Wen Chyi Shyu

Assessment of cytochrome P450-mediated drug–drug interaction potential of orteronel and exposure changes in patients with renal impairment using physiologically based pharmacokinetic modeling and simulation

Orteronel is a nonsteroidal, selective inhibitor of 17,20‐lyase that was recently in phase 3 clinical development as a treatment for castration‐resistant prostate cancer. In humans, the primary clearance route for orteronel is renal excretion. Human liver microsomal studies indicated that orteronel weakly inhibits CYP1A2, 2C8, 2C9 and 2C19, with IC50 values of 17.8, 27.7, 30.8 and 38.8 µm, respectively, whereas orteronel does not inhibit CYP2B6, 2D6 or 3A4/5 (IC50 > 100 µm). Orteronel also does not exhibit time‐dependent inhibition of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6 or 3A4/5. The results of a static model indicated an [I]/Ki ratio >0.1 for CYP1A2, 2C8, 2C9 and 2C19. Therefore, a physiologically based pharmacokinetic (PBPK) model was developed to assess the potential for drug–drug interactions (DDIs) between orteronel and theophylline, repaglinide, (S)‐warfarin and omeprazole, which are sensitive substrates of CYP1A2, 2C8, 2C9 and 2C19, respectively. Simulation of the area under the plasma concentration–time curve (AUC) of these four CYP substrates in the presence and absence of orteronel revealed geometric mean AUC ratios <1.25. Therefore, in accordance with the 2012 US FDA Draft Guidance on DDIs, orteronel can be labeled a ‘non‐inhibitor’ and further clinical DDI evaluation is not required. In PBPK models of moderate and severe renal impairment, the AUC of orteronel was predicted to increase by 52% and 83%, respectively. These results are in agreement with those of a clinical trial in which AUC increases of 38% and 87% were observed in patients with moderate and severe renal impairment, respectively. Copyright

Translational Exposure–Efficacy Modeling to Optimize the Dose and Schedule of Taxanes Combined with the Investigational Aurora A Kinase Inhibitor MLN8237 (Alisertib)

Aurora A kinase orchestrates multiple key activities, allowing cells to transit successfully into and through mitosis. MLN8237 (alisertib) is a selective Aurora A inhibitor that is being evaluated as an anticancer agent in multiple solid tumors and heme-lymphatic malignancies. The antitumor activity of MLN8237 when combined with docetaxel or paclitaxel was evaluated in in vivo models of triple-negative breast cancer grown in immunocompromised mice. Additive and synergistic antitumor activity occurred at multiple doses of MLN8237 and taxanes. Moreover, significant tumor growth delay relative to the single agents was achieved after discontinuing treatment; notably, durable complete responses were observed in some mice. The tumor growth inhibition data generated with multiple dose levels of MLN8237 and paclitaxel were used to generate an exposure–efficacy model. Exposures of MLN8237 and paclitaxel achieved in patients were mapped onto the model after correcting for mouse-to-human variation in plasma protein binding and maximum tolerated exposures. This allowed rank ordering of various combination doses of MLN8237 and paclitaxel to predict which pair would lead to the greatest antitumor activity in clinical studies. The model predicted that 60 and 80 mg/m2 of paclitaxel (every week) in patients lead to similar levels of efficacy, consistent with clinical observations in some cancer indications. The model also supported using the highest dose of MLN8237 that can be achieved, regardless of whether it is combined with 60 or 80 mg/m2 of paciltaxel. The modeling approaches applied in these studies can be used to guide dose-schedule optimization for combination therapies using other therapeutic agents. Mol Cancer Ther; 13(9); 2170–83. ©2014 AACR.

Dose Schedule Optimization and the Pharmacokinetic Driver of Neutropenia

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

Abstract 4649: Using pharmacokinetic/efficacy modeling to identify the optimal schedule for MLN0264, an anti- guanylyl cyclase C (GCC) antibody-drug conjugate, in a range of xenograft models

