Investigational Drug Research Articles

Abstract 6566: Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas

Abstract Purpose: Soft tissue sarcomas (STS) are a collection of heterogenous cancers with limited range of treatment options and poor outcomes. First line treatment for most STS is anthracycline-based, with gemcitabine-based combinations, pazopanib or ifosfamide as treatment options in refractory/relapsed STS. There is a clinical unmet need to improve treatment options for STS as median time to progression following second- and third-line treatments is less than 4 months. We have previously demonstrated in a 71-patient relapsed/refractory non-Hodgkin’s lymphoma clinical study that a functional precision medicine platform can effectively guide treatment, with several exceptional responders (Science Translational Medicine, 2022). This ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), analyses a predesigned array of 155 test combinations performed on a primary patient sample to rank and compare all possible therapeutic combinations from a 12-drug set. In this study, we evaluated clinical concordance of QPOP to predict treatment outcomes in an STS cohort. We also explored QPOP’s combination therapy ranking function to identify novel combinations that may benefit larger groups of STS patients when appropriately guided. Experimental Detail: A 12-drug set comprised of STS standard of care, FDA approved drugs and promising investigational drugs was evaluated in QPOP analysis of primary STS tumor samples. Ex vivo cultures of primary STS samples were treated with a predesigned array of 155 test combinations to provide a quantitative phenotypic output for each test combination. This dataset was analyzed by QPOP to rank all possible drug combinations towards a predicted treatment outcome. Concordance analysis was then performed comparing patient sample results with either parallel treatment initiated at time of sample collection or physician choice to use QPOP-guided treatment. Validation of QPOP-derived top-ranked drug combinations was also performed. Results: 12-drug, 3-level QPOP was successfully performed on 45 of 51 primary patient samples. Clinical concordance analysis showed QPOP had a total predictive value (TPV) of 76.9% and an AUCROC of 85.7%. Exceptional responders to guided treatment with pazopanib or eribulin were observed. Additionally, top-ranked epigenetic-based combinations were identified as effective across multiple STS subtypes and validated preclinically. Conclusion: The results show the potential for QPOP to predict clinical response in solid cancers, such as STS. Furthermore, QPOP can improve drug development pipelines through patient samples-based design of effective combinations with investigational drugs or underutilized drugs that may benefit larger populations of patients or specific subgroups of patients. Citation Format: Edward Kai-Hua Chow, Sharon P. Chan, Tan Boon Toh, Masturah Rashid, Valerie S. Yang. Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6566.

Abstract 742: PYX-201, a stroma-targeting ADC composed of an anti-EDB+FN antibody conjugated to Auristatin0101, demonstrates strong anti-tumor efficacy across multiple human cancer indications in pre-clinical PDX tumor models

Abstract Stroma is crucial to support solid tumor growth, metastasis and resistance to treatment. While most antibody-drug conjugates (ADCs) directly bind to cancer cells, PYX-201, an investigational drug, is designed to target tumor stroma by binding to the extra-domain B splice variant of fibronectin (EDB+FN), a matrix protein abundantly expressed in the tumor microenvironment of many solid tumors with absent or low expression in normal adult tissues. In vitro studies demonstrated PYX-201 was strongly cytotoxic to EDB+FN positive Caki2 cells, but not to EDB+FN negative HT29 cells. In addition, PYX-201 was 30-times more potent than a non-targeted ADC control, demonstrating PYX-201 cytotoxic effects were EDB+FN dependent. Furthermore, when Caki2 and HT29-luciferase cancer cells were co-cultured, the luciferase activity was used to quantify the amount of EDB+FN negative HT29 cells only. Interestingly, PYX-201 was able to kill EDB+FN negative cancer cells in a co-culture assay as shown by a decrease in luciferase activity, demonstrating PYX-201 bystander activity. Pre-clinical mouse model studies were conducted to evaluate PYX-201 anti-tumor efficacy and pharmacokinetic (PK) properties. First, a comprehensive patient-derived xenograft (PDX) mouse model trial was designed to evaluate the anti-tumor efficacy of PYX-201. To fully represent the diversity and characteristics of human tumors, PDX models were pre-selected from ten solid tumor indications based on different levels of EDB+FN protein expression and stromal density. PYX-201 (Q4D × 4 at 3mg/kg) was well tolerated, and strong anti-tumor responses (% Tumor Growth Inhibition [TGI] >90%) were observed in PDX models across various cancer indications. Potential correlations between EDB+FN protein expression with anti-tumor activity will be evaluated. Remarkably, long-term anti-tumor efficacy of PYX-201 was observed as tumors did not relapse even after >100 days in a breast cancer PDX model. Additionally, plasma samples were analyzed from a cell line derived xenograft mouse model after a single dose of PYX-201 to determine its PK profile. At 3 mg/kg, the maximum concentration (Cmax) was 28.1 μg/mL, area under the curve (AUC) was 1,732.7 h*μg/mL, with a low clearance of 1.5 mL/h/kg, and the half-life of the mAb from PYX-201 was calculated at 81.2 h. These PK parameters were associated with strong PYX-201 anti-tumor efficacy (% TGI >90%) in the H1975 xenograft model. Altogether, PYX-201 was well tolerated and demonstrated strong anti-tumor efficacy in a variety of pre-clinical human PDX models. PYX-201 is a promising and innovative ADC which is currently under investigation in a Phase I clinical trial (NCT05720117). Citation Format: Nicolas Severe, Amanda Facklam, Liz McMichael, Biplab Das, Sara Lewandowski, Justin Trickett, Liyang Diao, Chengfeng Merriman, Chuan Shen, Jianwen Feng, Shawn Harriman, Marsha Crochiere, Philipp Steiner, Jan Pinkas. PYX-201, a stroma-targeting ADC composed of an anti-EDB+FN antibody conjugated to Auristatin0101, demonstrates strong anti-tumor efficacy across multiple human cancer indications in pre-clinical PDX tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 742.

