Greg Hather

Nedd8-Activating Enzyme Inhibitor MLN4924 Provides Synergy with Mitomycin C through Interactions with ATR, BRCA1/BRCA2, and Chromatin Dynamics Pathways

MLN4924 is an investigational small-molecule inhibitor of the Nedd8-activating enzyme currently in phase I clinical trials. MLN4924 induces DNA damage via rereplication in most cell lines. This distinct mechanism of DNA damage may affect its ability to combine with standard-of-care agents and may affect the clinical development of MLN4924. As such, we studied its interaction with other DNA-damaging agents. Mitomycin C, cisplatin, cytarabine, UV radiation, SN-38, and gemcitabine demonstrated synergy in combination with MLN4924 in vitro. The combination of mitomycin C and MLN4924 was shown to be synergistic in a mouse xenograft model. Importantly, depletion of genes within the ataxia telangiectasia and Rad3 related (ATR) and BRCA1/BRCA2 pathways, chromatin modification, and transcription-coupled repair reduced the synergy between mitomycin C and MLN4924. In addition, comet assay demonstrated increased DNA strand breaks with the combination of MLN4924 and mitomycin C. Our data suggest that mitomycin C causes stalled replication forks, which when combined with rereplication induced by MLN4924 results in frequent replication fork collisions, leading to cell death. This study provides a straightforward approach to understand the mechanism of synergy, which may provide useful information for the clinical development of these combinations. Mol Cancer Ther; 13(6); 1625–35. ©2014 AACR.

KRAS Genotype Correlates with Proteasome Inhibitor Ixazomib Activity in Preclinical In Vivo Models of Colon and Non-Small Cell Lung Cancer: Potential Role of Tumor Metabolism

In non-clinical studies, the proteasome inhibitor ixazomib inhibits cell growth in a broad panel of solid tumor cell lines in vitro. In contrast, antitumor activity in xenograft tumors is model-dependent, with some solid tumors showing no response to ixazomib. In this study we examined factors responsible for ixazomib sensitivity or resistance using mouse xenograft models. A survey of 14 non-small cell lung cancer (NSCLC) and 6 colon xenografts showed a striking relationship between ixazomib activity and KRAS genotype; tumors with wild-type (WT) KRAS were more sensitive to ixazomib than tumors harboring KRAS activating mutations. To confirm the association between KRAS genotype and ixazomib sensitivity, we used SW48 isogenic colon cancer cell lines. Either KRAS-G13D or KRAS-G12V mutations were introduced into KRAS-WT SW48 cells to generate cells that stably express activated KRAS. SW48 KRAS WT tumors, but neither SW48-KRAS-G13D tumors nor SW48-KRAS-G12V tumors, were sensitive to ixazomib in vivo. Since activated KRAS is known to be associated with metabolic reprogramming, we compared metabolite profiling of SW48-WT and SW48-KRAS-G13D tumors treated with or without ixazomib. Prior to treatment there were significant metabolic differences between SW48 WT and SW48-KRAS-G13D tumors, reflecting higher oxidative stress and glucose utilization in the KRAS-G13D tumors. Ixazomib treatment resulted in significant metabolic regulation, and some of these changes were specific to KRAS WT tumors. Depletion of free amino acid pools and activation of GCN2-eIF2α-pathways were observed both in tumor types. However, changes in lipid beta oxidation were observed in only the KRAS WT tumors. The non-clinical data presented here show a correlation between KRAS genotype and ixazomib sensitivity in NSCLC and colon xenografts and provide new evidence of regulation of key metabolic pathways by proteasome inhibition.

