Benjamin Zeskind

Comparing the Biological Impact of Glatiramer Acetate with the Biological Impact of a Generic

For decades, policies regarding generic medicines have sought to provide patients with economical access to safe and effective drugs, while encouraging the development of new therapies. This balance is becoming more challenging for physicians and regulators as biologics and non-biological complex drugs (NBCDs) such as glatiramer acetate demonstrate remarkable efficacy, because generics for these medicines are more difficult to assess. We sought to develop computational methods that use transcriptional profiles to compare branded medicines to generics, robustly characterizing differences in biological impact. We combined multiple computational methods to determine whether differentially expressed genes result from random variation, or point to consistent differences in biological impact of the generic compared to the branded medicine. We applied these methods to analyze gene expression data from mouse splenocytes exposed to either branded glatiramer acetate or a generic. The computational methods identified extensive evidence that branded glatiramer acetate has a more consistent biological impact across batches than the generic, and has a distinct impact on regulatory T cells and myeloid lineage cells. In summary, we developed a computational pipeline that integrates multiple methods to compare two medicines in an innovative way. This pipeline, and the specific findings distinguishing branded glatiramer acetate from a generic, can help physicians and regulators take appropriate steps to ensure safety and efficacy.

Functional effects of the antigen glatiramer acetate are complex and tightly associated with its composition

Glatiramer acetate (Copaxone®; GA) is a non-biological complex drug for multiple sclerosis. GA modulated thousands of genes in genome-wide expression studies conducted in THP-1 cells and mouse splenocytes. Comparing GA with differently-manufactured glatiramoid Polimunol (Synthon) in mice yielded hundreds of differentially expressed probesets, including biologically-relevant genes (e.g. Il18, adj p<9e-6) and pathways. In human monocytes, 700+ probesets differed between Polimunol and GA, enriching for 130+ pathways including response to lipopolysaccharide (adj. p<0.006). Key differences were confirmed by qRT-PCR (splenocytes) or proteomics (THP-1). These studies demonstrate the complexity of GAs mechanisms of action, and may help inform therapeutic equivalence assessment.

Gene expression studies of a human monocyte cell line identify dissimilarities between differently manufactured glatiramoids

Glatiramer Acetate (GA) has provided safe and effective treatment for multiple sclerosis (MS) patients for two decades. It acts as an antigen, yet the precise mechanism of action remains to be fully elucidated, and no validated pharmacokinetic or pharmacodynamic biomarkers exist. In order to better characterize GA’s biological impact, genome-wide expression studies were conducted with a human monocyte (THP-1) cell line. Consistent with previous literature, branded GA upregulated anti-inflammatory markers (e.g. IL10), and modulated multiple immune-related pathways. Despite some similarities, significant differences were observed between expression profiles induced by branded GA and Probioglat, a differently-manufactured glatiramoid purported to be a generic GA. Key results were verified using qRT-PCR. Genes (e.g. CCL5, adj. p < 4.1 × 10−5) critically involved in pro-inflammatory pathways (e.g. response to lipopolysaccharide, adj. p = 8.7 × 10−4) were significantly induced by Probioglat compared with branded GA. Key genes were also tested and confirmed at the protein level, and in primary human monocytes. These observations suggest differential biological impact by the two glatiramoids and warrant further investigation.

Leveraging existing data sets to generate new insights into Alzheimer's disease biology in specific patient subsets.

To generate new insights into the biology of Alzheimer’s Disease (AD), we developed methods to combine and reuse a wide variety of existing data sets in new ways. We first identified genes consistently associated with AD in each of four separate expression studies, and confirmed this result using a fifth study. We next developed algorithms to search hundreds of thousands of Gene Expression Omnibus (GEO) data sets, identifying a link between an AD-associated gene (NEUROD6) and gender. We therefore stratified patients by gender along with APOE4 status, and analyzed multiple SNP data sets to identify variants associated with AD. SNPs in either the region of NEUROD6 or SNAP25 were significantly associated with AD, in APOE4+ females and APOE4+ males, respectively. We developed algorithms to search Connectivity Map (CMAP) data for medicines that modulate AD-associated genes, identifying hypotheses that warrant further investigation for treating specific AD patient subsets. In contrast to other methods, this approach focused on integrating multiple gene expression datasets across platforms in order to achieve a robust intersection of disease-affected genes, and then leveraging these results in combination with genetic studies in order to prioritize potential genes for targeted therapy.