Abstract 987: Novel ALK mutations in EML4::ALK+ NSCLC resistant to TKIs identified by liquid biopsy

Abstract The identification of Anaplastic Lymphoma Kinase (ALK) chromosomal translocations led to the development of effective targeted therapies in a subset of Non-Small Cell Lung Cancer (NSCLC) patients. Several drugs are currently in clinical practice for advanced ALK+ NSCLC. However, resistance remains a challenge in these patients. The possibility to analyze circulating tumor DNA (ctDNA) with non-invasive methods can greatly improve prognostic capabilities, by allowing early detection of relapse and of drug-resistant mutations from peripheral blood. We report a series of 15 consecutive relapsed NSCLC patients (12 ALK+, 3 ROS1+) treated at the Fondazione IRCCS-San Gerardo dei Tintori (Monza, Italy) that were investigated by liquid biopsy at tyrosine kinase inhibitor (TKI) failure, through amplicon deep sequencing of the ALK/ROS1 kinase domains. Median DNA yield was 4.8 ng per ml of plasma and mean fragment size was 176 bp. Mutational hotspot coding regions of ALK and ROS1 genes were amplified by high-fidelity PCR and sequenced. Mean coverage of called variants was 24,687x. The threshold for mutation calling was set at 0.6%, after correcting for mapping quality, minimum coverage, and strand bias. Analysis of background signal on healthy control DNA identified 0.0024 mismatches per mapped base. Overall, plasma ctDNA analysis detected an ALK mutation in 7/18 relapse samples from 7/15 patients. No ROS1 mutations were found in ROS1 fusion-positive patients. Among 12 patients carrying an EML4::ALK fusion, 10 variants potentially driving resistance were found. Along with mutations that were previously identified (L1196M/G1202R, E1154K, F1174L), novel ALK variants were detected such as L1198R, T1211I, C1235R, C1237Y, L1196P. Mutations were characterized for sensitivity to ALK inhibitors in a BaF3-EML4::ALK cell model, using cell proliferation assays and western blotting to evaluate the effects on ALK phosphorylation. C1237Y and L1196P mutants appeared to resist all TKIs; hence, we further tested their sensitivity to novel investigational drugs, zotizalkib and repotrectinib. Both mutants demonstrated resistance, suggesting that these two mutations confer broad resistance to TKIs. Molecular modelling of C1237Y and L1196P mutants was run to elucidate the mechanisms of resistance. The ADP-C1237Y complex showed a much smaller root-mean-square deviation than ADP-WT, indicating a higher affinity/activity of the mutant. L1196 interacts hydrophobically with a methyl group of lorlatinib, P1196 is predicted to lose such interaction, while ATP binding is not affected. The presence of C1237Y and L1196P variants, is a relevant finding due to their resistance, even after the exposure to the fourth-generation inhibitor zotizalkib. The clonal selection of these mutations, made by second/third generation ALK TKIs may lead to a complete refractory status, which would then render TKIs useless in a sequential therapeutic strategy. Citation Format: Federica Malighetti, Matteo Villa, Elisa Sala, Geeta Sharma, Giulia Arosio, Maria Gemelli, Chiara Manfroni, Diletta Fontana, Nicoletta Cordani, Raffaella Meneveri, Alfonso Zambon, Rocco Piazza, Fabio Pagni, Diego Cortinovis, Luca Mologni. Novel ALK mutations in EML4::ALK+ NSCLC resistant to TKIs identified by liquid biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 987.

Abstract 5703: Development of RVL-101, a CDK4/6-selective, small molecule inhibitor with superior CNS penetration and combinability potential for the effective treatment of primary and metastatic brain tumors