Efficient drug-pathway association analysis via integrative penalized matrix decomposition

Traditional drug discovery practice usually follows the “one drug - one target” approach, seeking to identify drug molecules that act on individual targets, which ignores the systemic nature of human diseases. Pathway-based drug discovery recently emerged as an appealing approach to overcome this limitation. An important first step of such pathway-based drug discovery is to identify associations between drug molecules and biological pathways. This task has been made feasible by the accumulating data from high-throughput transcription and drug sensitivity profiling. In this paper, we developed “iPaD”, an integrative Penalized Matrix Decomposition method to identify drug-pathway associations through jointly modeling of such high-throughput transcription and drug sensitivity data. A scalable bi-convex optimization algorithm was implemented and gave iPaD tremendous advantage in computational efficiency over current state-of-the-art method, which allows it to handle the ever-growing large-scale data sets that current method cannot afford to. On two widely used real data sets, iPaD also significantly outperformed the current method in terms of the number of validated drug-pathway associations that were identified. The Matlab code of our algorithm publicly available at http://licong-jason. github.io/iPaD/

Systematic review with meta-analysis: real-world effectiveness and safety of vedolizumab in patients with inflammatory bowel disease

BackgroundSelective patient recruitment can produce discrepancies between clinical trial results and real-world effectiveness.MethodsA systematic literature review and meta-analysis were conducted to assess vedolizumab real-world effectiveness and safety in patients with ulcerative colitis (UC) or Crohn’s disease (CD). MEDLINE, MEDLINE In-Process, EMBASE, and Cochrane databases were searched for real-world studies of vedolizumab in adult patients with UC/CD reporting clinical response, remission, corticosteroid-free remission, UC/CD-related surgery or hospitalization, mucosal healing, or safety published from May 1, 2014–June 22, 2017. Response and remission rates were combined in random-effects meta-analyses.ResultsAt treatment week 14, 32% of UC patients [95% confidence interval (CI) 27–39%] and 30% of CD patients (95% CI 25–34%) were in remission; and at month 12, 46% for UC (95% CI 37–56%) and 30% for CD (95% CI 20–42%). For UC, the rates of corticosteroid-free remission were 26% at week 14 (95% CI 20–34%) and 42% at month 12 (95% CI 31–53%); for CD they were 25% at week 14 (95%, CI 20–31%) and 31% at month 12 (95%, CI 20–45%). At month 12, 33–77% of UC and 6–63% of CD patients had mucosal healing. Nine percent of patients reported serious adverse events.ConclusionsVedolizumab demonstrated real-world effectiveness in patients with moderate-to-severely active UC or CD, with approximately one-half and one-third of patients, respectively, in remission at treatment month 12. These findings are consistent with clinical trial data and support the long-term benefit–risk profile of vedolizumab.

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 3747: Predicting Maximum Tolerated Dose during Phase I using anticipatory toxicity models

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA A critical component of the early development of anticancer agents is the assessment of the Maximum Tolerated Dose during Phase I. In practice, clinical toxicities are graded on a scale of severity, ranging from Grade 1 (least severe) to Grade 4. This categorical scale is also used for continuous readouts, such as neutropenia. As the underlying pathophysiology can be expected to remain the same, and vary by degree or intensity alone, the dose-response (or concentration- response) of lower-grade toxicities should foreshadow the incidence of higher-grade toxicities, which represent different thresholds on the same concentration-response curve. If the concentration- response of Grade 4 toxicity can be predicted from that of Grade 1 toxicity, then the accurate estimation of the MTD should be feasible from a careful estimation of the Grade 1 toxicity. In this work, we demonstrate the development of anticipatory toxicity models that are capable of predicting higher-grade toxicities during dose-escalation. First, we used a simple empirical approach that relies on a minimum of assumptions. We have previously shown that the nadir of neutropenia is strongly correlated with the maximum moving average concentration over the dosing window. Using this pharmacokinetic parameter as an independent variable, we developed a set of analytical expressions to connect the concentration- response of each lower grade of toxicity to the corresponding higher grades of toxicity. Next, we tested this family of anticipatory toxicity models on a simulated dataset of neutropenia generated from a previously published model of clinical neutropenia for a range of chemotherapeutics (Friberg et al., 2002). Finally, we validated our anticipatory toxicity models with in-house preclinical data. Next, we extended the development of anticipatory toxicity models to a model-based approach. We fitted the lower grades of toxicity from the simulated dataset described above to fit a semi-mechanistic model of neutropenia, showing that the model can predict higher grades of toxicity. Next, we again validated the anticipatory toxicity models built on a semi-mechanistic basis using in-house preclinical data. Each of the two approaches presented here may be better suited for different situations. The empirical approach is better suited for toxicities with a poorly understood mechanistic basis, while the semi-mechanistic approach is well suited for neutropenia and other hematological toxicities. In either case, the anticipatory toxicity models were able to accurately predict the incidence of higher-grade toxicities in the given datasets. Thus, a careful analysis of these lower-grade toxicities may provide the opportunity to accelerate clinical development through the design of alternative clinical dose-escalation schemes. Such alternative escalation schemes may also be safer, as they focus on the forward prediction of the MTD before it is reached. Citation Format: Ekta Kadakia, Snehal Samant, Christopher J. Zopf, Dean Bottino, Greg Hather, Santhosh Palani, Wen Chyi Shyu, Arijit Chakravarty. Predicting Maximum Tolerated Dose during Phase I using anticipatory toxicity 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 3747. doi:10.1158/1538-7445.AM2014-3747