MLN0264 is an investigational antibody-drug conjugate (ADC) that consists of the human anti-guanylyl cyclase C (GCC) antibody linked to a microtubule-disrupting agent (monomethyl auristatin). As ADCs have a very long clearance half-life, the potential exists for a highly infrequent dosing schedule. A quantitative understanding of the relationship between exposure and preclinical antitumor biological activity is thus applied to support dose schedule selection in the clinic. In this study, we develop a pharmacokinetic/efficacy (PK/E) relationship in xenograft models to evaluate the predictive contributions of exposure and xenograft characteristics to MLN0264 biological activity. Single dose pharmacokinetic (PK) data were obtained for a range of time points, and a linear two-compartment PK model was built. Xenograft biological activity studies were conducted in which MLN0264 was administered at various dose levels and dosing schedules to mice bearing one of six different xenograft models. We used multiple linear regression and tumor dynamic modeling to understand the factors contributing to the biological activity. First, the growth rate of each tumor under control and treatment conditions was established by fitting the biological activity data within the dosing period to an exponential growth function. Then, we assessed the relationship of AUC to antitumor biological activity. Within each xenograft model, AUC was strongly correlated with biological activity across a range of schedules (R2>0.9, p Although biological activity within a xenograft model was strongly correlated with exposure, when the data from different xenograft models were pooled together, the correlation was weaker (R2= 0.30, p= 0.0004). This result suggests that, although schedule is not a major determinant of biological activity, other xenograft-specific factors may be contributing to biological activity. To test the contribution of some possible factors, we used multiple linear regression to determine which covariates (such as GCC expression level and baseline growth rate of xenograft models) are predictive of biological activity. The results suggest baseline growth rate does not correlate with biological activity while GCC expression is weakly correlated. This finding formed the basis for the development of a mechanistic model of GCC expression and biological activity. Taken together, this work demonstrates the use of a PK/E framework to identify the scheduling effect for a first-in-man protocol for an ADC. We have also demonstrated that this PK/E framework can be further leveraged to assess the contribution of other potential predictors of ADC biological activity in xenograft models. Citation Format: Shu-Wen Teng, Christopher Zopf, Johnny Yang, Brad Stringer, Julie Zhang, Wen Chyi Shyu, Arijit Chakravarty, Petter Veiby, Jerome Mettetal. Using pharmacokinetic/efficacy modeling to identify the optimal schedule for MLN0264, an anti- guanylyl cyclase C (GCC) antibody-drug conjugate, in a range of xenograft models. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4649. doi:10.1158/1538-7445.AM2014-4649

Abstract B154: Application of preclinical combination pharmacokinetic(PK)/efficacy(E) modeling to investigate and translate the preclinical scheduling effect for MLN1117 and Taxotere combination

During preclinical development of investigational compound, MLN1117, combination efficacy studies of MLN1117 and Taxotere in mice bearing lung cancer tumor xenografts indicated the presence of a scheduling effect. In many instances, pre-dosing Taxotere resulted in improved tumor growth inhibition as compared to concomitant dosing with MLN1117. Furthermore both in vitro and in vivo PD studies demonstrated that sequential administration of MLN1117 (PI3Ka inhibitor) and Taxotere resulted in increased apoptosis as compared to concomitant treatment. To further investigate and translate these observations, combination PK/E modeling was performed on preclinical PK and efficacy data. A scheduling efficacy study was executed in small cell lung cancer NCI-H1048 model. The study was designed to compare the anti-tumor activity of the combination under varying levels of PK concomitance between the two compounds. Using a non-linear mixed effects approach, dynamic PK/E modeling was performed to describe the individual mouse tumor growth curves as a function of the instantaneous plasma drug concentration of MLN1117 and Taxotere. The combination effect in the dynamic model was described using the following expression: (PK/E)MLN1117 + (PK/E)Taxotere + Tau* (PK/E)Taxotere * (PK/E)MLN1117 The combination interaction (Tau) between MLN1117 and Taxotere was estimated to be negative but associated with significant inter-tumor variability. A negative Tau implied the combination behaved sub-additively under conditions of concomitant dosing. Pre-dosing Taxotere made the combination non-concomitant which explains the improved anti-tumor activity associated with it. The variability associated with the positive effects of pre-dosing Taxotere or non-concomitant dosing was attributed to the variability in Tau. In general, the modeling results favored the use of non-concomitant dosing for the MLN1117/Taxotere combination to eliminate the dependence of the combination efficacy on the negative Tau. Although the emphasis of this study has been on SCLC cancer model NCI-H1048, early preclinical data from other cancer models of different origin indicate that this phenomenon could be ubiquitous. The combination PK/E model though empirical in nature, provided a useful and translatable tool to guide combination schedule selection. The modeling framework focused on understanding the behavior of the combination i.e. combination interaction term (Tau) and translating this understanding to optimize combination scheduling choices for the clinic. Citation Format: Ekta Kadakia, Natasha Iartchouk, Karuppiah Kannan, Keli Song, Dong Mei Zhang, Christopher Zopf, Christopher Zopf, Munjal Patel, Chirag Patel, Swapan Chowdhury, Wen Chyi Shyu, Jing-Tao Wu, Arijit Chakravarty. Application of preclinical combination pharmacokinetic(PK)/efficacy(E) modeling to investigate and translate the preclinical scheduling effect for MLN1117 and Taxotere combination. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B154.