Abstract Cyclin-dependent kinases 4 and 6 (CDK4/6) are key regulators of the cell cycle that are frequently dysregulated in cancer, leading to unchecked cell growth. CDK4/6 inhibitors have emerged as a promising class of anticancer agents that selectively target this pathway. The primary mode of action of CDK4/6 inhibitors relies on the disruption of retinoblastoma (Rb) protein phosphorylation, which thereby blocks cell cycle progression from G1 to S phase and reduces proliferation of cancer cells by a cytostatic mechanism. However, despite their efficacy, approved and clinical-stage CDK4/6 inhibitors have modest penetration of unbound drug into the brain, limiting their utility in the treatment of brain metastases and primary brain tumors. Additionally, despite the potential benefits of CDK4/6 inhibitors as combination therapies with cytotoxic agents, adjunctive treatment options remain underexplored due to the poor safety profiles of existing therapies. RVL-101 is a preclinical, investigational drug with a novel pharmacophore that has a low nanomolar activity against CDK4, is CDK2,6,7,9-sparing, and maintains >500-fold selectivity over CDK1. To maximize the potential of RVL-101 as a preferred combination partner, this novel agent has been engineered to have exquisite kinome- and safety-panel selectivity with no measured CYP inhibition or induction liabilities. RVL-101 is differentiated from approved and clinical stage CDK4/6 inhibitors having both high partitioning into the brain (Kp) and high free-brain (Kpu,u = 0.7) levels resulting in equal levels of unbound drug in all relevant compartments at steady state. Like other CDK4/6 agents, RVL-101 is broadly efficacious in vivo in ER+/HER2- xenograft models at well-tolerated doses. However, RVL-101 has also demonstrated robust activity in HR-/HER2+ xenograft models as a single-agent and has uniquely shown improved tumor inhibition effect in combination with approved HER2 cytotoxic therapeutics tucatinib and fam-trastuzumab-deruxtecan. RVL-101 was also tested in a GBM model as a monotherapy and in conjunction with the cytotoxic agent temozolomide (TMZ). Remarkably, QD dosing of the cytostatic agent, RVL-101, after pulsatile pretreatment with TMZ led to dramatic synergistic efficacy compared to either agent alone, leading to 100% survival of all animals on the dual therapy arm for more than 3 weeks post discontinuation of dosing. RVL-101 offers the combination of substantial therapeutic benefit and consistent free-drug levels in plasma and CNS that could lead to safe and effective treatment for a broader patient population. Citation Format: John E. Campbell, Bo-Sheng Pan, Michael Bower, Peter Bertinato, Nisha Perez, Victoria Gras Andreu, Neha Rana, Sophia Tabchouri, Ankit Gupta, Jonah Kallenbach. Development of RVL-101, a CDK4/6-selective, small molecule inhibitor with superior CNS penetration and combinability potential for the effective treatment of primary and metastatic brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5703.

Abstract 3545: Atlas of therapeutic targets: Evolution of druggable proteome

Abstract Selection of therapeutic targets and modalities is of fundamental importance in drug discovery, as it can have a direct impact on the economics of drug discovery as well as its success rate. With the advancement of chem-bio and proteomics methods together with advent of artificial intelligence, the scope of the druggable proteome is expanding. The choice of drug targets gains further importance as we link it to potential clinical efficacy and safety, genetic variation and patient populations. Drug targets are often selected based on inadequate understanding of target biology, tractability by existing toolbox of therapeutic modalities and insufficient insight into possible clinical outcome. We have developed an “Atlas of Therapeutic Targets”, an interactive resource that provides classification of drug target families with linked information on clinical and investigational drugs. The atlas also provides key insights and score for tractability of targets with various therapeutic modalities. Particularly, our scoring system grades the targets on dimensions such as ligandability, involvement in protein-protein complexes, structural disorder, hotspot analysis and cross-referencing with known targets, thus yielding a total tractability score of 1-10. Dissection of targets based on such dimensions, enables critical evaluation therapeutic modalities as well tools and methodologies needed for their conversion into successful drug discovery programs. We curate 900+ human drug targets, analysis of which indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms. This atlas will be available as a resource for the wide drug discovery community. Citation Format: Ivan Babic, Elmar Nurmemmedov. Atlas of therapeutic targets: Evolution of druggable proteome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3545.

Abstract 5093: Analysis of protein expression in head and neck cancers as therapeutic targets for antibody drug conjugates

Abstract Introduction: Antibody Drug Conjugates (ADCs) represent a new category of therapeutic molecular targeting for solid tumors. To date 6 ADCs have been approved in solid tumors against 5 targets (HER2/ERBB2, Folate alpha, Tissue Factor, Nectin-4, and Trop-2). Thus far, no ADCs have been approved for head and neck cancers. Methods: We queried protein expression data from a publically available consortium of datasets (TNMPlot.com) containing protein expression data from over 56,000 tumor and normal samples from GEO, GTex, TCGA, and TARGET databases. Genes examined for protein targets, included those with ADC targets of both presently approved and investigational drugs from clinical trials.The level of overexpression was determined as the percent of tumors over the 75th percentile of expression in normal tissue. Results: Protein expression data were available for 99 HNSCC samples. Among targets with ADCs available, increased protein expression was seen in HNSCC for 0% of ERBB2 (n=0/99), 38% of FOLR1 (n=38/99), 0% of TACSTD2 (n=0/99), and 42% of F2 (n=42/99) genes.Among gene targets with investigational ADCs, increased protein expression was seen in HNSCC for 35% of of AXL (n=35/99), 13% of B7H3/CD27 (n=12/99), 50% of EGFR (n=50/99), 63% of VTCN1 (n=63/99), 10% of MSLN (n=10/99), 77% of CDH3 (n=76/99), and 39% of PDGFRA (n=39/99). Conclusion: Increased protein expression for approved ADC targets were infrequent in HNSCC. However, increased protein expression is seen for several ADCs under clinical investigation supporting the potential therapeutic approaches for investigational ADC targets. Citation Format: Justin Tyler Moyers, Seth L. Smith, Miranda M. Mora, Roberto Carmagnani Pestana. Analysis of protein expression in head and neck cancers as therapeutic targets for antibody drug conjugates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5093.