Compositional differences between Copaxone and Glatopa are reflected in altered immunomodulation ex vivo in a mouse model

Copaxone (glatiramer acetate, GA), a structurally and compositionally complex polypeptide nonbiological drug, is an effective treatment for multiple sclerosis, with a well‐established favorable safety profile. The short antigenic polypeptide sequences comprising therapeutically active epitopes in GA cannot be deciphered with state‐of‐the‐art methods; and GA has no measurable pharmacokinetic profile and no validated pharmacodynamic markers. The study reported herein describes the use of orthogonal standard and high‐resolution physicochemical and biological tests to characterize GA and a U.S. Food and Drug Administration–approved generic version of GA, Glatopa (USA‐FoGA). While similarities were observed with low‐resolution or destructive tests, differences between GA and USA‐FoGA were measured with high‐resolution methods applied to an intact mixture, including variations in surface charge and a unique, high‐molecular‐weight, hydrophobic polypeptide population observed only in some USA‐FoGA lots. Consistent with published reports that modifications in physicochemical attributes alter immune‐related processes, genome‐wide expression profiles of ex vivo activated splenocytes from mice immunized with either GA or USA‐FoGA showed that 7–11% of modulated genes were differentially expressed and enriched for immune‐related pathways. Thus, differences between USA‐FoGA and GA may include variations in antigenic epitopes that differentially activate immune responses. We propose that the assays reported herein should be considered during the regulatory assessment process for nonbiological complex drugs such as GA.

Physicochemical, biological, functional and toxicological characterization of the European follow-on glatiramer acetate product as compared with Copaxone

For more than 20 years, Copaxone (glatiramer acetate, Teva), a non-biological complex drug, has been a safe and effective treatment option for multiple sclerosis. In 2016, a follow-on glatiramer acetate product (FOGA, Synthon) was approved in the EU. Traditional bulk-based methods and high-resolution assays were employed to evaluate the physicochemical, functional, and bio-recognition attributes, as well as the in vivo toxicity profile of the active substances in Copaxone and Synthon EU FOGA lots. These tests included quality control tests applied routinely in release of Copaxone lots, as well as additional characterization assays, gene expression studies and a rat toxicity study. Even though the Synthon FOGA was designed to copy and compete with Copaxone, the active substances were found to be similar in only 7 of the tested 14 (50%) methods (similar is defined as within approved specifications or within the inherent microheterogeneity range of tested Copaxone batches, or not showing statistically significant differences). With additional methods applied, consistent compositional differences in attributes of surface charge distribution, molecular size, and spatial arrangement were observed. These marked differences were concordantly observed with higher biological activity of some of the Synthon EU FOGA lots compared with Copaxone lots, including potency and cytotoxicity activities as well as gene expression of pathways that regulate apoptosis, IL-2, and inflammation signaling. These observations raise concerns for immunogenicity differences, particularly in (repeated) substitution settings. Another orthogonal finding demonstrated increased frequency of injection-site local toxicity observations for the Synthon EU FOGA in an in vivo daily dosing rat study, thus warranting further qualification of the link between compositional and functional differences in immunogenicity, and potential impact on long-term efficacy and safety.

Abstract 789: Leveraging transcriptomic and genomic data to better select models for preclinical oncology therapeutic development to identify cell lines most similar to patient tumors