Abstract 4527: Dosing schedule effects on combination activity from first principles

Introduction Finding the ideal dosing schedule to optimize efficacy while balancing toxicity is a challenge for single agent clinical development, and takes on added complexity for combinations. In this work, we use theoretical modeling to investigate the role of schedule in determining combination activity, and propose a novel method of using dosing schedule to identify the in vivo interaction strength pre-clinically. Methods To evaluate the effect of schedule on tumor growth inhibition, we built a dynamic pharmacokinetic (PK)/effect (E) model to simulate tumor volume as a function of plasma concentration of a drug combination. The PK of each single agent was simulated using a one compartment model, and the inhibition of tumor growth rate was modeled as the sum of single agent effects and their product scaled by an interaction coefficient. We first simulated this model for various relative dosing frequencies and offsets between the two drugs to understand the relationship between schedule and activity for a synergistic combination. We next assessed the accuracy of estimating the interaction parameter using the activity discrepancy between in- and off-phase dosing schedules versus a traditional isobologram analysis. Using Fourier analysis of single agent PK profiles, we then identified an efficient study design to identify both the interaction parameter and optimal relative dosing schedule of the two drugs. Results Simulation of the PK/E model with parameter sweeps of dosing frequency and offsets led to a phase plot for combination activity showing peaks and valleys related to the concomitant exposure of the two drugs. From this, we surmised the interaction could be estimated based on the difference in tumor growth inhibition between schedules, and a comparison of in- and off-phase schedules performed well compared to isobologram analysis. Fourier analysis of simulated PK profiles of the two drugs revealed the concomitance as a function of dosing offset, and the pattern was maintained in the simulated activity. Using a study design with only four dose groups with different dosing offsets, we show it is possible to determine both the interaction coefficient (comparing one group at the peak and one at the valley of concomitance) and any effect of dose-ordering (two groups at different offsets but with the same predicted concomitance). Additionally, we demonstrate the difference in activity between synergistic and additive combinations manifests as beat frequencies present in the frequency spectrum, another possibility to identify synergy. Conclusions While combination development offers unique challenges, building an understanding of the PK/E relationship from first principles provides a framework to investigate drug interaction effects. The insights gained from studying combinations in vivo with a pre-clinical scheduling study may provide translational guidance on clinical questions around concomitance and dose-ordering. Citation Format: Andrew Chen, Jing-Tao Wu, Wen Chyi Shyu, Arijit Chakravarty, Christopher J. Zopf. Dosing schedule effects on combination activity from first principles. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4527. doi:10.1158/1538-7445.AM2015-4527

Abstract 791: Rational dose optimization for multi-drug cocktails

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA The design of an optimal combination therapy can be thought of as a set of tradeoffs designed to maximize the efficacy of the combination for a given toxicity budget. As anticancer agents (even targeted ones) often have overlapping toxicities, an efficient approach to making efficacy-toxicity tradeoffs is critical. In this context, a major impediment is that the search space grows exponentially with each additional drug while in vivo studies have a fixed upper size limit. Here, we model the efficacy and toxicity landscapes of multidrug cocktails, and describe an approach to visualize and design efficient studies for three-drug combinations. We further demonstrate the application of our approach in a practical drug development context by showing its impact on actual study design, and validation with in vivo datasets. As our approach relies on equations from first principles, it is in theory extensible to an arbitrary number of drugs, subject to the practical constraints of drug dosing. First, we demonstrate an approach to visually determine the optimal dose combination for a three-drug combination with fully overlapping toxicity. Efficacy and toxicity isoboles visualized as surfaces in three-drug space demonstrate the optimal dose combination, which corresponds to the point of tangency between the MTD and efficacy isobole surfaces. Next, we derive an analytical method to efficiently estimate this point with a minimum of data, and without using graphical methods (important for extending the work beyond three dimensions). The combined efficacy and toxicity of a three-drug combination were modeled from first principles using isobolograms as a sum of the single-agent PK/Efficacy (PK/E) relationships and four combination terms (three binary and one ternary) to account for potential interactions between the drugs. Borrowing from Microeconomic Utility Theory, we found the point of greatest efficacy along the Maximum Tolerated Dose (MTD) toxicity contour, which corresponds to the efficacy isobole tangentially intersecting the MTD contour. Through both analytical and numerical approaches, we determined the the single- and double-agent efficacy parameters uniquely determine the dose escalation path which provides the best estimate of combination efficacy. Simulated experiments demonstrate the optimal observation path up to MTD performs acceptably compared to a uniformly-gridded exposure space. Finally, we demonstrate the application of this approach in a proposed experimental design for three-drug combinations, and validate it with experimental data. While developing multi-drug cocktails poses many scientific and operational challenges, the approach presented here provides a straightforward route to preclinical testing and validation through the design of parsimonious studies that can explicitly define the contribution of each individual drug (and each two drug combination) to the overall efficacy and toxicity landscape of the cocktail. Citation Format: Christopher J. Zopf, Andrew Chen, Santhosh Palani, Rachael Brake, Mark Manfredi, Jeffrey Ecsedy, Wen Chyi Shyu, Arijit Chakravarty. Rational dose optimization for multi-drug cocktails. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 791. doi:10.1158/1538-7445.AM2014-791