Abstract 5784: Structure-based drug design against caPCNA for selective cancer chemotherapy development

Abstract Proliferating cell nuclear antigen (PCNA) is critical to DNA replication and repair processes, and it is also a proliferation biomarker in a variety of human tumors. A unique cancer-associated isoform of the protein, caPCNA, has been previously identified, allowing for rational-based drug discovery studies to develop leads with the potential for selective therapeutic targeting of cancer cells. Several strategies have been employed to develop agents targeting caPCNA, including peptide and small molecule-based inhibitors, but the success in developing therapeutically tractable compounds has been limited so far. Here, we present the crystal structures and corresponding biochemical characterizations of our novel small molecule-based caPCNA inhibitors, which bind into the PIP-box binding pocket of PCNA. One of our lead compounds is now an investigational new drug that we observed selectively kills cancer cells, and it appears to induce replication stress, apoptosis and increase cancer cell sensitivity to genotoxic agents, while these effects are not observed in non-malignant cell controls. This caPCNA inhibitor is orally administrable, metabolic stable and it suppresses tumor growth as a monotherapy or as a combination treatment, and it has entered clinical trials in the United States. PCNA plays a critical role in temporarily dislodging RNA polymerase II from transcription replication conflict sites, to enable the replication fork to proceed. Thus, structure-based drug discovery approaches to target transcription replication conflict resolution machinery, such as PCNA protein, may open a novel therapeutic avenue for exploiting this cancer-selective vulnerability. Citation Format: Jennifer Jossart, Pouya Haratipour, Long Gu, Carone M. li, Robert Lingeman, Yilun Liu, Robert J. Hickey, Linda H. Malkas, Jeff J. Perry. Structure-based drug design against caPCNA for selective cancer chemotherapy development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5784.

Abstract 3537: Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft

Abstract Accurate prediction of drug response using machine learning (ML) remains a significant challenge in drug development as well as in personalized cancer therapy. Furthermore, there is a scarcity of rigorous external validation studies for evaluating drug response prediction models. Even when available, such validations are often confined to in-vitro studies. Orthotopic patient-derived xenograft (O-PDX) mice serve as essential preclinical models that closely replicate the human tumor microenvironment and assess the therapeutic response to cancer treatments. We present CertisAITM, a novel ensemble of machine learning models that have been trained on a vast array of experimental high-throughput screenings of both monotherapies and combination therapies, incorporating over 4500 investigational and FDA-approved drugs across 10 major cancer indications. The results of our ML drug response prediction models show an average R2 of 0.75 and RMSE of 0.62 (across internal 5-fold cross-validation) for all 10 cancer prediction models. Our evaluation of CertisAI's drug response predictions within O-PDX pharmacology studies has shown a notable overall correlation between actual observed tumor growth inhibition (TGI) and predicted TGI, with an r value of 0.45 across seven indications. The most accurate prediction model achieved an r value of 0.7 for colorectal cancer, encompassing 37 treatments in 6 O-PDX studies. Altogether, these results highlight CertisAI’s potential in enhancing pre-clinical model selection as well as personalized treatment strategies in oncology. Citation Format: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bianca Carapia, Emily Eastwood, Jantzen Sperry, Jonathan Nakashima, Long Do. Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3537.

Abstract 4296: Investigating metabolic sensitivity and organization of renal cell carcinoma: To enhance effective therapies and patient survival

Abstract Background: Renal cell carcinoma (RCC) is the 6th most common cancer in men and the 9th most common in women in the United States. RCC tissues are metabolically dysfunctional, often resulting from loss of von-Hippel Lindau (VHL) and consequent upregulation of hypoxia-inducible factor (HIF1/2). HIF1a is a transcription factor that upregulates glycolytic gene expression. Current therapies, including immune checkpoint inhibitors and tyrosine kinase inhibitors, have shown improvements in the outcomes of RCC patients, however, there are no biomarkers clinically available to predict the efficacy of these drugs. While genomic data and transcriptomic signatures have been investigated, they are not used in clinical practice due to lack of prospective validation. Understanding additional pathways pertaining to metabolic features of RCC, is critical. Our bulk gene expression analysis of primary RCC samples from TCGA-KIRC show a predominance of genes expressing mitochondrial ribosomal proteins and electron transport chain in tumors that relapsed early after nephrectomy, warranting further validation. Aim: We aim to investigate changes in metabolic adaptation in RCC cells from kidney cancer cell lines (VHL mutant) and primary cells from human-matched primary and metastatic kidney cancer sites. Using a novel biosensor-based technique developed in the Albeck lab, we aim to identify the dependence of live RCC cells on specific metabolic pathways and test the effect of standard-of-care (SOC) pathway inhibitors such as tyrosine kinase, HIF pathway, metabolic pathway inhibitors impacting tumorigenicity. Methods: To examine functional metabolic behavior in single cells, we generated immortalized cell lines (MCF10A, 184A1, FL83B and HBE-1) stably transfected with biosensors for the activity of AKT, glycolysis, mTOR and AMPK pathway. Data from Live-cells treated with metabolic inhibitors were collected and analyzed using our MATLAB-based pipeline. We have extended the analysis to RCC cell lines (786-O cell lines) and primary cells collected from nephrectomy specimens and matched metastatic biopsy samples, using current SOC pathway inhibitors (including sorafenib, sunitinib, belzutifan) and investigational metabolic drugs (metformin, IACS-010759, niclosamide). Results & Conclusion: Our preliminary results show an association of high OX-PHOS with primary tumors from early relapse after nephrectomy than the relapsed late tumors. Our biosensor single cell data show that even genetically homogeneous cell populations can vary in their usage of OXPHOS and glycolysis to supply ATP for cell growth. Tumor gene expression profile coupled with biosensor data, will address mechanism behind this metabolic shift in RCC tumors in primary cells isolated from primary and metastatic sites. Our research will improve tumor prognosis and allow designing therapeutic trials. Citation Format: Madhura Patankar, John Albeck, Shuchi Gulati. Investigating metabolic sensitivity and organization of renal cell carcinoma: To enhance effective therapies and patient survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4296.