Cancer cell lines represent the front line of new compound testing, and results from these experiments often decide which compounds go on for further testing. Genomic context plays a critical role in drug response and now genomic data for tumors and cell lines are widely available. However, cell lines are often chosen based on ease of access, literature prevalence, and ease of culture. We combined gene expression and CNV/mutation profiling from four pancreatic cancer tumor datasets (GSE21501, GSE28735, ICGC, TCGA,) and three pancreatic cancer cell line datasets (Klijn et al, Collisson et al, and CCLE) to identify which cell lines best match patient tumors. CNV comparison revealed that popular cell lines do not always have the best CNV correlation with tumors: when comparing pancreatic cancer tumors to cell lines, the citations of the top five cell lines by CNV correlation were less than 10% of the pancreatic cancer cell line total. Next we filtered for driver mutations including SMAD4 and CDKN2A using mutation scoring algorithms and clustered tumors and cell lines. We found that many cell lines with few citation counts clustered readily amongst tumors (such as L33). Leveraging the hypothesis that different hits in the same pathway can have a similar downstream effect, we combined CNV, expression and mutation data and clustered cell lines together with tumors based on overall aberrations in MSigDB cancer pathways. L33 and YAPC clustered near tumors while the majority of other cell lines clustered together. To identify coexpressed gene clusters, we ran WGCNA individually in all seven datasets and discovered modules consistent in cell line and tumor datasets using iGraph. One of the most interesting modules (interferon regulated genes) is expressed highly in the majority of tumors profiled. About half of cell lines also express this module highly, suggesting that they may be more ideal models for high interferon expression tumors than other cell lines. Here we present evidence demonstrating that certain cell lines mimic pancreatic tumor genomes more closely while others represent patterns of genomic features not commonly observed in vivo. We also show that certain biologically relevant tumor subtypes may be better represented by some cell lines than others. Our analysis highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Adam Labradorf, Joseph Perez-Rogers, Brian Haas, Andrew Lysaght, Brian Weiner, Fadi Towfic, Kevin Fowler, Benjamin Zeskind, Sarah Kolitz, Badri Vardarajan, Maxim Artyomov, Rebecca L. Kusko. Leveraging transcriptomic and genomic data to better select models for preclinical oncology therapeutic development to identify cell lines most similar to patient tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 789.

Abstract A78: Leveraging genomics to optimize models for accelerated pancreatic cancer drug development

Cell lines used for pre-clinical testing of oncology compounds are not always chosen based on how well they models patient tumors. Instead they are often chosen based on availability and literature prevalence. The advent of high throughput genomic profiling demonstrates a causative relationship between genomic features and drug response, suggesting that cancer drug discovery could be accelerated by using genomics as a criteria to find ideal cell lines for a given cancer type. The overall oncology clinical trial success rate is dismally low, especially in pancreatic cancer. Pancreatic cancer has a five year survival of 5-6% and is predicted to be the second leading cause of cancer by 2030 with a dearth of promising medicines currently in trials. In order to forecast optimal cell lines for drug testing in pancreatic cancer, we leveraged gene expression, mutations, and copy number variation (CNV) data to compare tumors from The Cancer Genome Atlas (TCGA) to cell lines in Cancer Cell Line Encyclopedia (CCLE). To approximate cell line usage, the number of hits for each cell line in PubMed and Google Scholar were combined. Less than 20% of queried pancreatic cancer cell lines represented more than 88% of the total search hits, demonstrating a robust bias towards certain cell lines. We calculated the CNV correlation between each cell line and each tumor. The cell lines that were popular in literature, such as DAN-G (24% of citations), were often ranked worst by CNV correlation with tumors while some cell lines which were rarely cited such as L33 had among the highest CNV correlation. Next, we filtered mutation data using publicly available mutation scoring algorithms to select the most cancer driving mutations. Hierarchical clustering was applied to the tumor samples and cell lines together based on the presence or absence of the top scoring mutations in order to pinpoint cell lines with mutational spectra similar to tumors. In support of observations made in CNV data, popular cell lines such as DAN-G clustered with other cell lines and L33 clustered predominantly amongst tumor samples, providing further evidence that L33 may be an ideal cell line for modeling pancreatic cancer drug response. In order to leverage all available data types, the selected CNVs and mutations were combined into a pathway level event matrix based on the number of relevant mutations or CNVs within a given pathway and then clustered. Unsurprisingly these results show that most cell lines are much more similar to each other than to tumors. However, a few cell lines (including L33) cluster with tumor samples. Overall our results demonstrate that comprehensively L33 shows the best similarity to pancreatic cancer tumors. We believe that selecting preclinical screening methods that best match relevant tumor biology and genomic drivers could help accelerate the development of new medicines for a variety of cancers. Citation Format: Yoonjeong Cha, Andrew Lysaght, Brian Weiner, Sarah Kolitz, Fadi Towfic, Kevin Fowler, Badri Vardarajan, Maxim Artyomov, Benjamin Zeskind, Rebecca Kusko. Leveraging genomics to optimize models for accelerated pancreatic cancer drug development. [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 A78.

