life of both proteins is ca. 2 min. Degradation is energy dependent and putatively involves the Lon protease.

Variation in HU composition during growth of Escherichia coli: the heterodimer is required for long term survival.

The histone-like dimeric HU protein of Escherichia coli is encoded by two closely related genes, hupA and hupB. We show here that expression from the single hupA promoter and from the three hupB promoters varies during growth phase. The weak hupB-P4 promoter is active immediately after dilution. Transcription of the hupA gene is activated early in logarithmic phase. A little later, at mid to late exponential phase, RNA originating at the hupB-P2 promoter is detected. The hupB-P3 promoter is activated last when the cells enter stationary phase. Although the hup mRNAs are unstable, the HU protein is very stable so that the variations in the mRNAs synthesis are reflected in the level of the two HU subunits and in the composition of HU dimers. Cells growing exponentially contain a mixture of homodimeric alpha 2 and heterodimeric alpha beta but no beta 2 is detected. In stationary cells, the predominant form is the heterodimer alpha beta. The presence of the heterodimeric form is required for optimal survival of E. coli after prolonged starvation. The three forms of HU are not equivalent, since beta 2 is incapable of promoting formation of DNA supercoiling like alpha beta and alpha 2 do. The putative roles of each form of HU are discussed.

Regulation of HU alpha and HU beta by CRP and FIS in Escherichia coli.

The dimeric histone-like protein HU, one of the most abundant DNA binding proteins of Escherichia coli, is encoded by two closely related but unlinked genes, hupA and hupB. Overproduction of one or the other of the subunits has been shown to induce the SOS response and mucoidy. To understand how the synthesis of this protein is coordinated, we studied the transcription control of the two hup genes. We show here that CRP stimulated the transcription of both genes. In contrast, the FIS protein, one of the major positive regulators of the stable RNA operons, stimulated the transcription of the hupA gene, whereas it repressed that of the hupB gene. Moreover, stringent control, which like FIS also regulates the transcription of the stable RNA operons, affected the hupB transcription while it had no effect on hupA. This opposite regulation of the transcription of the two HU genes is reflected at the protein level signifying that changes in the composition of HU occur upon changes in the environment. It is rather unexpected that such divergent transcriptional regulation controls the two genes encoding a dimeric protein.