Abstract 4719: Modulation of HR-HPV viral protein E7 by SHetA2 in cervical cancer

Abstract Introduction: The purpose of this study is to determine the effect of the chemotherapeutic sulfur heteroarotinoid A2 (SHetA2) on high-risk human papillomavirus (HR-HPV) proteins and the modulation of cell survival and proliferation in cervical cancer. Cervical cancer is caused by persistent HR-HPV infection. HR-HPV tumorigenesis is driven by its early 6 and 7 (E6, E7) oncoproteins, which increase cell proliferation and survival. Cellular pathways regulated by E6 and E7 are counter-regulated by the investigational new drug SHetA2, which is currently in phase 1 clinical trial for advanced and recurrent cancers. SHetA2 disrupts complexes of 70-kDa heat shock protein (HSP70) family members, hsc70, GRP78, and mortalin, with their client proteins. We hypothesized that SHetA2 reduces E6 and E7 levels and their binding to HSP70 chaperones in association with the reduction of cervical cancer cells and tumor growth. Methods: SHetA2 effects on specific genes and proteins in HR-HPV-positive cervical cancer cell lines and xenograft tumors were assessed by western blot, quantitative polymerase chain reaction, immunofluorescence ligation assays, and coimmunoprecipitation assays. Results: SHetA2 significantly reduced E6 and E7 mRNA, and protein levels of E7 and the E7-regulated p16, but not E6 in cervical cancer cells and xenograft tumors. MG132 inhibition of proteasome function attenuated the E7 reduction. HSP70/hsc70 proteins bound E7 and SHetA2 disrupted these complexes. Conclusions/Implications: SHetA2 reduces E7 proteins in cervical cancer cells through a mechanism that involves proteasomal degradation in association with disruption of HSP70/E7 complexes, p16 reduction, and decreased cell and tumor growth. This is the first demonstration of HSP70/E7 complexes. SHetA2-induced release of E7 from HSP70s could be causing increased susceptibility of E7 to proteasomal degradation. This data supports the development of SHetA2 and HSP70 inhibitors for cervical cancer. Citation Format: Justin Garland, Showket Hussain, Doris M. Benbrook. Modulation of HR-HPV viral protein E7 by SHetA2 in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4719.

Abstract 5724: Preclinical evaluation of a novel CHK1 inhibitor encapsulated within the liposome, SMP-3124, for the treatment of solid cancer

Abstract Checkpoint kinase 1 (CHK1) is a serine/threonine kinase which plays a critical role for DNA damage responses (DDR) throughout the cell cycle in human cells. CHK1 is mainly activated by replication stress resulting in single-stranded DNA breaks induced by chemotherapeutic agents or mutations in DNA repair related genes. These kinds of stresses are reported to increase the dependency on DDR pathway molecule including CHK1. Therefore, targeting CHK1 is promising strategy for drug discovery. In fact, CHK1 inhibition induces S phase arrest and inhibits cell growth in wide range of cancer cell lines. Recently, a number of drugs in this pathway are tested in clinical trials. Although some of them showed obvious clinical responses in both monotherapy and combination therapy, their efficacies are limited because of their toxicities. To maximize the efficacy, we utilized CHK1 inhibitor with liposomal formulation which changes drug distribution to achieve longer drug retention in plasma and accumulation in tumor. Here, we generated SMP-3124, a novel and selective CHK1 inhibitor encapsulated within the liposome. SMP-3124 showed durable pharmacodynamic response due to the liposomal accumulation in the tumor in xenograft model. We confirmed potent anti-tumor activities of SMP-3124 in multiple subcutaneous xenografted models without severe side effects. In addition, SMP-3124 showed prolonged overall survival in both peritoneal dissemination and orthotopic model using human ovarian cancer cells. SMP-3124 synergistically inhibited tumor growth in combination with gemcitabine without additional hematological toxicities induced by gemcitabine. We are planning to an investigational new drug application in 2024. Citation Format: Yuka Kumagai, Ryosaku Inagaki, Kana Shimizu, Wataru Hirose, Naoaki Shimada, Kento Hayashi, Yoshiko Fukuoka, Makoto Matsuoka, Seiji Kamioka, Hitoshi Ban, Hiroki Umehara, Yusuke Sawayama. Preclinical evaluation of a novel CHK1 inhibitor encapsulated within the liposome, SMP-3124, for the treatment of solid cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5724.