Abstract 4741: Improving pancreatic cancer drug discovery by leveraging genomics to select better in vitro models

Currently, pancreatic cancer has an estimated 5-year survival rate of only 5-6%. The projection that pancreatic cancer will be the second leading cause of cancer related death by 2020 compounded by the numerous clinical trial failures precipitates the need for novel approaches to accelerate progress in new medicine development. Cell lines used for screening pre-clinical compounds prior to animal models and human testing are usually chosen based on ease of access and literature prevalence. However, the constellation of genomic derangements in cell lines commonly used for in vitro studies may not be representative of pancreatic cancer. In this study, we leveraged copy number variation (CNV) and targeted sequencing data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) to predict optimal cell lines that mirror pancreatic cancer genomes most closely. We calculated the frequency of each CCLE pancreatic cancer cell line in literature and compared this to how well each cell line recapitulates the pancreatic cancer population. Unsurprisingly, we observed that CCLE pancreatic cancer cell lines overall have more frequent CNVs and mutations than TCGA pancreatic cancer tumors. This observation is likely due to inherent genomic instability of cell lines and underscores the importance of using low passage cells. Next, we directly compared the median per gene CNV values in TCGA pancreatic cancer tumors and pancreatic cancer cell lines in CCLE. Contrary to our expectation, the top five cell lines by CNV correlation with TCGA pancreatic tumors represented only 6% out of all literature search hits for all CCLE pancreatic cancer cell lines, indicating the availability of more optimal cell lines from a genomics perspective. Additionally, we leveraged targeted sequencing data to compare the most frequent mutations with medium to high Mutation Assessor scores in TCGA pancreatic cancer tumors to CCLE pancreatic cancer cell lines. The seven most common mutations by this method in TCGA pancreatic cancer tumors were: KRAS, TP53, MYH8, TAOK2, PCDH15, ATRX, and CDKN2A. Using hierarchical clustering based on the presence or absence of these 7 mutations in pancreatic cancer CCLE cell lines and TCGA tumors, we showed that some cell lines readily clustered amongst TCGA tumors (such as BXPC3), while others occupied discrete branches of the dendrogram exclusive of most TCGA tumors such as PK1 and PANC1. This implies that while some cell line mimic pancreatic tumor mutations closely, others represent mutation constellations not commonly observed in patients. It is possible to apply this method to other cancer types, given consideration for potentially different cancer biology. In summary, our work reports that many popular pancreatic cancer cell lines harbor distinct genomic aberration profiles from pancreatic cancer tumors and highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Andrew Lysaght, Rain Cui, Brian Weiner, Sarah Kolitz, Fadi Towfic, Jason Funt, Kevin Fowler, Badri Vardarajan, Maxim Artyomov, Benjamin Zeskind, Rebecca Kusko. Improving pancreatic cancer drug discovery by leveraging genomics to select better in vitro models. [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 4741. doi:10.1158/1538-7445.AM2015-4741

Use of genetic technologies to compare medicines.

In order to ensure that patients receive the safest and most effective medicines possible, it is often necessary to compare medicines and assess the extent to which they are similar in their clinical impact. Full clinical trials with appropriate endpoints remain the only method to compare the clinical impact of two medicines with absolute certainty. Other available methods (including physicochemical analysis, genomics, and transcriptomics) can provide partial information about certain aspects of a medicines biological impact, with possible clinical implications. Especially for biologics and non‐biological complex drugs, which are more difficult to characterize by physicochemical means than small molecules, genomics and transciptomic studies can yield valuable insights for physicians, regulators, and drug developers. In this review, we cite and summarize a variety of studies that exemplify the emerging science of applying genomics and transcriptomics technologies to compare medicines. We discuss key aspects of experimental design, conduct of genetic assays, and advanced data analysis, all of which are critical for the successful execution of such studies. Finally, we propose new areas for which such studies can be applied to maximize patient benefit and reduce safety issues.