Abstract 2791: Just how noisy is xenograft data? Using growth rate modeling and bootstrapping to optimize xenograft study design.

The evaluation of tumor growth inhibition in xenograft models, generated by subcutaneous implantation of human cancer cell lines, is an integral component of the drug discovery and development process. While xenograft data is ubiquitous, and opinions on its utility are just as common, systematic examinations of signal-to-noise ratio, overall precision of xenograft data and the best metric to report tumor growth inhibition are, remarkably, absent from the literature. To this end, we present a thorough retrospective analysis of xenograft studies from a large in-house database of 225 human xenograft efficacy studies performed from 2006 to present, using a wide range of anti-neoplastic drugs. Individual xenograft tumor growth curves were fit to an exponential model, and a novel measure for data analysis was developed (the model-fitted T/C ratio on Day 21). This novel measure possesses several advantages over a traditional (raw) T/C ratio, notably the use of all available data (not just Day 21), the absence of bias due to informative right-censoring (nonrandom removal of mice in the control group whose tumor volume reached a pre-defined humane endpoint), and the absence of bias due to the inherent slowing of growth kinetics in the control group. The model-fitted T/C ratio on Day 21 was then compared to the raw T/C ratio. For each pair of comparisons between treatment group and control, bootstrapping was used to determine the mean (μ) and standard deviation (σ) of the T/C ratio (either model-fitted or raw). A Z-score measure, (1-μ)/σ, was computed as a measure of the signal-to-noise ratio of the xenograft studies, for 1167 comparisons. For xenograft studies conducted with 10 mice per group over 21 days, the model-fitted T/C ratio outperformed the raw T/C ratio in terms of median Z-score (7.1 and 5.4 respectively). When the number of mice in each group was reduced to 6, and the study length was decreased from 21 to 14 days, a high Z-score (5.1) was still achieved for the model-fitted T/C ratio. The power of xenograft study comparisons for T/C in the range of 0.35 to 0.45 was examined and found to be 0.95 for studies with 6 animals per group using the model-fitted T/C measure. Finally, the misclassification frequency (fraction of comparisons where the treatment was misclassified using a binary cutoff of efficacious vs. non-efficacious) was calculated, and found to be 0.04 using the model-fitted T/C. Our calculations demonstrate that switching to a model-fitted T/C makes more efficient use of the data, yielding considerable cost savings while maintaining a high power and low misclassification rate. The exact degree of benefit from this novel measure and alternative design may vary for other animal facilities, since the noise levels could vary. However, the methods developed here to evaluate potential designs should still be widely applicable, and represent a general approach for the optimization of xenograft studies. Citation Format: Jill Donelan, Greg Hather, Ray Liu, Syamala Bandi, Wen Chyi Shyu, Mark Manfredi, Arijit Chakravarty. Just how noisy is xenograft data? Using growth rate modeling and bootstrapping to optimize xenograft study design. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2791. doi:10.1158/1538-7445.AM2013-2791