Abstract 6583: Developing a selective cancer chemotherapy by small molecule-based targeting of PCNA protein

Abstract Proliferating cell nuclear antigen (PCNA) is a proliferation biomarker in a variety of human tumors and has critical cellular functions in DNA replication and repair. We previously identified a unique cancer-associated isoform of the protein, termed caPCNA, which allows for structure-based drug discovery studies to develop inhibitors for selective therapeutic targeting of cancer cells. Distinct strategies have been used to develop caPCNA targeting agents, which includes peptide and small molecule-based compounds, although the success in developing therapeutically tractable compounds has been limited so far. Here, we present the crystal structures and corresponding biochemical characterizations of our novel small molecule-based caPCNA inhibitors, which bind into the PIP-box binding pocket of PCNA. Our lead compound selectively kills cancer cells, and it appears to induce replication stress, apoptosis and increase cancer cell sensitivity to genotoxic agents, while these effects are not observed in non-malignant cell controls. This caPCNA inhibitor is orally administrable, metabolic stable and it suppresses tumor growth as a monotherapy or as a combination treatment and is now an investigational new drug in Phase 1 clinical trials. PCNA plays a critical role enabling the replication fork to proceed from transcription replication conflict sites, by temporarily dislodging RNA polymerase II. Thus, structure-based drug discovery against caPCNA may open a novel therapeutic avenue for the development of selective chemotherapeutics, through targeting a cancer cell’s vulnerability to unresolved transcription replication conflicts. Citation Format: Jennifer Jossart, Long Gu, Pouya Haratipour, Caroline Li, Robert Lingeman, Robert Hickey, Linda Malkas, John J. Perry. Developing a selective cancer chemotherapy by small molecule-based targeting of PCNA protein [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6583.

Abstract 3527: Application of a deep learning based drug sensitivity prediction model on a novel anticancer drug

Abstract Discovery of a candidate drug molecule through the preclinical stages is an iterative multiparametric process until a candidate is selected for investigational new drug submission enabling studies. This iterative approach takes up significant time and effort to design, synthesize, and test new compounds. A specific testing step is evaluation of half maximal inhibitory concentration (IC50) of compounds on cell line (CL) models. To augment CL testing, computational approaches predicting the IC50 of a drug can be used to guide choice of optimization as the molecule goes through hit to lead optimization onto candidate selection. DeepDSC [1] is a deep learning (DL) model developed to predict the IC50 in a specific CL of a given drug by integrating public data from large-scale drug screens and high-throughput RNAseq profiling. The Cancer Cell Line Encyclopedia (CCLE) [2] and Genomics of Drug Sensitivity (GDSC) [3] projects compiled pharmacological profiles of 23 and 139 drugs across 491 and 655 cancer CLs, respectively. A 10-fold cross validation (CV) approach was employed to train DeepDSC on the datasets independently and reported the average root mean squared error (RMSE) and the coefficient of determination (R2) on the held-out sets across the folds. However, the model’s generalizability to novel compounds not in the dataset, specifically compounds that are structurally dissimilar, is yet to be tested. In this regard, the DeepDSC model was retrained, with modifications, on a subset of the GDSC dataset - GDSC2 - to access the generalizability of DL models on BRG399, a proprietary microtubule targeting agent (MTA) developed by BPGbio Inc. GDSC2 was used since data generated for BRG399 and GDSC2 share the same potency assay (CellTiter-Glo [6]), The GDSC2 set was split into training and held-out sets using a 80-20 random split. Further, we chose to optimize the learning rate for both the autoencoder and feed forward networks (FFN), and the L2 regularization factor for the FFN only. Hyperparameters were optimized using 5-fold CV on the training set. Finally, we evaluated the predictive performance of the model on BRG399, which was not present in the training set. We encoded each compound in GDSC2 (n=234) using Circular Fingerprints [4] and observed an average Jaccard (or Tanimoto) distance (JD) [5] of 0.892 across 27261 drug pairs. BRG399 showed an average JD of 0.873 across 234 drug pairs, with the most similar compound JD of 0.785. Our retrained DeepDSC model had RMSE 1.15 and R2 0.82 on the training set and RMSE 1.19 and R2 0.86 on the held-out set. However, on BRG399 tested CLs (n=101) model had RMSE 1.84 and R2 < 0, which indicated that predictions were on average worse compared to a mean-predictor model for BRG399. These results suggest that DL models that can generalize predictions to a held-out set for compounds that were present in the training set may not accurately predict IC50 of novel compounds not in the training set. Citation Format: Nischal M. Chand, Vivek Vishnudas, Michael A. Kiebish, Anjan Thakurta, Niven R. Narain, Stephane Gesta, Gregory M. Miller. Application of a deep learning based drug sensitivity prediction model on a novel anticancer drug [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3527.

Abstract 1099: Combined MEK and RET inhibition overcomes resistance to single agent RET inhibition in medullary thyroid cancer