Abstract 3746: Anticipating the maximum tolerated dose for combinations based on early toxicity signals

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA In theory, the development of combination therapies in Oncology holds the promise of improved short-term response and better disease control. In practice, the added toxicity burden of a second antineoplastic agent can often limit the potential of combination therapies. Many antineoplastic agents, targeted or not, result in severe toxicities in clinical practice, and the paradigm for Oncology drug development still involves dosing to the Maximum Tolerated Dose (MTD). Thus, proactive identification and management of combination toxicity should help combination therapies achieve their full clinical potential. In this work, we devise a mathematical framework for assessing the toxicity potential for a pair of drugs with a single overlapping toxicity, using neutropenia- a very common adverse event for antineoplastics- as a motivating example. First, we introduce a mathematical framework for modeling combination toxicities, based on a combination index that is a measure of the interaction between two agents (analogous to antitumor activity, the overlapping toxicities for a pair of agents can be synergistic, additive or antagonistic). Next, we demonstrate the application of isobolograms for toxicity, which are a set of lines connecting all equally toxic combinations of the two drugs. When the MTD of a pair of combination therapies is determined by an overlapping toxicity, it can thus be expressed in terms of a minimal toxicity model that uses three components- the concentration/toxicity relationship for each individual therapy and the combination index of the toxicities. We show that the magnitude of the combination index remains unchanged whether the toxicity readout is continuous (such as Absolute Neutrophil Count) or categorical (Grade 1,2, 3, or 4 neutropenia). This result enables us to use the combination index calculated from a lower-grade toxicity (e.g. Grade 1 or Grade 2 neutropenia) to anticipate the MTD. We validate this approach using simulated datasets generated from a previously published model of neutropenia. This validation is further extended using experimental datasets based on preclinical toxicities for combinations of targeted agents. Finally, we demonstrate the extension of this approach to the prediction of the Dose Limiting Toxicity for a combination. This method relies on developing several toxicity models in parallel for each potential dose-limiting toxicity, and calculating the combination index from early-stage toxicities for each of them. The combination index for each toxicity is then used to predict the MTD that would result from it, and the toxicity that results in the lowest MTD is then the putative Dose Limiting Toxicity. Taken together, the approaches described here can be used to derive critical information directly from clinical data, and enable the design of rational escalation schemes in Phase I trials for combinations that are at once faster and safer. Citation Format: Ekta Kadakia, Christopher J. Zopf, Mayankbhai Patel, Dean Bottino, Greg Hather, Wen Chyi Shyu, Arijit Chakravarty. Anticipating the maximum tolerated dose for combinations based on early toxicity signals. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3746. doi:10.1158/1538-7445.AM2014-3746

Abstract 698: Variability in xenograft growth rates can be explained by intra-tumor evolutionary dynamics