Abstract Medullary thyroid carcinoma (MTC) is a rare neuroendocrine cancer, caused by activating mutations in the RET proto-oncogene, contributing to 13% of all thyroid cancer mortalities. Patients with metastatic MTC treated with RET inhibitors (RETi) can develop resistance through mechanisms currently being investigated. We aim to understand the molecular mechanisms of acquired resistance to RETi and develop novel combination therapies to circumvent resistance to these drugs. We used TT, an MTC cell line, to develop vandetanib-resistant (VanR) lines, at two independent sites, the Medical College of Wisconsin (MCW) and the Pediatric Oncology Branch (POB) at the NCI. We applied a multiomic approach to investigate the mechanisms of resistance, including Spectral karyotyping (SKY) and multiplex interphase Fluorescence in situ Hybridization (miFISH) to study the karyotype, ploidy, and heterogeneity at a single-cell level. Whole exome and RNA-sequencing was done to identify genetic alterations such as acquired mutations, copy number variations, and gene expression changes. In parallel, we performed genome-wide CRISPR gene knockout (KO) in TT cells treated with selpercatinib or vehicle to develop potential synergistic targeted combination therapy with RETi. Additionally, we performed a cytotoxicity screening using the Mechanism Interrogation Plate (MIPE6.0) library containing 2803 investigational drugs, novel agents, and FDA-approved drugs with known mechanisms of action. SKY and miFISH results showed that, for the parental TT_POB cell line, there were 4 copies of chromosome 10 (location of the RET gene), whereas there were 3 copies in TT_POB VanR. This was associated with an increase in the RET variant allele frequency, indicating a loss of heterozygosity of the wildtype allele as a mechanism of resistance, which was confirmed by whole exome sequencing. For TT_MCW VanR, we identified a new RET solvent front (G810S), as well as an increased copy number of RET with an increase in expression. CRISPR KO demonstrated that the knockout of NF1 led to resistance and survival to selpercatinib treatment. The MIPE6.0 screening confirmed resistance to RETi in TT_MCW VanR and sensitivity to multiple MEK inhibitors. In vivo testing in a xenograft mouse model confirmed the synergistic/additive activity of combined selpercatinib with trametinib, a MEK inhibitor. In conclusion, using a multiomic approach, we show that resistance to RETi occurs by an adaptive increase in RET signaling through secondary mutations in RET, and an increase in expression or dose of the mutant RET allele. These genetic events are associated with an activation of RAS signaling, in VanR cell lines, through the ERK/MAPK pathway. Furthermore, we demonstrate that the combination of RET and ERK/MAPK inhibition is synergistic against RET-mutant MTC. Future plans include translating our findings through clinical trials at the NCI. Citation Format: Arwa Fallatah, Abdelrahman Rahmy, Sophia Khan, Hsien-chao Chou, Kerstin Heselmeyer-Haddad, Darawalee Wangsa, David Milewski, Yong Kim, Robert Hawley, Young Song, Xiaohu Zhang, Craig Thomas, Jun S. Wei, Jong-In Park, John Glod, Javed Khan. Combined MEK and RET inhibition overcomes resistance to single agent RET inhibition in medullary thyroid cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1099.

Abstract 4035: Lipid-based nanoparticle combination immunotherapy with sitravatinib for the systemic treatment of high-risk neuroblastoma

Abstract We seek to develop a systemic, innate immunomodulatory nanoparticle therapy that combines synergistically with sitravatinib for the treatment of high-risk neuroblastoma, where metastasis is the largest hurdle for current treatment modalities. Here, we utilize a unique lipid-based nanoparticle (LNP) system comprised of co-encapsulated agonists of the Stimulator of Interferon Genes (STING) and Toll-like Receptor 4 (TLR4) pathways (immuno-NPs) that can be safely administered in the systemic blood circulation for delivery to the tumor microenvironment (TME) and uptake by perivascular innate antigen-presenting cells (APCs). STING/TLR4 agonists together promote the synergistic production of Type I interferon β (IFNβ), a key activator of anti-tumor immunity from the TME itself. Here, we also focus on sitravatinib, an investigational drug shown to inhibit receptor tyrosine kinases such as discoidin domain receptor 2 (DDR2), which can inhibit the sustained remodeling of collagen around primary tumors. The inhibition of such is vital to treatment as sustained collagen remodeling can facilitate the metastasis of cancer cells via epithelial-mesenchymal transition (EMT). We hypothesize that the downstream effects of sitravatinib treatment will augment tumor clearance and increase the therapeutic efficacy of immuno-NPs with respect to recruiting APCs. Our early pilot studies in humanized mice bearing orthotopic SH-5YSY tumors show that immuno-NPs are effective at tumor clearance. We found that mice inoculated subcutaneously with syngeneic 9464D-GD2 tumors showed decreased tumor growth when treated with our novel combination therapy in comparison to monotherapy controls. In both humanized and syngeneic tumor models, our ongoing mechanistic studies involve quantification of EMT in primary tumors, systemic nanoparticle deposition at metastatic sites, and the mechanistic impact of combination therapy on IFNβ-promoted elimination of metastasis. Here, we have proposed and tested an immunotherapy with great promise for treating neuroblastoma. However, the impact of nanoparticle-based immunotherapy reaches far beyond neuroblastoma: it has the potential to be a platform for the development of immunotherapies specific to other highly metastatic cancer types. Citation Format: Miranda Diaz-Infante, Griffin Kane, Christina Lusi, Meghan Brassil, Kim Wigglesworth, Christian Alarcon, Jason Shohet, Prabhani Atukorale. Lipid-based nanoparticle combination immunotherapy with sitravatinib for the systemic treatment of high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4035.

Hormone-based pharmacotherapy for metabolic dysfunction-associated fatty liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions globally in parallel to the rising prevalence of obesity. Despite its significant burden, there is no approved pharmacotherapy specifically tailored for this disease. Many potential drug candidates for MAFLD have encountered setbacks in clinical trials, due to safety concerns or/and insufficient therapeutic efficacy. Nonetheless, several investigational drugs that mimic the actions of endogenous metabolic hormones, including thyroid hormone receptor β (THRβ) agonists, fibroblast growth factor 21 (FGF21) analogues, and glucagon-like peptide-1 receptor agonists (GLP-1RAs), showed promising therapeutic efficacy and excellent safety profiles. Among them, resmetirom, a liver-targeted THRβ-selective agonist, has met the primary outcomes in alleviation of metabolic dysfunction-associated steatohepatitis (MASH), the advanced form of MAFLD, and liver fibrosis in phase-3 clinical trials. These hormone-based pharmacotherapies not only exhibit varied degrees of therapeutic efficacy in mitigating hepatic steatosis, inflammation and fibrosis, but also improve metabolic profiles. Furthermore, these three hormonal agonists/analogues act in a complementary manner to exert their pharmacological effects, suggesting their combined therapies may yield synergistic therapeutic benefits. Further in-depth studies on the intricate interplay among these metabolic hormones are imperative for the development of more efficacious combination therapies, enabling precision management of MAFLD and its associated comorbidities.

Standardization of key performance indicators and metrics for investigational drug services: Consensus recommendations among IDS specialists.

There are currently no consensus guidelines on establishing metrics for investigational drug services (IDS). Because of the complexity of research protocols, it remains difficult for sites to track pharmacy productivity and create a baseline for IDS growth within the institution, as well as to perform benchmarking with peer institutions. The goal of this study was to help establish practical guidance for IDS metrics and site utility as applicable. This was a survey-based project conducted by the metrics subgroup of the Hematology/Oncology Pharmacy Association (HOPA) IDS special interest group (SIG), which was formed specifically for this analysis. Three surveys developed by the metrics subgroup were sent to members of the IDS HOPA SIG to gather metrics. The first survey included questions about what metrics IDS sites currently collect. The identified metrics were then condensed into categories. Through a consensus-based approach, standardized definitions were established and applied to future surveys. The 2 subsequent surveys sent to HOPA SIG members helped create a list of top recommended metrics that are recommended for every IDS site to track. A total of 3 surveys were sent to 75 recipients, with the response rate ranging from 24% to 38%. From these surveys and consensus with the metrics subgroup, 5 top recommended metrics were identified: (1) active protocols; (2) dispenses; (3) new clinical trials initiated; (4) patients treated; and (5) clinical interventions. These recommended metrics should serve as guidance and allow for standardization to help ensure adequate resources are available for IDS pharmacy staff. These recommendations should serve as a basis for standardization and benchmarking with peer institutions.

Interventions for Pancreatitis-New Approaches, Knowledge Gaps, and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

There exists no cure for acute, recurrent acute or chronic pancreatitis and treatments to date have been focused on managing symptoms. A recent workshop held by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) focused on interventions that might disrupt or perhaps even reverse the natural course of this heterogenous disease, aiming to identify knowledge gaps and research opportunities that might inform future funding initiatives for NIDDK. The breadth and variety of identified active or planned clinical trials traverses the spectrum of the disease and was conceptually grouped for the workshop into behavioral, nutritional, pharmacologic and biologic, and mechanical interventions. Cognitive and other behavioral therapies are proven interventions for pain and addiction, but barriers exist to their use. Whilst a disease specific instrument quantifying pain is now validated, an equivalent is lacking for nutrition - and both face challenges in ease and frequency of administration. Multiple pharmacologic agents hold promise. Ongoing development of Patient Reported Outcome (PRO) measurements can satisfy Investigative New Drug (IND) regulatory assessments. Despite multiple randomized clinical trials demonstrating benefit, great uncertainty remains regarding patient selection, timing of intervention, and type of mechanical intervention (endoscopic versus surgery). Challenges and opportunities to establish beneficial interventions for patients were identified.

Evaluation of a remote hybrid staffing model for investigational drug service pharmacists.

This project compared the time to complete investigational prescription drug order verification by investigational drug service (IDS) pharmacists in an onsite vs offsite (hybrid) staffing model, evaluating the impact of remote work on verification time. Parenteral IDS drug orders from August 2019 through June 2022 were analyzed. Electronic medical records were timestamped for each order, first verification, second verification, and medication administration. The project was divided into four 6-month time periods to allow for 2 independent comparisons of onsite vs hybrid staffing models. A survey was given retrospectively to IDS pharmacists for the last hybrid timeframe to assess work satisfaction and burnout. A total of 4,398 orders were evaluated. The time from order entry to first verification was 14 minutes (n = 1,248) during the first onsite time period compared to 13 minutes (n = 1,042) during the first hybrid period (P = 0.003). The time for order verification was similar between the second onsite and hybrid periods (12 minutes [n = 1,041] vs 10 minutes [n = 1,067], respectively; P < 0.0001). The staff satisfaction survey for the hybrid model showed high levels of job satisfaction and no self-reported burnout. The remote hybrid staffing model did not result in a meaningful change in the time from order placement to medication administration within the IDS pharmacy. Hybrid staffing also resulted in greater job satisfaction among IDS pharmacists. This study highlights the benefits of implementing remote work practices in the pharmacy practice setting.

Metabolite Bioanalysis in Drug Development: Recommendations from the IQ Consortium Metabolite Bioanalysis Working Group.

The intent of this perspective is to share the recommendations of the International Consortium for Innovation and Quality in Pharmaceutical Development Metabolite Bioanalysis Working Group on the fit-for-purpose metabolite bioanalysis in support of drug development and registration. This report summarizes the considerations for the trigger, timing, and rigor of bioanalysis in the various assessments to address unique challenges due to metabolites, with respect to efficacy and safety, which may arise during drug development from investigational new drug (IND) enabling studies, and phase I, phase II, and phase III clinical trials to regulatory submission. The recommended approaches ensure that important drug metabolites are identified in a timely manner and properly characterized for efficient drug development.