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Growing evidence frames cancer as a disease of somatic Darwinian evolution. Recent reports establish both inter- and intra-tumor genetic heterogeneity within patients, and evolution in response to treatment. Cell lines and xenografts also display genetic heterogeneity arising from chromosomal instability, and evolve in response to experimental conditions. Although selection pressures in culture or in a mouse differ from those in a patient, studying the evolutionary process pre-clinically may yield insights useful in the design of experimental systems of cancer. Using the soft agar colony growth assay, we previously showed that individual colonies in soft agar have widely divergent growth rates that are mostly inherited over experimental timescales. In this work we ask what the impact of that divergence is on xenograft growth kinetics, and examine the implications for the development of resistance. First, we applied a stochastic, dynamic model of tumor kinetics to simulate clonal heterogeneity in xenograft growth rates. Each simulated “virtual tumor” consisted of a set of independently growing subclone cells, whose growth rates were bootstrapped from a distribution derived from 104 individual HCT-116 colonies in the soft agar colony growth assay. Both the sampling distribution variance and the number of subclones sampled to found each xenograft were varied to test the effect of intercell heterogeneity and starting population size on overall xenograft growth rate. We simulated tumor growth profiles over both a two-week implant and a three-week observation period to reflect experimental conditions. Three stochastic simulation scenarios were considered for possible cellular events: birth only; birth and death; and birth, death, and mutation. We compared the simulation results to an experimentally determined distribution derived from 308 individual HCT-116 xenograft tumors. Our findings suggest the variability of whole tumors is related to the clonal growth rate diversity within the HCT-116 cell line. In particular, the observed xenograft growth rate heterogeneity can be explained entirely by a scenario where many orders of magnitude fewer cells survive to initiate each tumor than the millions of cells implanted experimentally. This finding is consistent with previously published experimental results that have been interpreted to suggest the existence of cancer stem cells. We demonstrate the small number of founder cells in each xenograft leads to evolutionary drift under treatment conditions, when a resistant mutant may come to dominate stochastically due to the small population size.We then extend this work to ask questions about experimental design - how to develop a xenograft model system that is either more reproducible (less heterogeneity) or more heterogeneous (less reproducible). The latter system may be particularly valuable in studying the emergence of resistance. Citation Format: Christopher J. Zopf, Andrew Chen, Mayank Patel, Santhosh Palani, Syamala Bandi, Derek Blair, Wen Chyi Shyu, Arijit Chakravarty. Variability in xenograft growth rates can be explained by intra-tumor evolutionary dynamics. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 698. doi:10.1158/1538-7445.AM2014-698

Abstract 365: Using radionuclear imaging and mechanistic modeling to assess the therapeutic potential of antibody-drug conjugates (ADCs)

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Antibody-drug conjugates (ADCs) comprise a drug class that allows for direct delivery of a cytotoxic agent to the target tumor cell. The successful development of an ADC involves the assessment of patient characteristics such as tumor antigen expression and tumor vascularity. There is a need to develop a mechanistic understanding of the effect these tumor parameters have on ADC biological activity. This mechanistic insight can be derived from a mathematical model integrating quantitative preclinical data, and will support ADC development. MLN0264 is an investigational ADC consisting of a human anti-guanylyl cyclase C (GCC) antibody linked to the microtubule-disrupting agent monomethyl auristatin (MMAE). Here, we use mathematical modeling, in conjunction with in vivo imaging, to decouple the contribution of different tumor parameters to overall ADC biological activity. We constructed a mathematical model of ADC biological activity by integrating experimental results from (1) in vivo antibody imaging studies, (2) in vitro viability assays, and (3) in vivo xenograft biological activity studies. First, blood pharmacokinetics and tumor disposition were quantitatively constrained using in vivo radiolabeled antibody single-photon emission computed tomography (SPECT) data for both blood and tumor tissues. SPECT data from three xenografts with various antigen expression levels were used to link antigen expression level to ADC uptake. Second, the relationship between bound GCC receptor concentration and cell viability was established using viability assays run on an engineered cell line (293-GCC) with high antigen expression and high sensitivity to MMAE. Finally, using this relationship, we built a tumor growth dynamics model to describe in vivo xenograft biological activity, and to estimate the growth inhibition coefficient of 293-GCC. This mechanistic model can be used to gain insights into the factors driving response of a tumor that is intrinsically sensitive to MMAE. Our results indicate the process of ADC vascular permeability is one of the limiting factors of ADC disposition. This outcome is reasonable given that a large ADC molecular weight decreases the permeability. Furthermore, the model simulations suggest some tumors that are intrinsically sensitive to MMAE may not be affected by the ADC if GCC antigen expression levels are very low. Taken together, the mechanistic model developed here forms the basis of a quantitative understanding for several factors influencing MLN0264 patient selection. Citation Format: Shu-Wen Teng, Ozlem Yardibi, Julie Zhang, Donna Cvet, Johnny Yang, Kelly Orcutt, Melissa Gallery, Arijit Chakravarty, Wen Chyi Shyu, Jerome Mettetal, Daniel Bradley, Petter Veiby. Using radionuclear imaging and mechanistic modeling to assess the therapeutic potential of antibody-drug conjugates (ADCs). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 365. doi:10.1158/1538-7445.AM2014-365